nils-herrmann/clean_scirepeval huggingface dataset:
Real-time simulation of lightweight rigid bodies

Unlike common rigid bodies, lightweight rigid bodies have special and spectacular motions that are known as free fall, such as fluttering (oscillation from side to side) and tumbling (rotation and sideways drifting). However, computer graphics applications cannot simulate the dynamics of lightweight rigid bodies in various environments realistically and efficiently. In this study, we first analyze the physical characteristics of free-fall motions in quiescent flow and propose a new procedural motion-synthesis method for modeling free-fall motions in interactive environments. Six primitive motions of lightweight rigid bodies are defined in a phase diagram and analyzed separately using a trajectory-search tree and precomputed trajectory database. The global paths of free-fall motions are synthesized on the basis of these primitive motions by using a free-fall motion graph whose edges are connected in the Markov-chain model. Then, our approach integrates external forces (e.g., a wind field) by using an improved noise-based algorithm under different force magnitudes and object release heights. This approach exhibits not only realistic simulation results in various environments but also fast computation to meet real-time requirements.
Everything You Wanted to Know About the Blockchain: Its Promise, Components, Processes, and Problems

In 2008, the emergence of the blockchain as the foundation of the first-ever decentralized cryptocurrency not only revolutionized the financial industry but proved a boon for peer-to-peer (P2P) information exchange in the most secure, efficient, and transparent manner. The blockchain is a public ledger that works like a log by keeping a record of all transactions in chronological order, secured by an appropriate consensus mechanism and providing an immutable record. Its exceptional characteristics include immutability, irreversibility, decentralization, persistence, and anonymity.
NeuroPipe-Chip: A digital neuro-processor for spiking neural networks

Computing complex spiking artificial neural networks (SANNs) on conventional hardware platforms is far from reaching real-time requirements. Therefore we propose a neuro-processor, called NeuroPipe-Chip, as part of an accelerator board. In this paper, we introduce two new concepts on chip-level to speed up the computation of SANNs. These concepts are implemented in a prototype of the NeuroPipe-Chip. We present the hardware structure of the prototype and evaluate its performance in a system simulation based on a hardware description language (HDL). For the computation of a simple SANN for image segmentation, the NeuroPipe-Chip operating at 100 MHz shows an improvement of more than two orders of magnitude compared to an Alpha 500 MHz workstation and approaches real-time requirements for the computation of SANNs in the order of 10(6) neurons. Hence, such an accelerator would allow for applications of complex SANNs to solve real-world tasks like real-time image processing. The NeuroPipe-Chip has been fabricated in an Alcatel 0.35-mum digital CMOS technology.
Beyond the Privacy Paradox: Objective Versus Relative Risk in Privacy Decision Making

Privacy decision making has been investigated in the Information Systems literature using two contrasting frameworks. A first framework has largely focused on deliberative, rational processes by which individuals weigh the expected benefits of privacy allowances and disclosure against their resulting costs. Under this framework, consumer privacy decision making is broadly constructed as driven by stable, and therefore predictable, individual preferences for privacy. More recently, a second framework has leveraged theories and results from behavioral decision research to construe privacy decision making as a process in which cognitive heuristics and biases often occur, and individuals are significantly influenced by non-normative factors in choosing what to reveal or to protect about themselves. In three experiments, we combine and contrast these two perspectives by evaluating the impact of changes in objective risk of disclosure (normative factors), and the impact of changes in relative, and in particular reference-dependent, perceptions of risk (non-normative factors) on individual privacy decision making. We find that both relative and objective risks can impact individual privacy decisions. However, and surprisingly, we find that in experiments more closely modeled on real world contexts, and in experiments that capture actual privacy decisions as opposed to hypothetical choices, relative risk is a more pronounced driver of privacy decisions compared to objective risk. Our results suggest that while normative factors can influence consumers’ self-predicted, hypothetical behavior, nonnormative factors may sometimes be more important and consistent drivers of actual privacy choices.
Mapping Generative Models onto a Network of Digital Spiking Neurons

Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative tasks. Inference and learning in these algorithms use a Markov Chain Monte Carlo procedure called Gibbs sampling, where a logistic function forms the kernel of this sampler. On the other side of the spectrum, neuromorphic systems have shown great promise for low-power and parallelized cognitive computing, but lack well-suited applications and automation procedures. In this work, we propose a systematic method for bridging the RBM algorithm and digital neuromorphic systems, with a generative pattern completion task as proof of concept. For this, we first propose a method of producing the Gibbs sampler using bio-inspired digital noisy integrate-and-fire neurons. Next, we describe the process of mapping generative RBMs trained offline onto the IBM TrueNorth neurosynaptic processor-a low-power digital neuromorphic VLSI substrate. Mapping these algorithms onto neuromorphic hardware presents unique challenges in network connectivity and weight and bias quantization, which, in turn, require architectural and design strategies for the physical realization. Generative performance is analyzed to validate the neuromorphic requirements and to best select the neuron parameters for the model. Lastly, we describe a design automation procedure which achieves optimal resource usage, accounting for the novel hardware adaptations. This work represents the first implementation of generative RBM inference on a neuromorphic VLSI substrate.
iDedup: latency-aware, inline data deduplication for primary storage

Deduplication technologies are increasingly being deployed to reduce cost and increase space-efficiency in corporate data centers. However, prior research has not applied deduplication techniques inline to the request path for latency sensitive, primary workloads. This is primarily due to the extra latency these techniques introduce. Inherently, deduplicating data on disk causes fragmentation that increases seeks for subsequent sequential reads of the same data, thus, increasing latency. In addition, deduplicating data requires extra disk IOs to access on-disk deduplication metadata. In this paper, we propose an inline deduplication solution, iDedup, for primary workloads, while minimizing extra IOs and seeks. Our algorithm is based on two key insights from realworld workloads: i) spatial locality exists in duplicated primary data; and ii) temporal locality exists in the access patterns of duplicated data. Using the first insight, we selectively deduplicate only sequences of disk blocks. This reduces fragmentation and amortizes the seeks caused by deduplication. The second insight allows us to replace the expensive, on-disk, deduplication metadata with a smaller, in-memory cache. These techniques enable us to tradeoff capacity savings for performance, as demonstrated in our evaluation with real-world workloads. Our evaluation shows that iDedup achieves 60-70% of the maximum deduplication with less than a 5% CPU overhead and a 2-4% latency impact.
Environmental factors that influence the cutaneous production of vitamin D.

All vertebrates, including humans, obtain most of their daily vitamin D requirement from casual exposure to sunlight. During exposure to sunlight, the solar ultraviolet B photons (290-315 nm) penetrate into the skin where they cause the photolysis of 7-dehydrocholesterol to precholecalciferol. Once formed, precholecalciferol undergoes a thermally induced rearrangement of its double bonds to form cholecalciferol. An increase in skin pigmentation, aging, and the topical application of a sunscreen diminishes the cutaneous production of cholecalciferol. Latitude, season, and time of day as well as ozone pollution in the atmosphere influence the number of solar ultraviolet B photons that reach the earth's surface, and thereby, alter the cutaneous production of cholecalciferol. In Boston, exposure to sunlight during the months of November through February will not produce any significant amounts of cholecalciferol in the skin. Because windowpane glass absorbs ultraviolet B radiation, exposure of sunlight through glass windows will not result in any production of cholecalciferol. It is now recognized that vitamin D insufficiency and vitamin D deficiency are common in elderly people, especially in those who are infirm and not exposed to sunlight or who live at latitudes that do not provide them with sunlight-mediated cholecalciferol during the winter months. Vitamin D insufficiency and deficiency exacerbate osteoporosis, cause osteomalacia, and increase the risk of skeletal fractures. Vitamin D insufficiency and deficiency can be prevented by encouraging responsible exposure to sunlight and/or consumption of a multivitamin tablet that contains 10 micrograms (400 IU) vitamin D.
The Mediating Effect of Perceived Usefulness towards Tax Service Quality and the Continuance Usage Intention of the E-Filing System in Malaysia

This study examines the mediating effect of perceived usefulness on the relationship between tax service quality (correctness, response time, system support) and continuance usage intention of e-filing system in Malaysia. A total of 116 data was analysed using Partial Least Squared Method (PLS). The result showed that Perceived Usefulness has a partial mediating effect on the relationship between tax service quality (Correctness, Response Time) with the continuance usage intention and tax service quality (correctness) has significant positive relationship with continuance usage intention. Perceived usefulness was found to be the most important predictor of continuance usage intention.
Proximal Subungual Onychomycosis in a Patient with Classic Kaposi Sarcoma Caused by Trichophyton rubrum

A 58-year-old man presented with whitish patches on both great toenails for four weeks prior to visiting our hospital; the patches spread rapidly to other finger- and toe-nails. Prior to presentation, the patient had been diagnosed with idiopathic thrombocytopenic purpura two months ago and Kaposi's sarcoma three weeks ago. The patient was treated with human immunoglobulin for five days, and then received prednisolone 40 mg bid. Serology showed that the patient was negative for HIV and results of other laboratory tests were normal. The KOH slide preparation of the nail scraping showed long septated hyphae and numerous arthrospores. The fungus culture revealed whitish downy colonies on the front side and wine-red reverse pigmentation on Sabouraud's dextrose agar. Trichophyton rubrum was isolated on fungus culture and slide culture. The internal transcribed space (ITS) regions of ribosomal DNA of the cultured fungus were identical to Trichophyton rubrum. Proximal subungual onychomycosis (PSO) is the rarest form of onychomycosis. PSO initially presents as whitish patch(es) on the proximal side of the nail plate(s). Because PSO shows whitish to yellowish patches on the nail plate, the result of KOH examination of nail scrapings from the nail plate is almost always negative. Herein, we report on a case of multiple PSO in a patient with classic Kaposi sarcoma and suggest a method for easy KOH scraping on PSO.
A Temporal Text Mining Application in Competitive Intelligence

In this paper we describe an application of our approach to temporal text mining in Competitive Intelligence for the biotechnology and pharmaceutical industry. The main objective is to identify changes and trends of associations among entities of interest that appear in text over time. Text Mining (TM) exploits information contained in textual data in various ways, including the type of analyses that are typically performed in Data Mining . Information Extraction (IE) facilitates the semi-automatic creation of metadata repositories from text. Temporal Text mining combines Information Extraction and Data Mining techniques upon textual repositories and incorporates time and ontologies‟ issues. It consists of three main phases; the Information Extraction phase, the ontology driven generalisation of templates and the discovery of associations over time. Treatment of the temporal dimension is essential to our approach since it influences both the annotation part (IE) of the system as well as the mining part.
Hindi character recognition using RBF neural network and directional group feature extraction technique

In this paper, Radial Basis Function (RBF) neural Network has been implemented on eight directional values of gradient features for handwritten Hindi character recognition. The character recognition system was trained by using different samples in different handwritings collected of various people of different age groups. The Radial Basis Function network with one input and one output layer has been used for the training of RBF Network. Experiment has been performed to study the recognition accuracy, training time and classification time of RBF neural network. The recognition accuracy, training time and classification time achieved by implementing the RBF network have been compared with the result achieved in previous related work i.e. Back propagation Neural Network. Comparative result shows that the RBF with directional feature provides slightly less recognition accuracy, reduced training and classification time.
Image-based logical document structure recognition

The paper presents a complete solution for recognition of textual and graphic structures in various types of documents acquired from the Internet. In the proposed approach, the document structure recognition problem is divided into sub-problems. The first one is localizing logical structure elements within the document. The second one is recognizing segmented logical structure elements. The input to the method is an image of document page, the output is the XML file containing all graphic and textual elements included in the document, preserving the reading order of document blocks. This file contains information about the identity and position of all logical elements in the document image. The paper describes all details of the proposed method and shows the results of the experiments validating its effectiveness. The results of the proposed method for paragraph structure recognition are comparable to the referenced methods which offer segmentation only.
WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

Until recently, those deep steganalyzers in spatial domain are all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of “linear collusion attack” which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of “linear collusion attack”. As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images, with different demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperform other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature.
Diet and hair loss: effects of nutrient deficiency and supplement use

Patients presenting with hair loss should be screened by medical history, dietary history and physical exam for risk factors for nutrient deficiency. If warranted, laboratory studies may be performed. In patients with no risk factors, further laboratory evaluation searching for nutritional deficiencies is not warranted. For patients with nutritional deficiencies, it is clear that those deficiencies should be corrected. Further research is required to determine whether any benefit exists for nutrient supplementation in the absence of documented deficiency. At this time, patients must be informed that such research is lacking and that in fact some supplements carry the risk of worsening hair loss or the risk of toxicity.
AC3N: Anonymous communication in Content-Centric Networking

Content-Centric Networking (CCN) is an emerging (inter-)networking architecture with the goal of becoming an alternative to the IP-based Internet. To be considered a viable candidate, CCN must at least have parity with existing solutions for confidential and anonymous communication, e.g., TLS, tcpcrypt, and Tor. ANDa̅NA (Anonymous Named Data Networking Application) was the first proposed solution that addressed the lack of anonymous communication in Named Data Networking (NDN)-a variant of CCN. However, its design and implementation led to performance issues that hinder practical use. In this paper we introduce AC3N: Anonymous Communication for Content-Centric Networking. AC3N is an evolution of the ANDa̅NA system that supports high-throughput and low-latency anonymous content retrieval. We discuss the design and initial performance results of this new system.
Hyaluronic Acid in Inflammation and Tissue Regeneration.

Hyaluronic acid (HA), the main component of extracellular matrix, is considered one of the key players in the tissue regeneration process. It has been proven to modulate via specific HA receptors, inflammation, cellular migration, and angiogenesis, which are the main phases of wound healing. Studies have revealed that most HA properties depend on its molecular size. High molecular weight HA displays anti-inflammatory and immunosuppressive properties, whereas low molecular weight HA is a potent proinflammatory molecule. In this review, the authors summarize the role of HA polymers of different molecular weight in tissue regeneration and provide a short overview of main cellular receptors involved in HA signaling. In addition, the role of HA in 2 major steps of wound healing is examined: inflammation and the angiogenesis process. Finally, the antioxidative properties of HA are discussed and its possible clinical implication presented.
Miniature computers built into clothes , shoes and eyeglasses may become the “ smartest ” new fashion accessories

investigating how brains work or building intelligent machines or creating “smart” environments such as a house that can identify and track its occupants. But what about making people smarter? To accomplish this goal, one can consider biochemistry or bioimplants, but the easiest way to improve intelligence is by augmenting the items we wear all the time—glasses, wristwatches, clothes and shoes—with miniature computers, video displays, cameras and microphones. These high-tech “wearables,” which are being developed at the Massachusetts Institute of Technology Media Laboratory, can extend one’s senses, improve memory, aid the wearer’s social life and even help him or her stay calm and collected. The idea of increasing intelligence with wearable devices is very old. English physicist Robert Hooke wrote in 1665 (in the preface to Micrographia): “The next care to be taken, in respect of the Senses, is a supplying of their infirmities with Instruments, and as it were, the adding of artificial Organs to the natural.... And as Glasses have highly promoted our seeing ... there may be found many mechanical inventions to improve our other senses of hearing, smelling, tasting and touching.” One must draw a distinction between wearable devices and those that are merely Miniature computers built into clothes, shoes and eyeglasses may become the “smartest” new fashion accessories
The Political Economy of the Peace Process in a Changing Middle East

UNU World Institute for Development Economics Research (UNU/WIDER) was established by the United Nations University as its first research and training centre and started work in Helsinki, Finland, in 1985. The principal purpose of the Institute is policy-oriented research on the main strategic issues of development and international cooperation, as well as on the interaction between domestic and global changes. Its work is carried out by staff researchers and visiting scholars in Helsinki and through networks of collaborating institutions and scholars around the world. The views expressed in this publication are those of the author(s). Publication does not imply endorsement by the Institute or the United Nations University of any of the views expressed.  A historical overview 3 1.3 Regionalism and disintegration; ideology and politics of the last 50 years 9 1.4 The peace process in the Middle East 16 II REGIONAL DEVELOPMENT COOPERATION: THE INFLUENCE OF PEACE 29 2.1 The hopes, realities and prospects of economic cooperation-integration 29 2.2 Israel, its modern economy, and the Palestinians 31 2.3 Cooperation and Palestinian self-rule (self-government) arrangements 33 2.4 A peace dividend for the region 44 III WATER-THE CRITICAL ECO-POLITICAL ISSUE 49 3.1 Water-a key for the future 49 3.2 The international nature of Middle East water 50 3.3 Water resources of Israel, the West Bank, and the Gaza Strip 54 3.4 Jordan's water resources 59 IV INSTEAD OF A CONCLUSION 63 4.1 Efforts made-efforts needed 63 4.2 The role and response of the UN, international and regional organizations to the changing Middle East 66 4.3 Limitations of studies and research 66 BIBLIOGRAPHY 68 iii IV FOREWORD The character and consequences of the interconnectedness and interaction between international political and economic processes in the post-cold war era has changed in many respects, as compared to the previous decades. During the cold war years, economic factors played of course important roles in shaping the global power structure, and influencing international politics but in many respects, the political agenda of the two global powers dominated the global system. In the post-cold war world, the influence of the economic factors became much stronger in shaping global changes. Capital flows, technology and international entrepreneurship are the key components of the globalization process. The relative economic power of countries, international economic competitiveness and competition has a much greater impact on interstate relations than in the past. The pressure of the domestic socioeconomic problems …