nils-herrmann/clean_scirepeval huggingface dataset:
Intravenous pulse cyclophosphamide and steroids induce immunological and clinical remission in New-incident and relapsing primary membranous nephropathy.

Primary membranous nephropathy is associated with progression to end stage renal diseasein some patients. Standard therapy with cyclical cyclophosphamide and corticosteroids can be associated with significant adverse effects. We aimed to assess immunological and clinical response with intravenous pulse cyclophosphamide and oral steroids in patients with severe nephrotic syndrome - in a prospective observational cohort study.



A total of 17 consecutive patients (nine new-incident and eight relapses) with severe nephrotic syndrome received monthly intravenous pulse cyclophosphamide and oral steroids after failure to achieve remission with supportive therapy. Immunosuppressive therapy was discontinued at 6 months or earlier if proteinuria regressed to <100 mg/mmol and patients were followed for 12 months. Achievement of partial remission was primary outcome; changes in clinical parameters and anti-PLA2 R were secondary outcomes.



Dose of cyclophosphamide received was 5.4 g in New-incident patients and 4.2 g in patients with relapses. All 17 patients achieved partial remission within 6 months: proteinuria improved from 656 to 102 mg/mmol at 6 months and 55 mg/mmol at 12 months (P < 0.001); eGFR improved from 31 to 48 mL/min per 1.73 m2 at 6 months and 45 mL/min per 1.73 m2 at 12 months (P < 0.05). Anti-PLA2 R levels reduced from 244 to 10 U/L at 6 months and 10 U/L at 12 months (P < 0.001). Two out of nine patients in the New-incident group developed subsequent relapse. Cumulative doses of cyclophosphamide and steroids that patients received was about half of the standard regime.



Pulse cyclophosphamide with oral steroids induced immunological and clinical partial remission at significantly reduced doses in primary membranous nephropathy.
Query expansion for mixed-script information retrieval

For many languages that use non-Roman based indigenous scripts (e.g., Arabic, Greek and Indic languages) one can often find a large amount of user generated transliterated content on the Web in the Roman script. Such content creates a monolingual or multi-lingual space with more than one script which we refer to as the Mixed-Script space. IR in the mixed-script space is challenging because queries written in either the native or the Roman script need to be matched to the documents written in both the scripts. Moreover, transliterated content features extensive spelling variations. In this paper, we formally introduce the concept of Mixed-Script IR, and through analysis of the query logs of Bing search engine, estimate the prevalence and thereby establish the importance of this problem. We also give a principled solution to handle the mixed-script term matching and spelling variation where the terms across the scripts are modelled jointly in a deep-learning architecture and can be compared in a low-dimensional abstract space. We present an extensive empirical analysis of the proposed method along with the evaluation results in an ad-hoc retrieval setting of mixed-script IR where the proposed method achieves significantly better results (12% increase in MRR and 29% increase in MAP) compared to other state-of-the-art baselines.
General Techniques Involved in Phytochemical Analysis

Plants are recognized in the pharmaceutical industry for their broad structural diversity as well as their wide range of pharmacological activities. The biologically active compounds present in plants are called phytochemicals. These phytochemicals are derived from various parts of plants such as leaves, flowers, seeds, barks, roots and pulps. These phytochemicals are used as sources of direct medicinal agents. They serve as a raw material base for elaboration of more complex semi-synthetic chemical compounds. This paper mainly deals with the collection of plants, the extraction of active compounds from the various parts of plants, qualitative and quantitative analysis of the phytochemicals.
Modelling Dynamics in Semantic Web Knowledge Graphs with Formal Concept Analysis

In this paper, we propose a novel data-driven schema for largescale heterogeneous knowledge graphs inspired by Formal Concept Analysis (FCA). We first extract the sets of properties associated with individual entities; these property sets (aka. characteristic sets) are annotatedwith cardinalities and used to induce a lattice based on set-containment relations, forming a natural hierarchical structure describing the knowledge graph. We then propose an algebra over such schema lattices, which allows to compute diffs between lattices (for example, to summarise the changes from one version of a knowledge graph to another), to add diffs to lattices (for example, to project future changes), and so forth.While we argue that this lattice structure (and associated algebra) may have various applications, we currently focus on the use-case of modelling and predicting the dynamic behaviour of knowledge graphs. Along those lines, we instantiate and evaluate our methods for analysing how versions of the Wikidata knowledge graph have changed over a period of 11 weeks. We propose algorithms for constructing the lattice-based schema from Wikidata, and evaluate their efficiency and scalability. We then evaluate use of the resulting schema(ta) for predicting how the knowledge graph will evolve in future versions.
BackPropagation Through Time

This report provides detailed description and necessary derivations for the BackPropagation Through Time (BPTT) algorithm. BPTT is often used to learn recurrent neural networks (RNN). Contrary to feed-forward neural networks, the RNN is characterized by the ability of encoding longer past information, thus very suitable for sequential models. The BPTT extends the ordinary BP algorithm to suit the recurrent neural architecture. 1 Basic Definitions For a two-layer feed-forward neural network, we notate the input layer as x indexed by variable i, the hidden layer as s indexed by variable j, and the output layer as y indexed by variable k. The weight matrix that map the input vector to the hidden layer is V, while the hidden layer is propagated through the weight matrix W, to the output layer. In a simple recurrent neural network, we attach every neural layer a time subscript t. The input layer consists of two components, x(t) and the privious activation of the hidden layer s(t − 1) indexed by variable h. The corresponding weight matrix is U. Table 1 lists all the notations used in this report: Neural layer Description Index variable x(t) input layer i s(t− 1) previous hidden (state) layer h s(t) hidden (state) layer j y(t) output layer k Weight matrix Description Index variables V Input layer → Hidden layer i, j U Previous hidden layer → Hidden layer h, j W Hidden layer → Output layer j, k Table 1: Notations in the recurrent neural network. Then, the recurrent neural network can be processed as the following: • Input layer → Hidden layer sj(t) = f(netj(t)) (1)
Gray S-Box for Advanced Encryption Standard

We present Gray S-box for advanced encryption standard. Gray S-box is constructed by adding binary Gray code transformation as a preprocessing step to original AES S-box. Gray S-box corresponds to a polynomial with all 255 non-zero terms in comparison with 9-term polynomial of original AES S-box. This increases the security for S-box against algebraic attacks and interpolation attacks. Besides, as Gray S-box reuses AES S-box as a whole, Gray S-box inherits all advantages and efficiency of any existing optimized implementation of AES S-box. Gray S-box also achieves important cryptographic properties of AES S-box, including strict avalanche criterion, nonlinearity, and differential uniformity.
Hepcidin, a key regulator of iron metabolism and mediator of anemia of inflammation.

Human hepcidin, a 25-amino acid peptide made by hepatocytes, may be a new mediator of innate immunity and the long-sought iron-regulatory hormone. The synthesis of hepcidin is greatly stimulated by inflammation or by iron overload. Evidence from transgenic mouse models indicates that hepcidin is the predominant negative regulator of iron absorption in the small intestine, iron transport across the placenta, and iron release from macrophages. The key role of hepcidin is confirmed by the presence of nonsense mutations in the hepcidin gene, homozygous in the affected members, in 2 families with severe juvenile hemochromatosis. Recent evidence shows that deficient hepcidin response to iron loading may contribute to iron overload even in the much milder common form of hemochromatosis, from mutations in the HFE gene. In anemia of inflammation, hepcidin production is increased up to 100-fold and this may account for the defining feature of this condition, sequestration of iron in macrophages. The discovery of hepcidin and its role in iron metabolism could lead to new therapies for hemochromatosis and anemia of inflammation.
Towards CT-quality Ultrasound Imaging using Deep Learning

The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflected ultrasound radio-frequency(RF) data obtained by simulation from real CT scans of a human body. We also show that CNN is able to imitate existing computationally heavy despeckling methods, thereby saving orders of magnitude in computations and making them amenable to real-time applications.
EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century

People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.
Analysis of Phase Noise in Phase/Frequency Detectors

The phase noise of phase/frequency detectors can significantly raise the in-band phase noise of frequency synthesizers, corrupting the modulated signal. This paper analyzes the phase noise mechanisms in CMOS phase/frequency detectors and applies the results to two different topologies. It is shown that an octave increase in the input frequency raises the phase noise by 6 dB if flicker noise is dominant and by 3 dB if white noise is dominant. An optimization methodology is also proposed that lowers the phase noise by 4 to 8 dB for a given power consumption. Simulation and analytical results agree to within 3.1 dB for the two topologies at different frequencies.
RpStacks-MT: A High-Throughput Design Evaluation Methodology for Multi-Core Processors

Computer architects put significant efforts on the design space exploration of a new processor, as it determines the overall characteristics (e.g., performance, power, cost) of the final product. To thoroughly explore the space and achieve the best results, they need high design evaluation throughput – the ability to quickly assess a large number of designs with minimal costs. Unfortunately, the existing simulators and performance models are either too slow or too inaccurate to meet this demand. As a result, architects often sacrifice the design space coverage to end up with a sub-optimal product. To address this challenge, we propose RpStacks-MT, a methodology to evaluate multi-core processor designs with high throughput. First, we propose a graph-based multi-core performance model, which overcomes the limitations of the existing models to accurately describe a multi-core processor's key performance behaviors. Second, we propose a reuse distance-based memory system model and a dynamic scheduling reconstruction method, which help our graph model to quickly track the performance changes from processor design changes. Lastly, we combine these models with a state of the art design exploration idea to evaluate multiple processor designs in an efficient way. Our evaluations show that RpStacks-MT achieves extremely high design evaluation throughput – 88× higher versus a conventional cycle-level simulator and 18× higher versus an accelerated simulator (on average, for evaluating 10,000 designs) – while maintaining simulator-level accuracy.
Catalytic conversion of methane to more useful chemicals and fuels : a challenge for the 21 st century

The very large reserves of methane, which often are found in remote regions, could serve as a feedstock for the production of chemicals and as a source of energy well into the 21st century. Although methane currently is being used in such important applications as the heating of homes and the generation of hydrogen for ammonia synthesis, its potential for the production of ethylene or liquid hydrocarbon fuels has not been fully realized. A number of strategies are being explored at levels that range from fundamental science to engineering technology. These include: (a) stream and carbon dioxide reforming or partial oxidation of methane to form carbon monoxide and hydrogen, followed by Fischer–Tropsch chemistry, (b) the direct oxidation of methane to methanol and formaldehyde, (c) oxidative coupling of methane to ethylene, and (d) direct conversion to aromatics and hydrogen in the absence of oxygen. Each alternative has its own set of limitations; however, economical separation is common to all with the most important issues being the separation of oxygen from air and the separation of hydrogen or hydrocarbons from dilute product streams. Extensive utilization of methane for the production of fuels and chemicals appears to be near, but current economic uncertainties limit the amount of research activity and the implementation of emerging technologies.
HIV-1 capsid uncoating initiates after the first strand transfer of reverse transcription

Correct disassembly of the HIV-1 capsid shell, called uncoating, is increasingly recognised as central for multiple steps during retroviral replication. However, the timing, localisation and mechanism of uncoating are poorly understood and progress in this area is hampered by difficulties in measuring the process. Previous work suggested that uncoating occurs soon after entry of the viral core into the cell, but recent studies report later uncoating, at or in the nucleus. Furthermore, inhibiting reverse transcription delays uncoating, linking these processes. Here, we have used a combined approach of experimental interrogation of viral mutants and mathematical modelling to investigate the timing of uncoating with respect to reverse transcription. By developing a minimal, testable, model and employing multiple uncoating assays to overcome the disadvantages of each single assay, we find that uncoating is not concomitant with the initiation of reverse transcription. Instead, uncoating appears to be triggered once reverse transcription reaches a certain stage, namely shortly after first strand transfer. Using multiple approaches, we have identified a point during reverse transcription that induces uncoating of the HIV-1 CA shell. We propose that uncoating initiates after the first strand transfer of reverse transcription.
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set

A large set of machine learning and pattern classification algorithms trained and tested on KDD intrusion detection data set failed to identify most of the user-to-root and remote-to-local attacks, as reported by many researchers in the literature. In light of this observation, this paper aims to expose the deficiencies and limitations of the KDD data set to argue that this data set should not be used to train pattern recognition or machine learning algorithms for misuse detection for these two attack categories. Multiple analysis techniques are employed to demonstrate, both objectively and subjectively, that the KDD training and testing data subsets represent dissimilar target hypotheses for user-to-root and remote-to-local attack categories. These techniques consisted of switching the roles of original training and testing data subsets to develop a decision tree classifier, crossvalidation on merged training and testing data subsets, and qualitative and comparative analysis of rules generated independently on training and testing data subsets through the C4.5 decision tree algorithm. Analysis results clearly suggest that no pattern classification or machine learning algorithm can be trained successfully with the KDD data set to perform misuse detection for user-to-root or remote-to-local attack categories. It is further noted that the analysis techniques employed to assess the similarity between the two target hypotheses represented by the training and the testing data subsets can readily be generalized to data set pairs in other problem domains.
Isolated thickened greater auricular nerve due to leprosy

Leprosy is caused by Mycobacterium leprae bacillus and despite recommendation of multidrug therapy by World Health Organisation in 1981 and eradication programme in various countries; disease prevails and new cases added annually. Variable clinical presentation ranges from limited tuberculoid to widespread lepromatous leprosy. The neuritic presentation varies from mononeuropathy to mononeuropathy multiplex. The disease commonly affects the ulnar, radial in upper and common peroneal, posterior tibial in lower extremity. The neuritic leprosy is easily suspected when there is hypoanesthetic skin lesion with thickened and tender nerve. Involvement of uncommon nerve and pure neuritic presentation, a rare form of leprosy in which skin is spared often leads to diagnostic challenge. Biopsy is not needed to initiate treatment but sometimes required to rule our other diseases. We report a rare case of isolated thickening of greater auricular nerve and diagnostic dilemma encountered in the era of evidence-based medicine.
Paratrigeminal, Paraclival, Precavernous, or All of the Above? A Circumferential Anatomical Study of the C3-C4 Transitional Segment of the Internal Carotid Artery.

Although the term paraclival carotid pervades recent skull base literature, no clear consensus exists regarding boundaries or anatomical segments.



To reconcile various internal carotid artery (ICA) nomenclatures for transcranial and endoscopic-endonasal perspectives, we reexamined the transition between lacerum (C3) and cavernous (C4) segments using a C1-C7 segments schema. In this cadaveric study, we obtained a 360°-circumferential view integrating histological, microsurgical, endoscopic, and neuroradiological analyses of this C3-C4 region and identified a distinct transitional segment.



In 13 adult, silicone-injected, formalin-fixed cadaveric heads (26 sides), transcranial-extradural-subtemporal and endoscopic-endonasal CT-guided dissections were performed. A quadrilateral area was noted medial to Meckel's cave between cranial nerve VI, anterolateral and posterolateral borders of the lateral-paratrigeminal aspect of the precavernous ICA, and posterior longitudinal ligament. Endoscopically, a medial-paraclival aspect was defined. Anatomical correlations were made with histological and neuroradiological slides.



We identified a distinct precavernous C3-C4 transitional segment. In 18 (69%) specimens, venous channels were absent at the quadrilateral area, on the paratrigeminal border of the precavernous ICA. A trigeminal membrane, seen consistently on the superior border of V2, defined the lateral aspect of the cavernous sinus floor. The medial aspect of the precavernous ICA corresponded with the paraclival ICA.



Our study revealing the juncture of 2 complementary borders of the ICA, endoscopic endonasal (paraclival) and transcranial (paratrigeminal), reconciles various nomenclature. A precavernous segment may clarify controversies about the paraclival ICA and support the concept of a "safe door" for lesions involving Meckel's cave, cavernous sinus, and petrous apex.
Investigating Electronic Word-of-Mouth Effects on Online Discussion Forums: The Role of Perceived Positive Electronic Word-of-Mouth Review Credibility

Electronic word of mouth (eWOM) has been an important factor influencing consumer purchase decisions. Using the ABC model of attitude, this study proposes a model to explain how eWOM affects online discussion forums. Specifically, we propose that platform (Web site reputation and source credibility) and customer (obtaining buying-related information and social orientation through information) factors influence purchase intentions via perceived positive eWOM review credibility, as well as product and Web site attitudes in an online community context. A total of 353 online discussion forum users in an online community (Fashion Guide) in Taiwan were recruited, and structural equation modeling (SEM) was used to test the research hypotheses. The results indicate that Web site reputation, source credibility, obtaining buying-related information, and social orientation through information positively influence perceived positive eWOM review credibility. In turn, perceived positive eWOM review credibility directly influences purchase intentions and also indirectly influences purchase intentions via product and Web site attitudes. Finally, we discuss the theoretical and managerial implications of the findings.
DocEmul: A Toolkit to Generate Structured Historical Documents

We propose a toolkit to generate structured synthetic documents emulating the actual document production process. Synthetic documents can be used to train systems to perform document analysis tasks. In our case we address the record counting task on handwritten structured collections containing a limited number of examples. Using the DocEmul toolkit we can generate a larger dataset to train a deep architecture to predict the number of records for each page. The toolkit is able to generate synthetic collections and also perform data augmentation to create a larger trainable dataset. It includes one method to extract the page background from real pages which can be used as a substrate where records can be written on the basis of variable structures and using cursive fonts. Moreover, it is possible to extend the synthetic collection by adding random noise, page rotations, and other visual variations. We performed some experiments on two different handwritten collections using the toolkit to generate synthetic data to train a Convolutional Neural Network able to count the number of records in the real collections.