Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Machine Learning to Examine Medication Adherence Thresholds and Risk of Hospitalization

Background:Quality improvement efforts are frequently tied to patients achieving ≥80% medication adherence. However, there is little empirical evidence that this threshold optimally predicts important health outcomes. Objective:To apply machine learning to examine how adherence to oral hypoglycemic medications is associated with avoidance of hospitalizations, and to identify adherence thresholds for optimal discrimination of hospitalization risk. Methods:A retrospective cohort study of 33,130 non–dual-eligible Medicaid enrollees with type 2 diabetes. We randomly selected 90% of the cohort (training sample) to develop the prediction algorithm and used the remaining (testing sample) for validation. We applied random survival forests to identify predictors for hospitalization and fit survival trees to empirically derive adherence thresholds that best discriminate hospitalization risk, using the proportion of days covered (PDC). Outcomes:Time to first all-cause and diabetes-related hospitalization. Results:The training and testing samples had similar characteristics (mean age, 48 y; 67% female; mean PDC=0.65). We identified 8 important predictors of all-cause hospitalizations (rank in order): prior hospitalizations/emergency department visit, number of prescriptions, diabetes complications, insulin use, PDC, number of prescribers, Elixhauser index, and eligibility category. The adherence thresholds most discriminating for risk of all-cause hospitalization varied from 46% to 94% according to patient health and medication complexity. PDC was not predictive of hospitalizations in the healthiest or most complex patient subgroups. Conclusions:Adherence thresholds most discriminating of hospitalization risk were not uniformly 80%. Machine-learning approaches may be valuable to identify appropriate patient-specific adherence thresholds for measuring quality of care and targeting nonadherent patients for intervention.
Talking Point

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Abstract: Priority-based Chunk Scheduling Algorithm for Large Scale P2P Live Streaming System

Chunk-based mesh-pull Peer-to-Peer (P2P) streaming is being considered as the most promising approach to deliver real-time video to large scale users over the Internet. Currently the majorities of chunk scheduling approaches focus on receiver side’s chunk/peer selection strategies and neglect the service order at supplier side, which will cause the user’s video quality descending in overloaded operating environments. In this paper, we first propose the supplier side chunk priority model considering requested chunks’ urgency of playback and rarity. Then based on the model, we formulate the supplier side scheduling problem as a linear programming problem and propose a greedy bandwidth resource allocation algorithm to solve it. The extensive experiments demonstrate the proposed scheme effective in improving the quality of experience of end users in overloaded operating environments comparing to the FCFS (First Come First Service) scheme.
Detection of DoS and DDoS attacks in NGMN using frequency domain analysis

Ensuring security of the infrastructure against external attacks across network boundaries constitutes one of primary attributes as well as challenges of the next generation mobile network (NGMN). To allay the possibility of such attacks emancipating the NGMN architecture, it is necessary to identify the attack types. However, detection of the attack types from various traffic flows (as is the case in network links) and their subsequent classification can be a very daunting task, especially when both the attack and the legitimate traffic exhibit similar statistical properties (such as denial-of-service (DoS) and distributed DoS (DDoS)). Furthermore, the attackerpsilas ability to spoof and forge the packet header information (including IP address) makes the detection process even more difficult. Conventional anomaly based attack detection mechanisms have been found wanting in such situations. In an attempt to provide a solution, this paper proposes a detection algorithm that identifies and characterizes network traffic by investigating the frequency spectrum distribution. The Lomb periodogram is utilized to determine the power spectrum of the observed traffic whereupon two deviation score parameters are employed to segregate the anomaly traffic flows from legitimate ones in a two-step method. For simplicity purposes, the efficiency of such classification effort is demonstrated for DoS and DDoS attacks only (for their statistical similarity to normal traffic).
Composing reusable software components through views

The ability to combine separate reusable software components to form a complete program is necessary for effective software reuse. Views provide a clean, flexible, and efficient mechanism for combining reusable software components. A view describes how an application data type implements features of an abstract type; it provides a bidirectional mapping between a generic concept and a particular implementation of that concept. Parameterizing a generic procedure by means of views allows a single copy of the procedure to be specialized for a variety of application data types and target languages. Both composition of views and multiple views of the same data as different abstract types are often required. Automated support makes it easy to create views and to generate specialized code for an application in a desired target language. These techniques have been implemented. Examples are presented that illustrate combination of components through views to specialize a generic procedure and to instantiate a software framework. <<ETX>>
Failure recovery method based on log for autonomous robotic system

According to the historical data before failure happened, a new failure recovery method based on log for autonomous robotic system is proposed in the paper. Transaction is the atomic unit of failure recovery. The transactions are registered into the log before the mechanical actions are performed. When the failure is recovered, the corresponding transactions are retrieved from the log, then redo or undo. The recovery is accelerated through using the log file with balance point and the restart file. The control hardware failure is recovered by mirror technology. The EMACRobot system showed that the method based on log would reduce the duration of failure recovery, inherit control work before failure happens, and improve the robustness of autonomous mobile robot.
Triosite Implants and Fibrin Glue in the Treatment of Atrophic Rhinitis: Technique and Results

Nine patients with primary or secondary atrophic rhinitis were treated by narrowing of the nasal fossae using a new surgical technique (derived from the Eryes procedure) in which a Triosite® and fibrin glue mixture is implanted via the labial vestibule route. The results were good or excellent in seven patients. No rejections occurred. Osseocoalescence, as evaluated by computed axial tomography at 6 months, was good. Inspiratory intrasnasal pain in patients with postsurgical atrophic rhinitis improved following the operation.
ClockWork-RNN Based Architectures for Slot Filling

A prevalent and challenging task in spoken language understanding is slot filling. Currently, the best approaches in this domain are based on recurrent neural networks (RNNs). However, in their simplest form, RNNs cannot learn long-term dependencies in the data. In this paper, we propose the use of ClockWork recurrent neural network (CW-RNN) architectures in the slot-filling domain. CW-RNN is a multi-timescale implementation of the simple RNN architecture, which has proven to be powerful since it maintains relatively small model complexity. In addition, CW-RNN exhibits a great ability to model long-term memory inherently. In our experiments on the ATIS benchmark data set, we also evaluate several novel variants of CW-RNN and we find that they significantly outperform simple RNNs and they achieve results among the state-of-the-art, while retaining smaller complexity.
From aromatic molecules to nanoarchitectures: Influencing factors in on-surface synthesis

The present thesis reports the bottom-up approach to create 1D and 2D organic nanostructures by on-surface polymerization on the basis of functionalized arene molecules. The experiments were carried out on metal single crystal surfaces under defined ultra-high vacuum conditions. The polymerization pathway and coupling mechanism, as well as the resulting nanostructures were studied by a complementary approach of surface-sensitive analysis methods (STM, XPS and NEXAFS).
Continuous Training for Production ML in the TensorFlow Extended (TFX) Platform

Large organizations rely increasingly on continuous ML pipelines in order to keep machine-learned models continuously up-to-date with respect to data. In this scenario, dis-ruptions in the pipeline can increase model staleness and thus degrade the quality of downstream services supported by these models. In this paper we describe the operation of continuous pipelines in the Tensorﬂow Extended (TFX) platform that we developed and deployed at Google. We present the main mechanisms in TFX to support this type of pipelines in production and the lessons learned from the deployment of the platform internally at Google.
Invited review: Microtubule severing enzymes couple atpase activity with tubulin GTPase spring loading

Microtubules are amazing filaments made of GTPase enzymes that store energy used for their own self‐destruction to cause a stochastically driven dynamics called dynamic instability. Dynamic instability can be reproduced in vitro with purified tubulin, but the dynamics do not mimic that observed in cells. This is because stabilizers and destabilizers act to alter microtubule dynamics. One interesting and understudied class of destabilizers consists of the microtubule‐severing enzymes from the ATPases Associated with various cellular Activities (AAA+) family of ATP‐enzymes. Here we review current knowledge about GTP‐driven microtubule dynamics and how that couples to ATP‐driven destabilization by severing enzymes. We present a list of challenges regarding the mechanism of severing, which require development of experimental and modeling approaches to shed light as to how severing enzymes can act to regulate microtubule dynamics in cells. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 547–556, 2016.
A regionalised strategy for improving motor vehicle-related highway driver deaths using a weighted averages method

Objective The state of Florida has some of the most dangerous highways in the USA. In 2006, Florida averaged 1.65 fatalities per 100 million vehicle miles travelled (VMT) compared with the national average of 1.42. A study was undertaken to find a method of identifying counties that contributed to the most driver fatalities after a motor vehicle collision (MVC). By regionalising interventions unique to this subset of counties, the use of resources would have the greatest potential of improving statewide driver death. Methods The Florida Highway Safety Motor Vehicle database 2000–2006 was used to calculate driver VMT-weighted deaths by county. A total of 3 468 326 motor vehicle crashes were evaluated. Counties that had driver death rates higher than the state average were sorted by a weighted averages method. Multivariate regression was used to calculate the likelihood of death for various risk factors. Results VMT-weighted death rates identified 12 out of 67 counties that contributed up to 50% of overall driver fatalities. These counties were primarily clustered in central and south Florida. The strongest independent risk factors for driver death attributable to MVC in these high-risk counties were alcohol/drug use, rural roads, speed limit ≥45 mph, adverse weather conditions, divided highways, vehicle type, vehicle defects and roadway location. Conclusions Using the weighted averages method, a small subset of counties contributing to the majority of statewide driver fatalities was identified. Regionalised interventions on specific risk factors in these counties may have the greatest impact on reducing driver-related MVC fatalities.
Science writers: How they keep up

This research examines how science communicators understand the fields that they write about. This thesis looks at how science communicators become aware of (or “learn about”) the increasingly specialized subfields of science. The thesis also examines how science communicators educate themselves (or “keep up”) with ongoing changes in the field. The study, which integrated a survey with 161 participants and five in-depth interviews, also uncovered the demographics of science communicators, as well as the pressures they face on-the-job. According to the study’s results, there are no ineffective methods for learning about or keeping up with science, indicating that science communicators are using everything at their disposal to become aware of and remain educated about science. Another area looked at was a potential reflection of the relationship between scientists and communicators based on how easy/difficult it was for communicators to get in contact with scientists, and how eager/willing they were to talk to communicators. The findings show that scientist and communicators seem to have positive relationships with each other. These findings all have significance because they can be used by science communicators to improve practices.
An Embedded Training Solution: FBCB2/Tactical Decision Making Intelligent Tutoring System

Abstract : We are developing for STRICOM an Intelligent Tutoring System (ITS) for tank and mechanized infantry company commanders that teaches tactical decision making and the tactical use of FBCB2, a C4I system. These are complex cognitive tasks that normally require the availability of an instructor. This prevents the effective use of embedded systems for training in the field, where an instructor is not typically present. Our ITS interfaces to a tactical simulation and FBCB2 and assumes the duties normally performed by the instructor. Instructors and experts both agree that company commanders need to improve their tactical decision-making and that this requires more tactical decision-making practice. Practice should include a mix of tactical planning and tactical execution in dynamic simulations that provide 3-D virtual terrain views. Additionally, FBCB2 training decays very quickly so that an embedded, scenario-based training aid would substantially increase combat readiness.
Automatic Malware Signature Generation

The times when malware researchers could spend weeks analyzing a new piece of malware are long gone. Today newmalicious programs are written and distributed at such speed that it just is not possible. Virus scanners are the most common countermeasure against malware attacks and they need up-to-date signatures to successfully identify malware. This thesis describes Autosig, a program for automatic generation of malware signatures. The generation of signatures is based on the fact that most malware come in many different variants, but still share some invariant code. Using statistical data on how often certain byte combinations appear in legitimate files, Autosig extracts a substring from this invariant code to generate a signature. The signatures are tested and those that fail to pass all tests are discarded. By remembering all discarded signatures, Autosig learns which code to avoid. This technique has turned out to be successful in many of the time consuming routine cases, leaving the human analysts more time to generate working signatures for more complicated malware. It has also turned out helpful in replacing overlapping and redundant signatures, leading to a smaller signature database.
DNA methylation polymorphism in flue-cured tobacco and candidate markers for tobacco mosaic virus resistance

DNA methylation plays an important role in the epigenetic regulation of gene expression during plant growth, development, and polyploidization. However, there is still no distinct evidence in tobacco regarding the distribution of the methylation pattern and whether it contributes to qualitative characteristics. We studied the levels and patterns of methylation polymorphism at CCGG sites in 48 accessions of allotetraploid flue-cured tobacco, Nicotiana tabacum, using a methylation-sensitive amplified polymorphism (MSAP) technique. The results showed that methylation existed at a high level among tobacco accessions, among which 49.3% sites were methylated and 69.9% allelic sites were polymorphic. A cluster analysis revealed distinct patterns of geography-specific groups. In addition, three polymorphic sites significantly related to tobacco mosaic virus (TMV) resistance were explored. This suggests that tobacco breeders should pay more attention to epigenetic traits.
DIFFRACTION AND SCATTERING OF IONIZING RADIATIONS Experimental Study of Two-Beam X-Ray Diffractometry Using Synchrotron Radiation

A new scheme of two-beam X-ray diffractometry on the “X-Ray Crystallography and Physical Materials Science” (XCPM) beamline at the Kurchatov Synchrotron Radiation Source (KSRS) has been experimentally investigated. The scheme includes a standard double-crystal monochromator and a narrow slit installed in front of the sample. Measurements have been performed for the Si 111 and 311 reflections in the monochromator and the Si 111 and 220 reflections in the sample crystal. It is shown that this scheme allows one to obtain a near-proper diffraction reflection curve even in the case of symmetric diffraction if the Bragg angle for the monochromator exceeds the Bragg angle for the crystal sample by a factor of 2 or more. The experimental results coincide well with the theory. DOI: 10.1134/S1063774519010139
Comparison of whole-brain to region-based fMRI analyses

Conventional fMRI analyses primarily utilize a general linear model composed of a single reference vector for the whole-brain. A single reference vector assumes the hemodynamic response is homogenous between different individuals and regions of interest. This study sought to determine whether a regional-based analysis that accounts for individual and regional differences would yield more sensitive results in terms spatial extent. Fifteen subjects without neurological dysfunction were scanned in a 3T MRI using an experimental block design of visual fixation compared to randomized vergence eye movements. Two temporal independent component analyses were conducted on individual subject data sets. The first used the data set composed of all the voxels within the brain while the second used the data from a masked region to isolate the voxels within the frontal eye fields, a critical cortical region needed to evoke vergence eye movements. A hierarchical temporal independent component analysis was used to compute group-level results. Spatial maps were compared. The regional based analysis resulted in fewer clusters which had greater spatial extent compared to the whole-brain analysis which had more dispersion within the functional activity. This technique has the potential to study individual regions while accounting for the variability of the natural heterogeneous nature of the hemodynamic response found in fMRI data.
Control humanoid robot using intelligent optimization algorithms fusion with fourier series

Robot walking on two feet is a complex motion. Researchers are trying to improve biped robots walking in case of walking speed. The analysis of biped walking patterns is used to obtain more detailed information in this field. Several researches have been done to achieve this purpose and the equation of walking trajectory is one of them. This article will introduce a new algorithm in which an evolutionary computing, based on learning automata along with a continual action on control signals of humanoid robot's motion, showing the success of the proposed method as the result to be used for optimizing the parameters of Truncated Fourier Series (TFS) after being compared with the results of Genetic Algorithm (GA) implementation. It is notable that the conditions of the experiment for these two algorithms are considered to be identical.
Insulin Resistance in Malnutrition-Related Diabetes Mellitus is not Mediated by Insulin Antibodies

M alnutrition-related diabetes mellitus (MRDM) comprises at least two different clinical patterns termed protein deficient (type J) diabetes and fihrocalculous pancreatic diabetes, which are both seen in young individuals and are distinguished by the exocrine pancreatic failure characteristically seen in the latter (1). Certain unique clinical and biochemical characteristics common to both, such as ketosis resistance, insulinopenia, and insulin resistance, in addition to the common denominator of moderate-to-severe malnutrition, justify their inclusion under the umbrella term MRDM (1). Thus far, no attempt has been made to identify the cause of the peculiar insulin resistance that results in an insulin requirement that exceeds 2.5 U • kg" l • day" l . There is reason to suspect that insulin antibodies may contribute, at least in part, to the insulin resistance, because patients with type J diabetes, who usually belong to the poorest sections of society, and are unable to afford the cost of continuous therapy with insulin, characteristically stop taking insulin for periods of weeks or months. This pattern of intermittent therapy is very conducive to antibody formation with the highly immunogenic, mixed beef-pork insulin mixtures that are taken for their lower cost and greater availability (2,3). The purpose of this study was specifically to determine whether circulating antibodies to insulin contribute to insulin resistance in MRDM, as a first attempt to identify the pathophysiological basis for the insulin resistance seen in MRDM.
A new sign algorithm for interference suppression in DS-CDMA systems

A blind adaptive step-size averaging blind sign algorithm (AS-asign) for suppression of multiple access interference (MAI) in direct-sequence/code-division multiple access (DS/CDMA) systems is proposed. It combines the sign-regressor algorithm and the concept of variable step-size, uses a second least mean square algorithm for the step size of blind averaging sign-regressor algorithm. Simulations indicate that this algorithm yields improvements over similar adaptive step-size algorithm in dynamic environments.
Electron Microscopic Studies of Tumor Viruses I. Entry of Murine Leukemia Virus into Mouse Embryo Fibroblasts

Entry of Rauscher leukemia virus into mouse embryo fibroblasts was studied by electron microscopy. The polycation diethylaminoethyl-dextran enhanced viral attachment and subsequent entry. At the site of viral attachment to the cell membrane, three distinct interactions occurred between the viral envelope and cell membrane, namely, (i) dissolution of viral envelopes on the cell membrane, which itself remained unaltered; (ii) simultaneous dissolution of both the envelope and cell membrane, resulting in passage of viral nucleoids directly into the cytoplasm; and (iii) dissolution of the cell membrane with direct penetration of intact enveloped particles into the cytoplasm, followed by intracytoplasmic disruption of the envelope, resulting in release of nucleoids into the cytoplasm. These interactions occurred with both mature and immature C-type particles. At no time was fusion of viral envelopes with the cell membrane observed. The mechanism of these interactions is discussed.