Miracle-dz/UniFAR-FTUs huggingface dataset:
An Architecture of FPGA-Based Controller on Automatic Control System for Test and Launch in Launch Site

Most1 FPGA applications in control systems are used for single devices and equipment, or auxiliary processing systems. Because FPGA operation has the characteristics of unchangeable operating logic, it possesses higher security and reliability than Programmable Logic Controller (PLC). The objects of the ground test and launch control system are much complex in space launch site, the FPGA-based controller can achieve higher usage requirements. On the basis of analyzing the model and working pattern of the control system, the architecture and the concrete implementation scheme are proposed to develop the FPGA-based core controller that is similar to PLC. The FPGA-based controller consists of General Logic Module (GLM), backplane and Rear Transport Module (RTM). GLM consists of three parts. The first part is FPGA chip composed of two functions, i.e. one function mainly completes the general logic, and another is applied to implement the user custom functions. The second part is that universal carrier board connects FPGA chip to I/O interfaces. The third part is multiple types of I/O interfaces. The backplane provides slots for GLM connector cascaded multiple GLMs with the same or different functions. RTM serves as an interface between GLM and field input/output signals. A prototype controller has been developed according to the proposed architecture. It has reached the corresponding technical specifications and verified the feasibility of the technical scheme in the paper.
Predictors of cardiorespiratory fitness improvement in phase II cardiac rehabilitation

Cardiac rehabilitation (CR) improves cardiorespiratory fitness (CRF) and has been shown to reduce cardiovascular events and death. However, data about predictors of fitness improvement during CR are limited and conflicting. The objective of this study was to determine predictors of improvement in metabolic equivalents of task (METs) based on formal exercise testing throughout phase II CR.
Neuroprotective effect of rLosac on supplement‐deprived mouse cultured cortical neurons involves maintenance of monocarboxylate transporter MCT2 protein levels

The recombinant Lonomia obliqua Stuart‐factor activator (rLosac) is a recombinant hemolin which belongs to the immunoglobulin superfamily of cell adhesion molecules. It is capable of inducing pro‐survival activity in serum‐deprived human umbilical vein endothelial cells (HUVECs) and fibroblasts by increasing mitochondrial metabolism. We hypothesize that it could promote neuronal survival by acting on neuroenergetics. Our study reveals that treatment of primary mouse cortical neurons cultured in neurobasal medium lacking B27 supplement with rLosac led to an enhancement of cell viability in a time‐ and concentration‐dependent manner. In parallel, preserved or enhanced phosphorylation of Akt, p44, and p42 MAPK, as well as mTOR was observed following treatment with rLosac. During deprivation, as assessed by western blot and qRT‐PCR, protein and mRNA expression of MCT2 (the predominant neuronal monocarboxylate transporter allowing lactate use as an alternative energy substrate) decreased significantly in B27 supplement‐deprived cortical neurons and was hardly detected after 24 h of deprivation. Interestingly, rLosac maintained MCT2 protein expression after 24 h of deprivation including at the cell surface without preventing mRNA loss. MCT2 knockdown reduced rLosac‐enhanced cell viability, confirming its involvement in rLosac effect. Enhanced uptake of lactate was detected following rLosac treatment and might contribute to rLosac‐enhanced viability during deprivation. In the presence of both lactate and rLosac, cell viability was higher than in the presence of lactate alone. Our observations suggest that rLosac promotes cell viability in stressed (B27 supplement‐deprived) neurons by facilitating the use of lactate as energy substrate via the preservation of MCT2 protein expression.
The noncervical lateral transverse foramina

The lateral vertebral foramen (LVF) is an osseous feature found in thoracic and lumbar vertebrae of some artiodactyls and perissodactyls. To learn more about the distribution and characteristics of the LVF, we examined museum specimens from the Smithsonian mammal collection and teaching specimens from the Cornell University College of Veterinary Medicine. We identified five anatomically different types of LVF and noted their occurrence in 60 species. The LVF varies from a deep lateral groove at the cranial intervertebral notch, to as many as three distinct foramina located bilaterally in the caudal half of each vertebra. A nomenclature was developed to describe these five distinctly different LVF forms. The interspecific distribution of the LVF varies from examples such as the gazelle Gazella spekei, where the LVF occurs only in the thoracic region, to others such as the Siberian musk deer Moschus berezovski, where the LVF is predominant only in the lumbar region. Others, such as the Bos (cows), have large LVF along most of both the thoracic and lumbar regions of the vertebral column. Some did not have any form of LVF, such as the Giraffidae (giraffes) and Cetacea (whales). No LVF were found in 15 species representing nine families of the outgroup Carnivora, thus the LVF appears to be a characteristic specific to the artiodactyls and perissodactyls.
Research on Privacy Preserving of Searchable Encryption

In the cloud computing applications, the researchers proposed a new cryptographic primitive searchable encryption (SE) in order to ensure data security. Searchable encryption can make full use of cloud server computing capacity to search the ciphertext. The privacy of the searchable encryption on this paper is discussed: First of all, security threat model and security definition of searchable encryption is discussed. Secondly, the privacy preserving definition of searchable encryption is established from two aspects of data privacy and identity privacy. Once again the existing searchable encryption schemes are analyzed and evaluated based on data privacy preserving (index privacy preserving, search pattern privacy preserving, access pattern privacy preserving) by treat model and privacy preserving definition. Fourthly, searchable encryption schemes of different application models are introduced based on identity privacy preserving, and anonymous searchable encryption schemes are analyzed and evaluated based on anonymity definitions of different application models. Finally, the design principle and method of safety analysis are provided for privacy preserving scheme of searchable encryption.
Large-scale Malware Automatic Detection Based On Multiclass Features and Machine Learning

In the1 modern global mobile phone market, Android OS is firmly occupying the first throne with the absolute user base and the proportion of mobile phones, and with the proliferation of android smart phones and other mobile devices, it has also attracted more and more attention from malware developers. There are a large number of malware in several major application markets around the world. In order to solve this problem, this paper proposes a large-scale malware detection system based on multiclass features and machine learning. There are two main problems in the traditional detection schemes, one is how to analyze and extract effectively features which can distinguish malware and benign software, the other is that how to select the most suitable algorithm to detect malware. For the first problem, we select the features that can reflect the android software's maliciousness by extracting android features and removing the features whose degree of distinction are less. For the second problem, we compare seven machine learning algorithms and select the most suitable algorithm that has the highest accuracy for android malware identification. Afterwards, many experiments are done to verify our solutions. First, we extract 234 features and select 76 features. Second, selecting the most suitable algorithm "ensemble learning" by comparing the detection accuracies of 7 algorithms, then adjusting and optimizing the related parameter to achieve the highest accuracy, 99.73%, which proves the effectiveness of our system and scheme.
Peroxidative Oxidation of Alkanes and Alcohols under Mild Conditions by Di- and Tetranuclear Copper (II) Complexes of Bis (2-Hydroxybenzylidene) Isophthalohydrazide

Bis(2-hydroxybenzylidene)isophthalohydrazide (H4L) has been used to synthesize the dinuclear [Cu2(1κNO2:2κN′O′2-H2L)(NO3)2(H2O)2] (1) and the tetranuclear [Cu4(μ-1κNO2:2κN′O2-H2L)2(μ-NO3)2(H2O)4]·2C2H5OH (2) complexes. The solvent plays an important role in determining the ligand behaviour in the syntheses of the complexes. An ethanol-acetonitrile mixture of solvents favours partials enolization in the case of 2. Both complexes have been characterized by elemental analysis, infrared radiation (IR), single crystal X-ray crystallography and electrochemical methods. The variable temperature magnetic susceptibility measurements of 2 show strong antiferromagnetic coupling between the central nitrato-bridged Cu (II) ions. The catalytic activity of both 1 and 2 has been screened toward the solvent-free microwave-assisted oxidation of alcohols and the peroxidative oxidation of alkanes under mild conditions. Complex 1 exhibits the highest activity for both oxidation reactions, leading selectively to a maximum product yield of 99% (for the 1-phenylethanol oxidation after 1 h without any additive) and 13% (for the cyclohexane oxidation to cyclohexyl hydroperoxide, cyclohexanol and cyclohexanone after 3 h).
An invariance principle for polytopes

Let X be randomly chosen from {-1,1}n, and let Y be randomly chosen from the standard spherical Gaussian on Rn. For any (possibly unbounded) polytope P formed by the intersection of k halfspaces, we prove that |Pr[X ∈ P] - Pr[Y ∈ P]| ≤ log8/5k • Δ, where Δ is a parameter that is small for polytopes formed by the intersection of "regular" halfspaces (i.e., halfspaces with low influence). The novelty of our invariance principle is the polylogarithmic dependence on k. Previously, only bounds that were at least linear in k were known. We give two important applications of our main result: A bound of logO(1)k • ε1/6 on the Boolean noise sensitivity of intersections of k "regular" halfspaces (previous work gave bounds linear in k). This gives a corresponding agnostic learning algorithm for intersections of regular halfspaces. A pseudorandom generator (PRG) with seed length O(log n, poly(log k,1/Δ)) that Δ-fools all polytopes with k faces with respect to the Gaussian distribution. We also obtain PRGs with similar parameters that fool polytopes formed by intersection of regular halfspaces over the hypercube. Using our PRG constructions, we obtain the first deterministic quasi-polynomial time algorithms for approximately counting the number of solutions to a broad class of integer programs, including dense covering problems and contingency tables.
Word-Driven and Context-Aware Review Modeling for Recommendation

Recently, convolutional neural networks(CNNs) has been demonstrated to effectively model reviews in recommender systems, due to the learning of contextual features such as surrounding words and word order for reviews. However, CNNs with max-pooling fails to capture the count information of contextual features, since the feature map generated by a convolution filter can only get a max feature value with max-pooling. If the max feature value appears more than once in the feature map, CNNs will lose the count information of the contextual feature. The count information is quite critical for modeling reviews, for example, ten "a good quality" is more credible than one "a good quality" for representing item properties, and five "the scenery is" shows that a user may pay more attention to the scenery than one "the scenery is" does. Our model, called WCN-MF, extends CNNs by introducing a new module named Deep Latent Dirichlet Allocation (DLDA) to capture the count information of contextual features. DLDA is inspired by the fact that contextual features consist of words, hence, we can first capture the count information of words, and second generate contextual features with words. By combining DLDA with CNNs, we can get a word-driven and context-aware review representation. Further, we incorporate the review representation with Matrix Factorization for recommendation. Our evaluations on three real- world datasets reveal that our model can significantly outperform the state-of-the-art recommendation models.
ShareJIT: JIT code cache sharing across processes and its practical implementation

Just-in-time (JIT) compilation coupled with code caching are widely used to improve performance in dynamic programming language implementations. These code caches, along with the associated profiling data for the hot code, however, consume significant amounts of memory. Furthermore, they incur extra JIT compilation time for their creation. On Android, the current standard JIT compiler and its code caches are not shared among processes—that is, the runtime system maintains a private code cache, and its associated data, for each runtime process. However, applications running on the same platform tend to share multiple libraries in common. Sharing cached code across multiple applications and multiple processes can lead to a reduction in memory use. It can directly reduce compile time. It can also reduce the cumulative amount of time spent interpreting code. All three of these effects can improve actual runtime performance. In this paper, we describe ShareJIT, a global code cache for JITs that can share code across multiple applications and multiple processes. We implemented ShareJIT in the context of the Android Runtime (ART), a widely used, state-of-the-art system. To increase sharing, our implementation constrains the amount of context that the JIT compiler can use to optimize the code. This exposes a fundamental tradeoff: increased specialization to a single process’ context decreases the extent to which the compiled code can be shared. In ShareJIT, we limit some optimization to increase shareability. To evaluate the ShareJIT, we tested 8 popular Android apps in a total of 30 experiments. ShareJIT improved overall performance by 9% on average, while decreasing memory consumption by 16% on average and JIT compilation time by 37% on average.
Generative Adversarial Product Quantisation

Product Quantisation (PQ) has been recognised as an effective encoding technique for scalable multimedia content analysis. In this paper, we propose a novel learning framework that enables an end-to-end encoding strategy from raw images to compact PQ codes. The system aims to learn both PQ encoding functions and codewords for content-based image retrieval. In detail, we first design a trainable encoding layer that is pluggable into neural networks, so the codewords can be trained in back-forward propagation. Then we integrate it into a Deep Convolutional Generative Adversarial Network (DC-GAN). In our proposed encoding framework, the raw images are directly encoded by passing through the convolutional and encoding layers, and the generator aims to use the codewords as constrained inputs to generate full image representations that are visually similar to the original images. By taking the advantages of the generative adversarial model, our proposed system can produce high-quality PQ codewords and encoding functions for scalable multimedia retrieval tasks. Experiments show that the proposed architecture GA-PQ outperforms the state-of-the-art encoding techniques on three public image datasets.
An Attentive Interaction Network for Context-aware Recommendations

Context-aware Recommendations (CARS) have attracted a lot of attention recently because of the impact of contextual information on user behaviors. Recent state-of-the-art methods represent the relations between users/items and contexts as a tensor, with which it is difficult to distinguish the impacts of different contextual factors and to model complex, non-linear interactions between contexts and users/items. In this paper, we propose a novel neural model, named Attentive Interaction Network (AIN), to enhance CARS through adaptively capturing the interactions between contexts and users/items. Specifically, AIN contains an Interaction-Centric Module to capture the interaction effects of contexts on users/items; a User-Centric Module and an Item-Centric Module to model respectively how the interaction effects influence the user and item representations. The user and item representations under interaction effects are combined to predict the recommendation scores. We further employ effect-level attention mechanism to aggregate multiple interaction effects. Extensive experiments on two rating datasets and one ranking dataset show that the proposed AIN outperforms state-of-the-art CARS methods. In addition, we also find that AIN provides recommendations with better explanation ability with respect to contexts than the existing approaches.
Power of the Dissolution Test in Distinguishing a Change in Dosage Form Critical Quality Attributes

For a dissolution method to be considered relevant to in vivo performance, the dissolution data profiles should show discrimination or meaningful change when there is a change in critical material attributes (CMAs) and critical product properties (CPPs). The dissolution test has been shown repeatedly to have the power to distinguish between significant changes in active pharmaceutical ingredient (API), formulation, and process that relate to the release mechanism of the in vivo performance. Examples will be discussed in the literature where the effects of formulation, drug substance, and manufacturing variables have been measured by dissolution testing. There will be a suggested plan on how to develop and challenge a discriminating method that may be utilized for regulatory purposes. A brief review of other challenges and considerations regarding discriminatory dissolution testing is presented.
Pharmaceutical Impact of Houttuynia Cordata and Metformin Combination on High-Fat-Diet-Induced Metabolic Disorders: Link to Intestinal Microbiota and Metabolic Endotoxemia

Purpose: Metformin and Houttuynia cordata are representative anti-diabetic therapeutic agents in western and oriental medicinal fields, respectively. The present study examined the therapeutic effects of houttuynia cordata extract (HCE) and metformin in combination in a dysmetabolic mouse model. Methods: Metabolic disorders were induced in C57BL/6J mice by high fat diet (HFD) for 14 weeks. Results: Combination of metformin and HCE significantly lowered body weight, abdominal fat, perirenal fat, liver and kidney weights, but did not change epididymal fat in HFD-fed animals. Metformin + HCE treatment markedly attenuated the elevated serum levels of TG, TC, AST, ALT, and endotoxin and restored the depleted HDL level. Both HCE and metformin + HCE treatment ameliorated glucose tolerance and high level of fasting blood glucose in association with AMPK activation. Moreover, treatment with HCE + metformin dramatically suppressed inflammation in HFD-fed animals via inhibition of proinflammatory cytokines (MCP-1 and IL-6) and LPS receptor (TLR4). Histopathological findings showed that exposure of HFD-treated animals to metformin + HCE ameliorated fatty liver, shrinkage of intestinal villi and adipocytes enlargement. Furthermore, HCE and metformin + HCE treatments markedly modulated the abundance of gut Gram-negative bacteria, including Escherichia coli and Bacteriodetes fragilis, but not universal Gram-positive bacteria. Conclusions: Overall, HCE and metformin cooperatively exert their therapeutic effects via modulation of gut microbiota, especially reduction of Gram-negative bacteria, resulting in alleviation of endotoxemia.
Where Enterprise Architecture and Early Software Engineering Meet: An approach to use cases definition

Software development involves the resolution of technical problems related to a certain domain. However, in order to provide a suitable technical solution, it is necessary to take the organizational environment related to the software into account. Use cases have been often used to elicit requirements and represent functionalities that the software must provide to its users. However, use cases are not expressive enough to represent the organizational environment. Moreover, this is not the purpose of use cases. In this context, Enterprise Architecture (EA) emerges as a way to describe the organization's domain. EA provides architectural descriptions that support the alignment between information technology (IT) and organizational processes and, thus, helps developers to properly understand the requirements the software must meet. In this paper, we propose an approach that uses EA models as a basis to define use cases, named CEA (use Cases definition oriented by Enterprise Architecture modeling). To demonstrate the proposal use, we applied it in a project in the Public Security domain. Additionally, CEA was evaluated in an experimental study. The results indicate that EA models helped requirements engineers to define use cases.
FactCheck: Validating RDF Triples Using Textual Evidence

With the increasing uptake of knowledge graphs comes an increasing need for validating the knowledge contained in these graphs. However, the sheer size and number of knowledge bases used in real-world applications makes manual fact checking impractical. In this paper, we employ sentence coherence features gathered from trustworthy source documents to outperform the state of the art in fact checking. Our approach, FactCheck, uses this information to score how likely a fact is to be true and provides the user the evidence used to validate the input facts. We evaluated our approach on two different benchmark datasets and two different corpora. Our results show that FactCheck outperforms the state of the art by up to 13.3% in F-measure and 19.3% AUC. FactCheck is open-source and is available at https://github.com/dice-group/FactCheck.
Benefits of Mobile Apps in Pain Management: Systematic Review

Background Pain is a common condition with a significant physical, psychosocial, and economic impact. Due to enormous progress in mobile device technology as well as the increase in smartphone ownership in the general population, mobile apps can be used to monitor patients with pain and support them in pain management. Objective The aim of this review was to assess the efficacy of smartphone or computer tablet apps in the management of patients with pain. Methods In December 2017, a literature search was performed in the following databases: MEDLINE, EMBASE, CINAHL, Cochrane, and PsycINFO. In addition, a bibliography search was conducted. We included studies with at least 20 participants per arm that evaluated the effects of apps on smartphones or computer tablets on improvement in pain. Results A total of 15 studies with 1962 patients met the inclusion criteria. Of these, 4 studies examined the effect of mobile apps on pain management in an in-clinic setting and 11 in an out-clinic setting. The majority of the original studies reported beneficial effects of the use of a pain app. Severity of pain decreased in most studies where patients were using an app compared with patients not using an app. Other outcomes, such as worst pain or quality of life showed improvements in patients using an app. Due to heterogeneity between the original studies—patient characteristics, app content, and study setting—a synthesis of the results by statistical methods was not performed. Conclusions Apps for pain management may be beneficial for patients, particularly in an out-clinic setting. Studies have shown that pain apps are workable and well liked by patients and health care professionals. There is no doubt that in the near future, mobile technologies will develop further. Medicine could profit from this development as indicated by our results, but there is a need for more scientific inputs. It is desirable to know which elements of apps or additional devices and tools may improve usability and help patients in pain management.