nils-herrmann/clean_scirepeval huggingface dataset:
Tactical resource allocation and elective patient admission planning in care pathways

Tactical planning of resources in hospitals concerns elective patient admission planning and the intermediate term allocation of resource capacities. Its main objectives are to achieve equitable access for patients, to meet production targets/to serve the strategically agreed number of patients, and to use resources efficiently. This paper proposes a method to develop a tactical resource allocation and elective patient admission plan. These tactical plans allocate available resources to various care pathways and determine the selection of patients to be served that are at a particular stage of their care pathways. Our method is developed in a Mixed Integer Linear Programming framework and copes with multiple resources, multiple time periods and multiple patient groups with various uncertain care pathways, thereby integrating decision making for a chain of hospital resources. Computational results indicate that our method leads to a more equitable distribution of resources and provides control of patient access times, the number of patients served and the faction of allocated resource capacity. Our approach is generic, as the base MIP and the solution approach allow for including various extensions to both the objective criteria and the constraints. Consequently, the proposed method is applicable in various settings of tactical hospital management.
Chemometrics and intelligent laboratory systems Tutorial Introduction to multi-layer feed-forward neural networks

Basic definitions concerning the multi-layer feed-forward neural networks are given. The back-propagation training algorithm is explained. Partial derivatives of the objective function with respect to the weight and threshold coefficients are derived. These derivatives are valuable for an adaptation process of the considered neural network. Training and generalisation of multi-layer feed-forward neural networks are discussed. Improvements of the standard back-propagation algorithm are reviewed. Example of the use of multi-layer feed-forward neural networks for prediction of carbon-13 NMR chemical shifts of alkanes is given. Further applications of neural networks in chemistry are reviewed. Advantages and disadvantages of multilayer feed-forward neural networks are discussed.
Unawareness of dyskinesias in Parkinson's and Huntington's diseases

We performed a clinical study to evaluate the unawareness of dyskinesias in patients affected by Parkinson's disease (PD) and Huntington's disease (HD). Thirteen PD patients with levodopa-induced dyskinesias and 9 HD patients were enrolled. Patients were asked to evaluate the presence of dyskinesias while performing specific motor tasks. The Abnormal Involuntary Movement Scale (AIMS) and Goetz dyskinesia rating scale were administered to determine the severity of dyskinesias. The Unified Parkinson's disease rating scale (UPDRS) and Unified Huntington's Disease Rating Scale (UHDRS) were used in PD and HD patients, respectively. In PD we found a significant negative relationship between unawareness score at hand pronation-supination and AIMS score for upper limbs. In HD we found a significant positive relationship between total unawareness score and disease duration. In PD the unawareness seems to be inversely related with severity of dyskinesias, while in HD it is directly related to disease duration and severity.
Holographic subsurface radar RASCAN-5

This paper describes the implementation of a holographic subsurface radar for sounding at shallow depths. The radar uses continuous signal with frequency switching and records phases of reflected signal at several operating frequencies. Two versions of the radar with frequency bandwidths 6.4-6.8 and 13.8-14.6 GHz were designed and tested. The data acquisition is accomplished by manual scanning along parallel equidistant lines. Upon acquisition, this two-dimensional interference pattern, or hologram, can be focused by the outlined Fourier-based back propagation algorithm into an image that reflects distribution of sources. Experimentally obtained and demonstrated in the paper images with plan view resolution of the order of 1 cm suggest application of the radar in civil engineering and non-destructive testing.
It's the institutions, stupid! Why comprehensive national health insurance always fails in America.

We argue that the United States does not have comprehensive national health insurance (NHI) because American political institutions are biased against this type of reform. The original design of a fragmented and federated national political system serving an increasingly large and diverse polity has been further fragmented by a series of political reforms beginning with the Progressive era and culminating with the congressional reforms of the mid-1970s. This institutional structure yields enormous power to intransigent interest groups and thus makes efforts by progressive reformers such as President Clinton (and previous reform-minded presidents before him) to mount a successful NHI campaign impossible. We show how this institutional structure has shaped political strategies and political outcomes related to NHI since Franklin D. Roosevelt. Finally, we argue that this institutional structure contributes to the antigovernment attitudes so often observed among Americans.
Methodology for Evaluating the Rural Tourism Potentials : A Tool to Ensure Sustainable Development of Rural Settlements

The paper analyses potentials, challenges and problems of the rural tourism from the point of view of its impact on sustainable rural development. It explores alternative sources of income for rural people by means of tourism and investigates effects of the rural tourism on agricultural production in local rural communities. The aim is to identify the existing and potential tourist attractions within the rural areas in Southern Russia and to provide solutions to be introduced in particular rural settlements in order to make them attractive for tourists. The paper includes the elaboration and testing of a methodology for evaluating the rural tourism potentials using the case of rural settlements of Stavropol Krai, Russia. The paper concludes with a ranking of the selected rural settlements according to their rural tourist capacity and substantiation of the tourism models to be implemented to ensure a sustainable development of the considered rural areas.
Efficient text summarization using lexical chains

The rapid growth of the Internet has resulted in enormous amounts of information that has become more difficult to access efficiently. Internet users require tools to help manage this vast quantity of information. The primary goal of this research is to create an efficient and effective tool that is able to summarize large documents quickly. This research presents a linear time algorithm for calculating lexical chains which is a method of capturing the “aboutness” of a document. This method is compared to previous, less efficient methods of lexical chain extraction. We also provide alternative methods for extracting and scoring lexical chains. We show that our method provides similar results to previous research, but is substantially more efficient. This efficiency is necessary in Internet search applications where many large documents may need to be summarized at once, and where the response time to the end user is extremely important.
CGM: A biomedical text categorization approach using concept graph mining

Text Categorization is used to organize and manage biomedical text databases that are growing at an exponential rate. Feature representations for documents are a crucial factor for the performance of text categorization. Most of the successful existing techniques use a vector representation based on key entities extracted from the text. In this paper we investigate a new direction where we represent a document as a graph. In this representation we identify high level concepts and build a rich graph structure that contains additional concepts and relationships. We then use graph kernel techniques to perform text categorization. The results show a significant improvement in accuracy when compared to categorization based on only the extracted concepts.
Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs

Recursive neural networks (RNN) and their recently proposed extension recursive long short term memory networks (RLSTM) are models that compute representations for sentences, by recursively combining word embeddings according to an externally provided parse tree. Both models thus, unlike recurrent networks, explicitly make use of the hierarchical structure of a sentence. In this paper, we demonstrate that RNNs nevertheless suffer from the vanishing gradient and long distance dependency problem, and that RLSTMs greatly improve over RNN’s on these problems. We present an artificial learning task that allows us to quantify the severity of these problems for both models. We further show that a ratio of gradients (at the root node and a focal leaf node) is highly indicative of the success of backpropagation at optimizing the relevant weights low in the tree. This paper thus provides an explanation for existing, superior results of RLSTMs on tasks such as sentiment analysis, and suggests that the benefits of including hierarchical structure and of including LSTM-style gating are complementary.
Posterior fixation of thoracolumbar burst fractures: Is it possible to protect one segment in the lumbar region?

The treatment for thoracolumbar burst fractures is controversial. The aim of this retrospective study was to compare intermediate-segment (IS) and long-segment (LS) instrumentation in the treatment for these fractures. IS instrumentation was considered as pedicle fixation two levels above and one level below the fractured vertebra (infra-laminar hooks attached to lower vertebra with pedicle screws). LS instrumentation was done two levels above and two levels below the fractured vertebra. Among a total of 25 consecutive patients, Group 1 included ten patients treated by IS pedicle fixation, whereas Group 2 included fifteen patients treated by LS instrumentation. The measurements of local kyphosis (p = 0.955), sagittal index (p = 0.128), anterior vertebral height compression (p = 0.230) and canal diameter expansion (p = 0.839) demonstrated similar improvement at the final follow-up between the two groups. However, there was a significant difference (p < 0.05) between Group 1 and Group 2 regarding clinical outcome [Hannover scoring system, Oswestry disability questionnaire and the range of motion of the lumbar region compared to neutral (0°)]. The radiographic parameters were the same between the two groups. However, the clinical parameters demonstrated that IS instrumentation is a more effective management of thoracolumbar burst fractures.
Jannovar: a java library for exome annotation.

Transcript-based annotation and pedigree analysis are two basic steps in the computational analysis of whole-exome sequencing experiments in genetic diagnostics and disease-gene discovery projects. Here, we present Jannovar, a stand-alone Java application as well as a Java library designed to be used in larger software frameworks for exome and genome analysis. Jannovar uses an interval tree to identify all transcripts affected by a given variant, and provides Human Genome Variation Society-compliant annotations both for variants affecting coding sequences and splice junctions as well as untranslated regions and noncoding RNA transcripts. Jannovar can also perform family-based pedigree analysis with Variant Call Format (VCF) files with data from members of a family segregating a Mendelian disorder. Using a desktop computer, Jannovar requires a few seconds to annotate a typical VCF file with exome data. Jannovar is freely available under the BSD2 license. Source code as well as the Java application and library file can be downloaded from http://compbio.charite.de (with tutorial) and https://github.com/charite/jannovar.
CLOPE: a fast and effective clustering algorithm for transactional data

This paper studies the problem of categorical data clustering, especially for transactional data characterized by high dimensionality and large volume. Starting from a heuristic method of increasing the height-to-width ratio of the cluster histogram, we develop a novel algorithm -- CLOPE, which is very fast and scalable, while being quite effective. We demonstrate the performance of our algorithm on two real world datasets, and compare CLOPE with the state-of-art algorithms.
Modeling click and relevance relationship for sponsored search

Click-through rate (CTR) prediction and relevance ranking are two fundamental problems in web advertising. In this study, we address the problem of modeling the relationship between CTR and relevance for sponsored search. We used normalized relevance scores comparable across all queries to represent relevance when modeling with CTR, instead of directly using human judgment labels or relevance scores valid only within same query. We classified clicks by identifying their relevance quality using dwell time and session information, and compared all clicks versus selective clicks effects when modeling relevance.
 Our results showed that the cleaned click signal outperforms raw click signal and others we explored, in terms of relevance score fitting. The cleaned clicks include clicks with dwell time greater than 5 seconds and last clicks in session. Besides traditional thoughts that there is no linear relation between click and relevance, we showed that the cleaned click based CTR can be fitted well with the normalized relevance scores using a quadratic regression model. This relevance-click model could help to train ranking models using processed click feedback to complement expensive human editorial relevance labels, or better leverage relevance signals in CTR prediction.
CoupledLP: Link Prediction in Coupled Networks

We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any information of the target network. Moreover, the source and target networks are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to leverage the heterogeneous interactions between the two networks for the prediction task?
 We propose a unified framework, CoupledLP, to solve the problem. Given two coupled networks, we first leverage atomic propagation rules to automatically construct implicit links in the target network for addressing the challenge of target network incompleteness, and then propose a coupled factor graph model to incorporate the meta-paths extracted from the coupled part of the two networks for transferring heterogeneous knowledge. We evaluate the proposed framework on two different genres of datasets: disease-gene (DG) and mobile social networks. In the DG networks, we aim to use the disease network to predict the associations between genes. In the mobile networks, we aim to use the mobile communication network of one mobile operator to infer the network structure of its competitors. On both datasets, the proposed CoupledLP framework outperforms several alternative methods. The proposed problem of coupled link prediction and the corresponding framework demonstrate both the scientific and business applications in biology and social networks.
A 2-Port Stable Negative Capacitance Circuit Design With Unilateral Gain Boosting Technique

Herein, we present a floating 2-port nonreciprocal negative capacitance (NCAP) circuit that employs a unilateral gain boosting technique by including an asymmetric cross-coupled differential cascode transistor pair. The proposed circuit provides an asymmetric gain characteristic. The forward gain (<inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula>) is increased at the cost of the backward gain (<inline-formula> <tex-math notation="LaTeX">$S_{12}$ </tex-math></inline-formula>). The technique not only provides a boosted forward gain but also stable operation resulting from this reduced backward gain. We further investigate the stability of the NCAP circuit in terms of the output resistances of the current sources in the circuit. The proposed NCAP circuit was implemented in IBM 7RF 180-nm CMOS technology and was designed to have a supply voltage of 3 V and a current of 10 mA. The S-parameter measurements showed a self-resonance frequency of 2.25 GHz. The maximum <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> was −0.2 dB at 1 GHz. In the frequency range from 0.5 to 1.5 GHz, the capacitance was approximately −4 pF, and the difference between <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$S_{12}$ </tex-math></inline-formula> was 8.7–9.6 dB. The size of the core chip was <inline-formula> <tex-math notation="LaTeX">$150 \times 190~\mu \text{m}^{2}$ </tex-math></inline-formula>.
Reassessing Melanonychia Striata in Phototypes IV, V, and VI Patients.

Racial melanonychia striata is a nail plate dyschromia described as a gray coloration of the background in dermatoscopy and hypermelanosis in histology. However, many cases with brown or black coloration of the background in the dermatoscopic examination have been followed.



Evaluate racial melanonychia striata dermatoscopical patterns and histological diagnosis in Fitzpatrick skin Types IV, V, or VI population.



482 Fitzpatrick skin Types IV, V, or VI patients, between 19 and 72 years of age, were clinically evaluated and underwent nail plate dermatoscopic examination. Fifty-eight patients presenting melanonychia striata and dermatoscopic patterns with brown or black coloration of the background were submitted to a nail matrix biopsy. When occurring in more than 1 digit, biopsy was performed in the lesion with the darkest coloration presented in dermatoscopy.



All skin Types IV, V, or VI patients who had melanonychia striata, with brown or black coloration of the background in the dermatoscopic examination, revealed histopathological analysis compatible with hypermelanosis.



Racial melanonychia in skin Types IV, V, or VI patients, resulting from hypermelanosis, may present brown or black coloration of the background in the dermatoscopic examination.
Deep Learning on Retina Images as Screening Tool for Diagnostic Decision Support

In this project, we developed a deep learning system applied to human retina images for medical diagnostic decision support. The retina images were provided by EyePACS (Eyepacs, LLC). These images were used in the framework of a Kaggle contest (Kaggle INC, 2017), whose purpose to identify diabetic retinopathy signs through an automatic detection system. Using as inspiration one of the solutions proposed in the contest, we implemented a model that successfully detects diabetic retinopathy from retina images. After a carefully designed preprocessing, the images were used as input to a deep convolutional neural network (CNN). The CNN performed a feature extraction process followed by a classification stage, which allowed the system to differentiate between healthy and ill patients using five categories. Our model was able to identify diabetic retinopathy in the patients with an agreement rate of 76.73% with respect to the medical expert's labels for the test data.
Data Security, Privacy, Availability and Integrity in Cloud Computing: Issues and Current Solutions

Cloud computing changed the world around us. Now people are moving their data to the cloud since data is getting bigger and needs to be accessible from many devices. Therefore, storing the data on the cloud becomes a norm. However, there are many issues that counter data stored in the cloud starting from virtual machine which is the mean to share resources in cloud and ending on cloud storage itself issues. In this paper, we present those issues that are preventing people from adopting the cloud and give a survey on solutions that have been done to minimize risks of these issues. For example, the data stored in the cloud needs to be confidential, preserving integrity and available. Moreover, sharing the data stored in the cloud among many users is still an issue since the cloud service provider is untrustworthy to manage authentication and authorization. In this paper, we list issues related to data stored in cloud storage and solutions to those issues which differ from other papers which focus on cloud as general.
Generations of Mobile Wireless Technology: A Survey

Wireless communication is the transfer of information over a distance without the use of enhanced electrical conductors or "wires”. The distances involved may be short (a few meters as in television remote control) or long (thousands or millions of kilometers for radio communications). When the context is clear, the term is often shortened to "wireless". It encompasses various types of fixed, mobile, and portable two-way radios, cellular telephones, Personal Digital Assistants (PDAs), and wireless networking. In this paper we will throw light on the evolution and development of various generations of mobile wireless technology along with their significance and advantages of one over the other. In the past few decades, mobile wireless technologies have experience 4 or 5 generations of technology revolution and evolution, namely from 0G to 4G. Current research in mobile wireless technology concentrates on advance implementation of 4G technology and 5G technology. Currently 5G term is not officially used. In 5G researches are being made on development of World Wide Wireless Web (WWWW), Dynamic Adhoc Wireless Networks (DAWN) and Real Wireless World.
Digital video quality metric based on human vision

The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (Digital Video Quality). Here we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the Discrete Cosine Transform. It incorporates aspects of early visual processing, including light adaptation, luminance and chromatic channels, spatial and temporal filtering, spatial frequency channels, contrast masking, and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers.