Miracle-dz/UniFAR-FTUs huggingface dataset:
Architectural Support for NVRAM Persistence in GPUs

Non-volatile Random Access Memories (NVRAM) have emerged in recent years to bridge the performance gap between the main memory and external storage devices, such as Solid State Drives (SSD). In addition to higher storage density, NVRAM provides byte-addressability, higher bandwidth, near-DRAM latency, and easier access compared to block devices such as traditional SSDs. This enables new programming paradigms taking advantage of durability and larger memory footprint. With the range and size of GPU workloads expanding, NVRAM will present itself as a promising addition to GPU's memory hierarchy. To utilize the non-volatility of NVRAMs, programs should allow durable stores, maintaining consistency through a power loss event. This is usually done through a logging mechanism that works in tandem with a transaction execution layer which can consist of a transactional memory or a locking mechanism. Together, this results in a transaction processing system that preserves the ACID properties. GPUs are designed with high throughput in mind, leveraging high degrees of parallelism. Transactional memory proposals enable fine-grained transactions at the GPU thread-level. However, with lower write bandwidths compared to that of DRAMs, using NVRAM as-is may yield sub-optimal overall system performance when threads experience long latency. To address this problem, we propose using Helper Warps to move persistence out of the critical path of transaction execution, alleviating the impact of latencies. Our mechanism achieves a speedup of 4.4 and 1.5 under bandwidth limits of 1.6 GB/s and 12 GB/s and is projected to maintain speed advantage even when NVRAM bandwidth gets as high as hundreds of GB/s in certain cases. Due to the speedup, our proposed method also results in reduction in overall energy consumption.
A Prediction-Based Approach for Computing Robust Rating Scores

Assessing quality of products, especially when purchased online, is always a challenge. One of the widely used approaches for addressing this challenge is to rely on the scores computed by online rating systems, based on the feedback received from other users. For several reasons, like gaining benefits, personal interests or collusion, rating systems have always been facing with challenge of dishonest feedback. Although many techniques have been proposed for collusion detection, there are still issues that need more investigations. One of these issues is dealing with the sparsity problem, i.e., small number of votes per product, which makes it easier to manipulate scores. In this paper we propose a novel technique for calculating robust rank scores which relies on feedback prediction. In our model, we improve quality of computed scores by predicting feedback, for the people who have not assessed a product. This will result in decreasing sparsity. Then, we propose an iterative technique to calculate product rating scores based on the real and predicted feedbacks. We have implemented our method and compared its performance with three well-known related works. The result of comparison shows the superiority of our model.
Analog-Mixed-Signal Simulation of DC-DC Boost-Based MPPT System Taking into Account Weather Conditions Variations

DC-DC converters are widely used as interfaces between photovoltaic (PV) sources and loads in different applications. These devices use inductors as energy storage elements for controlling the power flow from the PV source to the load. These systems are usually designed using conventional linear small-signal approaches in the vicinity of an operating point. However, the operating point of a PV system is highly dependent on the environmental conditions such as the irradiance and the temperature. Irradiance and temperature changes make the system work at different power and current levels. The inductance of a nonlinear real inductor strongly depends on the operating current. In this paper, a study of a boost converter used for maximum power point tracking is presented by taking into account the inductor nonlinearity till saturation and the variation of its inductance with the weather conditions. To this end analog-mixed-signal circuit simulations are used to show the effects of the weather conditions on the dynamical behavior of the overall PV system.
Analysis of Factors Affecting User Loyalty on Bitcoin Exchange

Bitcoin Exchange industry in Indonesia continues to grow, as evidenced by the emergence of new exchanges that offer competing features and facilities, to determine strategies for creating and increasing user loyalty. The study examines the factors that influence bitcoin exchange user loyalty by determining the essential factors that affect user satisfaction and user loyalty in the bitcoin exchange business. The factors used in this study are Image, Cost, Promotion, Security & Privacy, Reliability, Efficiency, Information, Communication and Value-Added Services. The results of this study are, based on the largest order of influence, Promotion, Cost, Image, and Security & Privacy have a direct influence on Satisfaction. Promotion, Cost, and Security & Privacy also have an indirect influence on Loyalty. Bitcoin Exchange Users Satisfaction has a very large effect on Loyalty of 76.7%.
Nonlinear dynamics of coupled inductor-capacitor-memristor oscillators

In this work, the dynamical phenomena of coupled parallel inductor-capacitor-memristor oscillators connected in a ring structure are studied. Various dynamical phenomena are observed including perfect synchronization and formation of clusters. Conditions for synchronization are derived using the master stability function approach. Computer simulations are carried out to verify theoretical predictions.
CR-U-Net: Cascaded U-Net with Residual Mapping for Liver Segmentation in CT Images*

Abdominal computed tomography (CT) is a common modality to detect liver lesions. Liver segmentation in CT scan is important for diagnosis and analysis of liver lesions. However, the accuracy of existing liver segmentation methods is slightly insufficient. In this paper, we propose a liver segmentation architecture named CR-U-Net, which is composed of cascade U-Net combined with residual mapping. We make use of the MDice loss function for training in CR-U-Net, and the second-level of cascade network is deeper than the first-level to extract more detailed image features. Morphological algorithms are utilized as an intermediate-processing step to improve the segmentation accuracy. In addition, we evaluate our proposed CR-U-Net on liver segmentation task under the dataset provided by the 2017 ISBI LiTS Challenge. The experimental result demonstrates that our proposed CR-U-Net can outperform the state-of-the-art methods in term of the performance measures, such as Dice score, VOE, and so on.
Simulation of integrate-and-fire neuron circuits using HfO2-based ferroelectric field effect transistors

Inspired by neurobiological systems, Spiking Neural Networks (SNNs) are gaining an increasing interest in the field of bio-inspired machine learning. Neurons, as central processing and short-term memory units of biological neural systems, are thus at the forefront of cutting-edge research approaches. The realization of CMOS circuits replicating neuronal features, namely the integration of action potentials and firing according to the all-or-nothing law, imposes various challenges like large area and power consumption. The non-volatile storage of polarization states and accumulative switching behavior of nanoscale HfO2 - based Ferroelectric Field-Effect Transistors (FeFETs), promise to circumvent these issues. In this paper, we propose two FeFET-based neuronal circuits emulating the Integrate-and-Fire (I&F) behavior of biological neurons on the basis of SPICE simulations. Additionally, modulating the depolarization of the FeFETs enables the replication of a biology-based concept known as membrane leakage. The presented capacitor-free implementation is crucial for the development of neuromorphic systems that allow more complex features at a given area and power constraint.
A fully reconfigurable waveguide Bragg grating for programmable photonic signal processing

Since the discovery of the Bragg’s law in 1913, Bragg gratings have become important optical devices and have been extensively used in various systems. In particular, the successful inscription of a Bragg grating in a fiber core has significantly boosted its engineering applications. However, a conventional grating device is usually designed for a particular use, which limits general-purpose applications since its index modulation profile is fixed after fabrication. In this article, we propose to implement a fully reconfigurable grating, which is fast and electrically reconfigurable by field programming. The concept is verified by fabricating an integrated grating on a silicon-on-insulator platform, which is employed as a programmable signal processor to perform multiple signal processing functions including temporal differentiation, microwave time delay, and frequency identification. The availability of ultrafast and reconfigurable gratings opens new avenues for programmable optical signal processing at the speed of light. Bragg gratings are versatile elements used to perform spectral filtering in optical circuits. Here, the authors develop a scalable, reconfigurable grating device which can be electrically tuned to actively change its behaviour and control of optical pathways, for use in photonic signal processing.
Automatic Full and Partial Shoeprint Retrieval System for Use in Forensic Investigations

Forensic identification methods can be divided into physical forensics, chemical forensics, and biological forensics categories. Robust automatic identification or retrieval systems, such as AFIS and DNA-STR analysis systems, for fingerprints and biological evidence have been introduced. However, there are still challenges for shoeprint evidence. For example, most of identification or retrieval methods can only be applied to intact shoeprints, and do not compare shoeprints with different sizes. In addition, the shoeprints at a real crime scene are commonly incomplete, noisy, and unclear. In this paper, we propose a new shoeprint retrieval system that normalizes the size of shoeprints and is efficient in retrieving full- and partial-print shoeprints. Experimental results demonstrate our proposed method is capable of handling shoeprint identification in varied situations such as dusting environment, partial print, etc. In particular, our proposed method outperforms Ho's method when dealing with different shoe sizes.
An Electrocardiogram Acquisition and Analysis System for Detection of Human Stress

This paper introduces a system for acquiring electrocardiogram (ECG) signals of subjects using GE B30 monitor, and analyzing the data to classify the state of subjects into a mentally relaxed or stressed state. The analysis of the data is based on the heart rate variability (HRV) features extracted from ultra-short-term ECG segments. This system includes a database with 120 labeled ECG signal data and their corresponding HRV features for the training of machine learning models. The ECG signal data were recorded from 15 subjects whom are tested under both the mentally relaxed and stressed states. Test data show that the classifier of the system comes up with an accuracy of 80.56%.
Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk

Statistics based privacy-aware recommender systems make suggestions more powerful by extracting knowledge from the log of social contacts interactions, but unfortunately, they are static - moreover, advice from local experts effective in finding specific business categories in a particular area. We propose a dynamic recommender algorithm based on a lazy random walk that recommends top-rank shopping places to potentially interested visitors. We consider local authority and topical authority. The algorithm tested on FourSquare shopping data sets of 5 cities in Indonesia with k-steps={5,7,9} (lazy) random walks and compared the results with other state-of-the-art ranking techniques. The results show that it can reach high score precisions (0.5, 0.37, and 0.26 respectively on p@1, p@3, and p@5 for k=5). The algorithm also shows scalability concerning execution time. The advantage of dynamicity is the database used to power the recommender system; no need to be very frequently updated to produce a good recommendation.
Active Dual Level Gate Driver for Switching Losses Reduction in IGBTs

Nowadays, most of the motor driving systems are still using IGBTs as power switches. Moreover, also for this kind of applications, energy saving is of the utmost importance. IGBTs driving solutions are traditionally implemented using a fixed output current for charging and discharging the switch's gate. This solution, however, does not take into account Miller Capacitance (Crss) non-linearity effects in recent device generation. As Crss value spreads at least of one order of magnitude over the collector-to-emitter voltage (VCE) swing, the fixed current produces two different dV/dt, causing additional switching losses. In order to reduce them, a two-step gate driver is proposed in this paper. The presented solution has been firstly validated on a discrete concept board, then implemented in an integrated circuit developed in a $1\boldsymbol{\mu}\mathbf{m}$ High-Voltage (HV) SOI technology. Simulation and bench results are presented, highlighting the advantages of this solution and defining future improvements.
200 Gbps/Lane IM/DD Technologies for Short Reach Optical Interconnects

Client-side optics are facing an ever-increasing upgrading pace, driven by upcoming 5G related services and datacenter applications. The demand for a single lane data rate is soon approaching 200 Gbps. To meet such high-speed requirement, all segments of traditional intensity modulation direct detection (IM/DD) technologies are being challenged. The characteristics of electrical and optoelectronic components and the performance of modulation, coding, and digital signal processing (DSP) techniques are being stretched to their limits. In this context, we witnessed technological breakthroughs in several aspects, including development of broadband devices, novel modulation formats and coding, and high-performance DSP algorithms for the past few years. A great momentum has been accumulated to overcome the aforementioned challenges. In this article, we focus on IM/DD transmissions, and provide an overview of recent research and development efforts on key enabling technologies for 200 Gbps per lane and beyond. Our recent demonstrations of 200 Gbps short-reach transmissions with 4-level pulse amplitude modulation (PAM) and discrete multitone signals are also presented as examples to show the system requirements in terms of device characteristics and DSP performance. Apart from digital coherent technologies and advanced direct detection systems, such as Stokes–vector and Kramers–Kronig schemes, we expect high-speed IM/DD systems will remain advantageous in terms of system cost, power consumption, and footprint for short reach applications in the short- to mid- term perspective.
A Solar-Cell-Assisted, 99.66% Biofuel Cell Area Reduced, Biofuel-Cell-Powered Wireless Biosensing System in 65-nm CMOS for Continuous Glucose Monitoring Contact Lenses

This work proposes a solar-cell-assisted wireless biosensing system that operates using a biofuel cell (BFC). To facilitate the reduction of the BFC area and apply this system to area-constrained continuous glucose monitoring (CGM) contact lenses, an energy harvester combined with an on-chip solar cell is introduced as a dedicated power source for the transmitter. A dual-oscillator-based supply voltage monitor is employed to convert the BFC output into digital codes. From measurements of the test chip fabricated with the 65-nm CMOS technology, the proposed system can achieve BFC area reduction of 99.66%.
PointAGCN: Adaptive Spectral Graph CNN for Point Cloud Feature Learning

Feature learning on unstructured 3D point clouds using deep networks is gaining attention due to its wide applications in robotics, self-driving and so on. Among existing methods, PointNet has achieved promising results by directly working with point cloud data. However, it does not take full advantages of neighboring points that contain fine-grained structural information which is helpful to better semantic learning. In the paper, we propose an adaptive spectral graph convolutional network for 3D point cloud feature processing, named PointAGCN. Our model use localized spectral graph convolution to capture local geometric features instead of designing of a powerful localized filter manually. The topology of the graph in each layer can be dynamically updated in each layer, which can bring more flexibility and generality. A novel graph pooling operation is carried out on the k-nearest neighbor graph, which aggregates features at graph nodes. Through extensive experiments on various datasets, the results show that the proposed approach achieves competitive performance on standard benchmarks.
Alzheimer's Disease Diagnosis Model Based on Three-Dimensional Full Convolutional DenseNet

Dementia has become one of the major diseases causing death and disability in the elderly. Alzheimer's disease is the primary cause of dementia, and its clinical diagnosis, especially early diagnosis, is very urgent. The research on the diagnosis of Alzheimer's disease based on deep convolutional neural network has made good progress. However, it is still difficult to meet the requirements of clinical application. In clinic, most of them are structural MRI images, with limited sample size and few scanning layers. But deep learning needs enough annotated data. Aiming at the practical needs of clinical diagnosis of Alzheimer's disease, this paper proposes a increment method of dataset by weighted combination of positive and negative samples and a learning method for small number of samples, and establishes a 3D full convolutional DenseNet classification model, which can not only obtain better image feature information, but also improve the generalization ability of the model.
High-Order Uncertainty of LiDAR for Environment Perception in Automated Driving Systems

Environment Perception is the basic function in Automated Driving Systems. The inaccurate result of perception leads to subsequent erroneous decisions and erroneous control commands. Light Detection And Ranging (LiDAR) is an important sensor for environment perception. Aiming at the uncertainty caused by environment influences on LiDAR perception, this paper analyzes the high-order uncertainty of the sensor based on hierarchical Bayesian regression model. Experiments on the influence of rainfall on the accuracy of the LiDAR were carried out. The experimental results show that the measurement error does not depend distance, but rainfall lead to more uncertainty of LiDAR perception.
Classification-oriented Feature Extraction from Time Series by Comparing Learning

Feature extraction plays a key role for time series classification (TSC). In this paper, we propose a noval feature extraction model for time series based on comparing learning, which can effectively extract the features in a classification-oriented way. Unlike the previous works that take subsequences or intervals as features, we employ a comparing learning model constructed by a two-branch convolutional neural network which has great feature extraction ability to find the intrinsic characteristics between time series. Concretely, we first reorganize the training time series and their labels to construct a binary classification problem, and then a comparable network architecture with two branches is designed to get the similarities and differences of time series. To obtain the powerful features, we employ the contrastive loss function instead of the traditional loss function which can minimize the intra-class distance and maximum the inter-class distance. We demonstrate the effectiveness of our model on real time series datasets and find that our model can obtain well performance features for the problem of TSC and gets better results than other time series feature extraction methods.
Classifying Diabetic Retinopathy Images Using Induced Deep Region of Interest Extraction

Classification and segmentation tasks on biomedical images have proven to be challenging and advocate the development of radically different approaches in machine learning. Machine learning methods involving deep neural networks have also acquired a phase-shift due to the inherent complexity associated with the representation, preprocessing, analysis, learning, inference and post-processing of bio-medical images. In this work, we consider the task of classifying diabetic retinopathy images coming from a small real-world data set. Taking advantage of the recent advances in bio-medical image segmentation to identify regions of interest, we propose a semi-automatic two-stage approach where the regions of interest identified by segmentation task fuel the classification task with additional discriminative information. We also devise a new loss function which improves the quality of segmentation tasks in diabetic retinopathy. Extensive experiments on segmentation demonstrate the utility of the proposed loss function. Further, classification experiments show a significant improvement in the generalization performance and validate the utility of the proposed two-stage approach.
Lane Based Platoon Control of Homogeneous Platoons

The benefits of both homogeneous and heterogeneous platooning of automated vehicles have been reported by many studies. One commonality among such studies has been that platooning does provide a positive impact on traffic dynamics through increased average speeds, flow and capacity. Also due to platooning, fuel consumption is reduced and hence positively impacts the environment through reduced emissions. This is even more so when the percentage of automated vehicles is significantly higher than non-automated vehicles. While obvious that today's and near future highways will have automated and non-automated vehicles coexisting, research points towards homogeneity in order to fully realize the benefits of platooning. Research also indicates that when platooning allows maneuvers such as merging and splitting of platoons, these maneuvers present reduced benefits in traffic dynamics and consequently its impact on the environment. In light of these observations, this study presents a highway architecture that implements and controls lane and destination based fixed-routed platoons of automated vehicles with minimal lane changes. The study demonstrates that confining platoons to vehicles of same off-ramp destinations reduces the need for lane changes to a predetermined number, allows for more stable and longer strings of vehicle platoons of up to 24 vehicles per platoon. When compared to conventional traffic, the model presented shows between 135% and 156% increase in average speeds, 70% reduction in travel times and between 39% and 43.5% increase in highway throughput. The open-source traffic simulator, Simulation of Urban Mobility (SUMO) is used to simulate the model presented in this study.