nils-herrmann/clean_scirepeval huggingface dataset:
REDUNDANT IMUS FOR PRECISE TRAJECTORY DETERMINATION

A redundant inertial measurement unit (IMU) is an inertial sensing device composed by more than three accelerometers and three gyroscopes. This paper analyses the performance of redundant IMUs and their potential benefits and applications in airborne remote sensing and photogrammetry. The theory of redundant IMUs is presented through two different algorithmic approaches. The first approach is to combine the inertial observations, in the observation space, to generate a “synthetic” non-redundant IMU. The second approach modifies the INS mechanization equations so that they directly account for the observational redundancy. The paper ends with an empirical assesment of the concept. For this purpose, redundant IMU data was generated by combining two IMUs in a non-orthogonal configuration and flying them. Preliminary results of this flight are presented.
200V Super Junction MOSFET Fabricated by High Aspect Ratio Trench Filling

In this paper, a new filling process using an anisotropic epitaxial growth was proposed as the fabrication method of a super junction (SJ) MOSFET. The anisotropic growth controlled with silicon (Si) and chlorine (Cl) source gases was applied to filling of the high-aspect-ratio trenches without voids. Using the process we succeeded in filling trenches with aspect ratio of 18, which is the highest aspect ratio works previously reported, and we fabricated a 200V SJ-MOSFET with a 2.7mum pitch trench gate structure. Consequently, the MOSFET showed electrical characteristics of the lowest on-resistance of 1.5 m-ohm-cm 2 and a breakdown voltage of 225V
Do Personal Ethics Influence Corporate Ethics ?

In this paper, we introduce a new measure of personal ethics in the form of marital cheating to examine the relationship between personal ethics and corporate misconduct. Firms with CEOs and CFOs who use a marital infidelity website are more than twice as likely to engage in two forms of corporate misconduct. The relationship is not explained by a wide range of regional, firm, and executive characteristics or by the infidelity website usage of other executives. Additionally, white-collar SEC defendants also have elevated levels of infidelity website usage. Our findings suggest that personal and professional ethics are closely related.
High-Speed (MHz) Series Resonant Converter (SRC) Using Multilayered Coreless Printed Circuit Board (PCB) Step-Down Power Transformer

In this paper, design and analysis of an isolated low-profile, series resonant converter (SRC) using multilayered coreless printed circuit board (PCB) power transformer was presented. For obtaining the stringent height switch mode power supplies, a multilayered coreless PCB power transformer of approximately 4:1 turn's ratio was designed in a four-layered PCB laminate that can be operated in megahertz switching frequency. The outermost radius of the transformer is 10 mm with achieved power density of 16 W/cm2. The energy efficiency of the power transformer is found to be in the range of 87-96% with the output power level of 0.1-50 W operated at a frequency of 2.6 MHz. This designed step-down transformer was utilized in the SRC and evaluated. The supply voltage of the converter is varied from 60-120 VDC with a nominal input voltage of 90 V and has been tested up to the power level of 34.5 W. The energy efficiency of the converter under zero-voltage switching condition is found to be in the range of 80-86.5% with the switching frequency range of 2.4-2.75 MHz. By using a constant off-time frequency modulation technique, the converter was regulated to 20 VDC for different load conditions. Thermal profile with converter loss at nominal voltage is presented.
Domain Isolation in a Multi-tenant Software-Defined Network

Software-Defined Networking (SDN) has evolved as a new networking paradigm to solve many of current obstacles and limitations in communication networks. While initially intended mainly for single-domain networks, SDN technology is going to be deployed also to large cloud-based data centers where several customers, called tenants, share network resources. In a multi-tenant environment, the SDN technology allows the customers to have higher level of control over the available network resources. However, as the underlying network elements and control logic are shared between multiple tenants, the isolation between tenant domains becomes an important factor in the design of all multi-tenant solutions. In this paper, we propose a scalable system architecture based on OpenFlow and packet rewriting that provides isolation and controlled sharing between tenants while enabling them to have control over their assigned resources. The architecture addresses different facets of isolation in a multi-tenant network including traffic, address space, and control isolation. Our solution improves on previous ones by putting special emphasis on inter-tenant communication, e.g. on subcontractor relations in cloud services. The evaluation of the prototype indicates that our solution puts only a small performance overhead on forwarding in a shared network.
Rational Trust Modeling

Trust models are widely used in various computer science disciplines. The primary purpose of a trust model is to continuously measure the trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of rational trust modeling is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is the novelty of our approach. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer’s perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivize trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict the behavior of the players in subsequent steps by game theoretical analyses.
The Therapeutic Potentials of Ayahuasca: Possible Effects against Various Diseases of Civilization

Ayahuasca is an Amazonian psychoactive brew of two main components. Its active agents are β-carboline and tryptamine derivatives. As a sacrament, ayahuasca is still a central element of many healing ceremonies in the Amazon Basin and its ritual consumption has become common among the mestizo populations of South America. Ayahuasca use amongst the indigenous people of the Amazon is a form of traditional medicine and cultural psychiatry. During the last two decades, the substance has become increasingly known among both scientists and laymen, and currently its use is spreading all over in the Western world. In the present paper we describe the chief characteristics of ayahuasca, discuss important questions raised about its use, and provide an overview of the scientific research supporting its potential therapeutic benefits. A growing number of studies indicate that the psychotherapeutic potential of ayahuasca is based mostly on the strong serotonergic effects, whereas the sigma-1 receptor (Sig-1R) agonist effect of its active ingredient dimethyltryptamine raises the possibility that the ethnomedical observations on the diversity of treated conditions can be scientifically verified. Moreover, in the right therapeutic or ritual setting with proper preparation and mindset of the user, followed by subsequent integration of the experience, ayahuasca has proven effective in the treatment of substance dependence. This article has two important take-home messages: (1) the therapeutic effects of ayahuasca are best understood from a bio-psycho-socio-spiritual model, and (2) on the biological level ayahuasca may act against chronic low grade inflammation and oxidative stress via the Sig-1R which can explain its widespread therapeutic indications.
Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings

A review and meta-analysis was performed of seventy-five articles concerned with innovation characteristics and their relationship to innovation adoption and implementation. One part of the analysis consisted of constructing a methodological profile of the existing studies, and contrasting this with a hypothetical optimal approach. A second part of the study employed meta-analytic statistical techniques to assess the generality and consistency of existing empirical findings. Three innovation characteristics (compatibility, relative advantage, and complexity) had the most consistent significant relationship to innovation adoption. Suggestions for future research in the area were made.
A Hybrid Switched Reactive-Based Visual Servo Control of 5-DOF Robot Manipulators for Pick-and-Place Tasks

This paper addresses the problem of visual servo control for a five-degree-of-freedom robot manipulator to perform pick-and-place tasks. A new hybrid switched reactive-based visual servo control structure, inspired from a sensing-and-reaction behavior without the inverse interaction matrix computing, is proposed to handle this problem efficiently. The proposed structure is similar to the logic-based approaches, but the requirements of the fuzzy modeling and/or the fuzzy-rule base learning are omitted. To achieve this, a novel hardware-aware reactive function approach is presented to directly map an image position error vector to a desired end-effector velocity vector under the consideration of hardware limitations. This approach helps to simplify the implementation of visual servo systems with improved reliability. Moreover, the proposed control system is a hybrid switching controller consisting of both image-based and position-based reactive planning schemes, which allows improving the robustness and effectiveness of the visual servo system. Experimental results validate the performance of the proposed visual servo control method in a realistic scenario setting.
A stochastic model of human-machine interaction for learning dialog strategies

In this paper, we propose a quantitative model for dialog systems that can be used for learning the dialog strategy. We claim that the problem of dialog design can be formalized as an optimization problem with an objective function reflecting different dialog dimensions relevant for a given application. We also show that any dialog system can be formally described as a sequential decision process in terms of its state space, action set, and strategy. With additional assumptions about the state transition probabilities and cost assignment, a dialog system can be mapped to a stochastic model known as Markov decision process(MDP). A variety of data driven algorithms for finding the optimal strategy (i.e., the one that optimizes the criterion) is available within the MDP framework, based on reinforcement learning. For an effective use of the available training data we propose a combination of supervised and reinforcement learning: the supervised learning is used to estimate a model of the user, i.e., the MDP parameters that quantify the user’s behavior. Then a reinforcement learning algorithm is used to estimate the optimal strategy while the system interacts with the simulated user. This approach is tested for learning the strategy in an air travel information system (ATIS) task. The experimental results we present in this paper show that it is indeed possible to find a simple criterion, a state space representation, and a simulated user parameterization in order to automatically learn a relatively complex dialog behavior, similar to one that was heuristically designed by several research groups.
Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data

Real-time optimization of traffic flow addresses important practical problems: reducing a driver’s wasted time, improving citywide efficiency, reducing gas emissions and improving air quality. Much of the current research in traffic-light optimization relies on extending the capabilities of traffic lights to either communicate with each other or communicate with vehicles. However, before such capabilities become ubiquitous, opportunities exist to improve traffic lights by being more responsive to current traffic situations within the current, already deployed, infrastructure. In this paper, we introduce a traffic light controller that employs bidding within micro-auctions to efficiently incorporate traffic sensor information; no other outside sources of information are assumed. We train and test traffic light controllers on large-scale data collected from opted-in Android cell-phone users over a period of several months in Mountain View, California and the River North neighborhood of Chicago, Illinois. The learned auction-based controllers surpass (in both the relevant metrics of road-capacity and mean travel time) the currently deployed lights, optimized static-program lights, and longer-term planning approaches, in both cities, measured using real user driving data.
Constrained model predictive control: Stability and optimality

Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a "nite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and the "rst control in this sequence is applied to the plant. An important advantage of this type of control is its ability to cope with hard constraints on controls and states. It has, therefore, been widely applied in petro-chemical and related industries where satisfaction of constraints is particularly important because e$ciency demands operating points on or close to the boundary of the set of admissible states and controls. In this review, we focus on model predictive control of constrained systems, both linear and nonlinear and discuss only brie#y model predictive control of unconstrained nonlinear and/or time-varying systems. We concentrate our attention on research dealing with stability and optimality; in these areas the subject has developed, in our opinion, to a stage where it has achieved su$cient maturity to warrant the active interest of researchers in nonlinear control. We distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the "nite horizon optimal control problem solved on-line is exactly equivalent to the same problem with an in"nite horizon; in other cases it is equivalent to a modi"ed in"nite horizon optimal control problem. In both situations, known advantages of in"nite horizon optimal control accrue.
Increased motor control of a phantom leg in humans results from the visual feedback of a virtual leg

Although previous research reported that the visual feedback of a 'virtual arm' increased the control of a phantom arm, it did not consider that the repeated attempt to move the phantom may have contributed to the effect. Twenty-one lower limb amputees reported the response of their phantom leg during repeated attempts to move both legs in one of two conditions, a control condition in which the amputee only viewed the movements of their intact leg and an experimental condition in which the amputee additionally viewed the movements of a 'virtual' leg. It was found that viewing a virtual leg resulted in amputees reporting a significantly greater number of movements of their phantom leg than with attempted movement alone. The implications were discussed in terms of visuo-motor adaptation and theories of motor control.
Serendipity in the Research Literature: A Phenomenology of Serendipity Reporting

The role of information sciences is to connect people with the information they need to accomplish the tasks that contribute to the greater societal good. While evidence of the wonderful contributions arising from serendipitous events abound, the framework describing the information behaviors exhibited during a serendipitous experience is just emerging and additional detail regarding the factors influencing those behaviors is needed in order to support these experiences effectively. Furthermore, it is important to understand the whole process of serendipity to fully appreciate the impact of research policies, disciplinary traditions and academic reporting practices on this unique type of information behavior. This study addresses those need by examining the phenomenon of serendipity as it is reported by biomedical and radiography researchers. A mixed method content analysis of existing research reports will be incorporated with semi-structured interviews of serendipity reporters to gain a robust understanding of the phenomenon of serendipity, and provide detail that may inform the design of information environments.
Grid-Connected Photovoltaic Generation System

This study addresses a grid-connected photovoltaic (PV) generation system. In order to make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency of conventional boost converters and to allow the parallel operation of low-voltage PV modules. Moreover, an adaptive total sliding-mode control system is designed for the current control of the PWM inverter to maintain the output current with a higher power factor and less variation under load changes. In addition, an adaptive step-perturbation method is proposed to achieve the objective of maximum power point tracking, and an active sun tracking scheme without any light sensors is investigated to make PV plates face the sun directly in order to capture maximum irradiation and enhance system efficiency. Experimental results are given to verify the validity of the high step-up converter, the PWM inverter control, the ASP method, and the active sun tracker for a grid-connected PV generation system.
Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing

Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.
Proposed Architecture of MongoDB-Hive Integration

There is tremendous growth in heterogeneous and unstructured data in last few years. Various NoSQL databases have been developed to store and query this humongous data. MongoDB is prevailing document oriented database among NoSQL databases. MongoDB has been chosen among other technologies because of its ability to work with variety of latest as well as conventional technologies. It provides drivers for almost every development language. This paper summarizes work of various authors who have compared and integrated MongoDB with other big data technologies. In most cases, MongoDB gives better performance in terms of various parameters. Further, after carrying out critical analysis , an architecture is being proposed to integrate MongoDB with Hive. It utilizes the SQL like features of Hive for SQL familiar users.
Logic-Based Models for the Analysis of Cell Signaling Networks†

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.
Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis.

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.