nils-herrmann/clean_scirepeval huggingface dataset:
Kozai – Lidov Disc Instability

Recent results by Martin et al. (2014) showed in 3D SPH simulations that tilted discs in binary systems can be unstable to the development of global, damped Kozai–Lidov (KL) oscillations in which the discs exchange tilt for eccentricity. We investigate the linear stability of KL modes for tilted inviscid discs under the approximations that the disc eccentricity is small and the disc remains flat. By using 1D equations, we are able to probe regimes of large ratios of outer to inner disc edge radii that are realistic for binary systems of hundreds of AU separations and are not easily probed by multidimensional simulations. For order unity binary mass ratios, KL instability is possible for a window of disc aspect ratios H/r in the outer parts of a disc that roughly scale as (nb/n) 2 < ∼ H/r< ∼ nb/n, for binary orbital frequency nb and orbital frequency n at the disc outer edge. We present a framework for understanding the zones of instability based on the determination of branches of marginally unstable modes. In general, multiple growing eccentric KL modes can be present in a disc. Coplanar apsidal-nodal precession resonances delineate instability branches. We determine the range of tilt angles for unstable modes as a function of disc aspect ratio. Unlike the KL instability for free particles that involves a critical (minimum) tilt angle, disc instability is possible for any nonzero tilt angle depending on the disc aspect ratio.
Cs229 Problem Set #2 Solutions

Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (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) 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. Consult Piazza post 35 for details. Otherwise, please submit your assignments at as a single PDF file under 20MB in size. If you have trouble submitting online, you can also 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. If you are scanning your document by cellphone, please check the Piazza forum for recommended cellphone scanning apps and best practices. 1.  Constructing kernels In class, we saw that by choosing a kernel K(x, z) = φ(x) T φ(z), we can implicitly map data to a high dimensional space, and have the SVM algorithm work in that space. One way to generate kernels is to explicitly define the mapping φ to a higher dimensional space, and then work out the corresponding K. However in this question we are interested in direct construction of kernels. I.e., suppose we have a function K(x, z) that we think gives an appropriate similarity measure for our learning problem, and we are considering plugging K into the SVM as the kernel function. However for K(x, z) to be a valid kernel, it must correspond to an inner product in some higher dimensional space resulting from some feature mapping φ. Mercer's theorem tells us that K(x, z) is a (Mercer) kernel if and only if for any finite set {x
Evidence-based interventions for myofascial trigger points

Myofascial trigger points (MTrPs) can be a significant source of pain. This manuscript reviews the literature on the numerous interventions used to abate the signs and symptoms of MTrPs. This includes spray and stretch, topical agents, injections, trigger point compression, ultrasound, electrical stimulation, laser, dry needling, taping, and instrument assisted soft tissue techniques. The manuscripts cited reveal the results of these interventions in isolation, as well as, in conjunction with other modalities. Correspondence to: Dawn T Gulick, PhD, PT, ATC, CSCS, Professor of Physical Therapy, Widener University, One University Place, 126 Cottee Hall, Chester, PA USA, Tel: 610-499-1287;
The Corporate Social responsibility Disclosure : A Study of Listed Companies in Bangladesh

a Assist. Prof., Department of Business Administration, City University, Bangladesh, roufbogra@yahoo.com Abstract: This is an exploratory study designed to investigate the extant and nature of corporate social responsibility disclosure (CSRD) in corporate annual reports (CAR) of listed companies in Bangladesh. Specifically, the report examines the relationship between corporate attributes and firm-specific factors and corporate social responsibility disclosures. Data are taken from annual reports of 2007 of the listed companies of Dhaka Stock Exchanges. The study uses ordinary least squares regression model to examine the relationship between explanatory variables and corporate social responsibility disclosure and un-weighted relative disclosure index to measure voluntary disclosure. The extent of CSRD level is measured using 39 items of information. The result shows a positive association between proportion of Independent Directors (INDs) and Corporate Social Responsibility Disclosure (CSRD). But, size of the firm does not affect the level of corporate social responsibility disclosure. Control variables suggest that Board Leadership Structure (BLS), Board Audit Committee (BAC) and Percentage Return on Equity (PROE) are positively associated with company‟s corporate social responsibility disclosure (CSRD). The result shows that a higher proportion of independent non-executive directors on a board is positively related to the level of corporate social responsibility disclosure but the extent of corporate social responsibility disclosure is negatively related for firm‟s size.
Diverse Few-Shot Text Classification with Multiple Metrics

We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more realistic setting, where tasks are diverse. However, it imposes tremendous difficulties to existing state-of-the-art metric-based algorithms since a single metric is insufficient to capture complex task variations in natural language domain. To alleviate the problem, we propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. Extensive quantitative evaluations on real-world sentiment analysis and dialog intent classification datasets demonstrate that the proposed method performs favorably against state-of-the-art few shot learning algorithms in terms of predictive accuracy. We make our code and data available for further
Classification of Twitter Accounts into Automated Agents and Human Users

Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.
TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.
Impact of 3-D Advertising on Product Knowledge, Brand Attitude, and Purchase Intention: The Mediating Role of Presence

The conceptualization of a virtual experience has emerged because advancements in computer technology have led to a movement toward more multi-sensory online experiences. Two studies designed to explore the concepts of virtual experience and presence are presented with the results largely supporting the proposition that 3-D advertising is capable of enhancing presence and to varying degrees ultimately influencing the product knowledge, brand attitude, and purchase intention of consumers. The marketing implications are immediate because the ability to create a compelling virtual product experience is not beyond the capability of interactive advertising today. By creating compelling online virtual experiences, advertisers can potentially enhance the value of product information presented and engage consumers in an active user-controlled product experience.
Memory Performance Attacks: Denial of Memory Service in Multi-Core Systems

We are entering the multi-core era in computer science. All m ajor high-performance processor manufacturers have integ rated at least two cores (processors) on the same chip — and it is predi cted that chips with many more cores will become widespread i n the near future. As cores on the same chip share the DRAM memory system , multiple programs executing on different cores can interf er with each others’ memory access requests, thereby adversely aff cting one another’s performance. In this paper, we demonstrate that current multi-core proce ssors are vulnerable to a new class of Denial of Service (DoS) attacks because the memory system is “unfairly” shared among multip le cores. An application can maliciously destroy the memory -related performance of another application running on the same chip . We call such an application a memory performance hog (MPH) . With the widespread deployment of multi-core systems in commodi ty desktop and laptop computers, we expect MPHs to become a pr v lent security issue that could affect almost all computer users. We show that an MPH can reduce the performance of another appl ic tion by 2.9 times in an existing dual-core system, withou being significantly slowed down itself; and this problem will beco me more severe as more cores are integrated on the same chip. O ur analysis identifies the root causes of unfairness in the design of the m emory system that make multi-core processors vulnerable to MPHs. As a solution to mitigate the performance impact of MPHs, we prop ose a new memory system architecture that provides fairness to different applications running on the same chip. Our evaluations show t at this memory system architecture is able to effectively contain the negative performance impact of MPHs in not only dual-core bu t also 4-core and 8-core systems.
Robust rotation invariant texture classification

The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most existing schemes are highly orientation dependent and thus cannot correctly classify textures after rotation. In this paper, two novel feature extraction techniques for rotation invariant texture classification are presented. These schemes, using the wavelet transform and Gaussian Markov random field modelling, are shown to give a consistently high performance for rotated textures in the presence of noise. Moreover, they use just four features to represent each texture and require only a 16x16 image area for their analysis leading to a significantly lower computational complexity than most existing schemes.
Document Summarization via Guided Sentence Compression

Joint compression and summarization has been used recently to generate high quality summaries. However, such word-based joint optimization is computationally expensive. In this paper we adopt the ‘sentence compression + sentence selection’ pipeline approach for compressive summarization, but propose to perform summary guided compression, rather than generic sentence-based compression. To create an annotated corpus, the human annotators were asked to compress sentences while explicitly given the important summary words in the sentences. Using this corpus, we train a supervised sentence compression model using a set of word-, syntax-, and documentlevel features. During summarization, we use multiple compressed sentences in the integer linear programming framework to select salient summary sentences. Our results on the TAC 2008 and 2011 summarization data sets show that by incorporating the guided sentence compression model, our summarization system can yield significant performance gain as compared to the state-of-the-art.
Android permissions: user attention, comprehension, and behavior

Android's permission system is intended to inform users about the risks of installing applications. When a user installs an application, he or she has the opportunity to review the application's permission requests and cancel the installation if the permissions are excessive or objectionable. We examine whether the Android permission system is effective at warning users. In particular, we evaluate whether Android users pay attention to, understand, and act on permission information during installation. We performed two usability studies: an Internet survey of 308 Android users, and a laboratory study wherein we interviewed and observed 25 Android users. Study participants displayed low attention and comprehension rates: both the Internet survey and laboratory study found that 17% of participants paid attention to permissions during installation, and only 3% of Internet survey respondents could correctly answer all three permission comprehension questions. This indicates that current Android permission warnings do not help most users make correct security decisions. However, a notable minority of users demonstrated both awareness of permission warnings and reasonable rates of comprehension. We present recommendations for improving user attention and comprehension, as well as identify open challenges.
Maximum Power Point Tracking using Model Predictive Control of a flyback converter for photovoltaic applications

Due to the variable, stochastic behavior of the solar energy resource, Maximum Power Point Tracking (MPPT) of photovoltaic (PV) is required to ensure continuous operation at the maximum power point to generate the most electrical energy. This paper presents a Model Predictive Control (MPC) MPPT technique. Extracting the maximum power from PV systems has been widely investigated within the literature; the main contribution of this paper is improvement of the Perturb and Observe (P&O) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the flyback DC/DC converter. Comparing the developed technique to the conventional P&O method indicates significant improvement in PV system performance. The proposed MPC-MPPT technique for a flyback converter is implemented using the dSpace CP 1103.
Biodiesel from microalgae.

Continued use of petroleum sourced fuels is now widely recognized as unsustainable because of depleting supplies and the contribution of these fuels to the accumulation of carbon dioxide in the environment. Renewable, carbon neutral, transport fuels are necessary for environmental and economic sustainability. Biodiesel derived from oil crops is a potential renewable and carbon neutral alternative to petroleum fuels. Unfortunately, biodiesel from oil crops, waste cooking oil and animal fat cannot realistically satisfy even a small fraction of the existing demand for transport fuels. As demonstrated here, microalgae appear to be the only source of renewable biodiesel that is capable of meeting the global demand for transport fuels. Like plants, microalgae use sunlight to produce oils but they do so more efficiently than crop plants. Oil productivity of many microalgae greatly exceeds the oil productivity of the best producing oil crops. Approaches for making microalgal biodiesel economically competitive with petrodiesel are discussed.
Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction

The goal of this research is to build a model to predict stock price movement using sentiments on social media. A new feature which captures topics and their sentiments simultaneously is introduced in the prediction model. In addition, a new topic model TSLDA is proposed to obtain this feature. Our method outperformed a model using only historical prices by about 6.07% in accuracy. Furthermore, when comparing to other sentiment analysis methods, the accuracy of our method was also better than LDA and JST based methods by 6.43% and 6.07%. The results show that incorporation of the sentiment information from social media can help to improve the stock prediction.
Bioactivity and Applications of Sulphated Polysaccharides from Marine Microalgae

Marine microalgae have been used for a long time as food for humans, such as Arthrospira (formerly, Spirulina), and for animals in aquaculture. The biomass of these microalgae and the compounds they produce have been shown to possess several biological applications with numerous health benefits. The present review puts up-to-date the research on the biological activities and applications of polysaccharides, active biocompounds synthesized by marine unicellular algae, which are, most of the times, released into the surrounding medium (exo- or extracellular polysaccharides, EPS). It goes through the most studied activities of sulphated polysaccharides (sPS) or their derivatives, but also highlights lesser known applications as hypolipidaemic or hypoglycaemic, or as biolubricant agents and drag-reducers. Therefore, the great potentials of sPS from marine microalgae to be used as nutraceuticals, therapeutic agents, cosmetics, or in other areas, such as engineering, are approached in this review.
Third generation biofuels from microalgae

Biofuel production from renewable sources is widely considered to be one of the most sustainable alternatives to petroleum sourced fuels and a viable means for environmental and economic sustainability. Microalgae are currently being promoted as an ideal third generation biofuel feedstock because of their rapid growth rate, CO2 fixation ability and high production capacity of lipids; they also do not compete with food or feed crops, and can be produced on non-arable land. Microalgae have broad bioenergy potential as they can be used to produce liquid transportation and heating fuels, such as biodiesel and bioethanol. In this review we present an overview about microalgae use for biodiesel and bioethanol production, including their cultivation, harvesting, and processing. The most used microalgal species for these purposes as well as the main microalgal cultivation systems (photobioreactors and open ponds) will also be discussed.
Reinforcement Learning Neural Turing Machines - Revised

The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract external Interfaces and to learn a parametric model that interacts with them. The capabilities of a model can be extended by providing it with proper Interfaces that interact with the world. These external Interfaces include memory, a database, a search engine, or a piece of software such as a theorem verifier. Some of these Interfaces are provided by the developers of the model. However, many important existing Interfaces, such as databases and search engines, are discrete. We examine feasibility of learning models to interact with discrete Interfaces. We investigate the following discrete Interfaces: a memory Tape, an input Tape, and an output Tape. We use a Reinforcement Learning algorithm to train a neural network that interacts with such Interfaces to solve simple algorithmic tasks. Our Interfaces are expressive enough to make our model Turing complete.
Meta-analysis of scientometric research of knowledge management: discovering the identity of the discipline

The purpose of this study is to conduct a meta-analysis of prior scientometric research of the knowledge management (KM) field.  A total of 108 scientometric studies of the KM discipline were subjected to meta-analysis techniques.  The overall volume of scientometric KM works has been growing, reaching up to ten publications per year by 2012, but their key findings are somewhat inconsistent. Most scientometric KM research is published in non-KM-centric journals. The KM discipline has deep historical roots. It suffers from a high degree of over-differentiation and is represented by dissimilar research streams. The top six most productive countries for KM research are the USA, the UK, Canada, Germany, Australia, and Spain. KM exhibits attributes of a healthy academic domain with no apparent anomalies and is progressing towards academic maturity.  Scientometric KM researchers should use advanced empirical methods, become aware of prior scientometric research, rely on multiple databases, develop a KM keyword classification scheme, publish their research in KM-centric outlets, focus on rigorous research of the forums for KM publications, improve their cooperation, conduct a comprehensive study of individual and institutional productivity, and investigate interdisciplinary collaboration. KM-centric journals should encourage authors to employ under-represented empirical methods and conduct meta-analysis studies and should discourage conceptual publications, especially the development of new frameworks. To improve the impact of KM research on the state of practice, knowledge dissemination channels should be developed.  This is the first documented attempt to conduct a meta-analysis of scientometric research of the KM discipline.