Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiuser Random Beamforming in Millimetre-Waves Channels

Cellular communications exploiting the mmWaves frequency range are com- ing within our technological reach. However the specificities of propagation at these frequencies calls for new transmission schemes. Concerning the downlink there are signs that opportunistic beamforming may be an effec- tive solution. This thesis aims to show that in mmWaves channels, schemes based on randomly-directional beamforming allow to harness both the spatial multiplexing and multiuser diversity characterizing the broadcast channel by using only limited feedback and a simple transmitter architecture. It is well- known that performances of random beamforming schemes become optimal when the number of users tends to infinity. Hence, the number of necessary users with respect to the number of transmitting antennas is investigated and the necessity of a linear relation between the two is confirmed. Opportunis- tic beamforming is furthermore analysed under the aspect of fairness. The possibility to combine it with proportional-fair scheduling with only a small sum-rate loss is shown. Finally, the allocation of multiple users per beam is considered and the advantage of NOMA over OMA under the point of view of fairness is displayed.
Parameter-Free Predictive Control of IPM Motor Drives With Direct Selection of Optimum Inverter Voltage Vectors

Parameter dependence is a drawback of the model predictive control of electric motor drives. In this article, a predictive current control (PCC) for an interior permanent magnet (IPM) motor independent of the motor parameters is presented. The motor current time-derivatives (slopes) are expressed as functions of the phase angles of the inverter basic voltage vectors. The slopes are then predicted independent of the motor parameters and are used in selecting the optimum inverter voltage vectors. In addition, a method is used for avoiding the time-consuming evaluations of a cost function to select an optimum inverter voltage vector. By this method, the reference current slopes are used for a direct optimum voltage vector selection. As a result, the control performance is improved under the parametric uncertainties and the execution time of the control code is shortened in comparison to the conventional predictive method. The effectiveness of the proposed method and its superiority over the conventional and a recently presented PCC method are confirmed through the simulation and experimental results.
Intentional chlorpyrifos poisoning in pregnant woman and subsequent fetal death

Organophosphate poisoning is an important medical emergency exist in agriculture-oriented countries such as India. This case report describes the treatment strategies followed for a management of suicidal intoxication of a pregnant woman by chlorpyrifos compound at a secondary care public hospital, Udhagamandalam, India. The patient was unable to perceive fetal movements and had classic clinical symptoms of organophosphate poisoning such as excess salivation and pinpoint pupil. The patient was administered with 2 g of pralidoxime and 10 ampoules of atropine sulfate (1.2 mg each). The fetotoxic evaluation showed fetal death. The antidote given to the patient was according to the criteria given by the World Health Organization. The late admission of the patient may be considered as a reason for fetal death. Psychosocial, educational programs are highly recommended for the population in this region to reduce the number of intentional poisoning attempts.
Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automated delineation approaches. In the recent years Computer Vision witnessed a paradigm shift from mathematical modelling to data-driven methods based on Machine Learning. This led to improvements in performance and robustness of the detection algorithms. Nonetheless, most Machine Learning methods are general-purpose and they do not exploit the specificity of the delineation problem. In this thesis, we present learning methods suited for this task and we apply them to various kinds of microscopic and natural images, proving the general applicability of the presented solutions. First, we introduce a topology loss a new training loss term, which captures higher-level features of curvilinear networks such as smoothness, connectivity and continuity. This is in contrast to most Deep Learning segmentation methods that do not take into account the geometry of the resulting prediction. In order to compute the new loss term, we extract topology features of prediction and ground-truth using a pre-trained network, whose filters are activated by structures at different scales and orientations. We show that this approach yields better results in terms of conventional segmentation metrics and overall topology of the resulting delineation. Although segmentation of curvilinear structures provides useful information, it is not always sufficient. In many cases, such as neuroscience and cartography, it is crucial to estimate the network connectivity. In order to find the graph representation of the structure depicted in the image, we propose an approach for joint segmentation and connection classification. Apart from pixel probabilities, this approach also returns the likelihood of a proposed path being a part of the reconstructed network. We show that segmentation and path classification are closely related tasks and can benefit from the synergy. The aforementioned methods rely on Machine Learning, which requires significant amounts of annotated ground-truth data to train models. The labelling process often requires expertise, it is costly and tiresome. To alleviate this problem, we introduce an Active Learning method that significantly decreases the time spent on annotating images. It queries the annotator only about the most informative examples, in this case the hypothetical paths belonging to the structure of interest. Contrary to conventional Active Learning methods, our approach exploits
Audio-Visual Fusion And Conditioning With Neural Networks For Event Recognition

Video event recognition based on audio and visual modalities is an open research problem. The mainstream literature on video event recognition focuses on the visual modality and does not take into account the relevant information present in the audio modality. We propose to study several fusion architectures for the audio-visual recognition task of video events. We first build classical fusion architectures using concatenation, addition or Multimodal Compact Bilinear pooling (MCB). Then, we propose to create connections between visual and audio processing with Feature-Wise Linear Modulation (FiLM) layers. For instance, the information present in the audio modality is exploited to change the visual classification behaviour. We found that multimodal event classification performance is always better than unimodal performance, whatever the fusion or conditioning method used. Classification accuracy based on one modality improves when we add the modulation of the other modality through FiLM layers.
Clinical Medication Review and Falls in Older People—What Is the Evidence Base?

Background: This paper reports findings from a literature review undertaken to assess the current evidence base for clinical medication review and falls in older people. This forms part of a larger, organisational supported project design work-stream, where the objectives are to define the operational details for clinical medication review as part of multi-factorial assessment for elderly fallers in the community. Patients will be identified and targeted through an integrated care pathway mapping and elderly patient care screening service. Objective: A review of national and best practice guidance to help our understanding of how clinical medication review could be optimised. Methods: A PubMed database search was undertaken with search terms including “elderly” and “falls” and “medicines” followed by study of relevant publications in English and including cited referenced publications within selected papers. Results: Our findings were that both medication over-use and under-use in the elderly occur frequently and can be harmful. Many drugs commonly used by older persons have not been systematically studied as risk factors for falls. The screening tool of older people’s prescriptions (STOPP) and screening tool to alert to right treatment (START), validated for assessment of potentially inappropriate prescribing in the elderly, offer the possibility of provision of a structured clinical medication review to patients, with a need for more research on the impact of the STOPP START interventions on both the rates of falls and risk of falls in the elderly.
Sharing stories: An exploration of genres in storytimes

Exposure to a variety of stories can support many early literacy skills for young children. Using video recordings from a previous study that examined early literacy in public library storytimes, this study investigated the use of genre and story variety in storytimes for young children (birth to 60 months). Findings showed that attendees were primarily exposed to stories from the storybook/narrative genre with limited use of both non-narrative and narrative informational genres in number of stories and time spent reading each genre. Story variety and the use of current titles can be improved to better support early literacy development in public library storytimes.
Exploring the Effects of Frontalization and Data Synthesis on Face Recognition

by Sandipan Banerjee Automatic face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques like convolutional neural networks (CNNs). But the training process of CNNs requires a large amount of clean and correctly labelled data. In the first part of this work, we try to find the ideal orientation (facial pose, shape, context) of this data for training and testing such CNNs. If a CNN is intended to work with non-frontal face images, should this training data be diverse in terms of facial poses, or should face images be frontalized as a pre-processing step? To answer these questions we evaluate a set of popular facial landmarking and pose frontalization algorithms to understand their effect on facial recognition performance. We also introduce a new landmarking and frontalization scheme that operates over a single image without the need for a subject-specific 3D model, and perform a comparative analysis between the new scheme and other methods in the literature. Secondly, we analyze the usefulness of synthetic images in improving the face recognition pipeline while taking into account its practicality from a computation stand-point. In this regard, we propose a novel face synthesis method for augmentation of existing face image datasets. An augmented dataset reduces overfitting, which in turn, can enhance the face representation capability of a CNN. Our method, start-
The Role Of Intuitive Arithmetic In Developing Mathematical Skill

Symbolic mathematics allows humans to represent and describe the logic of the world around us. Although we typically think about math symbolically, humans across the lifespan and a wide variety of animal species spontaneously exhibit numerical competence without reference to formal mathematics. This intuitive ability to approximately compare, estimate, and manipulate large non-symbolic numerical quantities without language or symbols is called the Approximate Number System. The four chapters of this dissertation explore whether non-symbolic, approximate calculation can function as a bridge between our Approximate Number System and symbolic mathematics for children at the beginning of formal math education and university undergraduates. Chapter 1 explores how non-symbolic and symbolic ratio reasoning relates to general math skill and Approximate Number System acuity in elementary school children. Chapter 2 examines whether children and adults can perform a non-symbolic, approximate division computation, and how this ability relates to non-symbolic and symbolic mathematical skill. Chapter 3 tests the robustness and mechanism of a non-symbolic, approximate addition and subtraction training paradigm designed to improve arithmetic fluency in university undergraduates. Chapter 4 investigates whether the negative relation between math anxiety and symbolic math performance extends to approximate, non-symbolic calculation. Together, Chapters 1 and 2 provide evidence that nonsymbolic calculation ability functions as a mechanism of the relation between Approximate Number System acuity and symbolic math. Chapters 3 and 4 identify populations of students for whom practice with non-symbolic calculation may or may not be beneficial. In sum, this dissertation describes how nonsymbolic, approximate calculation allows students harness their intuitive sense of number in a mathematical context. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Psychology First Advisor Elizabeth M. Brannon
Performance Comparison of Data Mining Algorithm to Predict Approval of Credit Card

Credit analysis needs to identify and assess the factors that can affect customers in returning credit. Accurate measurement and good management ability in dealing with credit risk is an effort to save the economic operations unit and be beneficial for a stable and healthy financial system. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which consists of several stages, namely Business Understanding (dataset), Data Processing (Feature Selection PCA & Dimension Reduce), Algorithm Models (NN+PSO, SVM, LR), Evaluation (Validation and Accuracy). This study has tested the model using a neural network using the PCA selection feature and optimized with the Particle Swarm Optimize (PSO) algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove that the use of a single Neural Network method produces an accuracy of 80.33%. whereas the use of PCA + Neural Network + PSO hybrid method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the Neural Network was optimized using PSO and used feature selection. The purpose of this study is to implement and compare Support Vector Machine, Logistic Regression and Neural Network algorithms based on PCA and optimize PSO (Particle Swarm Optimization) to improve accuracy in predictions of credit card approvals.
Arterial blood pressure and blood gas composition of white rhinoceroses under etorphine anaesthesia

Arterial blood pressure and blood gas composition were investigated in white rhinoceroses immobilized with either etorphine / fentanyl (n = 6) or etorphine / azaperone (n = 6) mixtures. In the etorphine / fentanyl group, arterial pressure was higher (183 ± 16 mm Hg) than the mean value observed in the latter group (141 ± 24 mm Hg). In both groups, anaesthesia was accompanied by hypoxaemia and hypercapnia. Assuming that such effects could contribute to post-capture morbidity and / or mortality, either oxygen supplementation or the administration of a respiratory stimulant as soon as possible after recumbency is indicated. In addition, the use of azaperone may alleviate possible blood pressure elevation in these animals during immobilization. Arteriele bloeddruk en bloedgassamestelling is ondersoek in witrenosters wat met mengsels van of etorfien / fentaniel (n = 6) of etorfien / asaperoon (n = 6) geimmobiliseer is. Die arteriele bloeddruk van die etorfien / fentanielgroep (183 ± 16 mm Hg) was hoer as die gemiddelde waarde (141 ± 24 mm Hg) van die ander groep. In beide groepe het narkose met hipoksemie en hiperkapnie gepaard gegaan. Indien aanvaar word dat hierdie uitwerking tot morbiditeit en / of mortaliteit na die vangs mag bydra, sou dit gerade wees om suurstof of 'n asemhalingstimulant so gou moontlik nadat die dier gaan le het, toe te dien. Die gebruik van asaperoon mag bydra tot die verligting van moontlike bloeddrukverhogingtydens immobilisasie .
Cascaded Boundary Network for High-Quality Temporal Action Proposal Generation

Creating high-quality temporal action proposals is fundamental yet challenging for accurate action detection in untrimmed videos due to the complexity of the background and variation in actions’ durations and magnitudes. In this paper, we propose a cascaded boundary network (CBN) to predict the action boundaries by considering the importance of precise boundary information to develop accurate action proposals. Specifically, the first stage of CBN locates the temporal boundaries by predicting the probability that each frame corresponds to an action, the start position, and the end position. A temporal convolutional network is used in this stage to capture short-term context information. Next, the predicted probabilities are forwarded to the second stage, in which a long short-term memory (LSTM) network is utilized for further refinement by exploiting the correlation between the predicted probabilities to capture long-term context information. Finally, we combine the results from both stages to produce a long- and short-term information fusion. The experiments on THUMOS14 and ActivityNet-1.3 show that CBN achieves state-of-the-art recall performance. The performance improvement is especially remarkable for a small average number (AN) of retrieved proposals; e.g., the average recall at AN=50 on THUMOS14 is improved from 37.46% to 43.06%. Further experiments are performed by introducing proposals generated by CBN into an existing action detection framework. CBN also achieves state-of-the-art average mAP@tIoU on the THUMOS14 detection benchmark.
Relapsing Transient Neonatal Diabetes Mellitus due to ABCC8 Mutation

Neonatal diabetes mellitus with onset in the first six months of life is predominantly monogenic. Genetic evaluation of such infants is mandatory irrespective of the diabetic duration. Management of sulphonylurea responsive mutation induced diabetes is rewarding as it improves the metabolic control without exogenous insulin lifelong. Here we present a child with neonatal onset diabetes who remitted at 10 months of age and relapsed at 10 years, 8 months of age and was effectively switched over to oral sulphonylurea following identification of mutation in the ABCC8- SUR1 subunit of the K-ATP channel.
Logarithmic Pyramid Vector Quantization—Design and Theoretical Analysis

Vector quantization is an integral part of modern speech and audio codecs. This study proposes the logarithmic pyramid vector quantizer (LPVQ), which is a gain-shape vector quantizer specifically designed for the quantization of Laplace distributed memoryless sources. The objective of the study is a theoretical analysis of the LPVQ: We determine the distortion of the shape quantizer with respect to rate and vector dimension for the quantization of an i.i. d. Laplace source. Furthermore, we derive formulas for the quantization signal-to-noise ratio (SNR) of the shape quantizer and the quantization SNR of the LPVQ, giving reliable results for an effective bit rate per vector coordinate of 2bits and higher. We study the SNR-behavior of the LPVQ for infinite dimensions and compare it with the maximal achievable SNR according to the rate-distortion bound. After having proposed a strategy for the allocation of the bit rate for the gain quantization and the quantization of the shape vector, respectively, we verify the derived formulas by simulations.
Efficient Delivery of Video Services to Unicast and Multicast Users by Subgroup Formation based on Channel Quality and Distance

: As we knew that the mobile multimedia is one of the main sources of revenue for telecommunication industries, subscribers are often interested to receive data with the same rate and so it is necessary to handle both unicast and multicast traffic together efficiently and to increase user satisfaction. This paper proposes a virtual unified group approach (VUG), they forms sub-groups base on channel quality and the distance between the users within the unicast virtual groups and multicast groups. This will enhance the system performance as shown in the simulation results, in terms of throughput and the distribution of resources to unicast and multicast traffic.
Consensus-Based Cooperative Droop Control for Accurate Reactive Power Sharing in Islanded AC Microgrid

The conventional droop control technology, which can achieve autonomous power sharing among the distributed generation units, suffers from inaccurate reactive power sharing due to its dependence on line impedances. In this article, a novel consensus-based cooperative droop control technique where the droop slope gain is adaptively adjusted is proposed to improve the reactive power-sharing accuracy with the help of a sparse communication network. Through detailed small-signal analysis, it is verified that the proposed droop technique presents excellent immunity against communication delay. Laboratory test results are presented and the performances of the proposed control technique under different operation scenarios are verified, including the load change, communication link failure, and plug-and-play operation.
TOSwitch: Programmable and High-Throughput Switch Using Hybrid Switching Chips

The future of networking switches and routers foresees in two key features: Programmability and Throughput. In this letter, we propose a Traffic Offload Switch (TOSwitch) model for such a versatile switch, using different breeds of switching chips, an ASIC and an FPGA, to achieve the above two key features. Our TOSwitch model is useful in many real-world use cases. The TOSwitch gives line-rate throughput and low latency for current and future network protocols. In our simulation on Xilinx Zynq 7000 (ONetSwitch45) FPGA board, the results show that the traffic offload model does not introduce considerable overhead in terms of latency, throughput and switching chip resources.
Dialysis adequacy in chronic hemodialysis patients in educational center Vali-Asr in Birjand

،يبرم ؛لوؤسم هدنسيون ناريا ،دنجريب ،دنجريب يكشزپ مولع هاگشناد ،يراتسرپ هورگ . سردآ : دنجريب يكشزپ مولع هاگشناد ،يرافغ نابايخ ،دنجريب . يكينورتكلا تسپ : mogh1344@yahoo.com 2 ،يبرم ناريا ،دنجريب ،دنجريب يكشزپ مولع هاگشناد ،يراتسرپ هورگ . 3 راتسرپرس ، زيلايدومه شخب ، يشزومآ زكرم – يلو ينامرد رصع ) جع ( ناريا ،دنجريب ،دنجريب . 4 راتسرپ ، دومه شخب زيلاي ، يشزومآ زكرم – يلو ينامرد رصع ) جع ( ناريا ،دنجريب ،دنجريب .