Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Edge Machine Learning: Resource Allocation and Trade-Offs

The aim of this paper is to propose a resource allocation strategy for dynamic training and inference of machine learning tasks at the edge of the wireless network, with the goal of exploring the trade-off between energy, delay and learning accuracy. The scenario of interest is composed of a set of devices sending a continuous flow of data to an edge server that extracts relevant information running online learning algorithms, within the emerging framework known as Edge Machine Learning (EML). Taking into account the limitations of the edge servers, with respect to a cloud, and the scarcity of resources of mobile devices, we focus on the efficient allocation of radio (e.g., data rate, quantization) and computation (e.g., CPU scheduling) resources, to strike the best trade-off between energy consumption and quality of the EML service, including service end-to-end (E2E) delay and accuracy of the learning task. To this aim, we propose two different dynamic strategies: (i) The first method aims to minimize the system energy consumption, under constraints on E2E service delay and accuracy; (ii) the second method aims to optimize the learning accuracy, while guaranteeing an E2E delay and a bounded average energy consumption. Then, we present a dynamic resource allocation framework for EML based on stochastic Lyapunov optimization. Our low-complexity algorithms do not require any prior knowledge on the statistics of wireless channels, data arrivals, and data probability distributions. Furthermore, our strategies can incorporate prior knowledge regarding the model underlying the observed data, or can work in a totally data-driven fashion. Several numerical results on synthetic and real data assess the performance of the proposed approach.
Obese Adipose Tissue as a Driver of Breast Cancer Growth and Development: Update and Emerging Evidence

Obesity is an established risk factor for breast cancer growth and progression. A number of advances have been made in recent years revealing new insights into this link. Early events in breast cancer development involve the neoplastic transformation of breast epithelial cells to cancer cells. In obesity, breast adipose tissue undergoes significant hormonal and inflammatory changes that create a mitogenic microenvironment. Many factors that are produced in obesity have also been shown to promote tumorigenesis. Given that breast epithelial cells are surrounded by adipose tissue, the crosstalk between the adipose compartment and breast epithelial cells is hypothesized to be a significant player in the initiation and progression of breast cancer in individuals with excess adiposity. The present review examines this crosstalk with a focus on obese breast adipose-derived estrogen, inflammatory mediators and adipokines, and how they are mechanistically linked to breast cancer risk and growth through stimulation of oxidative stress, DNA damage, and pro-oncogenic transcriptional programs. Pharmacological and lifestyle strategies targeting these factors and their downstream effects are evaluated for feasibility and efficacy in decreasing the risk of obesity-induced breast epithelial cell transformation and consequently, breast cancer development.
Dynamism of Metabolic Carbon Flow of Starch and Lipids in Chlamydomonas debaryana

Microalgae have the potential to recycle CO2 as starch and triacylglycerol (TAG), which provide alternative source of biofuel and high added-value chemicals. Starch accumulates in the chloroplast, whereas TAG accumulates in the cytoplasmic lipid droplets (LD). Preferential accumulation of starch or TAG may be achieved by switching intracellular metabolic carbon flow, but our knowledge on this control remains limited. Are these two products mutually exclusive? Or, does starch act as a precursor to TAG synthesis, or vice versa? To answer these questions, we analyzed carbon flow in starch and lipids using a stable isotope 13C in Chlamydomonas debaryana NIES-2212, which accumulates, without nutrient limitation, starch in the exponential growth phase and TAG in the stationary phase. Pulse labeling experiments as well as pulse labeling and chase experiments were conducted, and then, gas chromatography-mass spectrometry (GC-MS) analysis was performed on starch-derived glucose and lipid-bound fatty acids. We exploited the previously developed method of isotopomer analysis to estimate the proportion of various pools with different isotopic abundance. Starch turned over rapidly to provide carbon for the synthesis of fatty acids in the exponential phase cells. Most fatty acids showed rapid and slow components of metabolism, whereas oleic acid decayed according to a single exponential curve. Highly labeled population of fatty acids that accumulated during the initial labeling decreased rapidly, and replaced by low abundance population during the chase time, indicating that highly labeled fatty acids were degraded and the resulting carbons were re-used in the re-synthesis with about 9-fold unlabeled, newly fixed carbons. Elongation of C16–C18 acids in vivo was indicated by partially labeled C18 acids. The accumulation of TAG in the stationary growth phase was accounted for by both de novo synthesis and remodeling of membrane lipids. These results suggest that de novo synthesis of starch and TAG was rapid and transient, and also almost independent to each other, but there is a pool of starch quickly turning over for the synthesis of fatty acids. Fatty acids were also subject to re-synthesis. Evidence was also provided for remodeling of lipids, namely, re-use of acyl groups in polar lipids for TAG synthesis.
Dogs Supporting Human Health and Well-Being: A Biopsychosocial Approach

Humans have long realized that dogs can be helpful, in a number of ways, to achieving important goals. This is evident from our earliest interactions involving the shared goal of avoiding predators and acquiring food, to our more recent inclusion of dogs in a variety of contexts including therapeutic and educational settings. This paper utilizes a longstanding theoretical framework- the biopsychosocial model- to contextualize the existing research on a broad spectrum of settings and populations in which dogs have been included as an adjunct or complementary therapy to improve some aspect of human health and well-being. A wide variety of evidence is considered within key topical areas including cognition, learning disorders, neurotypical and neurodiverse populations, mental and physical health, and disabilities. A dynamic version of the biopsychosocial model is used to organize and discuss the findings, to consider how possible mechanisms of action may impact overall human health and well-being, and to frame and guide future research questions and investigations.
Cross-Demographic Portability of Deep NLP-Based Depression Models

Deep learning models are rapidly gaining interest for real-world applications in behavioral health. An important gap in current literature is how well such models generalize over different populations. We study Natural Language Processing (NLP) based models to explore portability over two different corpora highly mismatched in age. The first and larger corpus contains younger speakers. It is used to train an NLP model to predict depression. When testing on unseen speakers from the same age distribution, this model performs at AUC=0.82. We then test this model on the second corpus, which comprises seniors from a retirement community. Despite the large demographic differences in the two corpora, we saw only modest degradation in performance for the senior-corpus data, achieving AUC=0.76. Interestingly, in the senior population, we find AUC=0.81 for the subset of patients whose health state is consistent over time. Implications for demographic portability of speech-based applications are discussed.
A New Technique for Shaping Axis-Symmetric Dual-Reflector Antennas Using Conic Sections

This work investigates a neiv procedure for the shaping of axis-symmetric dual-reflector antennas to control the amplitude and phase of the aperture-field. The generatrices of sub- and main-reflectors are shaped through consecutive concatenation of conic sections. To illustrate the efficacy of the algorithm, an axis-displaced Cassegrain configuration is shaped to provide an uniform aperture mumination, under Geometrical Optics principles. The results are validated using the electromagnetic analysis provided by the method-of-moments.
Personalizing Speech Start Point and End Point Detection in ASR Systems from Speaker Embeddings

Start Point Detection (SPD) and End Point Detection (EPD) in Automatic Speech Recognition (ASR) systems are the tasks of detecting the time at which the user starts speaking and stops speaking respectively. They are crucial problems in ASR as inaccurate detection of SPD and/or EPD leads to poor ASR performance and bad user experience. The main challenge involved in SPD and EPD is accurate detection in noisy environments, especially when speech noise is significant in the background. The current approaches tend to fail to distinguish between the speech of the real user and speech in the background. In this work, we aim to improve SPD and EPD in a multi-speaker environment. We propose a novel approach that personalizes SPD and EPD to a desired user and helps improve ASR quality and latency. We combine user-specific information (i-vectors) with traditional speech features (log-mel) and build a Convolutional, Long Short-Term Memory, Deep Neural Network (CLDNN) model to achieve personalized SPD and EPD. The proposed approach achieves a relative improvement of 46.53% and 11.31% in SPD accuracy, and 27.87% and 5.31% in EPD accuracy at SNR 0 and 5 dB respectively over the standard non-personalized models.
Improving Power System Reliability using Distributed Wind Generation

Due to the increased dependence on the power grid, it is important to look for solutions that will relieve the grid from its increasing amount of stress. The reliability of the grid can be improved by adding distributed generation (DG) sources in various locations in it, which can potentially reduce the amount of power flowing in its transmission network, and partially supply that power locally through the various DG sources. In this paper, we evaluate the effect of adding wind powered DG sources at selected locations in the grid, and measure the effect of this addition on the overall grid reliability. Results show that, the addition of DG sources will improve the grid reliability, if the location of the DG sources was chosen properly. In addition, we show that constant power at the location of DG sources provides a better performance than variable power. A characteristic of wind generation, variable power can still provide a significant improvement in grid reliability.
The Effect of Quadriceps Muscle Length on Maximum Neuromuscular Electrical Stimulation Evoked Contraction, Muscle Architecture, and Tendon-Aponeurosis Stiffness

Muscle-tendon unit length plays a crucial role in quadriceps femoris muscle (QF) physiological adaptation, but the influence of hip and knee angles during QF neuromuscular electrical stimulation (NMES) is poorly investigated. We investigated the effect of muscle length on maximum electrically induced contraction (MEIC) and current efficiency. We secondarily assessed the architecture of all QF constituents and their tendon-aponeurosis complex (TAC) displacement to calculate a stiffness index. This study was a randomized, repeated measure, blinded design with a sample of twenty healthy men aged 24.0 ± 4.6. The MEIC was assessed in four different positions: supine with knee flexion of 60° (SUP60); seated with knee flexion of 60° (SIT60); supine with knee flexion of 20° (SUP20), and seated with knee flexion of 20° (SIT20). The current efficiency (MEIC/maximum tolerated current amplitude) was calculated. Ultrasonography of the QF was performed at rest and during NMES to measure pennation angle (θp) and fascicle length (Lf), and the TAC stiffness index. MEIC and current efficiency were greater for SUP60 and SIT60 compared to SUP20 and SIT20. The vastus lateralis and medialis showed lower θp and higher Lf at SUP60 and SIT60, while for the rectus femoris, in SUP60 there were lower θp and higher Lf than in all positions. The vastus intermedius had a similar pattern to the other vastii, except for lack of difference in θp between SIT60 compared to SUP20 and SIT20. The TAC stiffness index was greater for SUP60. We concluded that NMES generate greater torque and current efficiency at 60° of knee flexion, compared to 20°. For these knee angles, lengthening the QF at the hip did not promote significant change. Each QF constituent demonstrated muscle physiology patterns according to hip and/or knee angles, even though a greater Lf and lower θp were predominant in SUP60 and SIT60. QF TAC index stiffened in more elongated positions, which probably contributed to enhanced force transmission and slightly higher torque in SUP60. Our findings may help exercise physiologist better understand the impact of hip and knee angles on designing more rational NMES stimulation strategies. Clinical Trial Registration www.ClinicalTrials. gov, identifier NCT03822221.
An Automotive-Use Battery-to-Load GaN-Based Switching Power Converter With Anti-Aliasing MR-SSM and In-Cycle Adaptive ZVS Techniques

In modern automotive electronics, there are uprising demands on enhanced power density and reliability for power delivery systems. To achieve high power density, single-stage gallium–nitride (GaN)-based power converters at high switching frequencies (<inline-formula> <tex-math notation="LaTeX">$f_{\mathrm {SW}}$ </tex-math></inline-formula>) are highly desirable but face formidable reliability challenges in the aspects of electromagnetic interference (EMI) suppression and consequential output voltage <inline-formula> <tex-math notation="LaTeX">$V_{O}$ </tex-math></inline-formula> regulation. To address these challenges, this article presents a GaN-based switching power converter that employs an anti-aliasing multi-rate spread-spectrum modulation (MR-SSM) technique for EMI suppression and an in-cycle adaptive zero-voltage switching (ZVS) technique to minimize switching power losses. Compared with classic fixed-rate SSM (FR-SSM), the proposed MR-SSM technique spreads EMI spectra in a wider frequency range without aliasing spikes and, thus, reduces peak EMI more effectively. To improve efficiency, an elastic dead-time (<inline-formula> <tex-math notation="LaTeX">$t_{\mathrm {dead}}$ </tex-math></inline-formula>) control facilitates in-cycle ZVS despite of a continuously changing <inline-formula> <tex-math notation="LaTeX">$f_{\mathrm {SW}}$ </tex-math></inline-formula>. An IC prototype based on this design was fabricated on a 180-nm HV BCD process, with an active die area of 0.87 mm<sup>2</sup>. The converter can handle a variable automotive-use battery voltage from 5 to 24 V and delivers up to 1.2-W power to a regulated output <inline-formula> <tex-math notation="LaTeX">$V_{O}$ </tex-math></inline-formula> at 1 V, with a peak efficiency of 90.2%. It accomplishes a 29% further peak EMI reduction compared with the FR-SSM counterpart.
Study of optimal placement of compact optical current sensor for practical applications

A study of the magnetic field in the vicinity of a magnetic split core to determine the optimal placement of a compact optical sensor was carried out to allow the development of a practical optical current meter for medium voltage (1 to 34.5 kV) applications. The magnetic split core structure enables a greater confinement of the magnetic field related to the current to be measured and reduced influence of external magnetic fields, as well as a mean to plug and unplug of the optical current meter on an electrical distribution line by allowing the conductor to be easily inserted into the magnetic core.
GPU-Accelerated CatBoost-Forest for Hyperspectral Image Classification Via Parallelized mRMR Ensemble Subspace Feature Selection

In this article, the graphics processing unit (GPU)-accelerated CatBoost (GPU-CatBoost) algorithm for hyperspectral image classification is first introduced and comparatively studied using diverse features. To further foster the classification performance from both accurate and efficient viewpoints, an ensemble version of GPU-CatBoost, the GPU-accelerated CatBoost-Forest (GPU-CatBF) algorithm, is proposed by adopting the parallelized minimum redundancy maximum relevance (mRMR) ensemble (PmRMRE) feature selection (FS) algorithm. To evaluate the performance and suitability of mRMR and PmRMRE, 11 other state-of-the-art FS algorithms are comprehensively investigated. Experimental results on three widely acknowledged hyperspectral benchmarks showed that: 1) GPU-CatBoost is also an advanced ensemble learning (EL) algorithm for hyperspectral image classification using diverse features; 2) mRMR and PmRMRE have advanced properties for highly discriminative features and band selection, and the best results are achieved by PmRMRE in most cases in terms of both the robustness and computational efficiency; and 3) GPU-CatBF always outperforms CatBoost and GPU-CatBoost, while compatible and even better results are reachable without losing much computational efficiency in contrast with other selected decision tree-based EL algorithms.
Evaluation of Antidiabetic Potential of Ipomoea turpethum R.Br. and Ipomoea batata L. (Convolvulaceae) in Alloxan Induced Diabetes in Rats: A Comparative Study.

In order to explore the intra-genera variation for biological activity, the antidiabetic activity of Ipomoea turpethum and Ipomoea batata was carried out in alloxan induced diabetes in rats. Dried powdered material of both plants was defatted with petroleum ether 60-80 °C and cold macerated with hydro-ethanol for seven days. These extracts were screened for antidiabetic activity using alloxan induced diabetes in rats. Diabetes was induced by intra-peritoneal administration of alloxan monohydrate (120 mg/kg) on days 1 and 12 and blood glucose levels were estimated on 15 th day. Hydro-ethanol extracts of both the plants at the dose of 500 mg/kg, oral, significantly lowered the blood glucose levels in diabetic rats treated for 7 days. The antidiabetic activity of these plants was insignificantly different from each other. This suggests that, different species from same genera may have similar chemical constituents and pharmacological activity.
NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery

Scene classification is one of the most important applications of remote sensing. Researchers have proposed various datasets and innovative methods for remote sensing scene classification in recent years. However, most of the existing remote sensing scene datasets are collected uniquely from a single data source: Google Earth. In addition, scenes in different datasets are mainly human-made landscapes with high similarity. The lack of richness and diversity of data sources limits the research and applications of remote sensing classification. This article describes a large-scale dataset named “NaSC-TG2,” which is a novel benchmark dataset for remote sensing natural scene classification built from Tiangong-2 remotely sensed imagery. The goal of this dataset is to expand and enrich the annotation data for advancing remote sensing classification algorithms, especially for the natural scene classification. The dataset contains 20 000 images, which are equally divided into ten scene classes. The dataset has three primary advantages: 1) it is large scale, especially in terms of the number of each class, and the numbers of scenes are evenly distributed; 2) it has a large number of intraclass differences and high interclass similarity, because all images are carefully selected from different regions and seasons; and 3) it offers natural scenes with novel spatial scale and imaging performance compared with other datasets. All images are acquired from the new generation of wideband imaging spectrometer of Tiangong-2. In addition to RGB images, the corresponding multispectral scene images are also provided. This dataset is useful in supporting the development and evaluation of classification algorithms, as demonstrated in the present study.
Effect of Nano-Size Magnetic Additions on Low Temperature Flux Pinning of Y-Ba-Cu-O Thin Films

Manufacturing of coated conductors can be tailored for future use operating parameters, such as high temperature and low field, mid temperature and mid field and low temperature and high field. Different methods of flux pinning are being tested world-wide to enhance critical currents of high temperature superconductor yttrium barium copper oxide coated conductors for these various operating conditions. Magnetic materials are interesting to consider as flux pinning additions because of their potential for very strong pinning strength. This research describes the study of different magnetic phase additions to superconducting thin films, including: BaFe12O19, La0.67Ca0. 33MnO3, Y3Fe5O12, and SrRuO3. Nano-size additions with volume percent ranging from 0.5% to 5% were incorporated by utilizing pulsed laser deposition to form alternating layers of magnetic dopants and yttrium barium copper oxide to minimize degradation of Tc. Experimental critical current density (Jcm(H,T)) results for mid temperature (30 K–65 K) / mid field (1 T–5 T) and low temperature (<30 K) / high field (>5 T) and microstructural TEM are presented. Results illustrate the benefits and interplay of combining magnetic flux pinning and core pinning by the magnetic additions and associated film defects, and suggest applications suitable for each system studied.
Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset

Land cover classification with SAR images mainly focuses on the utilization of fully polarimetric SAR (PolSAR) images. The conventional task of PolSAR classification is single-pixel-based region-level classification using polarimetric target decomposition. In recent years, a large number of high-resolution SAR images have become available, most of which are single-polarization. This article explores the potential of object-level semantic segmentation of high-resolution single-pol SAR images, in particular tailored for the Gaofen-3 (GF-3) sensor. First, a well-annotated GF-3 segmentation dataset “FUSAR-Map” is presented for SAR semantic segmentation. It is based on four data sources: GF-3 single-pol SAR images, Google Earth optical remote sensing images, Google Earth digital maps, and building footprint vector data. It consists of 610 high-resolution GF-3 single-pol SAR images with the size of 1024 × 1024. Second, an encoder–decoder network based on transfer learning is employed to implement semantic segmentation of GF-3 SAR images. For the FUSAR-Map dataset, an optical image pretrained deep convolution neural network (DCNN) is fine-tuned with the SAR training dataset. Experiments on the FUSAR-Map dataset demonstrate the feasibility of object-level semantic segmentation with high-resolution GF-3 single-pol SAR images. Also, our algorithm obtains fourth place about the PolSAR image semantic segmentation on the “2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation. ” The new dataset and the encoder–decoder network are intended as the benchmark data and baseline algorithm for further development of semantic segmentation with high-resolution SAR images. The FUSAR-Map and our algorithm are available at github.com/fudanxu/FUSAR-Map/.