Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiobjective evolutionary algorithm with constraint handling for aircraft landing scheduling

Aircraft landing scheduling is a multiobjective optimization problem with lots of constraints, which is difficult to be dealt with by traditional multiobjective evolutionary algorithms with general constraint handling strategies such as constraint-dominate definition. In this paper we pertinently designed an effective constraint handling method, and then presented a multiobjective evolutionary algorithm using the constraint handing method to solve the aircraft landing scheduling problem. Experiments show that our method is able to locate the feasible region in the search space, obtain the jagged Pareto front, and thereby provide efficient schedule for aircraft landing.
Data Driven Congestion Trends Prediction of Urban Transportation

Smart traffic prediction system provides significant benefits in solving the city traffic congestion. However, existing smart transportation system needs a lot of real-time traffic data and accurate location information to display the traffic condition. We hope that we can use the data which is easy to be obtained, and then predict a reliable congestion time. To address this problem, this paper studied a smart traffic forecasting system based on SWARIMA model. The system includes three steps: 1) use the sliding windows to calculate and process real-time data stream; 2) establish the SWARIMA model and make regression analysis; and 3) from a statistical point of view, calculate the elastic interval and predict the congestion trend. Our system is capable of accepting the real-time traffic data stream for the congestion prediction, in addition, we reduce the actual running parameters to three attributes: 1) speed; 2) time; and 3) location information. When faced with the challenges of real-time traffic congestion, the system can timely and effectively calculate the congestion trends and provide three reliable elastic intervals: 1) warning; 2) congestion; and 3) mitigation, which has significance to improve traffic condition and alleviate urban road congestion.
A pr 2 01 8 Low Complexity Secure Code ( LCSC ) Design for Big Data in Cloud Storage Systems

In the era of big data, reducing the computational complexity of servers in data centers will be an important goal. We propose Low Complexity Secure Codes (LCSCs) that are specifically designed to provide information theoretic security in cloud distributed storage systems. Unlike traditional coding schemes that are designed for error correction capabilities, these codes are only designed to provide security with low decoding complexity. These sparse codes are able to provide (asymptotic) perfect secrecy similar to Shannon cipher. The simultaneous promise of low decoding complexity and perfect secrecy make these codes very desirable for cloud storage systems with large amount of data. The design is particularly suitable for large size archival data such as movies and pictures. The complexity of these codes are compared with traditional encryption techniques.
Using the internet to evaluate the opinion of patients with inflammatory bowel disease with regard to the available information.

BACKGROUND AND AIMS patients need information for shared decision making. The aims of the study were to ascertain how patients with inflammatory bowel disease (IBD) felt about the information available and the way that their doctors informed them. In addition, how patients used the internet and factors that predicted a positive information evaluation were also assessed. METHOD a 39-item survey was designed that was distributed via the internet, principally using a Spanish Facebook site. RESULTS four hundred and twenty completed surveys were received. Patients rated the information available with a mean of 8 points (maximum of 10) and 71% felt that their doctor informed them well or very well. Various deficiencies were found such as little information available at the time of diagnosis (58%); doubts after seeing the specialist (70%); insufficient information about IBD complications, disease course and dietary aspects; and a lack of appropriate internet webpage recommendations from specialists or pharmacists. Patients ranked the internet fourth as an information source, followed by their specialist, patient associations and other patients. Independent predictive factors of feeling well informed (≥ 8) included age, OR 1.539 (CI 1.047-2.261), p = 0.028; education, OR 1.544 (CI 1.110-2.147), p = 0,010; IBD evolution time, OR 1.267 (CI 1.003-1,601), p = 0.047; and good or very good information from the specialist, OR 3.262 (CI 2.425-4.388), p < 0.001. CONCLUSIONS patients generally felt that they were well informed. However, there were aspects that needed improvement, such as information for younger patients or a lower education level, the information provided at diagnosis, information about specific or insufficiently covered IBD aspects and suggestions from doctors about high quality websites.
Selective Anti-Herpesvirus Agents

This review article focuses on the anti-herpesvirus agents effective against herpes simplex virus, varicella-zoster virus and cytomegalovirus, which have either been licensed for clinical use (idoxuridine, trifluridine, brivudin, acyclovir, valaciclovir, valganciclovir, famciclovir and foscarnet) or are under clinical development (CMX001 [the hexadecyloxypropyl prodrug of cidofovir], the helicase-primase inhibitor BAY 57–1293 [now referred to as AIC316], FV-100 [the valine ester of Cf 1743] and the terminase inhibitor letermovir [AIC246]).
Prescription Opioids and Labor Market Pains

We examine the effect of prescription opioids on county labor market outcomes, using data from the Prescription Drug Monitoring Programs of ten U.S. states and labor data from the Bureau of Labor Statistics. We achieve causal identification by exploiting plausibly exogenous variation in the concentration of high-volume prescribers as instruments (using Medicare Part D prescriber data). We find strong adverse effects on labor force participation rates, employment-to-population ratios, and unemployment rates. Notably, a 10 percent increase in prescriptions causes a 0.53 percentage point reduction in labor force participation, similar to the drop attributed to the 1984 liberalization of Disability Insurance.
Task scheduling and merging in space and time

Every day, robots are being deployed in more challenging environments, where they are required to perform complex tasks. In order to achieve these tasks, robots rely on intelligent deliberation algorithms. In this thesis, we study two deliberation approaches – task scheduling and task planning. We extend these approaches in order to not only deal with temporal and spatial constraints imposed by the environment, but also exploit them to be more efficient than the state-of-the-art approaches. Our first main contribution is a scheduler that exploits a heuristic based on Allen’s interval algebra to prune the search space to be traversed by a mixed integer program. We empirically show that the proposed scheduler outperforms the state of the art by at least one order of magnitude. Furthermore, the scheduler has been deployed on several mobile robots in long-term autonomy scenarios. Our second main contribution is the POPMERX algorithm, which is based on merging of partially ordered temporal plans. POPMERX first reasons with the spatial and temporal structure of separately generated plans. Then, it merges these plans into a single final plan, while optimising the makespan of the merged plan. We empirically show that POPMERX produces better plans that the-state-ofthe- art planners on temporal domains with time windows.
A four-channel radar system for rear seat occupancy detection in the 24 GHz ISM band

A measurement system to detect people sitting on the rear bench of a car has been developed for which a four-channel imaging radar system at 24 GHz SRD (ISM) with a bandwidth of 250 MHz is used [1]. On the radar system an antenna array with a dedicated radiation pattern is applied [2]. Based on a FMCW modulation approach it is possible to measure the range and the Doppler signal due to the motion of the passenger in the back of the car. A DBF approach is applied on the four receiver channels in order to determine the angle of arrival. Thus it would be possible to determine the seat location of the passenger. The data acquisition is done with an ADC from National Instruments. The signal processing and the evaluation of the results are performed with MATLAB.
An All-Memristor Deep Spiking Neural Computing System: A Step Toward Realizing the Low-Power Stochastic Brain

Deep analog artificial neural networks (ANNs) perform complex classification problems with remarkably high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain, on the other hand, is significantly more powerful than such networks and consumes orders of magnitude less power, indicating us about some conceptual mismatch. Given that the biological neurons communicate using energy efficient trains of spikes, and the behavior is nondeterministic, incorporating these effects in deep artificial neural networks may drive us few steps toward a more realistic neuron. In this paper, we propose how the inherent stochasticity of nanoscale resistive devices can be harnessed to emulate the functionality of a spiking neuron that can be incorporated in deep stochastic spiking neural networks (SNN). At the algorithmic level, we propose how the training can be modified to convert an ANN to an SNN while supporting the stochastic activation function offered by these devices. We devise circuit architectures to incorporate stochastic memristive neurons along with memristive crossbars, which perform the functionality of the synaptic weights. We tested the proposed all-memristor deep stochastic SNN for image classification and observed only about ${\text{1}}\%$ degradation in accuracy with the ANN baseline after incorporating the circuit and device related nonidealities. We witnessed that the network is robust to certain variations and consumes $\sim {\text{6.4}}\;\times$ less energy than its complementary metal oxide semiconductor (CMOS) counterpart.
Parallel and Distributed MCMC via Shepherding Distributions

In this paper, we present a general algorithmic framework for developing easily parallelizable/distributable Markov Chain Monte Carlo (MCMC) algorithms. Our framework relies on the introduction of an auxiliary distribution called a shepherding distribution (SD) that is used to control several MCMC chains that run in parallel. The SD is an introduced prior on one or more key parameters (or hyperparameters) of the target distribution. The shepherded chains then collectively explore the space of samples, communicating via the shepherding distribution, to reach high likelihood regions faster. The method of SDs is simple, and it is often easy to develop a shepherded sampler for a particular problem. Other advantages include wide applicability–the method can easily be used to draw samples from discrete distributions, or distributions on the simplex. Further, the method is asymptotically correct, since the method of SDs trivially maintains detailed balance.
Proteomics of contrasting rice genotypes: Identification of potential targets for raising crops for saline environment.

High salinity is one of the major problems in crop productivity, affecting seed germination as well as yield. In order to enhance tolerance of crops towards salinity, it is essential to understand the underlying physiological and molecular mechanisms. In this endeavor, study of contrasting genotypes of the same species differing in their response towards salinity stress can be very useful. In the present study, we have investigated temporal differences in morphological, physiological and proteome profiles of two contrasting genotypes of rice to understand the basis of salt tolerance. When compared to IR64 rice, Pokkali, the salt-tolerant wild genotype, has enhanced capacity to cope with stress, better growth rate and possesses efficient antioxidant system, as well as better photosynthetic machinery. Our proteome studies revealed a higher and an early abundance of proteins involved in stress tolerance and photosynthesis in Pokkali in comparison with IR64, which, in contrast, showed greater changes in metabolic machinery even during early duration of stress. Our findings suggest important differences in physicochemical and proteome profiles of the two genotypes, which may be the basis of observed stress tolerance in the salt-tolerant Pokkali.
Scalability, elasticity, and efficiency in cloud computing: A systematic literature review of definitions and metrics

Context In cloud computing, there is a multitude of definitions and metrics for scalability, elasticity, and efficiency. However, stakeholders have little guidance for choosing fitting definitions and metrics for these quality properties, thus leading to potential misunderstandings. For example, cloud consumers and providers cannot negotiate reliable and quantitative service level objectives directly understood by each stakeholder. Objectives Therefore, we examine existing definitions and metrics for these quality properties from the viewpoint of cloud consumers, cloud providers, and software architects with regard to commonly used concepts. Methods We execute a systematic literature review (SLR), reproducibly collecting common concepts in definitions and metrics for scalability, elasticity, and efficiency. As quality selection criteria, we assess whether existing literature differentiates the three properties, exemplifies metrics, and considers typical cloud characteristics and cloud roles. Results Our SLR yields 418 initial results from which we select 20 for in-depth evaluation based on our quality selection criteria. In our evaluation, we recommend concepts, definitions, and metrics for each property. Conclusions Software architects can use our recommendations to analyze the quality of cloud computing applications. Cloud providers and cloud consumers can specify service level objectives based on our metric suggestions.
A Generic Method for Design of Oligomer-Specific Antibodies

Antibodies that preferentially and specifically target pathological oligomeric protein and peptide assemblies, as opposed to their monomeric and amyloid counterparts, provide therapeutic and diagnostic opportunities for protein misfolding diseases. Unfortunately, the molecular properties associated with oligomer-specific antibodies are not well understood, and this limits targeted design and development. We present here a generic method that enables the design and optimisation of oligomer-specific antibodies. The method takes a two-step approach where discrimination between oligomers and fibrils is first accomplished through identification of cryptic epitopes exclusively buried within the structure of the fibrillar form. The second step discriminates between monomers and oligomers based on differences in avidity. We show here that a simple divalent mode of interaction, as within e.g. the IgG isotype, can increase the binding strength of the antibody up to 1500 times compared to its monovalent counterpart. We expose how the ability to bind oligomers is affected by the monovalent affinity and the turnover rate of the binding and, importantly, also how oligomer specificity is only valid within a specific concentration range. We provide an example of the method by creating and characterising a spectrum of different monoclonal antibodies against both the Aβ peptide and α-synuclein that are associated with Alzheimer's and Parkinson's diseases, respectively. The approach is however generic, does not require identification of oligomer-specific architectures, and is, in essence, applicable to all polypeptides that form oligomeric and fibrillar assemblies.
Low-Rank and Eigenface Based Sparse Representation for Face Recognition

In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC). Firstly, the low-rank images of the face images of each individual in training subset are extracted by the Robust Principal Component Analysis (Robust PCA) to alleviate the influence of noises (e.g., illumination difference and occlusions). Secondly, Singular Value Decomposition (SVD) is applied to extract the eigenfaces from these low-rank and approximate images. Finally, we utilize these eigenfaces to construct a compact and discriminative dictionary for sparse representation. We evaluate our method on five popular databases. Experimental results demonstrate the effectiveness and robustness of our method.
Predicting Dactyloscopic Examiner Fingerprint Image Quality Assessments

We work towards a system which can assist dactyloscopic examiners in assessing the quality and decision value of a fingerprint image and eventually a fingermark. However when quality assessment tasks of datyloscopic examiners are replaced by automatic quality assessment then we need to ensure that the automatic measurement is in agreement with the examiner opinion. Under the assumption of such agreement, we can predict the examiner opinion. We propose a method for determining the examiner agreement on ordinal scales and show that there is a high level of agreement between examiners assessing the ground truth quality of fingerprints. With ground truth quality information on 749 fingerprints and using 10-fold cross validation we construct models using Support Vector Machines and Proportional Odds Logistic Regression which predicts median examiner quality assessments 35% better than when using the prior class distribution.
Role of recoverin in rod photoreceptor light adaptation

Recoverin is a small molecular‐weight, calcium‐binding protein in rod outer segments that can modulate the rate of rhodopsin phosphorylation. We describe two additional and perhaps more important functions during photoreceptor light adaptation. Recoverin influences the rate of change of adaptation. In wild‐type rods, sensitivity and response integration time adapt with similar time constants of 150–200 ms. In Rv–/– rods lacking recoverin, sensitivity declines faster and integration time is already shorter and not significantly altered. During steady light exposure, rod circulating current slowly increases during a time course of tens of seconds, gradually extending the operating range of the rod. In Rv–/– rods, this mechanism is deleted, steady‐state currents are already larger and rods saturate at brighter intensities. We propose that recoverin modulates spontaneous and light‐activated phophodiesterase‐6, the phototransduction effector enzyme, to increase sensitivity in dim light but improve responsiveness to change in brighter illumination.
Social Role Identification via Dual Uncertainty Minimization Regularization

In this paper, we study a challenging problem of inferring individuals' role and statuses in a professional social network, which is of central importance in workforce optimization and human capital management. Realizing the natural setting of social nodes associated with dual view information, i.e., The local node characteristics and the global network influence, we present a novel model that explores graph regularization techniques and integrates such information to achieve improved prediction performance. In particular, our prediction model is built upon the graph transductive learning framework that encodes an uncertainty regularization term in the conventional empirical risk minimization principle. Through taking advantage of the information from both the local profile and the global network characteristics, the final inference of the role or statues achieves minimum an empirical loss on the labeled set, as well as a minimum uncertainty on the unlabeled social nodes. We perform extensive empirical study using real-world data and compare with representative peer approaches. The experimental results on three real social network data sets show that the proposed model greatly outperforms a number of baseline models and is able to effectively infer in a wide range of scenarios.
Path based MIMO channel model for hybrid beamforming architecture analysis

Hybrid beamforming communication systems allow the reduction of digital channels by employing an analogue beamforming network. This approach offers a large reduction in computational effort and energy consumption. Still there are only few practical realizations of such hybrid beamforming communication systems. Hence, there is a nearly infinite number of possible implementations of such an architecture. To analyze and compare the performance of different hybrid beamforming architectures, we present a path based multiple input multiple output (MIMO) channel model. This tool allows us to generate arbitrary propagation paths with distributions of direction of departure (DOD), direction of arrival (DOA) as well as phase and amplitudes. Therefore, an insight of the channel capacity and signal-to-noise ratio (SNR) can be given for different kinds of hybrid beamforming architectures. The results show that the newly developed path based channel model is beneficial for the analysis of hybrid beamforming architectures.
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis

Computing accurate estimates of the Fourier transform of analog signals from discrete data points is important in many fields of science and engineering. The conventional approach of performing the discrete Fourier transform of the data implicitly assumes periodicity and bandlimitedness of the signal. In this paper, we use Gaussian process regression to estimate the Fourier transform (or any other integral transform) without making these assumptions. This is possible because the posterior expectation of Gaussian process regression maps a finite set of samples to a function defined on the whole real line, expressed as a linear combination of covariance functions. We estimate the covariance function from the data using an appropriately designed gradient ascent method that constrains the solution to a linear combination of tractable kernel functions. This procedure results in a posterior expectation of the analog signal whose Fourier transform can be obtained analytically by exploiting linearity. Our simulations show that the new method leads to sharper and more precise estimation of the spectral density both in noise-free and noise-corrupted signals. We further validate the method in two real-world applications: the analysis of the yearly fluctuation in atmospheric CO2 level and the analysis of the spectral content of brain signals.
From Maxwell Garnett to Debye Model for Electromagnetic Simulation of Composite Dielectrics Part I: Random Spherical Inclusions

A semianalytical approach to obtain an equivalent Debye frequency dependence of effective permittivity for biphasic materials with random spherical inclusions from the well-known Maxwell Garnett (MG) mixing rule is proposed. Different combinations of frequency characteristics of mixture phases (host and inclusions) are considered: when at least one of the phases is frequency independent; lossy (with dc conductivity); or with a known single-term Debye frequency dependence. The equivalent Debye models approximate very well the frequency characteristics obtained directly from MG mixing rule. In some cases, there is an exact match between the two models, and a good approximation is achieved in the other cases and is quantified by the feature selective validation technique. The parameters of the derived equivalent Debye model can be employed in full-wave time-domain numerical electromagnetic codes and tools. This will allow for efficient wideband modeling of complex electromagnetic structures containing composite materials with effective dielectric parameters obtained through MG mixing rule.