Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Investigation of Deep Neural Networks on Object Detection

Object detection in surveillance system has to meet two requirements: high accuracy and realtime response. To achieve high accuracy, we adopt TensorBox architecture [1] as the realization that integrates the Long Short Time Memory (LSTM) of Recurrent Neural Network (RNN) into Convolutional Neural Network (CNN). Such the deep neural networks have been already known for their superior ability of object detection. Besides, the deep neural networks are usually computation-intensive and demand more resources to meet the realtime requirement. However, the system configuration is not properly selected and often leads to low utilization of the resources, which in turn makes the redundancy in the system with the expensive cost. In our performance investigation, we use the drone to capture the video and propose it as the scenario of a surveillance system. In addition, we experiment on different number of CPU cores and GPU with different available memory. Our performance investigation concludes that GPU outperforms CPU even though we use all of the CPU cores and the parameter tuning doesn’t matter much with respect to the time.
Kalman Filter Based Channel Tracking for Precoded GFDM Systems

Generalized Frequency Division Multiplexing (GFDM) offers the simultaneous benefits of flexibility, low latency and excellent spectral properties over conventional OFDM for modern wireless systems, making it a promising candidate for 5G and other wireless technologies. However, several implementation related issues remain, including estimation and equalization. We consider the problem of efficient estimation and channel tracking in GFDM systems. In particular, we discuss a scattered (and tessellated) pilot based channel estimation approach, and derive a Kalman filter based channel tracker to handle time varying channels. We extend this work to the GFDM-MIMO case. Simulations reveal that the proposed tracker minimizes estimation errors which significantly improves the BER performance of the system.
Addressing slow rates of long-term genetic gain in sugarcane

For the last 50 years, rates of genetic gain in sugarcane breeding programs worldwide appear to be slow, particularly when compared with gains being delivered in some other crops. Likely reasons include the high proportion of genetic variation present as non-additive variation in sugarcane for key traits and long generation interval between crossing and parent selection. The high non-additive genetic variation, especially for cane yield, contributes to low narrow-sense heritability and means that selection of new parents based on phenotype poorly predicts breeding value. DNA markers may better predict breeding value because prediction models directly related to additive genetic effects (ie. presence versus absence of alleles) can be applied. An experiment was set up in test this hypothesis. A set of 1135 clones typical of those evaluated in stage 2 in the Australian breeding program was taken as a starting population, and cane yield and commercial cane sugar (CCS) measured in all clones. A set of 384 pre-selected DNA (DArT) markers was also screened across this population and genomic predictions of cane yield and CCS made. Progeny from 141 crosses derived from a subset of 41 clones were evaluated to measure breeding value. As expected, phenotypic performance of the 41 clones for cane yield or CCS poorly predicted breeding value (r = 0.26, P<0.10; 0.02, ns; for CCS and cane yield, respectively), reflecting low narrow sense heritability. However genomic predictions using markers gave better predictions (r = 0.68*** and 0.35*, for CCS and cane yield, respectively). The results in this study are encouraging regarding the use of DNA markers for rapid improvement of parents. This is particularly so considering that an accurate marker-assisted breeding program should derive even more accurate predictions of breeding value, and use an approximately 3-year generation interval. Suggestions are made on how this may be done in practice.
Analysis, Diagnosis and Correction of Rain Streaks

In any unique and dynamic climate, for example, rain and snow, it causes varieties in local intensities of the rainy pictures. Such changes can remarkably influence the frameworks that rely upon highlights of the picture for object recognition, tracking, object detection, segmentation etc. Accordingly, there is a requirement for quick and precise detection [1]–[3] and evacuation of rain streaks or the marks of snow. Here, we build up a strategy for diagnosis and dismissal of rain components. Rain components have a higher intensity compare to the non-rain component of the image. This property has been utilized to recognize the rain components in a picture. To decrease the false alarm cases, we use the attributes of raindrops, (for example, major and minor axis) and recognize the identified rain and non-rain component. Correction of the color is utilized to reestablish shading of rain-component with the color of the background. We exhibit that our proposed technique performs well for rain-component identification with more prominent precision compare to other state-of-the-art methods.
Efficient Design of Binary Sequences With Low Autocorrelation Sidelobes

Sequences with low autocorrelation sidelobes are needed in a diverse set of active sensing applications, including radar, sonar, communications, and biomedicine. The recently proposed and widely adopted methods of the Cyclic Algorithm-New (CAN) and Periodic CAN (PeCAN) are known to be computationally efficient, particularly as they employ fast Fourier transform (FFT) operations to design unimodular (i.e., unit-modulus) sequences with good autocorrelation properties. However, these cyclic algorithms cannot be directly used to design binary sequences with good autocorrelation properties due to the extremely multimodal nature of the associated optimization objectives. In this paper, we combine the notion of converging functions with the CAN and PeCAN frameworks, to considerably enhance their performances for binary sequence synthesis. Moreover, the convergence of these algorithms are established and their convergence properties are thoroughly analyzed. Numerical examples are provided to show that the proposed algorithms can outperform existing approaches for aperiodic and periodic binary sequence synthesis, especially for long sequence designs.
Ab initio prediction of micro-RNA like structures in sugarcane viruses and their cellular targets.

During a viral infection in a plant, miRNAs interplay between the host and the pathogen. Sugarcane yellow leaf virus (SCYLV), Sugarcane streak mosaic virus (SCSMV), and Sugarcane bacilliform virus (SCBV) are serious diseases affecting sugarcane productivity worldwide. As a viable strategy to utilize resistance to the viruses in sugarcane, we used computational approaches to predict virus-encoded miRNAs in these viruses. We used an in silico approach to analyse SCYLV, SCSMV and SCBV virus genome sequences. Pre-miRNAs were extracted through VMir software, and screened using web tools such as Mir Para, MiPred, Mature Pred and Mature Bayes. The potential target genes were predicted using psRNATarget. From computational tools, different viral pre-miRNA hairpin sequences and 11, 8 and 13 putative mature miRNAs were predicted from SCYLV, SCSMV and SCBV genomes, respectively. These predicted miRNAs have hybridized with numerous targets. The present study is the first computational prediction of SCYLV and SCBV encoded viral miRNA and their targets in sugarcane and other monocot ESTs. This is also the first study that has identified miRNAs in P0 (SCYLV)-ORF, which is suppressor of RNA silencing. This new findings will fine-tune our strategy of using pathogenderived resistance in sugarcane and developing virus-resistant varieties through conventional breeding.
Removable Denture Patient Awareness toward Dental Implants

Background: Today’s modern implant dentistry appeals to a wide population. Willingness to have implant treatment and its success depends on patient knowledge and expectations as well as the care, skill, and judgment of clinicians. Since the introduction of dental implants, the efficacy of treatment has been confirmed by long-term clinical studies. Aim of the study: To assess removable denture patient awareness toward dental implants depending on the educational level of the patient. Materials and methods: a survey was conducted to determine the awareness of patients wearing removable prosthesis toward dental implant. A total of 100 patients were examined in the prosthodontics department after they were informed and gave their consent. A questionnaire with 7 questions was recorded including the basic information (age and gender), educational level of the patient, and did he/she has knowledge about dental implant and the source of this knowledge. The results were gathered and statistically analyzed using SPSS computer software. Results: A total of 100 subjects asked about the knowledge about implants, 61% had heard of implants as a treatment modality and dentists were the main source of information. The level of awareness increased with education. Conclusion: More than half of the questioned participants were aware of DIs as an option in replacing missing teeth (61%). Dentists were the main sources of information regarding dental implants among all participants.
Complex Satellite Lifetime Optimization Based on Bayesian Network Reliability Compression Inference Algorithm

In the satellite lifetime optimization, reliability is a critical issue. For the complex satellite system, Bayesian network (BN) is an important method for reliability modeling and inference. As the number of system’s components increases dramatically, the memory storage requirements of the system’s node probability table (NPT) will exceed the computer’s random access memory (RAM). To solve this challenge, compression methods have been proposed to reduce the memory storage requirements of NPT. However, for the complex satellite system with extreme large number of components and the explosion of probable state combination, the existing methods still face big challenge in compression efficiency. Therefore, an improved encoding compression algorithm is proposed to further enhance the NPT compression effect in this paper. For the hierarchical complex satellite system that has multiple subsystems which are further composed of multiple components, the multilevel BN reliability model is first constructed based on the proposed encoding compression algorithm. By the variable elimination algorithm, a multilevel BN reliability inference algorithm is proposed to perform the inference of the multilevel BN reliability model. Based on the basis of the reliability model above, further considering system mass, power and cost requirement, the satellite lifetime is properly designed by optimizing the system component configuration, including component model/type selection and number determination for redundancy. Finally, two cases are studied to demonstrate and validate the proposed algorithms.
Transient Characteristics of YBCO Tapes Under Pulsed Current and Pressures

Taking the advantage of quench behavior, high-temperature superconductors (HTS), such as YBCO tapes, are gradually used in the resistive superconducting fault current limiters (RSFCLs) for the direct current power system. However, due to the high cost, as well as the mechanical vulnerability, the complexity of HTS still remains. In this article, first YBCO tapes were sustained under different pressures, and then the transient characteristics of the tapes were tested under a huge pulsed current. Results show that while applying pressures on YBCO tapes, the resistance would increase significantly. Thus, a new approach to increase the quench resistance of RSFCLs was proposed, which could potentially reduce the consumption of YBCO tapes and cut down the cost.
Extracting more from boosted decision trees: A high energy physics case study

Particle identification is one of the core tasks in the data analysis pipeline at the Large Hadron Collider (LHC). Statistically, this entails the identification of rare signal events buried in immense backgrounds that mimic the properties of the former. In machine learning parlance, particle identification represents a classification problem characterized by overlapping and imbalanced classes. Boosted decision trees (BDTs) have had tremendous success in the particle identification domain but more recently have been overshadowed by deep learning (DNNs) approaches. This work proposes an algorithm to extract more out of standard boosted decision trees by targeting their main weakness, susceptibility to overfitting. This novel construction harnesses the meta-learning techniques of boosting and bagging simultaneously and performs remarkably well on the ATLAS Higgs (H) to tau-tau data set (ATLAS et al., 2014) which was the subject of the 2014 Higgs ML Challenge (Adam-Bourdarios et al., 2015). While the decay of Higgs to a pair of tau leptons was established in 2018 (CMS collaboration et al., 2017) at the 4.9$\sigma$ significance based on the 2016 data taking period, the 2014 public data set continues to serve as a benchmark data set to test the performance of supervised classification schemes. We show that the score achieved by the proposed algorithm is very close to the published winning score which leverages an ensemble of deep neural networks (DNNs). Although this paper focuses on a single application, it is expected that this simple and robust technique will find wider applications in high energy physics.
How cells die

Flow cytometry is a technology that measures and analyzes the optical properties of mono-dispersed single particles, such as cells, bacteria, picoplankton, microbeads, yeast, platelets, nuclei and other similarly-sized particles, passing single file through a focused laser beam. The laser can excite fluorophores that have been used to mark various molecules or physiological functions of the particles. The use of fluorophores with different fluorescence characteristics, multiple lasers and multiple photo detectors allows flow cytometers to measure many characteristics of each particle simultaneously. An important feature of flow cytometry is that large numbers, for example thousands of particles per second, are analyzed and therefore provide a statistically significant picture of a specimen's physical and biochemical make-up. In this inaugural address the focus will be on how we implemented flow cytometry in cellular toxicology or the study of how cell dies.
Molecular Detection of Pseudomonas aeruginosa and Study the Effect of Fresh Garlic juice on Some Virulence Factors of this Bacteria.

The present study included the collection of 169 samples from various clinical and environmental sources for investigation of existence of Pseudomonas aeruginosa in those sources, the clinical samples collected from some hospitals in Baghdad city and included 126 samples of wound, burns, cystic fibrosis, ear infections and urinary tract infections (35, 52,4, 22 and 13) samples respectively, while environmental samples were distributed by 25 samples from soil, and 18 samples from wastewater and some swamps. Samples were identified based on the morphological and microscopical characteristics of the colonies when they were culturing in a number of culture media as well as biochemical tests. The molecular detection was used to confirm diagnostic test for the isolates based on the 16SrRNA gene, as a detection gene, which has specific sequences for DNA of P.aeruginosa bacteria.The results of molecular detection showed that 51 isolates belong to target bacteria were distributed as 20 of burns, 9 isolates of wounds, 7 isolates of ear infection, 4 isolates of cystic fibrosis, 2 isolates of urinary tract infection, 5 isolates of soil and 4 isolates of water. The results of phenotypic detection of some virulence factors showed that 49 isolates with percent (95%) produced beta-hemolysis, while 46 isolates (90%) showed variance in the strength of pycocyanin production with a absorbance ranging from (0 to 0.52) at 690 nm, and 47 (92%) of isolates were producing protease enzyme, 44 (86%) of the isolates showed a differences in their ability to biofilm formation. As a result of the increased resistance of P.aeruginosa bacteria towards antibiotics, it is became a problem in the treatment of the infectious diseases caused by this bacteria therefore, the current study focused on the use of fresh garlic extract towards of some virulence factors because it being easy and commonly used. The results of the statistical analysis showed a significant difference when P-value (<0.05) in the production of some virulence factors, the isolates lost their ability to form biofilm, and decreased their ability to produce protease enzyme, and pyocyanin pigment, while did not affect the production of hemolycin. Key word: Pseudomonas aeruginosa, Molecular detection, Garlic ,Virulence Factors.
The Environment Is a Key Factor in Determining the Anti-Amyloid Efficacy of EGCG

Millions of people around the world suffer from amyloid-related disorders, including Alzheimer’s and Parkinson’s diseases. Despite significant and sustained efforts, there are still no disease-modifying drugs available for the majority of amyloid-related disorders, and the overall failure rate in clinical trials is very high, even for compounds that show promising anti-amyloid activity in vitro. In this study, we demonstrate that even small changes in the chemical environment can strongly modulate the inhibitory effects of anti-amyloid compounds. Using one of the best-established amyloid inhibitory compounds, epigallocatechin-3-gallate (EGCG), as an example, and two amyloid-forming proteins, insulin and Parkinson’s disease-related α-synuclein, we shed light on the previously unexplored sensitivity to solution conditions of the action of this compound on amyloid fibril formation. In the case of insulin, we show that the classification of EGCG as an amyloid inhibitor depends on the experimental conditions select, on the method used for the evaluation of the efficacy, and on whether or not EGCG is allowed to oxidise before the experiment. For α-synuclein, we show that a small change in pH value, from 7 to 6, transforms EGCG from an efficient inhibitor to completely ineffective, and we were able to explain this behaviour by the increased stability of EGCG against oxidation at pH 6.
Automatic Morphing and Edge Map for Face Recognition

Face recognition has received much attention during the past several years. Principal component analysis (PCA) is one of the most successful methods for face recognition but it is not highly accurate when the illumination and pose of the facial images vary considerably. Many researches have discussed some solutions to solve the illumination and pose problems, but most of them need multiple training images. This paper presents a novel face recognition system based on PCA, named Automatic Pose normalization and Edge map face Recognizer (APER). The idea is to automatically re-render a pose invariant reference model to accommodate varying pose of the images. Face edge images, which are insensitive to illumination changes, are incorporated. The APER requires only a single face image for training per person. The APER and the PCA method are evaluated using ORL database. The experimental results demonstrate that the APER can improve the performance of conventional PCA approach under varying pose and illumination with single training image. Keyword: morphing, automatic correspondence, edge detection, face recognition
Inheritance of resistance to orange rust in sugarcane

Orange rust, caused by Puccinia kuehnii, is an economically important disease in the Florida sugarcane industry. In this study, orange rust reactions of seedlings in progenies originating from 12 crosses between female and male parents with differing resistance to orange rust (three of each category: resistant x resistant, resistant x susceptible, susceptible x resistant, and susceptible x susceptible) were determined. Ratings for orange rust for the progeny of the 12 crosses were obtained by spraying seedlings with a spore suspension (10 urediniospores/mL) and incubating them overnight in dark at 25°C in dew chambers. In addition, two reciprocal crosses between orange rust susceptible CP881762 (S) and orange rust resistant CP95-1039 (R) parents were assessed for their reaction to orange rust. The progeny of two reciprocal crosses (same parents but with the male/female parents reversed) showed that the resistant female parent had twice as many orange rust resistant progeny compared with the cross with the susceptible female parent. Progenies of crosses from all combinations were composed of higher numbers of resistant plants than expected. A very high ratio of resistant individuals (R:S = 4:1) was present in the progeny of a self-cross (X03-1060) of a highly susceptible parent CP94-2203. Similar results (R:S = 3:1) were observed in the progeny of a cross between two susceptible parents (CP94-2203 X CP84-1198). Therefore, it is important not to eliminate parents susceptible to orange rust but with high agronomic value because of their ability to produce resistant genotypes in relatively high proportions.
DELIVERABLE 7.4 EXTENDED SYSTEM EVALUATION: UNCERTAIN CONDITIONS

In this paper we present results from a user evaluation of a robot bartender system which handles state uncertainty derived from speech input by using belief tracking and generating appropriate clarification questions. We present a combination of state estimation and action selection components in which state uncertainty is tracked and exploited, and compare it to a baseline version that uses standard speech recognition confidence score thresholds instead of belief tracking. The results suggest that users are served fewer incorrect drinks when the uncertainty is retained in the state. Mary Ellen Foster and Ronald P. A. Petrick. Planning for social interaction with sensor uncertainty. In Proceedings of the ICAPS 2014 Scheduling and Planning Applications woRKshop (SPARK), pages 19–20, Portsmouth, NH, USA, June 2014. Abstract: A robot coexisting with humans must not only be able to perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In this paper, we describe an extension of prior A robot coexisting with humans must not only be able to perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In this paper, we describe an extension of prior work on planning for task-based social interaction using a robot that must interact with multiple human agents in a simple bartending domain. We describe how the initial state representation developed for this robot has been extended to handle the full range of uncertainty resulting from the input sensors, and outline how the planner will use the resulting uncertainty in the state during plan generation.