nils-herrmann/clean_scirepeval huggingface dataset:
The mechanics of state-dependent neural correlations

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.
Miller compensation using current buffers in fully differential CMOS two-stage operational amplifiers

Several Miller compensation schemes using a current buffer in series with the compensation capacitor to modify the right-half-plane zero in fully differential two-stage CMOS operational amplifiers are analyzed. One scheme uses a current mirror as a current buffer, while the rest use a common-gate transistor as a current buffer. The gain transfer functions are derived for each topology, and approximate transfer-function coefficients are found that allow accurate estimation of the zero(s) and poles.
Overview of epidemiologic studies of diabetic retinopathy.

Diabetic retinopathy has been an important cause of blindness in young and middle age adults in the United States. Epidemiologic studies have quantitated the risk and have described potentially causal factors associated with many ocular complications of diabetes and other facets of this disease. A review of recent advances in diagnosis, treatment, temporal trends, and health care for diabetic retinopathy was conducted. Since the early 1980's, there have been studies of the variability of diabetic retinopathy in populations around the world and subpopulations in the United States which have demonstrated the high prevalences and incidences of this condition. Observational studies and clinical trials have documented the importance of glycemic and blood pressure control in the development and progression of this disease. There are some differences in the importance of confounders in different populations. Epidemiologic data have helped understand the importance of health care and health education in prevention and treatment of this condition. Observational studies have documented the importance of this disease on quality of life. Although there have been advances in understanding the distribution, causes, and severity of diabetic retinopathy, this is ever changing and requires continued monitoring. This is important because the increasing burden of diabetes will place a greater burden on the population and the medical care systems that will be caring for them.
MQTT of IoT Classification in Energy Saving

Industries today face the growing problems from scarce energy. Energy awareness will help industrial organizations survive in terms of availability and cost of energy. With IoT technology, MQTT serves as an application protocol for energy saving. However, there are few reviews on evidence of energy savings by MQTT of IoT after commercialization. The MQTT classification is proposed to identify MQTT energy technology and related technologies. This article surveys experimented MQTT application articles published between 2011 and 2017 to confirm this classification. Survey results indicate energy saving in all articles. The findings show that MQTT is a suitable IoT protocol for industrial applications that supports energy saving for industries.
Refactoring to Patterns

class Loan { ... protected Loan(...) { ... } } public class TermLoan extends Loan { public TermLoan(...) { super(...); } } public class Revolver extends Loan { public Revolver(...) { super(...); } } public class RCTL extends Loan { public RCTL(...) super(...); } } Refactoring To Patterns,   Loan superclass constructor is protected, and the constructors for the three subclasses are public. We’ll focus on the TermLoan class. The first step is to protect its constructor: public class TermLoan extends Loan { protected TermLoan(...){
Engraftable human neural stem cells respond to development cues, replace neurons, and express foreign genes

Stable clones of neural stem cells (NSCs) have been isolated from the human fetal telencephalon. These self-renewing clones give rise to all fundamental neural lineages in vitro. Following transplantation into germinal zones of the newborn mouse brain they participate in aspects of normal development, including migration along established migratory pathways to disseminated central nervous system regions, differentiation into multiple developmentally and regionally appropriate cell types, and nondisruptive interspersion with host progenitors and their progeny. These human NSCs can be genetically engineered and are capable of expressing foreign transgenes in vivo. Supporting their gene therapy potential, secretory products from NSCs can correct a prototypical genetic metabolic defect in neurons and glia in vitro. The human NSCs can also replace specific deficient neuronal populations. Cryopreservable human NSCs may be propagated by both epigenetic and genetic means that are comparably safe and effective. By analogy to rodent NSCs, these observations may allow the development of NSC transplantation for a range of disorders.
Calcium Carbonate.

Calcium carbonate is a chemical compound with the formula CaCO3 formed by three main elements: carbon, oxygen, and calcium. It is a common substance found in rocks in all parts of the world (most notably as limestone), and is the main component of shells of marine organisms, snails, coal balls, pearls, and eggshells. CaCO3 exists in different polymorphs, each with specific stability that depends on a diversity of variables.
Universal Dependencies v1: A Multilingual Treebank Collection

Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.
Prediction of Movies popularity Using Machine Learning Techniques

Number of movies are released every week. There is a large amount of data related to the movies is available over the internet, because of that much data available, it is an interesting data mining topic. The prediction of movies is complex problem. Every viewer, producer, director’s production houses all are curious about the movies that how it will perform in the theatre. Many work has been done relating to movies using social networking, blogs articles but much less has been explored by the data and attributes related to a movie which is continuous and in different dimensions. We have used IMDB for our experimentation. We created dataset and then transformed it and applied machine learning approaches to build efficient models that can predict the movies popularity.
Data Management on Non-Volatile Memory: A Perspective

The large performance gap between main memory and secondary storage accounts for many design decisions of traditional database systems. With the upcoming availability of Non-Volatile Memory (NVM), which has latencies in the same order of magnitude as DRAM, is byte-addressable and persistent, a completely new type of technology is added to the memory stack. This changes some basic assumptions such as slow storage, block granular access, and that sequential accesses are much faster than random accesses. New ideas are therefore needed to efficiently leverage NVM. Although several new approaches can be found in the literature, the exact role of NVM is not yet clear. In this paper, we survey recent work in this area and classify the existing approaches. We focus on two key challenges: (1) integration of NVM into the memory hierarchy and (2) the design of NVM-aware data structures. We contrast the different approaches, highlight their advantages and limitations, and make recommendations.
DFPS: Distributed FP-growth algorithm based on Spark

Frequent Itemset Mining (FIM) is the most important and time-consuming step of association rules mining. With the increment of data scale, many efficient single-machine algorithms of FIM, such as FP-growth and Apriori, cannot accomplish the computing tasks within reasonable time. As a result of the limitation of single-machine methods, researchers presented some distributed algorithms based on MapReduce and Spark, such as PFP and YAFIM. Nevertheless, the heavy disk I/O cost at each MapReduce operation makes PFP not efficient enough. YAFIM needs to generate candidate frequent itemsets in each iterative step. It makes YAFIM time-consuming. And if the scale of data is large enough, YAFIM algorithm will not work due to the limitation of memory since the candidate frequent itemsets need to be stored in the memory. And the size of candidate itemsets is very large especially facing the massive data. In this work, we propose a distributed FP-growth algorithm based on Spark, we call it DFPS. DFPS partitions computing tasks in such a way that each computing node builds the conditional FP-tree and adopts a pattern fragment growth method to mine the frequent itemsets independently. DFPS doesn't need to pass messages between nodes during mining frequent itemsets. Our performance study shows that DFPS algorithm is more excellent than YAFIM, especially when the length of transactions is long, the number of items is large and the data is massive. And DFPS has an excellent scalability. The experimental results show that DFPS is more than 10 times faster than YAFIM for T10I4D100K dataset and Pumsb_star dataset.
Lumbar spondylolysis: a review

Spondylolysis is an osseous defect of the pars interarticularis, thought to be a developmental or acquired stress fracture secondary to chronic low-grade trauma. It is encountered most frequently in adolescents, most commonly involving the lower lumbar spine, with particularly high prevalence among athletes involved in certain sports or activities. Spondylolysis can be asymptomatic or can be a cause of spine instability, back pain, and radiculopathy. The biomechanics and pathophysiology of spondylolysis are complex and debated. Imaging is utilized to detect spondylolysis, distinguish acute and active lesions from chronic inactive non-union, help establish prognosis, guide treatment, and to assess bony healing. Radiography with satisfactory technical quality can often demonstrate a pars defect. Multislice CT with multiplanar reformats is the most accurate modality for detecting the bony defect and may also be used for assessment of osseous healing; however, as with radiographs, it is not sensitive for detection of the early edematous stress response without a fracture line and exposes the patient to ionizing radiation. Magnetic resonance (MR) imaging should be used as the primary investigation for adolescents with back pain and suspected stress reactions of the lumbar pars interarticularis. Several imaging pitfalls render MR imaging less sensitive than CT for directly visualizing the pars defects (regional degenerative changes and sclerosis). Nevertheless, the presence of bone marrow edema on fluid-sensitive images is an important early finding that may suggest stress response without a visible fracture line. Moreover, MR is the imaging modality of choice for identifying associated nerve root compression. Single-photon emission computed tomography (SPECT) use is limited by a high rate of false-positive and false-negative results and by considerable ionizing radiation exposure. In this article, we provide a review of the current concepts regarding spondylolysis, its epidemiology, pathogenesis, and general treatment guidelines, as well as a detailed review and discussion of the imaging principles for the diagnosis and follow-up of this condition.
Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data

The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, such as logistic regression, even in their rare event and regularized forms, perform poorly at prediction. We compare the performance of Random Forests with three versions of logistic regression (classic logistic regression, Firth rare events logistic regression, and L1-regularized logistic regression), and find that the algorithmic approach provides significantly more accurate predictions of civil war onset in out-of-sample data than any of the logistic regression models. The article discusses these results and the ways in which algorithmic statistical methods like Random Forests can be useful to more accurately predict rare events in conflict data.
ASR — A real-time speech recognition on portable devices

This paper presents the implementation of real-time automatic speech recognition (ASR) for portable devices. The speech recognition is performed offline using PocketSphinx which is the implementation of Carnegie Mellon University's Sphinx speech recognition engine for portable devices. In this work, machine Learning approach is used which converts graphemes into phonemes using the TensorFlow's Sequence-to-Sequence model to produce the pronunciations of words. This paper also explains the implementation of statistical language model for ASR. The novelty of ASR is its offline speech recognition and thus requires no Internet connection compared to other related works. A speech recognition service currently provides the cloud based processing of speech and therefore has access to the speech data of users. However, the speech is processed on the handheld device in offline ASR and therefore enhances the privacy of users.
NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning

We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on thedevelopment of advanced applications of neuralmachine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning. NMT-Keras is based on an extended version of the popular Keras library, and it runs on Theano and Tensorflow. State-of-the-art neural machine translation models are deployed and used following the high-level framework provided by Keras. Given its high modularity and flexibility, it also has been extended to tackle different problems, such as image and video captioning, sentence classification and visual question answering.
Planar microwave lens based beam-forming phased antenna array system using non-coplanar SIW fed bowtie antenna

A novel beam-forming phased array system using a substrate integrated waveguide (SIW) fed Yagi-Uda array antenna is presented. This phase array antenna employs an integrated waveguide structure lens as a beam forming network (BFN). A prototype phased array system is designed with 7 beam ports, 9 array ports, and 8 dummy ports. A 10 GHz SIW-fed Bow-tie linear array antenna is proposed with a nonplanar structure to scan over (-24°, +24°) with SIW lens.
CT of Normal Developmental and Variant Anatomy of the Pediatric Skull: Distinguishing Trauma from Normality.

The use of computed tomography (CT) in clinical practice has been increasing rapidly, with the number of CT examinations performed in adults and children rising by 10% per year in England. Because the radiology community strives to reduce the radiation dose associated with pediatric examinations, external factors, including guidelines for pediatric head injury, are raising expectations for use of cranial CT in the pediatric population. Thus, radiologists are increasingly likely to encounter pediatric head CT examinations in daily practice. The variable appearance of cranial sutures at different ages can be confusing for inexperienced readers of radiologic images. The evolution of multidetector CT with thin-section acquisition increases the clarity of some of these sutures, which may be misinterpreted as fractures. Familiarity with the normal anatomy of the pediatric skull, how it changes with age, and normal variants can assist in translating the increased resolution of multidetector CT into more accurate detection of fractures and confident determination of normality, thereby reducing prolonged hospitalization of children with normal developmental structures that have been misinterpreted as fractures. More important, the potential morbidity and mortality related to false-negative interpretation of fractures as normal sutures may be avoided. The authors describe the normal anatomy of all standard pediatric sutures, common variants, and sutural mimics, thereby providing an accurate and safe framework for CT evaluation of skull trauma in pediatric patients.
Bacterial Spectrum and Antibiotic Resistance Patterns of Ocular Infection: Differences between External and Intraocular Diseases

This study aimed to compare the differences of microbial spectrum and antibiotic resistance patterns between external and intraocular bacterial infections in an eye hospital in South China. A total of 737 bacteria isolates from suspected ocular infections were included in this retrospective study covering the period 2010-2013. The organisms cultured from the ocular surface (cornea, conjunctiva) accounted for the majority of the isolates (82.77%, n = 610), followed by the intraocular (aqueous humor, vitreous fluid), which accounted for 17.23% (n = 127). The top three species accounting for the external ocular infections were S. epidermidis (35.25%), P. aeruginosa (8.03%), and S. simulans (4.43%). The top three species for the intraocular infections were S. epidermidis (14.96%), S. hominis (8.66%), and B. subtilis (7.87%). The bacteria from the external ocular surface were more sensitive to neomycin, while those from the intraocular specimens were more sensitive to levofloxacin (P < 0.01). Multidrug resistance was found in 89 bacteria (12.08%), including isolates from both external (13.28%) and intraocular samples (6.30%). The results of this study indicate that the bacteria spectrum of external and intraocular infections is variable in the setting. A high percentage of bacterial organisms were found to be primarily susceptible to neomycin for external infection and levofloxacin for intraocular infection.
Corrugated Textile based Triboelectric Generator for Wearable Energy Harvesting

Triboelectric energy harvesting has been applied to various fields, from large-scale power generation to small electronics. Triboelectric energy is generated when certain materials come into frictional contact, e.g., static electricity from rubbing a shoe on a carpet. In particular, textile-based triboelectric energy-harvesting technologies are one of the most promising approaches because they are not only flexible, light, and comfortable but also wearable. Most previous textile-based triboelectric generators (TEGs) generate energy by vertically pressing and rubbing something. However, we propose a corrugated textile-based triboelectric generator (CT-TEG) that can generate energy by stretching. Moreover, the CT-TEG is sewn into a corrugated structure that contains an effective air gap without additional spacers. The resulting CT-TEG can generate considerable energy from various deformations, not only by pressing and rubbing but also by stretching. The maximum output performances of the CT-TEG can reach up to 28.13 V and 2.71 μA with stretching and releasing motions. Additionally, we demonstrate the generation of sufficient energy from various activities of a human body to power about 54 LEDs. These results demonstrate the potential application of CT-TEGs for self-powered systems.
Remote, wireless, ambulatory monitoring of implantable pacemakers, cardioverter defibrillators, and cardiac resynchronization therapy systems: analysis of a worldwide database.

To describe the daily routine application of a new telemonitoring system in a large population of cardiac device recipients.



Data transmitted daily and automatically by a remote, wireless Home Monitoring system (HM) were analyzed. The average time gained in the detection of events using HM versus standard practice and the impact of HM on physician workload were examined. The mean interval between device interrogations was used to compare the rates of follow-up visits versus that recommended in guidelines.



3,004,763 transmissions were made by 11,624 recipients of pacemakers (n = 4,631), defibrillators (ICD; n = 6,548), and combined ICD + cardiac resynchronization therapy (CRT-D) systems (n = 445) worldwide. The duration of monitoring/patient ranged from 1 to 49 months, representing 10,057 years. The vast majority (86%) of events were disease-related. The mean interval between last follow-up and occurrence of events notified by HM was 26 days, representing a putative temporal gain of 154 and 64 days in patients usually followed at 6- and 3-month intervals, respectively. The mean numbers of events per patient per month reported to the caregivers for the overall population was 0.6. On average, 47.6% of the patients were event-free. The mean interval between follow-up visits in patients with pacemakers, single-chamber ICDs, dual chamber ICDs, and CRT-D systems were 5.9 +/- 2.1, 3.6 +/- 3.3, 3.3 +/- 3.5, and 1.9 +/- 2.9 months, respectively.



This broad clinical application of a new monitoring system strongly supports its capability to improve the care of cardiac device recipients, enhance their safety, and optimize the allocation of health resources.
IoT based dynamic road traffic management for smart cities

All metropolitan cities face traffic congestion problems especially in the downtown areas. Normal cities can be transformed into “smart cities” by exploiting the information and communication technologies (ICT). The paradigm of Internet of Thing (IoT) can play an important role in realization of smart cities. This paper proposes an IoT based traffic management solutions for smart cities where traffic flow can be dynamically controlled by onsite traffic officers through their smart phones or can be centrally monitored or controlled through Internet. We have used the example of the holy city of Makkah Saudi Arabia, where the traffic behavior changes dynamically due to the continuous visitation of the pilgrims throughout the year. Therefore, Makkah city requires special traffic controlling algorithms other than the prevailing traffic control systems. However the scheme proposed is general and can be used in any Metropolitan city without the loss of generality.