nils-herrmann/clean_scirepeval huggingface dataset:
Ginkgo biloba Food Supplements on the European Market - Adulteration Patterns Revealed by Quality Control of Selected Samples.

The aim of this study was to prove whether Ginkgo biloba food supplements on the European market comply with pharmaceutical quality, and whether their composition satisfies the European Pharmacopoeia criteria. Medicinal products containing a standardised Ginkgo leaf extract are used for the improvement of cognitive impairment and quality of life in mild dementia. Further, Ginkgonis folium is used for the treatment of peripheral circulation disorders. Pharmacopoeial Ginkgo dry extract contains 22.0 - 27.0% flavonoids and 5.4 - 6.6% terpene lactones (ginkgolides, bilobalide). In addition to its widespread use as an herbal medicine (herbal medicinal product), the same extract can be an ingredient in food supplements. The content of active secondary metabolites was quantified in a number of European food supplements containing Ginkgo dry extract or Ginkgo leaf. Flavonoids were quantified using a modified pharmacopoeial HPLC-UV method, and terpene lactones (ginkgolides A, B, C, and bilobalide) using LC-MS/MS. Some Ginkgo leaf supplement samples were also analysed by microscopy. The quality of food supplements on the European market is dubious. In this paper, we present selected examples of several methods of adulteration and falsification, including higher/lower doses of Ginkgo dry extract or Ginkgo leaf than declared and the addition of undeclared extraneous materials. These examples reveal several patterns in the manufacturing of adulterated products.
The Maximum Resource Bin Packing Problem

Usually, for bin packing problems, we try to minimize the number of bins used or in the case of the dual bin packing problem, maximize the number or total size of accepted items. This paper presents results for the opposite problems, where we would like to maximize the number of bins used or minimize the number or total size of accepted items. We consider off-line and on-line variants of the problems. For the off-line variant, we require that there be an ordering of the bins, so that no item in a later bin fits in an earlier bin. We find the approximation ratios of two natural approximation algorithms, First-Fit-Increasing and First-Fit-Decreasing for the maximum resource variant of classical bin packing. For the on-line variant, we define maximum resource variants of classical and dual bin packing. For dual bin packing, no on-line algorithm is competitive. For classical bin packing, we find the competitive ratio of various natural algorithms. We study the general versions of the problems as well as the parameterized versions where there is an upper bound of 1 k on the item sizes, for some integer k. ∗The work of Boyar, Favrholdt, Kohrt, and Larsen was supported in part by the Danish Natural Science Research Council (SNF). The work of Epstein was supported in part by the Israel Science Foundation (ISF). A preliminary version of this paper appeared in the proceedings of the Fifteenth International Symposium on Fundamentals of Computation Theory, 2005.
Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence

Blockchain represents a technology for establishing a shared, immutable version of the truth between a network of participants that do not trust one another, and therefore has the potential to disrupt any financial or other industries that rely on third-parties to establish trust. In order to better understand the current ecosystem of Blockchain applications, a scalable proof-of-concept pipeline for analysis of multiple streams of semi-structured data posted on social media is demonstrated, based on open source components. Deep Web as well as conventional social media are considered. Preliminary analysis suggests that data found in the Deep Web is complimentary to that available on the conventional web. Future work is described that will scale the system to cloud-based, real-time, analysis of multiple data streams, with Information Extraction (IE) (ex. sentiment analysis) and Machine Learning capability.
Trends in crypto-currencies and blockchain technologies: A monetary theory and regulation perspective

The internet era has generated a requirement for low cost, anonymous and rapidly verifiable transactions to be used for online barter, and fast settling money have emerged as a consequence. For the most part, e-money has fulfilled this role, but the last few years have seen two new types of money emerge. Centralised virtual currencies, usually for the purpose of transacting in social and gaming economies, and crypto-currencies, which aim to eliminate the need for financial intermediaries by offering direct peer-to-peer online payments. We describe the historical context which led to the development of these currencies and some modern and recent trends in their uptake, in terms of both usage in the real economy and as investment products. As these currencies are purely digital constructs, with no government or local authority backing, we then discuss them in the context of monetary theory, in order to determine how they may be have value under each. Finally, we provide an overview of the state of regulatory readiness in terms of dealing with transactions in these currencies in various regions of the world.
Effective self-regulation of goal attainment

Self-regulatory strategies of goal setting and goal striving are analyzed in three experiments. Experiment 1 uses fantasy realization theory (Oettingen,  Action and Self Development: Theory and Research through the Life Span, Sage Publications Inc, Thousand Oaks, CA, 1999, ) to analyze the self-regulatory processes of turning free fantasies about a desired future into binding goals. School children 8-12 years of age who had to mentally elaborate a desired academic future as well as present reality standing in its way, formed stronger goal commitments than participants solely indulging in the desired future or merely dwelling on present reality (Experiment 1). Effective implementation of set goals is addressed in the second and third experiments (Gollwitzer,  (1999) 493-503). Adolescents who had to furnish a set educational goal with relevant implementation intentions (specifying where, when, and how they would start goal pursuit) were comparatively more successful in meeting the goal (Experiment 2). Linking anticipated si tuations with goal-directed behaviors (i.e., if-then plans) rather than the mere thinking about good opportunities to act makes implementation intentions facilitate action initiation (Experiment 3).  People have to first turn their desires into binding goals, and second they have to attain the set goal. Both tasks benefit from selfregulatory strategies. In this article we describe a series of experiments with children, adolescents, and young adults that investigate self-regulatory processes facilitating effective goal setting and successful goal striving. The experimental studies investigate (1) different routes to goal setting depending on how
INFLUENCE OF SOCIAL MEDIA ADS ON CONSUMER ’ S PURCHASE

The Social media is changing traditional marketing communication. Consumers are increasingly using social media to search for information and turning away from traditional media such as television, magazines, radio. Companies, therefore, need to create effective online advertising strategies to capitalize on current market segment by incorporating effective features in online advertising, especially in social media. This study offers theoretical contributions and conceptual model to existing research on Social Media Ads and Consumer’s purchase Intention. This study highlights the characteristics of online ads and its influence on intention to purchase. While prior studies have investigated different features of online ads towards attitude and consumer’s buying behavior, this study provides a new set of consumer’s response towards Social Media ads with respect to Purchase Intention.
Efficient object localization using Convolutional Networks

Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introduce invariance and prevent over-training. These benefits of pooling come at the cost of reduced localization accuracy. We introduce a novel architecture which includes an efficient `position refinement' model that is trained to estimate the joint offset location within a small region of the image. This refinement model is jointly trained in cascade with a state-of-the-art ConvNet model [21] to achieve improved accuracy in human joint location estimation. We show that the variance of our detector approaches the variance of human annotations on the FLIC [20] dataset and outperforms all existing approaches on the MPII-human-pose dataset .
MaskGAN: Better Text Generation via Filling in the ______

Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maximum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model.
Real-Time Monocular Object-Model Aware Sparse SLAM

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While sparse point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other hand, state of the art object detection methods provide rich information about entities present in the scene from a single image. This work incorporates a real-time deep-learned object detector to the monocular SLAM framework for representing generic objects as quadrics that permit detections to be seamlessly integrated while allowing the real-time performance. Finer reconstruction of an object, learned by a CNN network, is also incorporated and provides a shape prior for the quadric leading further refinement. To capture the dominant structure of the scene, additional planar landmarks are detected by a CNN-based plane detector and modelled as landmarks in the map. Experiments show that the introduced plane and object landmarks and the associated constraints, using the proposed monocular plane detector and incorporated object detector, significantly improve camera localization and lead to a richer semantically more meaningful map.
Application of BCI systems in neurorehabilitation: a scoping review.

To review various types of electroencephalographic activities of the brain and present an overview of brain-computer interface (BCI) systems' history and their applications in rehabilitation.



A scoping review of published English literature on BCI application in the field of rehabilitation was undertaken. IEEE Xplore, ScienceDirect, Google Scholar and Scopus databases were searched since inception up to August 2012. All experimental studies published in English and discussed complete cycle of the BCI process was included in the review.



In total, 90 articles met the inclusion criteria and were reviewed. Various approaches that improve the accuracy and performance of BCI systems were discussed. Based on BCI's clinical application, reviewed articles were categorized into three groups: motion rehabilitation, speech rehabilitation and virtual reality control (VRC). Almost half of the reviewed papers (48%) concentrated on VRC. Speech rehabilitation and motion rehabilitation made up 33% and 19% of the reviewed papers, respectively. Among different types of electroencephalography signals, P300, steady state visual evoked potentials and motor imagery signals were the most common.



This review discussed various applications of BCI in rehabilitation and showed how BCI can be used to improve the quality of life for people with neurological disabilities. It will develop and promote new models of communication and finally, will create an accurate, reliable, online communication between human brain and computer and reduces the negative effects of external stimuli on BCI performance.  The field of brain-computer interfaces (BCI) is rapidly advancing and it is expected to fulfill a critical role in rehabilitation of neurological disorders and in movement restoration in the forthcoming years. In the near future, BCI has notable potential to become a major tool used by people with disabilities to control locomotion and communicate with surrounding environment and, consequently, improve the quality of life for many affected persons. Electrical field recording at the scalp (i.e. electroencephalography) is the most likely method to be of practical value for clinical use as it is simple and non-invasive. However, some aspects need future improvements, such as the ability to separate close imagery signal (motion of extremities and phalanges that are close together).
Learning to Refine Object Segments

Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture rich spatial information, while upper layers encode object-level knowledge but are invariant to factors such as pose and appearance. In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach. The resulting bottom-up/top-down architecture is capable of efficiently generating high-fidelity object masks. Similarly to skip connections, our approach leverages features at all layers of the net. Unlike skip connections, our approach does not attempt to output independent predictions at each layer. Instead, we first output a coarse ‘mask encoding’ in a feedforward pass, then refine this mask encoding in a topdown pass utilizing features at successively lower layers. The approach is simple, fast, and effective. Building on the recent DeepMask network for generating object proposals, we show accuracy improvements of 1020% in average recall for various setups. Additionally, by optimizing the overall network architecture, our approach, which we call SharpMask, is 50% faster than the original DeepMask network (under .8s per image).
Deep Reinforcement Learning of Video Games

The ability to learn is arguably the most crucial aspect of human intelligence. In reinforcement learning, we attempt to formalize a certain type of learning that is based on rewards and penalties. These supervisory signals should guide an agent to learn optimal behavior. In particular, this research focuses on deep reinforcement learning, where the agent should learn to play video games solely from pixel input. This thesis contributes to deep reinforcement learning research by assessing several variations to an existing state-of-the-art algorithm. First, we provide an extensive analysis on how the design decisions of the agent’s deep neural network affect its performance. Second, we introduce a novel neural layer that allows for local specializations in the visual input of the agents, as opposed to the global weight sharing that occurs in convolutional layers. Third, we introduce a ‘what’ and ‘where’ neural network architecture, inspired by the information flow of the visual cortical areas in the human brain. Finally, we explore prototype based deep reinforcement learning by introducing a novel output layer that is largely inspired by learning vector quantization. In a subset of our experiments, we show substantial improvements compared to existing alternatives.
1.5GHz fully differential latched current comparator with 20nA of sensitivity

Current comparison becomes critical in those data conversion systems where it is highly desirable to achieve speed without sacrificing either accuracy or power consumption. This paper presents a novel circuital solution to perform current comparison at very high speed. Furthermore, we deal with the comparator input offset causes proposing a mismatch aware design to get the desired accuracy. The comparator consists of a fully differential class-AB current input stage, two regenerative flip-flops, and an SR latch. We simulated the circuit in a 0.13mum technology with 1.2V supply voltage. The comparator speed can be pushed up to 1.5GHz achieving a current difference sensitivity up to 20nA and only 800muA of both static and dynamic current consumption
Emergence of Communication in an Interactive World with Consistent Speakers

Training agents to communicate with one another given taskbased supervision only has attracted considerable attention recently, due to the growing interest in developing models for human-agent interaction. Prior work on the topic focused on simple environments, where training using policy gradient was feasible despite the non-stationarity of the agents during training. In this paper, we present a more challenging environment for testing the emergence of communication from raw pixels, where training using policy gradient fails. We propose a new model and training algorithm, that utilizes the structure of a learned representation space to produce more consistent speakers at the initial phases of training, which stabilizes learning. We empirically show that our algorithm substantially improves performance compared to policy gradient. We also propose a new alignment-based metric for measuring context-independence in emerged communication and find our method increases context-independence compared to policy gradient and other competitive baselines.
2 Toward An Efficient Fingerprint Classification

Biometrics technology is keep growing substantially in the last decades with great advances in biometric applications. An accurate personal authentication or identification has become a critical step in a wide range of applications such as national ID, electronic commerce, and automated and remote banking. The recent developments in the biometrics area have led to smaller, faster, and cheaper systems such as mobile device systems. As a kind of human biometrics for personal identification, fingerprint is the dominant trait due to its simplicity to be captured, processed, and extracted without violating user privacy. In a wide range of applications of fingerprint recognition, including civilian and forensics implementations, a large amount of fingerprints are collected and stored everyday for different purposes. In Automatic Fingerprint Identification System (AFIS) with a large database, the input image is matched with all fields inside the database to identify the most potential identity. Although satisfactory performances have been reported for fingerprint authentication (1:1 matching), both time efficiency and matching accuracy deteriorate seriously by simple extension of a 1:1 authentication procedure to a 1:N identification system (Manhua, 2010). The system response time is the key issue of any AFIS, and it is often improved by controlling the accuracy of the identification to satisfy the system requirement. In addition to developing new technologies, it is necessary to make clear the trade-off between the response time and the accuracy in fingerprint identification systems. Moreover, from the versatility and developing cost points of view, the trade-off should be realized in terms of system design, implementation, and usability. Fingerprint classification is one of the standard approaches to speed up the matching process between the input sample and the collected database (K. Jain et al., 2007). Fingerprint classification is considered as indispensable step toward reducing the search time through large fingerprint databases. It refers to the problem of assigning fingerprint to one of several pre-specified classes, and it presents an interesting problem in pattern recognition, especially in the real and time sensitive applications that require small response time. Fingerprint classification process works on narrowing down the search domain into smaller database subsets, and hence speeds up the total response time of any AFIS. Even for
Predicting Questions' Scores on Stack Overflow

Developer support forums are becoming more popular than ever. Crowdsourced knowledge is an essential resource for many developers yet it can raise concerns about the quality of the shared content. Most existing research efforts address the quality of answers posted by Q&A community members. In this paper, we explore the quality of questions and propose a method of predicting the score of questions on Stack Overflow based on sixteen factors related to questions' format, content and interactions that occur in the post. We performed an extensive investigation to understand the relationship between the factors and the scores of questions. The multiple regression analysis shows that the question's length of the code, accepted answer score, number of tags and the count of views, comments and answers are statistically significantly associated with the scores of questions. Our findings can offer insights to community-based Q&A sites for improving the content of the shared knowledge.
Design and Characterization of Novel Paper-based Inkjet-Printed RFID and Microwave Structures for Telecommunication and Sensing Applications

In this paper, inkjet-printed microwave circuits fabricated on paper-based substrates were investigated, as a system-level solution for ultra-low-cost mass production. The RF characteristics of the paper-based substrate were studied by using the cavity resonator method and the Transmission Line method in order to characterize the dielectric constant (epsivr) and loss tangent (tandelta) of the substrate. A UHF RFID tag module was then developed with the inkjet-printing technology which could function as a technology for much simpler and faster fabrication on/in paper. Simulation and well-agreed measurement results verify a good performance of the tag module. In addition, for the first time, the possibility of paper-based substrate for multilayer microwave structures was explored, and a 2.4 GHz multilayer patch resonator bandpass filter with insertion loss < 0.6dB was demonstrated. These results show that the paper material can serve for the purpose of economical multilayer structures for telecommunication and sensing applications.
The Lattice Boltzmann Equation Method : Theoretical Interpretation , Numerics and Implications

During the last ten years the Lattice Boltzmann Equation (LBE) method has been developed as an alternative numerical approach in computational fluid dynamics (CFD). Originated from the discrete kinetic theory, the LBE method has emerged with the promise to become a superior modeling platform, both computationally and conceptually, compared to the existing arsenal of the continuum-based CFD methods. The LBE method has been applied for simulation of various kinds of fluid flows under different conditions. The number of papers on the LBE method and its applications continues to grow rapidly, especially in the direction of complex and multiphase media.