Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep convolutional neural networks for molecular subtyping of gliomas using magnetic resonance imaging

Purpose: Knowledge of molecular subtypes of gliomas can provide valuable information for tailored therapies. This study aimed to investigate the use of deep convolutional neural networks (DCNNs) for noninvasive glioma subtyping with radiological imaging data according to the new taxonomy announced by the World Health Organization in 2016. Methods: A DCNN model was developed for the prediction of the five glioma subtypes based on a hierarchical classification paradigm. This model used three parallel, weight-sharing, deep residuallearning networks to process 2.5-dimensional input of trimodal MRI data, including T1-weighted, T1-weighted with contrast enhancement, and T2-weighted images. A data set comprising 1,016 real patients was collected for evaluation of the developed DCNN model. The predictive performance was evaluated via the area under the curve (AUC) from the receiver operating characteristic analysis. For comparison, the performance of a radiomicsbased approach was also evaluated. Results: The AUCs of the DCNN model for the four classification tasks in the hierarchical classification paradigm were 0.89, 0.89, 0.85, and 0.66, respectively, as compared to 0.85, 0.75, 0.67, and 0.59 of the radiomics approach. Conclusion: The results showed that the developed DCNN model can predict glioma subtypes with promising performance, given sufficient, non-ill-balanced training data.
Proposal to Encode North Indic Number Forms in ISO/IEC 10646

Author(s): Pandey, Anshuman | Abstract: This is a proposal to encode a set of Common Indic Number Forms in the international character encoding standard Unicode. This set of characters was published in Unicode Standard version 5.2 in October 2009. The characters were used in the traditional representation of fractions in various scripts of northern India, Pakistan, and some areas of Nepal. They were also used in a number of scripts of South India, including Kannada. They are attested from at least the 16c CE until 1970s, and are occasionally used today. The fraction signs were used to write currency, time, measure, and other units.
A Discriminative Multi‐Channel Facial Shape (MCFS) Representation and Feature Extraction for 3D Human Faces

Building an effective representation for 3D face geometry is essential for face analysis tasks, that is, landmark detection, face recognition and reconstruction. This paper proposes to use a Multi‐Channel Facial Shape (MCFS) representation that consists of depth, hand‐engineered feature and attention maps to construct a 3D facial descriptor. And, a multi‐channel adjustment mechanism, named filtered squeeze and reversed excitation (FSRE), is proposed to re‐organize MCFS data. To assign a suitable weight for each channel, FSRE is able to learn the importance of each layer automatically in the training phase. MCFS and FSRE blocks collaborate together effectively to build a robust 3D facial shape representation, which has an excellent discriminative ability. Extensive experimental results, testing on both high‐resolution and low‐resolution face datasets, show that facial features extracted by our framework outperform existing methods. This representation is stable against occlusions, data corruptions, expressions and pose variations. Also, unlike traditional methods of 3D face feature extraction, which always take minutes to create 3D features, our system can run in real time.
Home Security System with IOT Based Sensors Running On House Infra Structure Platform

In this research a system that is based on IOT technology was developed. The system itself is a subsystem of a home security system. The subsystem is one of the essential parts of a home security system that protects its inhabitants, the house itself and the environment around it. It is based on technology that is related to Internet of Things (IOT). The IOT technology being utilized depends on the data communication infrastructure that is running in a house. The infrastructure is built and based on Lora (long Range) technology and in particular Lorawan. The Infrastructure complies with the demand that it is useful and fulfils the requirements of IOT. Lorawan is quite recent but technically comply with the demands for IOT. This research focused on gas leakage detection using a detector. Gas leakage that needs immediate detection is generally used for home use such as LPG. LPG is generally used in house kitchens or sometimes bath rooms. The developed module is for detection and issuance of warning the existence of LPG in its environment. The data for issuance of warning is communicated to a system called Premises Controller. The protection of a house against gas leakage is essential due to its sensitivity to fire sparks. Fire sparks can trigger fire and often explosion. A sensor’s sensitivity and the time of early warning determine the effectiveness of mitigating efforts.
An Intelligent Optimization Method for Optimal Virtual Machine Allocation in Cloud Data Centers

A cloud computing paradigm has quickly developed and been applied widely for more than ten years. In a cloud data center, cloud service providers offer many kinds of cloud services, such as virtual machines (VMs), to users. How to achieve the optimized allocation of VMs for users to satisfy the requirements of both users and providers is an important problem. To make full use of VMs for providers and ensure low makespan of user tasks, we formulate an optimal allocation model of VMs and develop an improved differential evolution (IDE) method to solve this optimization problem, given a batch of user tasks. We compare the proposed method with several existing methods, such as round-robin (RR), min–min, and differential evolution. The experimental results show that it can more efficiently decrease the cost of cloud service providers while achieving lower makespan of user tasks than its three peers. Note to Practitioners—VM allocation is one of the challenging problems in cloud computing systems, especially when user task makespan and cost of cloud service providers have to be considered together. We propose an IDE approach to solve this problem. To show its performance, this article compares the commonly used methods, i.e., RR and min–min, as well as the classic differential evolution method. A cloud simulation platform called CloudSim is used to test these methods. The experimental results show that the proposed one can well outperform its compared ones, and its VM allocation results can achieve the highest satisfaction of both users and providers. The proposed method can be readily applicable to industrial cloud computing systems.
Evaluation of locally available feed resources for striped catfish (Pangasianodon hypophthalmus)

This thesis investigated and compared inputs and outputs, economic factors and current feed use in small-scale farming systems producing striped catfish (Pangasianodon hypophthalmus) in the Mekong Delta. The nutrient content of locally available natural feed resources for striped catfish was determined and growth performance, feed utilisation and body indices were analysed in pond-cultured striped catfish fed diets where fish meal protein was replaced with protein from local feed resources. A survey showed that around 15 feed ingredients are used in striped catfish pond culture in the region. The combination of feed ingredients used in farm-made feeds varied among fish farms. The cost of producing 1 kg of fish using farm-made feeds was usually 8˗10% lower than that of using commercial feeds. Digestibility trials on selected potential feedstuffs showed that the apparent digestibility (AD) of DM, CP, OM and energy was highest in soybean meal, groundnut cake, broken rice, shrimp head meal, golden apple snail and catfish by-product meal and earthworm meal, whilst the digestibility was in lower cassava leaf meal and sweet potato leaf meal. The average digestibility of most essential amino acids (EAA) in selected feed ingredients was high (range 70˗92%), indicating high protein quality of these feedstuffs. In general, the AD of individual EAA was high for all diets except those with cassava leaf meal, rice bran and earthworm meal, where the AD of EAA was reduced. Two different growth experiments with the same diet (20˗100% replacement of fish meal) were performed in an indoor and an outdoor culture system. A significant finding was that daily weight gain (DWG) was much higher (3.2˗ to 6˗fold) in outdoor culture conditions compared with indoor. Feed conversion rate and feed utilisation were also 0.2˗0.7 units (kg feed DM/kg weight gain) higher in the outdoor system. The results suggest that fish meal protein in feed for striped catfish fingerlings can be replaced with protein from locally available plant and animal ingredients without compromising growth performance, feed utilisation or carcass traits.
Exceptionally preserved Mickwitzia from the Indian Springs Lagerstätte (Cambrian Stage 3), Nevada

Abstract. Exceptionally preserved specimens of the Cambrian stem-group brachiopod Mickwitzia occidens Walcott, 1908 are described in detail from the Indian Springs Lagerstätte in Nevada, USA. Shell structure and preserved mantle setae from these specimens reveal a variable diagenetic (taphonomic) history and provide insight into the phylogenetic position of mickwitziids. Morphologic and morphometric comparison to M. monilifera (Linnarsson, 1869) from Sweden and M. muralensis Walcott, 1913 from British Columbia, Canada reveals clear species-level distinctions. Scanning electron microscopic analysis allows revision of the generic diagnosis. The Mickwitzia shell is characterized by the presence of inwardly pointing phosphatic cones and tangential setae-bearing tubes. The inwardly pointing cone structures are not consistent with setal bearing structures as previously thought, but rather represent endopunctae-like structures. Acrotretid-like shell structures and shell-penetrating setae in M. occidens strengthen the previously proposed close relationship between stem-group brachiopods and tommotiids, a group of small shelly fossils.
Nonnegative matrix factorization‐based privacy‐preserving collaborative filtering on cloud computing

The security of user personal information on cloud computing is an important issue for the recommendation system. In order to provide high‐quality recommendation services, privacy of user is often obtained by untrusted recommendation systems. At the same time, malicious attacks often use the recommendation results to try to guess the private data of user. This article proposes a hybrid algorithm based on nonnegative matrix factorization and random perturbation technology, which implements the recommendation system and solves the protection problem of user privacy data in the recommendation process on cloud computing. Compared with the privacy protection algorithm of SVD, the elements of the matrix after the decomposition of the new algorithm are nonnegative elements, avoiding the meaninglessness of negative numbers in the matrix formed by texts, images, and so on, and it has a good explanation for the local characteristics of things. Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of protecting users' personal privacy on cloud computing.
A visual SLAM method based on point-line fusion in weak-matching scene

Visual simultaneous localization and mapping (SLAM) is well-known to be one of the research areas in robotics. There are many challenges in traditional point feature-based approaches, such as insufficient point features, motion jitter, and low localization accuracy in low-texture scenes, which reduce the performance of the algorithms. In this article, we propose an RGB-D SLAM system to handle these situations, which is named Point-Line Fusion (PLF)-SLAM. We utilize both points and line segments throughout the process of our work. Specifically, we present a new line segment extraction method to solve the overlap or branch problem of the line segments, and then a more rigorous screening mechanism is proposed in the line matching section. Instead of minimizing the reprojection error of points, we introduce the reprojection error based on points and lines to get a more accurate tracking pose. In addition, we come up with a solution to handle the jitter frame, which greatly improves tracking success rate and availability of the system. We thoroughly evaluate our system on the Technische Universität München (TUM) RGB-D benchmark and compare it with ORB-SLAM2, presumably the current state-of-the-art solution. The experiments show that our system has better accuracy and robustness compared to the ORB-SLAM2.
An inverse relationship between resistance to 2-deoxy-D-glucose and glucose starvation in tumor cells under normoxia

of a dissertation at the University of Miami. Dissertation supervised by Dr. Theodore J. Lampidis. No. of pages in text. (88) A distinguishing feature of tumor cells is their upregulation of glucose metabolism. This metabolic change from oxidative phosphorylation to aerobic glycolysis creates a selective window that can be exploited for anticancer therapy. Here we investigate mechanisms by which pancreatic cancer cells respond to therapeutic (2-deoxy-D-glucose, 2-DG) and physiologic (glucose starvation, GS) forms of glucose restriction. 2-Deoxy-d-Glucose (2-DG) is a sugar analog of both glucose and mannose and thus has established activities inhibiting glycolysis and interfering with glycosylation, respectively. Due to 2-DG’s dual functions, it has classically been thought of as a glucose deprivation mimetic. Additionally, these roles render 2-DG an attractive anticancer agent with potential to kill both hypoxic and normoxic tumor cells. In order to anticipate and understand resistance mechanisms that may arise when 2-DG is used clinically, here we investigate survival mechanisms in cell lines that are either selected for resistance or intrinsically resistant to the sugar analog. Generally, the resistant cell lines examined display muted induction of the unfolded protein response (UPR) when treated with 2-DG. Additionally, we find multiple mechanisms upstream of ER stress/UPR that appear to contribute to resistance including: lower 2-DG uptake and reduced glycosylation interference. Due to 2-DG’s inhibitory activities, it is thought to mimic glucose starvation (GS). Surprisingly, cell lines that were resistant to 2-DG were found to be more sensitive to GS than the 2-DG sensitive cell line, 1420. Sensitivity to GS coincided with reduced breakdown of glycogen to glucose, which correlated with lowered levels of glycogen phosphorylase (PYGB). Inhibition of PYGB prevented glycogen breakdown and restored sensitivity to GS in 1420. Overall the data demonstrate that the manner in which glucose is restricted in tumor cells, i.e. therapeutic or physiologic, leads to differential biological responses involving distinct glucose metabolic pathways. Moreover, in evolving tumors where glucose restriction occurs, the identification of PYGB as a metabolic target may have clinical application.
Foraging behaviour of juvenile New Zealand sea lions (Phocarctos hookeri)

I! ACKNOWLEDGEMENTS III! Animal ethics approval vi! TABLE OF CONTENTS VII! List of appendices xiii! List of tables xv! List of figures xviii! CHAPTER 1 GENERAL INTRODUCTION AND LITERATURE REVIEW: BACKGROUND ON JUVENILE FORAGING BEHAVIOUR AND THE NEW ZEALAND SEA LION 1! Foraging ecology 4! Juvenile foraging ability: why is it important to study juveniles? 5! Foraging behaviour of juvenile otariids 6! Maternal foraging education 8! Methods for studying the foraging behaviour of wild marine animals 9! Limitations of satellite telemetry 9! New Zealand sea lion 11! Distribution 11! Conservation status, abundance and trends 12! Breeding biology 14! Foraging ecology 15! Marginal foraging environment 16! NZ sea lion-fisheries interactions 16! Thesis objectives and outline 20! Thesis outline and division of labour 22!
Automated data inspection in jet engines

Rolls Royce accumulate a large amount of sensor data throughout the testing and deployment of their engines. The availability of this rich source of data offers exciting opportunities to automate the monitoring and testing of the engines. In this thesis we have developed statistical models to make meaningful insights from engine test data. We have built a classification model to identify different types of engine running in Pass-Off tests. The labels can be used for post-analysis and highlight problematic engine tests. The model has been applied to two different types of engines, in which it gives close to perfect classification accuracy. We have also created an unsupervised approach when there are no defined classes of engine running. These models have been incorporated into Rolls Royce systems. Early warnings for potential issues can enable relatively cheap maintenance to be performed and reduce the risk of irreparable engine damage. We have therefore developed an outlier detection model to identify abnormal temperature behaviour. The capabilities of the model are shown theoretically and tested on experimental and real data. Lastly, in a test decisions are made by engineers to ensure the engine complies with certain standards. To support the engineers we have developed a predictive model to identify segments of the engine test that should be retested. The model is tested against the current decision making of the engineers, and gives good predictive performance. The model highlights the possibility of automating the decision making process within a test.
A survey on quantum error correcting codes

Quantum error-correcting codes is used to protect quantum information against noise. In this thesis we explain the structure and error correction process of the Calderbank-Shor-Stean codes(CSS) codes and the stabilizer codes. Also we describe CSS codes in the context of stabilizer formalism. Furthermore we will discuss the logical basis states and the encoding circuit of stabilizer codes.
Short-Filter Design for Intrinsic Interference Reduction in QAM-FBMC Modulation

The waveform design for beyond 5G mobile communication networks is a challenge due to asynchronous and heterogeneous scenarios. Compared to OFDM, superior spectrum confinement must be considered for future radio access technologies. Due to its characteristics, QAM-FBMC is a potential candidate. We propose a method to design a short optimal filter for QAM-FBMC system in order to reduce the total interference at the receiver. We model the total interference with and without channel effects, and describe the optimization procedure for the prototype filter design. Performance results show that the designed filters outperform QMF, NPR and Gaussian filters.
Resume: A Robust Framework for Professional Profile Learning & Evaluation

Professional Proﬁle Extraction is a crucial challenge for any HR department. In this paper, we propose an approach to learn and evaluate professional embeddings. We ﬁrst highlight the technical issues associated with this speciﬁc data; then, we propose an architecture that compares diﬀerent language models to encode the textual information; ﬁnally, we learn user proﬁles and propose three original evaluation tasks to illustrate the strengths and weaknesses of our approach.
Proposed APs Distribution Optimization Algorithm: Aware of Interference (APD-AI)

Indoor networks became the focus of attention of many researchers due to its important role to connect to the wide networks. Many algorithms have been applied or proposed to maximize the coverage of indoor networks. In this paper, a multi-objective algorithm has been introduced to optimize the coverage and maximize the Signal-to-Interference Ratio (SIR) based on Binary Particle Swarm Optimization (BPSO) using Matlab software. It has been applied to the installed network which is consist of four AP with a heterogeneous distribution. It has been evaluated the optimized network and proves its reliability. The results obtained show the flexibility and efficiency of the proposed algorithm which produce an optimal network maximizes the coverage area and enhances the SIR by 9.03 dB.
Model Predictive Control of Modular Multilevel Converter

This chapter discusses how the predictive control often referred to as a model predictive control approach is becoming more popular and is widely employed to control the power converters. The cost function consists of multiple control objectives, which are optimized for weighting factors to reach the desired system behavior. The resulting switching state is then applied to the converter. The digital implementation of model predictive control (MPC) approach requires a discrete‐time model of the MMC system. The discrete‐time (DT) model is obtained from the discretization of continuous‐time (CT) model. The front‐end rectifier of MMC is modeled as a constant DC source. The rectifier output is connected to the positive and negative bars of each leg of an MMC system through a DC‐line. The DC‐line is modeled as an inductor and a series resistance. The output current flows through the converter legs to the load. The difference between the upper and lower arm current gives the magnitude of the current owing through the load. The circulating current flows among the converter legs due to the difference between the upper and lower arm voltages. The circulating current mainly consists of even‐order harmonic components corresponding to the multiples of twice the fundamental frequency.
Prediction of Heart Diseases through Artificial Intelligence and Data Mining

Data mining is the computational procedure of discovering styles in huge statistics sets regarding strategies on the intersection of artificial intelligence, gadget studying, facts, and database systems. It is an interdisciplinary subfield of computer technological know-how. In now a day’s lifestyle illnesses are increasing increasingly more. Data mining is one of the solutions for it, it helps us to overcome this problem by exploring old datasets. For any disease if it is identified at early stage treatment can be done easily. A wide range of data is produced in health care institutions, we will use that data to get some useful information. Data mining in medical sector helps doctors for diagnosis and treatment of diseases, this paper makes an effort to study and find interesting patterns from the data of patients.