nils-herrmann/clean_scirepeval huggingface dataset:
Generating Handwritten Chinese Characters Using CycleGAN

Handwriting of Chinese has long been an important skill in East Asia. However, automatic generation of handwritten Chinese characters poses a great challenge due to the large number of characters. Various machine learning techniques have been used to recognize Chinese characters, but few works have studied the handwritten Chinese character generation problem, especially with unpaired training data. In this work, we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style. We further propose DenseNet CycleGAN to generate Chinese handwritten characters. Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values. Furthermore, we propose content accuracy and style discrepancy as the evaluation metrics to assess the quality of the handwritten characters generated. We then use our proposed metrics to evaluate the generated characters from CASIA dataset as well as our newly introduced Lanting calligraphy dataset.
Study of ectoparasitism of ultramicrobacteria of the genus Kaistia, strains NF1 and NF3 by electron and fluorescence microscopy

Transmission electron and fluorescence microscopy was used to study the character of the interaction of free-living ultramicrobacterial (UMB) strains NF1 and NF3, affiliated with the genus Kaistia, and seven species of gram-positive and gram-negative heterotrophic bacteria. Strains NF1 and NF3 were found to exhibit parasitic activity against gram-positive Bacillus subtilis and gram-negative Acidovorax delafildii. UMB cells are tightly attached to the envelopes of the victim cells and induce their lysis, thus demonstrating the features of typical ectoparasitism. The selectivity of parasitism of the studied UMB to the victim bacteria has been shown: only two soil microorganisms of the seven test objects, B. subtilis ATCC 6633 and an aerobic gramnegative bacterium A. delafildii 39, were found to be sensitive to UMB attack. Other bacteria (Micrococcus luteus VKM Ac-2230, Staphylococcus aureus 209-P, Pseudomonas putida BS394, Escherichia coli C 600, and Pantoea agglomerans ATCC 27155) were not attacked by UMB. It was established for the first time that free-living UMB may be facultative parasites not only of phototrophic bacteria, as we have previously demonstrated , but of heterotrophic bacteria as well. The UMB under study seem to play an important role in the regulation of the quantity of microorganisms and in the functioning of microbial communities in some natural ecotopes.
SGD: General Analysis and Improved Rates

We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form minibatches. This is the first time such an analysis is performed, and most of our variants of SGD were never explicitly considered in the literature before. Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on the variance of the stochastic gradients. By specializing our theorem to different mini-batching strategies, such as sampling with replacement and independent sampling, we derive exact expressions for the stepsize as a function of the mini-batch size. With this we can also determine the mini-batch size that optimizes the total complexity, and show explicitly that as the variance of the stochastic gradient evaluated at the minimum grows, so does the optimal mini-batch size. For zero variance, the optimal mini-batch size is one. Moreover, we prove insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime.
Learning User Embedding Representation for Gender Prediction

Predicting the gender of users in social media has aroused great interests in recent years. Almost all existing studies rely on the the content features extracted from the main texts like tweets or reviews. It is sometimes difficult to extract content information since many users do not write any posts at all. In this paper, we present a novel framework which uses only the users' ids and their social contexts for gender prediction. The key idea is to represent users in the embedding connection space. A user often has the social context of family members, schoolmates, colleagues, and friends. This is similar to a word and its contexts in documents, which motivates our study. However, when modifying the word embedding technique for user embedding, there are two major challenges. First, unlike the syntax in language, no rule is responsible for the composition of the social contexts. Second, new users were not seen when learning the representations and thus they do not have embedding vectors. Two strategies circular ordering and incremental updating are proposed to solve these problems. We evaluate our methodology on two real data sets. Experimental results demonstrate that our proposed approach is significantly better than the traditional graph representation and the state-of-the-art graph embedding baselines. It also outperforms the content based approaches by a large margin.
Beyond the G-spot: clitourethrovaginal complex anatomy in female orgasm

The search for the legendary, highly erogenous vaginal region, the Gräfenberg spot (G-spot), has produced important data, substantially improving understanding of the complex anatomy and physiology of sexual responses in women. Modern imaging techniques have enabled visualization of dynamic interactions of female genitals during self-sexual stimulation or coitus. Although no single structure consistent with a distinct G-spot has been identified, the vagina is not a passive organ but a highly dynamic structure with an active role in sexual arousal and intercourse. The anatomical relationships and dynamic interactions between the clitoris, urethra, and anterior vaginal wall have led to the concept of a clitourethrovaginal (CUV) complex, defining a variable, multifaceted morphofunctional area that, when properly stimulated during penetration, could induce orgasmic responses. Knowledge of the anatomy and physiology of the CUV complex might help to avoid damage to its neural, muscular, and vascular components during urological and gynaecological surgical procedures.
Collaborative Networks of Cognitive Systems

Blockchain, a distributed secure digital ledger technology, is a relatively recent development with potentially transformational implications for economy and society. Its specific characteristics enable new decentralized models of distributed and trusted transactions. This position paper explores the implications of blockchain for collaborative networked organizations. In particular we aim at understanding the implications for companies in various economic sectors, and how new forms of networked organizations and new business models will be enabled. We also will focus on enablers of blockchain innovations, in particular with respect to governance of blockchain-based platforms and business networks. The paper results in a discussion of research challenges in the field of blockchain-enabled collaborative networked
A Sub-2 W 39.8–44.6 Gb/s Transmitter and Receiver Chipset With SFI-5.2 Interface in 40 nm CMOS

A 39.8-44.6 Gb/s transmitter and receiver chipset designed in 40 nm CMOS is presented. The line-side TX implements a 2-tap FIR filter with delay-based pre-emphasis. The line-side RX uses a quarter-rate CDR architecture. The TX output shows 0.9 pspp ISI and 0.2 psrms RJ at 0.87 W. The RX achieves a jitter tolerance of 0.6 UIpp at 100 MHz and an input sensitivity of 20 mV pp\mathchar"702D diff at 1.05 W. The combined transmitter/receiver equalization enables 44.6 Gb/s data transmission using 231-1 PRBS at BER 10-12 over a channel with >21 dB loss at Nyquist frequency.
A meta-analysis of the incidence of anterior cruciate ligament tears as a function of gender, sport, and a knee injury-reduction regimen.

The literature has shown that anterior cruciate ligament (ACL) tear rates vary by gender, by sport, and in response to injury-reduction training programs. However, there is no consensus as to the magnitudes of these tear rates or their variations as a function of these variables. For example, the female-male ACL tear ratio has been reported to be as high as 9:1. Our purpose was to apply meta-analysis to the entire applicable literature to generate accurate estimates of the true incidences of ACL tear as a function of gender, sport, and injury-reduction training.



A PubMed literature search was done to identify all studies dealing with ACL tear incidence. Bibliographic cross-referencing was done to identify additional articles. Meta-analytic principles were applied to generate ACL incidences as a function of gender, sport, and prior injury-reduction training.



Female-male ACL tear incidences ratios were as follows: basketball, 3.5; soccer, 2.67; lacrosse, 1.18; and Alpine skiing, 1.0. The collegiate soccer tear rate was 0.32 for female subjects and 0.12 for male subjects. For basketball, the rates were 0.29 and 0.08, respectively. The rate for recreational Alpine skiers was 0.63, and that for experts was 0.03, with no gender variance. The two volleyball studies had no ACL tears. Training reduced the ACL tear incidence in soccer by 0.24 but did not reduce it at all in basketball.



Female subjects had a roughly 3 times greater incidence of ACL tears in soccer and basketball versus male subjects. Injury-reduction programs were effective for soccer but not basketball. Recreational Alpine skiers had the highest incidences of ACL tear, whereas expert Alpine skiers had the lowest incidences. Volleyball may in fact be a low-risk sport rather than a high-risk sport. Alpine skiers and lacrosse players had no gender difference for ACL tear rate. Year-round female athletes who play soccer and basketball have an ACL tear rate of approximately 5%.
1.3 Mega pixels CCD pH imaging sensor with 3.75 μm spatial resolution

We have demonstrated for the first time a megapixel (1.3M pixels) CCD pH imager featuring a flash-injection-of-signal-electrons scheme. With a spatial resolution of 3.75μm and pH resolution of 0.16pH at 27.5fps we believe this is a promising device technology for applications such as high throughput DNA sequencing and label-free cell measurement system with sub-cell-size spatial resolution and real-time response.
OPTIMUM PERFORMANCE IN THE BADMINTON JUMP SMASH

The badminton smash is an essential component of a player’s repertoire and a significant stroke in gaining success as it is the most common winning shot, accounting for 53.9% of winning shots (Tsai and Chang, 1998; Tong and Hong, 2000; Rambely et al., 2005). The speed of the shuttlecock exceeds that of any other racket sport projectile with a maximum shuttle speed of 493 km/h (306 mph) reported in 2013 by Tan Boon Heong. If a player is able to cause the shuttle to travel at a higher velocity and give the opponent less reaction time to the shot, it would be expected that the smash would be a more effective weapon (Kollath, 1996; Sakurai and Ohtsuki, 2000).
Effectiveness of Mobile Applications in Vocabulary Teaching.

Smartphones are increasingly becoming an ordinary part of our daily lives. With their remarkable capacity, applications used in these devices are extremely varied. In terms of language teaching, the use of these applications has opened new windows of opportunity, innovatively shaping the way instructors teach and students learn. This 4 week-long study aimed to investigate the effectiveness of a mobile application on teaching 40 figurative idioms from the Michigan Corpus of Academic Spoken English (MICASE) corpus compared to traditional activities. Quasi-experimental research design with pretest and posttest was employed to determine the differences between the scores of the control (n=25) and the experimental group (n=25) formed with convenience sampling. Results indicate that participants in the experimental group performed significantly better in the posttest, demonstrating the effectiveness of the mobile application used in this study on learning idioms. The study also provides recommendations towards the use of mobile applications in teaching vocabulary.
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification

In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.
Mathematical modelling and simulation of pop-up books

The design and manufacturing of pop-up books are mainly manual at present, but a number of the processes therein can benefit from computerization and automation. This paper studies one aspect of the design of pop-up books: the mathematical modelling and simulation of the pieces popping up as a book is opened. It developes the formulae for the essential parameters in the pop-up animation. This animation enables the designer to determine on a computer if a particular set-up is appropriate to the theme which the page is designed to express, removing the need for the laborious and time-consuming task of making manual prototypes
Design and development of smart phone based ECG monitoring system

Heart diseases have caused millions of death worldwide in the past years because of the increase in the population and rising of health care costs. The main causes of heart attacks are a lack of pre-requisite care and also un-availability of medical care in emergency situations. Treatment and prevention of heart attack is very important. Periodic checkup is important in health maintenance. Electrocardiography(ECG), can be monitor anytime, anywhere and send the report of Electrocardiography(ECG), to the medical expert with this portable device and can effectively communicate with them. In case of any abnormality, doctor will effectively communicate with the patient. For regular checkup and home based checkup, there is need of low cost, portable, low power and time-saving ECG monitoring device. The main requirement of any portable device is low power supply and in this project, there is no need of external power supply because power requirement is completed by harvesting power from mobile phone through audio jack. This paper intends towards developing a device which monitors cardiac activity and will be compact, simple to handle, non-invasive, and reliable. Headphone jack interface could be used to develop modular platforms to monitor vital signs such as Pulse rate, SpO2, EKG.
Music and Dementia: Two Case-Studies

In this paper two current related projects on music and dementia are described. One is a project which culminated in a performance made up of memory jingles, some of which were algorithmically generated and developed for an early-stage dementia patient. She provided a list of her ordered daily activities and a description of one of her medication dosages. The daily activity list was rhythmically adjusted, and a tune was written to it, by a composer. However it is not possible for all people with dementia to have a “composer-inresidence”. Hence computer music techniques were also investigated towards some form of automation of composition. This algorithm was used to compose a tune for the medication description from the volunteer. A few weeks later she exhibited the ability to recall both tunes and words in multiple informal environments (including high pressure situations). The second project is a form of “research by production”. It details the making of a 60 minute BBC radio program which was produced in collaboration with people living with dementia and experts and carers. The program consisted of a mixture of tunes and documentary segments. These discussed various topic relating to dementia, radio and music (including an on the above memory jingles research). The structure of the program was designed in such a way as to make it more understandable to people living with dementias.
Generalized Residual Vector Quantization and Aggregating Tree for Large Scale Search

Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named residual vector quantization (RVQ). Next, we propose generalized residual vector quantization (GRVQ) to further improve over RVQ. Many vector quantization methods can be viewed as special cases of our proposed method. To enable GRVQ on billion scale data, we introduce a nonexhaustive search scheme named aggregating tree (A-Tree) for high dimensional data that uses GRVQ encodings to build a radix tree and perform the nearest neighbor search by beam search. To search accurately and efficiently, VQ-encodings should satisfy locally aggregating encoding criterion: For any node of the corresponding A-Tree, neighboring vectors should aggregate in fewer subtrees to make beam search efficient. We show that the proposed GRVQ encodings best satisfy the suggested criterion, and the joint use of GRVQ and A-Tree shows significantly better performances on billion scale datasets. Our methods are validated on several standard benchmark datasets. Experimental results and empirical analysis show the superior efficiency and effectiveness of our proposed methods compared to the state-of-the-art for large scale search.
Sediment patches , sediment supply , and channel morphology

Bed surface particle size patchiness may play a central role in bedload and morphologic response to changes in sediment supply in gravel-bed rivers. Here we test a 1-D model (from Parker ebook) of bedload transport, surface grain size, and channel profile with two previously published flume studies that documented bed surface response, and specifically patch development, to reduced sediment supply. The model over predicts slope changes and under predicts average bed surface grain size changes because it does not account for patch dynamics. Field studies reported here using painted rocks as tracers show that fine patches and coarse patches may initiate transport at the same stage, but that much greater transport occurs in the finer patches. A theory for patch development should include grain interactions (similar size grains stopping each other, fine ones mobilizing coarse particles), effects of boundary shear stress divergence, and sorting due to cross-stream sloping bed surfaces.
Customer churn prediction using a hybrid genetic programming approach

A churn consumer can be defined as a customer who transfers from one service provider to another service provider. Recently, business operators have investigated many techniques that identify the customer churn since churn rates leads to serious business loss. In this paper, a hybrid technique has been used which combines K-means clustering with Genetic Programming to predict churners in telecommunication companies. First, K-means clustering is used to filter the training dataset from outliers and non representative customer behaviors then Genetic Programming is applied in order to build classification trees that are able to classify customers into churners and non churners. The proposed approach is evaluated and compared with other common classification approaches. Experimental results show that K-means clustering with Genetic Programming has promising capabilities in predicting churners’ rates.
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control

Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties to compute inferences over unobserved function values. Provided a bound on the true best Lipschitz constant of the target function is known a priori they offer convergence guarantees as well as bounds around the predictions. Considering a more general setting that builds on Hölder continuity relative to pseudo-metrics, we propose an online method for estimating the Hölder constant online from function value observations that possibly are corrupted by bounded observational errors. Utilising this to compute adaptive parameters within a kinky inference rule gives rise to a nonparametric machine learning method, for which we establish strong universal approximation guarantees. That is, we show that our prediction rule can learn any continuous function in the limit of increasingly dense data to within a worst-case error bound that depends on the level of observational uncertainty. We apply our method in the context of nonparametric model-reference adaptive control (MRAC). Across a range of simulated aircraft roll-dynamics and performance metrics our approach outperforms recently proposed alternatives that were based on Gaussian processes and RBF-neural networks. For discrete-time systems, we provide stability guarantees for our learning-based controllers both for the batch and the online learning setting.
Tinea Corporis Bullosa Due to Trichophyton schoenleinii: Case Report

We report the first case of tinea corporis bullosa due to Trichophyton schoenleinii in a 41-year-old Romanian woman, without any involvement of the scalp and hair. The species identification was performed using macroscopic and microscopic features of the dermatophyte and its physiological abilities. Epidemiological aspects of the case are also discussed. The general treatment with terbinafine and topical applications of ciclopiroxolamine cream have led to complete healing, with the lesions disappearing in 2 weeks.
Analyzing Spatial Structure of IP Addresses for Detecting Malicious Websites

Web-based malware attacks have become one of the most serious threats that need to be addressed urgently. Several approaches that have attracted attention as promising ways of detecting such malware include employing one of several blacklists. However, these conventional approaches often fail to detect new attacks owing to the versatility of malicious websites. Thus, it is difficult to maintain up-to-date blacklists with information for new malicious websites. To tackle this problem, this paper proposes a new scheme for detecting malicious websites using the characteristics of IP addresses. Our approach leverages the empirical observation that IP addresses are more stable than other metrics such as URLs and DNS records. While the strings that form URLs or DNS records are highly variable, IP addresses are less variable, i.e., IPv4 address space is mapped onto 4-byte strings. In this paper, a lightweight and scalable detection scheme that is based on machine learning techniques is developed and evaluated. The aim of this study is not to provide a single solution that effectively detects web-based malware but to develop a technique that compensates the drawbacks of existing approaches. The effectiveness of our approach is validated by using real IP address data from existing blacklists and real traffic data on a campus network. The results demonstrate that our scheme can expand the coverage/accuracy of existing blacklists and also detect unknown malicious websites that are not covered by conventional approaches.