nils-herrmann/clean_scirepeval huggingface dataset:
Peripheral neuropathy with a syndrome of continuous motor unit activity

A chronic alcoholic who had also been submitted to partial gastrectomy developed a syndrome of continuous motor unit activity responsive to phenytoin therapy. There were signs of minimal distal sensorimotor polyneuropathy. Symptoms of the syndrome of continuous motor unit activity were fasciculation, muscle stiffness, myokymia, impaired muscular relaxation and percussion myotonia. Electromyography at rest showed fasciculation, doublets, triplets, multiplets, trains of repetitive discharges and myotonic discharges. Trousseau's and Chvostek's signs were absent. No abnormality of serum potassium, calcium, magnesium, creatine kinase, alkaline phosphatase, arterial blood gases and pH were demonstrated, but the serum Vitamin B12 level was reduced. The electrophysiological findings and muscle biopsy were compatible with a mixed sensorimotor polyneuropathy. Tests of neuromuscular transmission showed a significant decrement in the amplitude of the evoked muscle action potential in the abductor digiti minimi on repetitive nerve stimulation. These findings suggest that hyperexcitability and hyperactivity of the peripheral motor axons underlie the syndrome of continuous motor unit activity in the present case. Ein chronischer Alkoholiker, mit subtotaler Gastrectomie, litt an einem Syndrom dauernder Muskelfaseraktivität, das mit Diphenylhydantoin behandelt wurde. Der Patient wies minimale Störungen im Sinne einer distalen sensori-motorischen Polyneuropathie auf. Die Symptome dieses Syndroms bestehen in: Fazikulationen, Muskelsteife, Myokymien, eine gestörte Erschlaffung nach der Willküraktivität und eine Myotonie nach Beklopfen des Muskels. Das Elektromyogramm in Ruhe zeigt: Faszikulationen, Doublets, Triplets, Multiplets, Trains repetitiver Potentiale und myotonische Entladungen. Trousseau- und Chvostek-Zeichen waren nicht nachweisbar. Gleichzeitig lagen die Kalium-, Calcium-, Magnesium-, Kreatinkinase- und Alkalinphosphatase-Werte im Serumspiegel sowie O2, CO2 und pH des arteriellen Blutes im Normbereich. Aber das Niveau des Vitamin B12 im Serumspiegel war deutlich herabgesetzt. Die muskelbioptische und elektrophysiologische Veränderungen weisen auf eine gemischte sensori-motorische Polyneuropathie hin. Die Abnahme der Amplitude der evozierten Potentiale, vom M. abductor digiti minimi abgeleitet, bei repetitiver Reizung des N. ulnaris, stellten eine Störung der neuromuskulären Überleitung dar. Aufgrund unserer klinischen und elektrophysiologischen Befunde könnten wir die Hypererregbarkeit und Hyperaktivität der peripheren motorischen Axonen als Hauptmechanismus des Syndroms dauernder motorischer Einheitsaktivität betrachten.
Automatic unpaired shape deformation transfer

Transferring deformation from a source shape to a target shape is a very useful technique in computer graphics. State-of-the-art deformation transfer methods require either point-wise correspondences between source and target shapes, or pairs of deformed source and target shapes with corresponding deformations. However, in most cases, such correspondences are not available and cannot be reliably established using an automatic algorithm. Therefore, substantial user effort is needed to label the correspondences or to obtain and specify such shape sets. In this work, we propose a novel approach to automatic deformation transfer between two unpaired shape sets without correspondences. 3D deformation is represented in a high-dimensional space. To obtain a more compact and effective representation, two convolutional variational autoencoders are learned to encode source and target shapes to their latent spaces. We exploit a Generative Adversarial Network (GAN) to map deformed source shapes to deformed target shapes, both in the latent spaces, which ensures the obtained shapes from the mapping are indistinguishable from the target shapes. This is still an under-constrained problem, so we further utilize a reverse mapping from target shapes to source shapes and incorporate cycle consistency loss, i.e. applying both mappings should reverse to the input shape. This VAE-Cycle GAN (VC-GAN) architecture is used to build a reliable mapping between shape spaces. Finally, a similarity constraint is employed to ensure the mapping is consistent with visual similarity, achieved by learning a similarity neural network that takes the embedding vectors from the source and target latent spaces and predicts the light field distance between the corresponding shapes. Experimental results show that our fully automatic method is able to obtain high-quality deformation transfer results with unpaired data sets, comparable or better than existing methods where strict correspondences are required.
An Extensive Study on Currency Recognition System Using Image Processing

The need for currency recognition system is more relevant due to the advent of technology and machinery used to counterfeit the banknotes. There are many features to identify an authentic currency note physically. Although it may not be practical to accurately identify a counterfeit from an authentic currency note for a person. Several special features are included for strong security against counterfeit in the paper currency which only an intelligent machine can identify. We make use of this special visual properties and patterns to identify the original currency from a counterfeit. A system is applicable globally for the authentication of paper currency. Institutions like banks spend for expensive hardware and for their installation to resolve this issue of currency recognition. Image processing techniques are exploited for a better costeffective and highly efficient currency recognition system. Although image analysis is not sufficient for authentication, it makes the process easier and faster than the existing system and has easy availability.
Cs229 Problem Set #3 Solutions Cs 229, Autumn 2015 Problem Set #3 Solutions: Theory & Unsuper- Vised Learning

Notes: (1) These questions require thought, but they do not require long answers. (2) If you have a question about this homework, we encourage you to post your question on our Piazza forum, at https://piazza.com/stanford/fall2015/cs229. (3) If you missed the first lecture or are unfamiliar with the collaboration or honor code policy, please read the policy on Handout #1 (available from the course website) before starting work. (4) For problems that require programming, please include in your submission a printout of your code (with comments) and any figures that you are asked to plot. (5) Please do not just write the answers but also show your work. (6) If you are an on-campus (non-SCPD) student, please print, fill out, and include a copy of the cover sheet (enclosed as the final page of this document), and include the cover sheet as the first page of your submission. SCPD students: If you are submitting on time without using late-days, please submit your assignments through the SCPD office. Otherwise, please submit your assignments at https://www. stanford.edu/class/cs229/cgi-bin/submit.php as a single PDF file under 20MB in size. If you have trouble submitting online, email your submission to cs229-qa@cs.stanford.edu. However, we strongly recommend using the website submission method, as it will provide confirmation of submission and also allow us to track and return your graded homework to you more easily. 1.  The country of Elbania is organized into states, and there are only two candidates running in this election: One from the Elbanian Democrat party and another from the Labor Party of Elbania. The plan for making our electoral predictions is as follows: We'll sample m voters from each state and ask whether they're voting Democrat. We'll then publish, for each state, the estimated fraction of Democrat voters. In this problem, we'll work out how many voters we need to sample in order to ensure that we get good predictions with high probability. One reasonable goal might be to set m large enough that, with high probability, we obtain uniformly accurate estimates of the fraction of Democrat voters in every state. But this might require surveying very many people, which would be prohibitively expensive. So, we're instead going to demand only a slightly lower …
Hand gesture recognition and voice conversion system for dumb people

in our country around 2.78% of peoples are not able to speak (dumb). Their communications with others are only using the motion of their hands and expressions. We proposed a new technique called artificial speaking mouth for dumb people. It will be very helpful to them for conveying their thoughts to others. Some peoples are easily able to get the information from their motions. The remaining is not able to understand their way of conveying the message. In order to overcome the complexity the artificial mouth is introduced for the dumb peoples. This system is based on the motion sensor. According to dumb people, for every motion they have a meaning. That message is kept in a database. Likewise all templates are kept in the database. In the real time the template database is fed into a microcontroller and the motion sensor is fixed in their hand. For every action the motion sensors get accelerated and give the signal to the microcontroller. The microcontroller matches the motion with the database and produces the speech signal. The output of the system is using the speaker. By properly updating the database the dumb will speak like a normal person using the artificial mouth. The system also includes a text to speech conversion (TTS) block that interprets the matched gestures.
Simpler but More Accurate Semantic Dependency Parsing

While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture betweenword relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves stateof-the-art performance, beating the previous, substantially more complex stateof-the-art system by 1.9% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 2.6% labeled F1.
Single & multi beam laser grooving process parameter development and die strength characterization for 40nm node low-K/ULK wafer

This paper describes the development work on single and multi beam laser grooving technology for 40nm node low-k/ULK semiconductor device. A Nd:YAG ultraviolet (UV) laser diode operating at a wavelength of 355 nm was used in this study. The effects of single and multi beam laser micromachining parameters, i.e. laser power, laser frequency, feed speed, and defocus amount were investigated. The laser processed die samples were thoroughly inspected and characterized. This includes the die edge and die sidewall grooving quality, the grooving shape/profile and the laser grooving depth analysis. Die strength is important and critical. Die damage from thermal and ablation caused by the laser around the die peripheral weakens the mechanical strength within the die, causing a reduction in die strength. The strength of a laser grooved die was improved by optimizing the laser process parameter. High power optical microscopy, Scanning Electron Microscopy (SEM), and focused ion beam (FIB) were the inspection tools/methods used in this study. Package reliability and stressing were carried out to confirm the robustness of the multi beam laser grooving process parameter and condition in a mass production environment. The dicing defects caused by the laser were validated by failure analysis. The advantages and limitations of conventional single beam compared to multi beam laser grooving process were also discussed. It was concluded that, multi beam laser grooving is possibly one of the best solutions to consider for dicing quality and throughput improvements for low-k/ULK wafer dicing. The multi beam laser process is a feasible, efficient, and cost effective process compared to the conventional single beam laser ablation process.
Fingerprint recognition by combining global structure and local cues

As an important feature, orientation field describes the global structure of fingerprints. It provides robust discriminatory information other than traditional widely-used minutiae points. However, there are few works explicitly incorporating this information into fingerprint matching stage, partly due to the difficulty of saving the orientation field in the feature template. In this paper, we propose a novel representation for fingerprints which includes both minutiae and model-based orientation field. Then, fingerprint matching can be done by combining the decisions of the matchers based on the global structure (orientation field) and the local cue (minutiae). We have conducted a set of experiments on large-scale databases and made thorough comparisons with the state-of-the-arts. Extensive experimental results show that combining these local and global discriminative information can largely improve the performance. The proposed system is more robust and accurate than conventional minutiae-based methods, and also better than the previous works which implicitly incorporate the orientation information. In this system, the feature template takes less than 420 bytes, and the feature extraction and matching procedures can be done in about 0.30 s. We also show that the global orientation field is beneficial to the alignment of the fingerprints which are either incomplete or poor-qualitied.
on Heart Disease Prediction System using Data Mining Techniques

Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of heart disease are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is nearly 100% in one paper  and in . So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease.
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.
TweetSift: Tweet Topic Classification Based on Entity Knowledge Base and Topic Enhanced Word Embedding

Classifying tweets into topic categories is necessary and important for many applications, since tweets are about a variety of topics and users are only interested in certain topical areas. Many tweet classification approaches fail to achieve high accuracy due to data sparseness issue. Tweet, as a special type of short text, in additional to its text, also has other metadata that can be used to enrich its context, such as user name, mention, hashtag and embedded link. In this demonstration, we present TweetSift, an efficient and effective real time tweet topic classifier. TweetSift exploits external tweet-specific entity knowledge to provide more topical context for a tweet, and integrates them with topic enhanced word embeddings for topic classification. The demonstration will show how TweetSift works and how it is incorporated with our social media event detection system.
CHANGE OF DIRECTION SPEED IN SOCCER : HOW MUCH BRAKING IS ENOUGH ?

The aims of the present study were to examine: 1) the validity and reliability of a new timing system to assess running kinematics during change of direction (COD), and 2) the determinants of COD-speed. Twelve young soccer players performed three 20-m sprints, either in straight line or with one 45oor 90oCOD. Sprints were monitored using timing gates and two synchronized 100-Hz laser guns, to track players’ velocities before, during and after the COD. The validity analysis revealed trivial-to-small biases and smallto-moderate typical errors of the estimate with the lasers compared with the timing gates. The reliability was variable-dependent, with trivial(distance at peak speed) to-large (distance at peak deceleration) typical errors. Kinematic variables were angle-dependent, with likely lower peak speed, almost-certainly slower minimum speed during the COD and almost-certainly greater deceleration reached for 90o-COD vs. 45oCOD sprints. The minimum speed during the COD was largely correlated with sprint performance for both sprint angles. Correlations with most of the other independent variables were unclear. The new timing system showed acceptable levels of validity and reliability to assess some of the selected running kinematics during COD sprints. The ability to maintain a high speed during the COD may be the determinant of COD-speed.
Heuristic approach to online purchase prediction based on internet store visitors classification using data mining methods

Last years research gave some preliminary results in approaches to customer online purchase prediction. However, it still remains unclear what exact set of features of data instances should be incorporated in a model and is enough for prediction, what is the best data mining method (algorithm) to use, how stable over time could be such a model, whether a model is transferable from one online store to another. This study is focused on a heuristic approach to dealing with the problem under conditions of such theoretical and methodological diversity in order to find a quick and inexpensive first approximation to the solution or at least to find useful patterns and facts in the data.
Anticoccidial effects of Galla rhois extract on Eimeria tenella-infected chicken

Anticoccidial effects of Galla rhois (GR) extract were evaluated in chickens after oral infection with Eimeria tenella. This study was performed using 3-day-old chickens (n=30). The animals were divided into 3 groups as follows: GR 0.5%/infected (n=10), untreated/infected (n=10), and non-infected control (n=10). The chickens were fed a standard diet supplemented with or without GR for 1 week before infection with E. tenella (10,000 sporulated oocysts per chicken). The effects of GR on E. tenella infection were assessed by 2 parameters, number of fecal oocysts and body weight gain, and the results of the polymerase chain reaction (PCR). The GR-fed chickens produced significantly lower number of fecal oocysts (P<0.05) than the E. tenella-infected chickens who were fed the standard diet. In addition, GR-based diet improved the loss of body weight caused by E. tenella infection. Positive findings of PCR were identified by distinct bands in the samples of E. tenella-inoculated chickens. However, PCR analysis revealed no E. tenella oocysts in the feces of GR-fed chickens. Our data showed that GR extracts had remarkable anticoccidial activities against E. tenella. This finding might have implications for the development of novel anticoccidial drugs.
Opinion–Aspect Relations in Cognizing Customer Feelings via Reviews

Determining a consensus opinion on a product sold online is no longer easy, because assessments have become more and more numerous on the Internet. To address this problem, researchers have used various approaches, such as looking for feelings expressed in the documents and exploring the appearance and syntax of reviews. Aspect-based evaluation is the most important aspect of opinion mining, and researchers are becoming more interested in product aspect extraction; however, more complex algorithms are needed to address this issue precisely with large data sets. This paper introduces a method to extract and summarize product aspects and corresponding opinions from a large number of product reviews in a specific domain. We maximize the accuracy and usefulness of the review summaries by leveraging knowledge about product aspect extraction and providing both an appropriate level of detail and rich representation capabilities. The results show that the proposed system achieves F1-scores of 0.714 for camera reviews and 0.774 for laptop reviews.
An Iterative Global Structure-Assisted Labeled Network Aligner

Integrating data from heterogeneous sources is often modeled as merging graphs. Given two ormore “compatible”, but not-isomorphic graphs, the first step is to identify a graph alignment, where a potentially partial mapping of vertices between two graphs is computed. A significant portion of the literature on this problem only takes the global structure of the input graphs into account. Only more recent ones additionally use vertex and edge attributes to achieve a more accurate alignment. However, these methods are not designed to scale to map large graphs arising in many modern applications. We propose a new iterative graph aligner, gsaNA, that uses the global structure of the graphs to significantly reduce the problem size and align large graphs with a minimal loss of information. Concretely, we show that our proposed technique is highly flexible, can be used to achieve higher recall, and it is orders of magnitudes faster than the current state of the art techniques.  Abdurrahman Yaşar and Ümit V. Çatalyürek. 2018. An Iterative Global Structure-Assisted Labeled Network Aligner. In Proceedings of Special Interest Group on Knowledge Discovery and Data Mining, London, England, August 18 (SIGKDD’18), 10 pages.