Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Malware with an Ensemble Method Based on Deep Neural Network

Malware detection plays a crucial role in computer security. Recent researches mainly use machine learning based methods heavily relying on domain knowledge for manually extracting malicious features. In this paper, we propose MalNet, a novel malware detection method that learns features automatically from the raw data. Concretely, we first generate a grayscale image from malware file, meanwhile extracting its opcode sequences with the decompilation tool IDA. Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification. We perform experiments on more than 40,000 samples including 20,650 benign files collected from online software providers and 21,736 malwares provided by Microsoft. The evaluation result shows that MalNet achieves 99.88% validation accuracy for malware detection. In addition, we also take malware family classification experiment on 9 malware families to compare MalNet with other related works, in which MalNet outperforms most of related works with 99.36% detection accuracy and achieves a considerable speed-up on detecting efficiency comparing with two state-of-the-art results on Microsoft malware dataset.
Energy-Adaptive Downlink Resource Allocation in Wireless Cellular Systems

Mobile devices have increasingly been used to run multimedia applications which are extremely downlink-intensive. The conventional rate adaptive and/or margin adaptive approach for radio resource allocation may result in unnecessary energy consumption on mobile devices, which will not be energy efficient for mobile multimedia applications. In this paper, we develop an energy adaptive approach and design an energy-efficient downlink resource allocation scheme to support multimedia applications. The objective is to minimize the total energy consumption of mobile devices for data reception while meeting the data rate requirements at mobile devices and the transmit power constraint at the base station. We show that the optimization problem is NP-hard and then propose an efficient algorithm that has a provable performance guarantee under a certain condition. We have conducted extensive simulations to evaluate the efficacy of the proposed algorithm and our results provide useful insights into the design of energy-efficient resource allocation for wireless systems.
Enhancing the Sweetness of Yoghurt through Metabolic Remodeling of Carbohydrate Metabolism in Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus

ABSTRACT Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus are used in the fermentation of milk to produce yoghurt. These species normally metabolize only the glucose moiety of lactose, secreting galactose and producing lactic acid as the main metabolic end product. We used multiple serial selection steps to isolate spontaneous mutants of industrial strains of S. thermophilus and L. delbrueckii subsp. bulgaricus that secreted glucose rather than galactose when utilizing lactose as a carbon source. Sequencing revealed that the S. thermophilus strains had mutations in the galKTEM promoter, the glucokinase gene, and genes encoding elements of the glucose/mannose phosphotransferase system (PTS). These strains metabolize galactose but are unable to phosphorylate glucose internally or via the PTS. The L. delbrueckii subsp. bulgaricus mutants had mutations in genes of the glucose/mannose PTS and in the pyruvate kinase gene. These strains cannot grow on exogenous glucose but are proficient at metabolizing internal glucose released from lactose by β-galactosidase. The resulting strains can be combined to ferment milk, producing yoghurt with no detectable lactose, moderate levels of galactose, and high levels of glucose. Since glucose tastes considerably sweeter than either lactose or galactose, the sweetness of the yoghurt is perceptibly enhanced. These strains were produced without the use of recombinant DNA technology and can be used for the industrial production of yoghurt with enhanced intrinsic sweetness and low residual levels of lactose. IMPORTANCE Based on a good understanding of the physiology of the lactic acid bacteria Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus, we were able, by selecting spontaneously occurring mutants, to change dramatically the metabolic products secreted into the growth medium. These mutants consume substantially more of the lactose, metabolize some of the galactose, and secrete the remaining galactose and most of the glucose back into the milk. This allows production of yoghurt with very low lactose levels and enhanced natural sweetness, because humans perceive glucose as sweeter than either lactose or galactose.
Doxorubicin Kinetics and Effects on Lung Cancer Cell Lines Using In-vitro Raman Micro-Spectroscopy: Binding Signatures, Drug Resistance and DNA Repair

Raman micro-spectroscopy is a non-invasive, in vitro analytical tool which is being increasingly explored for its potential in clinical applications and monitoring the uptake, mechanism of action and cellular interaction at a molecular level of chemotherapeutic drugs, ultimately as a potential label-free preclinical screening and companion diagnostic tool. In this study, Doxorubicin (DOX), a “gold standard” chemotherapeutic drug, is employed as a model in the in vitro lung cancer cell line, A549, in order to demonstrate the potential of Raman micro-spectroscopy to screen and identify spectroscopic markers of its trafficking and mechanism of action. Confocal Laser Scanning Microscopy (CLSM) was used in parallel to illustrate the uptake and subcellular localisation, and cytotoxicity assays were employed to establish the toxicity profiles for early and late exposure times of A549 to DOX. Multivariate statistical analysis, consisting of principal components analysis (PCA), partial least squares regression (PLSR) and independent component analysis (ICA) were used to elucidate the spectroscopic signatures associated with DOX uptake and subcellular interaction. Raman spectroscopic profiling illustrates both drug kinetics and its pharmacodynamics in the cell and associated biochemical changes, demonstrating that DOX is mainly localised in the nuclear area, saturating the nucleolus first, within ~6hrs of exposure, before the surrounding nuclear areas after ~12hrs, and only accumulates in the cytoplasm after 48hrs. PLSR over varying time intervals enables identification of DOX-DNA binding at early stages of exposure (0-12hrs), while regression over the longer time periods (24-72hrs) reveals spectroscopic signatures associated with the metabolic cellular response.
A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products

Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.
A Diverse Family of Host-Defense Peptides (Piscidins) Exhibit Specialized Anti-Bacterial and Anti-Protozoal Activities in Fishes

Conventional antibiotics and other chemical-based drugs are currently one of the most common methods used to control disease-related mortality in animal agriculture. Use of the innate immune system to decrease disease related mortalities is a novel alternative to conventional drugs. One component of the innate immune system is the host-defense peptides, also known as antimicrobial peptides. Host-defense peptides are typically small, amphipathic, α-helical peptides with a broad-spectrum of action against viral, bacterial, fungal, and/or protozoal pathogens. Piscidins are host-defense peptides first discovered in the hybrid striped bass (white bass, Morone chrysops, x striped bass, M. saxatilis). In this paper we identify four new piscidin isoforms in the hybrid striped bass and describe their tissue distributions. We also determine the progenitor species of origin of each piscidin (orthology) and propose a revised nomenclature for this newly described piscidin family based on a three class system. The Class I piscidins (22 amino acids in length; striped bass and white bass piscidin 1 and piscidin 3) show broad-spectrum activity against bacteria and ciliated protozoans, while the Class III piscidins (55 amino acids in length; striped bass and white bass piscidin 6 and striped bass piscidin 7) primarily show anti-protozoal activity. The Class II piscidins (44–46 amino acids in length; striped bass and white bass piscidin 4 and white bass piscidin 5) have a level of activity against bacteria and protozoans intermediate to Classes I and III. Knowledge of piscidin function and activity may help in the future development of disease-resistant lines of striped bass and white bass that could be used to produce superior hybrids for aquaculture.
A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction

ABSTRACT Image clustering is a critical and essential component of image analysis to several fields and could be considered as an optimization problem. Cuckoo Search (CS) algorithm is an optimization algorithm that simulates the aggressive reproduction strategy of some cuckoo species. In this paper, a combination of CS and classical algorithms (KM, FCM, and KHM) is proposed for unsupervised satellite image classification. Comparisons with classical algorithms and also with CS are performed using three cluster validity indices namely DB, XB, and WB on synthetic and real data sets. Experimental results confirm the effectiveness of the proposed approach.
Engineered Disease Resistance in Cotton Using RNA-Interference to Knock down Cotton leaf curl Kokhran virus-Burewala and Cotton leaf curl Multan betasatellite Expression

Cotton leaf curl virus disease (CLCuD) is caused by a suite of whitefly-transmitted begomovirus species and strains, resulting in extensive losses annually in India and Pakistan. RNA-interference (RNAi) is a proven technology used for knockdown of gene expression in higher organisms and viruses. In this study, a small interfering RNA (siRNA) construct was designed to target the AC1 gene of Cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bu) and the βC1 gene and satellite conserved region of the Cotton leaf curl Multan betasatellite (CLCuMB). The AC1 gene and CLCuMB coding and non-coding regions function in replication initiation and suppression of the plant host defense pathway, respectively. The construct, Vβ, was transformed into cotton plants using the Agrobacterium-mediated embryo shoot apex cut method. Results from fluorescence in situ hybridization and karyotyping assays indicated that six of the 11 T1 plants harbored a single copy of the Vβ transgene. Transgenic cotton plants and non-transgenic (susceptible) test plants included as the positive control were challenge-inoculated using the viruliferous whitefly vector to transmit the CLCuKoV-Bu/CLCuMB complex. Among the test plants, plant Vβ-6 was asymptomatic, had the lowest amount of detectable virus, and harbored a single copy of the transgene on chromosome six. Absence of characteristic leaf curl symptom development in transgenic Vβ-6 cotton plants, and significantly reduced begomoviral-betasatellite accumulation based on real-time polymerase chain reaction, indicated the successful knockdown of CLCuKoV-Bu and CLCuMB expression, resulting in leaf curl resistant plants.
Optogenetics: illuminating the neural bases of rodent behavior

In vivo optogenetics has provided researchers with the ability to delve deeper into the neural basis of behavior by driving cell-type specific circuit connections within and between brain regions. The diverse toolbox available for circuit- and cell-specific manipula - tions is ever growing. Using these tools in conjunction with established and novel genetic and behavioral methods, neuroscience research has experienced an explosion in the understanding of the roles of specific cell subtypes in behavior. This review aims to outline recent advances in in vivo optogenetic tools for manipulation of behavior related to movement, pain and sensation, motivation, reward, emotion, learning, sleep, and epilepsy.
Semantic Search : Sentiment Analysis with Machine Learning Algorithms on German News Articles

The automatic analysis of complex text collections is of high interest for many companies and institutions. Analyzing the sentiments of documents is a current research topic. This paper presents and evaluates a classification approach we developed for the public relations department of the “Technische Universität Berlin”. The system is designed to predict the sentiment class of news articles. In the paper we discuss the creation of a training corpus, the approach for learning a classifier with different preprocessing steps as well as the evaluation of our system. The analysis of the baseline comparison with a 10-fold cross-validation shows that it predicts sentiment classes with a satisfying accuracy.
ENSO’s Modulation of Water Vapor Transport over the Pacific–North American Region

AbstractThe vertically integrated water vapor transport (IVT) over the Pacific–North American sector during three phases of ENSO in boreal winter (December–February) is investigated using IVT values calculated from the Climate Forecast System Reanalysis (CFSR) during 1979–2010. The shift of the location and sign of sea surface temperature (SST) anomalies in the tropical Pacific Ocean leads to different atmospheric responses and thereby changes the seasonal mean moisture transport into North America. During eastern Pacific El Nino (EPEN) events, large positive IVT anomalies extend northeastward from the subtropical Pacific into the northwestern United States following the anomalous cyclonic flow around a deeper Aleutian low, while a southward shift of the cyclonic circulation during central Pacific El Nino (CPEN) events induces the transport of moisture into the southwestern United States. In addition, moisture from the eastern tropical Pacific is transported from the deep tropical eastern Pacific into Mex.
Optimizing Variational Quantum Algorithms using Pontryagin's Minimum Principle

We use Pontryagin's minimum principle to optimize variational quantum algorithms. We show that for a fixed computation time, the optimal evolution has a bang-bang (square pulse) form, both for closed and open quantum systems with Markovian decoherence. Our findings support the choice of evolution ansatz in the recently proposed Quantum Approximate Optimization Algorithm. Focusing on the Sherrington-Kirkpatrick spin-glass as an example, we find a system-size independent distribution of the duration of pulses, with characteristic time scale set by the inverse of the coupling constants in the Hamiltonian. The optimality of the bang-bang protocols and the characteristic time scale of the pulses provide an efficient parameterization of the protocol and inform the search for effective hybrid (classical and quantum) schemes for tackling combinatorial optimization problems. For the particular systems we study, we find numerically that the optimal nonadiabatic bang-bang protocols outperform conventional quantum annealing in the presence of weak white additive external noise and weak coupling to a thermal bath modeled with the Redfield master equation.
Deriving fuel-based emission factor thresholds to interpret heavy-duty vehicle roadside plume measurements

ABSTRACT Remote sensing devices have been used for decades to measure gaseous emissions from individual vehicles at the roadside. Systems have also been developed that entrain diluted exhaust and can also measure particulate matter (PM) emissions. In 2015, the California Air Resources Board (CARB) reported that 8% of in-field diesel particulate filters (DPF) on heavy-duty (HD) vehicles were malfunctioning and emitted about 70% of total diesel PM emissions from the DPF-equipped fleet. A new high-emitter problem in the heavy-duty vehicle fleet had emerged. Roadside exhaust plume measurements reflect a snapshot of real-world operation, typically lasting several seconds. In order to relate roadside plume measurements to laboratory emission tests, we analyzed carbon dioxide (CO2), oxides of nitrogen (NOX), and PM emissions collected from four HD vehicles during several driving cycles on a chassis dynamometer. We examined the fuel-based emission factors corresponding to possible exceedances of emission standards as a function of vehicle power. Our analysis suggests that a typical HD vehicle will exceed the model year (MY) 2010 emission standards (of 0.2 g NOX/bhp-hr and 0.01 g PM/bhp-hr) by three times when fuel-based emission factors are 9.3 g NOX/kg fuel and 0.11 g PM/kg using the roadside plume measurement approach. Reported limits correspond to 99% confidence levels, which were calculated using the detection uncertainty of emissions analyzers, accuracy of vehicle power calculations, and actual emissions variability of fixed operational parameters. The PM threshold was determined for acceleration events between 0.47 and 1.4 mph/sec only, and the NOX threshold was derived from measurements where after-treatment temperature was above 200°C. Anticipating a growing interest in real-world driving emissions, widespread implementation of roadside exhaust plume measurements as a compliment to in-use vehicle programs may benefit from expanding this analysis to a larger sample of in-use HD vehicles. Implications: Regulatory agencies, civil society, and the public at large have a growing interest in vehicle emission compliance in the real world. Leveraging roadside plume measurements to identify vehicles with malfunctioning emission control systems is emerging as a viable new and useful method to assess in-use performance. This work proposes fuel-based emission factor thresholds for PM and NOx that signify exceedances of emission standards on a work-specific basis by analyzing real-time emissions in the laboratory. These thresholds could be used to prescreen vehicles before roadside enforcement inspection or other inquiry, enhance and further develop emission inventories, and potentially develop new requirements for heavy-duty inspection and maintenance (I/M) programs, including but not limited to identifying vehicles for further testing.
Errors in GNSS radio occultation data: relevance of the measurement geometry and obliquity of profiles

Abstract. Atmospheric profiles retrieved from GNSS (Global Navigation Satellite System) radio occultation (RO) measurements are increasingly used to validate other measurement data. For this purpose it is important to be aware of the characteristics of RO measurements. RO data are frequently compared with vertical reference profiles, but the RO method does not provide vertical scans through the atmosphere. The average elevation angle of the tangent point trajectory (which would be 90° for a vertical scan) is about 40° at altitudes above 70 km, decreasing to about 25° at 20 km and to less than 5° below 3 km. In an atmosphere with high horizontal variability we can thus expect noticeable representativeness errors if the retrieved profiles are compared with vertical reference profiles. We have performed an end-to-end simulation study using high-resolution analysis fields (T799L91) from the European Centre for Medium-Range Weather Forecasts (ECMWF) to simulate a representative ensemble of RO profiles via high-precision 3-D ray tracing. Thereby we focused on the dependence of systematic and random errors on the measurement geometry, specifically on the incidence angle of the RO measurement rays with respect to the orbit plane of the receiving satellite, also termed azimuth angle, which determines the obliquity of RO profiles. We analyzed by how much errors are reduced if the reference profile is not taken vertical at the mean tangent point but along the retrieved tangent point trajectory (TPT) of the RO profile. The exact TPT can only be determined by performing ray tracing, but our results confirm that the retrieved TPT – calculated from observed impact parameters – is a very good approximation to the "true" one. Systematic and random errors in RO data increase with increasing azimuth angle, less if the TPT is properly taken in to account, since the increasing obliquity of the RO profiles leads to an increasing sensitivity to departures from horizontal symmetry. Up to an azimuth angle of 30°, however, this effect is small, even if the RO profiles are assumed to be vertical. For applications requiring highest accuracy and precision it is advisable to exclude RO profiles with ray incidence angles beyond an azimuth of 50°. Errors in retrieved atmospheric profiles decrease significantly, by up to a factor of 2, if the RO data are exploited along the retrieved TPT. The tangent point trajectory of RO profiles should therefore be exploited whenever this is possible.
An Introduction to the Research on Scratchpad Memory : Definition , Hardware , Known Implementations and WCET Optimisation

Together with [13], this papers aims to be an introduction to the concept and a summary of the current research on the topic of Scratchpad Memory(SPM). The topics I focus on are the hardware aspects, energy efficiency, both the general concept of SPM and known implementations and applications, as well as the issue of worst-case execution time in hard real-time systems.