Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Massive Amounts of Small Files in All-Flash Storage

All-flash array is a popular memory device available for use in modern high-performance storage systems. Compared with other types of devices such as DRAM, NVRAM, and EEPROM, flash array combines the best features: shock resistance, low cost, low power consumption, and fast access. Moreover, the ever-increasing density of flash memory has led to a dramatic increase in the capacity, which allows the storage of large volume of data. However, flash memory is not optimal for managing a large number of small files because: 1. the small and random write operation is inefficient in flash memory; 2. massive metadata information occupies a significant portion of the namespace, which is relatively limited or scarce in big data storage systems. This paper introduces a novel approach, hash partitioning-based file compaction (HFC), to improve the efficiency of storing and accessing small files in all-flash storage systems. HFC consists of a file compaction tool and an access interface. The compaction tool merges a group (usually a directory) of small files into a set of "big files" to reduce the metadata required to be maintained in the on-chip memory. The data locality and tree structure of those small files are preserved. The access interface is designed to provide transparent access to the small files in the HFC big files. Experimental results confirm that the proposed method significantly enhances the efficiency of managing massive amounts of small files in flash memory in terms of namespace usage and access speed.
Discriminativeness-Preserved Domain Adaptation for Few-Shot Learning

Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples. However, this assumption is often invalid in practice – the target classes could come from a different domain. This poses an additional domain adaptation (DA) challenge with few training samples. In this article, the problem of cross-domain few-shot learning (CD-FSL) is approached, which requires solving FSL and DA in a unified framework. To this end, we propose a novel discriminativeness-preserved domain adaptive prototypical network (DPDAPN) model. It is designed to address a specific challenge in CD-FSL: the DA objective means that the source and target data distributions need to be aligned, typically through a shared domain adaptive feature embedding space, but the FSL objective dictates that the target domain per-class distribution must be different from that of any source domain class, meaning aligning the distributions across domains may harm the FSL performance. How to achieve global domain distribution alignment while maintaining source/target per-class discriminativeness thus becomes the key. Our solution is to explicitly enhance the source/target per-class separation before domain adaptive feature embedding learning in DPDAPN to alleviate the negative effect of domain alignment on FSL. Extensive experiments show that our DPDAPN outperforms the state-of-the-art FSL and DA models, as well as their naive combinations.
DVL Aided SINS Coarse Alignment Solution With High Dynamics

To accelerate the strapdown inertial navigation system (SINS)/Doppler velocity logs (DVL) based underwater vehicle’s coarse alignment process, an improved optimization based alignment (OBA) methodology is proposed, where the dynamic motions are taken into account. First, the DVL velocity measurements are analyzed to specify the extra velocity errors brought by the velocity difference between the transmitting and receiving epochs, where the influence of the dynamic motions is indicated. Then, a novel SINS/DVL coarse alignment method that accommodates dynamic motions is proposed, exploring the velocity relationship between the transmitting and receiving moments using SINS information. To evaluate the coarse alignment performance of the proposed method, simulations and field test are operated under different trajectories including various motions, such as turning and acceleration. Comparing to the traditional OBA method, the proposed coarse alignment solution shows its superiority in pitch, roll and yaw attitudes, where faster convergence rate and higher precision are obtained.
Effective Matrix-Vector Multiplication Computations on Account of Physically Based Matrix Distribution

This paper considers a parallel algorithm for Cholesky factorization on account of PBMD. Compared with the traditional data distribution for matrix-vector multiplication computation, the algorithm proposed incurs strictly less communication overhead and improves the load balancing. Experimental results demonstrate that the PBMD based matrix-vector multiplication computation makes the implementation of Cholesky factorization easier and enhances performance effectively.
Using Fine-Grained Test Cases for Improving Novice Program Fault Localization

Online Judge (OJ) system, which can automatically evaluate the results (right or wrong) of programs by executing them on standard test cases, is widely used in programming education. While an OJ system with personalized feedback can not only give execution results, but also provide information to assist students in locating their problems quickly. Automatically fault localization techniques are designed to find the exact faults in programs automatically, experimental results showed their effect on locating artificial faults, but their effectiveness on novice programs needs to be investigated. In this paper, we first evaluate the effectiveness of several widely-studied fault localization techniques on novice programs, and then we use fine-grained test cases to improve the fault localization accuracy. Empirical studies are conducted on 77 real student programs and the results show that, compared with original test cases in OJ system, the fault localization accuracy can be improved obviously when using fine-grained test cases. More specifically, in terms of TOP-1, TOP-3 and TOP-5 metrics, the maximum results can be improved from 5, 22, 37 to 9, 24, 48, respectively. The results indicate that more faults can be located when checking the top 1, 3 or 5 statements, so the fault localization accuracy is enhanced. Furthermore, a Test Case Granularity (TCG) concept is introduced to describe fine-grained test cases, and empirically studies demonstrate that there is a strong correlation between TCG and fault localization accuracy.
Towards a Cloud Native Big Data Platform using MiCADO

In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware $(e. g$., cluster nodes) and software $(e. g$., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms $(e. g$., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoop-based big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling).
Path planning based on the analytical inverse kinematics of the rail-mounted industrial robot for continuous- trajectory tracking

In this paper, the path planning method of a redundant robotic work cell consisting of an overhead linear rail and a FANUC 6-joint robot mounted on it is studied for the continuous-trajectory tracking task. The existence of the solution of the inverse kinematics problem of the rail-mounted industrial robot (RMIR) is discussed and an analytical redundancy resolution scheme for RMIR is proposed based on the workspace. The path planning method with the performance evaluation mechanism makes the end-effector (EE) of robot reach the target position and posture and avoid joint limit, which makes the best of the redundant degree of freedom to improve the robot performance. The results of simulation and experiment prove that the proposed method is effective in the application of the long and closed-trajectory tracking. The above research findings are integrated into the robot off-line programming (OLP) software developed based on the UG platform and successfully applied to the robotic induction hardening for the chamfers of the large automobile panel dies.
When Blockchain Meets SGX: An Overview, Challenges, and Open Issues

As a decentralized, public, and digital ledger technology in Peer-to-Peer network, blockchain has received much attention from various fields, including finance, healthcare, supply chain, etc. However, some challenges (e.g., scalability, privacy, and security issues) severely affects the wide adoption of blockchain technology. Recently, Intel software guard extensions (SGX), as new trusted computing technologies, have provided a new solution to the above challenges in the blockchain area. Although many studies have focused on using SGX technology to enhance their schemes in the blockchain areas, no comprehensive survey has systematically analyzed and delineated these studies. This article is the first to systematically discuss the application status of SGX in the blockchain area. In this article, we study the scheme designs, advantages, and disadvantages of the existing works using a six-layer hierarchical structure of the blockchain. We also summarize the functions of SGX and formally analyze the advantages and disadvantages of SGX. Finally, we review the remaining challenges and present a list of possible directions for future research.
Pathogenic LRRK2 requires secondary factors to induce cellular toxicity

Abstract Pathogenic mutations in the leucine-rich repeat kinase 2 (LRRK2) gene belong to the most common genetic causes of inherited Parkinson’s disease (PD) and variations in its locus increase the risk to develop sporadic PD. Extensive research efforts aimed at understanding how changes in the LRRK2 function result in molecular alterations that ultimately lead to PD. Cellular LRRK2-based models revealed several potential pathophysiological mechanisms including apoptotic cell death, LRRK2 protein accumulation and deficits in neurite outgrowth. However, highly variable outcomes between different cellular models have been reported. Here, we have investigated the effect of different experimental conditions, such as the use of different tags and gene transfer methods, in various cellular LRRK2 models. Readouts included cell death, sensitivity to oxidative stress, LRRK2 relocalization, α-synuclein aggregation and neurite outgrowth in cell culture, as well as neurite maintenance in vivo. We show that overexpression levels and/or the tag fused to LRRK2 affect the relocalization of LRRK2 to filamentous and skein-like structures. We found that overexpression of LRRK2 per se is not sufficient to induce cellular toxicity or to affect α-synuclein-induced toxicity and aggregate formation. Finally, neurite outgrowth/retraction experiments in cell lines and in vivo revealed that secondary, yet unknown, factors are required for the pathogenic LRRK2 effects on neurite length. Our findings stress the importance of technical and biological factors in LRRK2-induced cellular phenotypes and hence imply that conclusions based on these types of LRRK2-based assays should be interpreted with caution.
EEG and ECG in Reducing Smartphone Overuse by Multiple Leisure Activities

The major purpose is to develop the balanced patterns of leisure activity participation to solve the problem of smart phone overuse. For this, physiological indices from electroencephalograph (EEG) and electrocardiograph (ECG) and data from self-reported questionnaires were collected. As a result, multiple leisure activities have the potential for diversion from smart phone use. The results also showed that basketball games and calligraphy experiences might decrease smart phone overuse.
On Semantic Organization and Fusion of Trajectory Data

With the proliferation of positioning mobile devices, people’s trajectory data are posted on the net including spatial locations and semantic contexts such as in the form of text like twitter posted text. How to organize or fuse the raw spatial trajectories and context semantic data into a structured whole for analysis further is a problem, the focus of which is mostly how to annotate episodes in raw trajectories. In this paper we examine a structured and partially self-describing way for semantic organization and fusion of trajectory data. We annotate episodes with structured sentiments, events, or topic words, where sentiments given in a self-describing way and events are represented using the form from the natural language processing literature. Besides, all the data in the whole model are represented with JSON.
Proteomics‐Informed Prediction of Rosuvastatin Plasma Profiles in Patients With a Wide Range of Body Weight

Rosuvastatin is a frequently used probe to study transporter‐mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient’s individual time‐concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293‐transfected cells, and developed a semimechanistic model with a bottom‐up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two‐fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (Cmax; 76%; AFE: 1.05), and terminal half‐life (t1/2; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T
1.6-Octave Coherent OAM Supercontinuum Generation in As2S3 Photonic Crystal Fiber

We design and simulate an all-normal dispersion arsenic trisulfide (As<sub>2</sub>S<sub>3</sub>) photonic crystal fiber (PCF) with high nonlinearity to enable a flat and coherent orbital angular momentum (OAM) supercontinuum (SC) generation. The photonic crystal fiber features a near-zero and flat negative dispersion with variation between −96.5 and −36.5 ps/(nm<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>km) over a 940-nm wavelength range from 1740 to 2680 nm. A 1946-nm supercontinuum forms from 959 to 2905 nm at −20 dB level which covers a 1.6-octave bandwidth, by launching a 100-fs 5-kW chirp-free hyperbolic secant pulse with wavelength at 2000 nm into a 1.0-cm designed fiber. The generated supercontinuum of the other two vortex modes (TE<sub>01</sub> and TM<sub>01</sub>) can cover more than two octaves by optimizing the proposed fiber structures. The coherence of the generated supercontinuum of the three modes all shows nearly perfect property over the whole bandwidth. In general, we found that the designed ring-core As<sub>2</sub>S<sub>3</sub> PCF with all-normal dispersion could be used for broadband coherent supercontinuum generation of various vortex modes.
Implement an IOT Platform of Biosignal Monitoring for Nurse Station

To enhance up the processing of biosignal monitoring in clinic, the main purpose of this study is focused to implement three Raspberry pi 3B embedded systems become as a biosig-nal monitoring platform. The developed microsystem will acquire and decode the physiological parameters (including ECG, BP, SPO2, TP and RP) from SY-3000 module based on a Raspberry pi 3B embedded system. Then the embedded system will translate the data to the designed website homepage and backup on another cloud-based Raspberry pi 3B through the TCP/IP or Wifi as user's required. The designed platform are based on the integrated operation system among Linux, QT and MQTT. Finally, the families of the patient or the nurses can observe the physiological condition and biosignal trend through any mobile devices at anytime and anywhere. In addition, the VoIP and TLS/SSL techniques are also utilized in this platform to improve the voice communication ability and the cyber security in practice
Novel SJ-Mosfet With Disconnected P-Pillars For High Robustness

A novel SJ-MOSFET with Disconnected P-pillar (DP-SJMOSFET) is proposed to improve the Unclamped Inductance Switching (UIS) capability and reduce the body diode reverse recovery spike current in this paper. The DP-SJ-MOSFET features the disconnected ppillar at the 6th epitaxy. By employing the disconnected p-pillar, the triggering of the parasitic BJT under the UIS is suppressed, moreover, the hole current path is compressed to reduce the body diode reverse recovery spike current. As a result, the UIS capability is improved by 115% and the body diode reverse recovery current spike is reduced by 32.7% without sacrificing the other characteristics basically.
Isolation, toxicity and detection of cry gene in Bacillus thuringiensis isolates in Krabi province, Thailand

One hundred twenty one isolates of Bacillus thuringiensis were isolated from 91 soil samples collected in the national park and wildlife sanctuary in Krabi province. All isolates of B. thuringiensis were tested for their insecticidal activity against Spodoptera litura, S. exigua and Plutella xylostella larvae. Seven isolates of B. thuringiensis named JCPT7, JCPT16, JCPT18, JCPT64, JCPT68, JCPT74 and JCPT89 exhibited toxic activities against the insects, more than 90% mortality. The detection of cry gene of these isolates was done by a method based on polymerase chain reaction (PCR). The PCR result indicated that cry1Ab, cry1Ac, cry1C, cry1D, cry1I, cry9A, cry9B and cry2A were on chromosomal DNA and cry1Aa, cry1Ab, cry1Ac, cry1C, cry1D, cry1I and cry2A were on plasmid DNA. This study has introduced the promising B. thuringiensis isolates collected from soil samples which could be developed as an effective biocontrol agent for Lepidopterous pest.
Direct and Moderating Causal Effects of Network Support on Sleep Quality: Findings From the UC Berkeley Social Network Study.

BACKGROUND Sleep is an important, restorative behavior for health, yet many adults report troubled sleep. The existence of a support network may be beneficial for sleep quality, including as a buffer for stressful events, yet few studies have examined these relationships longitudinally. PURPOSE To examine the causal effect of changes in personal network support on sleep quality both directly and as a buffer of negative life events among young and older adults. METHODS The UC Berkeley Social Network survey collected data from young (21-30 year old, n = 475) and late middle-age (50-70 year old, n = 637) adults across three waves between 2015 and 2018. Participants reported on personal network characteristics, negative life events, and number of nights with trouble falling and staying asleep. Fixed effects models are used to examine causal relationships among each age cohort. RESULTS Direct effects of network support on sleep quality were observed among older adults. Insufficient practical support predicted higher rates of trouble falling asleep (incident rate ratio [IRR] = 1.40, p < .01), while a desire for more social companions predicted lower rates of trouble staying asleep (IRR = 0.81, p < .01). Buffering effects of network support on sleep quality were observed among young adults. Changes in partnership status buffer the negative effects of the death of a close tie on trouble falling asleep (IRR = 0.75, p < .01) and persistent difficulties paying bills on trouble staying asleep (IRR = 0.45, p < 0.001) among young adults. CONCLUSIONS This study provides evidence for the direct and buffering role of network support on sleep quality. Our results indicate that efforts to improve sleep quality should address personal networks and the support they provide, perhaps especially during times of stress for younger adults.
MAR: A Study of the Impact of Positive and Negative Reinforcement on Medication Adherence Reminders

This study investigates the effectiveness of three versions of a mobile persuasive application called Medication Adherence Reminder (MAR) in promoting medication adherence level among patients. MAR-1 offers a reminder feature, similar to most mobile applications on the market today, and a medications history, intended to promote self-awareness of medication adherence behavior. MAR-2 adds a points system to reward adherence, while MAR-3 adds a demerit system to punish nonadherence. An experimental comparative approach was employed to evaluate and compare the effectiveness of the three versions of MAR. The sample consisted of 30 participants over 18 years of age and on long-term medication regimens. A four-week field study with 10 participants for each MAR version was conducted. The majority of the participants found the app useful in helping them stick to their prescriptions. Most importantly, both the reward and penalty features were found to promote medication adherence behavior above the MAR-1 baseline level. While participants favored rewards over penalty, no statistically significant difference in medication adherence was found between the two approaches.
Deep Based Recommender System For Relevant K Pick-up Points

Recommender Systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. They are widely used and influence the daily life of almost everyone in different domains like e-commerce, social media, entertainment, or transportation in the mobility industry. The efficient generation of relevant recommendations in large-scale systems is a very complex task. This paper describes a deep recommender system for the most relevant K pick-up points for a driver. It is based on spatiotemporal features, points of interest (i.e POIs) and weather data using deep neural networks. Putting through a test with various features sets, we were able to achieve positive results. The effictiveness of our approach is demonstrated by the results, achieving a low loss value in most cases.