Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Analysis of RC Buildings Based on Incremental Dynamic Analysis

Earthquake is a natural disaster that can induce immense damage to properties and human lives. Fragility curve, defined as the probability of reaching or exceeding a specific damage state under an earthquake excitation, provides a prediction of damage that may occur during an earthquake. Incremental Dynamic Analysis Curve (IDA curve) developed from Incremental Dynamic analysis can be considered as the initial step of fragility curve development. The present study summarises the development of IDA curve for a RC building considering maximum roof displacement and storey drift as Damage Measure (DM) and peak ground acceleration as Intensity Measure (IM). The fragility curves developed using incremental dynamic analysis provide an overview of the probability of exceedance of damage limits in an existing building when acted upon by different levels of earthquake excitations and the performance level can be improved by incorporating appropriate design changes.
A novel two-stage Lung nodule detection in medical images based on deep learning

In order to solve the problems of low detection rate and high false positive of nodules in lung CT images by traditional computer-aided diagnosis systems. This paper introduces the real-time detection algorithm of medical images using the deep learning method. According to the three-dimensional nature of lung CT images, 3DFasterR-CNN is used to extract features to detect candidate nodules; then, the 3D convolutional neural network is used to remove false positive nodules. That saves the operation of filtering and difference map construction. The detection process mainly includes four steps: building a deep neural network, constructing suitable samples, training the network, and detecting. The results show that the algorithm has a good effect on the real-time change detection of medical images and can improve detection accuracy and efficiency. The average FROC value was 82.8%, compared with the traditional diagnosis and treatment. The recognition rate of the method has been significantly improved. The model can be used to assist doctors in diagnosing lung nodules and has a certain clinical application value.
Predicting new mineral occurrences and planetary analog environments via mineral association analysis

Abstract The locations of minerals and mineral-forming environments, despite being of great scientific importance and economic interest, are often difficult to predict due to the complex nature of natural systems. In this work, we embrace the complexity and inherent “messiness” of our planet's intertwined geological, chemical, and biological systems by employing machine learning to characterize patterns embedded in the multidimensionality of mineral occurrence and associations. These patterns are a product of, and therefore offer insight into, the Earth's dynamic evolutionary history. Mineral association analysis quantifies high-dimensional multicorrelations in mineral localities across the globe, enabling the identification of previously unknown mineral occurrences, as well as mineral assemblages and their associated paragenetic modes. In this study, we have predicted (i) the previously unknown mineral inventory of the Mars analogue site, Tecopa Basin, (ii) new locations of uranium minerals, particularly those important to understanding the oxidation–hydration history of uraninite, (iii) new deposits of critical minerals, specifically rare earth element (REE)- and Li-bearing phases, and (iv) changes in mineralization and mineral associations through deep time, including a discussion of possible biases in mineralogical data and sampling; furthermore, we have (v) tested and confirmed several of these mineral occurrence predictions in nature, thereby providing ground truth of the predictive method. Mineral association analysis is a predictive method that will enhance our understanding of mineralization and mineralizing environments on Earth, across our solar system, and through deep time.
Meteosat-Based Characterization of the Spatiotemporal Evolution of Warm Convective Cloud Fields over Central Europe

AbstractThe spatiotemporal evolution of warm convective cloud fields over central Europe is investigated on the basis of 30 cases using observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary Meteosat platforms. Cloud fields are tracked in successive satellite images using cloud motion vectors. The time-lagged autocorrelation is calculated for spectral reflectance and cloud property fields using boxes of 16 × 16 pixels and adopting both Lagrangian and Eulerian perspectives. The 0.6-μm reflectance, cloud optical depth, and water path show a similar characteristic Lagrangian decorrelation time of about 30 min. In contrast, significantly lower decorrelation times are observed for the cloud effective radius and droplet density. It is shown that the Eulerian decorrelation time can be decomposed into an advective component and a convective component using the spatial autocorrelation function. In an Eulerian frame cloud fields generally decorrelate faster than in a.
Neurovascular Structures at Risk During Surgical Access to Trigeminal Ganglion and Meckel's Cave – An Anatomical Study

Background: It is difficult to approach the Trigeminal Ganglion (TG) and Meckel's cave (MC) during surgeries. Therefore, the exact knowledge of the relationship of surgical landmarks to related anatomical structures is vital to reduce the associated postoperative morbidity. The aim of the present study was to enhance the knowledge of the surgical anatomy of structures that are present in the conduit of all surgical approaches to TG and MC, their distances from surrounding neurovascular structures, and their variations. Material and Methods: The study was carried out on 40 embalmed cadavers (Eight Female) of the anatomy department of a teaching hospital in Central India. Meticulous dissection of cranial fossae was done to locate TG, MC, and related anatomical structures. All distances from TG and MC were measured using an electronic digital calliper. Results: Length, width, and thickness of TG were 15.39 mm, 4.39 mm, and 2.54 mm, respectively. The distance from zygomatic arch, the lateral end of the petrous ridge, arcuate eminence, foramen ovale, and foramen spinosum to MC was 26.10 mm and 37.94 mm, 16.46 mm, 4.54 mm, and 11.23 mm, respectively. The sixth, fourth, and third cranial nerves were 6.26 mm, 4.94 mm, and 2.53 mm from MC, respectively. The MC was 42.72 mm and 33.87 mm anteromedial from posterior and anterior limits of the sigmoid sinus. Conclusion: Findings of the present study will aid surgical planning and decide the approach to TG and MC and avoid surgical complications.
PICK1-Deficient Mice Maintain Their Glucose Tolerance During Diet-Induced Obesity

Abstract Context Metabolic disorders such as obesity represent a major health challenge. Obesity alone has reached epidemic proportions, with at least 2.8 million people worldwide dying annually from diseases caused by overweight or obesity. The brain–metabolic axis is central to maintain homeostasis under metabolic stress via an intricate signaling network of hormones. Protein interacting with C kinase 1 (PICK1) is important for the biogenesis of various secretory vesicles, and we have previously shown that PICK1-deficient mice have impaired secretion of insulin and growth hormone. Objective The aim was to investigate how global PICK1-deficient mice respond to high-fat diet (HFD) and assess its role in insulin secretion in diet-induced obesity. Methods We characterized the metabolic phenotype through assessment of body weight, composition, glucose tolerance, islet morphology insulin secretion in vivo, and glucose-stimulated insulin secretion ex vivo. Results PICK1-deficient mice displayed similar weight gain and body composition as wild-type (WT) mice following HFD. While HFD impaired glucose tolerance of WT mice, PICK1-deficient mice were resistant to further deterioration of their glucose tolerance compared with already glucose-impaired chow-fed PICK1-deficient mice. Surprisingly, mice with β-cell–specific knockdown of PICK1 showed impaired glucose tolerance both on chow and HFD similar to WT mice. Conclusion Our findings support the importance of PICK1 in overall hormone regulation. However, importantly, this effect is independent of the PICK1 expression in the β-cell, whereby global PICK1-deficient mice resist further deterioration of their glucose tolerance following diet-induced obesity.
Chromosome-scale genome sequence assemblies of the ‘Autumn Bliss’ and ‘Malling Jewel’ cultivars of the highly heterozygous red raspberry (Rubus idaeus L.) derived from long-read Oxford Nanopore sequence data

Red raspberry (Rubus idaeus L.) is an economically valuable soft-fruit species with a relatively small (~300 Mb) but highly heterozygous diploid (2n = 2x = 14) genome. Chromosome-scale genome sequences are a vital tool in unravelling the genetic complexity controlling traits of interest in crop plants such as red raspberry, as well as for functional genomics, evolutionary studies, and pan-genomics diversity studies. In this study, we developed genome sequences of a primocane fruiting variety (‘Autumn Bliss’) and a floricane variety (‘Malling Jewel’). The use of long-read Oxford Nanopore Technologies sequencing data yielded long read lengths that permitted well resolved genome sequences for the two cultivars to be assembled. The de novo assemblies of ‘Malling Jewel’ and ‘Autumn Bliss’ contained 79 and 136 contigs respectively, and 263.0 Mb of the ‘Autumn Bliss’ and 265.5 Mb of the ‘Malling Jewel’ assembly could be anchored unambiguously to a previously published red raspberry genome sequence of the cultivar ‘Anitra’. Single copy ortholog analysis (BUSCO) revealed high levels of completeness in both genomes sequenced, with 97.4% of sequences identified in ‘Autumn Bliss’ and 97.7% in ‘Malling Jewel’. The density of repetitive sequence contained in the ‘Autumn Bliss’ and ‘Malling Jewel’ assemblies was significantly higher than in the previously published assembly and centromeric and telomeric regions were identified in both assemblies. A total of 42,823 protein coding regions were identified in the ‘Autumn Bliss’ assembly, whilst 43,027 were identified in the ‘Malling Jewel’ assembly. These chromosome-scale genome sequences represent an excellent genomics resource for red raspberry, particularly around the highly repetitive centromeric and telomeric regions of the genome that are less complete in the previously published ‘Anitra’ genome sequence.
Social Prescribing Outcomes for Trials (SPOT): Protocol for a modified Delphi study on core outcomes

Purpose This is a study protocol to co-create with knowledge users a core outcome set focused on middle-aged and older adults (40 years+) for use in social prescribing research. Methods We will follow the Core Outcome Measures in Effectiveness Trials (COMET) guide and use modified Delphi methods, including collating outcomes reported in social prescribing publications, online surveys, and discussion with our team to finalize the core outcome set. We intentionally center this work on people who deliver and receive social prescribing and include methods to evaluate collaboration. Our three-part process includes: (1) identifying published systematic reviews on social prescribing for adults to extract reported outcomes; and (2) up to three rounds of online surveys to rate the importance of outcomes for social prescribing. For this part, we will invite people (n = 240) who represent the population experienced in social prescribing, including researchers, members of social prescribing organizations, and people who receive social prescribing and their caregivers. Finally, we will (3) convene a virtual team meeting to discuss and rank the findings and finalize the core outcome set and our knowledge mobilization plan. Conclusion To our knowledge, this is the first study designed to use a modified Delphi method to co-create core outcomes for social prescribing. Development of a core outcome set contributes to improved knowledge synthesis via consistency in measures and terminology. We aim to develop guidance for future research, and specifically on the use of core outcomes for social prescribing at the person/patient, provider, program, and societal-level.
A Highly Scalable, Hybrid, Cross-Platform Timing Analysis Framework Providing Accurate Differential Throughput Estimation via Instruction-Level Tracing

Differential throughput estimation, i.e., predicting the performance impact of software changes, is critical when developing applications that rely on accurate timing bounds, such as automotive, avionic, or industrial control systems. However, developers often lack access to the target hardware to perform on-device measurements, and hence rely on instruction throughput estimation tools to evaluate performance impacts. State-of-the-art techniques broadly fall into two categories: dynamic and static. Dynamic approaches emulate program execution using cycle-accurate microarchitectural simulators resulting in high precision at the cost of long turnaround times and convoluted setups. Static approaches reduce overhead by predicting cycle counts outside of a concrete runtime environment. However, they are limited by the lack of dynamic runtime information and mostly focus on predictions over single basic blocks which requires developers to manually construct critical instruction sequences. We present MCAD, a hybrid timing analysis framework that combines the advantages of dynamic and static approaches. Instead of relying on heavyweight cycle-accurate emulation, MCAD collects instruction traces along with dynamic runtime information from QEMU and streams them to a static throughput estimator. This allows developers to accurately estimate the performance impact of software changes for complete programs within minutes, reducing turnaround times by orders of magnitude compared to existing approaches with similar accuracy. Our evaluation shows that MCAD scales to real-world applications such as FFmpeg and Clang with millions of instructions, achieving < 3% geo. mean error compared to ground truth timings from hardware-performance counters on x86 and ARM machines.
GAN-based Vertical Federated Learning for Label Protection in Binary Classification

Split learning (splitNN) has emerged as a popular strategy for addressing the high computational costs and low modeling efficiency in Vertical Federated Learning (VFL). However, despite its popularity, vanilla splitNN lacks encryption protection, leaving it vulnerable to privacy leakage issues, especially Label Leakage from Gradients (LLG). Motivated by the LLG issue resulting from the use of labels during training, we propose the Generative Adversarial Federated Model (GAFM), a novel method designed specifically to enhance label privacy protection by integrating splitNN with Generative Adversarial Networks (GANs). GAFM leverages GANs to indirectly utilize label information by learning the label distribution rather than relying on explicit labels, thereby mitigating LLG. GAFM also employs an additional cross-entropy loss based on the noisy labels to further improve the prediction accuracy. Our ablation experiment demonstrates that the combination of GAN and the cross-entropy loss component is necessary to enable GAFM to mitigate LLG without significantly compromising the model utility. Empirical results on various datasets show that GAFM achieves a better and more robust trade-off between model utility and privacy compared to all baselines across multiple random runs. In addition, we provide experimental justification to substantiate GAFM's superiority over splitNN, demonstrating that it offers enhanced label protection through gradient perturbation relative to splitNN.
A fully packaged multi-channel cryogenic module for optical quantum memories

Realizing a quantum network will require long-lived quantum memories with optical interfaces incorporated into a scalable architecture. Color centers quantum emitters in diamond have emerged as a promising memory modality due to their optical properties and compatibility with scalable integration. However, developing a scalable color center emitter module requires significant advances in the areas of heterogeneous integration and cryogenically compatible packaging. Here we report on a cryogenically stable and network compatible quantum-emitter module for memory use. This quantum-emitter module is a significant development towards advanced quantum networking applications such as distributed sensing and processing.
Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations

Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiative in leading the conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC systems with a tailor-designed schema that divides utterances into different types with speaker roles and initiative types. Four emotional support metrics are proposed to evaluate the mixed-initiative interactions. The analysis reveals the necessity and challenges of building mixed-initiative ESC systems. In the light of this, we propose a knowledge-enhanced mixed-initiative framework (KEMI) for ESC, which retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. Experimental results on two ESC datasets show the superiority of KEMI in both content-preserving evaluation and mixed initiative related analyses.
Active-matter isomorphs in the size-polydisperse Ornstein–Uhlenbeck Lennard–Jones model

This paper studies size-polydisperse Lennard–Jones systems described by active Ornstein–Uhlenbeck particle (AOUP) dynamics. The focus is on the existence of isomorphs (curves of invariant structure and dynamics) in the model’s three-dimensional phase diagram. Isomorphs are traced out from a single steady-state configuration by means of the configurational-temperature method. Good isomorph invariance of the reduced-unit radial distribution function and the mean-square displacement as a function of time is demonstrated for three uniform-distribution polydispersities, 12% , 23%, and 29%. Comparing to active-matter isomorphs generated by the analytical direct-isomorph-check method, the latter have poorer invariance of the structure, but better invariance of the dynamics. We conclude that both methods can be used to quickly get an overview of the phase diagram of polydisperse AOUP models involving a potential-energy function obeying the hidden-scale-invariance property required for isomorph theory to apply.
Study of collision and 
γ
-cascade times following neutron-capture processes in cryogenic detectors

The emission of $\gamma$-rays after a neutron capture in a cryogenic detector can generate mono-energetic nuclear recoils in the sub-keV regime, of direct interest for the calibration of Dark Matter and Coherent Elastic Neutrino Nucleus Scattering experiments. Here we show that accurate predictions of the nuclear recoil spectra induced by neutron captures require taking into account the interplay between the development in time of the de-excitation $\gamma$-cascade of the target nucleus and that of the associated atomic collisions in matter. We present detailed simulations coupling the FIFRELIN code for the description of the $\gamma$-cascades and the IRADINA code for the modelling of the fast atomic movements in matter. Nuclear recoil spectra are predicted, and made available to the community, for concrete cases of Al$_2$O$_3$, Si, Ge and CaWO$_4$ crystals exposed to a low intensity beam of thermal neutrons. We find that timing effects cause new calibration peaks to emerge in the recoil spectra and also impact the shape of the continuous recoil distribution. We discuss how they could give access to a rich physics program, spanning the accurate study of the response of cryogenic detectors in the sub-keV range, tests of solid state physics simulations and tests of nuclear models.
Skyrmion-mediated Nonvolatile Ternary Memory

Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that voltage-controlled magnetic anisotropy (VCMA) based writing is highly energy-efficient compared to other writing methods used in non-volatile nano-magnetic binary memory systems. In this study, we introduce a new, VCMA-based and skyrmion-mediated non-volatile ternary memory system using a perpendicular magnetic tunnel junction (p-MTJ) in the presence of room temperature thermal perturbation. We have also shown that ternary states {-1, 0, +1} can be implemented with three magnetoresistance values obtained from a p-MTJ corresponding to ferromagnetic up, down, and skyrmion state, with 99% switching probability in the presence of room temperature thermal noise in an energy-efficient way, requiring ~3 fJ energy on an average for each switching operation. Additionally, we show that our proposed ternary memory demonstrates an improvement in area and energy by at least 2X and ~60X respectively, compared to state-of-the-art spin-transfer torque (STT)-based non-volatile magnetic multistate memories. Furthermore, these three states can be potentially utilized for energy-efficient, high-density in-memory quantized deep neural network implementation.
Parameter optimization for low-rank matrix recovery in hyperspectral imaging

An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement in the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is tested on standard and openly available hyperspectral data sets, including Indian Pines, Pavia Centre, and Pavia University.
Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

In this paper, we take the initiative to investigate the performance of LLMs on complex planning tasks that require LLMs to understand a virtual spatial environment simulated via natural language and act correspondingly in text. We propose a benchmark named Natural Language Planning (NLP) composed of a set of novel tasks: Brick World, NLVR-based Manipulations, and Natural Language Navigation. We found that current popular LLMs such as ChatGPT still lack abilities in complex planning. This arises a question – do the LLMs have a good understanding of the environments described in natural language, or maybe other alternatives such as symbolic representations are neater and hence better to be understood by LLMs? To this end, we propose a novel method called C O S ( C hain- o f- S ymbol Prompting) that represents the complex environments with condensed symbolic spatial representations during the chained intermediate thinking steps. C O S is easy to use and does not need additional training on LLMs. Extensive experiments indicate that C O S clearly surpasses the performance of the Chain-of-Thought (CoT) Prompting in all three planning tasks with even fewer tokens used in the inputs compared with CoT on ChatGPT and InstructGPT. The performance gain is strong, by up to 60.8% accuracy (from 31.8% to 92.6%) on Brick World for ChatGPT. C O S also reduces the number of tokens in the prompt obviously, by up to 65.8% of the tokens (from 407 to 139) for the intermediate steps from demonstrations on Brick World.