Miracle-dz/UniFAR-FTUs huggingface dataset:
Consolidation treatment with yttrium-90 ibritumomab tiuxetan after new induction regimen in advanced stage follicular lymphoma: update results from the Spanish Lymphoma Oncology Group trial after a median follow-up of 8.5-years

Mariano Provencio, Fernando Franco, Jos e G omez-Codina, Cristina Quero Blanco, Marta Llanos, Francisco Garcia-Arroyo, Luis de la Cruz, Josep Gum a, Juan Ram on Delgado, Ruth Alvarez, Jos e Ignacio Chac on, Ana Royuela and Antonio Rueda Hospital Puerta de Hierro Majadahonda, Madrid, Spain; Department of Medical Oncology, Hospital Puerta de Hierro, Majadahonda, Spain; Department of Medical Oncology, Hospital La Fe, Valencia, Spain; Hospital Clinico de Malaga, Malaga, Spain; Department of Medical Oncology, Hospital Universitario de Canaras, Tenerife, Spain; Department of Medical Oncology, Hospital de Pontevedra, Pontevedra, Spain; Department of Medical Oncology, Hospital Universitario Virgen Macarena, Sevilla, Spain; Department of Medical Oncology, Hospital Universitario de Reus, Reus, Spain; Department of Medical Oncology, Hospital Virgen de las Nieves, Granada, Spain; Department of Medical Oncology, Hospital Universitario Virgen de la Salud, Toledo, Spain; Department of Biostatistic, Hospital Universitario Puerta del Hierro Majadahonda, Majadahonda, Spain; Department of Medical Oncology, Hospital Costa del Sol, Marbella, Spain
On the least upper bound for the settling time of a class of fixed-time stable systems

This paper deals with the convergence time analysis of a class of fixed-time stable systems with the aim to provide a new non-conservative upper bound for its settling time. Our contribution is threefold. First, we revisit the well-known class of fixed-time stable systems, given in [1], while showing the conservatism of the classical upper estimate of the settling time. Second, we provide the smallest constant that uniformly upper bounds the settling time of any trajectory of the system under consideration. Then, introducing a slight modification of the previous class of fixed-time systems, we propose a new predefined-time convergent algorithm where the least upper bound of the settling time is set a priori as a parameter of the system. This calculation is a valuable contribution toward online differentiators, observers and controllers in applications with real-time constraints.
Scrum+: An agile guide for the global software development (GSD) multi-model project management

Carrying out a project management is difficult in any situation, even more in global projects of software development, whose environment faces different aspects that make it even more difficult to manage this type of projects, for example: lack of coordination, lack of face to face communication, temporary differences, cultural diversity, application of different standards, models, approaches, among others. In this sense, this article presents an agile guide called Scrum+, which is based on Scrum, and defines a set of activities, tasks, roles and criteria to support the management of global software development projects when multiple reference models are present. Likewise, the evaluation of the proposal applied in a focus group with experts in different related areas is presented, whose recommendations were taken into account to improve the proposal and generate a final version.
Co-Robust-ADMM-Net: Joint ADMM Framework and DNN for Robust Sparse Composite Regularization

Symmetric <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-stable (<inline-formula> <tex-math notation="LaTeX">$\text{S}\alpha \text{S}$ </tex-math></inline-formula>) noise is a typical form of impulsive noise often generated in signal measurement and transmission systems. The problem of reconstructing an image from a small number of under-sampled data corrupted by impulsive noise is called robust compressive sensing (CS). In this paper, to effectively suppress the outliers and accurately reconstruct the image from compressive measured data in the presence of <inline-formula> <tex-math notation="LaTeX">$\text{S}\alpha \text{S}$ </tex-math></inline-formula> noise, a novel composite robust alternating direction method of multiplier network-based CS algorithm is proposed. Specifically, we first employ the <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>-norm as the estimator to depress the influence of <inline-formula> <tex-math notation="LaTeX">$\text{S}\alpha \text{S}$ </tex-math></inline-formula> noise, and then the ADMM framework is employed to address the resulting optimization problem. Moreover, a smoothing strategy is adopted to address the <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>-norm based non-smooth optimization problem. To exploit more prior knowledge and image features, a robust composite regularization model is proposed for training by the deep neural network (DNN). In the training phase, the DNN can be utilized to train the samples for the optimal parameters, the optimal shrinkage function and the optimal transform domain, which can be reserved as the network. In the reconstruction process, the obtained network can be employed for improving the reconstruction performance. Experiments show that our proposed algorithm can obtain higher reconstruction Peak signal-to-noise ratio than the existing state-of-the-art robust CS methods.
Physical Unclonable Functions based on silicon micro-ring resonators for secure signature delegation in Wireless Sensor Networks

With the arrival of the fourth industrial revolution and the era of Cyber-Physical Systems, Wireless Sensor Networks (WSN) are employed in a large catalogue of applications, many of them critical. At the beginning, encryption techniques were enough to protect WSN, but as more sophisticated applications are proposed, protection schemes turn also more complex. In particular, schemes based on digital signatures, hash functions and Public Key Infrastructures are extensively implemented. Nevertheless, all these solutions present a very important problem: nodes must store a secret key. As future systems will be unmanaged and be deployed in remote places without surveillance, the use of ROM memories where keys are stored in a permanent way is a critical risk. Therefore, in this paper we propose a new scheme for signature delegation in WSN, based on chaotic Physical Unclonable Functions (PUF). As the size of nodes is an essential parameter to be considered, the proposed technology is supported by micro-ring resonators. A simulation scenario is also deﬁned to validate the usability and performance of the proposed framework.
Load Shedding Scheme with Deep Reinforcement Learning to Improve Short-term Voltage Stability

In this paper, a novel load shedding scheme against voltage instability with deep reinforcement learning (DRL). The convolutional neural networks (CNNs) are chosen to automatically learn the features and exploit the local spatial correlations from TS data of voltages. The DRL load shedding controller interacts with system dynamics through a sequence of observations, actions and rewards with two convolutional neural networks, namely critic and actor networks. The critic network is used to evaluate the quality of the load shedding actions and the actor network to determine the time, location and amounts of load shedding (LS). A global reward function is defined considering the recovery characteristics of voltages and total load shedding amounts to guarantee the coordination of under voltage load shedding (UVLS). The simulation is performed on the modified New England 39-bus system, and the results validate the voltage recovery performance and load shedding amount of the proposed scheme under different scenario.
Image Processing Hardware Algorithm for Monitoring Environment

Recently, object recognition applications using cameras in various devices have been developed and it is expected to be used in many fields in the future. In this paper, we designed SURF based hardware to detect forest fire. We designed the hardware and used a parallel processing structure to enable real-time processing. We also proposed a method to reduce memory usage to reduce the area of hardware. We have designed the hardware in HDL and verified its operation on Modelsim simulation comparing to the result of Matlab software. This design enables real-time monitoring of environments such as fire detection and air pollution.
High-Resolution Snapshot SAR/ISAR Imaging of Ship Targets at Sea

Usually dedicated to ground imaging, Spot SAR processing can also be applied to moving targets, when proper autofocusing is used. In this case, the simultaneous motions of carrier and target both have an impact on the azimuth resolution. That is why these motions have to be estimated and their effects corrected. The process is then a joint SAR-Inverse SAR (ISAR) imaging. For ship targets, the typical roll, pitch and yaw motions (usually unknown for real, non co-operative targets) produce some residual uncorrected migrations of scatterers. The consequence of these migrations is a blurring of SAR/ISAR range-Doppler maps of the target. A "snapshot" technique, based on a short imaging time, allows some robustness to these residual migrations during imaging time, but it has two main disadvantages. First, it makes it difficult to achieve high azimuth resolution. Second, it produces a series of range-Doppler maps, which include both useful and unsuitable images for extracting the outline of the ship target. The interest of a particular image depends on the moment in the unknown rotation of the target. We developed a criterion enabling us to choose suitable snapshots in a series, and also a segmentation technique adapted to the typical shapes of ship targets. This criterion can be adapted to range undersampled data and the presumed Doppler spread of the target return. Its complexity and accuracy may therefore be adapted to the context. The criterion and the segmentation technique have both been tested on synthetic and real in-flight data. A highly effective way of producing easy recognizable height profiles of ship targets at sea has been achieved.
An Improved Golden Ball Algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery

A Golden Ball (IGB) algorithm was improved for the well-known vehicle routing problem, which simultaneously considers the customer demand from both delivery and pickup orders. The objective of this problem was to determine the optimal set of routes to totally satisfy both the delivery and pickup demand of the customer population. In this paper, the Golden Ball (GB) algorithm was improved using intra-move improvement algorithms modify process selection players and coaching procedure. The aim was to find the solution that minimizes the total cost. Computational results showed that the IGB algorithm was competitive and outperforms the Golden Ball (GB) algorithm in all directions. Moreover, the best known solutions were also obtained in 67.43% of tested instances (19 out of 28).
Online Programming Platform Based on Crowdsourcing

Computer programming has become a popular course in most universities. More and more students learn and practice programming on websites. However, many online programming websites such as online judges are oriented to users preparing for the International Collegiate Programming Contests. For other websites oriented to novice programmers, the maintenance and updating of the website content has become a significant and costly problem. So we designed a crowdsourcing-based online programming platform that allows most users to update and maintain together. Only administrators involving a small number of teachers and experienced teaching assistants are paid for managing it. As a learning community with a large number of users and exercises, if there are a few administrators that are responsible for the maintenance, the efficiency of construction will be quite limited. Crowdsourcing makes the content construction more efficient and the exercise's quality more reasonable.
K-means Text Dynamic Clustering Algorithm Based on KL Divergence

The random selection of the initial cluster center and distance measure function selection in the classical k-means algorithm have a great influence on the time and final precision of the clustering. Based on the above two problems, this paper proposes the classical VSM (vector space model) to represent textual materials. Based on the maximum distance method, K data points with large distribution difference are selected as the initial cluster centers, and the similarity between the cluster centers and the sample data is obtained through KL divergence. And then put what share the similarity in a cluster, form the cluster calculation formula and the distance measure function of the iterative center, and calculate the iteration until the sample data set is empty. The experiment proves that the improved text clustering algorithm proposed in this paper not only reduces the total consumption time of clustering, but also improves the accuracy of clustering at the same time.
A Ring Resonator with Liquid Crystal for Biosensing Application

In this work, a ring resonator is designed to study the resonance characteristics. The resonance characteristics of the ring resonator dependency on the ring diameter, waveguide width and the gap between the ring and waveguide are considered to be 6μm, $15\mu \text{m}$ and $0.1\mu \text{m}$ respectively. The resonance frequency, free spectral range (FSR), Quality (Q) factor of the resonances are determined. In this simulation, a liquid crystal (LC) is placed as a covering medium for biosensing application. Liquid crystal based Ring resonator is simulated at 1550 nm wavelength.
Detecting Data Tampering In Synchrophasors Using Power Flow Entropy

The continuous increase in demand for electric power has resulted in extremely complex power grids. The correct operation of these grids depends on the computerized control and monitoring using a communication network. These modern power grids are equipped with phasor measurement units which play a key role in providing crucial data for the state estimator of an energy management system. The integration of a communication network with the power grid makes it vulnerable to cyber attacks in the form of data tampering. These attacks can disrupt the operation of the state estimator resulting in significant financial as well as infrastructure damage. To solve this issue, this paper proposes a technique to detect tampered data in synchrophasors using the system entropy of a power network. The results show that the proposed technique can be used as an effective tool to detect attacks on synchrophasors.
Disciplinary Variations in Computer Programming

Programming bugs are quite normal and usual for novice programmers. This article reports the findings of our research investigating disciplinary variations in the computer programming field from three perspectives, which are (i) the knowledge structure implied by the performance of each disciplinary group of students in the 26 question categories covering different knowledge points, (ii) the programming ability illustrated by the common bugs made by them respectively, and (iii) the knowledge acquisition ability suggested by the changes happened to the common bugs over a one-year period. The results support the hypothesis that the thinking pattern and the knowledge structure of students is shaped by their disciplinary context, and call into question the extent to which the knowledge acquisition process differs between different disciplinary groups. Overall, Students may use our result to look up the most common bugs in attempt to avoid them in the future. While instructors can use this result to continuously improve their curriculum and teaching practices for different disciplinary group of students respectively.
Enhancing the Performance of Sentiment Analysis by Using Different Feature Combinations

With the growing accessibility and acceptance of social networking epoch, Sentiment Analysis has become one of the most prominent research domain in natural language processing. Each day, millions of people share their thoughts and ideas by posting in social media or writing online reviews. This massive participation, on one hand, makes these media opinion-rich; however, on the other hand, it poses some challenges in identifying the dominant opinion. In this work, tweets and movie reviews are classified according to the polarity of the opinions by using several features in combination. Performance of several feature combinations was evaluated by feeding those in different Machine Learning algorithms (NB, SVM, MaxEnt). Hence, the goal of the work was to evaluate how the performance of a classifier is affected when different feature combinations are used in Sentiment Analysis. Experiments were done on data from two different domain namely Stanford Twitter Sentiment 140 dataset and IMDb Movie Reviews dataset. Four different evaluation metrics: recall, precision, accuracy and F1 score are used for evaluating the investigational results of our system. This research demonstrates that by carefully choosing correct feature combination the classification accuracy can be increased while a random feature combination will provide little benefit.
An Energy Management Strategy of Hybrid Energy Storage Systems for Electric Vehicle Applications

In order to mitigate the power density shortage of current energy storage systems (ESSs) in pure electric vehicles (PEVs or EVs), a hybrid ESS (HESS), which consists of a battery and a supercapacitor, is considered in this research. Due to the use of the two ESSs, an energy management should be carried out for the HESS. An optimal energy management strategy is proposed based on the Pontryagin's minimum principle in this research, which instantaneously distributes the required propulsion power to the two ESSs during the vehicle's propulsion and also instantaneously allocates the regenerative braking energy to the two ESSs during the vehicle's braking. The objective of the proposed energy management strategy is to minimize the electricity usage of the EV and meanwhile to maximize the battery lifetime. A simulation study is conducted for the proposed energy management strategy and also for a rule-based energy management strategy. The simulation results show that the proposed strategy saves electricity compared to the rule-based strategy and the single ESS case for the three typical driving cycles studied in this research. Meantime, the proposed strategy has the effect of prolonging the battery lifetime compared to the other two cases.
Body mass index of patients with chronic obstructive pulmonary disease is associated with pulmonary function and exacerbations: a retrospective real world research.

Background Chronic obstructive pulmonary disease (COPD) is prevalent in China. The role of body mass index (BMI) in COPD progression and prognosis is unclear. We analyzed the association between BMI and pulmonary function, inflammation levels and exacerbation in Chinese COPD patients. Methods Our retrospective real world research included 744 patients with COPD diagnosed by spirometry and hospitalized from January 1st, 2014 to December 31st, 2016. The indicators were gathered from hospital records database and frequency of exacerbation in the three years were followed up. All 744 patients were divided into four groups by BMI grades. We analyzed the association between BMI and pulmonary function, inflammation levels and exacerbation by Spearman bivariate correlations, Kruskal-Wallis test, Mann-Whitney U test and logistic regression. Results The singly proportion (median of BMI) of these patients in underweight, normal weight, overweight and obesity was 7.80% (17.54), 45.97% (22.12), 27.96% (27.00) and 18.28% (31.25) respectively. With increasing of BMI grades, the values of forced expiratory volume in 1 second (FEV1), peak expiratory flow (PEF), forced expiratory flow (FEF25/50/75) and diffusing capacity of carbon monoxide (DLCO) were correspondingly increasing; the percentage of neutrophils and C-reactive protein (CRP) presented significant declining trend while the trend of the percentage of eosinophils was negative; the dose of systemic corticosteroid and length of stay present decreasing tendency; the frequency of exacerbation and hospitalization were decreasing. These were similar results in gender, smoking status COPD subgroups. Conclusions In our study, BMI was moderately correlated with pulmonary function positively and exacerbations negatively. To some extent, BMI might be a useful indicator to predict the prognosis of COPD patients and for long-term management.
Esaki Diodes Based on 2-D/3-D Heterojunctions

Esaki diodes based on interband tunneling have the characteristics of negative differential resistance (NDR) and ultrafast transient time, which lead to broad applications including oscillators, multivalue memories, and terahertz detectors. In this paper, we present the first demonstration of Esaki diodes based on 2-D/3-D heterojunctions—more specifically, chemical vapor deposition MoS2 on degenerately doped silicon. As compared to traditional 3-D heterostructures, these 2-D/3-D heterostructures have the following advantages: dislocation-free 2-D crystals even when the lattices are mismatched, dangling bond-free surface, and capability for large-scale synthesis at low cost. In this paper, monolayer, bilayer, and trilayer MoS2 are synthesized directly on degenerated Si substrate, forming the ultraclean heterostructures without surface contamination from tape and resist. Based on these pristine heterostructures, we are able to observe prominent NDR effect at room temperature. This NDR effect is attributed to the degenerately p-type doping in silicon and the natural n-type doping in MoS2. We also found that the peak voltage corresponding to the local maximum tunneling current depends on the MoS2 thickness. While MoS2 is changing from the monolayer (0.7 nm) to bulk (9.5 nm), the peak voltage increases from 0.8 to 1.6 V. This phenomenon can be explained by the energy-level differences between the monolayer and bulk MoS2. This paper provides the experimental groundwork for the synthesis of transition metal dichalcogenides on degenerately doped Si substrates and opens up new and exciting opportunities for electronic applications of 2-D/3-D heterostructures.
A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection

The closed-loop application of electrical stimulation via chronically implanted electrodes is a novel approach to stop seizures in patients with focal-onset epilepsy. To this end, an energy efficient seizure detector that can be implemented in an implantable device is of crucial importance. In this study, we first evaluated the performance of two machine learning algorithms (Random Forest classifier and support vector machine (SVM)) by using selected time and frequency domain features with a limited need of computational resources. Performance of the algorithms was further compared to a detection strategy implemented in an existing closed loop neurostimulation device for the treatment of epilepsy. The results show a superior performance of the Random Forest classifier compared to the SVM classifier and the reference approach. Next, we implemented the feature extraction and classification process of the Random Forest classifier on a microcontroller to evaluate the energy efficiency of this seizure detector. In conclusion, the feature set in combination with Random Forest classifier is an energy efficient hardware implementation that shows an improvement of detection sensitivity and specificity compared to the presently available closed-loop intervention in epilepsy while preserving a low detection delay.