nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Approach Toward Fabrication of High Performance Thin Film Composite Polyamide Membranes.

A practical method is reported to enhance water permeability of thin film composite (TFC) polyamide (PA) membranes by decreasing the thickness of the selective PA layer. The composite membranes were prepared by interfacial polymerization (IP) reaction between meta-phenylene diamine (MPD)-aqueous and trimesoyl chloride (TMC)-organic solvents at the surface of polyethersulfone (PES) microporous support. Several PA TFC membranes were prepared at different temperatures of the organic solution ranging from -20 °C to 50 °C. The physico-chemical and morphological properties of the synthesized membranes were carefully characterized using serval analytical techniques. The results confirmed that the TFC membranes, synthesized at sub-zero temperatures of organic solution, had thinner and smoother PA layer with a greater degree of cross-linking and wettability compared to the PA films prepared at 50 °C. We demonstrated that reducing the temperature of organic solution effectively decreased the thickness of the PA active layer and thus enhanced water permeation through the membranes. The most water permeable membrane was prepared at -20 °C and exhibited nine times higher water flux compared to the membrane synthesized at room temperature. The method proposed in this report can be effectively applied for energy- and cost-efficient development of high performance nanofiltration and reverse osmosis membranes.
HIDDEN COSTS OF QUALITY : MEASUREMENT &

Quality is the assurance of adherence to the customer specifications and it is a measure of excellence or a state of being free from defects, deficiencies and significant variation from standards. Customer specification of the product can be met by strictly adhering to the quality control measures in the production process and can be ensured in a cost effective manner only if the quality of each and every process in the organization is well defined and ensured without any lapses. Every activity in the supply chain line to be critically verified to identify the quality deviations incurring additional expense or loss to the organization. This is in line with the continual improvement principle of TQM philosophy . The cost of quality management system acts as the most significant tool in measuring, monitoring, controlling and decision making activities in a firm which aims on business excellence.
Hortaea werneckii isolated from silicone scuba diving equipment in Spain.

During a survey of black yeasts of marine origin, some isolates of Hortaea werneckii were recovered from scuba diving equipment, such as silicone masks and snorkel mouthpieces, which had been kept under poor storage conditions. These yeasts were unambiguously identified by phenotypic and genotypic methods. Phylogenetic analysis of both the D1/D2 regions of 26S rRNA gene and ITS-5.8S rRNA gene sequences showed three distinct genetic types. This species is the agent of tinea nigra which is a rarely diagnosed superficial mycosis in Europe. In fact this mycosis is considered an imported fungal infection being much more prevalent in warm, humid parts of the world such as the Central and South Americas, Africa, and Asia. Although H. werneckii has been found in hypersaline environments in Europe, this is the first instance of the isolation of this halotolerant species from scuba diving equipment made with silicone rubber which is used in close contact with human skin and mucous membranes. The occurrence of this fungus in Spain is also an unexpected finding because cases of tinea nigra in this country are practically not seen.
A Multi-tenant Database Architecture Design for Software Applications

Multi-tenant database is a new cloud computing paradigm that has recently attracted attention to deliver database functionalities for multiple tenants to create, store, and access their databases over the internet. This multi-tenant database should be highly configurable and secure to meet tenants' expectations and their different business requirements. In this paper, we propose an architecture design to build an intermediate database layer to be used between software applications and Relational Database Management Systems (RDBMS) to store and access multiple tenants' data in the Elastic Extension Table (EET) multi-tenant database schema. This database layer combines multi-tenant relational tables and virtual relational tables and makes them work together to act as one database for each tenant. This architecture design is suitable for multi-tenant database environment that can run any business domain database by using a combination of a database schema, which contains shared physical structured tables and virtual structured tenant's tables. Further, this multi-tenant database architecture design can be used as a base to build software applications in general and Software as a Service (SaaS) applications in particular.
Real-Time “Eye-Writing” Recognition Using Electrooculogram

Eye movements can be used as alternative inputs for human-computer interface (HCI) systems such as virtual or augmented reality systems as well as new communication ways for patients with locked-in syndrome. In this study, we developed a real-time electrooculogram (EOG)-based eye-writing recognition system, with which users can write predefined symbolic patterns with their volitional eye movements. For the “eye-writing” recognition, the proposed system first reconstructs the eye-written traces from EOG waveforms in real-time; then, the system recognizes the intended symbolic inputs with a reliable recognition rate by matching the input traces with the trained eye-written traces of diverse input patterns. Experiments with 20 participants showed an average recognition rate of 87.38% (F1 score) for 29 different symbolic patterns (26 lower case alphabet characters and three functional input patterns representing Space, Backspace, and Enter keys), demonstrating the promise of our EOG-based eye-writing recognition system in practical scenarios.
Hard Disk Drive Reliability Modeling and Failure Prediction

A reliability model for the hard disk drive (HDD) is developed, focusing on head-disk separation as the primary independent variable. Among the many factors contributing to reliability, the role of water vapor is described for the first time. An experimental method based on magnetic spacing loss theory is used to characterize the head-media separation as a function of temperature, altitude, humidity, and HDD operating mode. A statistical model based on these empirical data is developed to predict HDD reliability for various operating conditions. The predictive capability of the model is verified experimentally through application to HDD product reliability test data.
Discrimination Power of Short-Term Heart Rate Variability Measures for CHF Assessment

In this study, we investigated the discrimination power of short-term heart rate variability (HRV) for discriminating normal subjects versus chronic heart failure (CHF) patients. We analyzed 1914.40 h of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Association (NYHA) classification I, II, and III, extracted by public databases. Following guidelines, we performed time and frequency analysis in order to measure HRV features. To assess the discrimination power of HRV features, we designed a classifier based on the classification and regression tree (CART) method, which is a nonparametric statistical technique, strongly effective on nonnormal medical data mining. The best subset of features for subject classification includes square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), total power, high-frequencies power, and the ratio between low- and high-frequencies power (LF/HF). The classifier we developed achieved sensitivity and specificity values of 79.3% and 100 %, respectively. Moreover, we demonstrated that it is possible to achieve sensitivity and specificity of 89.7% and 100 %, respectively, by introducing two nonstandard features ΔAVNN and ΔLF/HF, which account, respectively, for variation over the 24 h of the average of consecutive normal intervals (AVNN) and LF/HF. Our results are comparable with other similar studies, but the method we used is particularly valuable because it allows a fully human-understandable description of classification procedures, in terms of intelligible “if ... then ...” rules.
Recognition of human activities using SVM multi-class classifier

Even great efforts have been made for decades, the recognition of human activities is still an unmature technology that attracted plenty of people in computer vision. In this paper, a system framework is presented to recognize multiple kinds of activities from videos by an SVM multi-class classifier with a binary tree architecture. The framework is composed of three functionally cascaded modules: (a) detecting and locating people by non-parameter background subtraction approach, (b) extracting various of features such as local ones from the minimum bounding boxes of human blobs in each frames and a newly defined global one, contour coding of the motion energy image (CCMEI), and (c) recognizing activities of people by SVM multi-class classifier whose structure is determined by a clustering process. The thought of hierarchical classification is introduced and multiple SVMs are aggregated to accomplish the recognition of actions. Each SVM in the multi-class classifier is trained separately to achieve its best classification performance by choosing proper features before they are aggregated. Experimental results both on a homebrewed activity data set and the public Schüldt’s data set show the perfect identification performance and high robustness of the system.
Count nouns , mass nouns and their transformations : a unified category-theoretic semantics

All natural languages seem to distinguish at the semantic level between count nouns (CNs) and mass nouns (MNs). Some natural languages, like English, mark the distinction at the syntactic level. Prototypical of CNs is ‘dog’ and of MNs is ‘matter’ (in the sense of physical stuff, not in the sense of concern or affair). One syntactic difference is that usually CNs take the plural (‘dogs’) whereas MNs do not. Other syntactic distinctions relate to the determiners and quantifiers. One can say a dog, another dog, many dogs, two dogs, etc.; one cannot correctly say *a matter, *another matter, *many matter, *two matter, etc. It seems that the distinction in English grammar was introduced by Otto Jespersen [6, p198].
Novel View Synthesis for Large-scale Scene using Adversarial Loss

Novel view synthesis aims to synthesize new images from different viewpoints of given images. Most of previous works focus on generating novel views of certain objects with a fixed background. However, for some applications, such as virtual reality or robotic manipulations, large changes in background may occur due to the egomotion of the camera. Generated images of a large-scale environment from novel views may be distorted if the structure of the environment is not considered. In this work, we propose a novel fully convolutional network, that can take advantage of the structural information explicitly by incorporating the inverse depth features. The inverse depth features are obtained from CNNs trained with sparse labeled depth values. This framework can easily fuse multiple images from different viewpoints. To fill the missing textures in the generated image, adversarial loss is applied, which can also improve the overall image quality. Our method is evaluated on the KITTI dataset. The results show that our method can generate novel views of large-scale scene without distortion. The effectiveness of our approach is demonstrated through qualitative and quantitative evaluation.
A challenge of 45 nm extreme low-k chip using Cu pillar bump as 1st interconnection

In this study, Cu pillar bump is firstly built on FCCSP with 65 nm low k chip. 7 DOE cells are designed to evaluate the effects of Cu pillar height, Cu pillar diameter, PI opening size and PI material on package reliability performance. No obvious failure is found after package assembly and long-term reliability test. The packages are still in good shape even though the reliability test is expanded to 3x test durations With the experiences of Cu pillar bump on 65 nm low k chip, Cu pillar bump is again built on FCBGA package with 45 nm ELK chip. White bump defect is found after chip bond via CSAM inspection, failure analysis shows that the white bump phenomenon is due to crack occurs inside ELK layer. A local heating bond tool (thermal compression bond) is used to improve ELK crack, test results illustrate ELK crack still exists, however the failure rate reduces from original 30%~50% to 5%~20%. Simulation analysis is conducted to study the effect of PI opening size and UBM size on stress concentration at ELK layer. Small PI opening size can reduce stress distribution at ELK layer. On the contrary, relatively large PI opening size and large UBM size also show positive effect on ELK crack. Assembly process and reliability test are conducted again to validate simulation results, experiment data is consistent with simulation result.
Interleaving generation for data race and deadlock reproduction

Concurrency errors, like data races and deadlocks, are difficult to find due to the large number of possible interleavings in a parallel program. Dynamic tools analyze a single observed execution of a program, and even with multiple executions they can not reveal possible errors in other reorderings. This work takes a single program observation and produces a set of alternative orderings of the synchronization primitives that lead to a concurrency error. The new reorderings are enforced under a happens-before detector to discard reorderings that are infeasible or do not produce any error report. We evaluate our approach against multiple repetitions of a state of the art happens-before detector. The results show that through interleaving inference more errors are found and the counterexamples enable easier reproducibility by the developer.
Chemical perturbation of Mcl-1 pre-mRNA splicing to induce apoptosis in cancer cells.

The myeloid cell leukemia-1 (MCL1) gene encodes antiapoptotic Mcl-1(L) and proapoptotic Mcl-1(S) proteins. In cancer, the Mcl-1(L)/Mcl-1(S) ratio is very high, accounting for the antiapoptotic nature of cancer cells. As such, reducing this ratio can render the cancer cells prone to apoptosis. The Mcl-1(L)/Mcl-1(S) ratio is determined in the alternative pre-mRNA splicing step that is regulated by splicing factor 3B1 (SF3B1). Here, we report that meayamycin B, a potent inhibitor of SF3B1, reversed the dominant isoform from Mcl-1(L) to Mcl-1(S) at the mRNA and protein levels. The resulting proapoptotic cellular environment was further exploited; when meayamycin B was combined with Bcl-x(L) inhibitor ABT-737, the combination treatment triggered apoptosis in nonsmall cell lung cancer A549 and H1299 cells that were otherwise resistant to ABT-737. These results demonstrate that perturbation of the MCL1 splicing with small molecule inhibitors of SF3B1 provides a means to sensitize cancer cells toward Bcl-x(L) inhibitors.
Learning Class-specific Word Representations for Early Detection of Hoaxes in Social Media

As people increasingly use social media as a source for news consumption, its unmoderated nature enables the diffusion of hoaxes, which in turn jeopardises the credibility of information gathered from social media platforms. To mitigate this problem, we study the development of a hoax detection system that can distinguish true and false reports early on. We introduce a semi-automated approach that leverages the Wikidata knowledge base to build large-scale datasets for veracity classification, which enables us to create a dataset with 4,007 reports including over 13 million tweets, 15% of which are fake. We describe a method for learning class-specific word representations using word embeddings, which we call multiw2v. Our approach achieves competitive results with F1 scores over 72% within 10 minutes of the first tweet being posted, outperforming other baselines. Our dataset represents a realistic scenario with a real distribution of true and false stories, which we release for further use as a benchmark in future research.
Bridging High-Level Synthesis to RTL Technology Libraries

The output of high-level synthesis typically consists of a netlist of generic RTL components and a state sequencing table. While module generators and logic synthesis tools can be used to map RTL components into standard cells or layout geometries, they cannot provide technology mapping into the data book libraries of functional RTL cells used commonly throughout the industrial design community. In this paper, we introduce an approach to implementing generic RTL components with technology-specific RTL library cells. This approach addresses the criticism of designers who feel that high-level synthesis tools should be used in conjunction with existing RTL data books. We describe how GENUS, a library of generic RTL components, is organized for use in high-level synthesis and how DTAS, a functional synthesis system, is used to map GENUS components into RTL library cells.
ESC Guidelines on the diagnosis and management of acute pulmonary embolism

Authors/Task Force Members: Stavros Konstantinides* (Chairperson) (Germany/ Greece), Adam Torbicki* (Co-chairperson) (Poland), Giancarlo Agnelli (Italy), Nicolas Danchin (France), David Fitzmaurice (UK), Nazzareno Galiè (Italy), J. Simon R. Gibbs (UK), Menno Huisman (The Netherlands), Marc Humbert† (France), Nils Kucher (Switzerland), Irene Lang (Austria), Mareike Lankeit (Germany), John Lekakis (Greece), Christoph Maack (Germany), Eckhard Mayer (Germany), Nicolas Meneveau (France), Arnaud Perrier (Switzerland), Piotr Pruszczyk (Poland), Lars H. Rasmussen (Denmark), Thomas H. Schindler (USA), Pavel Svitil (Czech Republic), Anton Vonk Noordegraaf (The Netherlands), Jose Luis Zamorano (Spain), Maurizio Zompatori (Italy)
Weighted Unsupervised Learning for 3D Object Detection

This paper introduces a novel weighted unsupervised learning for object detection using an RGB-D camera. This technique is feasible for detecting the moving objects in the noisy environments that are captured by an RGB-D camera. The main contribution of this paper is a real-time algorithm for detecting each object using weighted clustering as a separate cluster. In a preprocessing step, the algorithm calculates the pose 3D position X, Y, Z and RGB color of each data point and then it calculates each data point’s normal vector using the point’s neighbor. After preprocessing, our algorithm calculates k-weights for each data point; each weight indicates membership. Resulting in clustered objects of the scene. Weighted Unsupervised Learning, Object Detection, RGB-D camera, Kinect
Yawning as a behavioral marker of mild motion sickness and sopite syndrome.

Severe motion sickness is easily identifiable with sufferers showing obvious behavioral signs, including emesis (vomiting). Mild motion sickness and sopite syndrome lack such clear and objective behavioral markers. We postulate that yawning may have the potential to be used in operational settings as such a marker. This study assesses the utility of yawning as a behavioral marker for the identification of soporific effects by investigating the association between yawning and mild motion sickness/sopite syndrome in a controlled environment.



Using a randomized motion-counterbalanced design, we collected yawning and motion sickness data from 39 healthy individuals (34 men and 5 women, ages 27-59 yr) in static and motion conditions. Each individual participated in two 1-h sessions. Each session consisted of six 10-min blocks. Subjects performed a multitasking battery on a head mounted display while seated on the moving platform. The occurrence and severity of symptoms were assessed with the Motion Sickness Assessment Questionnaire (MSAQ).



Yawning occurred predominantly in the motion condition. All yawners in motion (N = 5) were symptomatic. Compared to nonyawners (MSAQ indices: Total = 14.0, Sopite = 15.0), subjects who yawned in motion demonstrated increased severity of motion sickness and soporific symptoms (MSAQ indices: Total = 17.2, Sopite = 22.4), and reduced multitasking cognitive performance (Composite score: nonyawners = 1348; yawners = 1145).



These results provide evidence that yawning may be a viable behavioral marker to recognize the onset of soporific effects and their concomitant reduction in cognitive performance.
Acceleration of Deep Learning on FPGA

In recent years, deep convolutional neural networks (ConvNet) have shown their popularity in various real world applications. To provide more accurate results, the state-of-the-art ConvNet requires millions of parameters and billions of operations to process a single image, which represents a computational challenge for general purpose processors. As a result, hardware accelerators such as Graphic Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), have been adopted to improve the performance of ConvNet. However, GPU-based solution consumes a considerable amount of power and a traditional RTL design on FPGA requires tedious development that is very time-consuming. In this work, we propose a scalable and parameterized end-to-end ConvNet design using Intel FPGA SDK for OpenCL. To validate the design, we implement VGG 16 model on two different FPGA boards. Consequently, our designs achieve 306.41 GOPS on Intel Stratix A7 and 318.94 GOPS on Intel Arria 10 GX 10AX115. To the best of our knowledge, this outperforms previous FPGA-based accelerators. Compared to the CPU (Intel Xeon E5-2620) and a mid-range GPU (Nvidia K40), our design is 24.3X and 1.7X more energy efficient respectively.
Non-invasive blood glucose monitoring using near-infrared spectroscopy

Blood glucose monitors are used to measure the amount of glucose in blood, especially of patients with symptoms or a history of abnormally high or low blood glucose levels. Most commonly, they enable diabetic patients to administer appropriate insulin doses. The availability of home-use glucometers, as opposed to clinical-use equipment, has greatly improved the quality of life of such individuals. However, such monitors require a blood draw through finger pricks for each test, which causes pain and inconvenience. Each test also requires a new test-strip, contributing to the recurring cost of such a device.
Reference Sequence Construction for Relative Compression of Genomes

Relative compression, where a set of similar strings are compressed with respect to a reference string, is a very effective method of compressing DNA datasets containing multiple similar sequences. Relative compression is fast to perform and also supports rapid random access to the underlying data. The main difficulty of relative compression is in selecting an appropriate reference sequence. In this paper, we explore using the dictionary of repeats generated by Comrad, Re-pair and Dna-x algorithms as reference sequences for relative compression. We show this technique allows better compression and supports random access just as well. The technique also allows more general repetitive datasets to be compressed using relative compression.