Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk for stroke and chronic kidney disease in patients with sleep apnea syndrome and heart failure with different ejection fractions

Abstract Background Patients with sleep apnea syndrome (SAS) and heart failure (HF) have concomitant different comorbidities and increased risk of morbidity. Aim The aim of this study was to analyze differences between patients with SAS and heart failure with preserved ejection fraction (HFpEF; ejection fraction [EF]≥50%) – group 1 and those with SAS and heart failure with reduced ejection fraction (HFrEF; EF<50%) – group 2. Methods We evaluated 51 patients with SAS and HF in the sleep laboratory of Timisoara Victor Babes Hospital. We collected general data, sleep questionnaires, anthropometric measurements (neck circumference [NC], abdominal circumference [AC]), somnography for apnea–hypopnea index (AHI), oxygen desaturation index (ODI), echocardiographic data, comorbidities, and laboratory test. Results The study included 51 patients who were divided into two groups depending on EF, with the following characteristics: Group 1 (HFpEF): 26 patients, 19 males, seven females, age 61.54±9.1 years, body mass index (BMI) 37±6.4 kg/m2, NC 45.4±3.6 cm, AC 126.6±12.9 cm, AHI 48.3±22.6 events/hour, central apnea 5.6±11.4 events/hour, obstructive apnea 25.7±18.7 events/hour, ODI 41.2±21.2/hour and lowest SpO2 –72.1±14%. Group 2 (HFrEF): 25 patients, 18 males, seven females, age 63.6±8.8 years, BMI 37.9±7.5 kg/m2, NC 46±4.4 cm, AC 127.2±13.9 cm, AHI 46.4±21.7 events/hour, central apnea 4.6±8.3 events/hour, obstructive apnea 25.9±18.5 events/hour, ODI 44.8±27.1/hour and lowest SpO2 –70.6±12.1%. Differences between groups regarding anthropometric and somnographic measurements and lipidic profile were not statistically significant. Significant differences were observed regarding stroke (23% vs. 4%, p=0.04) in the group with HFpEF and regarding creatinine measurements (1.1±0.2 vs. 1.4±0.7, p=0.049), aortic insufficiency (11.5% vs. 36%, p=0.04) and tricuspid insufficiency (6.1% vs. 80%, p=0.01) in the group with HFrEF. Conclusions Patients with SAS and HFpEF have a higher risk of stroke. Patients with SAS and HFrEF have a significantly increased risk of developing a life-long chronic kidney disease and aortic and tricuspid insufficiency. These results may suggest pathogenic links between SAS and the mentioned comorbidities, and this may explain the higher mortality when this association is present.
An Expression Feature Extraction Method Based on Improved LBP with Wavelet Decomposition

Facial expression feature extracted isn’t exquisite enough with the aid of original LBP (Local Binary Pattern) operator, merely focusing on differences between the center pixel gray value and the neighborhood pixel gray value. MLBP (Multiple Local Binary Patterns) is proposed in this paper, making some improvements to LBP algorithm. A novel approach to facial expression recognition based on MLBP operator with wavelet decomposition is put forward, effectively reducing the dimensionality of facial expression feature vector. Experimental results show that the method presented can efficaciously improve recognition rate.
A pipelined ROM-less architecture for sine-output direct digital frequency synthesizers using the second-order parabolic approximation

A mathematical approximation for the sine function is proposed which is so close to the sine function that it satisfies the accuracy requirements for sine computation in a typical sine-output direct digital frequency synthesizers (DDS). Then, it is shown that the proposed approximation is realizable by standard digital circuitry, so, a novel ROM-less architecture for sine-output DDS is developed, optimized, and implemented. Experimental results for the implemented ROM-less sine-output DDS are in complete agreement with simulation results in showing that the approximation error will cause harmonic levels below the spurious levels associated with the output quantization error. Another important advantage of the proposed architecture is that it dispenses with ROM, so it can be easily pipelined in order to be as fast as is required.
Review of pallet pooling studies

Abstract Development of sharing economy has brought new opportunities to the development of green logistics. Pallet pooling is a typical type of sharing economy. It has been recently promoted in almost every country in the world. Researchers have reached a common agreement that establishing an effective pallet pool is of great importance to the pallets use efficiency. It is also believed that the implementation of pallet pool will substantially contribute to the economic and social development. Detailed analysis of pallet pooling such as benefit analysis, mode choose, pallet allocation model & algorithm, quality control, etc. are discussed in this paper. Based on literature review, several important problems are listed for future study. These problems are information management, supply chain management, rental pricing, pallets tracking and allocation, quality control, sustainable development (carbon emissions), and “internet plus pallet pooling”. Some suggestions on China’s pallet pooling development are proposed as well.
Human error and human reliability

A short review is presented of the existing knowledge on the human error effects on the performance of system: special emphasis is placed on the human reliability. In addition, the associated technical procedures and illustrations are described briefly on the use of the human error failure rates (human reliabilities), and two examples of estimations are prepared tentatively, including the design of human tasks and specific items of man operated equipment.
Service discovery with higher order services in mobile hospitals

German Red Cross mobile hospitals must be operational in areas of war or disaster within a few days. In the ARDOR project, we collaborate with the German Red Cross in order to accelerate the workflows in these hospitals by developing and deploying a configurationless distributed information technology on top of a mobile ad hoc network . The main applications are scheduling of resources and their allocation. The computer-supported scheduling must enable the planning staff to query the currently available resources and services and the respective booking information. To achieve this, we introduce service discovery on the middleware layer. However, service discovery has to handle higher order (medical or logistic) services, as an entry in the schedule typically consists of the simultaneous allocation of many resources. Furthermore, service discovery must be scalable as the mobile ad hoc network might be highly dynamic and large in size. This paper claims that our self-organizing and scalable service discovery approach provides a way to enable reliable access to higher order services.
Shortest Path Algorithm for Data Transmission in Wireless Ad-Hoc Sensor Networks

Wireless sensor networks determine probable in military, environments, health and commercial applications. The process of transferring of information from a remote sensor node to other nodes in a network holds importance for such applications. Various constraints such as limited computation, storage and power makes the process of transferring of information routing interesting and has opened new arenas for researchers. The fundamental problem in sensor networks states the significance and routing of information through a real path as path length decides some basic performance parameters for sensor networks. This paper strongly focuses on a shortest path algorithm for wireless adhoc networks. The simulations are performed on NS2 and the results obtained discuss the role of transferring of information through a shortest path.
Contact Engineering High Performance n-Type MoTe2 Transistors.

Semiconducting MoTe2 is one of the few two-dimensional (2D) materials with a moderate band gap, similar to silicon. However, this material remains underexplored for 2D electronics due to ambient instability and predominantly p-type Fermi level pinning at contacts. Here, we demonstrate unipolar n-type MoTe2 transistors with the highest performance to date, including high saturation current (>400 µA/µm at 80 K and >200 µA/µm at 300 K) and relatively low contact resistance (1.2 to 2 kΩ∙µm from 80 to 300 K), achieved with Ag contacts and AlOx encapsulation. We also investigate other contact metals, extracting their Schottky barrier heights using an analytic subthreshold model. High-resolution X-ray photoelectron spectroscopy reveals that interfacial metal-Te compounds dominate the contact resistance. Among the metals studied, Sc has the lowest work function but is the most reactive, which we counter by inserting monolayer h-BN between MoTe2 and Sc. These metal-insulator-semiconductor (MIS) contacts partly de-pin the metal Fermi level and lead to the smallest Schottky barrier for electron injection. Overall, this work improves our understanding of n-type contacts to 2D materials, an important advance for low-power electronics.
Early Detection for Optimal-Latency Communications in Multi-Hop Links

Modern wireless machine-to-machine-type communications aim to provide both ultra reliability and low latency, stringent requirements that appear to be mutually exclusive. From the noisy channel coding theorem, we know that reliable communications mandate transmission rates that are lower than the channel capacity. To guarantee arbitrarily-low error probability, this implies the use of messages whose lengths tend to infinity. However, long messages are not suitable for low-latency communications. In this paper, we propose an early-detection scheme for wireless communications under a finite-blocklength regime that employs a sequential-test technique to reduce latency while maintaining reliability. We prove that our scheme leads to an average detection time smaller than the symbol duration. Furthermore, in multi-hop low-traffic or continuous-transmission links, we show that our scheme can reliably detect symbols before the end of their transmission, significantly reducing the latency, while keeping the error probability below a predefined threshold.
Uma proposta de escalonamento confiável para redes sem fio baseadas no padrão IEEE 802.11/11e

This work investigates the problem of scheduling network resources in the context of a cooperative mobile multi-robot system that exchange messages with firm real-time constraints using a wireless network compliant with the IEEE 802.11e amendment. Given the physical characteristics of wireless communication, messages retransmission becomes a crucial issue, especially in a real-time application. Experiments performed in the context of this work show that the regular approach for retransmission proposed in the IEEE 802.11e amendment is not enough for real-time applications that require a certain level of reliability. To tackle this problem, the current work proposes a new approach that integrates messages scheduling and retransmission in a combined manner at the MAC layer. According to the proposed solution, the scheduling algorithm is able to deal with residual transmission errors and increase the reliability level of the real-time messages. It provides flexibility to deal with unexpected failures and supports additional quality of service requirements increasing the robustness and adaptability of the system. The proposed approach is evaluated trough simulations for different scenarios of traffic load. Obtained results are analyzed comparatively with the standard approach for scheduling and retransmission of the IEEE 802.11e amendment. The results confirm the benefits of the proposal.
Local tail asymptotics for the joint distribution of length and of maximum of a random walk excursion

Эта заметка посвящена исследованию максимума экскурсии случайного блуждания с отрицательным сносом и однородными приращениями, распределение которых имеет легкий хвост. Точнее, мы находим асимптотику локальных вероятностей для совместного распределения длины экскурсии случайного блуждания, ее максимума и момента достижения максимума. Этот результат позволяет получить локальную центральную предельную теорему для длины экскурсии, при условии достижения ее максимумом больших значений.
Electromagnetic radiation detectors based on Josephson junctions: Effective Hamiltonian

We theoretically analyze two setups of low-energy single-photon counters based on Josephson junctions (JJs). For this, we propose two simple and general models based on the macroscopic quantum tunneling formalism (MQT). The first setup is similar to the photon counter based on the "cold-electron bolometer" (CEB), where the JJ replaces the CEB in the center of the superconducting antenna. In the second setup, the JJ is capacitively coupled to the antenna. We derive the Hamiltonians for the two setups, and we write the Schrodinger equations, taking into account both the antenna and the JJ. The quantum particles of the MQT models move in two-dimensional potential landscapes, which are parabolic along one direction and may have the form of a washboard potential along another direction. Such a potential landscape has a series of local minima, separated by saddle points. If the particle is prepared in the initial state in the metastable "ground state" of a local minimum, then the photon absorption causes it to jump into an excited state. If the excitation energy is bigger than the potential barrier seen by the quantum particle (the difference between the ground state and the saddle point), the photon is detected. The models are simple and allow us to do mostly analytical calculations. We show that the two setups are equivalent from the MQT point of view since one Hamiltonian can be transformed into the other by changes in variables. For typical values of the JJ and antenna parameters, the setups may work as counters of photons of wavelengths up to at least 1 cm. Dark count rates due to the phase particle tunneling directly from the ground state into the running state have also been evaluated.
Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information on big data collected by the Internet of Things (IoT). That is, it is pretty crucial to explore the valuable information associated with the rare events involved in minority subsets of the voluminous amounts of data. To do so, how to effectively measure the information with the importance of the small probability events from the perspective of information theory is a fundamental question. This paper first makes a survey of some theories and models with respect to importance measures and investigates the relationship between subjective or semantic importance and rare events in big data. Moreover, some applications for message processing and data analysis are discussed in the viewpoint of information measures. In addition, based on rare events detection, some open challenges related to information measures, such as smart cities, autonomous driving, and anomaly detection in the IoT, are introduced which can be considered as future research directions.
Life time evolution of coronal holes

We investigate the evolution of eight well-observed persistent coronal holes (CHs) with life spans of 5--12 solar rotations, that were observed between 2010 and 2015. The aim is to increase our understanding of the evolution of CHs, as well as to investigate the basic physical mechanisms that govern the CH behaviour over its lifetime. Using combined AIA/SDO and HMI/SDO data, we derive several CH parameters such as area, intensity, and magnetic field characteristics as function of time. Using in-situ data from the ACE satellite located at L1, we study the corresponding solar wind plasma measurements. We find that 6 out of 8 CHs in our data set reveal a steady increase in the area followed by a decrease. For those we derive the average absolute change of area between two points in the growing phase with (10.2 +/- 3.5 )x 10^8 km^2 per day, while for the decaying phase it is (8.6 +/- 3.7)x 10^8 km^2 per day. We further found that the CH magnetic field strength is strongly related to the amount of area strong flux tubes contribute to the CH area. However, there is no correlation between the magnetic field and the total CH area itself, hence, the magnetic field variation follows a different evolutionary pattern. With the in-situ proton bulk speed, we derive for the growing area phase a strong correlation (Pearson cc=0.69) and for the decaying phase a moderate one (cc=0.45).
Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks.
Deep Personalized Re-targeting

Predicting booking probability and value at the traveler level plays a central role in computational advertising for massive two-sided vacation rental marketplaces. These marketplaces host millions of travelers with long shopping cycles, spending a lot of time in the discovery phase. The footprint of the travelers in their discovery is a useful data source to help these marketplaces to predict shopping probability and value. However, there is no one-size-fits-all solution for this purpose. In this paper, we propose a hybrid model that infuses deep and shallow neural network embeddings into a gradient boosting tree model. This approach allows the latent preferences of millions of travelers to be automatically learned from sparse session logs. In addition, we present the architecture that we deployed into our production system. We find that there is a pragmatic sweet spot between expensive complex deep neural networks and simple shallow neural networks that can increase the prediction performance of a model by seven percent, based on offline analysis.
Dissimilarity learning via Siamese network predicts brain imaging data

The advent of deep learning has a profound effect on visual neuroscience. It paved the way for new models to predict neural data. Although deep convolutional neural networks are explicitly trained for categorization, they learn a representation similar to a biological visual system. But categorization is not the only goal of the human visual system. Hence, the representation of a classification algorithm may not completely explain the visual processing stages. Here, I modified the traditional Siamese network loss function (Contrastive loss) to train them directly on neural dissimilarity. This network takes image pair as input and predicts the correlation distance between their output features. For Algonauts challenge, using dissimilarity learning, I fine-tuned the initial layers of Alexnet to predict MEG early response/EVC data and all the layers of VGG-16 to predict MEG late response/IT data. This approach is ideal for datasets with high SNR. Therefore, my model achieved state-of-the-art performance on MEG dataset but not fMRI.
Design and Control of a Quasi-Direct Drive Soft Exoskeleton for Knee Injury Prevention during Squatting

This paper presents design and control innovations of wearable robots that tackle two barriers to widespread adoption of powered exoskeletons, namely restriction of human movement and versatile control of wearable co-robot systems. First, the proposed quasi-direct drive actuation comprising of our customized high torque density motors and low ratio transmission mechanism significantly reduces the mass of the robot and produces high backdrivability. Second, we derive a biomechanics model-based control that generates biological torque profile for versatile control of both squat and stoop lifting assistance. The control algorithm detects lifting postures using compact inertial measurement unit (IMU) sensors to generate an assistive profile that is proportional to the biological torque produced from our model. Experimental results demonstrate that the robot exhibits low mechanical impedance (1.5 Nm resistive torque) when it is unpowered and 0.5 Nm resistive torque with zero-torque tracking control. Root mean square (RMS) error of torque tracking is less than 0.29 Nm (1.21% error of 24 Nm peak torque). Compared with squatting without the exoskeleton, the controller reduces 87.5%, 80% and 75% of the of three knee extensor muscles (average peak EMG of 3 healthy subjects) during squat with 50% of biological torque assistance.
Lateral inhibition net and weighted matching algorithms for speech recognition in noise

The authors address the problem of speech recognition with signals corrupted by white Gaussian additive noise at moderate SNR. The energy of the noise is not required. A technique based on a lateral inhibition process approximation with a multilayer neural net (the lateral inhibition net (LIN)) and neural net processing efficacy weighting in acoustic pattern matching algorithms is proposed. In the recognition procedure, the local SNR is computed by means of the autocorrelation function and is employed to estimate the efficacy of LIN in noise cancelling which is taken into account as a weight in a pattern matching algorithm. A general criterion based on weighting the frame influence in decisions according to the reliability in noise reduction is suggested, and modified versions of both HMM and DTW algorithms have been designed. To be more coherent with the conditions that define LIN, a modification in the backpropagation algorithm is also proposed.