Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Study of Weather Variables for Rainfall Detection

Numerous weather parameters affect the occurrence and amount of rainfall. Therefore, it is important to study these parameters and their interdependency. In this paper, different weather and time-related variables - relative humidity, solar radiation, temperature, dew point, day-of-year, and time-of-day are analyzed systematically using Principal Component Analysis (PCA). We found that four principal components explain a cumulative variance of 85%. The first two principal components are applied to distinguish rain and no-rain scenarios as well. We conclude that all 7 variables have similar contribution towards rainfall detection.
AIS Electronic

Powered by the proliferation of social computing and user-generated content, new knowledge sharing platforms in China, including Q&A communities and live broadcasting, were launched and received widely attentions recently. This research is motivated by the tremendous growth of an online knowledge sharing platform, Zhihu Live (www.zhihu.com/lives). Built upon Zhihu community, the usability and functionality of Zhihu Live makes it easy for user to create their own broadcasting lives that can be shared in the community to a wide range of audiences, making this an attractive platform to content creators (speakers) and knowledge consumers (audiences). We therefore propose a two-phase model to investigate the daily sales of Zhihu lives. Hierarchical Linear model was employed to test our hypotheses. Our preliminary results suggest that number of “like” positively affects daily sales before a live starts (phase 1), whereas “like” number, audience review score, and interactions between speakers and audiences during the broadcasting process have significant effects on live’s daily sales after the live starts (phase 2). Implications are discussed and limitations are noted.
Osmophores and floral fragrance in Anacardium humile and Mangifera indica (Anacardiaceae): an overlooked secretory structure in Sapindales

Abstract Flowers of Anacardiaceae and other Sapindales typically produce nectar, but scent, often associated with a reward for pollinators, has surprisingly been mentioned only rarely for members of the family and order. However, flowers of Anacardium humile and Mangifera indica produce a strong sweet scent. The origin and composition of these floral scents is the subject of this study. Screening of potential osmophores on the petals and investigations of their anatomy were carried out by light, scanning and transmission electron microscopy. The composition of the floral fragrance was characterized by gas chromatography–mass spectrometry. In both species, the base of the adaxial side of each petal revealed specialized secretory epidermal cells which are essentially similar in structure and distinct from all other neighbouring cells. These cells also showed evidence of granulocrine secretory mechanisms and slight specific variations in their subcellular apparatus coinciding with the respective composition of the floral fragrance, predominantly composed of sesquiterpenes in A. humile and monoterpenes in M. indica. This study reports the presence of osmophores for the first time in flowers of Anacardiaceae and confirms the link between the ultrastructural features of their secretory cells and the volatiles produced by the flowers. The flowers of most Sapindales, including Anacardiaceae, are nectariferous. However, the presence of osmophores has only been described for very few genera of Rutaceae and Sapindaceae. Both the occurrence of osmophores and fragrance may have largely been overlooked in Anacardiaceae and Sapindales until now. Further studies are needed to better understand the nature and diversity of the interactions of their nectariferous flowers with their pollinators.
Dual-Channel Densenet for Hyperspectral Image Classification

Deep neural networks provide deep extracted features for image classification. As a high dimension data, hyperspectral image (HSI) feature extraction is unlike an RGB image whose feature representation could not be simply generated in the spatial domain. To take full advantage of HSI, a dual-channel convolutional neural network (CNN) is applied, 1D convolution for the spectral domain and 2D convolution for spatial domain. For pixel-wise classification of HSI, in our network model, one-dimensional customized DenseNet is for extracting the hierarchical spectral features and another customized DenseNet is applied to extract the hierarchical spatial-related feature. Furthermore, we experimentally tuned the several widen factors and dense-net growth rates to evaluate the impact of hyper-parameter. To compare our proposed method with HSI classification methods, we test other three DNNs based method in two real-world HSI dataset. The result demonstrated our approach outperformed the state-of-art method.
Improving Iturin A Production of Bacillus amyloliquefaciens by Genome Shuffling and Its Inhibition Against Saccharomyces cerevisiae in Orange Juice

Genome shuffling is an effective method for the rapid improvement of the production of secondary metabolites. This study used the principle of gene shuffling to enhance the yield of iturin A produced by Bacillus amyloliquefaciens LZ-5. Improvements in lipopeptide yield were evident among four strains subjected to recursive protoplast fusion. The four strains were obtained through mutagenesis processes: nitrosoguanidine, ultraviolet irradiation, and atmospheric and room temperature plasma. A high yield strain with 179.22 mg/l of iturin A was obtained after two rounds of genome shuffling, which was a 2.03-fold increase compared with the wild strain. To evaluate the efficacy of iturin A for control of spoilage yeast in food, the anti-yeast efficacy of iturin A was evaluated in orange juice incubated with Saccharomyces cerevisiae. The juice treated with 0.76 mg/ml iturin A showed a significant (p < 0.05) control on yeast population during the storage, similar to that of the 0.30 mg/ml natamycin. In addition, iturin A showed a tiny effect on chemical-physical characteristics of orange juice. Our results provide a basis for the application of antimicrobial lipopeptide in juice products.
Similarity-Preserving Deep Features for Hyperspectral Image Classification

Recently, deep learning has been introduced to extract hierarchical features of hyperspectral images (HSls) and achieved good classification performance. However, the previous deep learning based methods only consider the semantic information of individual pixel, which cannot effectively deal with the complex spectral-spatial characteristic of HSls. In this paper, we propose a novel deep learning based framework to learn the similarity-preserving deep features (SPDF) for HSI classification. Specifically, we firstly introduce a deep network that can take pairs of image patches as training samples, and then a loss function is elaborately designed to minimize the feature distance of similar pairs and maximize the feature distance of dissimilar pairs in feature space. Once the deep network is well trained, the SPDF can be obtained by propagating the samples through the trained network. Finally, these features are fed into the support vector machines (SVM) for HSI classification. Experimental results demonstrate the pro-nosed method outnerforms other comnetitive methods.
Cardiovascular Risk and Neurocognitive Assessment in Young Adults and Their Relationship to Body Adiposity

Background We assessed body composition, adiposity, cardiovascular risk, and cognitive functions in healthy young adult females and investigated the possible correlation between neurocognitive decline, adiposity, and cardiovascular risk markers. Material/Methods This cross-sectional study was conducted on 83 healthy, young adult, Saudi women (age 19–23 years). Subjects were classified into group (A) with 19 non-obese subjects and negative family history (FH) of cardiovascular diseases (CVD), group (B) with 38 non-obese subjects with a positive FH of CVD, and group (C) with 18 obese subjects with positive FH of CVD. Body composition was analyzed by bioelectrical impedance analysis. Cognitive functions were evaluated using the Cambridge Neuropsychological Automated Battery (CANTAB). The blood samples were tested for lipoprotein(a) [Lp(a)] and high-sensitivity C-reactive Protein (hs-CRP). Results There was significantly prolonged Attention-Switching Task (AST) latency in obese subjects with negative family history of CVD (p=0.014) and those with positive family history of CVD (p=0.026) compared to controls, but the difference in AST Percent Correct Trials, Intra-Extra Dimensional Set Shift (IED) Total Errors, and Simple Reaction Time (SRT) was not significant. Simple response time had a weak but significant inverse correlation with BMI (r=−0.227, p<0.05). BMI was correlated positively with Lp(a) and hs-CRP, while BF% was correlated with hs-CRP only. No correlation was observed between the CANTAB tests, Lp(a), and hs-CRP. Conclusions Cardiovascular risk increases with higher adiposity and the presence of a positive family history of cardiovascular disease. Neurocognitive function may decline with higher adiposity; however, no relationship was observed between neurocognitive functions and cardiovascular risk markers.
NURIP: Neural Interface Processor for Brain-State Classification and Programmable-Waveform Neurostimulation

The advancement of implantable medical devices for the treatment of neurological disorders demands energy-efficient, low-latency processors for responsive, safe, and personalized neuromodulation. A 130-nm CMOS neural interface processor is presented to perform the brain-state classification and closed-loop control using programmable-waveform electrical stimulation. The architecture features an autoencoder neural network for both spatial filtering and dimensionality reduction. Dedicated feature extraction blocks are implemented for univariate (signal-band energy) and multivariate (phase locking value, and cross-frequency coupling) neural signal processing. The proceeding exponentially decaying memory support vector machine (EDM-SVM) accelerator employs these features for hardware-efficient brain-state classification with a high temporal resolution. An integrated digitally charge-balanced waveform generator enables flexibility in finding optimal neuromodulation paradigms for pathological symptom suppression. The system on chip (SoC) is validated using the EU human intracranial electroencephalography epilepsy data set, achieving a seizure sensitivity of 97.7% and a false detection rate of 0.185/h while consuming 169 $\mu \text{J}$ per classification.
Boosting face recognition via neural Super-Resolution

We propose a two-step neural approach for face Super-Resolution (SR) to improve face recognition performance. It consists in ﬁrst performing generic SR on the entire image, based on Convolutional Neural Networks, followed by a spe-ciﬁc local SR step for each facial component, using neural autoencoders. Obtained results on the LFW dataset for a × 4 upscaling factor demonstrate that the method improves both image reconstruction ( + 2 . 80 dB in PSNR) and recognition performance ( + 3 . 94 points in mean accuracy), compared with × 4 bicubic interpolation.
Serum and Lipoprotein Particle miRNA Profile in Uremia Patients

microRNAs (miRNAs) are post-transcriptional regulators of messenger RNA (mRNA), and transported through the whole organism by—but not limited to—lipoprotein particles. Here, we address the miRNA profile in serum and lipoprotein particles of healthy individuals in comparison with patients with uremia. Moreover, we quantitatively determined the cellular lipoprotein-particle-uptake dependence on the density of lipoprotein particle receptors and present a method for enhancement of the transfer efficiency. We observed a significant increase of the cellular miRNA level using reconstituted high-density lipoprotein (HDL) particles artificially loaded with miRNA, whereas incubation with native HDL particles yielded no measurable effect. Thus, we conclude that no relevant effect of lipoprotein-particle-mediated miRNA-transfer exists under in vivo conditions though the miRNA profile of lipoprotein particles can be used as a diagnostic marker.
Crops Classification from Sentinel-2A Multi-spectral Remote Sensing Images Based on Convolutional Neural Networks

Deep learning technology such as convolutional neural networks (CNN) can extract the distinguishable and representative features of different land cover from remote sensing images in a hierarchical way to classify. However, in the field of agriculture, there are few application of crops classification from multi-spectral remote sensing images based on deep learning. In this context, we compared the classification methods of CNN and support vector machines (SVM) in extracting the spatial distribution of crops planting area from Sentineal-2A multi-spectral remote sensing images in Yuanyang county, China. For the region of study, both methods obtained reasonable spatial distribution of different crops, the verification results show that the overall accuracy of CNN is 95.6% which is superior to SVM.
Multi-Scale Investigation of Hydrocarbon Plays: An Assessment Based on Orbital Multispectral, Airborne and Close-Range Hyperspectral Data

This works aims to study tar sand outcrops in the Paraná basin, Brazil, using multi -scale spectral sensing technologies. Sites in this basin comprise well-exposed outcrops of tar sands for which a wealth of orbital, airborne, field and lab spectral data is currently available, including WorldView-3 (WV-3; DigitalGlobe) over the entire study area. The area was also surveyed by the AisaFENIX hyperspectral system both from field-based and airborne platforms. This was followed by outcrop studies and sampling in the field and close-range spectroscopy using both hyperspectral imaging (i.e. Specim's sisuCHEMA) and nonimaging (ASD‘s FieldSpec) instruments in the laboratory. The aim of the work is twofold: (i) to detect the tar sands at multiple scales, and (ii) to describe the alteration mineralogy associated with bitumen emplacement in sandstone beds, drawing an analogy between this phenomenon and the microseepage-induced alteration.
Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference

Particle-based variational inference offers a flexible way of approximating complex posterior distributions with a set of particles. In this paper we introduce a new particle-based variational inference method based on the theory of semi-discrete optimal transport. Instead of minimizing the KL divergence between the posterior and the variational approximation, we minimize a semi-discrete optimal transport divergence. The solution of the resulting optimal transport problem provides both a particle approximation and a set of optimal transportation densities that map each particle to a segment of the posterior distribution. We approximate these transportation densities by minimizing the KL divergence between a truncated distribution and the optimal transport solution. The resulting algorithm can be interpreted as a form of ensemble variational inference where each particle is associated with a local variational approximation.
Estimation of Ground Deformation of transmission corridors in mountain areas Using Stanford Method

Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterers interferometry (PSI) and small baseline methods(SB), which are optimized for different models of scattering. However, the scattering characteristics of transmission corridors in mountain areas usually lay between these two end-member models. In the experiment, we selected a set of SAR images of TerraSAR-X satellite, covered Mao County area, to detect the subsidence during the fifteen months, using stanford method for persistent scatterers-multi-temporal InSAR technology (StaMPS-MTI).
Impact of Diet-Modulated Butyrate Production on Intestinal Barrier Function and Inflammation

A major challenge in affluent societies is the increase in disorders related to gut and metabolic health. Chronic over nutrition by unhealthy foods high in energy, fat, and sugar, and low in dietary fibre is a key environmental factor responsible for this development, which may cause local and systemic inflammation. A low intake of dietary fibre is a limiting factor for maintaining a viable and diverse microbiota and production of short-chain fatty acids in the gut. A suppressed production of butyrate is crucial, as this short-chain fatty acid (SCFA) can play a key role not only in colonic health and function but also at the systemic level. At both sites, the mode of action is through mediation of signalling pathways involving nuclear NF-κB and inhibition of histone deacetylase. The intake and composition of dietary fibre modulate production of butyrate in the large intestine. While butyrate production is easily adjustable it is more variable how it influences gut barrier function and inflammatory markers in the gut and periphery. The effect of butyrate seems generally to be more consistent and positive on inflammatory markers related to the gut than on inflammatory markers in the peripheral tissue. This discrepancy may be explained by differences in butyrate concentrations in the gut compared with the much lower concentration at more remote sites.
Characteristic Basis Function Pattern Modeling of Wideband Reflector Antenna Beam Pattern Frequency Variations

This paper presents a discussion and examples of using the Characteristic Basis Function Pattern method for the modeling of radiation patterns of large, wideband reflector antenna systems. It is shown that beam normalization should be applied to reduce the number of required modes to achieve a specified accuracy in the reconstructed interpolated field, and that care should be taken to sample finely enough in frequency to capture any chromatic aberrations if high levels of accuracy are required.
Deep multi-task learning with evolving weights

Pre-training of deep neural networks has been abandoned in the last few years. The main reason is the diﬃculty to control the over-ﬁtting and tune the consequential raised number of hyper-parameters. In this paper we use a multi-task learning framework that gathers weighted supervised and unsupervised tasks. We propose to evolve the weights along the learning epochs in order to avoid the break in the sequential transfer learning used in the pre-training scheme. This framework allows the use of unlabeled data. Extensive experiments on MNIST showed interesting results.
Complications of percutaneous nephrostomy in a district general hospital.

OBJECTIVE Percutaneous nephrostomy (PCN) is one of the commonest procedures performed. There are currently no European recommendations on the accepted rate of complications. The aim of the present study is to report the complication rate of PCN with the specific emphasis on sepsis and septic shock, the causative organisms, sensitivities to antibiotics, and associated risk factors. MATERIAL AND METHODS Retrospectively collected data on patients undergoing acute or elective PCN at the Department of Radiology, Countess of Chester Hospital (COCH), in the UK between January 2014 and December 2016 were analyzed after the study was approved by Local Audit Department at COCH. RESULTS A total of 66 patients underwent 90 acute or elective PCNs. Three patients developed major post-PCN complication (two patients developed septic shock and the third suffered a hemorrhagic episode requiring blood transfusion). Nephrostomy tube complications (blockage, leaking, fracturing and kinking of the catheter) occurred in 4 patients. Complications were more common when the PCN was performed out of working hours (71.4% [10/14], and 17.3% [9/52] for PCNs performed within, and out of working hours, respectively: p<0.001). The age of the patients did not seem to correlate with the development of complications (p<0.001). Of all 25 patients, in whom septicemia was diagnosed prior to PCN tube insertion, 12 developed septic shock and 13 had signs of sepsis for longer than 24 h. Fifteen patients had positive urine cultures. The most common organism isolated was Escherichia coli. Blood culture isolates included: Escherichia coli, Eggerthella lenta, Enterococcus, Proteus mirabilis, Pseudomonas aeruginosa and Streptococcus pneumonia. CONCLUSION Our complication rates were within United States proposed target ranges. Our data may help to serve as a baseline for outcome targets in the European centres.
Fast Neural Chinese Word Segmentation for Long Sentences

Rapidly developed neural models have achieved competitive performance in Chinese word segmentation (CWS) as their traditional counterparts. However, most of methods encounter the computational inefficiency especially for long sentences because of the increasing model complexity and slower decoders. This paper presents a simple neural segmenter which directly labels the gap existence between adjacent characters to alleviate the existing drawback. Our segmenter is fully end-to-end and capable of performing segmentation very fast. We also show a performance difference with different tag sets. The experiments show that our segmenter can provide comparable performance with state-of-the-art.
POMCP with Human Preferences in Settlers of Catan

We present a suite of techniques for extending the Partially Observable Monte Carlo Planning algorithm to handle complex multi-agent games. We design the planning algorithm to exploit the inherent structure of the game. When game rules naturally cluster the actions into sets called types, these can be leveraged to extract characteristics and high-level strategies from a sparse corpus of human play. Another key insight is to account for action legality both when extracting policies from game play and when these are used to inform the forward sampling method. We evaluate our algorithm against other baselines and versus ablated versions of itself in the well-known board game Settlers of Catan.
An Investigation of the Fluvial Geomorphology and Associated Minerals in the Syrtis Major, Mars

Syrtis Major Planum is a major volcanic construct on Mars ridden with a large number of fluvial channels mostly draining into the adjacent Isidis basin. These channels have been attributed to have formed by lava flows from the calderas of the construct onto the basin. However, with availability of high spatial and spectral resolution remote sensing datasets, new findings in the region over time have indicated possible presence of hydrological channels in Syrtis Major. We explore one such feature in the volcanic construct that seems to have been altered by hydrological activity. High abundance of hydroxylated and hydrated minerals have been found in the region which support this conclusion that lava might not be the only factor causing potential channel generation in the region.