Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the Accuracy of Oculus Touch Controllers for Motor Rehabilitation Applications Using Quantifiable Upper Limb Kinematics: Validation Study

Background: As commercial motion tracking technology becomes more readily available, it is necessary to evaluate the accuracy of these systems before using them for biomechanical and motor rehabilitation applications. Objective: This study aimed to evaluate the relative position accuracy of the Oculus Touch controllers in a 2.4 x 2.4 m play-space. Methods: Static data samples (n=180) were acquired from the Oculus Touch controllers at step sizes ranging from 5 to 500 mm along 16 different points on the play-space floor with graph paper in the x (width), y (height), and z (depth) directions. The data were compared with reference values using measurements from digital calipers, accurate to 0.01 mm; physical blocks, for which heights were confirmed with digital calipers; and for larger step sizes (300 and 500 mm), a ruler with hatch marks to millimeter units. Results: It was found that the maximum position accuracy error of the system was 3.5 ± 2.5 mm at the largest step size of 500 mm along the z-axis. When normalized to step size, the largest error found was 12.7 ± 9.9% at the smallest step size in the y-axis at 6.23 mm. When the step size was <10 mm in any direction, the relative position accuracy increased considerably to above 2% (approximately 2 mm at maximum). An average noise value of 0.036 mm was determined. A comparison of these values to cited visual, goniometric, and proprioceptive resolutions concludes that this system is viable for tracking upper-limb movements for biomechanical and rehabilitation applications. The accuracy of the system was also compared with accuracy values from previous studies using other commercially available devices and a multicamera, marker-based professional motion tracking system. Conclusions: The study found that the linear position accuracy of the Oculus Touch controllers was within an agreeable range for measuring human kinematics in rehabilitative upper-limb exercise protocols. Further testing is required to ascertain acceptable repeatability in multiple sessions and rotational accuracy. (JMIR Biomed Eng 2019;4(1):e12291) doi: 10.2196/12291
Target leverage and speed of adjustment along the life cycle of the firm

This paper analyzes differences of target leverage and speed of adjustment across three life cycle stages of the firm: introduction, growth and maturity. We find that profitability and tangibility are the most stable determinants, whereas growth opportunities, tangibles, and size show changing effects accross stages. The speed of adjustment increases as the firms evolve, although firms in introduction using operating debt are able to adjust the fastest. Firms changing stage adjust leverage at a lower speed and their target is more affected by profitability and growth opportunities, mainly when the change is from introduction to growth. Finally, we confirm the existence of long‐term debt targets, evidencing that next‐year target is a relevant factor to explain current debt when firms change from one stage to another.
Efficiency of Chinese Real Estate Market Based on Complexity-Entropy Binary Causal Plane Method

Real estate market is a complex system. A rational real estate market is not only helpful to people's living standards but also beneficial to countries’ macroeconomic stability. Is Chinese real estate market rational? This paper attempts to study the efficiency of Chinese real estate market by using the complexity-entropy binary causal plane method. We firstly discuss the formation mechanism of real estate price, which provides a theoretical basis for testing the efficiency, and compute the real estate market efficiency of 70 main Chinese cities. The results show that neither the whole market nor the main cities have reached the weak efficiency, and the efficiency and complexity of each city are different, and the relationship between them is significantly negative. In addition, this paper also compares the efficiency and complexity of Chinese real estate market with American real estate market. Then, some suggestions for the healthy development of Chinese real estate market in the future are put forward.
Speculative Taint Tracking (STT): A Comprehensive Protection for Speculatively Accessed Data

Speculative execution attacks present an enormous security threat, capable of reading arbitrary program data under malicious speculation, and later exfiltrating that data over microarchitectural covert channels. Since these attacks first rely on being able to read arbitrary data (potential secrets), a conservative approach to defeat all attacks is to delay the execution of instructions that read those secrets, until those instructions become non-speculative. This paper's premise is that it is safe to execute and selectively forward the results of speculative instructions that read secrets, which improves performance, as long as we can prove that the forwarded results do not reach potential covert channels. We propose a comprehensive hardware protection based on this idea, called Speculative Taint Tracking (STT), capable of protecting all speculatively accessed data. Our work addresses two key challenges. First, to safely selectively forward secrets, we must understand what instruction(s) can form covert channels. We provide a comprehensive study of covert channels on speculative microarchitectures, and use this study to develop hardware mechanisms that block each class of channel. Along the way, we find new classes of covert channels related to implicit flow on speculative machines. Second, for performance, it is essential to disable protection on previously protected data, as soon as doing so is safe. We identify that the earliest time is when the instruction(s) producing the protected data become non-speculative, and design a novel microarchitecture for disabling protection at this moment. We provide an extensive formal analysis showing that STT enforces a novel form of non-interference, with respect to all speculatively accessed data. We further evaluate STT on 21 SPEC and 9 PARSEC workloads, and find it adds only 8.5%/14.5% overhead (depending on attack model) relative to an insecure machine, while reducing overhead by 4.7×/18.8× relative to a baseline secure scheme.
Relative probiotic potential of milk bacteria from Desi and cross-breed cows and its compatibility with prebiotics

• Received: 03 January 2018 • Revised: 12 February 2018 • Accepted: 28 February 2018 • Published: 03 March 2018 • ABSTRACT Fifty two lactic acid bacteria (LAB) were isolated from milk of Desi and cross-breed cows of India and its functional characteristics were investigated on the basis of morphological and biochemical properties. Among them four bacterial isolates were screened on the basis of in vitro probiotic attributes. Isolate CP-12 d and CP-8 d which were isolated from Desi Indian cows showed high tolerance to low pH, bile, NaCl and certain antibiotics. These isolates exhibited the highest adhesion to hydrocarbons xylene, n-octane and n-hectane. Isolate CP-8 d exhibited the highest auto-aggregation rate (62%). It was the most resistant isolate against different antibiotics. All the four isolates inhibited the enteric pathogens viz., Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae and Salmonella typhi but the isolate CP-12 d and CP-8 d exhibited high antagonistic activity against all pathogens. These two bacterial isolates exhibited good probiotic properties and also grew well in presence of inulin as compared to honey.
Electronic Health Record Sharing System Using Smart Contract

Due to enhancement of the information technology all over the world, electronic health records (EHRs) have become a general method to store the patients’ health record in hospital databases. When a patient visits a hospital, the health record is stored in that hospital database. If the same patient visits another hospital, that health record from the previous hospital is not accessible by the present hospital. Thus, sharing of sensitive information of patients among healthcare providers while maintaining the integrity of the data and protecting patient privacy has become a key issue for the healthcare industry nowadays. And, it is very tiresome and tedious task to construct a consolidated health record for a patient by collecting records from different hospital databases due to the security and privacy concerns. In healthcare Industry currently, involving patient at every stage of treatment and diagnosis, thereby building a coordinated care system is gaining attention. The patient health record must be accessible at each step of medications and multiple parties should be granted access to view, edit, and share records while preserving the precision and updation of records timely and securely. We know that, the personal health data is very sensitive and estimable. Thus, to address the key challenges faced by healthcare industry today, it is ideal to use blockchain technology for sharing health record. A blockchain Electronic Health Record sharing system utilizes smart contract to grant access to the medical records and it improves data sharing quality and maintains the privacy of records. In this paper, we will realize the true supremacy of using blockchain for sharing EHR involving patient at every step.
Intravenous Oxytocin and Misoprostol Equally Effective in Managing Uterine Atony Induced Postpartum Hemorrhage among Delivery Mothers: A Two-Year Case Series in Freetown, Sierra Leone

Background: Sierra Leone has one of the highest maternal mortality rates in the world. Postpartum hemorrhage is one of the common causes of maternal deaths in developing countries. Methods: We analysed the medical records of mothers who developed postpartum hemorrhage following delivery at the Rokupa Government Hospital in Freetown, Sierra Leone during the period of 1st January 2017 to the 31st December 2018. We reviewed the sociodemographic characteristics and medical features associated with postpartum hemorrhage and the management of PPH. Results: There were 2,236 deliveries during the period under review of which 0.89% (20/2236) resulted into the postpartum hemorrhage. The mean age of PPH patients was 25.4 years (Standard deviation =6.4 years). Misoprostol (n=5, 50%, X2=15.143, df=18, p-value=0.6521) and oxytocin (n=5, 50%, X2=15.143, df=18, p-value =0.6521) administered via intravenously were the most common methods used to manage postpartum hemorrhage caused by uterine atony. Conclusion: Our findings are different from similar studies in which most cases were either referred from homes, clinics, primary or tertiary care hospitals. We are calling for the adoption of the administration of intravenous oxytocin or misoprostol as part of a national algorithm for the management of postpartum hemorrhage in Sierra Leone.
Contestable Norms

To be sustainable without external enforcement, social norms, contracts, and other agreements must not only deter violations but also withstand proposals to move to other norms, contracts, or agreements. We introduce contestable norms, which achieve both objectives, analyze their efficiency, stability, design, and conceptual foundation, and characterize their payoffs. Contestable norms may be inefficient even when agents have arbitrarily frequent opportunities to interact and challenge the incumbent norm, to an extent that is determined by the amount of conflict inherent in agents’ strategic environment. The analysis sheds new light on the efficient institution hypothesis and the renegotiation paradox.
HSM: A Hybrid Slowdown Model for Multitasking GPUs

Graphics Processing Units (GPUs) are increasingly widely used in the cloud to accelerate compute-heavy tasks. However, GPU-compute applications stress the GPU architecture in different ways --- leading to suboptimal resource utilization when a single GPU is used to run a single application. One solution is to use the GPU in a multitasking fashion to improve utilization. Unfortunately, multitasking leads to destructive interference between co-running applications which causes fairness issues and Quality-of-Service (QoS) violations. We propose the Hybrid Slowdown Model (HSM) to dynamically and accurately predict application slowdown due to interference. HSM overcomes the low accuracy of prior white-box models, and training and implementation overheads of pure black-box models, with a hybrid approach. More specifically, the white-box component of HSM builds upon the fundamental insight that effective bandwidth utilization is proportional to DRAM row buffer hit rate, and the black-box component of HSM uses linear regression to relate row buffer hit rate to performance. HSM accurately predicts application slowdown with an average error of 6.8%, a significant improvement over the current state-of-the-art. In addition, we use HSM to guide various resource management schemes in multitasking GPUs: HSM-Fair significantly improves fairness (by 1.59x on average) compared to even partitioning, whereas HSM-QoS improves system throughput (by 18.9% on average) compared to proportional SM partitioning while maintaining the QoS target for the high-priority application in challenging mixed memory/compute-bound multi-program workloads.
Cryptocurrency Forecasting using α-Sutte Indicator, ARIMA, and Long Short-Term Memory

The purpose of these studies are to obtain bitcoin price predictions using three different approach in forecasting methods : ARIMA model, α-sutte indicator and LSTM algorithm, and to find out the accuracy level of the three methods in forecasting bitcoin’s price as well. Bitcoin closing’s price each day taken from website of coin market starting from April 29 2013 to February 06 2019 was analyzed using R and Python softwares. Based on the smallest value of MSE, MAPE, and MAD, the LSTM algorithm gave the best prediction, followed by the α-Sutte indicator and the ARIMA model.
EXPERIMENTAL VERIFICATION OF PSYCHOPHYSIOLOGICAL PERFORMANCE OF A SELECTED FLIGHT PERSONNEL AND SW: PRESURVEY FOR TRANSPORT SAFETY

Flight personnel have various factors that cause high psychological stress during the performance of flight tasks, thereby influencing their psycho-physiological experience, which may be a cause of a breach of air traffic safety. The paper presents the results of an experimental verification of task management under load, which allowed selection of probands from a sample of 100 participants in the presurvey. Overall, 70% of the probands achieved better results than the minimum level of success of simulated and the complex tasks, 60%. The main aim of the experiment, the presurvey, is to verify and prepare a final version of sets of test tasks in the field of measuring the psychophysiological performance of the candidates for aviation personnel and to complete the creation of a technical device and the software tool for these processes. The software will be filed for the patent protection of copyrights and subsequently used for solving an approved research project supported by a grant from the Agency for Science and Research of the Slovak Republic.
Twisted-Files Adaptive: A Novel Approach to Endodontic NiTi Instrumentation

A major goal of Endodontics is the prevention or elimination of “apical periodontitis”. Apical periodontitis is the general term for endodontic related periradicular/periapical inflammation, whether it is histologically a granuloma, cyst or abscess, in symptomatic or asymptomatic form. When root canal treatment is indicated, we rely on the triad of instrumentation, irrigation and obturation to achieve this goal.
Site-specific, nonlinear soil response using an active source

Next to the velocity structure, the most important site physical characteristic for prediction of Strong Ground Motion (SGM) is the material elastic nonlinear response: the modulus reduction and nonlinear dissipation. Site characterization is currently carried out by point measurements, most generally by laboratory analysis of core and/or using in situ borehole diagnostics (velocity and density logs, for instance). We are developing an alternative approach where the site linear and nonlinear elastic properties can be characterizedin situ. The approach, known as Nonlinear Resonant Ultrasound Spectroscopy (NRUS) is used to interrogate the nonlinear characteristics of the medium by applying an active source. NRUS relies on monitoring a resonance mode frequency change as a function of drive level. The elastic nonlinear behavior is extracted from the change in resonant frequency and dissipation characteristics with drive level. This type of experimental approach is commonly applied in the laboratory to characterize sample nonlinear response (1).
Structured approach therapy for PTSD in returning veterans and their partners: Pilot findings.

Seven married couples, each consisting of a veteran who had been deployed to Operation Iraqi Freedom and a cohabiting female spouse, participated in an uncontrolled trial of structured approach therapy (SAT), a couple-based treatment for posttraumatic stress disorder (PTSD). After completing treatment, the group of 7 returning veterans showed significant reductions in both selfand clinician-related PTSD with posttreatment Hedge g effect size improvements of 2.51 and 3.54, indicating an extremely high magnitude of change in posttraumatic stress. Paired t tests also indicated significant decreases in spousal anxiety, with a trend toward a significant decrease in spousal depression. Analyses of reliable change on the individual level indicated that 4 of 5 veterans and 3 of 4 spouses with dyadic adjustment scores in the distressed range prior to treatment showed reliable decreases in distress over the course of SAT placing them in the nondistressed range at posttreatment. Five of 7 spouses showed reliable decreases in depression, and 4 of 7 spouses showed reliable decreases in anxiety over the course of treatment with SAT. These results support the hypothesis that participation in SAT reduces PTSD in returning veterans while reducing relationship problems and distress in their spouses. More extensive research is being conducted with a larger sample in a randomized clinical trial.
K-NN active learning under local smoothness assumption

There is a large body of work on convergence rates either in passive or active learning. Here we first outline some of the main results that have been obtained, more specifically in a nonparametric setting under assumptions about the smoothness of the regression function (or the boundary between classes) and the margin noise. We discuss the relative merits of these underlying assumptions by putting active learning in perspective with recent work on passive learning. We design an active learning algorithm with a rate of convergence better than in passive learning, using a particular smoothness assumption customized for k-nearest neighbors. Unlike previous active learning algorithms, we use a smoothness assumption that provides a dependence on the marginal distribution of the instance space. Additionally, our algorithm avoids the strong density assumption that supposes the existence of the density function of the marginal distribution of the instance space and is therefore more generally applicable.
Sensor Fusion: Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data.

The Tactical Driver Behavior modeling problem requires understanding of driver actions in complicated urban scenarios from a rich multi modal signals including video, LiDAR and CAN bus data streams. However, the majority of deep learning research is focused either on learning the vehicle/environment state (sensor fusion) or the driver policy (from temporal data), but not both. Learning both tasks end-to-end offers the richest distillation of knowledge, but presents challenges in formulation and successful training. In this work, we propose promising first steps in this direction. Inspired by the gating mechanisms in LSTM, we propose gated recurrent fusion units (GRFU) that learn fusion weighting and temporal weighting simultaneously. We demonstrate it's superior performance over multimodal and temporal baselines in supervised regression and classification tasks, all in the realm of autonomous navigation. We note a 10% improvement in the mAP score over state-of-the-art for tactical driver behavior classification in HDD dataset and a 20% drop in overall Mean squared error for steering action regression on TORCS dataset.
Pangolin: An Efficient and Flexible Graph Pattern Mining System on CPU and GPU.

There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability. We present Pangolin, a high-performance and flexible in-memory GPM framework targeting shared-memory CPUs and GPUs. Pangolin is the first GPM system that provides high-level abstractions for GPU processing. It provides a simple programming interface based on the extend-reduce-filter model, which enables users to specify application-specific knowledge for search space pruning and isomorphism test elimination. We describe novel optimizations that exploit locality, reduce memory consumption, and mitigate the overheads of dynamic memory allocation and synchronization. Evaluation on a 28-core CPU demonstrates that Pangolin outperforms existing GPM frameworks Arabesque, RStream, and Fractal by 49x, 88x, and 80x on average, respectively. Acceleration on a V100 GPU further improves performance of Pangolin by 15x on average. Compared to state-of-the-art hand-optimized GPM applications, Pangolin provides competitive performance with less programming effort.
Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EMs), can be significantly affected by variations in atmospheric, illumination, and environmental conditions that typically occur within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the EMs, which propagates significant modeling errors throughout the whole unmixing process and compromises the quality of the results. Therefore, serious efforts have been dedicated to mitigating the effects of spectral variability in SU. This resulted in the development of algorithms that incorporate different strategies to enable the EMs to vary within a hyperspectral image, using, for instance, sets of spectral signatures known a priori as well as Bayesian, parametric, and local EM models.
Approximation Algorithms and LP Relaxations for Scheduling Problems Related to Min-Sum Set Cover

We consider single-machine scheduling problems that are natural generalizations or variations of the min-sum set cover problem and the min-sum vertex cover problem. For each of these problems, we give new approximation algorithms. Some of these algorithms rely on time-indexed LP relaxations. We show how a variant of alpha-point scheduling leads to the best-known approximation ratios, including a guarantee of 4 for an interesting special case of the so-called generalized min-sum set cover problem. We also make explicit the connection between the greedy algorithm for min-sum set cover and the concept of Sidney decomposition for precedence-constrained single-machine scheduling, and show how this leads to a 4-approximation algorithm for single-machine scheduling with so-called bipartite OR-precedence constraints.