Miracle-dz/UniFAR-FTUs huggingface dataset:
ENERGY MANAGEMENT APPROACH FOR CHARGE SUSTAINING HYBRID ELECTRIC VEHICLE

With gaining popularity, Hybrid Electric Vehicles (HEVs) are replacing conventional vehicles on the road. The presence of multiple energy sources allows for an additional degree of freedom and necessitates Energy Management Strategies (EMS) to determine power split between them. Different strategies are discussed in this paper. From basic management strategies like the rule-based to mathematically complex Equivalent consumption minimization strategies are analysed. Although they cannot be compared to each other directly due to the varying nature of the test element, Dynamic programming is used to benchmark each strategy. Strategies are compared based on fuel consumption and the Battery State of Charge. Using technologies like GPS, new variations of EMS are developed, which are easy to implement in real-time and have performance almost like DP, are also discussed. Recent and future trends are discussed in the end. This article potentially introduces the reader to different categories and types of Energy management strategies developed to aid power-split between onboard energy sources.
THE EFFECTS OF BRUSH CONTROL ON GRASS SPECIES ABUNDANCE AT SEMONKONG, LESOTHO

Shrub encroachment is one of the major threats to Lesotho’s degraded rangelands. The main drivers of rangeland degradation include prolonged droughts, overgrazing and frequent burning of rangelands. The Ministry of Forestry, Range and Soil Conservation, through the Department of Range Resources Management, has engaged in brush control activities for removal of shrub species from the rangelands. The encroaching shrub species include Chrysocoma ciliata, Helichrysum trilineatum, Relhania dieterlenii, Seriphium plumosum, Felicia filifolia, Erica dominans and Pentzia cooperi. The shrubs are usually less than 1.5 m in height, and they are referred to as brush species. This study aimed at investigating the abundance of different grass species on shrub-cleared areas in 2016, 2017 and 2018 as compared to the uncleared brush area in the Semonkong grazing area, and to determine the relative abundance of palatable and less palatable grass species on shrub-controlled areas and an area with brush species. The study was conducted at the Semonkong grazing area in Maseru District, Lesotho. The species composition of the grass community significantly changed in response to the clearance, which was mainly due to changes in the abundance of four species, Merxmuellera disticha, Festuca caprina, Eragrostis curvula and Helictotricon longifolia. The area cleared three years before had the highest abundance of highly palatable grass species and the lowest abundance of less palatable species. However, there was a noticeable regrowth of shrubs and introduction of new shrub species such as Erica dominans and Pentzia cooperi three years after removal of shrubs. The results strongly suggest that brush control activity is a successful rangeland management tool in improving productivity of rangelands in Lesotho due to an increase in the abundance of highly palatable grass species and decreased abundance of less palatable species.
Effects of Trade and Technology on the Mexican Labor Market

This paper assesses the effects of trade and technological change on Mexico's labor market between 1994 and 2019. The implications of the exposure of local labor markets to greater trade integration under NAFTA and to greater competition from China in the US market are analyzed, as are the consequences of the exposure of local labor markets to automation. The main results show that trade integration under NAFTA promoted employment in Mexico for all demographic groups, especially for women and the less educated. In addition, it is also found that trade integration reduced unemployment and the non-participation rate. China's competition in the US market had the opposite effects on these indicators. Finally, the analysis by sector (manufacturing and non-manufacturing) suggests that those markets susceptible to automation experienced a pattern of labor polarization.
BARRIERS TO THE USE OF METHYLPHENIDATE IN PAEDIATRIC NEURO-ONCOLOGY SERVICES

Background The increasing effectiveness of childhood cancer treatment has resulted in a greater number of children surviving previously incurable central nervous system tumours. This growing population of survivors report significant treatment-related difficulties, including attentional impairment associated with poor long-term intellectual development, academic attainment, and health-related quality of life. Clinical findings show benefit to attention and executive functions following methylphenidate administration. The current project explored barriers associated with use of methylphenidate in paediatric neuro-oncology services in the UK. Method Qualitative data was gathered by semi-structured questionnaire sent to clinical psychologists/neuropsychologists in 19 of the 21 NHS primary treatment oncology centres in the UK in May 2018. Thematic analytic methods were used to explore the data. Results 11 responses were received from primary treatment centres. Knowledge of the evidence base for methylphenidate in paediatric brain injury was limited. This was primarily attributable to the inadequate resource of psychology into many primary treatment centres, limiting provision to service to a restricted proportion of the patient group. Psychologists reported an interest in exploring the utility of methylphenidate in their patient group. Respondents highlighted the need for provision of accessible research summaries and treatment protocols addressing the potential use of psychostimulants, stating that these would support their team to consider expanding the interventions offered. Conclusions The development of shared resources for clinicians will be important in supporting the application of research findings to clinical practice. We anticipate national collaboration will support the advancement of intervention for the growing clinical population of long-term survivors.
AUTOMATIC KEYWORD EXTRACTION USING ARTIFICIAL NEURON NETWORK AND FEATURE EXTRACTION

: The growth of the internet and the digital library have created massive electronic documents that the manual analysis of these documents is no longer feasible. Hence, extracting keywords from these documents is vital to have the first concept about these documents and search for relevant documents. However, extracting a limited number of keywords from a document that is enough to find all related documents is challenging. Automatic keyword extraction is an important research direction in data mining, natural language processing, and search engine optimization. The advantages of traditional methods of keywords extraction, based on statistics and linguistic rules, are domain-independent and do not require any training data. However, keywords generated by this method are not good enough to find all relevant documents. This paper proposes a new approach that exploits word-level handcrafted features and machine learning models to solve this task. To evaluate the proposed solution, we compare our results with the latest supervised and unsupervised methods for automatic keyword extraction.s. Experiment results show that our model achieves the best results on the 9/20 data corpus. It points out that our proposed approach is promising.
Comparison of magnesium sulphate with placebo for prevention of atrial fibrillation in post Coronary Artery Bypass Grafting (CABG) patients

Background: Postoperative Atrial Fibrillation (AF) is the most common complication that develops after cardiovascular surgery and is associated with adverse outcomes. Magnesium is an essential element in the body, which is required as a cofactor for over 300 enzymatic reactions and reduced levels of magnesium in the blood have been found related with an increased risk of AF, both in ambulatory setting and after cardiac surgeries. Our study was aimed at determining the role of prophylactic magnesium sulphate in preventing postoperative AF in subjects undergoing Coronary Artery Bypass Graft (CABG).
Discovery of novel targets for diffuse large B-cell lymphoma

The newly emerging CD19-targeted Chimeric Antigen Receptor T-cells (CAR-T) therapy has revolutionized the treatment of advanced diffuse large B-cell lymphoma (DLBCL) and achieved a functional cure in some patients. However, a majority of patients suffer relapse. A major pathway for tumor relapse is the loss of the target antigen, CD19, on the cancer cell surface, essentially making them invisible to the therapy. Consequently, new targets are required for the continued treatment of relapsed patients. Facing this dilemma, we hypothesized that a systematic search of the human genome may reveal more novel targets of DLBCL with characteristics similar to CD19. These include high expression in DLBCL and other B-cell tumors, but minimal expression in normal tissues. To test this hypothesis, we used several well-annotated public genomic and proteomic databases and carried out in-depth screening to pinpoint optimal new targets for DLBCL. Our study uncovered several new targets that have not been extensively pursued in the public domain, such as CD79A and TNFRSF13C. Based on our refined criteria for target evaluation, we finally concluded that CD79A represents the most ideal novel target for DLBCL and potentially other B-cell tumor types. This target should be explored as the antigen of next-generation CAR-T therapy that has the potential to save and extend the lives of patients.
Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions

This paper deals with the following important research question. Traditionally, the neural network employs non-linear activation functions concatenated with linear operators to approximate a given physical phenomenon. They ”fill the space” with the concatenations of the activation functions and linear operators and adjust their coefficients to approximate the physical phenomena. We claim that it is better to ”fill the space” with linear combinations of smooth higher-order B-splines base functions as employed by isogeometric analysis and utilize the neural networks to adjust the coefficients of linear combinations. In other words, the possibilities of using neural networks for approximating the B-spline base functions’ coefficients and by approximating the solution directly are evaluated. Solving differential equations with neural networks has been proposed by Maziar Raissi et al. in 2017 [16] by introducing Physics-informed Neural Networks (PINN), which naturally encode underlying physical laws as prior information. Approximation of coefficients using a function as an input leverages the well-known capability of neural networks being universal function approximators [6]. In essence, in the PINN approach the network approximates the value of the given field at a given point. We present an alternative approach, where the physcial quantity is approximated as a linear combination of smooth B-spline basis functions, and the neural network approximates the coefficients of B-splines. This research compares results from the DNN approximating the coefficients of the linear combination of B-spline basis functions, with the DNN approximating the solution directly. We show that our approach is cheaper and more accurate when Preprint submitted to Elsevier January 5, 2022 ar X iv :2 20 1. 00 90 4v 1 [ m at h. N A ] 3 J an 2 02 2 approximating smooth physical fields.
Automated Graph Machine Learning: Approaches, Libraries and Directions

Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To tackle the challenge, automated graph machine learning, which aims at discovering the best hyper-parameter and neural architecture configuration for different graph tasks/data without manual design, is gaining an increasing number of attentions from the research community. In this paper, we extensively discuss automated graph machine approaches, covering hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We briefly overview existing libraries designed for either graph machine learning or automated machine learning respectively, and further in depth introduce AutoGL, our dedicated and the world's first open-source library for automated graph machine learning. Last but not least, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive discussion of approaches, libraries as well as directions for automated graph machine learning.
Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

The nonlinear relation between the spectral information and the corresponding objects (complex physiognomies) makes pixelwise classification challenging for conventional methods. To deal with nonlinearity issues in hyperspectral image classification (HSIC), convolutional neural networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3-D and 2-D inception net with the attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting attention-fused hybrid network (AfNet) is based on three attention-fused parallel hybrid subnets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models.
AI visualization in Nanoscale Microscopy

Artificial Intelligence (AI) & Nanotechnology are promising areas for the future of humanity. While Deep Learning based Computer Vision has found applications in many fields from medicine to automotive, its application in nanotechnology can open doors for new scientific discoveries. Can we apply AI to explore objects that our eyes can’t see such as nano scale sized objects? An AI platform to visualize nanoscale patterns learnt by a Deep Learning neural network can open new frontiers for nanotechnology. The objective of this paper is to develop a Deep Learning based visualization system on images of nanomaterials obtained by scanning electron microscope (SEM). This paper contributes an AI platform to enable any nanoscience researcher to use AI in visual exploration of nanoscale morphologies of nanomaterials. This AI is developed by a technique of visualizing intermediate activations of a Convolutional AutoEncoder (CAE). In this method, a nano scale specimen image is transformed into its feature representations by a Convolution Neural Network (CNN). The Convolutional AutoEncoder is trained on 100% SEM dataset from NFFA-EUROPE, and then CNN visualization is applied. This AI generates various conceptual feature representations of the nanomaterial. While Deep Learning based image classification of SEM images are widely published in literature, there are not much publications that have visualized Deep neural networks of nanomaterials. There is a significant opportunity to gain insights from the learnings extracted by machine learning. This paper unlocks the potential of applying Deep Learning based Visualization on electron microscopy to offer AI extracted features and architectural patterns of various nanomaterials. This is a contribution in Explainable AI in nano scale objects, and to learn from otherwise black box neural networks. This paper contributes an open source AI with reproducible results at URL (https://sites.google.com/view/aifornanotechnology)
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms

We study the class of first-order locally-balanced Metropolis--Hastings algorithms introduced in Livingstone&Zanella (2021). To choose a specific algorithm within the class the user must select a balancing function $g:\mathbb{R} \to \mathbb{R}$ satisfying $g(t) = tg(1/t)$, and a noise distribution for the proposal increment. Popular choices within the class are the Metropolis-adjusted Langevin algorithm and the recently introduced Barker proposal. We first establish a universal limiting optimal acceptance rate of 57% and scaling of $n^{-1/3}$ as the dimension $n$ tends to infinity among all members of the class under mild smoothness assumptions on $g$ and when the target distribution for the algorithm is of the product form. In particular we obtain an explicit expression for the asymptotic efficiency of an arbitrary algorithm in the class, as measured by expected squared jumping distance. We then consider how to optimise this expression under various constraints. We derive an optimal choice of noise distribution for the Barker proposal, optimal choice of balancing function under a Gaussian noise distribution, and optimal choice of first-order locally-balanced algorithm among the entire class, which turns out to depend on the specific target distribution. Numerical simulations confirm our theoretical findings and in particular show that a bi-modal choice of noise distribution in the Barker proposal gives rise to a practical algorithm that is consistently more efficient than the original Gaussian version.
A Survey on DNS Encryption: Current Development, Malware Misuse, and Inference Techniques

The domain name system (DNS) that maps alphabetic names to numeric Internet Protocol (IP) addresses plays a foundational role in Internet communications. By default, DNS queries and responses are exchanged in unencrypted plaintext, and hence, can be read and/or hijacked by third parties. To protect user privacy, the networking community has proposed standard encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and DNS over QUIC (DoQ) for DNS communications, enabling clients to perform secure and private domain name lookups. We survey the DNS encryption literature published from 2016 to 2021, focusing on its current landscape and how it is misused by malware, and highlighting the existing techniques developed to make inferences from encrypted DNS traffic. First, we provide an overview of various standards developed in the space of DNS encryption and their adoption status, performance, benefits, and security issues. Second, we highlight ways that various malware families can exploit DNS encryption to their advantage for botnet communications and/or data exfiltration. Third, we discuss existing inference methods for profiling normal patterns and/or detecting malicious encrypted DNS traffic. Several directions are presented to motivate future research in enhancing the performance and security of DNS encryption.
Sound and Visual Representation Learning with Multiple Pretraining Tasks

Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In this light, this work aims to combine Multiple SSL tasks (Multi-SSL) that generalizes wellfor all downstream tasks. For this study, we investigate binaural sounds and image data. For binaural sounds, we propose three SSL tasks namely, spatial alignment, temporal synchronization offore-ground objects and binaural sounds and temporal gap prediction. We investigate several approaches of Multi-SSL and give insights into the downstream task performance on video retrieval, spatial sound super resolution, and semantic prediction using OmniAudio dataset. Our experiments on binaural sound representations demonstrate that Multi-SSL via incremental learning (IL) of SSL tasks outperforms single SSL task models and fully supervised models in the downstream task performance. As a check of applicability on other modalities, we also formulate our Multi-SSL models for image representation learning and we use the recently proposed SSL tasks, MoCov2 and DenseCL. Here, Multi-SSL surpasses recent methods such as MoCov2, DenseCL and DetCo by 2.06%,3.27% and 1.19% on VOC07 classification and +2.83, +1.56 and +1.61 AP on COCO detection.
Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection

Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may be important for high throughput screening, is also not well explored.To address this challenge, we consider some contemporary CNN models for binary classification of breast histopathology images that involves (1) the data preprocessing with stain normalized images using an adaptive colour deconvolution (ACD) based color normalization algorithm to handle the stain variabilities; and (2) applying transfer learning based training of some arguably more efficient CNN models, namely Visual Geometry Group Network (VGG16), MobileNet and EfficientNet. We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images. The experimental analysis shows that pretrained networks in most cases yield better quality results on data augmented breast histopathology images with stain normalization, than the case without stain normalization. Further, we evaluated the performance and efficiency of popular lightweight networks using stain normalized images and found that EfficientNet outperforms VGG16 and MobileNet in terms of test accuracy and F1 Score. We observed that efficiency in terms of test time is better in EfficientNet than other networks; VGG Net, MobileNet, without much drop in the classification accuracy.
Binary Fuse Filters: Fast and Smaller Than Xor Filters

Bloom and cuckoo filters provide fast approximate set membership while using little memory. Engineers use them to avoid expensive disk and network accesses. The recently introduced xor filters can be faster and smaller than Bloom and cuckoo filters. The xor filters are within 23% of the theoretical lower bound in storage as opposed to 44% for Bloom filters. Inspired by Dietzfelbinger and Walzer, we build probabilistic filters—called binary fuse filters—that are within 13% of the storage lower bound—without sacrificing query speed. As an additional benefit, the construction of the new binary fuse filters can be more than twice as fast as the construction of xor filters. By slightly sacrificing query speed, we further reduce storage to within 8% of the lower bound. We compare the performance against a wide range of competitive alternatives such as Bloom filters, blocked Bloom filters, vector quotient filters, cuckoo filters, and the recent ribbon filters. Our experiments suggest that binary fuse filters are superior to xor filters.