Miracle-dz/UniFAR-FTUs huggingface dataset:
XR Carousel: A Visualization Tool For Volumetric Video

Recent years have seen a new uptake in immersive media and eXtended Reality (XR). And due to a global pandemic, computer-mediated communication over video conferencing tools became a new normal of everyday remote collaboration and virtual meetings. Social XR leverages XR technologies for remote communication and collaboration. But in order for XR to facilitate a high level of (social) presence and thus high-quality mediated social contact between users, we need high-quality 3D representation of users. One approach to providing detailed 3D user representations as new immersive media is to use point clouds or meshes, but these representation formats come with complexity on compression bitrate and processing time. In the example of virtual meetings, compression has to fulfill stringent requirements such as low latency and high quality. As the compression techniques for 3D immersive media steadily advance, it is important to be able to easily compare different compression techniques on their technical and visual merits in an easy way. The proposed demonstrator in this paper is a visualization tool that helps assessing the visual quality of a 3D representation employing various coding schemes. The complete end-to-end rendering/encoding chain can be easily assessed, allowing for subjective testing by showing the differences between the selected encoding parameters. The tool presented in this demo paper offers an improved and easy visual process for the comparison of encoders of immersive media.
Intermittent-Aware Neural Architecture Search

The increasing paradigm shift towards intermittent computing has made it possible to intermittently execute deep neural network (DNN) inference on edge devices powered by ambient energy. Recently, neural architecture search (NAS) techniques have achieved great success in automatically finding DNNs with high accuracy and low inference latency on the deployed hardware. We make a key observation, where NAS attempts to improve inference latency by primarily maximizing data reuse, but the derived solutions when deployed on intermittently-powered systems may be inefficient, such that the inference may not satisfy an end-to-end latency requirement and, more seriously, they may be unsafe given an insufficient energy budget. This work proposes iNAS, which introduces intermittent execution behavior into NAS to find accurate network architectures with corresponding execution designs, which can safely and efficiently execute under intermittent power. An intermittent-aware execution design explorer is presented, which finds the right balance between data reuse and the costs related to intermittent inference, and incorporates a preservation design search space into NAS, while ensuring the power-cycle energy budget is not exceeded. To assess an intermittent execution design, an intermittent-aware abstract performance model is presented, which formulates the key costs related to progress preservation and recovery during intermittent inference. We implement iNAS on top of an existing NAS framework and evaluate their respective solutions found for various datasets, energy budgets and latency requirements, on a Texas Instruments device. Compared to those NAS solutions that can safely complete the inference, the iNAS solutions reduce the intermittent inference latency by 60% on average while achieving comparable accuracy, with an average 7% increase in search overhead.
Practical application of in vivo MRI‐based brown adipose tissue measurements in infants

The role of brown adipose tissue (BAT) in infant metabolism remains poorly understood, primarily because of the inherent limitation of positron emission tomography/computed tomography imaging to measure BAT, which is not suitable for infants. The aims of this method development study were to assess the feasibility, intra‐rater reliability, interscan repeatability, and physiological relevance of measuring BAT in infants using magnetic resonance imaging (MRI).
Skeletal muscle energy metabolism in obesity

Comparing energy metabolism in human skeletal muscle and primary skeletal muscle cells in obesity, while focusing on glucose and fatty acid metabolism, shows many common changes. Insulin‐mediated glucose uptake in skeletal muscle and primary myotubes is decreased by obesity, whereas differences in basal glucose metabolism are inconsistent among studies. With respect to fatty acid metabolism, there is an increased uptake and storage of fatty acids and a reduced complete lipolysis, suggesting alterations in lipid turnover. In addition, fatty acid oxidation is decreased, probably at the level of complete oxidation, as β ‐oxidation may be enhanced in obesity, which indicates mitochondrial dysfunction. Metabolic changes in skeletal muscle with obesity promote metabolic inflexibility, ectopic lipid accumulation, and formation of toxic lipid intermediates. Skeletal muscle also acts as an endocrine organ, secreting myokines that participate in interorgan cross talk.
Refugees, Discrimination, and Barriers to Health

ABSTRACT Using an interpretive phenomenological analysis, we explored refugees’ experiences with discrimination and social determinants of health in a southern city of Texas, within the context of recent public policy changes and anti-refugee rhetoric. To create a separate, yet coextending, exploration of this phenomena, we interviewed both refugees and staff members who work with this population. Five main themes emerged: (a) economic instability, (b) unequal educational starting points, (c) refugee enclaves, (d) negative community and political messages towards refugees, and (e) connection to health and healthcare systems. Implications for counselors are also discussed.
Favourable inland wetland conditions increase apparent survival of migratory shorebirds in Australia

ABSTRACT Many migratory shorebird species using the East Asian–Australasian Flyway are declining rapidly. While the loss of staging habitats in East Asia is considered the primary cause, stressors to fitness often occur throughout the geographic range of declining species, and threats in the non-breeding grounds have been comparatively poorly studied. Three species of migratory shorebird, Curlew Sandpiper (Calidris ferruginea), Sharp-tailed Sandpiper (C. acuminata) and Red-necked Stint (C. ruficollis), use Australia’s dynamic temporary wetland systems opportunistically, yet these large wetland systems have become increasingly degraded, with reduced frequency and extent of flooding. Here, we test whether variables related to wetland availability in Australia’s interior can explain annual variation in apparent survival, abundance or immature to adult ratios at three well-monitored coastal shorebird areas in southern Australia (total area > 1315 km2). We show that coastal annual bird abundance and ratios of immatures at the coast were higher when inland Australia was relatively hot and dry. Also, a small but significant amount of variation in annual apparent survival can be explained by annual variation in inland conditions, with higher survival rates in years when inland conditions were relatively wet and cool. For the endangered Curlew Sandpiper, the impacts of Australian environmental conditions may be exacerbating the impacts of conditions experienced in other parts of its range on fitness and survival. While the effects we document here are relatively weak, they do suggest that management of inland wetlands for these shorebirds may positively affect survival rates of these sharply declining species.
Measurement method of traverse crossing distance based on moving edge calculation

In order to ensure the security of power system, the measurement method of traverse crossing distance is designed based on the principle of moving edge calculation. Taking ±800kV UHV DC transmission line as the research object, the interference value in the process of traverse cross measurement is collected by particle flow density algorithm, and the performance data of traverse is standardized and recorded according to the collection results. The sag value of traverse cross span distance is obtained by moving edge calculation method, and the traverse cross span distance is realized according to the sag value Accurate measurement. Finally, the experiment shows that the research method has higher accuracy in the practical application process, and can improve the security and stability of power system operation.
Efficiency of Buffer Caching in Computing-Intensive Workloads

With the recent advent of the fourth industrial revolution, computing-intensive workloads such as big data and machine learning emerge every day. Even though the workloads are computation-intensive, there are file I/Os to access storage, and we need to improve the I/O performance by using buffer caching. This paper analyzes the efficiency of the buffer caching in the emerging computing-intensive workloads, and observes some peculiar I/O patterns, which degrades the performance of the buffer caching significantly. To relieve this problem, we present a new buffer caching scheme for improving the I/O performance of computing-intensive workloads. Simulation experiments with real-world traces show that the proposed buffer caching scheme improves the cache miss rate against the well-known LRU buffer caching policy by 35.2% on average and up to 81.6%.
Scheduling Thermostatically Controlled Loads to Provide Regulation Capacity Based on a Learning-Based Optimal Power Flow Model

Thermostatically controlled load (TCL, such as heating, ventilation, and air conditioning system) is a desirable demand-side flexibility source in distribution networks. It can participate in regulation services and mitigate power imbalances from fluctuating distributed renewable generation. To effectively utilize the load flexibility from spatially and temporally distributed TCLs in a distribution network, it is necessary to consider power flow constraints to avoid possible voltage or current violations. Published works usually adopt optimal power flow models (OPF) to describe these constraints. However, these models require accurate topology of the distribution network that is often unobservable in practice. To bypass this challenge, this paper proposes a novel learning-based OPF to optimize TCLs for regulation services. This method trains three regression multi-layer perceptrons (MLPs) based on the distribution network's historical operation data to replicate its power flow constraints. The trained MLPs are further equivalently reformulated into linear constraints with binary variables so that the optimization problem becomes a mixed-integer linear program that can be effectively solved. Numerical experiments based on the IEEE 123-bus system validate that the proposed method can achieve better TCL power scheduling performance with guaranteed feasibility and optimality than other state-of-art models.
A Framework for Asymmetrical DNN Modularization for Optimal Loading

The modern era of artificial intelligence is mostly driven by Deep Neural Network (DNN). As a result most of the intelligent/smart apps running on edge devices (mobile phones, televisions etc.) use DNN for their predictive ability. DNN based prediction suffers with operational overheads, which is the summation of model loading latency and inference latency. Model loading latency affects the first response of the DNN powered apps, whereas inference latency affects the subsequent responses. As apps switching has become a common practice among edge device users, so it is of utmost interest to make the switching smooth by reducing the model loading latency. In this paper asymmetrical DNN modularization is proposed as a potential solution. The proposed method solves two distinct problems (a). Improves the model loading latency by parallel loading of all the modules (child models) of given DNN model. (b). Helps in on-device training by keeping the live gradients only for last child model. The decision about modularization index and their corresponding positions are taken by reinforcement learning unit (RLU). RLU takes into account the available hardware resources on-device (eg. h/w threads), loading latency of each layer on dedicated compute units. In response, it provides the best modularization index k and their corresponding positions $\vec{p}$ specific to the DNN model and device, where $P=(p_{1},p_{2},\ \ldots,p_{k})$ and $p_{i}$ is the end position of child $i$. The proposed method has shown significant loading time improvement (up to 7X) on popular DNNs, used for camera use-cases. Along with improving the loading latency the proposed modularization method facilitates for On-device personalization by separating the module with trainable layers and loading them particularly while training on-device.
An Efficient and Low-Cost FPGAs-Accelerated CNN-Based Edge Intelligent Garbage Classification System on ZYNQ

Garbage classification has achieved a high classification accuracy using convolutional neural network (CNN). However, due to the high complexity of CNNs, CPU or GPU-based solutions are too expensive for daily garbage classification. In this paper, we propose an efficient and low-cost field programmable logic gate arrays (FPGAs) accelerated CNN-based intelligent garbage classification system with two ARM+FPGA heterogeneous platforms. In this work, an image preprocessing module is deployed in the programmable logic of xc7z010clg400-1 to perform the real-time preprocessing of a garbage image. A pre-trained 18-layer CNN model is deployed in the programmable logic of xc7z020clg400-1 with the help of hardware computing module reusing to perform the garbage classification in real time. In the processing system of xc7z010clg400-1 and xc7z020clg400-1, a software to implement the scheduling of hardware is designed. According to the experiments, the system achieved a calculation speed of 21.709 times faster than the Intel(R) Core(TM) i7-7700HQ processor (CPU) and the power consumption is 1/17 of the CPU's. Its classification accuracy on the garbage dataset TrashNet is 93%, which is only 0.333% less than the pure software on the CPU. It is a cheaper solution and can also enhance the efficiency of daily garbage classification based on embedded devices. In the future work, the system we proposed will be a promising solution of the large consumption of time, energy and comnuting resource in previous solutions.
Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems, enabling vehicular communication, where the nodes are highly mobile, requires robust mobility management techniques to minimise signalling cost and interruptions during frequent handovers. This presents a major challenge that communication system engineers need to address to realise the promise of 5G systems for V2X and similar applications. In this paper, we provide an overview of recent progresses in the development of handover and beam management techniques in 5G communication systems. We conduct a critical appraisal of current research on beam level and cell level mobility management in 5G mmWave networks considering the ultra-reliable and low-latency communication requirements within the context of V2X applications. We also provide an insight into the open challenges and the emerging trends as well as the possible evolution beyond the horizon of 5G.
AdaptiveSystems: An Integrated Framework for Adaptive Systems Design and Development Using MPS JetBrains Domain-Specific Modeling Environment

This paper contains the design and development of an adaptive systems (AdaptiveSystems Domain-Specific Language - DSL) framework to assist language developers and data scientists in their attempt to apply Artificial Intelligence (AI) algorithms in several application domains. Big-data processing and AI algorithms are at the heart of autonomics research groups among industry and academia. Major advances in the field have traditionally focused on algorithmic research and increasing the performance of the developed algorithms. However, it has been recently recognized by the AI community that the applicability of these algorithms and their consideration in context is of paramount importance for their adoption. Current approaches to address AI in context lie in two areas: adaptive systems research that mainly focuses on implementing adaptivity mechanisms (technical perspective) and AI in context research that focuses on business aspects (business perspective). There is currently no approach that combines all aspects required from business considerations to an appropriate level of abstraction. In this paper, we attempt to address the problem of designing adaptive systems and therefore providing AI in context by utilising DSL technology. We propose a new DSL (AdaptiveSystems) and a methodology to apply this to the creation of a DSL for specific application domains such as AdaptiveVLE (Adaptive Virtual Learning Environment) DSL. The language developer will be able to instantiate the AdaptiveSystems DSL to any application domain by using the guidelines in this paper with an integrated path from design to implementation. The domain expert will then be able to use the developed DSL (e.g. AdaptiveVLE DSL) to design and develop their application. Future work will include extension and experimentation of the applicability of this work to more application domains within British Telecom (BT) and other areas such as health care, finance, etc.
A Multitask Learning Approach for Fake News Detection: Novelty, Emotion, and Sentiment Lend a Helping Hand

The recent explosion in false information on social media has led to intensive research on automatic fake news detection models and fact-checkers. Fake news and misinformation, due to its peculiarity and rapid dissemination, have posed many interesting challenges to the Natural Language Processing (NLP) and Machine Learning (ML) community. Admissible literature shows that novel information includes the element of surprise, which is the principal characteristic for the amplification and virality of misinformation. Novel and emotional information attracts immediate attention in the reader. Emotion is the presentation of a certain feeling or sentiment. Sentiment helps an individual to convey his emotion through expression and hence the two are co-related. Thus, Novelty of the news item and thereafter detecting the Emotional state and Sentiment of the reader appear to be three key ingredients, tightly coupled with misinformation. In this paper we propose a deep multitask learning model that jointly performs novelty detection, emotion recognition, sentiment prediction, and misinformation detection. Our proposed model achieves the state-of-the-art(SOTA) performance for fake news detection on three benchmark datasets, viz. ByteDance, Fake News Challenge(FNC), and Covid-Stance with 11.55%, 1.58%, and 21.76% improvement in accuracy, respectively. The proposed approach also shows the efficacy over the single-task framework with an accuracy gain of 11.53, 28.62, and 14.31 percentage points for the above three datasets. The source code is available at https://github.com/Nish-19/Multitask-Fake-News-NES.
A Novel Pre-Processing Method for Neural Network-Based Magnetic Field Approximation

In this article, a novel pre-processing method is proposed for magnetic field approximation based on neural networks. The process of calculating magnetic fields based on neural networks is presented first. In order to save the training time and reduce the estimation error, an excitation distance function, which is inspired by an analytical formula of magnetic potential, is proposed to integrate the input data, such as the geometry, excitations, and boundaries. This pre-processing or roughly reduces the dimension of the input layer by three quarters, while the training process is accelerated by a more integrated input layer. Preliminary experiments show that the predicted results by the proposed pre-processing method are close to the ground truth, with a significant reduction of computation time compared with the traditional finite element method (FEM).
Universal Transformer Hawkes process

The recent increase of asynchronous event sequence data in a diversity of fields, make researchers pay more attention to how to mine knowledge from them. In the initial research phase, researchers tend to make use of basic mathematical-based point process models, such as Poisson process and Hawkes process. And in recent years, recurrent neural network (RNN) based point process models are proposed which have significant model performance improvement, while it is still hard to describe the long-term relation between events. To address this issue, transformer Hawkes process is proposed. However, it is worth noting that transformer with a fixed stack of different layers is failure to implement the parallel processing, recursive learning, and abstracting the local salient properties, while they may be very important. In order to make up for this shortcoming, we present a Universal Transformer Hawkes Process (UTHP), which introduces the recurrent structure in encode process, and introduce convolutional neural network (CNN) in the position-wise-feed-forward neural network. Experiments on several datasets show that the performance of our model is improved compared to the performance of the state-of-the-art.
A Robust Method with DropBlock for Face Anti-Spoofing

Face anti-spoofing is an important component for reliable face recognition. Previous deep learning methods usually exploit a large network structure and are prone to be overfitting when the face anti-spoofing database is a small scale. And in some occasions, multi-frame information or other auxiliary information is not available. In order to address these issues, we propose an end-to-end deep learning approach with DropBlock layer to distinguish between a fake face and a genuine face, which makes use of frame-only RGB image. In this paper, we evaluate the proposed approach with different settings on three popular databases, i.e., REPLAY-ATTACK, CASIA-FASD, and CASIA-SURF. The proposed approach can achieve competitive results on these databases. The experimental results show that our model is more robust and can learn more generalized features for face anti-spoofing.
Cross-Network Community Sensing for Anchor Link Prediction

Anchor link prediction focuses on finding accounts related to the same natural person in different online platforms and can benefit several services like user modeling and cross-network recommendation systems. Recently, community structure information has attracted interest from researchers, while most of existing methods only treat community structure information as additional information and lack utilizing the interaction between communities. To address these limitations, we propose a cross-network community sensing model for anchor link prediction (CCALP). Our CCALP model regards communities as special nodes in each network and further models community level inter-network relationships by existing anchor links. We further utilize an attention mechanism to integrate cross-network community neighbors' vectors. This attention mechanism can help us balance the degree of utilization of community knowledge. Through extensive experiments on two real-world datasets, we demonstrate that CCALP outperforms the existing baseline approaches.