nils-herrmann/clean_scirepeval huggingface dataset:
Predictors of failure in infants with viral bronchiolitis treated with high-flow, high-humidity nasal cannula therapy*.

Viral bronchiolitis is an acute infection and inflammatory disease of the respiratory tract, with infants typically presenting with the most severe symptoms. Medical management of bronchiolitis is mostly supportive. Several preliminary studies suggest potential benefit from the use of high-flow nasal cannula systems. Although high-flow nasal cannula is a well-established modality in the newborn intensive care unit, its use in the pediatric intensive care unit for acute respiratory failure is far less established. The objective of this study was to identify any laboratory and clinical variables that may predict high-flow nasal cannula failure in management of bronchiolitis in the pediatric intensive care unit.



The study design was a retrospective chart review of all patients admitted to the pediatric intensive care unit from 2006 to 2010 with a diagnosis of viral bronchiolitis. Inclusion criteria included the initiation of high flow nasal cannula therapy at the time of admission and age ≤ 12 months. Exclusion criteria were intubation prior to admission, age >12 months, and the presence of a tracheostomy.



A total of 113 patients with viral bronchiolitis met the inclusion criteria.



Academic free standing Children's Hospital in the Midwest.



Retrospective chart review.



The data were analyzed by comparing those patients who responded to high-flow nasal cannula (n = 92) with those who were nonresponders to high-flow nasal cannula and required intubation (n = 21). No differences were noted between the groups for age, sex, or ethnicity. Mean weight and weight-for-corrected-age percentiles were significantly lower for patients who failed high-flow nasal cannula (p = .016 and .031, respectively), but weight-for-corrected-age percentile was not significant in logistic regression controlling for other variables. Respiratory rate prior to the initiation of high-flow nasal cannula also correlated strongly with respiratory deterioration (p < .001). The PCO2 was significantly higher for both before (p < .001) and after (p < .001) initiation of therapy in the nonresponder group. Pediatric Risk of Mortality III scores for the patients who failed high-flow nasal cannula were significantly higher (p < .001) than those of patients who tolerated this therapy.



History of prematurity and the patient's age did not increase a patient's risk of failure. Nonresponders to high-flow nasal cannula therapy were on the onset, more hypercarbic, were less tachypnic prior to the start of high-flow nasal cannula, and had no change in their respiratory rate after the initiation of high-flow nasal cannula therapy. Nonresponders had higher pediatric risk of mortality scores in the first 24 hrs.
Quantification of time in Digital Libraries: Temporal Zipf's law

The temporal dimension of a text document defines the temporal scope of its narrated event. This temporal dimension acquires more importance in corpora created along several years of production, such as digital libraries. Temporal aspects of text have been the subject of many researches with specific tasks, notably information retrieval and event detection, while no studies have been conducted to quantify and analyze the richness of the temporal dimension of different text collections. Analysing thirteen text collections we show how the extent and characteristics of the time presence in text varies among collections that have different scopes, although time intervals are mentioned in almost all the text units analyzed. We found that unique intervals follow the same distribution, given by the Zipf's law, that holds for single words.
An IoT Data Communication Framework for Authenticity and Integrity

Internet of Things has been widely applied in everyday life, ranging from transportation, healthcare, to smart homes. As most IoT devices carry constrained resources and limited storage capacity, sensing data need to be transmitted to and stored at resource-rich platforms, such as a cloud. IoT applications retrieve sensing data from the cloud for analysis and decision-making purposes. Ensuring the authenticity and integrity of the sensing data is essential for the correctness and safety of IoT applications. We summarize the new challenges of the IoT data communication framework with authenticity and integrity and argue that existing solutions cannot be easily adopted. We present two solutions, called Dynamic Tree Chaining (DTC) and Geometric Star Chaining (GSC) that provide authenticity, integrity, sampling uniformity, system efficiency, and application flexibility to IoT data communication. Extensive simulations and prototype emulation experiments driven by real IoT data show that the proposed system is more efficient than alternative solutions in terms of time and space.
Design of Carry Skip Adder Using Han Carlson Adder for Low Power and High Speed VLSI Applications

Adders are the basic building block in the arithmetic circuits. In order to achieve high speed and low power consumption a 32bit carry skip adder is proposed. In the conventional technique, a hybrid variable latency extension is used with a method called as parallel prefix network (Brent-Kung). As a result, larger delay along with higher power consumption is obtained, which is the main drawback for any VLSI applications. In order to overcome this, Han Carlson adder along with CSA is used to design parallel prefix network. Therefore it reduces delay and power consumption. The proposed structure is designed by using HSPICE simulation tool. Therefore, a lower delay and low power consumption can be achieved in the benchmark circuits.  High speed, low delay, efficient power consumption and size.
Learning Safe Policies with Expert Guidance

We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consistent with its existing knowledge. We propose two methods to solve the resulting optimization: an exact ellipsoid-based method and a method in the spirit of the "follow-the-perturbed-leader" algorithm. Our experiments demonstrate the behavior of our algorithm in both discrete and continuous problems. The trained agent safely avoids states with potential negative effects while imitating the behavior of the expert in the other states.
VoxTester, software for digital evaluation of speech changes in Parkinson disease

Patients with Parkinson's disease may have difficulties in speaking because of the reduced coordination of the muscles that control breathing, phonation, articulation and prosody. Symptoms that may occur because of changes are weakening of the volume of the voice, voice monotony, changes in the quality of the voice, speed of speech, uncontrolled repetition of words. The evaluation of some of the disorders mentioned can be achieved through measuring the variation of parameters in an objective manner. It may be done to evaluate the response to the treatments with intra-daily frequency pre / post-treatment, as well as in the long term. Software systems allow these measurements also by recording the patient's voice. This allows to carry out a large number of tests by means of a larger number of patients and a higher frequency of the measurements. The main goal of our work was to design and realize Voxtester, an effective and simple to use software system useful to measure whether changes in voice emission are sensitive to pharmacologic treatments. Doctors and speech therapists can easily use it without going into the technical details, and we think that this goal is reached only by Voxtester, up to date.
Measuring thin-client performance using slow-motion benchmarking

Modern thin-client systems are designed to provide the same graphical interfaces and applications available on traditional desktop computers while centralizing administration and allowing more efficient use of computing resources. Despite the rapidly increasing popularity of these client-server systems, there are few reliable analyses of their performance. Industry standard benchmark techniques commonly used for measuring desktop system performance are ill-suited for measuring the performance of thin-client systems because these benchmarks only measure application performance on the server, not the actual user-perceived performance on the client. To address this problem, we have developed slow-motion benchmarking, a new measurement technique for evaluating thin-client systems. In slow-motion benchmarking, performance is measured by capturing network packet traces between a thin client and its respective server during the execution of a slow-motion version of a conventional benchmark application. These results can then be used either independently or in conjunction with conventional benchmark results to yield an accurate and objective measure of the performance of thin-client systems. We have demonstrated the effectiveness of slow-motion benchmarking by using this technique to measure the performance of several popular thin-client systems in various network environments on Web and multimedia workloads. Our results show that slow-motion benchmarking solves the problems with using conventional benchmarks on thin-client systems and is an accurate tool for analyzing the performance of these systems.
Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.
Benchmarking and Improving Recovery of Number of Topics in Latent Dirichlet Allocation Models

Latent Dirichlet Allocation (LDA) is a generative model describing the observed data as being composed of a mixture of underlying unobserved topics, as introduced by Blei et al. (2003). A key hyperparameter of LDA is the number of underlying topics k, which must be estimated empirically in practice. Selecting the appropriate value of k is essentially selecting the correct model to represent the data; an important issue concerning the goodness of fit. We examine in the current work a series of metrics from literature on a quantitative basis by performing benchmarks against a generated dataset with a known value of k and evaluate the ability of each metric to recover the true value, varying over multiple levels of topic resolution in the Dirichlet prior distributions. Finally, we introduce a new metric and heuristic for estimating k and demonstrate improved performance over existing metrics from the literature on several benchmarks.
Cost-sensitive online active learning with application to malicious URL detection

Malicious Uniform Resource Locator (URL) detection is an important problem in web search and mining, which plays a critical role in internet security. In literature, many existing studies have attempted to formulate the problem as a regular supervised binary classification task, which typically aims to optimize the prediction accuracy. However, in a real-world malicious URL detection task, the ratio between the number of malicious URLs and legitimate URLs is highly imbalanced, making it very inappropriate for simply optimizing the prediction accuracy. Besides, another key limitation of the existing work is to assume a large amount of training data is available, which is impractical as the human labeling cost could be potentially quite expensive. To solve these issues, in this paper, we present a novel framework of Cost-Sensitive Online Active Learning (CSOAL), which only queries a small fraction of training data for labeling and directly optimizes two cost-sensitive measures to address the class-imbalance issue. In particular, we propose two CSOAL algorithms and analyze their theoretical performance in terms of cost-sensitive bounds. We conduct an extensive set of experiments to examine the empirical performance of the proposed algorithms for a large-scale challenging malicious URL detection task, in which the encouraging results showed that the proposed technique by querying an extremely small-sized labeled data (about 0.5% out of 1-million instances) can achieve better or highly comparable classification performance in comparison to the state-of-the-art cost-insensitive and cost-sensitive online classification algorithms using a huge amount of labeled data.
Integrated electromicrobial conversion of CO2 to higher alcohols.

One of the major challenges in using electrical energy is the efficiency in its storage. Current methods, such as chemical batteries, hydraulic pumping, and water splitting, suffer from low energy density or incompatibility with current transportation infrastructure. Here, we report a method to store electrical energy as chemical energy in higher alcohols, which can be used as liquid transportation fuels. We genetically engineered a lithoautotrophic microorganism, Ralstonia eutropha H16, to produce isobutanol and 3-methyl-1-butanol in an electro-bioreactor using CO(2) as the sole carbon source and electricity as the sole energy input. The process integrates electrochemical formate production and biological CO(2) fixation and higher alcohol synthesis, opening the possibility of electricity-driven bioconversion of CO(2) to commercial chemicals.
Enhancing Chinese Word Segmentation Using Unlabeled Data

This paper investigates improving supervised word segmentation accuracy with unlabeled data. Both large-scale in-domain data and small-scale document text are considered. We present a unified solution to include features derived from unlabeled data to a discriminative learning model. For the large-scale data, we derive string statistics from Gigaword to assist a character-based segmenter. In addition, we introduce the idea about transductive, document-level segmentation, which is designed to improve the system recall for out-ofvocabulary (OOV) words which appear more than once inside a document. Novel features1 result in relative error reductions of 13.8% and 15.4% in terms of F-score and the recall of OOV words respectively.
A Beacon Interval Shifting Scheme for Interference Mitigation in Body Area Networks

This paper investigates the issue of interference avoidance in body area networks (BANs).  presented several schemes to reduce such interference, but these schemes are still not proper solutions for BANs. We present a novel distributed TDMA-based beacon interval shifting scheme that reduces interference in the BANs. A design goal of the scheme is to avoid the wakeup period of each BAN coinciding with other networks by employing carrier sensing before a beacon transmission. We analyze the beacon interval shifting scheme and investigate the proper back-off length when the channel is busy. We compare the performance of the proposed scheme with the schemes presented in IEEE 802.15 Task Group 6 using an OMNeT++ simulation. The simulation results show that the proposed scheme has a lower packet loss, energy consumption, and delivery-latency than the schemes of IEEE 802.15 Task Group 6.
Host Identity Protocol (HIP): Connectivity, Mobility, Multi-Homing, Security, and Privacy over IPv4 and IPv6 Networks

The Host Identity Protocol (HIP) is an inter-networking architecture and an associated set of protocols, developed at the IETF since 1999 and reaching their first stable version in 2007. HIP enhances the original Internet architecture by adding a name space used between the IP layer and the transport protocols. This new name space consists of cryptographic identifiers, thereby implementing the so-called identifier/locator split. In the new architecture, the new identifiers are used in naming application level end-points (sockets), replacing the prior identification role of IP addresses in applications, sockets, TCP connections, and UDP-based send and receive system calls. IPv4 and IPv6 addresses are still used, but only as names for topological locations in the network. HIP can be deployed such that no changes are needed in applications or routers. Almost all pre-compiled legacy applications continue to work, without modifications, for communicating with both HIP-enabled and non-HIP-enabled peer hosts. The architectural enhancement implemented by HIP has profound consequences. A number of the previously hard networking problems become suddenly much easier. Mobility, multi-homing, and baseline end-to-end security integrate neatly into the new architecture. The use of cryptographic identifiers allows enhanced accountability, thereby providing a base for easier build up of trust. With privacy enhancements, HIP allows good location anonymity, assuring strong identity only towards relevant trusted parties. Finally, the HIP protocols have been carefully designed to take middle boxes into account, providing for overlay networks and enterprise deployment concerns. This article provides an in-depth look at HIP, discussing its architecture, design, benefits, potential drawbacks, and ongoing work.
Automatic Inference and Enforcement of Kernel Data Structure Invariants

Kernel-level rootkits affect system security by modifying key kernel data structures to achieve a variety of malicious goals. While early rootkits modified control data structures, such as the system call table and values of function pointers, recent work has demonstrated rootkits that maliciously modify non-control data. Prior techniques for rootkit detection fail to identify such rootkits either because they focus solely on detecting control data modifications or because they require elaborate, manually-supplied specifications to detect modifications of non-control data. This paper presents a novel rootkit detection technique that automatically detects rootkits that modify both control and non-control data. The key idea is to externally observe the execution of the kernel during a training period and hypothesize invariants on kernel data structures. These invariants are used as specifications of data structure integrity during an enforcement phase; violation of these invariants indicates the presence of a rootkit. We present the design and implementation of Gibraltar, a tool that uses the above approach to infer and enforce invariants. In our experiments, we found that Gibraltar can detect rootkits that modify both control and non-control data structures, and that its false positive rate and monitoring overheads are negligible.
Corpas na Gaeilge ( 1882-1926 ) : Integrating Historical and Modern Irish Texts

This paper describes the processing of a corpus of seven million words of Irish texts from the period 1882-1926. The texts which have been captured by typing or optical character recognition are processed for the purpose of lexicography. Firstly, all historical and dialectal word forms are annotated with their modern standard equivalents using software developed for this purpose. Then, using the modern standard annotations, the texts are processed using an existing finite-state morphological analyser and part-of-speech tagger. This method enables us to retain the original historical text, and at the same time have full corpus-searching capabilities using modern lemmas and inflected forms (one can also use the historical forms). It also makes use of existing NLP tools for modern Irish, and enables integration of historical and modern Irish corpora.
Research of Output-Pin-Wheel Cycloid Drive Reducer

By analyzing the shortcomings of various types of cycloid drive, a kind of new cycloid drive that named as output-pin-wheel cycloid drive is innovated. The structure of reducer and the structural dimensions of specific components are designed by analyzing its working principle. In order to validating the correctness of drive model and assembly relations of parts, a three-dimensional solid modeling and virtual prototyping are built. Dynamic simulation analysis of virtual prototyping is done in the no-load and full load conditions, which provides the theory basis for manufacture and optimization of the reducer.
Malignant struma ovarii: an analysis of 88 cases, including 27 with extraovarian spread.

Struma ovarii that display extraovarian spread or later recurrence is exceedingly rare. Among 88 patients with "malignant" struma ovarii followed for prolonged periods, several features helped to predict the adverse clinical course. Adhesions (graded 2 to 4+), peritoneal fluid (> or =1 L) or ovarian serosal rent were worrisome features, occurring in 74% of 27 biologically malignant tumors but only 10% of 61 clinically benign tumors. The size of the strumal component rather than the overall size of the ovarian teratoma also had some predictive value. Tumors with a strumal component < or =6 cm recurred rarely (7%), whereas 33% of the consult and 88% of the literature cases > or =12 cm were clinically malignant. Except for a papillary pattern or poorly differentiated cancer, no microscopic feature reliably predicted the clinical outcome, including those typically associated with malignancy in primary thyroid tumors. Among the consult cases, 7% with histologic follicular adenomas and 29% with papillary carcinomas were clinically malignant. Unequivocal vascular invasion was rare, precluding assessment of its effect. Optically clear nuclei, when extensive, were useful to diagnose papillary carcinoma, but were present nevertheless in smaller numbers in both macrofollicular and microfollicular adenomas. Eight tumors confined initially to the ovary (stage 1) recurred. Papillary carcinomas recurred earlier (average 4 y) than follicular adenomatous neoplasms (average 11 y, range: 1-29 y). Overall, the survival rate for all patients was 89% at 10 years and 84% at 25 years, indicating the need for routine long-term follow-up.
Design, Modeling, Fabrication and Characterization of 2–5- $\mu \text{m}$  Redistribution Layer Traces by Advanced Semiadditive Processes on Low-Cost Panel-Based Glass Interposers

This paper presents the latest advances in extending semiadditive process (SAP) methods to 2-5 μm lines and spaces, achieved using dry film photoresists on thin glass substrates, toward meeting the routing requirements for 20-μm bump pitch interposers. High-density chip-to-chip interconnections on 2.5-D interposers are a key enabler to meet the high logic to memory bandwidth needs of next-generation electronic systems. Such 2.5-D interposers require ultrafine redistribution layer (RDL) traces with line widths and spacing below 5 μm. This paper reports on the extension of panel scale and lower cost SAPs to achieve less than 5 μm lines and spaces, based on the ultrasmooth surface and improved dimensional stability of thin glass panels. A modified low-cost SAP method with newly developed differential seed layer etching was employed to fabricate the fine line and space patterns and coplanar waveguide (CPW) transmission on thin glass panels. Fine lines down to 2-μm lines and spaces and CPW lines with signal lengths up to 5 mm and ground-to-signal gaps down to 5.5 μm at 15-μm signal widths were successfully fabricated on ultra-thin glass panels. For comparison, the same processes were also applied to a silicon wafer. The signal insertion losses of CPW lines on the glass were 0.024 dB/mm better at 15 GHz than those on the silicon, as confirmed by simulations as well as VNA measurements. The measured insertion loss of 5-mm long CPW lines on glass interposer was 0.7 dB at 10 GHz and matched well to the simulated values.