Miracle-dz/UniFAR-FTUs huggingface dataset:
A Method for Automatic Android Malware Detection Based on Static Analysis and Deep Learning

The computers nowadays are being replaced by the smartphones for the most of the internet users around the world, and Android is getting the most of the smartphone systems’ market. This rise of the usage of smartphones generally, and the Android system specifically, leads to a strong need to effectively secure Android, as the malware developers are targeting it with sophisticated and obfuscated malware applications. Consequently, a lot of studies were performed to propose a robust method to detect and classify android malicious software (malware). Some of them were effective, some were not; with accuracy below 90%, and some of them are being outdated; using datasets that became old containing applications for old versions of Android that are rarely used today. In this paper, a new method is proposed by using static analysis and gathering as most useful features of android applications as possible, along with two new proposed features, and then passing them to a functional API deep learning model we made. This method was implemented on a new and classified android application dataset, using 14079 malware and benign samples in total, with malware samples classified into four malware classes. Two major experiments with this dataset were implemented, one for malware detection with the dataset samples categorized into two classes as just malware and benign, the second one was made for malware detection and classification, using all the five classes of the dataset. As a result, our model overcomes the related works when using just two classes with F1-score of 99.5%. Also, high malware detection and classification performance was obtained by using the five classes, with F1-score of 97%.
Mean-variance portfolio selection and variance hedging with random coefficients: closed-loop equilibrium strategy

In this paper, both dynamic mean-variance portfolio selection problems and dynamic variance hedging problems are discussed under non-Markovian framework. Explicit closed-loop equilibrium strategies of these problems are respectively obtained via a unified approach for the first time. Several new interesting facts arise in mean-variance problems with constant risk aversion. For example, it is shown that equilibrium strategies are still allowed to rely on initial wealth as long as risk-free return rate is random. In addition, the closed-loop equilibrium strategy and open-loop equilibrium strategy have the same connection with initial wealth in non-Markovian setting, and they happen to equal to each other if only risk-free return rate is deterministic.
Concealed Object Detection With Microwave Imaging Using Vivaldi Antennas Utilizing Novel Time-Domain Beamforming Algorithm

A time-domain novel beamforming algorithm for ultra-wideband (UWB) microwave imaging is furnished in this paper. A monostatic and bistatic antenna setup is utilized to transmit and receive custom UWB pulses. This setup is employed to image various metallic and non–metallic threats attached to a human body. The imaging results demonstrate that the proposed beamforming algorithm allows effective reconstruction of different targets for both monostatic and bistatic cases. An ultra-wideband high gain Antipodal Vivaldi Antenna (AVA) is used as the transducer for the proposed imaging setup. The designed transducer demonstrates measured impedance bandwidth from 660 MHz to 15 GHz (182%) with a peak gain of 15 dBi at 8 GHz. The quality and accuracy of the proposed beamforming algorithm is tested outside the anechoic chamber just to make it more convincing in a real-world dynamic scenario. The result shows that different concealed materials can be detected accurately with the beamforming algorithm. To the best of the authors’ knowledge, the proposed algorithm can effectively add up the target responses to obtain a clear and distinct image of the metallic and non-metallic targets.
Geometry of folded hypercomplex structures

We investigate the geometry of the Kodaira moduli space $M$ of sections of $\pi:Z\to {\mathbb P}^1$, the normal bundle of which is allowed to jump from ${\mathcal O}(1)^{n}$ to ${\mathcal O}(1)^{n-2m}\oplus {\mathcal O}(2)^{m}\oplus {\mathcal O}^{m}$. In particular, we identify the natural assumptions which guarantee that the Obata connection of the hypercomplex part of $M$ extends to a logarithmic connection on $M$.
Mixed semi-Lagrangian/finite difference methods for plasma simulations

In this paper, we present an efficient algorithm for the long time behavior of plasma simulations. We will focus on 4D drift-kinetic model, where the plasma's motion occurs in the plane perpendicular to the magnetic field and can be governed by the 2D guiding-center model. Hermite WENO reconstructions, already proposed in [25], are applied for solving the Vlasov equation. Here we consider an arbitrary computational domain with an appropriate numerical method for the treatment of boundary conditions. Then we apply this algorithm for plasma turbulence simulations. We first solve the 2D guiding-center model in a D-shape domain and investigate the numerical stability of the steady state. Then, the 4D drift-kinetic model is studied with a mixed method, i.e. the semi-Lagrangian method in linear phase and finite difference method during the nonlinear phase. Numerical results show that the mixed method is efficient and accurate in linear phase and it is much stable during the nonlinear phase. Moreover, in practice it has better conservation properties.
An L1 ergodic theorem with values in a non-positively curved space via a canonical barycenter map

Abstract We give a general version of the Birkhoff ergodic theorem for functions taking values in non-positively curved spaces. In this setting, the notion of a Birkhoff sum is replaced by that of a barycenter along the orbits. The construction of an appropriate barycenter map is the core of this note. As a byproduct of our construction, we prove a fixed point theorem for actions by isometries on a Buseman space.
Immunogenic Cell Death (ICD)-Related Gene Signature Could Predict the Prognosis of Patients with Diffuse Large B-Cell Lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is the most prevalent type of lymphoma that is potentially curable by chemotherapy. Immunogenic cell death (ICD) is regarded as an essential process for the clearance of residual tumor cells. However, the impact of ICD on DLBCL remains unknown. Here, we tried to explore the prognostic role of ICD in DLBCL. Methods: A gene expression microarray of DLBCL was downloaded from the Gene Expression Omnibus (GEO). The genes involved in ICD were obtained via literature reviews. Then, based on univariate, multivariate, and LASSO Cox regression analysis, the ICD-related gene signature was identified. The effect of the ICD-related gene signature on DLBCL was explored. The chi-square test was used to compare complete response rate (CRR) and recurrence rate between high- and low-risk groups. Results: The signature based on 12 ICD-related genes could independently predict the overall survival of DLBCL. Furthermore, high risk was linked to lower CRR and higher recurrence rate. Then, a nomogram based on the ICD-related gene signature was established. The area under the curve of the prediction model reached 0.820 in the training set and 0.780 in the validation set. Conclusions: This study suggested that the ICD-related gene signature could be a novel prognostic indicator for DLCBL.
Entropic Central Limit Theorem for Order Statistics

It is well known that central order statistics exhibit a central limit behavior and converge to a Gaussian distribution as the sample size grows. This paper strengthens this known result by establishing an entropic version of the central limit theorem that ensures a stronger mode of convergence using the relative entropy. This upgrade in convergence is shown at the expense of extra regularity conditions, which can be considered as mild. To prove this result, ancillary results on order statistics are derived, which might be of independent interest. For instance, a rather general bound on the moments of order statistics, and an upper bound on the mean squared error of estimating the $p \in (0,1)$ -th quantile of an unknown cumulative distribution function, are derived. Finally, a discussion on the necessity of the derived conditions for convergence and on the rate of convergence and monotonicity of the relative entropy is provided.
Stabilising falling liquid film flows using feedback control

Falling liquid films become unstable due to inertial effects when the fluid layer is sufficiently thick or the slope sufficiently steep. This free surface flow of a single fluid layer has industrial applications including coating and heat transfer, which benefit from smooth and wavy interfaces, respectively. Here, we discuss how the dynamics of the system are altered by feedback controls based on observations of the interface height, and supplied to the system via the perpendicular injection and suction of fluid through the wall. In this study, we model the system using both Benney and weighted-residual models that account for the fluid injection through the wall. We find that feedback using injection and suction is a remarkably effective control mechanism: the controls can be used to drive the system towards arbitrary steady states and travelling waves, and the qualitative effects are independent of the details of the flow modelling. Furthermore, we show that the system can still be successfully controlled when the feedback is applied via a set of localised actuators and only a small number of system observations are available, and that this is possible using both static (where the controls are based on only the most recent set of observations) and dynamic (where the controls are based on an approximation of the system which evolves over time) control schemes. This study thus provides a solid theoretical foundation for future experimental realisations of the active feedback control of falling liquid films.
Harvesting Curvatures for Communication-Efficient Distributed Optimization

This paper considers the design of distributed optimization algorithms in the server-client setting, with the goal of achieving better communication efficiency, a crucial desiderata in modern distributed machine learning applications. One popular approach to reduce communication is to impose more computation between communication rounds, by letting each client optimize an approximation of the global objective function-constructed using the local objective function and aggregated history information-and exchange the updates with the parameter server for global consensus. In particular, judiciously incorporating second-order information in the local approximation often leads to better communication efficiency when the problem is ill-conditioned. However, existing methods construct the second-order correction term in an isotropic manner, without taking into account the curvature information of the global objective function accumulated over the course of the algorithm. This paper proposes a novel algorithm that refines this idea by constructing a second-order correction term using a BFGS-style update formula, where the kernel matrix is updated recursively using only history gradients to harvest curvature information for accelerating convergence. Numerical experiments demonstrate improved communication efficiency over competitive baseline algorithms on both synthetic and real datasets.
Operational Impact of the Through-Traffic Signal Prioritization for Heavy Commercial Vehicle Platooning on Urban Arterials

This study investigated the operational impact of heavy commercial vehicle (HCV) platooning on urban arterials. HCV platooning is an important application of vehicle-to-vehicle (V2V) technology, with urban arterials facilitating an essential component of HCV movements when picking up and delivering goods. HCV platooning has the potential to reduce fuel consumption and emissions. Moreover, the increasing HCV driver shortage problem can be alleviated if the vehicle following behind a lead vehicle can function without a driver by using autonomous technology enabling Society of Automotive Engineers Level 4 or higher. PTV VISSIM was used to develop a set of micro-simulation models that investigated the impact of traffic signal priority (TSP) and low levels (0%, 5%, and 10%) of HCV platooning. The performance measures include travel time and the number of stops. With the existing traffic control system, HCV platooning increased travel time and increased the number of stops for all vehicles including passenger cars and HCVs. TSP with 5% HCV platooning improved travel time and decreased the number of stops for all vehicles. TSP with 10% HCV platooning, however, only decreased travel time and the number of stops for passenger vehicles. The results suggest that a higher penetration rate of HCV platooning may create significant delays and overwhelm the traffic system even with the assistance of TSP. The findings of this study highlight the potential for TSP to mitigate the impact of HCV platooning on traffic congestion. However, the TSP system may not be a panacea that works for all traffic compositions.
On a differential geometric viewpoint of Jaynes' MaxEnt method and its quantum extension

We present a differential geometric viewpoint of the quantum MaxEnt estimate of a density operator when only incomplete knowledge encoded in the expectation values of a set of quantum observables is available. Finally, the additional possibility of considering some prior bias towards a certain density operator (the prior) is taken into account and the unsolved issues with its quantum relative entropic inference criterion are pointed out.
Acquisition and Analysis of Microcirculation Image in Septic Model Rats

Background: Microcirculation is a vital sign that supplies oxygen and nutrients to maintain normal life activities. Sepsis typically influences the operation of microcirculation, which is recovered by the administration of medicine injection. Objective: Sepsis-induced variation and recovery of microcirculation are quantitatively detected using microcirculation images acquired by a non-contact imaging setup, which might assist the clinical diagnosis and therapy of sepsis. Methods: In this study, a non-contact imaging setup was first used to record images of microcirculation on the back of model rats. Specifically, the model rats were divided into three groups: (i) the sham group as a control group; (ii) the cecum ligation and puncture (CLP) group with sepsis; and (iii) the CLP+thrombomodulin (TM) group with sepsis and the application of TM alfa therapy. Furthermore, considering the sparsity of red blood cells (RBCs), the blood velocity is estimated by robust principal component analysis (RPCA) and U-net, and the blood vessel diameter is estimated by the contrast difference between the blood vessel and tissue. Results and Effectiveness: In the experiments, the continuous degradation of the estimated blood velocity and blood vessel diameter in the CLP group and the recovery after degradation of those in the CLP+TM group were quantitatively observed. The variation tendencies of the estimated blood velocity and blood vessel diameter in each group suggested the effects of sepsis and its corresponding therapy.
Vaccination History, Body Mass Index, Age, and Baseline Gene Expression Predict Influenza Vaccination Outcomes

Seasonal influenza is a primary public health burden in the USA and globally. Annual vaccination programs are designed on the basis of circulating influenza viral strains. However, the effectiveness of the seasonal influenza vaccine is highly variable between seasons and among individuals. A number of factors are known to influence vaccination effectiveness including age, sex, and comorbidities. Here, we sought to determine whether whole blood gene expression profiling prior to vaccination is informative about pre-existing immunological status and the immunological response to vaccine. We performed whole transcriptome analysis using RNA sequencing (RNAseq) of whole blood samples obtained prior to vaccination from 275 participants enrolled in an annual influenza vaccine trial. Serological status prior to vaccination and 28 days following vaccination was assessed using the hemagglutination inhibition assay (HAI) to define baseline immune status and the response to vaccination. We find evidence that genes with immunological functions are increased in expression in individuals with higher pre-existing immunity and in those individuals who mount a greater response to vaccination. Using a random forest model, we find that this set of genes can be used to predict vaccine response with a performance similar to a model that incorporates physiological and prior vaccination status alone. A model using both gene expression and physiological factors has the greatest predictive power demonstrating the potential utility of molecular profiling for enhancing prediction of vaccine response. Moreover, expression of genes that are associated with enhanced vaccination response may point to additional biological pathways that contribute to mounting a robust immunological response to the seasonal influenza vaccine.
A Comparison Study between IFRS 9 and IAS 39 in GCC Countries

IFRS 9 was introduced as a replacement for IAS 39, which spurred a wide debate since its implementation. This study aims to show the impact of IFRS 9 on the performance, solvency, credit ratio, capital adequacy ratio, expected credit loss, non-performing credits and write-offs for local banks operating within the Gulf Cooperation Council. To achieve the objectives of this study, data from financial disclosures was collected for 53 GCC banks for the period 2012-2020, which had 477 years of observation. The cross-sectional data was analyzed using the logit model (panel logistic regression model), capable of extracting non-linear relationships. This study found that there was a substantial positive difference in the percentage of the provision for credit losses, write-offs, and credit and non-performing credit ratios after the implementation of the IFRS 9. In contrast, no substantial difference was found in financial performance indicators, solvency, and capital adequacy ratio. The study offered a set of recommendations based on these findings and offered a number of potential future research opportunities to enrich the topic.
A Pan-Draft Metabolic Model Reflects Evolutionary Diversity across 332 Yeast Species

Yeasts are increasingly employed in synthetic biology as chassis strains, including conventional and non-conventional species. It is still unclear how genomic evolution determines metabolic diversity among various yeast species and strains. In this study, we constructed draft GEMs for 332 yeast species using two alternative procedures from the toolbox RAVEN v 2.0. We found that draft GEMs could reflect the difference in yeast metabolic potentials, and therefore, could be utilized to probe the evolutionary trend of metabolism among 332 yeast species. We created a pan-draft metabolic model to account for the metabolic capacity of every sequenced yeast species by merging all draft GEMs. Further analysis showed that the pan-reactome of yeast has a “closed” property, which confirmed the great conservatism that exists in yeast metabolic evolution. Lastly, the quantitative correlations among trait similarity, evolutionary distances, genotype, and model similarity were thoroughly investigated. The results suggest that the evolutionary distance and genotype, to some extent, determine model similarity, but not trait similarity, indicating that multiple mechanisms shape yeast trait evolution. A large-scale reconstruction and integrative analysis of yeast draft GEMs would be a valuable resource to probe the evolutionary mechanism behind yeast trait variety and to further refine the existing yeast species-specific GEMs for the community.
Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

Internal Combustion Engine based transportation rapidly increases the impact on the environment. The burning of fossil fuels in the industries and transportation sector breadth of global warming. However, Hybrid Electric Vehicle is not a permanent solution for emission-less road vehicles. Therefore, electric vehicles are the most feasible technologies to attain the goals of energy savings and zero-emission road vehicles. This paper sequentially surveys the key point of vehicles like; Powertrain strategy, configuration, fuel economy, reduced emission, and control strategy expounds in terms of its basic principles, pros, and cons. These key points make the energy management strategy more conclusive for the more efficient vehicle. In addition, this paper review systematically qualitative and quantitative algorithm in all type of EMS used in HEV and compare them with existing approaches in terms of pros & cons through a comprehensive analysis. Furthermore, addressed the potential research gap and provide the directives for further development in power train and ems in every respect.
Additive units of product system

We introduce the notion of additive units and roots of a unit in a spatial product system. The set of all roots of any unit forms a Hilbert space and its dimension is the same as the index of the product system. We show that a unit and all of its roots generate the type I part of the product system. Using properties of roots, we also provide an alternative proof of the Powers' problem that the cocycle conjugacy class of Powers sum is independent of the choice of intertwining isometries. In the last section, we introduce the notion of cluster of a product subsystem and establish its connection with random sets in the sense of Tsirelson and Liebscher.
Influence of substitution of wheat flour with modified potato starch on the quality of Chinese steamed bread

The application of potato starch in flour products is very important for potato staple food. The gluten protein of wheat flour will be weakened after being mixed with potato starch, which could have inhibitory effect on quality properties of flour products. Therefore, it is, it is necessary to study the influence of substitution wheat flour with potato starch on the quality of Chinese steamed bread (CSB), which is an important staple food in North China. This study investigated the effect of potato starch modified by heat-moisture treatment (HMTS) and microwave treatment (MWS) as wheat flour substitute in the making of CSB. The research results showed that the specific volume of CSB decreased with more incorporation of HMTS or MWS. The differences color (ΔE>3) between the control and experimental CSB were detectable by the human eye when the substitution level of HMTS or MWS was higher than 30 % or 20 %, respectively. Texture properties of CSB were affected with substitution due to the disruption of dough structure, and the incorporation of HMTS or MWS led to firmer and denser structure of CSB. The total sensory score of CSB decreased with more incorporation of HMTS or MWS. CSB can be accepted by consumers when the substitution level of wheat flour with HMTS or MWS was lower than 30 %. In general, the research results revealed that modified potato starch (HMTS and MWS) incorporation levels affected the specific volume, color, texture properties and sensory evaluation of CSB. This research can provide comprehension of the influence of modified potato starch (HMTS or MWS) on CSB and provide valuable guidance for further application of potato starch in wheat-based products