Miracle-dz/UniFAR-FTUs huggingface dataset:
DepGraph: A Dependency-Driven Accelerator for Efficient Iterative Graph Processing

Many graph processing systems have been recently developed for many-core processors. However, for iterative graph processing, due to the dependencies between vertices’ states, the propagations of new states of vertices are inherently conducted along graph paths sequentially and are also dependent on each other. Despite the years’ research effort, existing solutions still severely underutilize many-core processors to quickly propagate the new states of vertices, suffering from slow convergence speed. In this paper, we propose a dependency-driven programmable accelerator, DepGraph, which couples with the core architecture of the many-core processor and can fundamentally alleviate the challenge of dependencies for faster state propagation. Specifically, we propose an effective dependency-driven asynchronous execution approach into novel microarchitecture designs for faster state propagations. DepGraph prefetches the vertices for the core on-the-fly along the dependency chains between their states and the active vertices’ new states, aiming to effectively accelerate the propagations of the active vertices’ new states and also ensure better data locality. Through transforming the dependency chains along the frequently-used paths into direct ones at runtime and maintaining these calculated direct dependencies as a set of fast shortcuts, called hub index, DepGraph further accelerates most state propagations. Also, many propagations do not need to wait for the completion of other propagations, which enables more propagations to be effectively conducted along the paths with higher degree of parallelism. The experimental results show that for iterative graph processing on a simulated 64-core processor, a cutting-edge software graph processing system can achieve 5.0–22.7 times speedup after integrating with our DepGraph while incurring only 0.6% area cost. In comparison with three state-of-the-art hardware solutions, i.e., HATS, Minnow, and PHI, DepGraph improves the performance by up to 3.0–14.2, 2.2–5.8, and 2.4–10.1 times, respectively.
Demonstration of MOCVD-Grown Long-Wavelength Infrared InAs/GaSb Superlattice Focal Plane Array

High-performance InAs/GaSb type-II superlattice infrared detectors and focal plane arrays (FPAs) are normally grown by molecular beam epitaxy (MBE). In this work, we demonstrate the first long-wavelength infrared InAs/GaSb superlattice FPA grown by metalorganic chemical vapor deposition (MOCVD) with clear image. High-quality superlattice material was obtained evidenced by sharp X-ray diffraction peaks and atomic flat surface. Electrical and optical measurements performed on single element detectors showed a 50% cut-off wavelength of <inline-formula> <tex-math notation="LaTeX">$\sim 10.1~\mu \text{m}$ </tex-math></inline-formula>, a dark current density of <inline-formula> <tex-math notation="LaTeX">$2.5\times 10^{-5}$ </tex-math></inline-formula> A/cm<sup>2</sup>, a peak responsivity of 0.88 A/W and a peak detectivity of <inline-formula> <tex-math notation="LaTeX">$1.7\times 10^{11}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>Hz<sup>1/2</sup>/W at 80 K. A <inline-formula> <tex-math notation="LaTeX">$320\times256$ </tex-math></inline-formula> FPA with <inline-formula> <tex-math notation="LaTeX">$30~\mu \text{m}$ </tex-math></inline-formula> pixel pitch was then fabricated. With an integration time of 1.9 ms and an applied bias of -0.1 V, the FPA shows an average operability of 96.96%, a non-uniformity of 4.97%, a noise equivalent temperature difference of 51.1 mK and a peak detectivity of <inline-formula> <tex-math notation="LaTeX">$2.3\times 10^{10}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>Hz<sup>1/2</sup>/W at 80 K without thinning down the substrate.
Stress Engineering as a Strategy to Achieve High Ferroelectricity in Thick Hafnia Using Interlayer

Binary oxides of Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub> (HZO) have attracted considerable attention of the ferroelectric research community, owing to their excellent ferroelectric properties and CMOS compatibility. In particular, HZO films of a relatively high thickness (>10 nm) are studied widely for sensor and display applications. However, one of the major constraints of HZO materials is the formation of monoclinic phases (m-phase) with increasing film thickness resulting in the degradation of its remanent polarization (<inline-formula> <tex-math notation="LaTeX">${P}_{r}$ </tex-math></inline-formula>). Herein, we present a stress engineering method to achieve high ferroelectricity in thick hafnia using an interlayer. In our work, we attempted to address the aforesaid limitation of HZO by inserting a dielectric interlayer and elucidated the influence of interlayer on the relatively thick HZO films. high resolution TEM (HRTEM) analysis revealed that the presence of interlayer allows the growth of the top and bottom HZO layer in an independent direction thereby preventing the loss of ferroelectricity in HZO films with higher thickness by controlling its grain size. Similarly, grain angle incidence X-ray diffraction (GIXRD) and residual stress measurements suggest that the interlayer affects the o-phase formation from the t-phase owing to the tensile stress applied to the HZO films because of the coefficient of thermal expansion (CTE) mismatch between the HZO and interlayer. In our study, an improved <inline-formula> <tex-math notation="LaTeX">$2{P}_{r}$ </tex-math></inline-formula> value of 30.2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{C}$ </tex-math></inline-formula>/cm<sup>2</sup> was achieved by inserting a TiO<sub>2</sub> dielectric interlayer in a relatively thicker HZO film. We believe that this approach can be adopted in various applications such as sensors, displays, and memory devices.
Deep Learning for SARS COV-2 Genome Sequences

The SARS-CoV-2 virus which originated in Wuhan, China has since spread throughout the world and is affecting millions of people. When there is a novel virus outbreak, it is crucial to quickly determine if the epidemic is a result of the novel virus or a well-known virus. We propose a deep learning algorithm that uses a convolutional neural network (CNN) as well as a bi-directional long short-term memory (Bi-LSTM) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) amongst Coronaviruses. Besides, we classify whether a genome sequence contains candidate regulatory motifs or otherwise. Regulatory motifs bind to transcription factors. Transcription factors are responsible for the expression of genes. The experimental results show that at peak performance, the proposed convolutional neural network bi-directional long short-term memory (CNN-Bi-LSTM) model achieves a classification accuracy of 99.95%, area under curve receiver operating characteristic (AUC ROC) of 100.00%, a specificity of 99.97%, the sensitivity of 99.97%, Cohen’s Kappa equal to 0.9978, Mathews Correlation Coefficient (MCC) equal to 0.9978 for the classification of SARS CoV-2 amongst Coronaviruses. Also, the CNN-Bi-LSTM correctly detects whether a sequence has candidate regulatory motifs or binding-sites with a classification accuracy of 99.76%, AUC ROC of 100.00%, a specificity of 99.76%, a sensitivity of 99.76%, MCC equal to 0.9980, and Cohen’s Kappa of 0.9970 at peak performance. These results are encouraging enough to recognise deep learning algorithms as alternative avenues for detecting SARS CoV-2 as well as detecting regulatory motifs in the SARS CoV-2 genes.
CARE: Coordinated Augmentation for Elastic Resilience on DRAM Errors in Data Centers

As the computation density and memory capacity continues to grow, DRAM errors have become the leading cause of server crashes and/or system failures in modern data centers. While myriads of techniques have been proposed to mitigate their impact on system reliability, these solutions either incur significant overhead on performance, power and memory capacity or require modifying multiple system components; hence, they are impractical to implement or deploy. This paper proposes CARE, a novel error tolerance framework for efficient and elastic resilience on DRAM errors. It introduces a cache-like structure in the memory controller for dynamic error tracking and proactive resilience enhancement to achieve high error tolerance economically and practically. Experiment results show that with around 58KB area overhead in the memory controller, CARE achieves near Chipkill reliability without any memory capacity penalty and incurs negligible performance overhead compared with the baseline SEC-DED systems. CARE provides an attractive alternative to enhance the reliability in data centers.
Pansharpening Via Neighbor Embedding of Spatial Details

The spatial details injection model has been considered as a general framework in the literature of pansharpening, and recently there have been significant advances in this framework based on sparse representation (SR) of spatial details. However, the SR-based methods have greater computational burden in estimating the sparse vectors and limited ability in detail edge preservation. In this article, we introduce the neighbor embedding (NE) instead of SR-based model and the edge-preserving filter into the spatial detail injection framework to address the aforementioned two drawbacks. By utilizing the best quality of NE, we propose the detail injection via NE (DINE) algorithm for pansharpening, and DINE+, an improved variant of DINE by using the edge-preserving filter to enhance the spatial details. Experiments carried on three datasets captured by different satellite sensors and compared with current state-of-the-art methods validate the effectiveness of the proposed methods.
Sloping-Invariance for Nonferrous Metallic Slabs at Multiple Frequencies by Eddy Current Sensors

Eddy current sensors have been widely applied to various measurements, whereas it is still obscure if these measurement techniques are workable for sloping samples. We start from a modified Dodd and Deeds’s analytical solution for finite-size samples and find that the pseudo-linearity exists in the magnitude-phase curve of the theoretical mutual inductance. The curves for different conductivities have no intersections. The experiments for verifying the pseudo-linearity are conducted at multiple frequencies from 20 kHz to 100 kHz. We subsequently involve the sloping samples in our simulations and experiments at 20 kHz. The pseudo-linearity preserves in both the simulated and experimental results. To characterize this pseudo-linearity, we resort to the method of least squares. The obtained intercepts for the same conductivity at different tilting angles are almost the same. Hence the intercept is independent of the tilting angle. The intercepts for different conductivities are clearly separated. Thus, the intercepts for non-sloping samples can be directly utilized as the criterion to classify sloping samples. We then test the classification process at multiple frequencies, which works properly at all the frequencies. Our classification rates are advanced compared to those in the literature. This sloping-invariance (that is the tilting-angle-independent intercept) might make the eddy current sensors find wider applications.
Small-Signal Stability Analysis of Different Outer-Loop Control Combinations under AC/DC Decoupled Control Frame for Hybrid MMC

The AC/DC decoupled control is applicable for improving the DC fault-ride-through performance of the hybrid modular multilevel converters. There exist multiple possible control combinations for the outer loops of the decoupled control frame. This paper analyzes the impact of several outer-loop control combinations on the small-signal stability characteristics. The unstable modes and corresponding participation factors are compared via time-domain eigen-value studies. The propagation of oscillation between AC and DC network, and the maximum transferable active power are also analyzed and validated by electromagnetic simulation.
Dead Page and Dead Block Predictors: Cleaning TLBs and Caches Together

The last level TLB (LLT) and the last level cache (LLC) play a critical role in the overall performance of memory-intensive applications. While management of LLC content has received significant attention, the same may not be true for LLT. In this work, we first explore the well-known concept of dead blocks in caches for TLBs. We find that dead pages are fairly common in the LLT. Different from dead blocks in LLCs, dead pages in LLTs are most often dead-on-arrival, i.e., they produce zero hits in the TLB. We design a storage-efficient dead page predictor that works with a fraction of storage compared to typical dead block predictors. This is important since an LLT itself requires only a few KBs of storage compared to MBs in LLC. We then leverage the dead page information to guide a simple dead block predictor in LLC. This is driven by the observation that dead blocks are often concentrated within dead pages. In effect, we designed a dead page predictor and a correlating dead block predictor with a total storage overhead of only 11KB to bypass predicted dead pages and dead blocks in LLTs and LLCs, respectively. Together, these predictors help improve the IPC of a set of 14 memory-intensive workloads by 8.3%, on average.
Development and integration of serious games with focus in the training of different cognitive abilities in elderly people to improve their quality of life

It is well known that each year the amount of elderly people is increasing and the life expectancy is longer in EU countries. So, obviously, it is important to include new tools in the socio-sanitary system to maintain and improve the quality of life of the target group. The objective of the proposal described in this paper is to train cognitive abilities (memory, attention, spacial processing…) to promote independent living though a set of serious games (4). These video games serve as a tool to measure partially the cognitive skills of the users and helps to train and improve them. The tests were carried out with 100 people from 3 countries in different socio-cultural contexts. The first levels of each game are played by users several times with good scores, a fact that does not occur in the higher levels that are usually abandoned. However, the subjective feedback received is very promising as they feel they are having fun.
Research on Real-time and Efficient Alignment Algorithm for License Plate Detection Based on Neural Network A lightweight network for license plate detection and alignment

As one of the main identification marks of vehicles, vehicle license plate plays an important role in intelligent traffic management, and vehicle license plate detection and recognition is also a hot topic in recent years. Low accuracy of traditional method for license plate detection under the natural scene, the accuracy is higher based on the deep learning of license plate detection, but the real-time performance is poorer, for distortion correction of license plate alignment with special network return the four vertices of license plate, then the perspective transform[1] correct license plate area, the process to further increase the computational complexity. For existing detection and license plate four vertices of regression model, such as MTCNN[2], but for the big input picture, MTCNN's image pyramid has more computation, so this paper proposes a mobilenet[3] backbone network and sampling at the three different scale layer on the license plate of the network to forecast the results, the network reference yolov4[4] design ideas, so called mobilenet - yolov4, in addition to regress the category of the license plate and boundary box. Four vertices regress mission add to the license plate detection. The integration of these three tasks into a simple network improves the multiplexing of the network layer and reduces the computation of subsequent license plate alignment. Experiments show that the algorithm has higher real-time and accuracy, and has higher research and application value.
Effect of improved and traditional smoking on the storage life and nutritional quality of cat, croaker and sardine fishes.

ABSTRACTThis study assessed the effect of improved and traditional smoking on the storage life and nutritional quality of cat, croaker and sardine. The experimental fishes were divided into two batches for smoking in improved and traditional kiln. The fish was stunned, eviscerated, cleaned, brined and smoked in either improved or traditional smoking kiln. The smoked fish from both traditional and improved smoking kiln were also divided into six groups for storage over a period of five weeks. Weekly samples were taken and analyzed. The proximate composition (Moisture, fat, protein, ash) and chemical analysis (Total volatile base nitrogen (TVBN), peroxide value (PV), free fatty acid (FFA), trimethylamine (TMA) and thiobabutaric value (TBA)) were determine for fresh, smoked and stored smoked three fish species. The result of this study showed a general significant increase (P<0.05) in the proximate and chemical content of the three fish species smoked in the two smoking method used which could probably be due to product dehydration and oxidation respectively. The proximate contents of the fish species significantly decreased (P<0.05) over the storage period while the moisture content increased from; for smoked cat, croaker, sardine for improved and traditional kiln ranged from 10.23 to 11.28, 10.27 to 11.34%; 10.18 to 11.25, 10.26 to 11.27%; 10.40 to 11.01, 10.44 to 11.90% respectively at the end of 35 days of storage. There was general increase in the chemical factors over the period of storage but higher in traditional smoking. The traditional smoked sardine fish had the highest TVBN of 3.3 to 3.5 mgN/100g and PV of 3.46 to 3.61 meqO2/kg at the end of 35 days of storage. The general increase of TVBN over the storage period could be due to gradual degradation of the initial protein to more volatile products while PV could be due to oxidation of lipids. The value of FFA for the three fish species increased with the range of 0.25 to 0.38%. The highest value of trimethylamine obtained was from 2.15 to 2.57mg/100g and TBA from 0.04 – 0.13mg malonaldehyde/kg from traditionally smoked croaker fish at the 35 days of storage. The increase in TMA may be due to protein breakdown. Nevertheless, the spoilage factors were all within the acceptable limit, but indicated the beginning of spoilage. The study showed that smoking fish species with the improved kiln had a better nutrient retention and keeping quality than traditional smoking kiln.
On the nonlocal computational modelling of damage in brittle and quasi-brittle materials

Computational prediction of progressive micro-cracking, observed namely in brittle and quasi-brittle materials, resulting in their softening behaviour, needs some regularization approach. Its social relevance increases with the massive application of steel fibre-reinforced concrete and similar composites in buildings and engineering structures. However, most approaches contain ad hoc computational procedures and tricks, without a proper mathematical existence and convergence analysis for the time development of damage. This paper demonstrates the possibility of such analysis, at least for a simplified model problem of structural dynamics, with numerous references to available generalizations and some application examples.
A Deep Residual Shrinkage Neural Network-based Deep Reinforcement Learning Strategy in Financial Portfolio Management

Reinforcement Learning algorithms are widely applied in many fields, such as price index prediction, image recognition, and natural language processing. This paper introduces a novel algorithm based on the classical Deep Reinforcement Learning algorithm and Deep Residual Shrinkage Neural Network for portfolio management. In this algorithm, the Ensemble of Identical Independent Evaluators framework put forward by Jiang et al. is adopted in the policy function. Following this, we adopt the Deep Residual Shrinkage Neural Network to function as the identical independent evaluator to optimize the algorithm. We use the cryptocurrency market in this research to assess the efficacy of our strategy with eight traditional portfolio management strategies as well as Jiang et al.'s reinforcement learning strategy. In our experiments, the Accumulated Yield is used to reflect the profit of the algorithm. Despite having a high commission rate of 0.25% in back-tests, results show that our algorithm can achieve 44.5%, 105.4%, and 148.8% returns in three different 50-days back-tests, which is five times more than the profit of other non-reinforcement learning strategies and Jiang et al.'s strategy. Furthermore, the Sharpe ratio demonstrates that the extra reward per unit risk of the our strategy is still better than other traditional portfolio management strategies and Jiang et al.'s strategy by at least 50% in different time horizons.
Leakage-Prevention Mechanism to Maintain Driving Capability of Compensation Pixel Circuit for Low Frame Rate AMOLED Displays

A new leakage-prevention pixel circuit is proposed to maintain the driving capability of its driving thin-film transistors (TFTs) at a low frame rate of 15 Hz for use in active-matrix organic light-emitting diode (AMOLED) displays. The proposed leakage-prevention mechanism generates a compensating leakage current that reduces the source-to-drain voltage of the switching TFT, thereby lessening the fluctuation of the stored driving voltage when the frame rate is reduced from 60 to 15 Hz. Moreover, the proposed circuit can compensate for the threshold voltage variation in low-temperature polycrystalline silicon TFTs (LTPS TFTs) and current–resistance drops in VDD lines. To validate the performance of the compensation and leakage prevention, simulation models based on the measurement of the fabricated LTPS TFTs and an organic light-emitting diode (OLED) are established. The experimental results confirm that the leakage current of the gate node is successfully suppressed from 50.34 to 1.25 pA by the proposed leakage-prevention mechanism. Also, within the long emission period, the highly uniform OLED currents at low, medium, and high gray levels are achieved, verifying the feasibility of the proposed pixel circuit.
Aging Estimation of an AC Adapter from Generated Electromagnetic Noise

Capacitors are the parts of a power supply unit that deteriorate most easily. Among types of power supply unit, AC adapters are the ones for which it is not possible to check the leakage or bulging of capacitors, because they are sealed and invisible. Therefore, we focused on the electromagnetic noise which deteriorated AC adapters emit on the power line. We measured their noise and analyzed them with our own method of mode decomposition. It was found that the intensity of the noise is proportional to the internal resistance of the deteriorated capacitors measured in the hot condition.