Search

The coupled axial and transverse currents (CATI) method was recently introduced to model the AC loss and magnetization in twisted composite superconducting strands with low computational cost and high accuracy. This method involves two-dimensional finite element (FE) models coupled with circuit equations representing the periodicity of the strand. In this paper, we propose to adapt the CATI method to Rutherford cables, which are periodic structures made of transposed superconducting strands. We focus on reproducing the interstrand coupling currents flowing across contact resistances between the strands and we analyze the associated AC loss. We show that results of a reference three-dimensional FE model are accurately reproduced with a strongly reduced computational cost., Comment: 5 pages, 5 figures, presented at ASC 2024

Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations are difficult to achieve due to the challenges of coordinating and deploying heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which addresses many of these challenges. We developed a workflow Software Development Toolkit (SDK), a curated collection of workflow technologies that can be composed and interoperated through a common interface, engineered following current best practices, and specifically designed to work on HPC platforms. ExaWorks also developed PSI/J, a job management abstraction API, to simplify the construction of portable software components and applications that can be used over various HPC schedulers. The PSI/J API is a minimal interface for submitting and monitoring jobs and their execution state across multiple and commonly used HPC schedulers. We also describe several leading and innovative workflow examples of ExaWorks tools used on DOE leadership platforms. Furthermore, we discuss how our project is working with the workflow community, large computing facilities, and HPC platform vendors to address the requirements of workflows sustainably at the exascale.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Recent self-supervised clustering-based pre-training techniques like DINO and Cribo have shown impressive results for downstream detection and segmentation tasks. However, real-world applications such as autonomous driving face challenges with imbalanced object class and size distributions and complex scene geometries. In this paper, we propose S3PT a novel scene semantics and structure guided clustering to provide more scene-consistent objectives for self-supervised training. Specifically, our contributions are threefold: First, we incorporate semantic distribution consistent clustering to encourage better representation of rare classes such as motorcycles or animals. Second, we introduce object diversity consistent spatial clustering, to handle imbalanced and diverse object sizes, ranging from large background areas to small objects such as pedestrians and traffic signs. Third, we propose a depth-guided spatial clustering to regularize learning based on geometric information of the scene, thus further refining region separation on the feature level. Our learned representations significantly improve performance in downstream semantic segmentation and 3D object detection tasks on the nuScenes, nuImages, and Cityscapes datasets and show promising domain translation properties., Comment: Accepted for WACV 2025

The R package bsvars provides a wide range of tools for empirical macroeconomic and financial analyses using Bayesian Structural Vector Autoregressions. It uses frontier econometric techniques and C++ code to ensure fast and efficient estimation of these multivariate dynamic structural models, possibly with many variables, complex identification strategies, and non-linear characteristics. The models can be identified using adjustable exclusion restrictions and heteroskedastic or non-normal shocks. They feature a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, statistical verification of identification and hypotheses on autoregressive parameters, and analyses of structural shocks, volatilities, and fitted values. These features differentiate bsvars from existing R packages that either focus on a specific structural model, do not consider heteroskedastic shocks, or lack the implementation using compiled code.

Human capabilities in understanding visual relations are far superior to those of AI systems, especially for previously unseen objects. For example, while AI systems struggle to determine whether two such objects are visually the same or different, humans can do so with ease. Active vision theories postulate that the learning of visual relations is grounded in actions that we take to fixate objects and their parts by moving our eyes. In particular, the low-dimensional spatial information about the corresponding eye movements is hypothesized to facilitate the representation of relations between different image parts. Inspired by these theories, we develop a system equipped with a novel Glimpse-based Active Perception (GAP) that sequentially glimpses at the most salient regions of the input image and processes them at high resolution. Importantly, our system leverages the locations stemming from the glimpsing actions, along with the visual content around them, to represent relations between different parts of the image. The results suggest that the GAP is essential for extracting visual relations that go beyond the immediate visual content. Our approach reaches state-of-the-art performance on several visual reasoning tasks being more sample-efficient, and generalizing better to out-of-distribution visual inputs than prior models., Comment: 10 pages of main text and 8 pages appendices

In this paper, we propose the Reduced Order Hysteretic Magnetization (ROHM) model to describe the magnetization and instantaneous power loss of composite superconductors subject to time-varying magnetic fields. Once the parameters of the ROHM model are fixed based on reference simulations, it allows to directly compute the macroscopic response of composite superconductors without the need to solve the detailed current density distribution. It can then be used as part of a homogenization method in large-scale superconducting models to significantly reduce the computational effort compared to detailed simulations. In this contribution, we focus on the case of a strand with twisted superconducting filaments subject to a time-varying transverse magnetic field. We propose two levels of ROHM models: (i) a rate-independent model that reproduces hysteresis in the filaments, and (ii) a rate-dependent model that generalizes the first level by also reproducing dynamic effects due to coupling and eddy currents. We describe the implementation and inclusion of these models in a finite element framework, discuss how to deduce the model parameters, and finally demonstrate the capabilities of the approach in terms of accuracy and efficiency over a wide range of excitation frequencies and amplitudes., Comment: Updated preprint (improved figures and added explanations), 20 pages, 28 figures

Quench protection of full-size high-field accelerator magnets poses significant challenges. Maintaining the hot-spot temperature and peak voltage-to-ground within acceptable limits requires a protection system that quickly transitions most of the coil turns to the normal state. Existing magnet protection technologies, such as quench protection heaters or the Coupling Loss Induced Quench system (CLIQ), have been successfully applied. However, they both present shortcomings since they require either thin insulation between the heaters and the magnet conductor or direct electrical connections to the magnet coil. A novel quench protection method, Energy Shift with Coupling (ESC), is presented which can achieve excellent quench protection performance without the above-mentioned drawbacks. ESC relies on normal-conducting auxiliary coils strongly magnetically coupled with the magnet coils to protect. Upon quench detection capacitive units connected across such coils introduce a high current change in the auxiliary coils causing a rapid shift of magnet stored energy from the magnet coils to the auxiliary coils. This has three beneficial effects: sudden reduction of ohmic loss in the normal zone of the magnet conductor, introduction of high transient losses in the magnet conductor, thus causing a quick transition to the normal state, and extraction of a part of the magnet stored energy to the auxiliary coils. The applicability of the ESC concept on an existing magnet design is analyzed with electromagnetic and thermal transient simulations performed with the STEAM-LEDET program. The advantages and disadvantages of ESC are discussed and compared to other conventional quench protection methods. Simulation results show that ESC can be applied to protect full-scale magnets with reasonable requirements in terms of size and location of the auxiliary coils and of capacitive unit parameters.

2D magnetic semiconductors, which intrinsically couple a rich landscape of magnetic orders with tightly bound electron-hole pairs (excitons), present an exciting platform to investigate the interplay between optical and magnetic phenomena at the atomic scale. In such systems, the strength of magneto-optical effects determines how deeply the magnetic properties can be revealed. Here, we report the observation of remarkably strong magneto-excitonic effects in the 2D magnetic semiconductor CrSBr that allow probing its magnetic order with unprecedented sensitivity. By investigating optical transitions above the fundamental exciton energy, we discover a massive spectral shift approaching 100 meV under applied magnetic fields - an order of magnitude larger than previously observed magneto-excitonic responses. Our comprehensive magneto-optical experiments accompanied by detailed DFT calculations indicate the possible origin of the transitions exhibiting such intriguing behavior. These findings open avenues for exploiting magneto-excitonic phenomena at newly accessible regimes, enabling novel opto-spintronic applications previously limited by weak magnetic responses., Comment: submitted to Nature Communications, Supplementary Material included

We consider edge colorings of a graph in such a way that each two different triangles have distinct colorings. It is an extension of the well-known idea of distinguishing all maximal stars in a graph. It was introduced in literature in 1985 and studied by many authors in various variants, but always for stars. We estimate new invariants regarding triangles for proper and general colorings., Comment: 5 pages

Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be mapped, scheduled, and launched on different computing. That requires a software stack that enables users to code their workflows and automate resource management and workflow execution. Currently, there are many workflow technologies with diverse levels of robustness and capabilities, and users face difficult choices of software that can effectively and efficiently support their use cases on HPC machines, especially when considering the latest exascale platforms. We contributed to addressing this issue by developing the ExaWorks Software Development Kit (SDK). The SDK is a curated collection of workflow technologies engineered following current best practices and specifically designed to work on HPC platforms. We present our experience with (1) curating those technologies, (2) integrating them to provide users with new capabilities, (3) developing a continuous integration platform to test the SDK on DOE HPC platforms, (4) designing a dashboard to publish the results of those tests, and (5) devising an innovative documentation platform to help users to use those technologies. Our experience details the requirements and the best practices needed to curate workflow technologies, and it also serves as a blueprint for the capabilities and services that DOE will have to offer to support a variety of scientific heterogeneous workflows on the newly available exascale HPC platforms.

The evolution of smart home technologies, particularly agentic ones such as conversational agents, robots, and virtual avatars, is reshaping our understanding of home and domestic life. This shift highlights the complexities of modern domestic life, with the household landscape now featuring diverse cohabiting units like co-housing and communal living arrangements. These agentic technologies present specific design challenges and opportunities as they become integrated into everyday routines and activities. Our workshop envisions smart homes as dynamic, user-shaped spaces, focusing on the integration of these technologies into daily life. We aim to explore how these technologies transform household dynamics, especially through boundary fluidity, by uniting researchers and practitioners from fields such as design, sociology, and ethnography. Together, we will develop a taxonomy of challenges and opportunities, providing a structured perspective on the integration of agentic technologies and their impact on contemporary living arrangements., Comment: 13 pages, no figures, ECSCW 2024 Workshop Proposal

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

Mobile network performance modeling typically assumes either a fixed cell's configuration or only considers a limited number of parameters. This prohibits the exploration of multidimensional, diverse configuration space for, e.g., optimization purposes. This paper presents a method for performance predictions based on a network cell's configuration and network conditions, which utilizes neural network architecture. We evaluate the idea by extensive experiments, with data from more than 50,000 5G cells. The assessment included a comparison of the proposed method against models developed for fixed configuration. Results show that combined configuration-performance modeling outperforms single-configuration models and allows for performance prediction of unknown configurations, i.e., it is not used for model training. A substantially lower mean absolute error was achieved (0.25 vs. 0.45 for fixed-configuration MLP-based models).

Let $f: E \mapsto \{1,2,\dots,k\}$ be an edge coloring of the $n$ - dimensional hypercube $H_n$. By the palette at a vertex $v$ we mean the sequence $\left(f(e_1(v)), f(e_1(v)),\dots, f(e_n(v))\right)$, where $e_i(v)$ is the $i$ - dimensional edge incident to $v$. In the paper, we show that two colors are enough to distinguish all vertices of the $n$ - dimensional hypercube $H_n$ ($n \geq 2$) by their palettes. We also show that if $f$ is a proper edge coloring of the hypercube $H_n$ ($n\geq 5$), then $n$ colors suffice to distinguish all vertices by their palettes., Comment: 11 pages, 6 figures

We present the census of H$\beta$+[OIII] 4960,5008 \AA emitters at 6.87$ galaxies. We report a rapid decline of the [OIII] luminosity density at $z\gtrsim 6-7$ which cannot be explained by the evolution of the cosmic star-formation rate density. Finally we find that FRESCO detects in only 2h galaxies likely accounting for $\sim 10-20\%$ of the ionising budget at $z=7-8$ (assuming an escape fraction of 10%), raising the prospect of directly detecting a significant fraction of the sources of reionisation with JWST., Comment: 20 pages + appendices. Accepted in MNRAS. Public catalogue release at https://github.com/rameyer/fresco. V3: matching accepted version

Thin layers can lead to unfavorable meshes in a finite element (FE) analysis. Thin shell approximations (TSAs) avoid this issue by removing the need for a mesh of the thin layer while approximating the physics across the layer by an interface condition. Typically, a TSA requires the mesh of both sides of the TSA interface to be conforming. To alleviate this requirement, we propose to combine mortar methods and TSAs for solving the heat equation. The mortar TSA method's formulation is derived and enables an independent discretization of the subdomains on the two sides of the TSA depending on their accuracy requirements. The method is verified by comparison with a reference FE solution of a thermal model problem of a simplified superconducting accelerator magnet., Comment: Pre-submission version (preprint). Presented at and submitted to the proceedings of "The 15th International Conference on Scientific Computing in Electrical Engineering" (SCEE 2024), March 4-8, 2024, Darmstadt, Germany

High-temperature superconductors (HTS) have the potential to enable magnetic fields beyond the current limits of low-temperature superconductors in applications like accelerator magnets. However, the design of HTS-based magnets requires computationally demanding transient multi-physics simulations with highly non-linear material properties. To reduce the solution time, we propose using Parareal (PR) for parallel-in-time magneto-thermal simulation of magnets based on HTS, particularly, no-insulation coils without turn-to-turn insulation. We propose extending the classical PR method to automatically find a time partitioning using a first coarse adaptive propagator. The proposed PR method is shown to reduce the computing time when fine engineering tolerances are required despite the highly nonlinear character of the problem. The full software stack used is open-source., Comment: Pre-submission version (preprint). Presented at and submitted to the proceedings of "The 15th International Conference on Scientific Computing in Electrical Engineering" (SCEE 2024), March 4-8, 2024, Darmstadt, Germany

We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions under which the matrix containing structural parameters is partially or globally unique; (ii) a statistical procedure to assess the validity of the conditions mentioned above; and (iii) a shrinkage prior distribution for conditional variances centred on a hypothesis of homoskedasticity. Such a prior ensures that the evidence for identifying a structural shock comes only from the data and is not favoured by the prior. We illustrate our new methods using a U.S. fiscal structural model.

In this paper, we propose the Coupled Axial and Transverse currents (I) (CATI) method, as an efficient and accurate finite element approach for modelling the electric and magnetic behavior of periodic composite superconducting conductors. The method consists of a pair of two-dimensional models coupled via circuit equations to account for the conductor geometrical periodicity. This allows to capture three-dimensional effects with two-dimensional models and leads to a significant reduction in computational time compared to conventional three-dimensional models. After presenting the method in detail, we verify it by comparison with reference finite element models, focussing on its application to twisted multifilamentary superconducting strands. In particular, we show that the CATI method captures the transition from uncoupled to coupled filaments, with accurate calculation of the interfilament coupling time constant. We then illustrate the capabilities of the method by generating a detailed loss map and magnetization curves of given strand types for a range of external transverse magnetic field excitations, with and without transport current., Comment: 18 pages. Updated after peer-review

One of the significant problems associated with imbalanced data classification is the lack of reliable metrics. This runs primarily from the fact that for most real-life (as well as commonly used benchmark) problems, we do not have information from the user on the actual form of the loss function that should be minimized. Although it is pretty common to have metrics indicating the classification quality within each class, for the end user, the analysis of several such metrics is then required, which in practice causes difficulty in interpreting the usefulness of a given classifier. Hence, many aggregate metrics have been proposed or adopted for the imbalanced data classification problem, but there is still no consensus on which should be used. An additional disadvantage is their ambiguity and systematic bias toward one class. Moreover, their use in analyzing experimental results in recognition of those classification models that perform well for the chosen aggregated metrics is burdened with the drawbacks mentioned above. Hence, the paper proposes a simple approach to analyzing the popular parametric metric $F_\beta$. We point out that it is possible to indicate for a given pool of analyzed classifiers when a given model should be preferred depending on user requirements.

The exploration of two-dimensional (2D) antiferromagnetic (AFM) materials has shown great promise and interest in tuning the magnetic and electronic properties as well as studying magneto-optical effects. The current work investigates the control of magneto-optical interactions in alloyed MnxZn1-xPS3 lamellar semiconductor single crystals, with the Mn/Zn ratio regulating the coupling strength. Magnetic susceptibility results show a retention of AFM order followed by a decrease in N\'eel temperatures down to ~ 40% Mn concentration, below which a paramagnetic behavior is observed. Absorption measurements reveal an increase in bandgap energy with higher Zn(II) concentration, and the presence of Mn(II) d-d transition below the absorption edge. DFT+U approach qualitatively explained the origin and the position of the experimentally observed mid band-gap states in pure MnPS3, and corresponding peaks visible in the alloyed systems MnxZn1-xPS3. Accordingly, emission at 1.3 eV in all alloyed compounds results from recombination from a 4T1g Mn(II) excited state to a hybrid p-d state at the valence band. Most significant, temperature-dependent photoluminescence (PL) intensity trends demonstrate strong magneto-optical coupling in compositions with x > 0.65. This study underscores the potential of tailored alloy compositions as a means to control magnetic and optical properties in 2D materials, paving the way for advances in spin-based technologies.

We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that improve expressivity while maintaining the inference efficiency characteristics of RNNs. We introduce a new multilingual corpus with 1.12 trillion tokens and a fast tokenizer based on greedy matching for enhanced multilinguality. We trained four Eagle models, ranging from 0.46 to 7.5 billion parameters, and two Finch models with 1.6 and 3.1 billion parameters and find that they achieve competitive performance across a wide variety of benchmarks. We release all our models on HuggingFace under the Apache 2.0 license. Models at: https://huggingface.co/RWKV Training code at: https://github.com/RWKV/RWKV-LM Inference code at: https://github.com/RWKV/ChatRWKV Time-parallel training code at: https://github.com/RWKV/RWKV-infctx-trainer

We use the large spectroscopic data set of the MOSFIRE Deep Evolution Field survey to investigate the kinematics and energetics of ionised gas outflows. Using a sample of 598 star-forming galaxies at redshift 1.4 < $z$ < 3.8, we decompose $\rm{H}\alpha$ and [OIII] emission lines into narrow and broad components, finding significant detections of broad components in 10% of the sample. The ionised outflow velocity from individual galaxies appears independent of galaxy properties, such as stellar mass, star-formation rate (SFR), and star-formation-rate surface density ($\Sigma_{\rm SFR}$). Adopting a simple outflow model, we estimate the mass-, energy- and momentum-loading factors of the ionised outflows, finding modest values with averages of 0.33, 0.04, and 0.22, respectively. The larger momentum- than energy-loading factors, for the adopted physical parameters, imply that these ionised outflows are primarily momentum-driven. We further find a marginal correlation (2.5$\sigma$) between the mass-loading factor and stellar mass in agreement with predictions by simulations, scaling as $\eta_{m}$ $\propto M_{\star}^{-0.45}$. This shallow scaling relation is consistent with these ionised outflows being driven by a combination of mechanical energy generated by supernovae explosions and radiation pressure acting on dusty material. In a majority of galaxies, the outflowing material does not appear to have sufficient velocity to escape the gravitational potential of their host, likely recycling back at later times. Together, these results suggest that the ionised outflows traced by nebular emission lines are negligible, with the bulk of mass and energy carried out in other gaseous phases., Comment: 17 pages, 9 figures, Submitted to Monthly Notices of the Royal Astronomical Society

Continual learning poses a fundamental challenge for modern machine learning systems, requiring models to adapt to new tasks while retaining knowledge from previous ones. Addressing this challenge necessitates the development of efficient algorithms capable of learning from data streams and accumulating knowledge over time. This paper proposes a novel approach to continual learning utilizing the weight consolidation method. Our method, a simple yet powerful technique, enhances robustness against catastrophic forgetting by interpolating between old and new model weights after each novel task, effectively merging two models to facilitate exploration of local minima emerging after arrival of new concepts. Moreover, we demonstrate that our approach can complement existing rehearsal-based replay approaches, improving their accuracy and further mitigating the forgetting phenomenon. Additionally, our method provides an intuitive mechanism for controlling the stability-plasticity trade-off. Experimental results showcase the significant performance enhancement to state-of-the-art experience replay algorithms the proposed weight consolidation approach offers. Our algorithm can be downloaded from https://github.com/jedrzejkozal/weight-interpolation-cl.

In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets. We focus on sparser point clouds, capturing scenarios from different perspectives: not just from vehicles on the road but also from mobile robots on sidewalks, which encounter significantly different environmental conditions and sensor configurations. We introduce Unsupervised Adversarial Domain Adaptation for 3D Object Detection (UADA3D). UADA3D does not depend on pre-trained source models or teacher-student architectures. Instead, it uses an adversarial approach to directly learn domain-invariant features. We demonstrate its efficacy in various adaptation scenarios, showing significant improvements in both self-driving car and mobile robot domains. Our code is open-source and will be available soon., Comment: Accepted for IEEE RA-L 2024

Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense of touch and expand their capability drastically. However, many tasks still need human intervention/guidance. For this reason, we present a teleoperation framework designed to provide haptic feedback to human operators based on the data from camera-based tactile sensors mounted on the robot gripper. Partial autonomy is introduced to prevent slippage of grasped objects during task execution. Notably, we rely exclusively on low-cost off-the-shelf hardware to realize an affordable solution. We demonstrate the versatility of the framework on nine different objects ranging from rigid to soft and fragile ones, using three different operators on real hardware., Comment: https://vision-tactile-manip.github.io/teleop/

Perception plays a crucial role in various robot applications. However, existing well-annotated datasets are biased towards autonomous driving scenarios, while unlabelled SLAM datasets are quickly over-fitted, and often lack environment and domain variations. To expand the frontier of these fields, we introduce a comprehensive dataset named MCD (Multi-Campus Dataset), featuring a wide range of sensing modalities, high-accuracy ground truth, and diverse challenging environments across three Eurasian university campuses. MCD comprises both CCS (Classical Cylindrical Spinning) and NRE (Non-Repetitive Epicyclic) lidars, high-quality IMUs (Inertial Measurement Units), cameras, and UWB (Ultra-WideBand) sensors. Furthermore, in a pioneering effort, we introduce semantic annotations of 29 classes over 59k sparse NRE lidar scans across three domains, thus providing a novel challenge to existing semantic segmentation research upon this largely unexplored lidar modality. Finally, we propose, for the first time to the best of our knowledge, continuous-time ground truth based on optimization-based registration of lidar-inertial data on large survey-grade prior maps, which are also publicly released, each several times the size of existing ones. We conduct a rigorous evaluation of numerous state-of-the-art algorithms on MCD, report their performance, and highlight the challenges awaiting solutions from the research community., Comment: Accepted by The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024

Experimental science is increasingly driven by instruments that produce vast volumes of data and thus a need to manage, compute, describe, and index this data. High performance and distributed computing provide the means of addressing the computing needs; however, in practice, the variety of actions required and the distributed set of resources involved, requires sophisticated "flows" defining the steps to be performed on data. As each scan or measurement is performed by an instrument, a new instance of the flow is initiated resulting in a "fleet" of concurrently running flows, with the overall goal to process all the data collected during a potentially long-running experiment. During the course of the experiment, each flow may need to adapt its execution due to changes in the environment, such as computational or storage resource availability, or based on the progress of the fleet as a whole such as completion or discovery of an intermediate result leading to a change in subsequent flow's behavior. We introduce a cloud-based decision engine, Braid, which flows consult during execution to query their run-time environment and coordinate with other flows within their fleet. Braid accepts streams of measurements taken from the run-time environment or from within flow runs which can then be statistically aggregated and compared to other streams to determine a strategy to guide flow execution. For example, queue lengths in execution environments can be used to direct a flow to run computations in one environment or another, or experiment progress as measured by individual flows can be aggregated to determine the progress and subsequent direction of the flows within a fleet. We describe Braid, its interface, implementation and performance characteristics. We further show through examples and experience modifying an existing scientific flow how Braid is used to make adaptable flows.

Current research in Mixed Reality (MR) presents a wide range of novel use cases for blending virtual elements with the real world. This yet-to-be-ubiquitous technology challenges how users currently work and interact with digital content. While offering many potential advantages, MR technologies introduce new security, safety, and privacy challenges. Thus, it is relevant to understand users' apprehensions towards MR technologies, ranging from security concerns to social acceptance. To address this challenge, we present the Mixed Reality Concerns (MRC) Questionnaire, designed to assess users' concerns towards MR artifacts and applications systematically. The development followed a structured process considering previous work, expert interviews, iterative refinements, and confirmatory tests to analytically validate the questionnaire. The MRC Questionnaire offers a new method of assessing users' critical opinions to compare and assess novel MR artifacts and applications regarding security, privacy, social implications, and trust.

The effect of pressure on Raman scattering (RS) in the bulk HfS$_2$ is investigated under hydrostatic and non-hydrostatic conditions. The RS lineshape does not change significantly in the hydrostatic regime, showing a systematic blueshift of the spectral features. In a non-hydrostatic environment, seven peaks emerge in the spectrum ($P$=7 GPa) dominating the lineshape up to $P$=10.5 GPa. The change in the RS lineshape manifests a pressure-induced phase transition in HfS$_2$. The simultaneous observation of both low-pressure (LP) and high-pressure (HP) related RS peaks suggests the corresponding coexistence of two different phases over a large pressure range. We found that the HP-related phase is metastable, persisting during the decompression cycle down to $P$=1.2 GPa with the LP-related features finally recovering at even lower pressures. The angle-resolved polarized RS (ARPRS) performed under $P$=7.4 GPa revealed a strong in-plane anisotropy of both the LP-related A$_{1g}$ mode and the HP peaks. The anisotropy is related to the possible distortion of the structure induced by the non-hydrostatic component of the pressure. We describe the obtained results by the influence of the non-hydrostatic pressure on the observed phase transition. We interpret our results in terms of a distorted $Pnma$ phase as a possible HP induced structure of HfS$_2$.

Large language models (LLMs) have significantly advanced Natural Language Processing (NLP) tasks in recent years. However, their universal nature poses limitations in scenarios requiring personalized responses, such as recommendation systems and chatbots. This paper investigates methods to personalize LLMs, comparing fine-tuning and zero-shot reasoning approaches on subjective tasks. Results demonstrate that personalized fine-tuning improves model reasoning compared to non-personalized models. Experiments on datasets for emotion recognition and hate speech detection show consistent performance gains with personalized methods across different LLM architectures. These findings underscore the importance of personalization for enhancing LLM capabilities in subjective text perception tasks., Comment: Accepted to SENTIRE 2024 (ICDM Workshops): https://sentic.net/sentire2024wozniak.pdf

The assumptions underpinning the adiabatic Born-Oppenheimer (BO) approximation are broken for molecules interacting with attosecond laser pulses, which generate complicated coupled electronic-nuclear wavepackets that generally will have components of electronic and dissociation continua as well as bound-state contributions. The conceptually most straightforward way to overcome this challenge is to treat the electronic and nuclear degrees of freedom on equal quantum-mechanical footing by not invoking the BO approximation at all. Explicitly correlated Gaussian (ECG) basis functions have proved successful for non-BO calculations of stationary molecular states and energies, reproducing rovibrational absorption spectra with very high accuracy. In this paper, we present a proof-of-principle study of the ability of fully flexible ECGs (FFECGs) to capture the intricate electronic and rovibrational dynamics generated by short, high-intensity laser pulses. By fitting linear combinations of FFECGs to accurate wave function histories obtained on a large real-space grid for a regularized 2D model of the hydrogen atom and for the 2D Morse potential we demonstrate that FFECGs provide a very compact description of laser-driven electronic and rovibrational dynamics.

We investigate the vibrational and magnetic properties of thin layers of chromium tribromide (CrBr$_3$) with a thickness ranging from three to twenty layers (3~L to 20~L) revealed by the Raman scattering (RS) technique. Systematic dependence of the RS process efficiency on the energy of the laser excitation is explored for four different excitation energies: 1.96 eV, 2.21 eV, 2.41 eV, and 3.06 eV. Our characterization demonstrates that for 12 L CrBr$_3$, 3.06~eV excitation could be considered resonant with interband electronic transitions due to the enhanced intensity of the Raman-active scattering resonances and the qualitative change in the Raman spectra. Polarization-resolved RS measurements for 12 L CrBr$_3$ and first-principles calculations allow us to identify five observable phonon modes characterized by distinct symmetries, classified as the A$_\textrm{g}$ and E$_\textrm{g}$ modes. The evolution of phonon modes with temperature for a 20 L CrBr$_3$ encapsulated in hexagonal boron nitride flakes demonstrates alterations of phonon energies and/or linewidths of resonances indicative of a transition between the paramagnetic and ferromagnetic state at Curie temperature ($T_\textrm{C} \approx 50$ K). The exploration of the effects of thickness on the phonon energies demonstrated small variations pronounces exclusively for the thinnest layers in the vicinity of 3 - 5 L. This observation is attributed to strong localization in the real space of interband electronic excitations, limiting the effects of confinement for resonantly excited Raman modes to atomically thin layers., Comment: 12 pages, 5 figures

Data annotated by humans is a source of knowledge by describing the peculiarities of the problem and therefore fueling the decision process of the trained model. Unfortunately, the annotation process for subjective natural language processing (NLP) problems like offensiveness or emotion detection is often very expensive and time-consuming. One of the inevitable risks is to spend some of the funds and annotator effort on annotations that do not provide any additional knowledge about the specific task. To minimize these costs, we propose a new model-based approach that allows the selection of tasks annotated individually for each text in a multi-task scenario. The experiments carried out on three datasets, dozens of NLP tasks, and thousands of annotations show that our method allows up to 40% reduction in the number of annotations with negligible loss of knowledge. The results also emphasize the need to collect a diverse amount of data required to efficiently train a model, depending on the subjectivity of the annotation task. We also focused on measuring the relation between subjective tasks by evaluating the model in single-task and multi-task scenarios. Moreover, for some datasets, training only on the labels predicted by our model improved the efficiency of task selection as a self-supervised learning regularization technique.

In the era of artificial intelligence, data is gold but costly to annotate. The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT for text augmentation in sentiment analysis. We leverage ChatGPT's generative capabilities to create synthetic training data that significantly improves the performance of smaller models, making them competitive with, or even outperforming, their larger counterparts. This innovation enables models to be both efficient and effective, thereby reducing computational cost, inference time, and memory usage without compromising on quality. Our work marks a key advancement in the cost-effective development and deployment of robust sentiment analysis models., Comment: 10 pages, 9 figures, presented at ICDM Workshop: SENTIRE 2023

We report calculations of the high harmonic generation spectra of the C\textsubscript{60} fullerene molecule, employing a diverse set of real-time time-dependent quantum chemical methods. All methodologies involve expanding the propagated electronic wavefunction in bases consisting of the ground and singly excited time-independent eigenstates obtained through the solution of the corresponding linear-response equations. We identify the correlation and exchange effect in the spectra by comparing the results from methods relying on the Hartree-Fock reference determinant with those obtained using approaches based on the density functional theory with different exchange-correlation functionals. The effect of the full random phase approximation treatment of the excited electronic states is also analyzed and compared with the configuration interaction singles and the Tamm-Dancoff approximation. We also showcase that the real-time extension of the semiempirical method INDO/S can be effectively applied for an approximate description of laser-driven dynamics in large systems., Comment: 50 pages, 9 figures

We report calculations of the high-order harmonic spectra of few-atomic clusters of two noble gases, helium and krypton, subjected to laser pulses of intensities above $10^{13}$ W/cm\textsuperscript{2}. We employ a fully \textit{ab intio} framework, namely the real-time time-dependent configuration interaction singles coupled to a discrete Gaussian basis set. Our model reproduces the observed higher than quadratic dependence between the high-order harmonic yield and the number of atoms, and the obtained value of the enhancement rate is in quantitative agreement with experimental results. We suggest a mechanism responsible for the high-order harmonic enhancement, according to which, after the tunneling ionization, the residual ion polarizes the electron densities of surrounding atoms in the cluster, which leads to increased electron-electron repulsion during the recollision. The proposed mechanism is not only fully consistent with both our calculations and the experimental data, but can also explain the limitations of previous theoretical studies on high-order harmonic generation in clusters., Comment: 15 pages, 7 figures

This paper deals with the modelling of superconducting and resistive wires with a helicoidal symmetry, subjected to an external field and a transport current. Helicoidal structures are three-dimensional, and therefore yield computationally intensive simulations in a Cartesian coordinate system. We show in this paper that by working instead with a helicoidal system of coordinates, the problem to solve can be made two-dimensional, drastically reducing the computational cost. We first introduce the state-of-the-art approach and apply it on the h-phi-formulation with helicoidally symmetric boundary conditions (e.g., axial external magnetic field, with or without transport current), with an emphasis on the function space discretization. Then, we extend the approach to general boundary conditions (e.g., transverse external magnetic field) and present numerical results with linear materials. In particular, we discuss the frequency-dependent losses in composite wires made of superconducting filaments embedded in a resistive matrix. Finally, we provide outlook to the application of the generalized model with nonlinear materials., Comment: updates after peer-review