Miracle-dz/UniFAR-FTUs huggingface dataset:
Adipokinetic Hormone Receptor Mediates Lipid Mobilization to Regulate Starvation Resistance in the Brown Planthopper, Nilaparvata lugens

Lipid storage must be efficiently mobilized to sustain the energy demands during processes of exercise or starvation. In insects, adipokinetic hormone (AKH) and brummer lipase are well-known regulators of lipid mobilization. We recently demonstrated that brummer-dependent lipolysis regulates starvation resistance in the brown planthopper, Nilaparvata lugens, one of the most destructive rice pests. The present work investigated the roles of the AKH signaling system in lipid mobilization during the starvation process in N. lugens. NlAKHR is a typical G protein-coupled receptor (GPCR) and possesses high structure and sequence similarity to other insect AKHRs. Spatial and developmental expression profiles suggested that NlAKH is released from the corpora cardiaca to activate NlAKHR mainly expressed in the fat body. Starvation significantly induced the expression of NlAKH and NlAKHR, indicating a potential role of the AKH signaling system in starvation resistance. To reveal the functions of the AKH signaling system, a double-stranded RNA (dsRNA)-mediated knockdown of NlAKHR and NlAKH peptide injection was performed. The results show NlAKHR silencing decreased the levels of 1,2-diacylglycerol (DAG) in the hemolymph and increased triacylglycerol (TAG) levels in the fat body, whereas NlAKH injection led to a critical accumulation of DAG in the hemolymph and a severe reduction of TAG content in the fat body. Knockdown of NlAKHR resulted in prolonged lifespan and high levels of whole-body TAG, indicating an inability to mobilize TAG reserves during starvation. Conversely, the NlAKH injection reduced the survival and accelerated TAG mobilization during starvation, which further confirms the role of NlAKH in lipolysis. Moreover, NlAKHR silencing caused obesity in N. lugens, whereas NlAKH injection depleted organismal TAG reserves in vivo and produced a slim phenotype. These results indicate that lipid mobilization is regulated by the AKH signaling system, which is essential for adjusting body lipid homeostasis and ensuring energy supplement during starvation in N. lugens.
MODELING ISSUES FOR REAL-TIME PERFORMANCE IN FLEXIBLE MULTIBODY SYSTEMS

c.es Abstract. In recent years, our group has worked on real-time methods for the dynamics of flexible and rigid multi-body systems. In order to develop such methods, attention must be paid, in an integrated way, to modeling issues, formulation of the equations of motion, nu- merical integration procedures, and implementation techniques. Achievement of real-time performance in flexible multi-body systems on conventional PC platforms is commonly more challenging than in the rigid case. It can be stated that most methods developed for rigid multi-body systems can be extended to consider flexible members. Consequently, conclusions and recommendations drawn when studying rigid mechanisms in order to achieve real-time performance can be extrapolated to the flexible case. However, some additional "degrees-of- freedom" arise when simulating the motion of flexible multi-body systems, which must be managed by the analyst. They are the choice, for each of the flexible bodies, of the accuracy level to be applied for the description of the corresponding flexible component. This is some- thing affecting the modeling stage, and occurs no matter the selected modeling technique is. In this paper, the described topic is addressed, in the context of a floating reference frame formulation with modal superposition of static and dynamic modes with fixed boundaries, previously developed by the authors. The modal selection process of a complex multi-body system is studied in detail in order to show the effect that the modeling of flexible bodies has on the computational efficiency of the method, emphasizing the need of reaching a compro- mise between accuracy and efficiency at the time of selecting both the static and dynamic modes. A good selection is considered to be a critical factor when pursuing real-time per- formance.
Testing of Digital Systems: Index

From the Publisher: As the complexity of modern digital systems increases, so does the need for ever more rigorous testing at all levels, from individual chips up to complete system architectures. This book is the most comprehensive introduction available to the range of techniques and tools used in digital testing. It covers every key topic, including fault simulation, CMOS testing, design for testability, and built-in self test. Aimed at graduate students of electrical and computer engineering, the book is also the most up-to-date reference on the market for practicing engineers.
Internet Routing Architectures, Second Edition

From the Publisher: This invaluable reference provides realistic, practical design solutions from Cisco Systems-the Internetworking authority. Teaches how to construct and support robust ISP connections to the Internet Illustrates Inter- and Intra-domain routing issues, topologies, and scenarios for practical application The only Internetworking book emphasizing domain routing issues and protocols endorsed by Cisco!
Quantizing Euclidean Motions via Double-Coset Decomposition

Concepts from mathematical crystallography and group theory are used here to quantize the group of rigid-body motions, resulting in a “motion alphabet” with which robot motion primitives are expressed. From these primitives it is possible to develop a dictionary of physical actions. Equipped with an alphabet of the sort developed here, intelligent actions of robots in the world can be approximated with finite sequences of characters, thereby forming the foundation of a language in which robot motion is articulated. In particular, we use the discrete handedness-preserving symmetries of macromolecular crystals (known in mathematical crystallography as Sohncke space groups) to form a coarse discretization of the space SE(3) of rigid-body motions. This discretization is made finer by subdividing using the concept of double-coset decomposition. More specifically, a very efficient, equivolumetric quantization of spatial motion can be defined using the group-theoretic concept of a double-coset decomposition of the form Γ\SE(3)/Δ, where Γ is a Sohncke space group and Δ is a finite group of rotational symmetries such as those of the icosahedron. The resulting discrete alphabet is based on a very uniform sampling of SE(3) and is a tool for describing the continuous trajectories of robots and humans. An efficient coarse-to-fine search algorithm is presented to round off any motion sampled from the continuous group of motions to the nearest element of our alphabet. It is shown that our alphabet and this efficient rounding algorithm can be used as a geometric data structure to accelerate the performance of other sampling schemes designed for desirable dispersion or discrepancy properties. Moreover, the general “signals to symbols” problem in artificial intelligence is cast in this framework for robots moving continuously in the world.
Genetic algorithm metaheuristic in the solution of forest management models.

This work aimed to test the Genetic Algorithm (GA) metaheuristic evaluating its effectiveness and efficiency in the solution of this kind of problem, and comparing its results with those obtained by the software CPLEX. A completely randomized design, as the factorial arrangement, was used to analyze the effect of different parameters on GA performance. Sizes of initial population (Pini), crossing-over rates (Tcross) and crossing-over methods breaks (Mcross) were the analyzed factors. In the cases that the interactions were significant by F test (P < 0.05) the differences among the means were tested by Tukey test at 5% probability level. The percentage distance (distance between the answer of AG and the exact answer) and the time of processing were used as a measure of effectiveness and efficiency, respectively. The initial population is the factor that most influenced the AG performance considering percentage distance and processing time, so that for larger Pini are found greater proximity of GA response to the exact response and also greater processing times.
Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network

Sketch has been employed as an effective communicative tool to express the abstract and intuitive meanings of object. Recognizing the free-hand sketch drawing is extremely useful in many real-world applications. While content-based sketch recognition has been studied for several decades, the instance-level Sketch-Based Image Retrieval (SBIR) tasks have attracted significant research attention recently. The existing datasets such as QMUL-Chair and QMUL-Shoe, focus on the retrieval tasks of chairs and shoes. However, there are several key limitations in previous instance-level SBIR works. The state-of-the-art works have to heavily rely on the pre-training process, quality of edge maps, multi-cropping testing strategy, and augmenting sketch images. To efficiently solve the instance-level SBIR, we propose a new Deep Triplet Classification Siamese Network (DeepTCNet) which employs DenseNet-169 as the basic feature extractor and is optimized by the triplet loss and classification loss. Critically, our proposed DeepTCNet can break the limitations existed in previous works. The extensive experiments on five benchmark sketch datasets validate the effectiveness of the proposed model. Additionally, to study the tasks of sketch-based hairstyle retrieval, this paper contributes a new instance-level photo-sketch dataset - Hairstyle Photo-Sketch dataset, which is composed of 3600 sketches and photos, and 2400 sketch-photo pairs.
Mobile, location-based games for learning: Developing, deploying and evaluating mobile game technology in education

Recent developments in mobile technology have facilitated the emergence of a vast number of games to be played on mobile phones. Several mobile games have also been developed with the explicit purpose of being used for learning. Studies of the educational practices related to these mobile games are not extensively available, however. The main aim for this research was to explore how mobile, location-based games can be used to facilitate teaching and learning practices within education. In particular, the aim was to fill the research gap on educational practices with mobile, location-based games, with an emphasis on mediated, situated social interaction with these games. For this purpose the technological framework of SILO — an authoring tool for creating location-based games — and the game Premierløitnant Bielke were designed, enacted and evaluated. Engagement with the game was studied in three different settings: first, with regard to usability and educational potential of the game; second, with regard to the opportunities for countering the experience of “onetimeness” of game playing and integration with other classroom tools and activities; and third, with regard to gaining insight into the interactional organisation and practical accomplishment of gameplay to discover what the players were actually doing when playing the game. A fourth study explored the educational potential of students creating location-based games for each other to play using the SILO framework. Inspired by design-based research, the methodological approach was to study naturally occurring gameplay in order to inform and improve, in practical ways, the design of both the technology and the activities within the scenarios in which the games were embedded. Based on a view of learning as a situated, mediated and socially originated phenomenon, an ethnographically inspired approach to data collection and analysis was adopted, with the view that learning practices should be studied in light of the context in which they take place. This choice was supported by the observation that the data material on learning practices with mobile, locationbased games for learning is still relatively scarce. Therefore, explorative studies that
Cyber-Resilient Platforms Overview

This document is a high-level overview of The Cyber Resilient Platforms Program (CyReP) – a Microsoft-led industry initiative to improve the security and resiliency of computers, with particular emphasis on cloud-managed IoT devices. The CyReP Program includes hardware and protocol specifications, as well as open-source software that enables the security features. One of the primary goals of CyReP is to enable a rich ecosystem of hardware and software components that can be used to build systems and devices that meet the requirements of NIST SP-800-193 (DRAFT) “Platform Firmware Resiliency Guidelines. ”
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving

With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 45 public driving datasets that enable learning and publish this dataset index at this https URL. Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.
Knowledge and attitude of men towards vasectomy as a family planning method in Edo State Nigeria.

In the past family-planning programs have focused attention primarily on women because of the need to free women from excessive child-bearing and to reduce maternal and infant mortality through the use of modern methods of contraception. Most of the family-planning services were offered within maternal and child health (MCH) centers most research and information campaigns focused on women and this has reinforced the misconception that family planning is largely a womans business with the man playing a very peripheral role especially in a society where male supremacy and virility is very important and is marked by Procreation. Hence this is a descriptive study designed to assess the knowledge and attitude of men towards vasectomy as a method of family planning in Okada community Edo state. Accidental sampling technique was used to select 140 married men and questionnaire was administered. However only 136 of the questionnaire was retrieved and same analyzed using both descriptive and inferential statistics with level of significance set at 5% (0.05). The study revealed that majority of the respondents was between the ages of 28 -37 years and have at least one sexual partner. Despite the respondents knowledge of at least one form of male family planning methods majority 85 (62.5%) had no knowledge of vasectomy while out of the 51 (37.5%) who claimed to have knowledge only 18 (13.2%) had high knowledge the remaining 33(24.3%) had poor few knowledge of vasectomy. Most of the respondents also showed negative attitude towards vasectomy and believed that vasectomy should not be done by men and that females should be responsible for family planning but the decision on the method to use should be demanded from them as the head of the family. Cultural acceptance112 (82.4%) religious acceptance98 (72.1%) ignorance 96 (70.6%) and accessibility of family planning clinic 90 (66.2%) were the major factors influencing attitude of men towards vasectomy. The study also revealed that there was no significant association between academic attainment of respondents under study and their attitude towards vasectomy [X2=3.534 P-value = 0.171] as well as between marital status between and their attitude towards vasectomy [X2=0.436 P-value =0.804] with p > 0.05; however significant association was found between the level of knowledge of respondents under study and their attitude towards vasectomy [X2= 4.918 P-value = 0.047] with p < 0.05. It was recommended that there is need to design effective information education and communication strategies to reach men in every part of the federation on the need to actively participate in family planning as well as intense value clarification and attitude transformation in order to improve mens attitudes towards vasectomy.
Artificial Intelligence: A Systems Approach with CD

This book offers students and AI programmers a new perspective on the study of artificial intelligence concepts. The essential topics and theory of AI are presented, but it also includes practical information on data input & reduction as well as data output (i.e., algorithm usage). Because traditional AI concepts such as pattern recognition, numerical optimization and data mining are now simply types of algorithms, a different approach is needed. This sensor / algorithm / effecter approach grounds the algorithms with an environment, helps students and AI practitioners to better understand them, and subsequently, how to apply them. The book has numerous up to date applications in game programming, intelligent agents, neural networks, artificial immune systems, and more. A CD-ROM with simulations, code, and figures accompanies the book.
Image Labeling with Markov Random Fields and Conditional Random Fields

Most existing methods for object segmentation in computer vision are formulated as a labeling task. This, in general, could be transferred to a pixel-wise label assignment task, which is quite similar to the structure of hidden Markov random field. In terms of Markov random field, each pixel can be regarded as a state and has a transition probability to its neighbor pixel, the label behind each pixel is a latent variable and has an emission probability from its corresponding state. In this paper, we reviewed several modern image labeling methods based on Markov random field and conditional random Field. And we compare the result of these methods with some classical image labeling methods. The experiment demonstrates that the introduction of Markov random field and conditional random field make a big difference in the segmentation result.
An Empirical Study of Machine Learning Techniques for Classifying Emotional States from EEG Data

With the great advancement in robot technology, smart human-robot interaction is considered to be the most wanted success by the researchers these days. If a robot can identify emotions and intentions of a human interacting with it, that would make robots more useful. Electroencephalography (EEG) is considered one effective way of recording emotions and motivations of a human using brain. Various machine learning techniques are used successfully to classify EEG data accurately. K-Nearest Neighbor, Bayesian Network, Artificial Neural Networks and Support Vector Machine are among the suitable machine learning techniques to classify EEG data. The aim of this thesis is to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. Different methods based on different signal processing techniques are studied to find a suitable method to process the EEG data. Various number of EEG data features are used to identify those which give best results for different classification techniques. Different methods are designed to format the dataset for EEG data. Formatted datasets are then evaluated on various machine learning techniques to find out which technique can accurately classify EEG data according to associated affective/emotional states. Research method includes conducting an experiment. The aim of the experiment was to find the various emotional states in subjects as they look on different pictures and record the EEG data. The obtained EEG data is processed, formatted and evaluated on various machine learning techniques to find out which technique can accurately classify EEG data according to associated affective/emotional states. The experiment confirms the choice of a technique for improving the accuracy of results. According to the results, Support Vector Machine is the first and Regression Tree is the second best to classify EEG data associated with specific affective/emotional states with accuracies up to 70.00% and 60.00% respectively. SVM is better in performance than RT. However, RT is famous for providing better accuracies for diverse EEG data.
Performance Evaluation of Cloud Database and Traditional Database in terms of Response Time while Retrieving the Data

Context: There has been an exponential growth in the size of the databases in the recent times and the same amount of growth is expected in the future. There has been a firm drop in the storage cost followed by a rapid increase in t he storage capacity. The entry of Cloud in the recent times has changed the equations. The Performance of the Database plays a vital role in the competition. In this research, an attempt has been made to evaluate and compare the performance of the traditional database and the Cloud Database. Objectives: This thesis investigates about the prior works on the issues that affect the performance of Cloud Database. And compares the performance of a Database in Traditional to that Cloud Environments Methods: Two different research methods are used to carry the research. They are Systematic Literature Review (SLR) and Quantitative Methodology. Articles from Scientific Databases are chosen for SLR process. Results: From the SLR process, 4 issues were identified. From the Experimentation results, Cloud Database is having poor performance compared to the Traditional Database. Conclusions: Issues that affect the performance of Cloud Database are identified and a test bed is created to test the performance of a Database. Attempts are to be made to improve the performance of Cloud Database.