Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed and Efficient Storage of High-Dimensional Data Using Artificial Neural Network

Artificial Neural Network (ANN) has traditionally been used as function approximator for tasks such as classification, and data regression. In this work, we demonstrate that ANN can also be used for data storage in a distributed and efficient manner. The specific network structure utilized here is known as Deep Transposed Convolutional Network (DTCN), constructed by stacking several layers of the Transposed Convolutional Network (TCN) module. The key idea is to uniquely associate each training example with a fixed low-dimensional input vector generated using a random number generator, using the index or identification number of the training example as the seed. After training, the DTCN effectively stores all the data within the network distributively across its layers in the form of network weights. Since the space required to store all the network weights necessary to reproduce the entire dataset with an acceptable level of accuracy is significantly less than that needed to store the original dataset, the dataset has been effectively compressed. The proposed application of ANN represents a notable departure from its typical usage. Moreover, this approach could potentially provide insights into how information is stored in biological systems. As a proof of concept, a DTCN was implemented and trained with a high-dimensional dataset (3 x 224 x 224), CelebA, which comprises hundreds of thousands of color images of human faces. Experimental results indicate that a DTCN with an appropriate capacity can effectively compress and store high-dimensional data.
Yoga Pose Recognition Based On Convolutional Neural Networks

People are more accepting of virtual yoga courses now than they were before the epidemic, and this preference for online forms is likely to continue even after the outbreak is over. Thus, an automated yoga self-training system is significant. Automatic yoga position recognition is necessary for the practitioner to carry out their yoga exercise with accurate poses at any time. Convolutional neural networks (CNN) is a deep learning architecture that has been proven to be a powerful tool in image classification. This dataset was subjected to an experiment using many common CNN architectures. The experiment, however, excludes the use of certain powerful image classification models such as VGG-16, AlexNet, and GoogleNet. Therefore, a study of classifying 82 yoga classes using the VGG16, AlexNet, and GoogleNet models via transfer learning is carried out to evaluate their performance in terms of sensitivity, precision, F1-score, specificity, Top-1 accuracy, and Top-5 accuracy. Transfer learning is a handy technique for eliminating time-consuming computations and allowing image classification with a smaller dataset. GoogleNet was the most appropriate model for classifying 82 yoga classes since it achieved the highest percentages of 99.94%, 80.14%, 92.97%, 85.93%, 87.89%, and 97.08% for specificity, sensitivity, precision, F1-score, Top-1 accuracy, and Top-5 accuracy, respectively.
Efficient Information Recognition for Machine-printed Invoices

Automatic recognition of information in machine-printed invoice images is significant to enterprises in saving labor. However, most invoice information identification methods rely on high-quality invoice images, and collecting these images requires the purchase of professional and expensive image acquisition equipment. In this paper, we propose a novel invoice recognition framework capable of identifying lower-quality invoice images captured by devices such as mobile phones. we present an image preprocessing algorithm to correct distorted invoice images based on perspective projection. Then, accurate content Key-Value (KV) structures are extracted by U-net and template matching combined with the coordinate fine-tuning method, and the Robust text recognizer with Automatic Rectification (RARE) is adapted to recognize characters. To further improve the proposed model in small data scenarios, we adapt the Generative Adversarial Networks (GAN) to extend training samples and improve the RARE by active learning method. Extensive experiments are conducted on both machine-printed invoice images collected from actual scenarios and the open-source ICDAR dataset. The comparison results demonstrate the superiority of the proposed method on both sufficient and small data scenarios.
All Optical 2R Regeneration Based on Optical Parabolic Pulses for High-Bit-Rate Links

The primary objective of this study is to develop an All-optical 2R regenerator (Reamplification and Reshaping) that integrates optical parabolic pulses. The key goal is to enhance transmission performance by decreasing noise and improving the quality of transmitted signals through the utilization of generated similaritons in Raman amplifier fiber. The results confirm that the proposed 2R regenerator was efficient to regenerate signals with high data rate ranging from 40 Gb/s to 160 Gb/s. The performance of this regenerator is evaluated via the quality factor (Q) and the eye diagram of the received signals.
SocialVessel: VAE-inspired Multi-Vessel Gated Recurrent Units for Vessel Trajectory Prediction in Port Areas

Accurate and robust trajectory prediction models are crucial for ensuring the safe navigation of autonomous vessels. To meet this demand, we develop SocialVessel, a novel framework that leverages a VAE-inspired archtitecture with Gated Recurrent Units to enhance vessel trajectory prediction in port areas. By considering the target vessel and its surroundings, SocialVessel simultaneously infers multiple predicted trajectories and employs clustering to determine the most probable destination and trajectory. Through the incorporation of an attention mechanism, the model effectively captures the influence of surrounding vessels by encoding essential features such as distance, DCPA, and TCPA. Additionally, a VAE-inspired framework aligns the predictive distribution with the observed distribution during training. Experimental results demonstrate the superior performance of SocialVessel compared to existing models, showcasing its potential to enhance the navigation capabilities of autonomous vessels.
Comparative Analysis of Machine Learning and Deep Learning Strategies for Solar Irradiation Forecasting

In this work, we conduct a comparative analysis of Machine Learning (ML) and Deep Learning (DL) approaches for solar irradiation forecasting at Batna station. Specifically, we examine six different ML methods: Linear Regression (LR), Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). Furthermore, we investigate a deep learning-based regression approach considered a relatively recent field technique. We propose to use three DL methods, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bi-directional Long Short-Term Memory (Bi-LSTM). We present and discuss the experimental outcomes obtained from the Batna station data.
The forest health monitoring national technical reports: examples of analyses and results from 2001-2004

This brochure presents examples of analyses included in the first four Forest Health Monitoring (FHM) national technical reports. Its purpose is to introduce the reader to the kinds of information available in these and subsequent FHM national technical reports. Indicators presented here include drought, air pollution, forest fragmentation, and tree mortality. These and other indicators were generally analyzed by broad ecological regions characterized by similar climate, vegetation, geology, and soils. Sources are provided for additional information about these analyses, as well as the FHM Program in general.
Development of the Lifelike Head Unit for a Humanoid Cybernetic Avatar ‘Yui’ and its Operation Interface

In avatar-mediated communication, the face-to-face interlocutor must sense the operator’s presence and emotions via an avatar. Although androids resembling humans have been developed to convey presence through appearance and movement, few studies have prioritized deepening the communication experience for operators and interlocutors using android robots as avatars. To address this gap, we introduced the “cybernetic avatar ‘Yui,’” featuring a human-like head unit with 28 actuation points, capable of expressing gaze, facial emotions, and speech-related mouth movements. Through an eye-tracking unit in a head-mounted display (HMD) and the degrees of freedom in both eyes of Yui, operators can naturally control the avatar’s gaze. Additionally, microphones embedded in Yui’s ears allowed operators to hear surrounding sounds in three dimensions, enabling them to discern the direction of calls based solely on auditory information. The HMD’s face-tracking unit synchronizes the facial movements of the avatar with those of the operator. This immersive interface, coupled with Yui’s human-like appearance, enabled real-time emotional transmission and communication, thus enhancing the sense of presence for both parties. Our experiments demonstrate Yui’s facial expression capabilities and validate the efficacy of the system through teleoperation trials, suggesting potential advancements in avatar technology.
Variation in the Care of Children with Inflammatory Bowel Disease Within and Across Canadian Provinces: A Multi-Province Population-Based Cohort Study

Purpose The incidence of childhood-onset inflammatory bowel disease (IBD) is rising. We described variation in health services utilization and need for surgery among children with IBD between six and 60 months following IBD diagnosis across Canadian pediatric centers and evaluated the associations between care provided at diagnosis at each center and the variation in these outcomes. Patients and Methods Using population-based deterministically-linked health administrative data from four Canadian provinces (Alberta, Manitoba, Nova Scotia, Ontario) we identified children diagnosed with IBD <16 years of age using validated algorithms. Children were assigned to a pediatric center of care using a hierarchical approach based on where they received their initial care. Outcomes included IBD-related hospitalizations, emergency department (ED) visits, and IBD-related abdominal surgery occurring between 6 and sixty months after diagnosis. Mixed-effects meta-analysis was used to pool results and examine the association between center-level care provision and outcomes. Results We identified 3784 incident cases of pediatric IBD, of whom 2937 (77.6%) were treated at pediatric centers. Almost a third (31.4%) of children had ≥1 IBD-related hospitalization and there were 0.66 hospitalizations per person during follow-up. More than half (55.8%) of children had ≥1 ED visit and there were 1.64 ED visits per person. Between-center heterogeneity was high for both outcomes; centers where more children visited the ED at diagnosis had more IBD-related hospitalizations and more ED visits during follow-up. Between-center heterogeneity was high for intestinal resection in Crohn’s disease but not colectomy in ulcerative colitis. Conclusion There is variation in health services utilization among children with IBD and risk of undergoing intestinal resection in those with Crohn’s disease, but not colectomy among children with ulcerative colitis, across Canadian pediatric tertiary-care centers. Improvements in clinical care pathways are needed to ensure all children have equitable and timely access to high quality care.
Swine industry perspectives on the future of pig farming

Understanding the views of those working along the value chain reliant on livestock is an important step in supporting the transition towards more sustainable farming systems. We recruited 31 delegates attending the Pig Welfare Symposium held in the United States to participate in one of six focus group discussions on the future of pig farming. Each of these six group discussions was subjected to a thematic analysis that identified four themes: (1) technical changes on the farm; (2) farm and industry culture; (3) the farm-public interface; and (4) sustainability. The results of this study illustrate the complexity and diversity of views of those working along the associated value chain within the swine industry. Participants spent the majority of their time discussing current challenges, including technical challenges on the farm and public perception of pig farms. Participants were more hesitant to discuss future issues, but did engage on the broader issue of sustainability, focusing upon economic and environmental aspects.
Domain-Specific STT-MRAM-Based In-Memory Computing: A Survey

In recent years, the rapid growth of big data and the increasing demand for high-performance computing have fueled the development of novel computing architectures. Among these, in-memory computing architectures that leverage the high-density and low-latency nature of modern memory technologies have emerged as promising solutions for domain-specific computing applications. STT-MRAM (Spin Transfer Torque Magnetic Random Access Memory) is one of such memory technology that holds great potential for in-memory computing due to numerous advantages such as non-volatility, high density, high endurance, and low power consumption. This survey paper aims to provide a comprehensive overview of the state-of-the-art in STT-MRAM-based domain-specific in-memory computing (DS-IMC) architectures. We examine the challenges, opportunities, and trade-offs associated with these architectures from the perspective of various application domains, like machine learning, image and signal processing, and data encryption. We explore different experimental research tools used in studying these architectures, guidelines for efficiently designing them, and gaps in the state-of-the-art that necessitate future research and development.
The Performance of the New KETEK Low-Noise SiPM Array Series: Characterization in a Compton Camera Study

Compton cameras (CCs) allow for spatially resolved <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> detection. The <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-ray origins are reconstructed by measuring the interaction positions and energy deposits in the scatter and absorber components, allowing to determine the scattering angle. Consequently, the energy resolution of the CC components plays a major role in component selection and detector design. For scintillators, the energy resolution strongly depends on the scintillation material, and also on the photosensor and signal processing electronics. For our CC prototype, built from a <inline-formula> <tex-math notation="LaTeX">$16\times16$ </tex-math></inline-formula> GAGG scintillation array (scatterer) with 1.6-mm crystal pitch and a monolithic LaBr 3:Ce block (<inline-formula> <tex-math notation="LaTeX">$51\times 51\times30$ </tex-math></inline-formula> mm3), we investigated the energy resolution of the components in a readout configuration involving the new KETEK low-noise SiPM array series (PA33XXWL-0808) for the individual camera components and compared the performance with that of the previous generation (PA33XXWB-0808). Furthermore, we commissioned a CC prototype equipped with the new SiPM series, evaluated its energy resolution, and performed a systematic study of the imaging capabilities for <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-rays of 1274 keV (using a 22Na point source). For the GAGG scatterer, an energy resolution of 9.4% was measured. An improvement between 0.9% and 1.8% was achieved due to the superior balance of SiPM saturation, PDE, and noise compared with KETEK’s previous SiPM array generation. For the absorber detector, a comparable energy resolution was obtained for SiPM arrays of the two generations with comparable microcell sizes (47 versus <inline-formula> <tex-math notation="LaTeX">$50 ~\mu \text{m}$ </tex-math></inline-formula>). However, the reduced noise of the WL-SiPM arrays allowed to obtain an excellent energy resolution of 4.1% over a wider range of applied bias voltages (5.0–6.0 V) and, thus, gains. The CC prototype provided a spatial resolution below 6 mm and was capable of resolving shifts of the radiation source of 2 mm with an accuracy well below 1 mm.
An Agent-Based Real-Time Game Model for Forecasting the Market Penetration of Vehicles in China

Internal combustion engine vehicles (ICEs), hybrid, plug-in hybrid and battery electric vehicles (HEVs, PHEVs, and BEVs) represent tradition, high technology, energy conservation and innovation respectively. The new energy vehicles (NEVs) include PHEVs and BEVs. This study innovatively proposes an agent-based real-time game model to simulate the market penetration of vehicles in China. The advantage of the model is in accord with the product diffusion theory and synchronous with the cognition of consumers. The purchase preference is described by a hierarchical variables structure, which forms a probit model with the approximately normal distribution based on the central limit theorem. The results show that when the individual purchase of ICEs will not bring any incentive for others to purchase ICEs, the market penetration of ICEs will decrease. The development of NEVs should focus on their own competitiveness, and the interaction between the proven technology of ICEs and the rapid development of NEVs is useful for NEVs. Restricted with the narrow price range, the market penetration of HEVs will keep steady. Finally, the high interaction frequency is good for NEVs, which indicates that WeMedias will play an important role.
Sixty-Five Years of Change in Montane Plant Communities in Western Colorado, U.S.A.

ABSTRACT Documenting and predicting patterns of vegetation change over time are challenging due to a lack of sufficiently detailed historical data for comparison. Montane plant communities are expected to respond to anthropogenic disturbance, including climate change, in complex ways dependent on component species' responses to changing abiotic and biotic conditions. To investigate the patterns and possible causes of temporal changes in montane plant communities, we resampled 121 transects surveyed by Jean Langenheim from 1948 to 1952 in the East River Basin near Crested Butte, Colorado, U.S.A. Langenheim quantified the composition of the four predominant community types (sagebrush, spruce-fir, upland-herbaceous, and alpine) at sites ranging from 2600 to 4100 m in elevation. Our resurvey of the same sites 65 years later revealed that all four communities currently have much higher levels of heterogeneity among sites and have experienced significant changes in species composition and dominance. Compositional changes include significant increases in bare ground, graminoid and shrub abundance, and loss of forbs, at higher elevations. Species' mean elevations shifted upward 41 m, and many species expanded their ranges into new communities. Elevation shifts were most pronounced from lower elevation communities, while many alpine species shifted their ranges into lower subalpine meadow communities.
Large-scale screening of clinical assessments to distinguish between states in the Integrated HD Progression Model (IHDPM)

Background Understanding the sensitivity and utility of clinical assessments across different HD stages is important for study/trial endpoint selection and clinical assessment development. The Integrated HD Progression Model (IHDPM) characterizes the complex symptom progression of HD and separates the disease into nine ordered disease states. Objective To generate a temporal map of discriminatory clinical measures across the IHDPM states. Methods We applied the IHDPM to all HD individuals in an integrated longitudinal HD dataset derived from four observational studies, obtaining disease state assignment for each study visit. Using large-scale screening, we estimated Cohen’s effect sizes to rank the discriminative power of 2,472 clinical measures for separating observations in disease state pairs. Individual trajectories through IHDPM states were examined. Discriminative analyses were limited to individuals with observations in both states of the pairs compared (N = 3,790). Results Discriminative clinical measures were heterogeneous across the HD life course. UHDRS items were frequently identified as the best state pair discriminators, with UHDRS Motor items – most notably TMS – showing the highest discriminatory power between the early-disease states and early post-transition period states. UHDRS functional items emerged as strong discriminators from the transition period and on. Cognitive assessments showed good discriminative power between all state pairs examined, excepting state 1 vs. 2. Several non-UHDRS assessments were also flagged as excellent state discriminators for specific disease phases (e.g., SF-12). For certain state pairs, single assessment items other than total/summary scores were highlighted as having excellent discriminative power. Conclusion By providing ranked quantitative scores indicating discriminatory ability of thousands of clinical measures between specific pairs of IHDPM states, our results will aid clinical trial designers select the most effective outcome measures tailored to their study cohort. Our observations may also assist in the development of end points targeting specific phases in the disease life course, through providing specific conceptual foci.
Review and application of Russian and Norwegian methods for measuring and estimating riverine inputs of heavy metals to the Barents Sea

NIVA provides government, business and the public with a basis for preferred water management through its contracted research, reports and development work. A characteristic of NIVA is its broad scope of professional disciplines and extensive contact network in Norway and abroad. Our solid professionalism, interdisciplinary working methods and holistic approach are key elements that make us an excellent advisor for government and society.
Organic Geochemistry of MiocenePliocene Laminated Diatomites on the East Flank of the Mid-Atlantic Ridge (Site 520, Leg 73)

SAMPLES The hypothesis was tested by analyzing 41 small samples of various lithofacies: nannofossil-diatom ooze, clayey nannofossil-diatom ooze, nannofossil-diatomite, laminated diatomite, and laminated clayey nannofossil-diatomite. All samples were taken from Lithologic Unit 2, between Cores 9 and 26 (256.70-390.98 m subbottom). In age, Core 9 is early Pliocene and the rest are late Miocene: Cores 10 to 17 are Messinian, Cores 18 to 25 are from the Messinian/Tortonian transition, and Core 26 is Tortonian.