nils-herrmann/clean_scirepeval huggingface dataset:
Reverse wire bonding and phosphor printing for LED wafer level packaging

Solid state lighting is a good alternative light source with reduced energy consumption. Light-emitting diode (LED) is very efficient in turning electrical energy into light. LED has a number of advantages over the traditional light sources. The optical performance of the LED component is very critical. In general, white light can be obtained by applying phosphor on a blue LED chip. The blue light from the LED excites the phosphor to emit yellow light. The blue and yellow light mixes together to give white light. In order to obtain a good optical performance, it is necessary to apply phosphor properly. It is challenging to distribute a right amount of phosphor on the LED die. Besides, phosphor dispensing is usually the slowest process when compared with die bonding and wire bonding. This controls the overall throughput of the LED packaging process. There are different methods to apply the phosphor. The phosphor is mixed with epoxy or silicone to form slurry and is then dispensed onto the chip. However, the spatial color distribution is poor if phosphor slurry is used. Conformal phosphor coating can be used to improve the spatial color distribution. In this paper, an innovative phosphor stencil printing method is proposed. This paper demonstrates the feasibility of the phosphor stencil printing process for wafer-level LED packaging. LEDs are first mounted on a wafer submount. Wire bonds are used as interconnect. The phosphor is stencil printed on the chip surface after wire bond. The minimum phosphor layer thickness is controlled by the wire bond loop height. In order to achieve a low loop height, reverse wire bonding is used. The first bond is on the wafer submount and the second bond is on the LED chip. The reverse wire bond has a very low profile which allows a thin layer of phosphor to be printed on the chip surface. Prototypes are successfully fabricated. A uniform layer of phosphor is stencil printed on the LED chip on the wafer submount. Experimental result shows that the proposed phosphor printing method is very effective in distributing the right amount of phosphor on the chip surface.
A Computational Approach to Grammatical Coding of English Words

As a firs l~ step in many computer language processing systems, each word in a natural language sentence must be coded as to its form-class or part of speech. This paper describes a computational grammar coder which has been completely programmed and is oper~tional on Lhe IBM 7090. It is part of a complete syntactic annlysis system for which it accomplishes word-class coding, using a computational approach rather than the usual method of dictionary lookup. The resulting system is completely contained in less than 1~,000 computer words. It processes running English text on the IBM 7090 at a rate of more than 1250 words per minute. Since the system is not dependent on large dictionaries, it operates on any ordinary English text. In preliminary experiments with scientific text, the system correctly and unambiguously coded over 90 percent of the words in two samples of scientific writing. A fair proportion of the remaining ambiguity can be removed at higher levels of synvactic analysis, but the problem of structural ambiguity in natural languages is seen to be a critical one in the development of practical language processing systems.
A synthetic integral feedback controller for robust tunable regulation in bacteria

We report on the first engineered integral feedback control system in a living cell. The controller is based on the recently published antithetic integral feedback motif  which has been analytically shown to have favorable regulation properties. It is implemented along with test circuitry in Escherichia coli using seven genes and three small-molecule inducers. The closed-loop system is highly tunable, allowing a regulated protein of interest to be driven to a desired level and maintained there with precision. Realized using a sigma/anti-sigma protein pair, the integral controller ensures that regulation is maintained in the face of perturbations that lead to the regulated protein’s degradation, thus serving as a proof-of-concept prototype of integral feedback implementation in living cells. When suitably optimized, this integral controller may be utilized as a general-purpose robust regulator for genetic circuits with unknown or partially-known topologies and parameters.
The Application of MIMO to Non-Orthogonal Multiple Access

This paper considers the application of multiple-input multiple-output (MIMO) techniques to nonorthogonal multiple access (NOMA) systems. A new design of precoding and detection matrices for MIMO-NOMA is proposed and its performance is analyzed for the case with a fixed set of power allocation coefficients. To further improve the performance gap between MIMO-NOMA and conventional orthogonal multiple access schemes, user pairing is applied to NOMA and its impact on the system performance is characterized. More sophisticated choices of power allocation coefficients are also proposed to meet various quality-of-service requirements. Finally, computer simulation results are provided to facilitate the performance evaluation of MIMO-NOMA and also demonstrate the accuracy of the developed analytical results.
Multiple Trench Split-gate SOI LDMOS Integrated With Schottky Rectifier

In this paper, a multiple trench split-gate silicon-on-insulator (SOI) lateral double-diffused MOSFET with a Schottky rectifier (MTS-SG-LDMOS) is proposed and its characteristics are studied using 2-D simulations. The new structure features double oxide trenches, a floating polysilicon split-gate, and a Schottky rectifier. Each oxide trench includes a vertical field plate which enhances the depletion of the drift region and modulates the bulk electric field. As the simulation results, when compared to the conventional SOI LDMOS (C-LDMOS), the breakdown voltage in the MTS-SG-LDMOS increases from 297 to 350 V, the specific on-state resistance (<inline-formula> <tex-math notation="LaTeX">${R} _{ \mathrm{\scriptscriptstyle ON},\mathsf {sp}}$ </tex-math></inline-formula>) decreases from 142.9 to 48.2 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \mathsf {cm}^{{\mathsf {2}}}$ </tex-math></inline-formula>, and the gate–drain charge (<inline-formula> <tex-math notation="LaTeX">${Q} _{{\mathsf {gd}}}$ </tex-math></inline-formula>) decreases from 19 to 9 pC. Moreover, the reverse recovery time of the proposed structure shows a 73.6% reduction as compared to the C-LDMOS device.
Value co-creation between firms and customers: The role of big data-based cooperative assets

To better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms. ã 2016 The Author(s).
Ontological Engineering: Principles, Methods, Tools and Languages

In 1991, the DARPA Knowledge Sharing Effort ([88], p. 37) envisioned a new way to build intelligent systems. It proposed the following: Building knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could be instead done by assembling reusable components. System developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, using them to perform some of its reasoning.
Vocabulary learning by mobile-assisted authentic content creation and social meaning-making: two case studies

In recent years, we have witnessed the concomitant rise of communicative and contextualised approaches as well as the paradigmatic development of the Mobile Assisted Language Learning (MALL) framework in analyzing language learning. The focus of MALL research has gradually shifted from content-based (delivery of learning content through mobile devices) to design-oriented (authentic and/or social mobile learning activities) study. In this paper, we present two novel case studies of MALL that emphasize learner-created content. In learning English prepositions and Chinese idioms respectively, the primary school students used the mobile devices assigned to them on a one-to-one basis to take photos in real-life contexts so as to construct sentences with the newly acquired prepositions or idioms. Subsequently, the learners were voraciously engaged in classroom or online discussion of their semantic constructions, thereby enhancing their understanding of the proper usage of the prepositions or idioms. This work shows the potential of transforming language learning into an authentic seamless learning experience.
Robust visual inertial odometry using a direct EKF-based approach

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).
Scam Detection in Twitter

Twitter is one among the fastest growing social networking services. This growth has led to an increase in Twitter scams (e.g., intentional deception). There is relatively little effort in identifying scams in Twitter. In this paper, we propose a semisupervised Twitter scam detector based on a small labeled data. The scam detector combines selflearning and clustering analysis. A suffix tree data structure is used. Model building based on Akaike and Bayes Information Criteria is investigated and combined with the classification step. Our experiments show that 87% accuracy is achievable with only 9 labeled samples and 4000 unlabeled samples, among other results.
Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I.

As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care.



We reviewed 30,121 randomly selected records from 51 randomly selected acute care, nonpsychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians.



Adverse events occurred in 3.7 percent of the hospitalizations (95 percent confidence interval, 3.2 to 4.2), and 27.6 percent of the adverse events were due to negligence (95 percent confidence interval, 22.5 to 32.6). Although 70.5 percent of the adverse events gave rise to disability lasting less than six months, 2.6 percent caused permanently disabling injuries and 13.6 percent led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi 2 = 21.04, P less than 0.0001). Using weighted totals, we estimated that among the 2,671,863 patients discharged from New York hospitals in 1984 there were 98,609 adverse events and 27,179 adverse events involving negligence. Rates of adverse events rose with age (P less than 0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (P less than 0.01). There were significant differences in rates of adverse events among categories of clinical specialties (P less than 0.0001), but no differences in the percentage due to negligence.



There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.
An Architecture Proposal for Network Traffic Monitoring with IoT Traffic Classification Support

Devices of the Internet-of-Things such as sensors, actuators and smart objects are being incorporated into homes and companies to allow more interaction and communication. As new devices are introduced in IoT ecosystems, there is a need to improve control, security and infrastructure management, since network operators will not be able to know which devices are being used or whether network operations are being performed correctly for these devices. IoT ecosystems are heterogeneous, dynamic with the constant inclusion of new services and require interoperability. Environments with these characteristics present a challenge for any classification methodology. This paper shows a proposal for the construction of a tool for network monitoring that supports IoT devices traffic classification and identification. The proposed tool will provide three main functionalities: a network monitor, a passive classifier, and anactive classifier.
New Developments in Space Syntax Software

The Spatial Positioning tool (SPOT) is an isovist-based spatial analysis software, and is written in Java working as a stand-alone program. SPOT differs from regular Space syntax software as it can produce integration graphs and intervisibility graphs from a selection of positions. The concept of the software originates from a series of field studies on building interiors highly influenced by organizations and social groups. We have developed SPOT as a prototype. Basic SPOT operations use selections of positions and creations of isovist sets. The sets are color-coded and layered; the layers can be activated and visible by being turned on or off. At this point, there are two graphs produced in SPOT, the isovist overlap graph that shows intervisibility between overlapping isovist fields and the network integration analysis built on visibility relations. The program aims to be used as a fast and interactive sketch tool as well as a precise analysis tool. Data, images, and diagrams can be exported for use in conjunction with other CAD or illustration programs. The first stage of development is to have a functioning prototype with the implementation of all the basic algorithms and a minimal basic functionality in respect to user interaction.
Grid Services for Distributed System Integration

ions and concepts that let applications access and share resources and services across distributed, wide area networks, while providing common security semantics, distributed resource management performance, coordinated fail-over, problem determination services, or other QoS metrics that are of importance in a particular context. For some time, such problems have been of central concern to developers of distributed systems for large-scale scientific research. Work within this community has led to the development of Grid technologies, which have been widely adopted in scientific and technical computing. Grid technologies and infrastructures support the sharing and coordinated use of diverse resources in dynamic, distributed virtual organizations—that is, the creation, from geographically distributed components operated by distinct organizations with differing policies, of virtual computing systems that are sufficiently integrated to deliver the desired QoS. In particular, the open source Globus Toolkit described in the “OGSA and the Globus ToolKit” sidebar has emerged as a de facto standard for construction of Grid systems. Projects building on the Globus Toolkit range from scientific collaborations concerned with remote access to specialized experimental facilities—for example, the Network for Earthquake Engineering Simulation, NEESgrid; (http://www.neesgrid.org)—to “data grids” for the The Open Grid Services Architecture enables the integration of services and resources across distributed, heterogeneous, dynamic virtual organizations—whether within a single enterprise or extending to external resource-sharing and service-provider relationships.
A Survey of CPU-GPU Heterogeneous Computing Techniques

As both CPUs and GPUs become employed in a wide range of applications, it has been acknowledged that both of these Processing Units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated a significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this article, we survey Heterogeneous Computing Techniques (HCTs) such as workload partitioning that enable utilizing both CPUs and GPUs to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler, and application levels. Further, we review both discrete and fused CPU-GPU systems and discuss benchmark suites designed for evaluating Heterogeneous Computing Systems (HCSs). We believe that this article will provide insights into the workings and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.
A comparative evaluation of outlier detection algorithms: Experiments and analyses

We survey unsupervised machine learning algorithms in the context of outlier detection. This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning. The selected methods are benchmarked on publicly available datasets and novel industrial datasets. Each method is then submitted to extensive scalability, memory consumption and robustness tests in order to build a full overview of the algorithms’ characteristics.
An Instrument for Measuring Customer Satisfaction Toward Web Sites That Market Digital Products and Services

MIS literature has not addressed the measurement of customer information satisfaction in electronic commerce. Current models for measuring user information satisfaction (UIS) and end-user computing satisfaction (EUCS) are perceived as inapplicable as they are targeted primarily towards either conventional data processing or the end-user computing environment. This study develops a comprehensive model and instrument for measuring customer information satisfaction (CIS) for web sites that market digital products and services. This paper first discusses the concepts and definitions of customer information satisfaction from the literature. We summarize our findings in a theoretical framework. Based on this framework, we develop a measurement instrument to measure customer information satisfaction. The procedures used in generating items, collecting data, and purifying a multiple-item scale are described. We have carefully examined evidences of reliability, content validity, criterion-related validity, convergent validity, discriminant validity, and nomological validity by analyzing data from a quota sample of 520 adult respondents. The norms of the instrument are then developed, and the potential applications for practitioners and researchers are explored. Finally, we conclude this study by discussing limitations and potential future research. We hope that our CIS instrument can be used by other researchers to develop and test Internet marketing and EC theories in the future.
An Overview of BioCreative II.5

We present the results of the BioCreative II.5 evaluation in association with the FEBS Letters experiment, where authors created Structured Digital Abstracts to capture information about protein-protein interactions. The BioCreative II.5 challenge evaluated automatic annotations from 15 text mining teams based on a gold standard created by reconciling annotations from curators, authors, and automated systems. The tasks were to rank articles for curation based on curatable protein-protein interactions; to identify the interacting proteins (using UniProt identifiers) in the positive articles (61); and to identify interacting protein pairs. There were 595 full-text articles in the evaluation test set, including those both with and without curatable protein interactions. The principal evaluation metrics were the interpolated area under the precision/recall curve (AUC iP/R), and (balanced) F-measure. For article classification, the best AUC iP/R was 0.70; for interacting proteins, the best system achieved good macroaveraged recall (0.73) and interpolated area under the precision/recall curve (0.58), after filtering incorrect species and mapping homonymous orthologs; for interacting protein pairs, the top (filtered, mapped) recall was 0.42 and AUC iP/R was 0.29. Ensemble systems improved performance for the interacting protein task.
Plant Leaf Recognition using Shape Based Features and Neural Network Classifiers

This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using two different shape modeling techniques, the first based on the Moments-Invariant (M-I) model and the second on the CentroidRadii (C-R) model. For the M-I model the first four normalized central moments have been considered and studied in various combinations viz. individually, in joint 2-D and 3-D feature spaces for producing optimum results. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the feature vector. To further improve the accuracy, a hybrid set of features involving both the M-I and C-R models has been generated and explored to find whether the combination feature vector can lead to better performance. Neural networks are used as classifiers for discrimination. The data set consists of 180 images divided into three classes with 60 images each. Accuracies ranging from 90%-100% are obtained which are comparable to the best figures reported in extant literature. Keywords-plant recognition; moment invariants; centroid-radii model; neural network; computer vision.