nils-herrmann/clean_scirepeval huggingface dataset:
High Flow Nasal Oxygen Therapy: From Physiology to Clinic

Oxygen support therapy should be given to the patients with acute hypoxic respiratory insufficiency in order to provide oxygenation of the tissues until the underlying pathology improves. The inspiratory flow rate requirement of patients with respiratory insufficiency varies between 30 and 120 L/min. Low flow and high flow conventional oxygen support systems produce a maximum flow rate of 15 L/min, and FiO2 changes depending on the patient’s peak inspiratory flow rate, respiratory pattern, the mask that is used, or the characteristics of the cannula. The inability to provide adequate airflow leads to discomfort in tachypneic patients. With high-flow nasal oxygen (HFNO) cannulas, warmed and humidified air matching the body temperature can be regulated at flow rates of 5–60 L/min, and oxygen delivery varies between 21% and 100%. When HFNO, first used in infants, was reported to increase the risk of infection, its long-term use was stopped. This problem was later eliminated with the use of sterile water, and its use has become a current issue in critical adult patients as well. Studies show that HFNO treatment improves physiological parameters when compared to conventional oxygen systems. Although there are studies indicating successful applications in different patient groups, there are also studies indicating that it does not create any difference in clinical parameters, but patient comfort is better in HFNO when compared with standard oxygen therapy and noninvasive mechanical ventilation (NIMV) (1-6). In this compilation, the physiological effect mechanisms of HFNO treatment and its use in various clinical situations are discussed in the light of current studies.
Fluctuations in the Radial Distribution Function for Simple Liquids

The equilibrium microstructure of a fluid may only be described exactly in terms of a complete set of n-body atomic distribution functions, where n is 1, 2, 3 , . . . , N, and N is the total number of particles in the system. The higher order functions, i. e. n > 2, are complex and practically inaccessible but con­ siderable qualitative information can already be derived from studies of the mean radial occupation function n(r) defined as the average number of atoms in a sphere of radius r centred on a particular atom. The function for a perfect gas of non-inter­ acting particles is
Design of Broadband Dual-Band Dipole for Base Station Antenna

A novel dual-band unidirectional antenna composed of an irregular shorted patch and planar dipole elements is presented and studied. This proposed antenna employs an L-shaped feeding strip for exciting dual-band operations. For the lower frequency band, a V-slot is loaded on the planar dipole for acting as a capacitive loading for having wide impedance matching of the antenna. Then, two additional smaller planar dipoles are placed and connected to the irregular short patch for achieving another wideband performance at the higher frequency band. The proposed antenna has wide dual-band impedance bandwidths of 34% and 49.5% in the lower and the upper bands, ranged from 0.78 GHz to 1.1 GHz and from 1.58 GHz to 2.62 GHz, respectively. More importantly, the antenna has stable gains of 7 dBi and 8 dBi across each band, demonstrating the high stability of the radiation characteristic for the two radiation modes. The radiation patterns are symmetric, low back-lobe radiation and low cross-polarization. Analysis and parametric studies of the proposed antenna are provided.
Spatial contrast sensitivity of birds

Contrast sensitivity (CS) is the ability of the observer to discriminate between adjacent stimuli on the basis of their differences in relative luminosity (contrast) rather than their absolute luminances. In previous studies, using a narrow range of species, birds have been reported to have low contrast detection thresholds relative to mammals and fishes. This was an unexpected finding because birds had been traditionally reported to have excellent visual acuity and color vision. This study reports CS in six species of birds that represent a range of visual adaptations to varying environments. The species studied were American kestrels (Falco sparverius), barn owls (Tyto alba), Japanese quail (Coturnix coturnix japonica), white Carneaux pigeons (Columba livia), starlings (Sturnus vulgaris), and red-bellied woodpeckers (Melanerpes carolinus). Contrast sensitivity functions (CSFs) were obtained from these birds using the pattern electroretinogram and compared with CSFs from the literature when possible. All of these species exhibited low CS relative to humans and most mammals, which suggests that low CS is a general characteristic of birds. Their low maximum CS may represent a trade-off of contrast detection for some other ecologically vital capacity such as UV detection or other aspects of their unique color vision.
Protein Sequence Alignment and Database Scanning

In the context of protein structure prediction, there are two principle reasons for comparing and aligning protein sequences: (a) To obtain an accurate alignment. This may be for protein modelling by comparison to proteins of known three-dimensional structure. (b) To scan a database with a newly determined protein sequence and identify possible functions for the protein by analogy with well-characterized proteins. In this chapter I review the underlying principles and techniques for sequence comparison as applied to proteins and used to satisfy these two aims. 2, Amino acid scoring schemes All algorithms to compare protein sequences rely on some scheme to score the equivalencing of each of the 2L0 possible pairs of amino acids, (i.e. 190 pairs of different amino acids plus 20 pairs of identical amino acids). Most scoring schemes represent the 210 pairs of scores as a 20 x 20 matrix of similarities where identical amino acids and those of similar character (e.g. I, L) give higher scores compared to those of different character (e.g. I, D). Since the first protein sequences were obtained, many different types of scoring scheme have been devised. The most commonly used are those based on observed substitution and of these, the t976 Dayhoff matrix for 250 PAMS (1) has until recently dominated. This and other schemes are discussed in the following sections. 2.1 Identity scoring This is the simplest scoring scheme: amino acid pairs are classified into two types; identical and non-identical. Non-identical pairs are scored zero and
Technical Report : Evaluating Formal Properties of Feature Diagram Languages

Feature Diagrams (FDs) are a family of popular modelling languages, mainly used for engineering requirements in software product lines. FDs were first introduced by Kang et al. as part of the FODA (Feature Oriented Domain Analysis) method back in 1990. Since then, various extensions of FODA FD were devised to compensate for purported ambiguity and lack of precision and expressiveness. Recently, we surveyed these notations and provided them with a generic formal syntax and semantics, called FFD (Free Feature Diagrams). We also started investigating the comparative semantics of FFD with respect to other recent formalisations of FD languages. Those results were targeted at improving the quality of FD languages and making the comparison between them more objective. In this paper, we briefly recall our previous results. Most importantly, we give a more comprehensive account of our research method: (1) we provide better illustration, motivation and discussion of the technical choices at the root of FFD and (2) we put our contributions in a wider perspective. The main questions to which we try to answer are: What are the basic concepts and principles on which our investigation relies and why? How are our contributions to be situated in the wider endeavour of improving the quality of FD languages? How must the investigation be pursued and how can it be complemented?
Reasoning within Fuzzy Description Logics

Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e. set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. In this paper we will present a fuzzy extension of ALC, combining Zadeh’s fuzzy logic with a classical DL. In particular, concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported. We will define its syntax, its semantics, describe its properties and present a constraint propagation calculus for reasoning in it.
Ensemble based systems in decision making

In matters of great importance that have financial, medical, social, or other implications, we often seek a second opinion before making a decision, sometimes a third, and sometimes many more. In doing so, we weigh the individual opinions, and combine them through some thought process to reach a final decision that is presumably the most informed one. The process of consulting "several experts" before making a final decision is perhaps second nature to us; yet, the extensive benefits of such a process in automated decision making applications have only recently been discovered by computational intelligence community. Also known under various other names, such as multiple classifier systems, committee of classifiers, or mixture of experts, ensemble based systems have shown to produce favorable results compared to those of single-expert systems for a broad range of applications and under a variety of scenarios. Design, implementation and application of such systems are the main topics of this article. Specifically, this paper reviews conditions under which ensemble based systems may be more beneficial than their single classifier counterparts, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined. We discuss popular ensemble based algorithms, such as bagging, boosting, AdaBoost, stacked generalization, and hierarchical mixture of experts; as well as commonly used combination rules, including algebraic combination of outputs, voting based techniques, behavior knowledge space, and decision templates. Finally, we look at current and future research directions for novel applications of ensemble systems. Such applications include incremental learning, data fusion, feature selection, learning with missing features, confidence estimation, and error correcting output codes; all areas in which ensemble systems have shown great promise
Improved splash-plate feed parabolic reflector antenna for Ka-Band VSAT applications

In this study, we propose a conical dielectric loaded circular waveguide opening to splash plate subreflector for parabolic antenna feed. Corrugations on the dielectric lens and splash-plate structure are optimized for antenna performance. The feed structure is designed as axi-symmetric to support zero-order azimuth currents for low cross-polarization. Resulting structure offers good gain, low sidelobe, good cross-polarization, and low VSWR for Ka-band VSAT applications where receive and transmit frequency bands are 20.2–21.2 GHz and 30–31 GHz, respectively.
Automatic Real-time Background Cut for Portrait Videos

We in this paper solve the problem of high-quality automatic real-time background cut for 720p portrait videos. We first handle the background ambiguity issue in semantic segmentation by proposing a global background attenuation model. A spatial-temporal refinement network is developed to further refine the segmentation errors in each frame and ensure temporal coherence in the segmentation map. We form an end-to-end network for training and testing. Each module is designed considering efficiency and accuracy. We build a portrait dataset, which includes 8,000 images with high-quality labeled map for training and testing. To further improve the performance, we build a portrait video dataset with 50 sequences to fine-tune video segmentation. Our framework benefits many video processing applications.
Finite-Sample Analysis of Proximal Gradient TD Algorithms

In this paper, we show for the first time how gradient TD (GTD) reinforcement learning methods can be formally derived as true stochastic gradient algorithms, not with respect to their original objective functions as previously attempted, but rather using derived primal-dual saddle-point objective functions. We then conduct a saddle-point error analysis to obtain finite-sample bounds on their performance. Previous analyses of this class of algorithms use stochastic approximation techniques to prove asymptotic convergence, and no finite-sample analysis had been attempted. Two novel GTD algorithms are also proposed, namely projected GTD2 and GTD2-MP, which use proximal “mirror maps” to yield improved convergence guarantees and acceleration, respectively. The results of our theoretical analysis imply that the GTD family of algorithms are comparable and may indeed be preferred over existing least squares TD methods for off-policy learning, due to their linear complexity. We provide experimental results showing the improved performance of our accelerated gradient TD methods.
Why Is My Classifier Discriminatory?

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.
Task-based instruction

This article is organised in five main sections. First, the sub-area of task-based instruction is introduced and contextualised. Its origins within communicative language teaching and second language acquisition research are sketched, and the notion of a task in language learning is defined. There is also brief coverage of the different and sometimes contrasting groups who are interested in the use of tasks. The second section surveys research into tasks, covering the different perspectives (interactional, cognitive) which have been influential. Then a third section explores how performance on tasks has been measured, generally in terms of how complex the language used is, how accurate it is, and how fluent. There is also discussion of approaches to measuring interaction. A fourth section explores the pedagogic and interventionist dimension of the use of tasks. The article concludes with a survey of the various critiques of tasks that have been made in recent years.
FILM , REPRESENTATION AND THE EXCLUSION OF ABORIGINAL IDENTITY : EXAMPLES FROM AUSTRALIAN CINEMA

Throughout the latter half of the past century cinema has played a significant role in the shaping of the core narratives of Australia. Films express and implicitly shape national images and symbolic representations of cultural fictions in which ideas about Indigenous identity have been embedded. In this paper, exclusionary practices in Australian narratives are analysed through examples of films representing Aboriginal identity. Through these filmic narratives the articulation, interrogation, and contestation of views about filmic representations of Aboriginal identity in Australia is illuminated. The various themes in the filmic narratives are examined in order to compare and contrast the ways in which the films display the operation of narrative closure and dualisms within the film texts.
Game theoretic mechanism design applied to machine learning classification

The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. Numerous approaches have been developed ranging from neural network models striving to replicate neurophysiology to more abstract mathematical manipulations which identify numerical similarities. Nevertheless a common theme amongst the varied approaches is that learning techniques incorporate a strategic component to try and yield the best possible decision or classification. The mathematics of game theory formally analyzes strategic interactions between competing players and is consequently quite appropriate to apply to the field of machine learning with potential descriptive as well as functional insights. Furthermore, game theoretic mechanism design seeks to develop a framework to achieve a desired outcome, and as such is applicable for defining a paradigm capable of performing classification. In this work we present a game theoretic chip-fire classifier which as an iterated game is able to perform pattern classification.
A Simple Machine Learning Method for Commonsense Reasoning? A Short Commentary on Trinh & Le (2018)

A program is then asked the question “what was too small” as a follow-up to (1a), and the question “what was too big” as a follow-up to (1b). Levesque et. al. call a sentence such as that in (1) “Google proof” since a system that processed a large corpus cannot “learn” how to resolve such references by finding some statistical correlations in the data, as the only difference between (1a) and (1b) are antonyms that are known to co-occur in similar contexts with the same frequency. In a recent paper Trinh and Le (2018) henceforth T&L suggested that they have successfully formulated a „simple‟ machine learning method for performing commonsense reasoning, and in particular, the kind of reasoning that would be required in the process of language understanding. In doing so, T&L use the Winograd Schema (WS) challenge as a benchmark. In simple terms, T&L suggest the following method for “learning” how to successfully resolve the reference “it” in sentences such as those in (1): generate two
A high-performance carbon nanotube-based field emission x-ray tube for commercial dental imaging

We developed a fully vacuum-sealed carbon nanotube (CNT)-based field emission x-ray tube with commercial-level performance for dental imaging application. The CNT x-ray tube was designed in a triode configuration with the focus-functional gate and had a compact size, much smaller than the conventional hot-cathode tubes. The small size of CNT x-ray tubes along with no cathode heating enables us to make a very compact portable dental imaging system. We have obtained a quite good x-ray image of a human tooth phantom by operating the CNT x-ray tube in an anode voltage of 65 kV, an emission current of 3 m A, and a pulse duration of 0.5 s, which is comparable to that of the conventional commercial hot-cathode tube.
Fault notification extension in support of BSS 2G siemens

Fault management system has critical responsibility in telecommunication networks to guarantee the networks reliability. One of important functions of fault management is fault notification. In this paper, fault notification extension for BSS 2G Siemens is proposed. Application to process and send the BSS 2G alarms automatically to sites engineers in form of Short Message Service (SMS) is designed and developed. Report status of the notification is accomplished with time-stamp and interval period. The experimental results showed that the notification extension for the BSS 2G alarm is received successfully by site engineers. Transmission time and response time of the notification is faster up to three times than using operator's phone call.
DTV coverage and protection contour estimation for spatial white space

TV white spaces are vacant, unused or interleaved frequencies located between broadcast TV channels in the VHF/UHF range, which can be found between 54 MHz and 806 MHz. Utilization of these vacant channels has become a major challenge since the idea of cognitive radio (CR) technology, which aims to exploit the available TV white space may not be guaranteed for deployment at the present stage. This calls for alternative spectrum management models and techniques that would allow efficient utilization of the TV white space. In this work, we develop an algorithm that predicts the service contour and makes decision on the availability of the white space. The algorithm recovers the region outside global no-talk zone of the primary user. Results of simulation show that the amount of white space recovered depends on the threshold rule applied. It was also found that the keep-out-distance varies as a function of the operating frequency and the propagation characteristics of the area.
Improving Data Quality through Deep Learning and Statistical Models

Outlier detection is an essential aspect of data quality control as it allows analysts and engineers the ability to identify data quality problems through the use of their own data as a tool. However, traditional data quality control methods are based on users’ experience or previously established business rules, and this limits performance in addition to being a very time consuming process and low accuracy. Utilizing big data, we can leverage computing resources and advanced techniques to overcome these challenges and provide greater value to the business. In this paper, we first review relevant works and discuss machine learning techniques, tools, and statistical quality models. Second, we offer a creative data profiling framework based on deep learning and statistical model algorithms for improving data quality. Third, authors use public Arkansas officials’ salaries, one of the open datasets available from the state of Arkansas’ official website, to demonstrate how to identify outlier data for improving data quality via machine learning. Finally, we discuss future works.
Searching for Google's Value: Using Prediction Markets to Forecast Market Capitalization Prior to an Initial Public Offering

To inform theory and to investigate the practical application of prediction markets in a setting where the distribution of information across agents is critical, we conducted markets designed to forecast post-IPO valuations before a particularly unique IPO: Google. Because prediction markets allow us to infer the distribution of information before the IPO, the combination of results from our markets and the unique features of the IPO help us distinguish between underpricing theories. The evidence leans against theories which require large payments to buyers to overcome problems of asymmetric information between issuers and buyers. It is most consistent with theories where underpricing is in exchange for future benefits. This is but one of many potential applications for prediction markets in testing information-based theories.
An Improved Weighted-Feature Clustering Algorithm for K-anonymity

Chiu proposed a clustering algorithm adjusting the numeric feature weights automatically for k-anonymity implementation and this approach gave a better clustering quality over the traditional generalization and suppression methods. In this paper, we propose an improved weighted-feature clustering algorithm which takes the weight of categorical attributes and the thesis of optimal k-partition into consideration. To show the effectiveness of our method, we do some information loss experiments to compare it with greedy k-member clustering algorithm.