Miracle-dz/UniFAR-FTUs huggingface dataset:
A 4-Transistors/1-Resistor Hybrid Synapse Based on Resistive Switching Memory (RRAM) Capable of Spike-Rate-Dependent Plasticity (SRDP)

Mimicking the cognitive functions of the brain in hardware is a primary challenge for several fields, including device physics, neuromorphic engineering, and biological neuroscience. A key element in cognitive hardware systems is the ability to learn via biorealistic plasticity rules, combined with the area scaling capability to enable integration of high-density neuron/synapse networks. To this purpose, resistive switching memory (RRAM) devices have recently attracted a strong interest as potential synaptic elements. Here, we present a novel hybrid 4-transistors/1-resistor synapse capable of spike-rate-dependent plasticity. The frequency-dependent learning behavior of the synapse is shown by experiments on HfO2 RRAM devices. Unsupervised learning, update, and recognition of one or more visual patterns in sequence is demonstrated at the level of neural network, thus, supporting the feasibility of hybrid CMOS/RRAM integrated circuits matching the learning capability in the human brain.
Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

Aim To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.
Which Part is Better: Multi-Part Competition Network for person Re-Identification

Person re-identification is a challenging task due to the background clutters, occlusion and illumination variations. In addition, the pedestrian misalignment always exists in some automatic-detection datasets. In this paper, we propose a Multi-Part Competition Network (MPCN) consisting of Multi-Part Network (MPN) and Part Competition Network (PCN), which aims to solve the misalignment problem caused by the detector errors and human pose variations. First, we construct original body parts and enlarged body parts using human pose estimation algorithm. These two kinds of body parts not only alleviate the misalignment from background and varying human pose but also solve the missing details and imprecise body parts introduced by human pose estimator. Then, we use MPN to acquire global features and two different body parts features. The components of MPN, a global branch and two part branches, are combined by ROI pooling layer. Finally, we apply PCN to achieve a tradeoff between the original body parts and the enlarged body parts and acquire discriminative part features from these two different body parts. Extensive evaluations on three widely used re-id datasets, Market-1501, CUHK03, VIPeR demonstrate that our proposed network have a competitive result compared to the state-of-the-art methods.
Morphology Variations of Primary Cu6Sn5 Intermetallics in Lead-Free Solder Balls

This study focuses on the morphologies of primary Cu6Sn5 intermetallics in small free standing SnCu (Ø 270 μm) solder balls. Those showed a large variety of different shapes and sizes ranging from facetted hexagonal rods, to partly facetted splitting crystals and parallel growing branches, to dendritic crystals without facets. The results of electron backscatter diffraction (EBSD) measurements confirm [0001] as the major growth direction and the {10I0} planes as facets of the hexagonal rods. The formation of splitting crystals parallel to the {10I0} planes may be caused by a slight deviation of the major growth direction towards <2II0>. Morphology transition to dendritic structures can be influenced primarily by increasing the Cu content of the alloy and the cooling rate. However, strong variations occur even if the composition and the cooling rate are constant. Differences in undercooling of the Cu6Sn5 phase have been discussed as a possible reason, since a decreasing solidification temperature promotes a faster initial phase growth due to the increasing oversaturation of the melt’s Cu content.
Efficient Object Region Discovery for Weakly-supervised Semantic Segmentation

Deep Convolutional Neural Networks (DCNNs) provide the leading performance in the semantic segmentation task. However, collecting large-scale pixel-level annotations for training such a DCNN is labor intensive and not cost-effective. In this paper, we propose a small to large (STL) Filed-Of-View framework to train semantic segmentation networks from image level annotations. Specifically, we first train a small Filed-Of-View segmentation network (SFN) with the image-level annotations to discover initial object regions effectively. These localized regions are then combined with saliency maps to construct hypotheses on pixel-level annotations, using which a large Filed-Of-View segmentation network (LFN) is learned. To further enhance the segmentation quality, the object regions generated by LFN are verified with saliency maps, and thus the hypotheses are refined in an iterative manner. The converged hypotheses serve as the supervision information to learn a more powerful LFN for semantic segmentation. Extensive experimental results on PASCAL VOC 2012 segmentation benchmark well demonstrates the superiority of our proposed methods compared with the state-of-the-art.
Depth-assisted RefineNet for Indoor Semantic Segmentation

This paper focuses on indoor semantic segmentation using RGB-D data. It has been shown that incorporating depth information into RGB information is helpful to improve segmentation accuracy. However, previous studies have revealed two problems. One is about the model size. Recent state-of-the-art methods generally build a network branch for depth images, inherently increasing the model size. The other is about boundary segmentation. The complex and various object configurations with severe occlusions influence the segmentation precision of object boundaries. To address these two problems, we propose a depth-assisted RefineNet (D-RefineNet) for refining the boundary segmentation. The proposed network only uses RGB images to predict segmentation results. Depth images are just used in the proposed loss function without increasing the model size. When the depth values of adjacent pixels change drastically but the adjacent pixels have the same predicted semantic labels, the proposed loss function penalizes the predicted result. Experimental evaluations demonstrate that the proposed method is effective on two challenging RGB-D indoor datasets, NYUDv2 and SUN RGB-D.
Graphene Based THz Electromagnetic Imaging System for the Analysis of Artworks

In this paper, a graphene-based electromagnetic imaging system working in 140–220 GHz frequency band for nonintrusive analysis of 3D artwork is presented. A single-stage high-order frequency multiplier is used to generate the signal at the transmitter module, whereas the single-stage high-order subharmonic mixer acts as a signal detector at the receiver. Both modules exploit the nonlinear response of graphene sheets excited by an incident electromagnetic wave, to generate harmonic components and mixing products of the input signals, respectively. The transmitter and the receiver modules were implemented through an additive manufacturing process. The resulting electromagnetic imaging system prototype was applied to the analysis of ceramic vessels, showing its ability to detect enclosed objects and to obtain information in cross-sectional planes. Furthermore, the geometrical parameters and filling material characterization can be obtained by processing the frequency response of the scattered field.
Bayesian Inference with Error Variable Splitting and Sparsity Enforcing Priors for Linear Inverse Problems

Regularization and Bayesian inference based methods have been successfully applied for linear inverse problems. In these methods, often simple Gaussian or Poisson models for the forward model errors have been considered. In this work, we use variable splitting for the errors to model different sources of errors and their possible non-stationarity or impulsive nature using Student-t or other heavy tailed distributions. Also, as a prior model, a sparsity enforcing hierarchical model of Infinite Gaussian Mixture model is introduced. With these prior models, we obtain a complete Bayesian inference framework which can efficiently be implemented for any linear inverse problem. Interestingly, many recent regularization-based algorithms such as Alternating Direction Method of Multipliers (ADMM) as well as more classical Bayesian based methods such as Sparse Bayesian Learning (SBL) are obtained as particular cases. One advantage of the Bayesian approach is the possibility to estimate, jointly with the reconstruction, the hyper-parameters such as the regularization parameter, thus the capability of proposing unsupervised methods. Examples of implementation of the proposed method in different signal and image processing such as deconvolution in mass spectrometry, estimation of periodic components estimation in biological signals and computed tomography are mentioned and referenced.
Survey of Available Experimental Data of Radio Wave Propagation for Wireless Transmission

This paper provides a survey of various experimental data available on the value of the propagation path loss of radio waves in a cellular wireless environment. It is shown starting with the Okumura et al.’s paper on propagation measurements and other available published experimental data that they all exhibit that the electric field varies as $\rho ^{-1.5}$ within a cellular radius of a few kilometers, where $\rho $ is the radial distance of the receiving antenna from the transmitting one. This decay in the fields is equivalent to a propagation loss of −9 dB/octave or −30 dB for a decade of the distance. This value is independent of the nature of the ground, whether it be composed of rural, urban, suburban, or water. This is the first time it is stated that the propagation path loss due to the presence of ground generates a path loss of 90 dB when the signals travel a distance of 1 km. This value is rather large when compared to a loss of 30–50 dB produced by buildings, trees, and similar artifacts. Therefore, the experimental data indicate that the effect of trees and buildings have a secondary influence on the decay of the electric field with distance, the dominant one is the propagation loss over an imperfect ground. Contemporary propagation models do not acknowledge this fact. Outside the cellular radius of a few kilometers, the path loss appears to be 12 dB/octave or 40 dB/decade of distance. In a companion paper, it will be demonstrated that the values for the path loss can be explained from an analytical standpoint without taking recourse to statistics which involves a lot of assumptions on the functional variation of the variables of interest.
Dual Linearly Polarized Microstrip Antenna Using a Slot-Loaded TM50 Mode

We present a high-gain, low-profile dual-polarized microstrip antenna operating in the hybridized higher order mode. The proposed antenna consists of a slot-loaded cross-shaped patch, which can be considered as orthogonally polarized radiating elements operated in the TM50 mode. Moreover, the four loaded slots can effectively excite the in-phase TM10 mode such that the superposition of both modes can lead to gain enhancement in the broadside direction, with suppressed sidelobes. The proposed antenna requires only a single-dielectric layer and a simple feeding method, while exhibiting good isolation between the two polarizations. We have developed a prototype, which has a total size of only 1.65 λ0 × 1.65 λ0 × 0.04 λ0 (λ0 is the free-space wavelength at the center frequency) and a measured maximum gain of 10.9 dBi. This dual-polarized microstrip antenna with the advantages of high gain, low profile, low cost, and good isolation, is envisioned to benefit many multiple-input, multiple-output and the fifth generation (5G) communication systems.
Distributed Offset Correction for Time Synchronization in Networks with Random Delays

A new distributed asynchronous offset correction algorithm for time synchronization in networks with random communication delays, communication dropouts, and measurement noise is proposed. The algorithm is based on a specially constructed error function which includes delay compensation and unboundedly-increasing-time compensation parameters. Convergence of the algorithm in the mean square sense and w.p.l is proved. A modification of the algorithm based on consensus on the delay compensation parameter is also proposed, offering better performance in practice. Illustrative simulation results are presented.
From Usage to Contribution: A Synthesized Typology of Technology Extra-Role Behavior

Beyond usage, users can contribute to IT services and bring competitive advantages to IT vendors. To understand how to better mobilize the powerful user workforce, this study aims at studying user voluntary contributions in a general context of consumer IT services. Drawing on the extra-role behavior literature, this study re-examines the definition and scope of voluntary contributions. Specifically, we theorize a new term, technology extra-role behavior (TERB), and synthesize a comprehensive typology of TERB. This study has both research and practical implications
“What is worse than death?”: experience of critical events among physicians

Background. It is noticeable that doctors’ avoidant behavior while dealing with emotional consequences of critical events not only lowers physician’s quality of sleep (Kahn, Sheppes, & Sadeh, 2013), brings them less satisfaction with the results of their work (Gleichgerrcht & Decety, 2013), but also worsens medical care as they provide poorer services related to the patient (Austin Saylor, & Finley, 2017; Meier, Back, & Morrison, 2001). The lack of scientific publications shows that this topic is underresearched and relevant. The purpose of the study is to reveal physicians’ experience of critical events. Methods. Five practicing physicians of anesthesiology-reanimatology and surgery participated in the qualitative part of the research. The data was collected using semi-structured interview and processed using inductive thematic Braun & Clarke (2006) analysis. Results. Qualitative analysis revealed the complicated experiences in a physician’s workplace, which doctors described as: taking responsibilities in the presence of a patient’s death, the risk of burnout and negative emotions experienced after a critical event. The analysis also emerged techniques used in dealing with emotions after critical events and consequences of the latter in one’s personal life. Conclusions. The study revealed that physicians in their work environment come across difficulties such as risk of burnout, balancing between formal and informal communication with patients and emotional strain which is caused by facing a patient’s death. Doctors tend to cope with negative emotions that emerge during critical events using various methods, but the most elucidated technique was the avoidance to deal with emotions. Analysis also revealed that experiences, gained through medics’ work, modify their attitude towards life and death and change the emotional connection between a physician and his relatives.
AlGaN Solar-Blind Avalanche Photodiodes With p-Type Hexagonal Boron Nitride

To improve the performances of AlGaN solar-blind avalanche photodiodes (APDs), we propose a separate absorption and multiplication AlGaN APD with a p-hBN layer. The simulated results show that the p-hBN/AlGaN APD significantly decreases the breakdown voltage almost by 23% in comparison with the conventional AlGaN APD due to the use of p-hBN, which has a wider bandgap, a higher p-type doping efficiency, and a smaller spontaneous polarization. Moreover, the designed APD keeps an intrinsic solar-blind characteristic even at large reverse bias because the h-BN is completely transparent in the long wavelength direction outside the solar-blind region. The result also confirms that the p-hBN/AlGaN APD has the lower Flicker and impact ionization noise at low and medium frequency under breakdown voltage.
Prediction of Students' Academic Success Using Data Mining Methods

Success is very important for all of us. Most people wants prosperity, reputation, and richness that can only be achieved with the success. A society that wants to be successful should pay attention to their new generation because they are the future of the world. If we want to invest to our future, we must contribute to success of our new generations. Therefore, in this study, the academic performance of the students that belong to different levels of education like primary, secondary, and high school levels is tried to be determined by applying various classification methods such as Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), Decision Tree (J48), and Voting classifiers. It is also observed which characteristics are more related to the improvement of academic performance of the students. Features like absence of student, parent's school satisfaction, raising hands on class, and parent who is responsible for the student can affect the success of the student. A comparison is made with other study that previously worked on the same data set. As a result, better classification accuracy is achieved. We observe the best classification accuracy as 80.6% by Voting classifier, while the previous study has the highest accuracy as 79.1 % by applying Artificial Neural Network (ANN) classifier. Also, in our study, Apriori algorithm is applied to detect relationships between features.
The Interaction of Ion Migration with Shockley-Read-Hall Recombination in the Bulk of Perovskite Solar Cells Explains Anomalous Voltage and Luminescence Transients

Slow transient responses in perovskite solar cells have been attributed to a range of possible mechanisms, but the migration of ionic charge is a leading candidate. In this paper, we present an overview of the results of a transient numerical model of drift-diffusion in a semiconductor device, to which we add mobile ionic charge, to demonstrate that a range of monotonic and non-monotonic transient voltage and luminescence responses described in the perovskite literature can be replicated with the movement on ionic charge. These include some unusual non-monotonic responses that have previously been attributed to multiple, competing mechanisms. We observe that the key mechanism instead is the interaction of the ionic charge on the distribution of electrons and holes and a subsequent change in the distribution of trap-mediated recombination that is modelled after Shockley-Read-Hall statistics. These results are evidence for the value of a rigorous computation of the interaction of ion migration and traditional semiconductor charge transport models to reveal interactions between ion migration and recombination in perovskite solar cells that might otherwise escape our intuition.
Recommending Paths: Follow or Not Follow?

Mobile social network applications constitute an important platform for traffic information sharing, helping users collect and share sensor information about the driving conditions they experience on the traveled path in real time. In this paper we analyse the simple but fundamental model of a platform choosing between two paths: one with known deterministic travel cost and the other that alternates over time between a low and a high random cost states, where the low and the high cost states are only partially observable and perform respectively better and worse on average than the fixed cost path. The more users are routed over the stochastic path, the better the platform can infer its actual state and use it efficiently.At the Nash equilibrium, if asked to take the riskier path, in many cases selfish users (that are allowed to have access to the information collected by the platform) will myopically disregard the optimal path suggestions of the platform, leading to a suboptimal system without enough exploration on the stochastic path. We prove the interesting result that if the past collected information is hidden from users, the system becomes incentive compatible and even ‘sophisticated’ users (in the sense that they have full capability to reverse-engineer the platform’s recommendation and derive the path state distribution conditional on the recommendation) prefer to follow the platform’s recommendations. In a more practical setting where the platform implements a model-free Q-learning algorithm to minimise the social travel cost, our analysis suggests that increasing the accuracy of the learning algorithm increases the range of system parameters for which sophisticated users follow the recommendations of the platform, becoming in the limit fully incentive compatible. Finally, we extend the two-path model to include more stochastic paths, and show that incentive compatibility holds under our information restriction mechanism.
Plane Wave Diffraction by Arbitrary-Angled Lossless Wedges: High-Frequency and Time-Domain Solutions

This paper concerns the diffraction phenomenon originated by a uniform plane wave impacting an arbitrary-angled lossless dielectric wedge with planar surfaces. The high-frequency diffraction coefficients are obtained by performing uniform asymptotic evaluations of the radiation integrals resulting from the physical optics approximation of the electric and magnetic equivalent surface currents located on the inner and outer faces of the wedge. The final expressions are in closed form and contain the standard transition function of the uniform theory of diffraction and the Fresnel reflection and transmission coefficients related to the geometrical optics propagation mechanisms. Moreover, they allow a simple physical interpretation of each contribution and they are easy to use and to implement in a computer code. The knowledge of such diffraction coefficients in the frequency domain permits to apply the inverse Laplace transform to obtain the time-domain counterparts, which enable the evaluation of the transient diffracted field originated by an arbitrary function plane wave.