cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Spatial Dependence of the Growth Factor in Scalar-Tensor Cosmology
Creators: name: Caruana, Maria
name: Farrugia, Gabriel
name: Said, Jackson Levi
name: Sultana, Joseph
Publisher: name: arXiv
Year: 2024
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: Scalar-tensor theories have taken on a key role in attempts to confront the growing open questions in standard cosmology. It is important to understand entirely their dynamics at perturbative level including any possible spatial dependence in their growth of large scale structures. In this work, we investigate the spatial dependence of the growth rate of scalar-tensor theories through the Mészáros equation. We confirm that at subhorizon level this dependence does not play a major role for viable models. However, we establish conditions on which this criterion is met which may be important for developing new models. In our work, we consider three specific models that exhibit spatial dependence of the growth rate at subhorizon modes, which may also be important for early Universe models.
Towards Neuro-Symbolic Video Understanding
Creators: name: Choi, Minkyu
name: Goel, Harsh
name: Omama, Mohammad
name: Yang, Yunhao
name: Shah, Sahil
name: Chinchali, Sandeep
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: The unprecedented surge in video data production in recent years necessitates efficient tools to extract meaningful frames from videos for downstream tasks. Long-term temporal reasoning is a key desideratum for frame retrieval systems. While state-of-the-art foundation models, like VideoLLaMA and ViCLIP, are proficient in short-term semantic understanding, they surprisingly fail at long-term reasoning across frames. A key reason for this failure is that they intertwine per-frame perception and temporal reasoning into a single deep network. Hence, decoupling but co-designing semantic understanding and temporal reasoning is essential for efficient scene identification. We propose a system that leverages vision-language models for semantic understanding of individual frames but effectively reasons about the long-term evolution of events using state machines and temporal logic (TL) formulae that inherently capture memory. Our TL-based reasoning improves the F1 score of complex event identification by 9-15% compared to benchmarks that use GPT4 for reasoning on state-of-the-art self-driving datasets such as Waymo and NuScenes.
description: Accepted by The European Conference on Computer Vision (ECCV) 2024
Statistical tests for comparing the associations of multiple exposures with a common outcome in Cox proportional hazard models
Creators: name: Hamaya, Rikuta
name: Wang, Peilu
name: Ge, Lin
name: Giovannucci, Edward L.
name: Wang, Molin
Publisher: name: arXiv
Year: 2024
Subjects: subject: Methodology (stat.ME)
subject: Applications (stat.AP)
subject: FOS: Computer and information sciences

description: With advancement of medicine, alternative exposures or interventions are emerging with respect to a common outcome, and there are needs to formally test the difference in the associations of multiple exposures. We propose a duplication method-based multivariate Wald test in the Cox proportional hazard regression analyses to test the difference in the associations of multiple exposures with a same outcome. The proposed method applies to linear or categorical exposures. To illustrate our method, we applied our method to compare the associations between alignment to two different dietary patterns, either as continuous or quartile exposures, and incident chronic diseases, defined as a composite of CVD, cancer, and diabetes, in the Health Professional Follow-up Study. Relevant sample codes in R that implement the proposed approach are provided. The proposed duplication-method-based approach offers a flexible, formal statistical test of multiple exposures for the common outcome with minimal assumptions.
CBX: Python and Julia packages for consensus-based interacting particle methods
Creators: name: Bailo, Rafael
name: Barbaro, Alethea
name: Gomes, Susana N.
name: Riedl, Konstantin
name: Roith, Tim
name: Totzeck, Claudia
name: Vaes, Urbain
Publisher: name: arXiv
Year: 2024
Subjects: subject: Optimization and Control (math.OC)
subject: FOS: Mathematics
subject: 49-04 (Primary) 90C26, 90C56, 93A16 (Secondary)

description: We introduce CBXPy and ConsensusBasedX.jl, Python and Julia implementations of consensus-based interacting particle systems (CBX), which generalise consensus-based optimization methods (CBO) for global, derivative-free optimisation. The raison d'être of our libraries is twofold: on the one hand, to offer high-performance implementations of CBX methods that the community can use directly, while on the other, providing a general interface that can accommodate and be extended to further variations of the CBX family. Python and Julia were selected as the leading high-level languages in terms of usage and performance, as well as their popularity among the scientific computing community. Both libraries have been developed with a common ethos, ensuring a similar API and core functionality, while leveraging the strengths of each language and writing idiomatic code.
description: 7 pages, 3 figures
Learning from Reduced Labels for Long-Tailed Data
Creators: name: Wei, Meng
name: Li, Zhongnian
name: Zhou, Yong
name: Xu, Xinzheng
Publisher: name: arXiv
Year: 2024
Subjects: subject: Machine Learning (cs.LG)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming. Unfortunately, despite being a common approach to mitigate labeling costs, existing weakly supervised learning methods struggle to adequately preserve supervised information for tail samples, resulting in a decline in accuracy for the tail classes. To alleviate this problem, we introduce a novel weakly supervised labeling setting called Reduced Label. The proposed labeling setting not only avoids the decline of supervised information for the tail samples, but also decreases the labeling costs associated with long-tailed data. Additionally, we propose an straightforward and highly efficient unbiased framework with strong theoretical guarantees to learn from these Reduced Labels. Extensive experiments conducted on benchmark datasets including ImageNet validate the effectiveness of our approach, surpassing the performance of state-of-the-art weakly supervised methods.
description: 11 pages, 3 figures
The Costs of Competition in Distributing Scarce Research Funds
Creators: name: Schweiger, Gerald
name: Barnett, Adrian
name: Besselaar, Peter van den
name: Bornmann, Lutz
name: De Block, Andreas
name: Ioannidis, John P. A.
name: Sandström, Ulf
name: Conix, Stijn
Publisher: name: arXiv
Year: 2024
Subjects: subject: General Economics (econ.GN)
subject: FOS: Economics and business

description: Research funding systems are not isolated systems - they are embedded in a larger scientific system with an enormous influence on the system. This paper aims to analyze the allocation of competitive research funding from different perspectives: How reliable are decision processes for funding? What are the economic costs of competitive funding? How does competition for funds affect doing risky research? How do competitive funding environments affect scientists themselves, and which ethical issues must be considered? We attempt to identify gaps in our knowledge of research funding systems; we propose recommendations for policymakers and funding agencies, including empirical experiments of decision processes and the collection of data on these processes. With our recommendations we hope to contribute to developing improved ways of organizing research funding.
description: 29 pages
Origin of competing charge density waves in kagome metal ScV$_6$Sn$_6$
Creators: name: Wang, Kang
name: Chen, Siyu
name: Kim, Sun-Woo
name: Monserrat, Bartomeu
Publisher: name: arXiv
Year: 2024
Subjects: subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: Understanding competing charge density wave (CDW) orders in the bilayer kagome metal ScV$_6$Sn$_6$ remains challenging. Experimentally, upon cooling, short-range order with wave vector $\mathbf{q}_{2}=(\frac{1}{3},\frac{1}{3},\frac{1}{2})$ forms, which is subsequently suppressed by the condensation of long-range $\mathbf{q}_{3}=(\frac{1}{3},\frac{1}{3},\frac{1}{3})$ CDW order at lower temperature. Theoretically, however, the $\mathbf{q}_{2}$ CDW is predicted as the ground state, leaving the CDW mechanism elusive. Here, using anharmonic phonon-phonon calculations combined with density functional theory, we predict a temperature-driven structural phase transitions from the high-temperature pristine phase to the $\mathbf{q}_{2}$ CDW, followed by the low-temperature $\mathbf{q}_{3}$ CDW, explaining experimental observations. We demonstrate that semi-core electron states stabilize the $\mathbf{q}_{3}$ CDW over the $\mathbf{q}_{2}$ CDW. Furthermore, we find that the out-of-plane lattice parameter controls the competing CDWs, motivating us to propose compressive bi-axial strain as an experimental protocol to stabilize the $\mathbf{q}_{2}$ CDW. Finally, we suggest Ge or Pb doping at the Sn site as another potential avenue to control CDW instabilities. Our work provides a full theory of CDWs in ScV$_6$Sn$_6$, rationalizing experimental observations and resolving earlier discrepancies between theory and experiment.
description: 19 pages, 5 figures
Supervised Multiple Kernel Learning approaches for multi-omics data integration
Creators: name: Briscik, Mitja
name: Tazza, Gabriele
name: Dillies, Marie-Agnes
name: Vidács, László
name: Dejean, Sébastien
Publisher: name: arXiv
Year: 2024
Subjects: subject: Machine Learning (stat.ML)
subject: Machine Learning (cs.LG)
subject: Applications (stat.AP)
subject: FOS: Computer and information sciences

description: Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining. We provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches. Multiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.
DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection
Creators: name: Fent, Felix
name: Palffy, Andras
name: Caesar, Holger
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: The perception of autonomous vehicles has to be efficient, robust, and cost-effective. However, cameras are not robust against severe weather conditions, lidar sensors are expensive, and the performance of radar-based perception is still inferior to the others. Camera-radar fusion methods have been proposed to address this issue, but these are constrained by the typical sparsity of radar point clouds and often designed for radars without elevation information. We propose a novel camera-radar fusion approach called Dual Perspective Fusion Transformer (DPFT), designed to overcome these limitations. Our method leverages lower-level radar data (the radar cube) instead of the processed point clouds to preserve as much information as possible and employs projections in both the camera and ground planes to effectively use radars with elevation information and simplify the fusion with camera data. As a result, DPFT has demonstrated state-of-the-art performance on the K-Radar dataset while showing remarkable robustness against adverse weather conditions and maintaining a low inference time. The code is made available as open-source software under https://github.com/TUMFTM/DPFT.
description: Accepted to IEEE Transactions on Intelligent Vehicles
Two bifurcation sets of expansive Lorenz maps with a hole at the critical point
Creators: name: Sun, Yun
name: Li, Bing
Publisher: name: arXiv
Year: 2024
Subjects: subject: Dynamical Systems (math.DS)
subject: FOS: Mathematics
subject: 37E05, 37B10

description: Let $f$ be an expansive Lorenz map on $[0,1]$ and $c$ be the critical point. The survivor set we are discussing here is denoted as $S^+_{f}(a,b):=\{x\in[0,1]:f(b)\leq f^{n}(x) \leq f(a)\ \forall n\geq0\}$, where the hole $(a,b)\subseteq [0,1]$ satisfies $a\leq c \leq b$ and $a\neq b$. Let $a\in[0,c]$ be fixed, we mainly focus on the following two bifurcation sets: $$ E_{f}(a):=\{b\in[c,1]:S^{+}_{f}(a,ε)\neq S^{+}_{f}(a,b) \ \forall \ ε>b\}, \ \ {\rm and} $$ $$ B_{f}(a):=\{b\in[c,1]:h_{top}(S^+_{f}(a,ε))\neq h_{top}(S^+_{f}(a,b)) \ \forall \ ε>b\}. $$
 By combinatorial renormalization tools, we give a complete characterization of the maximal plateau $P(b)$ such that for all $ε\in P(b)$, $h_{top}(S^+_{f}(a,ε))=h_{top}(S^+_{f}(a,b))$. Moreover, we obtain a sufficient and necessary condition for $E_{f}(a)=B_{f}(a)$, which partially extends the results in \cite{allaart2023} and \cite{baker2020}.
description: 18 pages
Photonic time crystals: Theory and applications
Creators: name: Asgari, M. M.
name: Garg, P.
name: Wang, X.
name: Mirmoosa, M. S.
name: Rockstuhl, C.
name: Asadchy, V.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Optics (physics.optics)
subject: Applied Physics (physics.app-ph)
subject: FOS: Physical sciences

description: This tutorial offers a comprehensive overview of photonic time crystals - artificial materials whose electromagnetic properties are periodically modulated in time at scales comparable to the oscillation period of light while remaining spatially uniform. Being the temporal analogs to traditional photonic crystals, photonic time crystals differ in that they exhibit momentum bandgaps instead of energy bandgaps. The energy is not conserved within momentum bandgaps, and eigenmodes with exponentially growing amplitudes exist in the momentum bandgap. Such properties make photonic time crystals a fascinating novel class of artificial materials from a basic science and applied perspective. This tutorial overviews the fundamental electromagnetic equations governing photonic time crystals and explores the groundbreaking physical phenomena they support. Based on these properties, we also oversee a diverse range of applications they unlock. Different material platforms suitable for creating photonic time crystals are discussed and compared. Furthermore, we elaborate on the connections between wave amplification in photonic time crystals and parametric amplification mechanisms in electrical circuits and nonlinear optics. The tutorial will be helpful for readers with physics or engineering backgrounds. It is designed to serve as an introductory guide for beginners and to establish a reference baseline reflecting the current understanding for researchers in the field.
description: 94 pages, 38 figures
Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility
Creators: name: Ma, Haoran
name: Ye, Liao
name: Ruan, Fanjie
name: Zhao, Zichao
name: Li, Maohui
name: Wang, Yuehai
name: Yang, Jianyi
Publisher: name: arXiv
Year: 2024
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Generative adversarial networks (GANs) have achieved remarkable success with realistic tasks such as creating realistic images, texts, and audio. Combining GANs and quantum computing, quantum GANs are thought to have an exponential advantage over their classical counterparts due to the stronger expressibility of quantum circuits. In this research, a two-qubit silicon quantum photonic chip is created, capable of executing arbitrary controlled-unitary (CU) operations and generating any 2-qubit pure state, thus making it an excellent platform for quantum GANs. To capture complex data patterns, a hybrid generator is proposed to inject nonlinearity into quantum GANs. As a demonstration, three generative tasks, covering both pure quantum versions of GANs (PQ-GAN) and hybrid quantum-classical GANs (HQC-GANs), are successfully carried out on the chip, including high-fidelity single-qubit state learning, classical distributions loading, and compressed image production. The experiment results prove that silicon quantum photonic chips have great potential in generative learning applications.
Collisions of Spin-polarized YO Molecules for Single Partial Waves
Creators: name: Burau, Justin J.
name: Mehling, Kameron
name: Frye, Matthew D.
name: Chen, Mengjie
name: Aggarwal, Parul
name: Hutson, Jeremy M.
name: Ye, Jun
Publisher: name: arXiv
Year: 2024
Subjects: subject: Atomic Physics (physics.atom-ph)
subject: FOS: Physical sciences

description: Efficient sub-Doppler laser cooling and optical trapping of YO molecules offer new opportunities to study collisional dynamics in the quantum regime. Confined in a crossed optical dipole trap, we achieve the highest phase-space density of $2.5 \times 10^{-5}$ for a bulk laser-cooled molecular sample. This sets the stage to study YO--YO collisions in the microkelvin temperature regime, and reveal state-dependent, single-partial-wave two-body collisional loss rates. We determine the partial-wave contributions to loss of specific rotational states (first excited $N=1$ and ground $N=0$) following two strategies. First, we measure the change of the collision rate in a spin mixture of $N=1$ by tuning the kinetic energy with respect to the p- and d-wave centrifugal barriers. Second, we compare loss rates between a spin mixture and a spin-polarized state in $N=0$. Using quantum defect theory with a partially absorbing boundary condition at short range, we show that the dependence on temperature for $N=1$ can be reproduced in the presence of a d-wave or f-wave resonance, and the dependence on a spin mixture for $N=0$ with a p-wave resonance.
description: 5 Pages, 4 Figures. Fixed typos. Collisional loss data in $N = 1$ and $N = 0$ is now presented as density versus time instead of number versus time. This work is published in Physical Review A (Copyright 2024 by the American Physical Society). The link to the article is https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.L041306
Action Model Learning with Guarantees
Creators: name: Aineto, Diego
name: Scala, Enrico
Publisher: name: arXiv
Year: 2024
Subjects: subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: This paper studies the problem of action model learning with full observability. Following the learning by search paradigm by Mitchell, we develop a theory for action model learning based on version spaces that interprets the task as search for hypothesis that are consistent with the learning examples. Our theoretical findings are instantiated in an online algorithm that maintains a compact representation of all solutions of the problem. Among these range of solutions, we bring attention to actions models approximating the actual transition system from below (sound models) and from above (complete models). We show how to manipulate the output of our learning algorithm to build deterministic and non-deterministic formulations of the sound and complete models and prove that, given enough examples, both formulations converge into the very same true model. Our experiments reveal their usefulness over a range of planning domains.
A Sentiment Analysis of Medical Text Based on Deep Learning
Creators: name: Chen, Yinan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computation and Language (cs.CL)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: The field of natural language processing (NLP) has made significant progress with the rapid development of deep learning technologies. One of the research directions in text sentiment analysis is sentiment analysis of medical texts, which holds great potential for application in clinical diagnosis. However, the medical field currently lacks sufficient text datasets, and the effectiveness of sentiment analysis is greatly impacted by different model design approaches, which presents challenges. Therefore, this paper focuses on the medical domain, using bidirectional encoder representations from transformers (BERT) as the basic pre-trained model and experimenting with modules such as convolutional neural network (CNN), fully connected network (FCN), and graph convolutional networks (GCN) at the output layer. Experiments and analyses were conducted on the METS-CoV dataset to explore the training performance after integrating different deep learning networks. The results indicate that CNN models outperform other networks when trained on smaller medical text datasets in combination with pre-trained models like BERT. This study highlights the significance of model selection in achieving effective sentiment analysis in the medical domain and provides a reference for future research to develop more efficient model architectures.
Existential Unforgeability in Quantum Authentication From Quantum Physical Unclonable Functions Based on Random von Neumann Measurement
Creators: name: Ghosh, Soham
name: Galetsky, Vladlen
name: Farré, Pol Juliá
name: Deppe, Christian
name: Ferrara, Roberto
name: Boche, Holger
Publisher: name: arXiv
Year: 2024
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Physical Unclonable Functions (PUFs) leverage inherent, non-clonable physical randomness to generate unique input-output pairs, serving as secure fingerprints for cryptographic protocols like authentication. Quantum PUFs (QPUFs) extend this concept by using quantum states as input-output pairs, offering advantages over classical PUFs, such as challenge reusability via public channels and eliminating the need for trusted parties due to the no-cloning theorem. Recent work introduced a generalized mathematical framework for QPUFs. It was shown that random unitary QPUFs cannot achieve existential unforgeability against Quantum Polynomial Time (QPT) adversaries. Security was possible only with additional uniform randomness. To avoid the cost of external randomness, we propose a novel measurement-based scheme. Here, the randomness naturally arises from quantum measurements. Additionally, we introduce a second model where the QPUF functions as a nonunitary quantum channel, which guarantees existential unforgeability. These are the first models in the literature to demonstrate a high level of provable security. Finally, we show that the Quantum Phase Estimation (QPE) protocol, applied to a Haar random unitary, serves as an approximate implementation of the second type of QPUF by approximating a von Neumann measurement on the unitary's eigenbasis.
description: 20 pages, 6 figures
Simulation-based inference of black hole ringdowns in the time domain
Creators: name: Pacilio, Costantino
name: Bhagwat, Swetha
name: Cotesta, Roberto
Publisher: name: arXiv
Year: 2024
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: Gravitational waves emitted by a ringing black hole allow us to perform precision tests of general relativity in the strong field regime. With improvements to our current gravitational wave detectors and upcoming next-generation detectors, developing likelihood-free parameter inference infrastructure is critical as we will face complications like nonstandard noise properties, partial data and incomplete signal modeling that may not allow for an analytically tractable likelihood function. In this work, we present a proof-of-concept strategy to perform likelihood-free Bayesian inference on ringdown gravitational waves using simulation based inference. Specifically, our method is based on truncated sequential neural posterior estimation, which trains a neural density estimator of the posterior for a specific observed data segment. We setup the ringdown parameter estimation directly in the time domain. We show that the parameter estimation results obtained using our trained networks are in agreement with well-established Markov-chain methods for simulated injections as well as analysis on real detector data corresponding to GW150914. Additionally, to assess our approach's internal consistency, we show that the density estimators pass a Bayesian coverage test.
description: 12 pages, 7 figures; v2: minor changes, matches published version; v3: metadata added
Numerical investigation of the late-time tails of the solutions of the Fackerell-Ipser equation
Creators: name: Racz, Istvan
name: Toth, Gabor Zsolt
Publisher: name: arXiv
Year: 2024
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: The late-time behaviour of the solutions of the Fackerell-Ipser equation (which is a wave equation for the spin-zero component of the electromagnetic field strength tensor) on the closure of the domain of outer communication of sub-extremal Kerr spacetime is studied numerically. Within the Kerr family, the case of Schwarzschild background is also considered. Horizon-penetrating compactified hyperboloidal coordinates are used, which allow the behaviour of the solutions to be observed at the event horizon and at future null infinity as well. For the initial data, pure multipole configurations that have compact support and are either stationary or non-stationary are taken. It is found that with such initial data the solutions of the Fackerell-Ipser equation converge at late times either to a known static solution (up to a constant factor) or to zero. As the limit is approached, the solutions exhibit a quasinormal ringdown and finally a power-law decay. The exponents characterizing the power-law decay of the spherical harmonic components of the field variable are extracted from the numerical data for various values of the parameters of the initial data, and based on the results a proposal for a Price's law relevant to the Fackerell-Ipser equation is made. Certain conserved energy and angular momentum currents are used to verify the numerical implementation of the underlying mathematical model. In the construction of these currents a discrete symmetry of the Fackerell-Ipser equation, which is the product of an equatorial reflection and a complex conjugation, is also taken into account.
description: 29 pages, LaTeX, minor changes
Record high superconducting transition temperature in Ti$_{1-x}$Mn$_x$ alloy with rich magnetic element Mn
Creators: name: Zhang, Ying-Jie
name: Zhu, Yijie
name: Li, Qing
name: Xiang, Zhe-Ning
name: Huang, Tianheng
name: Sun, Jian
name: Wen, Hai-Hu
Publisher: name: arXiv
Year: 2024
Subjects: subject: Superconductivity (cond-mat.supr-con)
subject: FOS: Physical sciences

description: It is well-known that magnetic moments are very harmful to superconductivity. A typical example is the element Mn whose compounds usually exhibit strong magnetism. Thus, it is very hard to achieve superconductivity in materials containing Mn. Here, we report enhanced superconductivity with the superconducting transition temperature ($T_\text{c}$) up to a record high-value of about 26 K in a beta-phase Ti$_{1-x}$Mn$_x$ alloy containing rich magnetic element Mn under high pressures. This is contrary to the intuition that the magnetic moments always suppress superconductivity. Under high pressures, we also found that in the middle-pressure regime, the Pauli limit of the upper critical field is surpassed. The synchrotron X-ray diffraction data shows an unchanged beta-phase with a continuous contraction of the cell volume, which is well supported by the first-principles calculations. Although the theoretical results based on electron-phonon coupling (EPC) can interpret the $T_\text{c}$ value in a certain pressure region, the monotonic enhancement of superconductivity by pressure cannot seek support from the theory. Our results show a surprising enhancement of superconductivity in Ti$_{1-x}$Mn$_x$ alloy with a considerable Mn content.
description: 28 pages, 5 figures. Comments are welcome and appreciated
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
Creators: name: Kirk, Hannah Rose
name: Whitefield, Alexander
name: Röttger, Paul
name: Bean, Andrew
name: Margatina, Katerina
name: Ciro, Juan
name: Mosquera, Rafael
name: Bartolo, Max
name: Williams, Adina
name: He, He
name: Vidgen, Bertie
name: Hale, Scott A.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computation and Language (cs.CL)
subject: FOS: Computer and information sciences

description: Human feedback is central to the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) of feedback processes. To navigate these questions, we introduce PRISM, a dataset that maps the sociodemographics and stated preferences of 1,500 diverse participants from 75 countries, to their contextual preferences and fine-grained feedback in 8,011 live conversations with 21 LLMs. With PRISM, we contribute (i) wider geographic and demographic participation in feedback; (ii) census-representative samples for two countries (UK, US); and (iii) individualised ratings that link to detailed participant profiles, permitting personalisation and attribution of sample artefacts. We target subjective and multicultural perspectives on value-laden and controversial issues, where we expect interpersonal and cross-cultural disagreement. We use PRISM in three case studies to demonstrate the need for careful consideration of which humans provide what alignment data.
Expressiveness of Commutative Quantum Circuits: A Probabilistic Approach
Creators: name: Ramirez, Jorge M.
name: Wong, Elaine
name: Alves, Caio
name: Chehade, Sarah
name: Bennink, Ryan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: This study investigates the frame potential and expressiveness of commutative quantum circuits. Based on the Fourier series representation of these circuits, we express quantum expectation and pairwise fidelity as characteristic functions of random variables, and expressiveness as the recurrence probability of a random walk on a lattice. A central outcome of our work includes formulas to approximate the frame potential and expressiveness for any commutative quantum circuit, underpinned by convergence theorems in probability theory. We identify the lattice volume of the random walk as means to approximate expressiveness based on circuit architecture. In the specific case of commutative circuits involving Pauli-$Z$ rotations, we provide theoretical results relating expressiveness and circuit structure. Our probabilistic representation also provide means for bounding and approximately calculating the frame potential of a circuit through sampling methods.
Study of energy backflow in unidirectional monochromatic and space-time waves
Creators: name: Saari, Peeter
name: Besieris, Ioannis
Publisher: name: arXiv
Year: 2024
Subjects: subject: Optics (physics.optics)
subject: FOS: Physical sciences

description: Backflow, or retropropagation, is a counterintuitive phenomenon whereby for a forward-propagating wave the energy locally propagates backward. In the context of backflow, physically most interesting are the so-called unidirectional waves, which contain only forward propagating plane wave constituents. Yet, very few such waves possessing closed-form analytic expressions for evaluation of the Poynting vector are known. In this study, we examine energy backflow in a novel (2+1)-dimensional unidirectional monochromatic wave and in a (2+1)D spatio-temporal wave packet, analytic expressions which we succeeded to find. We also present a detailed study of the backflow in the "needle" pulse. This is an interesting model object because well-known superluminal non-diffracting space-time wave packets can be derived from its factored wave function. Finally we study the backflow in an unidirectional version of the so-called focus wave mode--a pulse propagating luminally and without spread, which is the first and most studied representative of the (3+1)D non-diffracting space-time wave packets (also referred to as spatiotemporally localized waves).
description: 17 pages, 10 figures. This is a significantly expanded and enhanced version
On the Iwasawa theory of Cayley graphs
Creators: name: Ghosh, Sohan
name: Ray, Anwesh
Publisher: name: arXiv
Year: 2024
Subjects: subject: Number Theory (math.NT)
subject: Combinatorics (math.CO)
subject: Group Theory (math.GR)
subject: FOS: Mathematics
subject: 11R23, 05C25, 05C31, 05C50

description: This paper explores Iwasawa theory from a graph theoretic perspective, focusing on the algebraic and combinatorial properties of Cayley graphs. Using representation theory, we analyze Iwasawa-theoretic invariants within $\mathbb{Z}_\ell$-towers of Cayley graphs, revealing connections between graph theory, number theory, and group theory. Key results include the factorization of associated Iwasawa polynomials and the decomposition of $μ$- and $λ$-invariants. Additionally, we apply these insights to complete graphs, establishing conditions under which these invariants vanish.
description: Version 2: 23 pages, minor corrections following referee reports. Example 2 added. Accepted for publication in Research in the Mathematical Sciences
Multi-Robot Rendezvous in Unknown Environment with Limited Communication
Creators: name: Song, Kun
name: Chen, Gaoming
name: Liu, Wenhang
name: Xiong, Zhenhua
Publisher: name: arXiv
Year: 2024
Subjects: subject: Robotics (cs.RO)
subject: FOS: Computer and information sciences

description: Rendezvous aims at gathering all robots at a specific location, which is an important collaborative behavior for multi-robot systems. However, in an unknown environment, it is challenging to achieve rendezvous. Previous researches mainly focus on special scenarios where communication is not allowed and each robot executes a random searching strategy, which is highly time-consuming, especially in large-scale environments. In this work, we focus on rendezvous in unknown environments where communication is available. We divide this task into two steps: rendezvous based environment exploration with relative pose (RP) estimation and rendezvous point selection. A new strategy called partitioned and incomplete exploration for rendezvous (PIER) is proposed to efficiently explore the unknown environment, where lightweight topological maps are constructed and shared among robots for RP estimation with very few communications. Then, a rendezvous point selection algorithm based on the merged topological map is proposed for efficient rendezvous for multi-robot systems. The effectiveness of the proposed methods is validated in both simulations and real-world experiments.
description: Accepted by RAL. 8 pages, 6 figures. Code at https://github.com/KunSong-L/Distributed-Multi-Robot-Topological-Map
Polydisperse versus monodisperse microbubbles: A simulation study for contrast-enhanced ultrasound imaging
Creators: name: Matalliotakis, Agisilaos
name: Verweij, Martin D.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Medical Physics (physics.med-ph)
subject: Computational Physics (physics.comp-ph)
subject: FOS: Physical sciences

description: Contrast-enhanced ultrasound (CEUS) presents distinct advantages in diagnostic echography. Utilizing microbubbles (MBs) as conventional contrast agents enhances vascular visualization and organ perfusion, facilitating real-time, non-invasive procedures. There is a current tendency to replace the traditional polydisperse MBs by novel monodisperse formulations in an attempt to optimize contrast enhancement and guarantee consistent behavior and reliable imaging outcomes. This study investigates the contrast enhancement achieved by monodisperse MBs of different sizes, and their influence on nonlinear imaging artifacts observed in traditional CEUS. To explore the differences between monodisperse and polydisperse populations without excessive experimentation, numerical simulations are employed for delivering precise, objective and expeditious results. The Iterative Nonlinear Contrast Source (INCS) method has previously demonstrated its efficacy in simulating ultrasound propagation in large populations in which each bubble has individual properties and several orders of multiple scattering are significant. Our findings in CEUS imaging indicate that scattering from resonant monodisperse microbubbles is 11.8 dB stronger than scattering from the polydisperse population. Furthermore, the amplitude of nonlinear imaging artifacts downstream of the monodisperse population is 19.4 dB stronger compared to polydisperse suspension. Investigating the impact of multiple scattering on polydisperse populations compared to various monodisperse suspensions reveals that monodisperse MBs are more effective contrast agents, especially when on resonance. Despite the strong signal to noise ratio of monodisperse populations, the imaging artifacts due to nonlinear wave propagation are also enhanced, resulting in more missclassification of MBs as tissue.
Nonlocal free-energy density functional for warm dense matter
Creators: name: Ma, Cheng
name: Chen, Min
name: Xie, Yu
name: Xu, Qiang
name: Mi, Wenhui
name: Wang, Yanchao
name: Ma, Yanming
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computational Physics (physics.comp-ph)
subject: FOS: Physical sciences

description: Finite-temperature orbital-free density functional theory (FT-OFDFT) holds significant promise for simulating warm dense matter due to its favorable scaling with both system size and temperature. However, the lack of the numerically accurate and transferable noninteracting free energy functionals results in a limit on the application of FT-OFDFT for warm dense matter simulations. Here, a nonlocal free energy functional, named XWMF, was derived by line integrals for FT-OFDFT simulations. Particularly, a designed integral path, wherein the electronic density varies from uniform to inhomogeneous, was employed to accurately describe deviations in response behavior from the uniform electron gas. The XWMF has been benchmarked by a range of warm dense matter systems including the Si, Al, H, He, and H-He mixture. The simulated results demonstrate that FT-OFDFT within XWMF achieves remarkable performance for accuracy and numerical stability. It is worth noting that XWMF exhibits a low computational cost for large-scale ab~initio simulations, offering exciting opportunities for the realistic simulations of warm dense matter systems covering a broad range of temperatures and pressures.
description: 8 pages, 4 figures
Atomistic-informed phase field modeling of magnesium twin growth by disconnections
Creators: name: Hu, Yang
name: Kochmann, Dennis M.
name: Runnels, Brandon
Publisher: name: arXiv
Year: 2024
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: The nucleation and propagation of disconnections play an essential role during twin growth. Atomistic methods can reveal such small structural features on twin facets and model their motion, yet are limited by the simulation length and time scales. Alternatively, mesoscale modeling approaches (such as the phase field method) address these constraints of atomistic simulations and can maintain atomic-level accuracy when integrated with atomic-level information. In this work, a phase field model is used to simulate the disconnection-mediated twinning, informed by molecular dynamics (MD) simulations. This work considers the specific case of the growth of $\{10\bar{1}2\}$ twin in magnesium. MD simulations are first conducted to obtain the orientation-dependent interface mobility and motion threshold, and to simulate twin embryo growth and collect facet velocities, which can be used for calibrating the continuum model. The phase field disconnections model, based on the principle of minimum dissipation potential, provides the theoretical framework. This model incorporates a nonconvex grain boundary energy, elasticity and shear coupling, and simulates disconnections as a natural emergence under the elastic driving force. The phase field model is further optimized by including the anisotropic interface mobility and motion threshold suggested by MD simulations. Results agree with MD simulations of twin embryo growth in the aspects of final twin thickness, twin shape, and twin size, as well as the kinetic behavior of twin boundaries and twin tips. The simulated twin microstructure is also consistent with experimental observations, demonstrating the fidelity of the model.
Why Reinforcement Learning in Energy Systems Needs Explanations
Creators: name: Butt, Hallah Shahid
name: Schäfer, Benjamin
Publisher: name: arXiv
Year: 2024
Subjects: subject: Artificial Intelligence (cs.AI)
subject: Systems and Control (eess.SY)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: With economic development, the complexity of infrastructure has increased drastically. Similarly, with the shift from fossil fuels to renewable sources of energy, there is a dire need for such systems that not only predict and forecast with accuracy but also help in understanding the process of predictions. Artificial intelligence and machine learning techniques have helped in finding out wellperforming solutions to different problems in the energy sector. However, the usage of state-of-the-art techniques like reinforcement learning is not surprisingly convincing. This paper discusses the application of reinforcement techniques in energy systems and how explanations of these models can be helpful
A Good Foundation is Worth Many Labels: Label-Efficient Panoptic Segmentation
Creators: name: Vödisch, Niclas
name: Petek, Kürsat
name: Käppeler, Markus
name: Valada, Abhinav
name: Burgard, Wolfram
Publisher: name: arXiv
Year: 2024
Subjects: subject: Robotics (cs.RO)
subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: A key challenge for the widespread application of learning-based models for robotic perception is to significantly reduce the required amount of annotated training data while achieving accurate predictions. This is essential not only to decrease operating costs but also to speed up deployment time. In this work, we address this challenge for PAnoptic SegmenTation with fEw Labels (PASTEL) by exploiting the groundwork paved by visual foundation models. We leverage descriptive image features from such a model to train two lightweight network heads for semantic segmentation and object boundary detection, using very few annotated training samples. We then merge their predictions via a novel fusion module that yields panoptic maps based on normalized cut. To further enhance the performance, we utilize self-training on unlabeled images selected by a feature-driven similarity scheme. We underline the relevance of our approach by employing PASTEL to important robot perception use cases from autonomous driving and agricultural robotics. In extensive experiments, we demonstrate that PASTEL significantly outperforms previous methods for label-efficient segmentation even when using fewer annotations. The code of our work is publicly available at http://pastel.cs.uni-freiburg.de.
Search for new particles in events with a hadronically decaying W or Z boson and large missing transverse momentum at $\sqrt{s}$ = 13 TeV using the ATLAS detector
Creators: name: ATLAS Collaboration
Publisher: name: arXiv
Year: 2024
Subjects: subject: High Energy Physics - Experiment (hep-ex)
subject: FOS: Physical sciences

description: A search is presented for new particles produced in proton-proton collisions at a centre-of-mass energy of 13 TeV that result in final states comprising a massive vector (W or Z) boson that decays hadronically and large missing transverse momentum. The data sample was collected with the ATLAS experiment at the Large Hadron Collider from 2015 to 2018 and corresponds to an integrated luminosity of 140 fb$^{-1}$. No significant excess over the Standard Model expectation is observed. Model-independent 95% confidence-level limits on the visible cross-section that range from 0.3 fb to 79.5 fb are obtained for non-Standard-Model processes. Exclusion limits are also presented for models with axion-like particles, for two-Higgs-doublet models with a pseudo-scalar mediator between the Standard Model and the dark sector, for the invisible decay of the Higgs boson and for pair-produced weakly interacting dark matter candidates.
description: 51 pages in total, author list starting page 34, 13 figures, 6 tables, published in JHEP 11 (2024) 126. All tables and figures, including auxiliary figures, are available at: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/EXOT-2019-27/
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models
Creators: name: Máté, Bálint
name: Fleuret, François
name: Bereau, Tristan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Machine Learning (cs.LG)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: Thermodynamic integration (TI) offers a rigorous method for estimating free-energy differences by integrating over a sequence of interpolating conformational ensembles. However, TI calculations are computationally expensive and typically limited to coupling a small number of degrees of freedom due to the need to sample numerous intermediate ensembles with sufficient conformational-space overlap. In this work, we propose to perform TI along an alchemical pathway represented by a trainable neural network, which we term Neural TI. Critically, we parametrize a time-dependent Hamiltonian interpolating between the interacting and non-interacting systems, and optimize its gradient using a score matching objective. The ability of the resulting energy-based diffusion model to sample all intermediate ensembles allows us to perform TI from a single reference calculation. We apply our method to Lennard-Jones fluids, where we report accurate calculations of the excess chemical potential, demonstrating that Neural TI reproduces the underlying changes in free energy without the need for simulations at interpolating Hamiltonians.