November 2024 arXiv papers — page 131

Mohit Agarwal, Mimi Sun, Chaitanya Kamath, Arbaaz Muslim

Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Traditional approaches to these classes of

Andrew Kuznetsov, Michael Xieyang Liu, Aniket Kittur

Aiming to help people conduct online research tasks, much research has gone into tools for searching for, collecting, organizing, and synthesizing online information. However, outside of the lab, in-the-wild sensemaking sessions (with data on tasks, users, their tools and challenges) can ground us in the reality of such efforts and the state of tool support.

Nyle Siddiqui, Florinel Alin Croitoru, Gaurav Kumar Nayak, Radu Tudor Ionescu

With the recent exhibited strength of generative diffusion models, an open research question is if images generated by these models can be used to learn better visual representations. While this generative data expansion may suffice for easier visual tasks, we explore its efficacy on a more difficult discriminative task: clothes-changing person re-identifica

Arpana Saboo, Soumyadeep Halder, Mithun Thudiyangal, Sonjoy Majumder

We theoretically explore the spin texture dynamics of a harmonically trapped spin-1 Bose-Einstein condensate with Rashba spin-orbit coupling and ferromagnetic spin-exchange interactions under a sinusoidally varying magnetic field along the $x$-direction. This interplay yields an intrinsic spin texture in the ground state, forming a linear chain of alternatin

Haoyu Chen, Tiantian Mao, Fan Yang

In this paper, we modify the Bayes risk for the expectile, the so-called variantile risk measure, to better capture extreme risks. The modified risk measure is called the adjusted standard-deviatile. First, we derive the asymptotic expansions of the adjusted standard-deviatile. Next, based on the first-order asymptotic expansion, we propose two efficient est

Domitille Gérard, Stéphanie Buil, Jean-Pierre Hermier, Aymeric Delteil

We experimentally investigate a solid-state quantum emitter - a B center in hexagonal boron nitride (hBN) - that has lifetime-limited coherence at short times, and experiences inhomogeneous broadening due to spectral diffusion at longer times. By making use of power broadening in resonant laser excitation, we explore the crossover between the inhomogeneous a

Matthew Bunney, Urban F. P. Seifert, Stephan Rachel, Matthias Vojta

Superconductivity usually emerges from a metallic normal state which follows the Fermi-liquid paradigm. If, in contrast, the normal state is a fractionalized non-Fermi liquid, then pairing may either eliminate fractionalization via a Higgs-type mechanism leading to a conventional superconducting state, or pairing can occur in the presence of fractionalizatio

Karim Abdel Sadek, Matteo Nulli, Joan Velja, Jort Vincenti

This work investigates the reproducibility of the paper 'Explaining RL decisions with trajectories'. The original paper introduces a novel approach in explainable reinforcement learning based on the attribution decisions of an agent to specific clusters of trajectories encountered during training. We verify the main claims from the paper, which state that (i

Cong Wei, Zheyang Xiong, Weiming Ren, Xinrun Du

Instruction-guided image editing methods have demonstrated significant potential by training diffusion models on automatically synthesized or manually annotated image editing pairs. However, these methods remain far from practical, real-life applications. We identify three primary challenges contributing to this gap. Firstly, existing models have limited edi

Shane Chern

In this paper, we establish simple $k$-fold summation expressions for the Quot and motivic Cohen--Lenstra zeta functions associated with the $(2,2k)$ torus links. Such expressions lead us to some multiple Rogers--Ramanujan type identities and their finitizations, thereby confirming a conjecture of Huang and Jiang. Several other properties of the two zeta fun

T. Rao, L. Barron-Palos, I. Berkutov, C. Crawford

Metastability exchange optical pumping (MEOP) is a widely used technique for producing polarized $^{3}$He. In connection with an experiment to search for the electric dipole moment of the neutron (nEDM) we have built a MEOP based $^{3}$He polarization and injection system to prepare 80 % polarized $^{3}$He at room temperature which will be injected into a ~4

I. M. Morris, T. Lühmann, K. Klink, L. Crooks

The negatively charged nickel vacancy center (NiV$^-$) in diamond is a promising spin qubit candidate with predicted inversion symmetry, large ground state spin orbit splitting to limit phonon-induced decoherence, and emission in the near-infrared. Here, we experimentally confirm the proposed geometric and electronic structure of the NiV defect via magneto-o

Peng Zhang, Jing-Yuan Chen

Since the inception of lattice QCD, a natural definition for the Yang-Mills instanton on lattice has been long sought for. In a recent work, one of authors showed the natural solution has to be organized in terms of bundle gerbes in higher homotopy theory / higher category theory, and introduced the principles for such a categorical construction. To pave the

Hyewon Jeong, Suyeol Yun, Hammaad Adam

Electrocardiograms (ECGs) are an established technique to screen for abnormal cardiac signals. Recent work has established that it is possible to detect arrhythmia directly from the ECG signal using deep learning algorithms. While a few prior approaches with contrastive learning have been successful, the best way to define a positive sample remains an open q

Ziang Yu, Shiwei Zhang, Yuehaw Khoo

We propose a novel algorithm for calculating the ground-state energy of quantum many-body systems by combining auxiliary-field quantum Monte Carlo (AFQMC) with tensor-train sketching. In AFQMC, a good trial wavefunction to guide the random walk is crucial for improving the sampling efficiency and controlling the sign problem. Our proposed method iterates bet

Tomàs Ortega, Antonio Pascual-Iserte, Olga Muñoz

Designers of millimeter wave (mmWave) cellular systems need to evaluate line-of-sight (LOS) maps to provide good service to users in urban scenarios. In this letter, we derive estimators to obtain LOS maps in scenarios with potential blocking elements. Applying previous stochastic geometry results, we formulate the optimal Bayesian estimator of the LOS map u

Paulina Knees, Essodjolo Kpatcha, Iñaki Lara, Daniel E. López-Fogliani

We analyse relevant signals expected at the LHC for a squark of the first two families as the lightest supersymmetric particle (LSP). The discussion is established in the framework of the $\mu\nu$SSM, where the presence of $R$-parity violating couplings involving right-handed neutrinos solves simultaneously the $\mu$-problem and the accommodation of neutrino

Mario Rosenfelder, Lea Bold, Hannes Eschmann, Peter Eberhard

Advances in machine learning and the growing trend towards effortless data generation in real-world systems has led to an increasing interest for data-inferred models and data-based control in robotics. It seems appealing to govern robots solely based on data, bypassing the traditional, more elaborate pipeline of system modeling through first-principles and

Mengxia Yu, De Wang, Qi Shan, Colorado J Reed

Recent works have shown a surprising result: a small fraction of Large Language Model (LLM) parameter outliers are disproportionately important to the quality of the model. LLMs contain billions of parameters, so these small fractions, such as 0.01%, translate to hundreds of thousands of parameters. In this work, we present an even more surprising finding: P

Sergii Favorov

A simple necessary and sufficient condition is given for an absolutely convergent Dirichlet series with imaginary exponents and only real zeros to be a finite product of sines. The proof is based on Meyer's theorem on quasicrystals.

Hakan Akgun, Xianquan Yan, Tamer Taskiran, Muhamet Ibrahimi

Scale invariance is a hallmark of criticality in complex dynamical systems. While random external inputs or tunable stochastic interactions are typically required to produce critical behavior, it remains unclear whether scale-invariant dynamics can emerge from purely deterministic interactions. Here, we address this question by studying the asymptotic dynami

Barnabás Janzer, Oliver Janzer, Abhishek Methuku, Gábor Tardos

In 2006, Marcus and Tardos proved that if $A^1,\dots,A^n$ are cyclic orders on some subsets of a set of $n$ symbols such that the common elements of any two distinct orders $A^i$ and $A^j$ appear in reversed cyclic order in $A^i$ and $A^j$, then $\sum_{i} |A^i|=O(n^{3/2}\log n)$. This result is tight up to the logarithmic factor and has since become an impor

Akanksha Gautam, Kavita Dorai, Arvind

We generate different orders of quantum coherence in a three-qubit NMR system and study their dynamics in the presence of inherent noise. Robust dynamical decoupling (DD) sequences are applied to preserve the different coherence orders. Initially, DD sequences are implemented simultaneously on all three spins, which effectively protects third-order coherence

David Robinson, Marius Miron, Masato Hagiwara, Benno Weck

Large language models (LLMs) prompted with text and audio have achieved state-of-the-art performance across various auditory tasks, including speech, music, and general audio, showing emergent abilities on unseen tasks. However, their potential has yet to be fully demonstrated in bioacoustics tasks, such as detecting animal vocalizations in large recordings,

Marcel Neugebauer

Gaussian Processes face two primary challenges: constructing models for large datasets and selecting the optimal model. This master's thesis tackles these challenges in the low-dimensional case. We examine recent convergence results to identify models with optimal convergence rates and pinpoint essential parameters. Utilizing this model, we propose a Samplet

Yao Ma, Samuel Louvan, Zhunxuan Wang

Multi-source unsupervised domain adaptation aims to leverage labeled data from multiple source domains for training a machine learning model to generalize well on a target domain without labels. Source domain selection plays a crucial role in determining the model's performance. It relies on the similarities amongst source and target domains. Nonetheless, ex

Yunhan Yang, Yukun Huang, Yuan-Chen Guo, Liangjun Lu

3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harness the powerful Vision Language Models (VLMs) for 2D-to-3D knowledge distillation, achieving zero-shot 3D part segmentation. However, these methods are limited by their reliance on

Juntao He, Baxi Chong, Jianfeng Lin, Zhaochen Xu

Achieving robust legged locomotion on complex terrains poses challenges due to the high uncertainty in robot-environment interactions. Recent advances in bipedal and quadrupedal robots demonstrate good mobility on rugged terrains but rely heavily on sensors for stability due to low static stability from a high center of mass and a narrow base of support. We

Youssef Allouah, Akash Dhasade, Rachid Guerraoui, Nirupam Gupta

Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades the iterative exchange of models between clients and the server with a single round of communication, thereby sa

Yumeng Zeng, Shu Chen

The dynamical quantum phase transition (DQPT) is an important concept in nonequilibrium critical phenomena; however, its relation to the equilibrium quantum phase transition (EQPT) remains obscure. Substantial evidence has suggested that quenching across the underlying equilibrium phase boundary is neither a sufficient nor a necessary condition for the exist

Shauli Ravfogel, Anej Svete, Vésteinn Snæbjarnarson, Ryan Cotterell

Understanding and manipulating the causal generation mechanisms in language models is essential for controlling their behavior. Previous work has primarily relied on techniques such as representation surgery -- e.g., model ablations or manipulation of linear subspaces tied to specific concepts -- to \emph{intervene} on these models. To understand the impact

Ismail Cosandal, Sennur Ulukus, Nail Akar

Age of incorrect information (AoII) is a recently proposed freshness and mismatch metric that penalizes an incorrect estimation along with its duration. Therefore, keeping track of AoII requires the knowledge of both the source and estimation processes. In this paper, we consider a time-slotted pull-based remote estimation system under a sampling rate constr

Jinlu Li

Let C[0, 1] be the Banach space of all continuous real valued functions on [0, 1]. For an arbitrarily given nonnegative integer n, let Pn denote the set of all polynomials with degree less than or equal to n. Pn is a closed subspace of C[0, 1]. In this paper, we first prove (in details) that the metric projection operator from C[0, 1] to Pn is a single-value

Ang Lv, Ruobing Xie, Shuaipeng Li, Jiayi Liao

We propose a novel attention mechanism, named Cog Attention, that enables attention weights to be negative for enhanced expressiveness, which stems from two key factors: (1) Cog Attention enhances parameter flexibility. For example, unlike traditional softmax attention heads that use a static output-value (OV) matrix to delete or copy inputs that the heads a

Shuangyuan Lu, Penghao Zhu, Yuan-Ming Lu

We introduce the bilayer construction, as a specific purification scheme for a general mixed state, where each mixed state has a one-to-one correspondence with a bilayer pure state with two constraints: non-negativity of the bilayer wavefunction; and the presence of an anti-unitary layer-exchange symmetry T. Different from the Choi-Jamio{\l}kowski isomorphis

Dhruba Jyoti Gogoi, Jyatsnasree Bora, Filip Studnička, H. Hassanabadi

We investigate the temperature, photon and shadow radii, quasinormal modes (QNMs), time domain profiles, greybody factors, emission rates and topological characteristics of deformed black holes, focusing on the effects of the deformation parameter $ \alpha $ and control parameter $ \beta $. Increasing $ \alpha $ enhances the oscillation frequency and damping

M. Guzzetti, D. Zhang, C. Goodman, C. Hanretty

Axions are a well-motivated candidate for dark matter. The preeminent method to search for axion dark matter is known as the axion haloscope, which makes use of the conversion of axions to photons in a large magnetic field. Due to the weak coupling of axions to photons however, the expected signal strength is exceptionally small. To increase signal strength,

Dominic Sagers, Mark H. M. Winands, Dennis J. N. J. Soemers

Monte-Carlo Tree Search (MCTS) typically uses multi-armed bandit (MAB) strategies designed to minimize cumulative regret, such as UCB1, as its selection strategy. However, in the root node of the search tree, it is more sensible to minimize simple regret. Previous work has proposed using Sequential Halving as selection strategy in the root node, as, in theor

Cheng-Jun Xia, Wen-Jie Xie, Mohemmedelnazier Bakhiet

Utilizing various astrophysical constraints on neutron star structures, we carry out a Bayesian analysis on the density-dependent behaviors of coupling constants in RMF models as well as the nuclear matter properties at supranuclear densities. The effective nucleon interactions in the isoscalar-scalar, isoscalar-vector, and isovector-vector channels are cons

Andres Fernandez Herrero, Dario Weißmann, Xucheng Zhang

We introduce local invariants of algebraic spaces and stacks which measure how far they are from being a scheme. Using these invariants, we develop mostly topological criteria to determine when the moduli space of a stack is a scheme. As an application we study moduli of principal bundles on a smooth projective curve.

Raúl de la Fuente, Luciano Radrigan, Anibal S Morales

Mining machinery operating in variable environments faces high wear and unpredictable stress, challenging Predictive Maintenance (PdM). This paper introduces the Edge Sensor Network for Predictive Maintenance (ESN-PdM), a hierarchical inference framework across edge devices, gateways, and cloud services for real-time condition monitoring. The system dynamica

Gustavo Bergantiños, Juan D. Moreno-Ternero

We study an index to measure the popularity of artists in music streaming platforms. This index, which can be used to allocate the amount raised via paid subscriptions among participating artists, is based on the Shapley value, a centerpiece in cooperative game theory. We characterize this Shapley index combining several axioms formalizing principles with no

Nicola Menadeo, Miguel Zumalacárregui

The nature of gravity can be tested by how gravitational waves (GWs) are emitted, detected, and propagate through the universe. Propagation tests are powerful, as small deviations compound over cosmological distances. However, GW propagation tests of theories beyond Einstein's general relativity (GR) are limited by the high degree of symmetry of the average

Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin

Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such a

Lovedeep Gondara, Jonathan Simkin

We present an audit mechanism for language models, with a focus on models deployed in the healthcare setting. Our proposed mechanism takes inspiration from clinical trial design where we posit the language model audit as a single blind equivalence trial, with the comparison of interest being the subject matter experts. We show that using our proposed method,

S. Abdollahi, F. Acero, A. Acharyya, A. Adelfio

The recent detection of extended $\gamma$-ray emission around middle-aged pulsars is interpreted as inverse-Compton scattering of ambient photons by electron-positron pairs escaping the pulsar wind nebula, which are confined near the system by unclear mechanisms. This emerging population of $\gamma$-ray sources was first discovered at TeV energies and remain

Young-Min Cho, Raphael Shu, Nilaksh Das, Tamer Alkhouli

Effective group decision-making is critical in Multi-Agent Systems (MAS). Yet, how different mechanisms for reaching consensus impact collaboration quality and efficiency remains understudied. We conduct a systematic study on group decision-making mechanisms in a decentralized setting. Through controlled experiments, we analyze how different voting rules aff

Yingjian Wang, Yilun Hai, Buniechukwu Njoku, Koteswararao Kondepu

Error estimation is an important step for error correction in quantum key distribution. Traditional error estimation methods require sacrificing a part of the sifted key, forcing a trade-off between the accuracy of error estimation and the size of the partial sifted key to be used and discarded. In this paper, we propose a hybrid approach that aims to preser

Leonid Shvedov, Stefano Burrello, Maria Colonna, Hua Zheng

We investigate the pre-equilibrium dipole response in the charge-asymmetric reaction $^{40}$Ca+$^{152}$Sm, of recent experimental interest, at several beam energies within the range $[5, 11]$ AMeV and different collision centralities. By employing Skyrme-like effective interactions for the nuclear mean field, we probe the role of the different ingredients pe

Luis Fredes, Jean-François Marckert

The transition matrix of a Markov chain $(X_k,k\geq 0)$ on a finite or infinite rooted tree is said to be almost upper-directed if, given $X_k$, the node $X_{k+1}$ is either a descendant of $X_k$ or the parent of $X_k$. It is said to be almost lower-directed if given $X_k$, $X_{k+1}$ is either an ancestor of $X_k$ or a child of $X_k$. These models include ne

Ajay Chandra, Léonard Ferdinand

We show how the flow approach of Duch, with elementary differentials as coordinates, can be used to prove well-posedness for rough stochastic differential equations driven by fractional Brownian motion with Hurst index $H > \frac{1}{4}$. A novelty appearing here is that we use coordinates for the flow that are indexed by trees rather than multi-indices.

Brian E. Perron, Kelley A. Rivenburgh, Bryan G. Victor, Zia Qi

Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representati

Ahmadreza Sezavar, Catarina Brites, Joao Ascenso

Event cameras are a cutting-edge type of visual sensors that capture data by detecting brightness changes at the pixel level asynchronously. These cameras offer numerous benefits over conventional cameras, including high temporal resolution, wide dynamic range, low latency, and lower power consumption. However, the substantial data rates they produce require

Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan, Youssef Marzouk

Conditional simulation is a fundamental task in statistical modeling: Generate samples from the conditionals given finitely many data points from a joint distribution. One promising approach is to construct conditional Brenier maps, where the components of the map pushforward a reference distribution to conditionals of the target. While many estimators exist

Ahmad Salmanogli, Hesam Zandi, Saeed Hajihosseini, Mahdi Esmaeili

The Purcell effect, a common issue in qubit-resonator systems leading to both fidelity and nonclassicality losses is studied while its suppression is achieved using a novel qubit readout circuit design. Our approach utilizes a unique coupling architecture in which, the qubit first interacts with a filter resonator before coupling to the readout resonator. Th

Lingbo Mo, Shun Jiang, Akash Maharaj, Bernard Hishamunda

Task-Oriented Dialogue (TOD) systems assist users in completing tasks through natural language interactions, often relying on a single-layered workflow structure for slot-filling in public tasks, such as hotel bookings. However, in enterprise environments, which involve rich domain-specific knowledge, TOD systems face challenges due to task complexity and th

Carlos Pinzón

This paper studies the relationship between volume and surface uniform measures on n-dimensional p-balls under the p-norm. It is proved that for p=1, p=2 and p=infinity, and only for these values of p, radial projection maps a volumetrically uniform distribution to a surface-uniform distribution. Algorithms for uniform sampling on p-balls and p-spheres are p

Shifeng Xie, Jhony H. Giraldo

Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised learning (SSL) methods are widely used in GRL because they can avoid expensive human annotation. In this work, we propose a novel Subgraph Gaussian Embedding Contrast (SGEC) metho

Samuel Van Stroud, Philippa Duckett, Max Hart, Nikita Pond

Reconstructing charged particle tracks is a fundamental task in modern collider experiments. The unprecedented particle multiplicities expected at the High-Luminosity Large Hadron Collider (HL-LHC) pose significant challenges for track reconstruction, where traditional algorithms become computationally infeasible. To address this challenge, we present a nove

Emanuela Radici, Federico Stra

We study a deterministic particle scheme to solve a scalar balance equation with nonlocal interaction and nonlinear mobility used to model congested dynamics. The main novelty with respect to "Radici-Stra [SIAM J. Math. Anal. 55.3 (2023)]" is the presence of a source term; this causes the solutions to no longer be probability measures, thus requiring a suita

Md Sabir Ali, C. Fairoos, C. L. Ahmed Rizwan, T. K. Safir

In this paper, using the ensemble-averaged theory, we define the thermodynamic free energy of Einstein-Gauss-Bonnet (EGB) black holes in anti-de Sitter (AdS) spacetime. This approach derives the gravitational partition function by incorporating non-saddle geometries besides the classical solutions. Unlike the sharp transition points seen in free energy calcu

Yasra Chandio, Khotso Selialia, Joseph DeGol, Luis Garcia

Advancements in tracking algorithms have empowered nascent applications across various domains, from steering autonomous vehicles to guiding robots to enhancing augmented reality experiences for users. However, these algorithms are application-specific and do not work across applications with different types of motion; even a tracking algorithm designed for

Primož Kajdič, Xóchitl Blanco-Cano, Lucile Turc, Martin Archer

In recent years, it has become increasingly clear that space weather disturbances can be triggered by transient upstream mesoscale structures (TUMS), independently of the occurrence of large-scale solar wind (SW) structures, such as interplanetary coronal mass ejections and stream interaction regions. Different types of magnetospheric pulsations, transient p

Eduardo Ibarra-García-Padilla, Hannah Lange, Roger G Melko, Richard T Scalettar

Neural quantum states (NQS) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly-correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe

ATLAS Collaboration

A search for pair production of vector-like leptons coupling to first- and second-generation Standard Model leptons is presented. The search is based on a dataset of proton-proton collisions at $\sqrt{s}=13$ TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb$^{-1}$. Events are c

Peter Anderson, Mano Vikash Janardhanan, Jason He, Wei Cheng

Financial documents are filled with specialized terminology, arcane jargon, and curious acronyms that pose challenges for general-purpose text embeddings. Yet, few text embeddings specialized for finance have been reported in the literature, perhaps in part due to a lack of public datasets and benchmarks. We present BAM embeddings, a set of text embeddings f

Oliver M. Drozdowski, Büşra Kocameşe, Kim E. Boonekamp, Michael Boutros

Cell extrusion is an essential mechanism for controlling cell density in epithelial tissues. Another essential element of epithelia is curvature, which is required to achieve complex shapes, like in the lung or intestine. Here we introduce a three-dimensional bubbly vertex model to study the interplay between extrusion and curvature. We find a generic cellul

Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan

New LLM evaluation benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive Chinese benchmark to evaluate the factuality ability of language models to answer short questions, and Chinese SimpleQA mainly has five properties (i.e., Chinese, Diverse, High-qua

Maximilian Wackenhuth

We prove sphere packing density bounds in hyperbolic space (and more generally irreducible symmetric spaces of noncompact type), which were conjectured by Cohn and Zhao and generalize Euclidean bounds by Cohn and Elkies. We work within the Bowen-Radin framework of packing density and replace the use of the Poisson summation formula in the proof of the Euclid

Harish Haresamudram, Chi Ian Tang, Sungho Suh, Paul Lukowicz

In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to recognize activities. Substantial gains have been made since the days of hand-crafting heuristics as features, yet, progress has seemingly stalled on many popular benchmarks, with p

Martin Mayr, Julian Krenz, Katharina Neumeier, Anna Bub

Most datasets in the field of document analysis utilize highly standardized labels, which, while simplifying specific tasks, often produce outputs that are not directly applicable to humanities research. In contrast, the Nuremberg Letterbooks dataset, which comprises historical documents from the early 15th century, addresses this gap by providing multiple t

Lu Qi, Evan C. Reed, Boyan Yu, Kenneth R. Brown

Dipole-phonon quantum logic (DPQL) offers novel approaches for state preparation, measurement, and control of quantum information in molecular ion qubits. In this work, we demonstrate an experimental implementation of DPQL with a trapped calcium monoxide and calcium ion chain at room temperature. We present evidence for one DPQL signal in two hours of data c

Zhongquan Chen, Pim van der Hoorn, Björn Baumeier

This paper introduces a method to identify traps in molecular charge transport networks as obtained by multiscale modeling of organic semiconductors. Depending on the materials, traps can be defect-like single molecules or clusters of several neighboring ones, and can have a significant impact on the dynamics of charge carriers. Our proposed method builds on

NVIDIA, :, Maciej Bala, Yin Cui

We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation. Our method first synthesizes RGB and surface normal images of the described object at multiple viewpoints using a diffusion model. The multi-view observations are then used to reconstruct the shape, texture, and PBR materials of the object. Our method can generate high

David Hobson, Gechun Liang, Edward Wang

Zero-sum Dynkin games under Poisson constraints, where players can only stop at the event times of a Poisson process, have been studied widely in the recent literature. The constraint can be modelled in two ways: either both players share the same Poisson process (the common constraint) or each player has their own Poisson process (the independent constraint

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin

Instruction tuning has been widely adopted to ensure large language models (LLMs) follow user instructions effectively. The resulting instruction-following capabilities of LLMs heavily rely on the instruction datasets used for tuning. Recently, synthetic instruction datasets have emerged as an economically viable solution to provide LLMs diverse and high-qua

Taihang Hu, Linxuan Li, Joost van de Weijer, Hongcheng Gao

Although text-to-image (T2I) models exhibit remarkable generation capabilities, they frequently fail to accurately bind semantically related objects or attributes in the input prompts; a challenge termed semantic binding. Previous approaches either involve intensive fine-tuning of the entire T2I model or require users or large language models to specify gene

H. O. Cildiroglu

In this study, a novel experimental setup analogous to joint spin/polarization measurement experiments is proposed by establishing a direct relationship between path (momentum) entanglement and concurrence. The results demonstrate that joint-detection probabilities can be governed not only by phase shifts but also by concurrence, which arises from the angle

Kaijian Zou, Muhammad Khalifa, Lu Wang

Many-shot in-context learning (ICL) has emerged as a unique setup to both utilize and test the ability of large language models to handle long context. This paper delves into long-context language model (LCLM) evaluation through many-shot ICL. We first ask: what types of ICL tasks benefit from additional demonstrations, and how effective are they in evaluati

Tsz On Mario Chan

The effective freeness in Fujita's conjecture states that, for an ample line bundle $L$ on a complex projective manifold $X$, the adjoint bundle $K_X\otimes L^{\otimes m}$ is globally generated when $m \geq \dim_{\mathbb C} X + 1$. Following the approach of Angehrn and Siu, a solution is provided in this paper via the use of adjoint ideal sheaves, which prov

Diana Lin, Samarth Bhargav, Azuka Chiejina, Mohamed I. Ibrahem

The advancement of 5G and NextG networks through Open Radio Access Network (O-RAN) architecture enables a shift toward virtualized, modular, and disaggregated configurations. A core component of O-RAN is the RAN Intelligent Controller (RIC), which manages RAN using machine learning-driven xApps that access sensitive data from RAN and User Equipment (UE), sto

Shengwei Xu, Yuxuan Lu, Grant Schoenebeck, Yuqing Kong

We introduce the GEM (Generative Estimator for Mutual Information), an evaluation metric for assessing language generation by Large Language Models (LLMs), particularly in generating informative judgments, without the need for a gold standard reference. GEM broadens the scenarios where we can benchmark LLM generation performance-from traditional ones, like m

NVIDIA, :, Yuval Atzmon, Maciej Bala

We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in which image signals at different frequency bands are attenuated at varying rates. Edify Image supports a wide ra

Shi Feng, Balázs Gerencsér

Considering a Markov chain defined on a cycle, near-quadratic improvement of mixing is shown when only a subtle perturbation is introduced to the structure and non-reversible transition probabilities are used. More precisely, a mixing time of $O(n^{\frac{k+2}{k+1}})$ can be achieved by adding $k$ random edges to the cycle, keeping $k$ fixed while $n\to\infty

Albin Joy, Anand Srivastava, Rajib Biswas

Azurin and its derived peptides, notably p28, exhibit significant anticancer properties, primarily by stabilizing the tumor suppressor protein p53 and preventing its degradation. Previous studies have shown that p28 binds to p53's DNA-binding domain, protecting it from degradation mechanisms. Expanding on these findings, our research explored whether p28 act

Xingzhi Guo, Silong Wang, Baojian Zhou, Yanghua Xiao

Real-world graphs grow rapidly with edge and vertex insertions over time, motivating the problem of efficiently maintaining robust node representation over evolving graphs. Recent efficient GNNs are designed to decouple recursive message passing from the learning process, and favor Personalized PageRank (PPR) as the underlying feature propagation mechanism.

Ruben Härle, Felix Friedrich, Manuel Brack, Björn Deiseroth

Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, but their output may not be aligned with the user or even produce harmful content. This paper presents a novel approach to detect and steer concepts such as toxicity before generation. We introduce the Sparse Conditioned Autoencoder (SCAR), a single trained

Yanchen Wang, Adam Turnbull, Tiange Xiang, Yunlong Xu

Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades, advances in functional Magnetic Resonance Imaging (fMRI) and machine learning have greatly improved our ability to map visual stimuli to brain activity, especially in the visual co

Thien Hang Nguyen, Huy Le Nguyen

We introduce two complementary techniques for efficient optimization that reduce memory requirements while accelerating training of large-scale neural networks. The first technique, Subset-Norm step size, generalizes AdaGrad-Norm and AdaGrad(-Coordinate) through step-size sharing. Subset-Norm (SN) reduces AdaGrad's memory footprint from $O(d)$ to $O(\sqrt{d}

Julián Faúndez, Rodrigo Alves Fontenele, Sebastião dos Anjos Sousa-Júnior, Fakher F. Assaad

In this work, we investigate the impact of Rashba spin-orbit coupling (RSOC) on the formation of charge-density wave (CDW) and superconducting (SC) phases in the Holstein model on a half-filled square lattice. Using unbiased finite-temperature Quantum Monte Carlo simulations, we go beyond mean-field approaches to determine the ground state order parameter as

Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan

Transformer models have demonstrated remarkable success in many domains such as natural language processing (NLP) and computer vision. With the growing interest in transformer-based architectures, they are now utilized for gesture recognition. So, we also explore and devise a novel ConvMixFormer architecture for dynamic hand gestures. The transformers use qu

Haochen Zhao, Florian Mintert

We propose a new quantum simulation method for simulating N-body interactions, which are tensor products of N Pauli operators, in an analytically exact manner. This method iteratively attaches many two-body interactions on one two-body interaction to simulate an N-body interaction. Those controlled two-body interactions can be untunable and act only on neigh

A. Jabar, S. Benyoussef, L. Bahmad

One of the most active classes of nanostructures is Fullerene C$_{20}$, which has been exploited as an active component in significant applications. In this investigation, we used Monte Carlo simulations to investigate the magnetic and magnetocaloric properties of the mixed spins 2 and 3/2 Fullerene C$_{20}$ system. Ferrimagnetic and ferromagnetic phases are

Emilio J. Estrada, Juan Manuel Márquez, Diego Portillo-Sánchez, Pablo Roig

We analyze the proton$\text{-}$box contribution to the hadronic light$\text{-}$by$\text{-}$light part of the muon's anomalous magnetic moment, which is the first reported baryonic contribution to this piece. We follow the quark$\text{-}$loop analysis, incorporating the relevant data$\text{-}$driven and lattice proton form factors. Although the heavy mass exp

Jacob Huckelberry, Yuke Zhang, Allison Sansone, James Mickens

Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU capabilities two to three orders of magnitude smaller than conventional systems, make traditional software and hardware secur

Nicolas Dietrich, Wolfgang Trutschnig

Copulas, in particular Archimedean copulas are commonly viewed as analytically nice and regular objects. Motivated by a recently established result sta\-ting that the first partial derivatives of bivariate copulas can exhibit surprisingly pathological behavior, we focus on the class of $d$-dimensional Archimedean copulas denoted by $\mathcal{C}_{ar}^d$ and s

Xue Jiang, Yihong Dong, Yongding Tao, Huanyu Liu

Large language models (LLMs) have achieved impressive performance in code generation recently, offering programmers revolutionary assistance in software development. However, due to the auto-regressive nature of LLMs, they are susceptible to error accumulation during code generation. Once an error is produced, LLMs can merely continue to generate the subsequ

Chih-Kai Yang, Yu-Kuan Fu, Chen-An Li, Yi-Cheng Lin

This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorporates a decoder-only transformer architecture and aims to achieve seamless interaction while preserving the con

Luis F Seoane, Henry Secaira-Morocho, Ester Lázaro, Susanna Manrubia

Understanding how viral mutant spectra organize and explore genotype space is essential for unraveling the mechanisms driving evolution at the finest scale. Here we use deep-sequencing data of an amplicon in the A2 protein of the RNA bacteriophage Q$\beta$ to reconstruct genotype networks with tens of thousands of different haplotypes. The study of populatio

Beatrice Costeri, Claudio Dappiaggi, Michele Goi

We consider a self-interacting, massive, real scalar field on a four-dimensional globally hyperbolic spacetime and the associated stress-energy tensor. Using techniques proper of the algebraic approach to perturbative quantum field theory, we study the associated, Wick-ordered, quantum observable. In particular we generalize a construction, first developed i