Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of anti-obesity activity of duloxetine in comparison with sibutramine along with its anti-depressant activity: an experimental study in obese rats

5-HT and noradrenaline are important neurotransmitters that control increase in body mass and are involved in the pathophysiology of obesity and depression. Sibutramine, an established anti-obesity agent, and duloxetine, an anti-depressant agent, are serotonin noradrenaline reuptake inhibitors (SNRIs). The objective of the present study was to compare the anti-obesity effect of duloxetine with sibutramine along with its effect on blood pressure and depression in obese rats. The secondary objective of the study was to determine if a relationship exists between obesity and depression. Obesity was induced by high-fat diet (HFD) in healthy male Sprague–Dawley rats. After 5 weeks of feeding HFD, animals were overweight (17.57%) with high food intake (57.15%) in comparison with normal animals. These obese animals were treated with duloxetine (30 mg·kg–1, p.o.) and sibutramine (5 mg·kg–1, p.o.) for 4 weeks. Control animals were treated with duloxetine alone (30 mg·kg–1, p.o.). Our results depict that duloxetine .
A fine-grained recognition model of air targets based on bilayer faster R-CNN with feedback

The accuracy of air target identification is of great significance for air defense operations and civilian management. A fine-grained recognition model of aerial target based on bilayer faster regions with convolution neural network (Faster R-CNN) with feedback is proposed in the paper. Faster R-CNN model is a typical target detection model based on deep learning. However, its ability to distinguish categories with subtle differences is not enough. In the proposed model, Faster R-CNN model is used for the first training to get a classification model and the clustering analysis of the classification result is used to get confused categories. Then the first training model is fine-tuned to retrain the confusing categories. The model is tested in the FGVC-Aircraft-2013b data set, and the average training accuracy is raised from 88.7% to 89.3%, the accuracy of the classification is raised from 88.98% to 91.21%, which shows that this model is effective in improving the fine-grained identification of air targets.
Virtual Machine for Secure Embedded Systems

In the Broadcasting and Over-The-Top media-service industries, video content which is delivered to the customer needs to be secured by encrypting or scrambling the content. The content then needs to be decrypted or unscrambled at the end-users location. A common technique is to use a hardware device which includes a secret key for the cryptographic operations. Many television providers use a Set-top Box (STB) which is installed at the end-users home to execute this task. The security of the decryption or descrambling method is enforced in the Set-top box by a Secure Chipset which resides on a System on a Chip (SoC) in the Set-top box. In the market of Digital Video Broadcasting (DVB) or HTTP Live Streaming (HLS) many different chip vendors compete. The vendors develop their own framework and Application Programming Interface (API) which have to be used by the Conditional Access System (CAS) and Digital Rights Management (DRM) vendors. The hardware design and implementation decisions are left with the Silicon Vendor. This leads to fragmentation and the CAS and DRM vendors need to integrate with many different solutions. Often broadcasting companies have many different Set-top Boxes with different Chips deployed in the field. Updates for the program which runs on the Secure Chipset therefore have to be developed and tested for the different chip types. This leads to raising costs as, for each chip type, a different image has to be produced. Further in the world of Satellite communication it is not possible to unicast an image to a specific STB. All images are broadcast to all the STBs and the STB itself decides which image to use. It is not uncommon that six to ten different images have to be broadcast to all the STBs which leads to a significant increase of bandwidth. In the case of satellite communication this bandwidth usage is very expensive and undesired. In this thesis we propose a solution to decrease this bandwidth by creating an abstraction layer between the Secure Chip and the image running on the chip. We created a Virtual Machine (VM) which can interpret a suitable instruction set. This VM has to be integrated with the different chip vendors only once during production of the chip. The image which runs then on the VM can be deployed in later stages. The benefit is that only one image has to be produced and broadcast to the STBs which leads to decrease of the used bandwidth. In this thesis we analyze the code size and performance penalty of the VM compared to a native application running on a secure chip.
New Erythropoietic Proteins: Rationale and Clinical Data

Anemia is a common condition in patients with cancer. Patients who develop severe anemia are frequently treated with red blood cell transfusions. However, the benefits are transient and associated with a number of risks (such as infection and immunosuppression). An alternative treatment option, recombinant human erythropoietin (rHuEPO) was introduced into the clinical setting more than a decade ago. Today, rHuEPO is increasingly being used to treat anemic patients with cancer. However, despite the well-documented clinical benefits of rHuEPO therapy, it has not been universally adopted as a routine treatment in the oncology setting and there remains an unmet need for an effective and convenient anemia treatment for cancer patients. Longer-acting versions of rHuEPO would avoid the need for frequent injections, increase biological activity and, potentially, the clinical efficacy of rHuEPO. One agent, darbepoetin alfa, is now available for the treatment of anemia in patients with cancer who are receiving chemotherapy, another agent, pegylated-epoetin beta, is in phase II trials, and additional compounds are in early stages of preclinical and clinical development. This review focuses on the available clinical experience with such agents and discusses how dose- and schedule-finding studies are optimizing their clinical use to maximize patient benefits.
Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer

General sparse matrix-sparse matrix multiplication (SpGEMM) is one of the fundamental linear operations in a wide variety of scientific applications. To implement efficient SpGEMM for many large-scale applications, this paper proposes scalable and optimized SpGEMM kernels based on COO, CSR, ELL, and CSC formats on the Sunway TaihuLight supercomputer. First, a multi-level parallelism design for SpGEMM is proposed to exploit the parallelism of over 10 millions cores and better control memory based on the special Sunway architecture. Optimization strategies, such as load balance, coalesced DMA transmission, data reuse, vectorized computation, and parallel pipeline processing, are applied to further optimize performance of SpGEMM kernels. Second, we thoroughly analyze the performance of the proposed kernels. Third, a performance-aware model for SpGEMM is proposed to select the most appropriate compressed storage formats for the sparse matrices that can achieve the optimal performance of SpGEMM on the Sunway. The experimental results show the SpGEMM kernels have good scalability and meet the challenge of the high-speed computing of large-scale data sets on the Sunway. In addition, the performance-aware model for SpGEMM achieves an absolute value of relative error rate of 8.31 percent on average when the kernels are executed in one single process and achieves 8.59 percent on average when the kernels are executed in multiple processes. It is proved that the proposed performance-aware model can perform at high accuracy and satisfies the precision of selecting the best formats for SpGEMM on the Sunway TaihuLight supercomputer.
Efforts of the United States’ National Marrow Donor Program and Registry to improve utilization and representation of minority donors

summary Haematopoietic stem cell transplantation is often used as a therapy for patients with certain blood, metabolic or immune system disorders. The United States’ National Marrow Donor Program (NMDP) works to facilitate such life‐saving transplants by coordinating the donor search and match process. However, concern exists that the NMDP Registry is underutilized and under‐representative of racial and ethnic minorities. African‐Americans and Hispanics are somewhat under‐represented within the total number of donors, and it is estimated that the Registry is used by only approximately one‐third of patients needing transplants. The NMDP has instituted programmes that address such concerns, resulting in an increase in both the total number of donors and the minority representation on the Registry. It has also increased efforts to recruit donors of umbilical cord blood, often a viable alternative source of haematopoietic stem cells. Over the past 8 years, the Registry has grown by more than 30% to contain over seven million donors, and the proportional distribution of racial and ethnic groups on the Registry has steadily approached their proportional distribution in the US population. Continued efforts on the part of the NMDP to maintain a Registry that is large in number and ethnically diverse should help ensure access to haematopoietic stem cell transplants for all patients who need them. The procedures and experience of the NMDP and its Registry may have implications for registries elsewhere in the world as they confront similar issues of number and diversity.
Robust anomaly detection in urban environments using sensor and information fusion and a camera network

The paper presents a method for detection of abnormal behaviors related to violent events in urban environments. We have further developed a person detection method and introduced a person tracker as an intermediate stage between person detection and anomaly detection. The method is presented together with results from evaluations based on real sensor data that have been recorded in a realistic urban environment. Several scenarios have been recorded such as normal situations, a person loitering, a person shooting and people escaping from sudden smoke development. The method uses sensor data from a static video surveillance system, consisting of two visual and two thermal infrared cameras. The person detection method combines foreground segmentation with a trained machine learning algorithm which is based on boosting. The tracker associates incoming detections to objects (i.e. people), and generates tracks of world coordinates and velocities of objects. The anomaly detection method, which is based on the hidden Markov model (HMM), fuses information that is derived from the tracker. The information contains a number of persons, their positions and velocities. The results indicate that the person detection method in combination with the tracker can produce robust and accurate observations to the HMM, which in turn provide good conditions for accurate anomaly detection.
SyRA: Early System Reliability Analysis for Cross-Layer Soft Errors Resilience in Memory Arrays of Microprocessor Systems

Cross-layer reliability is becoming the preferred solution when reliability is a concern in the design of a microprocessor-based system. Nevertheless, deciding how to distribute the error management across the different layers of the system is a very complex task that requires the support of dedicated frameworks for cross-layer reliability analysis. This paper proposes SyRA, a system-level cross-layer early reliability analysis framework for radiation induced soft errors in memory arrays of microprocessor-based systems. The framework exploits a multi-level hybrid Bayesian model to describe the target system and takes advantage of Bayesian inference to estimate different reliability metrics. SyRA implements several mechanisms and features to deal with the complexity of realistic models and implements a complete tool-chain that scales efficiently with the complexity of the system. The simulation time is significantly lower than micro-architecture level or RTL fault-injection experiments with an accuracy high enough to take effective design decisions. To demonstrate the capability of SyRA, we analyzed the reliability of a set of microprocessor-based systems characterized by different microprocessor architectures (i.e., Intel x86, ARM Cortex-A15, ARM Cortex-A9) running both the Linux operating system or bare metal in the presence of single bit upsets caused by radiation induced soft errors. Each system under analysis executes different software workloads both from benchmark suites and from real applications.
Risk-aware navigation for UAV digital data collection

This thesis studies the navigation task for autonomous UAVs to collect digital data in a risky environment. Three problem formulations are proposed according to different real-world situations. First, we focus on uniform probabilistic risk and assume UAV has unlimited amount of energy. With these assumptions, we provide the graph-based Data-collecting Robot Problem (DRP) model, and propose heuristic planning solutions that consist of a clustering step and a tour building step. Experiments show our methods provide high-quality solutions with high expected reward. Second, we investigate non-uniform probabilistic risk and limited energy capacity of UAV. We present the Data-collection Problem (DCP) to model the task. DCP is a grid-based Markov decision process, and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve data-collection efficiency, design four deep neural networks that model the opponent’s behavior at different levels, and empirically prove that explicit opponent modeling with a dedicated network provides superior performance. RISK-AWARE NAVIGATION FOR UAV DIGITAL DATA COLLECTION by Zhi Xing B.S., Nankai University, 2010 M.S., Syracuse University, 2017 Dissertation Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer and Information Science and Engineering. Syracuse University August 2017 Copyright c © Zhi Xing 2017 All Rights Reserved
Maternal health care in five sub-Saharan African countries

This paper examines inequalities in access to maternal health care services and identifies demographic and socio-economic factors associated with poor maternal health outcomes using data from five Demographic and Health Surveys conducted in Ghana (2003), Kenya (2003), Nigeria (2003), Uganda (2000-2001) and Zambia (2001-2002). The six maternal health care indicators show that rural women are more disadvantaged than urban women. Home deliveries comprise more than half of total births. Getting money for treatment stands out as the most important problem women have in accessing health care. In general, Nigerian women experience poorer maternal health outcomes than women in the other four countries. Maternal educational attainment, urban/rural residence and partner’s occupation emerge as the most important predictors of inadequate antenatal care, institutional delivery and current use of any contraceptive method. Female education beyond secondary school level coupled with strenuous efforts to reduce poverty holds the key to keep women off the road to death.
New Algorithm for Generating Complex Password to Be Applied in Several Applications

The proposed paper present new algorithm for generate complex key or complex password that can be used in the information security, which is the most important filed in computer science today. This algorithm contains many stages; these stages promote formation of an efficient password that requests in many applications such as website, social networks and applications under any operating system. Always this applications needs to change the classic and traditional methods that are supposed responsible of provides maximum security for applications users. The system will produce password has nothing any relation with the initial password and the second important feature is any little change in initial password even if the change was substitutions places lead to different result, thus the proposed system achieves confusion and diffusion. Keywords: complex password, key generation, information security
Objective Human Affective Vocal Expression Detection and Automatic Classification with Stochastic Models and Learning Systems

This paper presents a widespread analysis of affective vocal expression classification systems. In this study, state-of-the-art acoustic features are compared to two novel affective vocal prints for the detection of emotional states: the Hilbert-Huang-Hurst Coefficients (HHHC) and the vector of index of non-stationarity (INS). HHHC is here proposed as a nonlinear vocal source feature vector that represents the affective states according to their effects on the speech production mechanism. Emotional states are highlighted by the empirical mode decomposition (EMD) based method, which exploits the non-stationarity of the affective acoustic variations. Hurst coefficients (closely related to the excitation source) are then estimated from the decomposition process to compose the feature vector. Additionally, the INS vector is introduced as dynamic information to the HHHC feature. The proposed features are evaluated in speech emotion classification experiments with three databases in German and English languages. Three state-of-the-art acoustic features are adopted as baseline. The $\alpha$-integrated Gaussian model ($\alpha$-GMM) is also introduced for the emotion representation and classification. Its performance is compared to competing stochastic and machine learning classifiers. Results demonstrate that HHHC leads to significant classification improvement when compared to the baseline acoustic features. Moreover, results also show that $\alpha$-GMM outperforms the competing classification methods. Finally, HHHC and INS are also evaluated as complementary features for the GeMAPS and eGeMAPS feature sets
Dynamic data fusion using multi-input models for malware classification

Criminals use malware to disrupt cyber-systems. The number of these malware-vulnerable systems is increasing quickly as common systems, such as vehicles, routers, and lightbulbs, become increasingly interconnected cyber-systems. To address the scale of this problem, analysts divide malware into classes and develop, for each class, a specialized defense. In this project we classified malware with machine learning. In particular, we used a supervised multi-class long short term memory (LSTM) model. We trained the algorithm with thousands of malware files annotated with class labels (the training set), and the algorithm learned patterns indicative of each class. We used disassembled malware files (provided by Microsoft) and separated the constituent data into parsed instructions, which look like human-readable machine code text, and raw bytes, which are hexadecimal values. We are interested in which format, text or hex, is more valuable as input for classification. To solve this, we investigated four cases: a text-only model, a hexadecimal-only model, a multi-input model using both text and hexadecimal inputs, and a model based on combining the individual results. We performed this investigation using the machine learning Python package Keras, which allows easily configurable deep learning architectures and training. We hoped to understand the trade-offs between the different formats. Due to the class imbalance in the data, we used multiple methods to compare the formats, using test accuracies, balanced accuracies (taking into account weights of classes), and an accuracy derived from tables of confusion. We found that the multi-input model, which allows learning on both input types simultaneously, resulted in the best performance. Our finding expedites malware classification research by providing researchers a suitable deep learning architecture to train a tailored version to their malware.
A Survey of Benchmarks to Evaluate Data Analytics for Smart-* Applications

The growth of ubiquitous sensor networks at an accelerating pace cuts across many areas of modern day life. They enable measuring, inferring, understanding and acting upon a wide variety of indicators, in fields ranging from agriculture to healthcare or to complex urban environments. The applications devoted to this task are designated as Smart-* Applications. They hide a staggering complexity, relying on multiple layers of data collection, transmission, aggregation, analysis and also storage, both at the network edge and on the cloud. Furthermore, Smart-* Applications raise additional specific challenges, such as the need to process and extract knowledge from diverse data, which is flowing at high velocity in near real-time or in the heavily distributed environment they rely on. How to assess the performance of such a complex stack, when faced with the specifics of \mbox{Smart-*} Applications, remains an open research question. In this article, the key specific characteristics and requirements of Smart-* Applications are initially detailed. Afterwards, for each of these requirements, there is a description of the benchmarks one can use to precisely evaluate the performance of the underlying systems and technologies. Finally, an identification of future research directions related to identified open issues for benchmarking Smart-* Applications is performed.
A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology

We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the differentiable properties of persistent homology, a concept used in topological data analysis, we can specify the desired topology of segmented objects in terms of their Betti numbers and then drive the proposed segmentations to contain the specified topological features. Importantly this process does not require any ground-truth labels, just prior knowledge of the topology of the structure being segmented. We demonstrate our approach in four experiments: one on MNIST image denoising and digit recognition, one on left ventricular myocardium segmentation from magnetic resonance imaging data from the UK Biobank, one on the ACDC public challenge dataset and one on placenta segmentation from 3-D ultrasound. We find that embedding explicit prior knowledge in neural network segmentation tasks is most beneficial when the segmentation task is especially challenging and that it can be used in either a semi-supervised or post-processing context to extract a useful training gradient from images without pixelwise labels.
Odd supersymmetric Kronecker elliptic function and Yang–Baxter equations

We introduce an odd supersymmetric version of the Kronecker elliptic function. It satisfies the genus one Fay identity and supersymmetric version of the heat equation. As an application we construct an odd supersymmetric extensions of the elliptic $R$-matrices, which satisfy the classical and the associative Yang-Baxter equations.