Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Classification Method Based on Convolutional Encoder and Multimodal Feature Fusion

To overcome the challenges posed by the lack of uniqueness, limited categories, and inaccurate representations of clouds, this paper has presented a cloud classification method based on convolutional encoder and multimodal feature fusion. Firstly, to extract the most representative cloud features, we designed a Cloud-SSAE model, which adds a convolution neural network on top of the autoencoder learning mechanism, incorporates a penalty term within the hidden layer to promote sparsity in the subsequent input layer, and utilizes max-pooling to increase the robustness of the features. Secondly, we improved VGG16 model by incorporating dilated convolution to obtain dense local features. We further fused the color, texture, morphology, temperature, and humidity into the multimodal features. Lastly, we combined Cloud-SSAE model features, VGG16 model features, and multimodal features, and input the fused features into Support vector machine for classification. Consequently, the method proposed herein has the capacity to facilitate the classification of 11 distinct cloud types, encompassing but not restricted to cirrus and cumulonimbus cloud, with an improved accuracy level of 92.1%.
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices

As neural networks (NN) are deployed across diverse sectors, their energy demand correspondingly grows. While several prior works have focused on reducing energy consumption during training, the continuous operation of ML-powered systems leads to significant energy use during inference. This paper investigates how the configuration of on-device hardware-elements such as GPU, memory, and CPU frequency, often neglected in prior studies, affects energy consumption for NN inference with regular fine-tuning. We propose PolyThrottle, a solution that optimizes configurations across individual hardware components using Constrained Bayesian Optimization in an energy-conserving manner. Our empirical evaluation uncovers novel facets of the energy-performance equilibrium showing that we can save up to 36 percent of energy for popular models. We also validate that PolyThrottle can quickly converge towards near-optimal settings while satisfying application constraints.
Summary of the Impacts of Freeway Traffic Incidents and Their Countermeasures

The frequency occurrence of freeway traffic incidents have significant effects on the stable operation of freeway.Through the analysis on the impacts of freeway traffic incidents,this paper presents the impacts of traffic incidents on freeway capacity,vehicle delay,traffic congestion and public safety.In view of the impacts,a freeway incident management system is proposed to reduce the impacts of traffic incidents.By expounding the phases of incident management system and the architecture,it provides basis for setting up the well-considered freeway incident management system.
Stochastic resetting with refractory periods: pathway formulation and exact results

We look into the problem of stochastic resetting with refractory periods. The model dynamics comprises diffusive and motionless phases. The diffusive phase ends at random time instants, at which the system is reset to a given position---where the system remains at rest for a random time interval, termed the refractory period. A pathway formulation is introduced to derive exact analytical results for the relevant observables in a broad framework, with the resetting time and the refractory period following arbitrary distributions. For the paradigmatic case of Poissonian distributions of the resetting and refractory times, in general with different characteristic rates, closed-form expressions are obtained that successfully describe the relaxation to the steady state. Finally, we focus on the single-target search problem, in which the survival probability and the mean first passage time to the target can be exactly computed. Therein, we also discuss optimal strategies, which show a non-trivial dependence on the refractory period.
Rashba spin splitting based on trilayer graphene systems

We establish a general Rashba Hamiltonian for trilayer graphene (TLG) by introducing an extrinsic layer-dependent Rashba spin-orbit coupling (SOC) arising from the off-plane inversion symmetry breaking. Our results indicate that the band spin splitting depends strongly on the layer-distribution and sign of Rashba SOC as well as the ABA or ABC stacking order of TLG. We find that spin splitting is significantly enhanced as the number of layers of the Rashba SOC with the same sign and magnitude increases. For the spatially-separated two Rashba SOCs of the same magnitude but the opposite sign, no spin splitting arises in ABC-TLG due to the preservation of inversion symmetry that ensures the complete cancellation of contributions from the opposite layers, whereas nonzero spin splitting is observed for ABA-TLG due to its own lack of inversion symmetry. We further illustrate that gate voltage is effective to modulate the spin-polarized states near the band edges. Moreover, we use density functional theory calculations to verify the Rashba splitting effect in the example of TLG interfaced by Au layer(s), which induce simultaneously the effective terms of Rashba SOC and gate voltage. Our results demonstrate the significance of layer and symmetry in manipulating spin and can be extended to multilayer graphene or other van der Waals interface systems.
Utilizing a Hybrid Model for Human Injury Severity Analysis in Traffic Accidents

Road safety has been prioritized by governments globally, resulting in the implementation of numerous initiatives aimed at curtailing traffic accidents. Despite these efforts, the complete eradication of accidents remains unattainable. Therefore, swift and accurate responses to accident sites, accompanied by appropriate medical aid, are paramount in saving lives. Existing systems, primarily designed to alert medical personnel in the aftermath of an accident, rely solely on Vehicle Damage (V d ) to assess accident severity, neglecting Human Injury (H i ) considerations. This study proposes a hybrid model equipped with an attention mechanism, designed to classify accident severity based on both V d and H i . The proposed model accepts video or image inputs and classifies accident severity levels accordingly. Moreover, an extension of the model has been developed to obfuscate sensitive areas in accident imagery based on severity, particularly when such images are disseminated on public platforms without obtaining necessary consent. The proposed hybrid model, therefore, not only facilitates a more comprehensive severity assessment of traffic accidents but also ensures the protection of privacy and promotes ethical image sharing practices.
A Two-Dimensional Precision Level for Real-Time Measurement Based on Zoom Fast Fourier Transform

This paper proposes a two-dimensional precision level for real-time measurement using a zoom fast Fourier transform (zoom FFT)-based decoupling algorithm that was developed and integrated in an FPGA. This algorithm solves the contradiction between obtaining high resolution and obtaining high measurement speed, and achieves both high angle-resolution measurement and real-time measurement. The proposed level adopts a silicone-oil surface as the angle-sensitive interface and combines the principle of homodyne interference. By analyzing the frequency of the interference fringes, the angle variation can be determined. The zoom-FFT-based decoupling algorithm improves the system’s frequency resolution of the interference fringes, thereby significantly enhancing the angle resolution. Furthermore, this algorithm improves the efficiency of angle decoupling, while the angle decoupling process can also be transplanted to the board to realize real-time measurement of the level. Finally, a prototype based on the level principle was tested to validate the effectiveness of the proposed method. The principle analysis and test results showed that the angle resolution of the prototype improved from 9 arcsec to about 0.1 arcsec using this angle-solution method. At the same time, the measurement repeatability of the prototype was approximately ±0.2 arcsec. In comparison with a commercial autocollimator, the angle measurement accuracy reached ±0.6 arcsec.
BUILDING SOCIAL CAPITAL : A Field Study of the Active, Beautiful and Clean Waters (ABC Waters) Programme in Singapore

This thesis presents a field study examining the effect of the Active, Beautiful and Clean Waters (ABC Waters) Programme in Singapore on social capital. Based on a multi-disciplinary approach and following the theoretical framework of bonding and bridging social capital developed by Robert Putnam and others, three different cases of the ABC Programme were compared, looking particularly at the level of blue-green landscape integration. The three cases comprised the ABC flagship project ‘Bishan-Ang Mo Kio Park’ with high level of blue-green integration as well as the green and the grey section of the Ulu Pandan Park Connector with medium and low level of blue-green integration, respectively. Quantitative data was obtained by counting the share of park users engaged in social interaction and by surveying 330 park users on the perceptions of social interaction and integration. In addition to the quantitative data, a total of 60 face-to-face interviews were carried out in the three park areas, providing an in-depth understanding of the perceptions of the surrounding areas. The study concludes that the ABC Waters Programme is a strong generator of social capital, offering an attractive place for social bonding and to some extent also for social bridging.
Contrastive Difference Predictive Coding

Predicting and reasoning about the future lie at the heart of many time-series questions. For example, goal-conditioned reinforcement learning can be viewed as learning representations to predict which states are likely to be visited in the future. While prior methods have used contrastive predictive coding to model time series data, learning representations that encode long-term dependencies usually requires large amounts of data. In this paper, we introduce a temporal difference version of contrastive predictive coding that stitches together pieces of different time series data to decrease the amount of data required to learn predictions of future events. We apply this representation learning method to derive an off-policy algorithm for goal-conditioned RL. Experiments demonstrate that, compared with prior RL methods, ours achieves $2 \times$ median improvement in success rates and can better cope with stochastic environments. In tabular settings, we show that our method is about $20 \times$ more sample efficient than the successor representation and $1500 \times$ more sample efficient than the standard (Monte Carlo) version of contrastive predictive coding.
The Mechanism of Transcription Factor Swi6 in Regulating Growth and Pathogenicity of Ceratocystis fimbriata: Insights from Non-Targeted Metabolomics

Ceratocystis fimbriata (C. fimbriata) is a notorious pathogenic fungus that causes sweet potato black rot disease. The APSES transcription factor Swi6 in fungi is located downstream of the cell wall integrity (CWI)-mitogen-activated protein kinase (MAPK) signaling pathway and has been identified to be involved in cell wall integrity and virulence in several filamentous pathogenic fungi. However, the specific mechanisms by which Swi6 regulates the growth and pathogenicity of plant pathogenic fungi remain elusive. In this study, the SWI6 deletion mutants and complemented strains of C. fimbriata were generated. Deletion of Swi6 in C. fimbriata resulted in aberrant growth patterns. Pathogenicity assays on sweet potato storage roots revealed a significant decrease in virulence in the mutant. Non-targeted metabolomic analysis using LC-MS identified a total of 692 potential differentially accumulated metabolites (PDAMs) in the ∆Cfswi6 mutant compared to the wild type, and the results of KEGG enrichment analysis demonstrated significant enrichment of PDAMs within various metabolic pathways, including amino acid metabolism, lipid metabolism, nucleotide metabolism, GPI-anchored protein synthesis, and ABC transporter metabolism. These metabolic pathways were believed to play a crucial role in mediating the growth and pathogenicity of C. fimbriata through the regulation of CWI. Firstly, the deletion of the SWI6 gene led to abnormal amino acid and lipid metabolism, potentially exacerbating energy storage imbalance. Secondly, significant enrichment of metabolites related to GPI-anchored protein biosynthesis implied compromised cell wall integrity. Lastly, disruption of ABC transport protein metabolism may hinder intracellular transmembrane transport. Importantly, this study represents the first investigation into the potential regulatory mechanisms of SWI6 in plant filamentous pathogenic fungi from a metabolic perspective. The findings provide novel insights into the role of SWI6 in the growth and virulence of C. fimbriata, highlighting its potential as a target for controlling this pathogen.
MICROALGAL BIOFUELS : A BRIEF INTRODUCTION

The cultivation of microalgae for biofuels in general, and oil production in particular, is not a near-term commercial prospect. Aside from some niche, but significant, applications in wastewater treatment, this technology still requires considerable, long-term R&D. This is due in part to the high costs of even the simplest of algal production systems (unlined, open, paddle wheelmixed, raceway-type ponds), in even larger part due to the presently undeveloped nature of algal mass culture technologies, from the selection of algal strains that can be stably maintained in the open ponds to their low-cost harvesting, and, most importantly, due to the need to achieve very high productivities of algal biomass with a high content of vegetable oils or other biofuel precursors, required by the high capital and operating costs of algae production. However, R&D to achieve these goals is justified by the potential to produce biofuels at very high productivities using saline water, land and other resources not useful in food production.
A comparative study on habitat suitability index of Chilean Jack Mackerel in Southeast Pacific Ocean

Based on the commercial fishing data of Chilean Jack Mackerel in Southeast Pacific from 2000 to 2007,the HSI(habitat suitability index) value was calculated with four conjunction modeling methods;and combined with oceanic environmental data,the spatial distribution map were drawn by suffer 8.0. By verifying these four models with fishing data of 2008,the results indicated apparent differences: when arithmetic mean model or geometric mean model was applied,the main yield of Jack Mackerel was distributed in the area where HSI ranges from 0.6 to 0.9;when maximum model was applied,the main yield was distributed in the area where HSI ranges from 0.8 to 1.0;and when minimum model was applied,the main yield was distributed in the area where HSI ranges from 0.3 to 0.6. By comparing the verified results of these four modeling methods,the study confirmed that both Arithmetic mean model and geometric mean model were good indicators of central fishing ground and distribution characters of Chilean Jack Mackerel,which could be helpful in fishery forecast.
Multiple metamorphic stages within an eclogite-facies terrane (Sesia Zone, Western Alps) revealed by Th-U-Pb petrochronology

Convergent plate margins typically experience a transition from subduction to collision dynamics as massive continental blocks enter the subduction channel. Studies of high-pressure rocks indicate that tectonic fragments are rapidly exhumed from eclogite facies to midcrustal levels, but the details of such dynamics are controversial.To understand the dynamics of a subduction channel we report the results of a petrochronological study from the central Sesia Zone, a key element of the internalWestern Alps.This comprises two polymetamorphic basement complexes (Eclogitic Micaschist Complex and Gneiss Minuti Complex) and a thin, dismembered cover sequence (Scalaro Unit) associated with pre-Alpine metagabbros and metasediments (Bonze Unit). Structurally controlled samples from three of these units (Eclogitic Micaschist Complex and Scalaro-Bonze Units) yield unequivocal petrological and geochronological evidence of two distinct high-pressure stages. Ages (U-Th-Pb) of growth zones in accessory allanite and zircon, combined with inclusion and textural relationships, can be tied to the multi-stage evolution of single samples.Two independent tectono-metamorphic ‘slices’ showing a coherent metamorphic evolution during a given time interval have been recognized: the Fondo slice (which includes Scalaro and Bonze rocks) and the Druer slice (belonging to the Eclogitic Micaschist Complex). The new data indicate separate stages of deformation at eclogite-facies conditions for each recognized independent kilometer-sized tectono-metamorphic slice, between ~85 and 60 Ma, with evidence of intermittent decompression (∆P~0.5 GPa) within only the Fondo slice. The evolution path of the Druer slice indicates a different P-T-time evolution with prolonged eclogite-facies metamorphism between ~85 and 75Ma. Our approach, combining structural, petrological and geochronological techniques, yields field-based constraints on the duration and rates of dynamics within a subduction channel.
A NEW BASAL CENTROSAURINE CERATOPSID FROM THE OLDMAN FORMATION, SOUTHEASTERN ALBERTA

Abstract A new centrosaurine ceratopsid, Albertaceratops nesmoi, is described from the lower Oldman Formation (Upper Cretaceous) of southern Alberta, and is based on a single, almost complete skull. Referred material is described from equivalent beds in the Judith River Formation of north-central Montana. A limited phylogenetic analysis of the Ceratopsidae places the new taxon as the basal member of the Centrosaurinae and indicates that robust, elongate postorbital horncores that form a synapomorphy of (Ceratopsidae + Zuniceratops) are also present in Centrosaurinae.
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network

In clinical scenarios, multiple medical images with different views are usually generated simultaneously, and these images have high semantic consistency. However, most existing medical report generation methods only consider single-view data. The rich multi-view mutual information of medical images can help generate more accurate reports, however, the dependence of multi-view models on multi-view data in the inference stage severely limits their application in clinical practice. In addition, word-level optimization based on numbers ignores the semantics of reports and medical images, and the generated reports often cannot achieve good performance. Therefore, we propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C^2M-DoT). Specifically, (i) a semantic-based multi-view contrastive learning medical report generation framework is adopted to utilize cross-view information to learn the semantic representation of lesions; (ii) a domain transfer network is further proposed to ensure that the multi-view report generation model can still achieve good inference performance under single-view input; (iii) meanwhile, optimization using a cross-modal consistency loss facilitates the generation of textual reports that are semantically consistent with medical images. Extensive experimental studies on two public benchmark datasets demonstrate that C^2M-DoT substantially outperforms state-of-the-art baselines in all metrics. Ablation studies also confirmed the validity and necessity of each component in C^2M-DoT.
Experimental Investigation of Dynamic Ground Effect

MODELS of an F-106B and an XB-70 aircraft were tested under static and dynamic conditions in ground effect in a 36 in. x 51 in. wind tunnel. Tests were made with and without flap deflections. Lift, drag, and pitching moments were measured by a movable strain gaged sting. Test results showed that the percentage increments in lift and drag coefficients in dynamic ground effect were always smaller than those measured under static conditions. The dynamic pitching moment data were masked by the system dynamics in initiating the dynamic tests. Contents To simulate the constant angle-of-attack flight-test technique in a wind tunnel, a test technique of moving a model toward a ground board was developed and is explained in Ref. 1. Only wing models were tested in the exploratory experiments of Ref. 1. These tests showed that the percentage increments in lift and drag coefficients ACL% and ACD% in dynamic ground effect were always smaller than those measured under static conditions for highly swept wings. In the present investigation, complete configuration models for the F-106B and XB-70 aircraft were tested to compare dynamic and static
Who Else Wants to Work Innovatively? The Role of Transformational Leadership in the Workplace

This study aims to determine the effect of transformational leadership and job satisfaction as mediators on teachers' innovative work behavior. By using a quantitative approach, 212 teachers from senior high schools and vocational high schools in three suburbs of Bandung City, Indonesia, were involved in this study. The research model in this study was tested using PLS-SEM assisted by SmartPLS 3.2. 9 for Windows software. The results indicate that transformational leadership has a positive and significant effect on innovative work behavior. Job satisfaction also played a significant role in mediating the relationship between transformational leadership and innovative work behavior. The findings also demonstrate that job satisfaction has a significant effect on innovative work behavior. Finally, this study proved that leadership is an indispensable factor in promoting innovative work behavior. These findings provide practical and theoretical contributions regarding the role of leadership in fostering a culture that values and pushes for innovation in the workplace.
From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.