nils-herrmann/clean_scirepeval huggingface dataset:
A convenient nucleic acid test on the basis of the capillary convective PCR for the on-site detection of enterovirus 71.

The recent and continuing epidemic of enterovirus 71 in China has affected millions of children and resulted in thousands of deaths. Timely diagnosis and management is essential for disease control. Current enterovirus 71 molecular tests require resources that are unavailable for on-site testing. We have developed a simple-to-operate nucleic acid test, the convenient and integrated nucleic acid test, for local medical institutions. It uses a convective PCR for rapid amplification, a dipstick for visual detection of PCR products, and a simple commercial kit for nucleic acid extraction. By using a specially designed reagent and reaction tube containing a dipstick, the amplification and detection processes are well integrated and simplified. Moreover, cross contamination that may be caused by an open-tube detection system can be avoided. On the basis of the convenient and integrated nucleic acid test, an enterovirus 71 assay for on-site testing was developed. After evaluating known hand, foot, and mouth disease virus stocks of 17 strains of 11 different serotypes, this assay showed a favorable detection spectrum and no cross-reactivity. Its clinical performance was established by testing 141 clinical samples and comparing the results with a nested RT-PCR method. The assay showed a clinical sensitivity and specificity of 98.5% and 100%, respectively. Our results suggest that this convenient and integrated nucleic acid test enterovirus 71 assay may serve as an on-site diagnosis tool.
Dissociation Following Traumatic Stress Etiology and Treatment

We postulate that the cascade ‘‘Freeze-Flight-Fight-Fright-Flag-Faint’’ is a coherent sequence of six fear responses that escalate as a function of defense possibilities and proximity to danger during life-threat. The actual sequence of trauma-related response dispositions acted out in an extremely dangerous situation therefore depends on the appraisal of the threat by the organism in relation to her/his own power to act (e.g., age and gender) as well as the perceived characteristics of threat and perpetrator. These reaction patterns provide optimal adaption for particular stages of imminence. Subsequent to the traumatic threats, portions of the experience may be replayed. The actual individual cascade of defense stages a survivor has gone through during the traumatic event will repeat itself every time the fear network, which has evolved peritraumatically, is activated again (i.e., through internal or external triggers or, e.g., during exposure therapy).When a parasympathetically dominated ‘‘shutdown’’ was the prominent peri-traumatic response during the traumatic incident, comparable dissociative responses may dominate responding to subsequently experienced threat and may also reappear when the traumatic memory is reactivated. Repeated experience of traumatic stress forms a fear network that can become pathologically detached from contextual cues such as time and location of the danger, a condition which manifests itself as posttraumatic stress disorder (PTSD). Intrusions, for example, can therefore be understood as repetitive displays of fragments of the event, which would then, depending on the dominant physiological response during the threat, elicit a corresponding combination of hyperarousal and dissociation. We suggest that trauma treatment must therefore differentiate between patients on two dimensions: those with peritraumatic sympathetic activation versus those who went down the whole defense cascade, which leads to parasympathetic dominance during the trauma and a corresponding replay of physiological and dissociative responding, when reminded. The differential management of dissociative stages (‘‘fright’’ and ‘‘faint’’) has important treatment implications.
Buck-Boost converter as power factor correction controller for plug-in electric vehicles and battery charging application

This paper discusses the working of buck-boost converter as power factor correction controller applicable for battery charging in electric vehicles application. This topology will work in buck mode and boost mode of operation reliant on the rectified voltage at input side and battery voltage at load side. This paper presents an informal and effective line frequency current shaping control arrangement used for achieving power factor nearer to unity. It also satisfies the harmonic compliance of source current in accordance to the IEEE519 recommendations. This objective is achieved by active power factor control circuit using continuous conduction mode (CCM) of buck-boost converter implementing adaptable duty cycle control scheme. The control is very simple and gives good performance. The performance of the control scheme is simulated for both open-loop and closed-loop control in Matlab/Simulink environment.
A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model

Often in biomedical research the aim of a study is to compare the outcomes of several treatment arms while adjusting for multiple clinical prognostic factors. In this paper we focus on computation of the direct adjusted survival curves for different treatment groups based on an unstratified or a stratified Cox model. The estimators are constructed by taking the average of the individual predicted survival curves. The method of direct adjustment controls for possible confounders due to an imbalance of patient characteristics between treatment groups. This adjustment is especially useful for non-randomized studies. We have written a SAS macro to estimate and compare the direct adjusted survival curves. We illustrate the SAS macro through the examples analyzing stem cell transplant data and Ewing's sarcoma data.
Forwarding in a content-based network

This paper presents an algorithm for content-based forwarding, an essential function in content-based networking. Unlike in traditional address-based unicast or multicast networks, where messages are given explicit destination addresses, the movement of messages through a content-based network is driven by predicates applied to the content of the messages. Forwarding in such a network amounts to evaluating the predicates stored in a router's forwarding table in order to decide to which neighbor routers the message should be sent. We are interested in finding a forwarding algorithm that can make this decision as quickly as possible in situations where there are numerous, complex predicates and high volumes of messages. We present such an algorithm and give the results of studies evaluating its performance.
Building Better Open-Source Tools to Support Fairness in Automated Scoring

Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all testtakers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.
Network medicine: a network-based approach to human disease

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
Architecture comparisons between Nvidia and ATI GPUs: Computation parallelism and data communications

In recent years, modern graphics processing units have been widely adopted in high performance computing areas to solve large scale computation problems. The leading GPU manufacturers Nvidia and ATI have introduced series of products to the market. While sharing many similar design concepts, GPUs from these two manufacturers differ in several aspects on processor cores and the memory subsystem. In this paper, we conduct a comprehensive study to characterize the architectural differences between Nvidia's Fermi and ATI's Cypress and demonstrate their impact on performance. Our results indicate that these two products have diverse advantages that are reflected in their performance for different sets of applications. In addition, we also compare the energy efficiencies of these two platforms since power/energy consumption is a major concern in the high performance computing.
Self-deception requires vagueness

The paper sets out to reveal conditions enabling diagnostic self-deception, people's tendency to deceive themselves about the diagnostic value of their own actions. We characterize different types of self-deception in terms of the distinction between intervention and observation in causal reasoning. One type arises when people intervene but choose to view their actions as observations in order to find support for a self-serving diagnosis. We hypothesized that such self-deception depends on imprecision in the environment that allows leeway to represent one's own actions as either observations or interventions. Four experiments tested this idea using a dot-tracking task. Participants were told to go as quickly as they could and that going fast indicated either above-average or below-average intelligence. Precision was manipulated by varying the vagueness in feedback about performance. As predicted, self-deception was observed only when feedback on the task used vague terms rather than precise values. The diagnosticity of the feedback did not matter.
Service-oriented Communities: Models and Concepts Towards Fractal Social Organizations

It is described an abstract model for the definition and the dynamic evolution of "communities of actants" originating from a given reference society of roles. Multiple representations are provided, showing how communities evolve with respect to their reference societies. In particular we show how such representations are self-similar and factorisable into "prime" constituents. An operating model is then introduced that describes the life-cycle of the communities of actants. After this a software component is presented -- the service-oriented community -- and its features are described in terms of the above mentioned models. Finally it is shown how such component can constitute the building block of a novel architecture for the design of fractal social organizations.
QUANTUM COMPUTING : AN INTRODUCTION

After some remarks on the fundamental physical nature of information, Bennett and Fredkin's ideas of reversible computation are introduced. This leads on to the suggestions of Benioff and Feynman as to the possibilit y of a new type of essentially ‘quantum computers’ . If we can build such devices, Deutsch showed that ‘quantum paralleli sm’ leads to new algorithms and new complexity classes. This is dramatically ill ustrated by Shor's quantum algorithm for factorization which is polynomial in time in contrast to algorithms for factorization on a classical Turing computer. This discovery has potentiall y important implications for the security of many modern cryptographic systems. The fundamentals of quantum computing are then introduced reversible logic gates, qubits and quantum registers. The key quantum property of ‘ entanglement’ is described, with due homage to Einstein and Bell . As an ill ustration of a quantum program, Grover's database search algorithm is described in some detail . After all this theory, the status of experimental attempts to build a quantum computer is reviewed: it will become evident that we have a long way to go before we can factorize even small numbers. Finally, we end with some thoughts about the process of ‘quantum compilation’ translating a quantum algorithm into actual physical operations on a quantum system and some comments on prospects for future progress.
Exploiting Parallelism on Keccak: FPGA and GPU Comparison

One of the methods to ensure information integrity is the use of hash functions, which generates a stream of bytes (hash) which must be unique. But most functions can no longer prevent malicious attacks and ensure that the information have just a hash. In order to solve this problem, the National Institute of Standards and Technology (NIST) convened the scientific community through a competition to create a new hash function standard, called SHA-3. This work is proposed to explore one of the finalist algorithms in the competition, the Keccak, and subsequently implement the propose pipeline architecture in FPGA with intuit to obtain performance data. Finally, it will be compared the pipeline implementation of keccak with implementations on GPUs. KeywordsInformation Integrity; Hash Functions; Pipeline Architecture; SHA-3 Keccak; GPU and FPGA
Understanding latent interactions in online social networks

Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques.
 In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.
Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce “deep compression”, a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9× to 13×; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35×, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49× from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3× to 4× layerwise speedup and 3× to 7× better energy efficiency.
A Process-Based Knowledge Management System for Schools: A Case Study in Taiwan

Knowledge management systems, or KMSs, have been widely adopted in business organizations, yet little research exists on the actual integration of the knowledge management model and the application of KMSs in secondary schools. In the present study, the common difficulties and limitations regarding the implementation of knowledge management into schools’ organizational cultures are reviewed and discussed. Furthermore, relevant theories of knowledge management models are summarized, and a model of process-based knowledge management appropriate for schools is proposed. Based on the proposed model, this study applied a low-cost, open-source software development framework to establish a process-based knowledge management system for schools, or PKMSS. We conducted a 30-day empirical observation and survey at a secondary school in Taiwan. This case study used methods including a satisfaction survey, qualitative content analysis of knowledge discussion, and unstructured interviews to explore the progress, performance, and limitations of PKMSS implementation. It was determined that PKMSS has some value in promoting schools’ knowledge management. It not only facilitates the externalization and combination of knowledge and effectively keeps the objectives of knowledge sharing in focus, but it also promotes inter-member interactions. However, this study also found certain restrictions in terms of the classification of knowledge content and system functions. Based on the above findings, we propose relevant suggestions as references for the evaluation and introduction of a KMS in educational organizations.
CaSE: Cache-Assisted Secure Execution on ARM Processors

Recognizing the pressing demands to secure embedded applications, ARM TrustZone has been adopted in both academic research and commercial products to protect sensitive code and data in a privileged, isolated execution environment. However, the design of TrustZone cannot prevent physical memory disclosure attacks such as cold boot attack from gaining unrestricted read access to the sensitive contents in the dynamic random access memory (DRAM). A number of system-on-chip (SoC) bound execution solutions have been proposed to thaw the cold boot attack by storing sensitive data only in CPU registers, CPU cache or internal RAM. However, when the operating system, which is responsible for creating and maintaining the SoC-bound execution environment, is compromised, all the sensitive data is leaked. In this paper, we present the design and development of a cache-assisted secure execution framework, called CaSE, on ARM processors to defend against sophisticated attackers who can launch multi-vector attacks including software attacks and hardware memory disclosure attacks. CaSE utilizes TrustZone and Cache-as-RAM technique to create a cache-based isolated execution environment, which can protect both code and data of security-sensitive applications against the compromised OS and the cold boot attack. To protect the sensitive code and data against cold boot attack, applications are encrypted in memory and decrypted only within the processor for execution. The memory separation and the cache separation provided by TrustZone are used to protect the cached applications against compromised OS. We implement a prototype of CaSE on the i.MX53 running ARM Cortex-A8 processor. The experimental results show that CaSE incurs small impacts on system performance when executing cryptographic algorithms including AES, RSA, and SHA1.
Variational Sequential Labelers for Semi-Supervised Learning

We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latentvariable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data.
Support Vector Machines with Applications 1

Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik’s statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques. The SVMs operate within the framework of regularization theory by minimizing an empirical risk in a well-posed and consistent way. A clear advantage of the support vector approach is that sparse solutions to classification and regression problems are usually obtained: only a few samples are involved in the determination of the classification or regression functions. This fact facilitates the application of SVMs to problems that involve a large amount of data, such as text processing and bioinformatics tasks.  and their applications, emphasizing their key features. In addition, some algorithmic extensions and illustrative real-world applications of SVMs are shown.
Fast , Interactive Origami Simulation using GPU Computation

We present an explicit method for simulating origami that can be rapidly computed on a Graphics Processing Unit (GPU). Previous work on origami simulation methods model the geometric or structural behaviors of origami with a focus on physical realism; in this paper we introduce a compliant, explicit numerical simulation method that emphasizes computational speed and interactivity. We do this by reformulating existing techniques for simulating origami so that they can be computed on highly parallel GPU architectures. We implement this method in an open-source, GPU-accelerated WebGL app that runs in any modern browser. We evaluate our method’s performance, stability, and scalability to existing methods (Freeform Origami, MERLIN) and demonstrate its capacity for real-time interaction through a traditional GUI and immersive virtual reality.