LLukas22/scidocs huggingface dataset:
Temporal Data in Relational Database Systems: A Comparison

Several database systems have implemented temporal data support, partly according to the model specified in the last SQL standard and partly according to other, older temporal models. In this article we use the most important temporal concepts to investigate their implementations in enterprise database systems. Also, we discuss strengths and weaknesses of these implementations and give suggestions for future extensions.
Hartstone Benchmark User's Guide, Version 1.0.

The ideas and findings in this report should not be construed as an official DoD position. It is published in the interest of scientific and technical information exchange. Use of any other trademarks in this report is not intended in any way to infringe on the rights of the trademark holder. Abstract: The Hartstone benchmark is a set of timing requirements for testing a system's ability to handle hard real-time applications. It is specified as a set of processes with well-defined workloads and timing constraints. The name Hartstone derives from HArd Real Time and the fact that the workloads are presently based on the well-known Whetstone benchmark. This report describes the structure and behavior of an implementation in the Ada programming language of one category of Hartstone requirements , the Periodic Harmonic (PH) Test Series. The Ada implementation of the PH series is aimed primarily at real-time embedded processors where the only executing code is the benchmark and the Ada runtime system. Guidelines for performing various Hartstone experiments and interpreting the results are provided. Also included are the source code listings of the benchmark, information on how to obtain the source code in machine-readable form, and some sample results for Version 1.0 of the Systems Designers XD Ada VAX/VMS-MC68020 cross-compiler.
A pooling based scene text proposal technique for scene text reading in the wild

Automatic reading texts in scenes has attracted increasing interest in recent years as texts often carry rich semantic information that is useful for scene understanding. In this paper, we propose a novel scene text proposal technique aiming for accurate reading texts in scenes. Inspired by the pooling layer in the deep neural network architecture, a pooling based scene text proposal technique is developed. A novel score function is designed which exploits the histogram of oriented gradients and is capable of ranking the proposals according to their probabilities of being text. An end-to-end scene text reading system has also been developed by incorporating the proposed scene text proposal technique where false alarms elimination and words recognition are performed simultaneously. Extensive experiments over several public datasets show that the proposed technique can handle multi-orientation and multi-language scene texts and obtains outstanding proposal performance. The developed end-to-end systems also achieve very competitive scene text spotting and reading performance. Preprint submitted to Journal of Pattern Recognition November 27, 2018 ar X iv :1 81 1. 10 00 3v 1 [ cs .C V ] 2 5 N ov 2 01 8
Effect of ivacaftor treatment in patients with cystic fibrosis and the G551D-CFTR mutation: patient-reported outcomes in the STRIVE randomized, controlled trial.

BACKGROUND
Cystic fibrosis (CF) is an inherited, rare autosomal recessive disease that results in chronically debilitating morbidities and high premature mortality. We evaluated how ivacaftor treatment affected CF symptoms, functioning, and well-being, as measured by the Cystic Fibrosis Questionnaire-Revised (CFQ-R), a widely-used patient-reported outcome (PRO) measure.


METHODS
STRIVE, a double-blind, placebo-controlled randomized trial, evaluated ivacaftor (150 mg) in CF patients aged 12+ with the G551D-CFTR mutation for 48 weeks. Treatment effect analysis used a mixed-effects repeated measures model. Treatment benefit analyses applied the cumulative distribution function and a categorical analysis of change scores ("improvement," "no change," or "decline"). Content-based interpretation examined treatment effect on specific item responses.


RESULTS
Data from 152 patients with a baseline CFQ-R assessment were analyzed. The treatment effect analysis favored treatment with ivacaftor over placebo on the Body Image, Eating, Health Perceptions, Physical Functioning, Respiratory, Social Functioning, Treatment Burden, and Vitality scales. Findings were supported by the analysis of categorical change. On all CFQ-R scales, the percentage of patients who improved was greater for ivacaftor. In the content-based analysis, the treatment benefit was characterized by better scores across a broad range of domains.


CONCLUSIONS
Results illustrate broad benefits of ivacaftor treatment across many domains: respiratory symptoms, physical and social functioning, health perceptions, and vitality, as measured by the CFQ-R. The breadth of improvements reflects the systemic mechanism of action of ivacaftor compared to other therapies. Findings support the patient-reported value of ivacaftor treatment in this patient population.


TRIAL REGISTRATION
ClinicalTrials.gov NCT00909532.
Smart wearable body sensors for patient self-assessment and monitoring

BACKGROUND
Innovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited. The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors.


METHODS
We reviewed the literature for connected device, sensor, trackers, telemonitoring, wireless technology and real time home tracking devices and their application for clinicians.


RESULTS
Smart wearable sensors are effective and reliable for preventative methods in many different facets of medicine such as, cardiopulmonary, vascular, endocrine, neurological function and rehabilitation medicine. These sensors have also been shown to be accurate and useful for perioperative monitoring and rehabilitation medicine.


CONCLUSION
Although these devices have been shown to be accurate and have clinical utility, they continue to be underutilized in the healthcare industry. Incorporating smart wearable sensors into routine care of patients could augment physician-patient relationships, increase the autonomy and involvement of patients in regards to their healthcare and will provide for novel remote monitoring techniques which will revolutionize healthcare management and spending.
Old, Sick, Alone, and Poor: A Welfare Analysis of Old-Age Social Insurance Programs

All individuals face some risk of ending up old, sick, alone and poor. Is there a role for social insurance for these risks and if so what is a good program? A large literature has analyzed the costs and benefits of pay as you go public pensions and found that the costs exceed the benefits. This paper, instead, considers means-tested social insurance programs for retirees such as Medicaid and food stamp programs. We find that the welfare gains from these programs are large. Moreover, the current scale of meanstested social insurance in the U.S. is too small in the following sense. If we condition on the current Social Security program, increasing the scale of means-tested social insurance by 1/3 benefits both the poor and the affluent when a payroll tax is used to fund the increase.
Is there any room for new physics in the muon g-2 problem?

Is there any room for new physics in the muon g-2 problem?
Towards execution time estimation in abstract machine-based languages

Abstract machines provide a certain separation between platform-dependent and platform-independent concerns in compilation. Many of the differences between architectures are encapsulated in the specific abstract machine implementation and the bytecode is left largely architecture independent. Taking advantage of this fact, we present a framework for estimating upper and lower bounds on the execution times of logic programs running on a bytecode-based abstract machine. Our approach includes a one-time, program-independent profiling stage which calculates constants or functions bounding the execution time of each abstract machine instruction. Then, a compile-time cost estimation phase, using the instruction timing information, infers expressions giving platform-dependent upper and lower bounds on actual execution time as functions of input data sizes for each program. Working at the abstract machine level makes it possible to take into account low-level issues in new architectures and platforms by just reexecuting the calibration stage instead of having to tailor the analysis for each architecture and platform. Applications of such predicted execution times include debugging/verification of time properties, certification of time properties in mobile code, granularity control in parallel/distributed computing, and resource-oriented specialization
Cython: The Best of Both Worlds

Cython is a Python language extension that allows explicit type declarations and is compiled directly to C. As such, it addresses Python's large overhead for numerical loops and the difficulty of efficiently using existing C and Fortran code, which Cython can interact with natively.
A generalized polynomial chaos based ensemble Kalman filter with high accuracy

As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states. This, however, introduces sampling errors that affect the accuracy of EnKF in a negative manner. Though sampling errors can be easily reduced by using a large number of samples, in practice this is undesirable as each ensemble member is a solution of the system of state equations and can be time consuming to compute for large-scale problems. In this paper we present an efficient EnKF implementation via generalized polynomial chaos (gPC) expansion. The key ingredients of the proposed approach involve (1) solving the system of stochastic state equations via the gPC methodology to gain efficiency; and (2) sampling the gPC approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost, to drastically reduce the sampling errors. The resulting algorithm thus achieves a high accuracy at reduced computational cost, compared to the classical implementations of EnKF. Numerical examples are provided to verify the convergence property and accuracy improvement of the new algorithm. We also prove that for linear systems with Gaussian noise, the first-order gPC Kalman filter method is equivalent to the exact Kalman filter. 2009 Elsevier Inc. All rights reserved.
Action recognition with deep neural networks

In this work, we have invesigated the action recognition problem using the Charades Action Recognition Dataset with 157 action classes. We have compared the results of different techniques such as extreme learning machines, support vector machines, and decision trees, applied on the features extracted with deep neural networks and the scene-action conditional probabilities.
A-voiding catastrophe: implementing a nurse-driven protocol.

PURPOSE
To evaluate the effectiveness of a nurse-driven indwelling urinary catheter removal protocol in an acute care setting.


BACKGROUND
The most common hospital-acquired infection is catheter-associated urinary tract infection (CAUTI). These infections account for 40% of all hospital-acquired infections. Although indwelling urinary catheters may be a needed intervention, studies have demonstrated over half of the patients who receive a urinary catheter do not have a valid indication for its use.


METHODOLOGY
A retrospective chart review allowed measurement of prevalence of catheter usage, dwell time, and CAUTIs before and after implementation of the protocol. Indwelling catheter usage was tabulated monthly from nursing clinical documentation. The sum of catheter days was divided by the total patient days and multiplied by 100 to get a percentage of catheter usage for each month. Dwell time was calculated by tabulating the length of time in days each patient had an indwelling catheter and dividing it by the total number of catheterized patients to determine the mean duration in days. The number of CAUTIs was divided by the total number of patients with urinary catheters and multiplied by 100 to get the percentage of CAUTI.


RESULTS
Prior to implementing the nurse-driven removal protocol, indwelling urinary catheter usage was 37.6%, mean dwell time was 3.35 days, and the CAUTI rate was 0.77%. After implementation of the protocol, catheter usage was 27.7%, mean dwell time was 3.46 days, and the CAUTI rate 0.35%.


IMPLICATIONS FOR NURSING
Findings support the use of a nurse-driven protocol to reduce the incidence and duration of urinary catheterization, to decrease incidence of CAUTI, and to improve the quality of care for hospitalized patients.
Language Generation with Recurrent Generative Adversarial Networks without Pre-training

Generative Adversarial Networks (GANs) have shown great promise recently in image generation. Training GANs for language generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximum-likelihood or used convolutional networks for generation. In this work, we show that recurrent neural networks can be trained to generate text with GANs from scratch using curriculum learning, by slowly teaching the model to generate sequences of increasing and variable length. We empirically show that our approach vastly improves the quality of generated sequences compared to a convolutional baseline. 1
Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records

This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to learn the temporal and spatial drifts between data from different satellite sensors. The resulting NNs are then used to sequentially adjust the satellite data for the creation of a global homogeneous long-term climate data record. The proposed technique has successfully been applied to the merging of ozone data from three European satellite sensors covering together a time period of more than 16 years. The resulting long-term ozone data record has an excellent long-term stability of 0.2 ± 0.2% per decade and can therefore be used for ozone and climate studies.
Delayed Gastric Emptying in Advanced Parkinson Disease: Correlation With Therapeutic Doses.

INTRODUCTION
Gastrointestinal dysfunction is often described in patients with Parkinson disease (PD), and gastrointestinal symptoms are usually attributed to gastroparesis. The consequent delayed gastric emptying (GE) may be an important pharmacokinetic mechanism underlying some of the response fluctuations that develop after long-term levodopa (L-dopa) therapy.The aim of this prospective study was to assess GE time by a liquid meal scintigraphy, in PD patients, and to correlate them with demographic, clinical, and therapeutic data.


METHODS
Scintigraphy with radiolabeled albumin nanocolloids added to acidified orange juice was performed in 51 consecutive PD patients 1 hour after their usual dopaminergic therapy first dose and after a 12-hour fast. Demographic, neurologic, gastrointestinal, and pharmacologic data were collected.


RESULTS
Fifty-one patients were divided into 2 groups using the cutoff point obtained in normal subjects (40 minutes): group 1 included 29 patients with GE T½ of 27.60 ± 7.30 minutes (normal), group 2 showed a GE T½ of 84.90 ± 53.80 minutes (delayed). The most striking significant difference between the 2 groups was the dopa-decarboxylase inhibitor mean dose that was significantly higher in the group of patients with delayed GE (201.32 ± 97.26 vs 127.65 ± 79.74; P = 0.005).


CONCLUSIONS
The impairment of gastric motility, frequently represented in PD patients, occurs in approximately 42% of patients with motor complications. A mechanism that may explain the GE delay is the effect of L-dopa on dopaminergic receptors in the stomach. Therefore, the dosage of dopa-decarboxylase inhibitor, increasing the L-dopa concentration, may contribute to GE delay and its consequent effect on drug delivery and efficacy.
Biofuels from microalgae — A review of technologies for production , processing , and extractions of biofuels and co-products

Sustainability is a key principle in natural resource management, and it involves operational efficiency, minimisation of environmental impact and socio-economic considerations; all of which are interdependent. It has become increasingly obvious that continued reliance on fossil fuel energy resources is unsustainable, owing to both depleting world reserves and the green house gas emissions associated with their use. Therefore, there are vigorous research initiatives aimed at developing alternative renewable and potentially carbon neutral solid, liquid and gaseous biofuels as alternative energy resources. However, alternate energy resources akin to first generation biofuels derived from terrestrial crops such as sugarcane, sugar beet, maize and rapeseed place an enormous strain on world food markets, contribute to water shortages and precipitate the destruction of the world’s forests. Second generation biofuels derived from lignocellulosic agriculture and forest residues and from nonfood crop feedstocks address some of the above problems; however there is concern over competing land use or required land use changes. Therefore, based on current knowledge and technology projections, third generation biofuels specifically derived from microalgae are considered to be a technically viable alternative energy resource that is devoid of the major drawbacks associated with first and second generation biofuels. Microalgae are photosynthetic microorganisms with simple growing requirements (light, sugars, CO2, N, P, and K) that can produce lipids, proteins and carbohydrates in large amounts over short periods of time. These products can be processed into both biofuels and valuable co-products. This study reviewed the technologies underpinning microalgae-to-biofuels systems, focusing on the biomass production, harvesting, conversion technologies, and the extraction of useful co-products. It also reviewed the synergistic coupling of microalgae propagation with carbon sequestration and wastewater treatment potential for mitigation of environmental impacts associated with energy conversion and utilisation. It was found that, whereas there are outstanding issues related to photosynthetic efficiencies and biomass output, microalgae-derived biofuels could progressively substitute a significant proportion of the fossil fuels required to meet the growing energy demand. 2009 Elsevier Ltd. All rights reserved.
Low dose linomide in Type I juvenile diabetes of recent onset: a randomised placebo-controlled double blind trial

The quinoline-3-carboxamide, linomide, protects non-obese diabetic mice from diabetes. The effects of linomide on insulin needs and beta cell function were studied in recent juvenile Type I diabetes in a double-blind trial. Patients with recent onset diabetes were randomly assigned to treatment with a fixed dose of 2.5 mg linomide (42 patients) or placebo (21 patients) for 1 year, in addition to insulin and diet. Glycated haemoglobin was 10–15 % lower at 9 months (p = 0.003) and 12 months (p < 0.05) in the linomide group. The insulin dose was 32–40 % smaller in the linomide group at 3 (p < 0.03), 6 (p < 0.02), 9 (p < 0.001) and 12 months (p = 0.01). Insulin doses correlated negatively with C peptide values (p = 0.001–0.002). The trend for higher C peptide values in the linomide group did not reach significance. In a post hoc subgroup analysis performed in 40 patients (25 from the linomide group and 15 from the placebo group) who still had detectable residual beta cell function at entry, linomide was associated with 45–59 % higher C peptide value at 6 months (p < 0.05), 9 months (p < 0.05) and 12 months (p < 0.05). The main adverse effects of linomide were mild transitory anaemia (45 vs 10 % in the linomide and placebo groups), thrombocytopenia (24 vs 10 %), and mild joint discomfort (45 vs 5 %) with no clinical signs. In conclusion, low-dose linomide reduced the insulin needs in patients with juvenile Type I diabetes of recent onset and improved beta cell function in patients who still had detectable beta cell function at entry. These results support further clinical and experimental studies to define the effects of linomide in Type I diabetes provided the safety of linomide is reliably established. [Diabetologia (1998) 41: 1040–1046]
Intraday Return Volatility Process: Evidence from NASDAQ Stocks

This paper presents a comprehensive analysis of the distributional and timeseries properties of intraday return. The purpose is to determine whether a GARCH model that allows for time varying variance in a process can adequately represent intraday return volatility. Our primary data set consists of 5-minute returns, trading volume, and bid-ask spreads during the period January 1, 1999 through March 31, 1999, for a subset of thirty stocks from NASDAQ 100 Index. The result of our study indicates that a GARCH (1,1) model best describes the volatility of intraday return. Current volatility can be explained by past volatility that tends to persist over time. This result is consistent with that of Akgiray (1989) who estimates volatility using various ARCH and GARCH specifications and finds GARCH (1,1) model performs the best. We add volume as an additional explanatory variable in the GARCH model to examine if volume can capture GARCH effects. Consistent with results of Najand and Yung (1991) and Foster (1995) and contrary to those of Lamoureux and Lastrapes (1990), our results show that the persistence in volatility remains in intraday return series even after volume is included in the model as an explanatory variable. We then substitute bid-ask spread for volume in the conditional volatility equation to examine if the latter can capture GARCH effects. The results show that GARCH effect remains strongly significant for many of the securities after the introduction of bid-ask spread. Consistent with results of Antoniou, Homes and Priestley (1998), intraday returns also exhibit significant asymmetric responses of volatility to flow of information into the market. Intraday Return Volatility Process: Evidence from NASDAQ Stocks