nils-herrmann/clean_scirepeval huggingface dataset:
U.S. decennial life tables for 1999-2001: state life tables.

This report, following publication of the national life tables (1,2) for 1999-2001, presents state-specific life tables for the 50 states and District of Columbia by race (white and black) and sex. These tables are the most recent in a series of decennial life tables for the United States.



Data used to prepare these state-specific life tables include population counts by age on the census date of April 1, 2000; deaths occurring in the 3-year period of 1999-2001; and counts of U.S. resident births during 1997-2001. Methods for calculating the life tables were modified from the previous decennial life tables to automate the smoothing of age-specific mortality data and to allow for the estimation of life tables for smaller population subgroups, which often had insufficient data available to estimate reliable life tables under the previous method. The current method allows for the estimation of life tables for the black population in six states, which were never previously published due to small numbers of deaths. Standard errors for estimating life expectancy and probability of dying are also presented in this report.



Among the 50 states, Hawaii had the highest life expectancy at birth during the 1999-2001 period at 80.23 years, and Mississippi had the lowest life expectancy at 73.88 years. Life expectancy for the District of Columbia was even lower at 73.09 years. State-specific life expectancy at birth improved from the previous decennial period (1989-1991) for all states and the District of Columbia. Life expectancy at age 65 ranged from 20.42 years in Hawaii to 16.61 years in Kentucky. Life expectancy at age 65 also improved for all states except Kentucky.
An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

In this correspondence we have not addressed the problem of constructing actual codebooks. Information theory indicates that, in principle , one can construct a codebook by drawing each component of each codeword independently, using the distribution obtained from the Blahut algorithm. This procedure is not in general practical. Practical ways to construct codewords may be found in the extensive literature on vector quantization (see, e.g., the tutorial paper by R. M. Gray [19] or the book [20]). It is not clear at this point if codebook constructing methods from the vector quantizer literature are practical in the setting of this correspondence. Alternatively, one can trade complexity and performance and construct a scalar quantizer. In this case, the distribution obtained from the Blahut algorithm may be used in the Max–Lloyd algorithm [21], [22]. Grenander, " Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recog-nition, " IEEE Trans.matic target recognition organized via jump-diffusion algorithms, " IEEE bounds for estimators on matrix lie groups for atr, " A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, a hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to account for pixel variability, similarity, and discrimination. In this correspondence, we present a new hyperspectral measure, Spectral Information Measure (SIM), to describe spectral variability and two criteria, spectral information divergence and spectral discriminatory probability, for spectral similarity and discrimination, respectively. The spectral information measure is an information-theoretic measure which treats each pixel as a random variable using its spectral signature histogram as the desired probability distribution. Spectral Information Divergence (SID) compares the similarity between two pixels by measuring the probabilistic discrepancy between two corresponding spectral signatures. The spectral discriminatory probability calculates spectral probabilities of a spectral database (library) relative to a pixel to be identified so as to achieve material identification. In order to compare the discriminatory power of one spectral measure relative to another , a criterion is also introduced for performance evaluation, which is based on the power of discriminating one pixel from another relative to a reference pixel. The experimental results demonstrate that the new hyper-spectral measure can characterize spectral variability more effectively than the commonly used Spectral Angle Mapper (SAM).
Predictive analytics for banking user data using AWS Machine Learning cloud service

The aim of the project is to develop a Machine Learning model to perform predictive analytics on the banking dataset. The banking data set consists of details about customers like and whether the customer will buy a product provided by the bank or not. The data set is obtained from University of California Irvine Machine Learning Repository. This data set is used to create a binary classification model using Amazon Web Service(AWS) Machine Learning platform. 70 % of the data is used to train the binary classification model and 30 % of the dataset is used to test the model. Depending upon the test result we evaluate the essential parameters like precision, recall, accuracy and false positive rates. These parameters evaluate the efficiency of our model. Once we design our model we test our model using two features in AWS Machine learning. One, using real time prediction where we give real time input data and test our model. Two, we do batch prediction, where we have a set of customer data and we upload our data to evaluate our prediction.
Artificial Intelligence and Software Engineering: Status and Future Trends

During the last decades the disciplines of Artificial Intelligence (AI) and Software Engineering (SE) have developed separately without much exchange of research results. In AI we researched techniques for the computations that made it possible to perceive, reason, and act. Research in SE was concerned with supporting human beings to develop better software faster. Today, several research directions of both disciplines come closer together and are beginning to build new research areas. Software Agents play an important role as research objects in Distributed AI (DAI) as well as in agentoriented software engineering (AOSE). Knowledge-Based Systems (KBS) are being investigated for learning software organizations (LSO) as well as knowledge engineering. Ambient Intelligence (AmI) is a new research area for distributed, non-intrusive, and intelligent software systems both from the direction of how to build these systems as well as how to design the collaboration between ambient systems. Last but not least, Computational Intelligence (CI) plays an important role in research about software analysis or project management as well as knowledge discovery in databases or machine learning. Furthermore, in the last five to ten years several books, journals, and conferences have focused on the intersection between AI and SE. The international conference and associated journal Automated Software Engineering (ASE) presents research about formal and autonomic approaches to support SE . Similar topics with a stronger focus on KBS and knowledge management are published in the international conference and associated journal of Software Engineering and Knowledge Engineering (IJSEKE) . In this paper, we give a short overview about the status and future trends in the intersection between AI and SE. We focus on the topics software agents, KBS, AmI, and CI as the areas covered by the contributions of this special issue. In Section 2 we describe the disciplines AI and SE.
Agent Based Framework for Scalability in Cloud Computing

Cloud computing focuses on delivery of reliable, secure, faulttolerant, sustainable, and scalable infrastructures for hosting internet-based application services. These applications have different composition, configuration, and deployment requirements. Cloud service providers are willing to provide large scaled computing infrastructure at a cheap prices. Quantifying the performance of scheduling and allocation policy on a Cloud infrastructure (hardware, software, services) for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is an extremely challenging problem to tackle. This problem can be tackle with the help of mobile agents. Mobile agent being a process that can transport its state from one environment to another, with its data intact, and is capable of performing appropriately in the new environment. This work proposes an agent based framework for providing scalability in cloud computing environments supported with algorithms for searching another cloud when the approachable cloud becomes overloaded and for searching closest datacenters with least response time of virtual machine (VM).
A randomized controlled trial comparing the AccuVein AV 400 device to standard insertion technique for peripheral intravenous cannulation by experienced nurse anesthetists in obese patients undergoing elective surgery

Obesity is a predictive factor for difficulty in peripheral intravenous insertion. The AccuVein AV400 device was developed to assist in peripheral vein identification and venipuncture. It can identify peripheral veins lying 10 mm subcutaneously using near infrared laser light. Objective was to evaluate the efficacy of the AccuVein AV400 device in improving the first-time success rate of peripheral intravenous cannulation in obese patients.  Seventy-two patients with a body mass index (BMI) of 30 kg/m2 or more were randomized into two groups; the AV400 group (using the AccuVein AV400 device for peripheral venous insertion) and a standard group (using the naked eye and/or palpation for peripheral venous insertion). The providers were three experienced nurse anesthetists. An observer recorded the number of attempts to successful cannulation and the time between tourniquet application and successful cannulation. Failure was defined as when more than four cannulation attempts were done in either technique.  The BMIs of the two groups were comparable [33.7 ± 4.6 kg/m2 in the AV400 group and 34.0 ± 4.6 kg/m2 in the standard group; (p = 0.77)]. There was no significant difference in the first-attempt success rate between the AV400 group (52.8%) and the standard group (58.3%) (p = 0.81). The time to success was 41.5 (17.5 62.5) seconds (95% CI, 36.1 115.8) using the AV400 device and 77  seconds (95% CI, 57.3 214.3) with the standard technique (p = 0.12).  Although the AccuVein AV400 improved visualization of the peripheral veins, we found no statistically and Conclusion: significant difference in first-attempt success rates between standard cannulation and the use of the AccuVein AV400 device in unselected obese patients.
A Comprehensive Review on Metabolic Syndrome

Metabolic syndrome is defined by a constellation of interconnected physiological, biochemical, clinical, and metabolic factors that directly increases the risk of cardiovascular disease, type 2 diabetes mellitus, and all cause mortality. Insulin resistance, visceral adiposity, atherogenic dyslipidemia, endothelial dysfunction, genetic susceptibility, elevated blood pressure, hypercoagulable state, and chronic stress are the several factors which constitute the syndrome. Chronic inflammation is known to be associated with visceral obesity and insulin resistance which is characterized by production of abnormal adipocytokines such as tumor necrosis factor α, interleukin-1 (IL-1), IL-6, leptin, and adiponectin. The interaction between components of the clinical phenotype of the syndromewith its biological phenotype (insulin resistance, dyslipidemia, etc.) contributes to the development of a proinflammatory state and further a chronic, subclinical vascular inflammation which modulates and results in atherosclerotic processes. Lifestyle modification remains the initial intervention of choice for such population.Modern lifestylemodification therapy combines specific recommendations on diet and exercise with behavioural strategies. Pharmacological treatment should be considered for those whose risk factors are not adequately reduced with lifestyle changes. This review provides summary of literature related to the syndrome’s definition, epidemiology, underlying pathogenesis, and treatment approaches of each of the risk factors comprising metabolic syndrome.
An integrated modeling method for assessment of quality systems applied to aerospace manufacturing supply chains

In highly regulated industries such as aerospace, the introduction of new quality standard can provide the framework for developing and formulating innovative novel business models which become the foundation to build a competitive, customer-centric enterprise. A number of enterprise modeling methods have been developed in recent years mainly to offer support for enterprise design and help specify systems requirements and solutions. However, those methods are inefficient in providing sufficient support for quality systems links and assessment. The implementation parts of the processes linked to the standards remain unclear and ambiguous for the practitioners as a result of new standards introduction. This paper proposed to integrate new revision of AS/EN9100 aerospace quality elements through systematic integration approach which can help the enterprises in business re-engineering process. The assessment capability model is also presented to identify impacts on the existing system as a result of introducing new standards.
Fast damped gauss-newton algorithm for sparse and nonnegative tensor factorization

Alternating optimization algorithms for canonical polyadic decomposition (with/without nonnegative constraints) often accompany update rules with low computational cost, but could face problems of swamps, bottlenecks, and slow convergence. All-at-once algorithms can deal with such problems, but always demand significant temporary extra-storage, and high computational cost. In this paper, we propose an all-at-once algorithm with low complexity for sparse and nonnegative tensor factorization based on the damped Gauss-Newton iteration. Especially, for low-rank approximations, the proposed algorithm avoids building up Hessians and gradients, reduces the computational cost dramatically. Moreover, we proposed selection strategies for regularization parameters. The proposed algorithm has been verified to overwhelmingly outperform “state-of-the-art” NTF algorithms for difficult benchmarks, and for real-world application such as clustering of the ORL face database.
A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes

We formulate a multi-armed bandit (MAB) approach to choosing expert policies online in Markov decision processes (MDPs). Given a set of expert policies trained on a state and action space, the goal is to maximize the cumulative reward of our agent. The hope is to quickly find the best expert in our set. The MAB formulation allows us to quantify the performance of an algorithm in terms of the regret incurred from not choosing the best expert from the beginning. We first develop the theoretical framework for MABs in MDPs, and then present a basic regret decomposition identity. We then adapt the classical Upper Confidence Bounds algorithm to the problem of choosing experts in MDPs and prove that the expected regret grows at worst at a logarithmic rate. Lastly, we validate the theory on a small MDP. ar X iv :1 70 7. 05 71 4v 1 [ cs .S Y ] 1 8 Ju l 2 01 7
Evaluation of different substrates for inkjet printing of rasagiline mesylate.

The main goal of the present study was to evaluate applicability of the different model substrates, namely orodispersible films (ODFs), porous copy paper sheets, and water impermeable transparency films (TFs) in preparation of the inkjet-printed drug-delivery systems. Rasagiline mesylate (RM) was used as a low-dose active pharmaceutical ingredient (API). Flexible doses of the drug in a single unit were obtained by printing several subsequent layers on top of the already printed ones, using an off-the-shelf consumer thermal inkjet (TIJ) printer. The produced drug-delivery systems were subjected to microscopic and chemical analysis together with solid-state characterization and content uniformity studies. The results revealed that RM recrystallized on the surface of ODFs and TFs, and the printed crystals were arranged in lines. No drug crystals were detected after printing on the surface of the copy paper due to absorption of the ink into the matrix of the substrate. The best linear correlation between the dose of the drug and the number of the printing layers was obtained for the porous copy paper. The other two substrates showed poor linearity and unacceptable standard deviations of the printed drug substance due to limited absorption of the API ink into the carrier. The shear stress between the substrate, the print head, and the paper feeding rollers caused smearing of the drug that had been surface-deposited during the earlier printing cycles. In conclusion, this study indicates that the edible substrates with absorption properties similar to copy paper are favorable for successful preparation of drug-delivery systems by TIJ printers.
Improving Smart Home Security: Integrating Logical Sensing Into Smart Home

This paper explains various security issues in the existing home automation systems and proposes the use of logic-based security algorithms to improve home security. This paper classifies natural access points to a home as primary and secondary access points depending on their use. Logic-based sensing is implemented by identifying normal user behavior at these access points and requesting user verification when necessary. User position is also considered when various access points changed states. Moreover, the algorithm also verifies the legitimacy of a fire alarm by measuring the change in temperature, humidity, and carbon monoxide levels, thus defending against manipulative attackers. The experiment conducted in this paper used a combination of sensors, microcontrollers, Raspberry Pi and ZigBee communication to identify user behavior at various access points and implement the logical sensing algorithm. In the experiment, the proposed logical sensing algorithm was successfully implemented for a month in a studio apartment. During the course of the experiment, the algorithm was able to detect all the state changes of the primary and secondary access points and also successfully verified user identity 55 times generating 14 warnings and 5 alarms.
Exploring Trade-off&#8217;s Between Centralized versus Decentralized Automotive Architectures Using a Virtual Integration Environment

The large variety of architectural dimensions in automotive electronics design, for example, bus protocols, number of nodes, sensors and actuators interconnections and power distribution topologies, makes architecture design task a very complex but crucial design step especially for OEMs. This situation motivates the need for a design environment that accommodates the integration of a variety of models in a manner that enables the exploration of design alternatives in an efficient and seamless fashion. Exploring these design alternatives in a virtual environment and evaluating them with respect to metrics such as cost, latency, flexibility and reliability provide an important competitive advantage to OEMs and help minimize integration risks later in the design cycle. In particular, the choice of the degree of decentralization of the architecture has become a crucial issue in automotive electronics. In this paper, we demonstrate how a rigorous methodology (platform-based design) and the Metropolis framework can be used to find the balance between centralized and decentralized architectures
Human Factors and Ergonomics in transportation control systems

Employing case studies taken from work experience in the UK, USA, Canada and Japan this paper observes the evolution of Human Factors (HF) and ergonomics in the railroadfrom a practitioner’s point of view. Practical areas for application of HF at specific points in railroadsignaling and control systems are described. HF considerations in advanced train control systems and the movement towards automation are discussed as well as the impact of these new technologies on the context of operation itself. There is now a greater reliance on the operator to remain vigilant and react efficiently when intervention on automation is required both within the control room and driver cab environments. This paper illustrates some of the human performance concerns for novel transportation control systems that are faced todayand discusses how this area of cognitive attention, human error and workload is difficult to assess and predict.
Physics and Medicine 1 Physics and medicine: a historical perspective

Nowadays, the term medical physics usually refers to the work of physicists employed in hospitals, who are concerned mainly with medical applications of radiation, diagnostic imaging, and clinical measurement. This involvement in clinical work began barely 100 years ago, but the relation between physics and medicine has a much longer history. In this report, I have traced this history from the earliest recorded period, when physical agents such as heat and light began to be used to diagnose and treat disease. Later, great polymaths such as Leonardo da Vinci and Alhazen used physical principles to begin the quest to understand the function of the body. After the scientifi c revolution in the 17th century, early medical physicists developed a purely mechanistic approach to physiology, whereas others applied ideas derived from physics in an eff ort to comprehend the nature of life itself. These early investigations led directly to the development of specialties such as electrophysiology, biomechanics, and ophthalmology. Physics-based medical technology developed rapidly during the 19th century, but it was the revolutionary discoveries about radiation and radioactivity at the end of the century that ushered in a new era of radiation-based medical diagnosis and treatment, thereby giving rise to the modern medical physics profession. Subsequent developments in imaging in particular have revolutionised the practice of medicine. We now stand on the brink of a new revolution in post-genomic personalised medicine, with physics-based techniques again at the forefront. As before, these techniques are often the unpredictable fruits of earlier investment in basic physics research.
Fundamentals of Molecular Information and Communication Science

Molecular communication (MC) is the most promising communication paradigm for nanonetwork realization since it is a natural phenomenon observed among living entities with nanoscale components. Since MC significantly differs from classical communication systems, it mandates reinvestigation of information and communication theoretical fundamentals. The closest examples of MC architectures are present inside our own body. Therefore, in this paper, we investigate the existing literature on intrabody nanonetworks and different MC paradigms to establish and introduce the fundamentals of molecular information and communication science. We highlight future research directions and open issues that need to be addressed for revealing the fundamental limits of this science. Although the scope of this development encompasses wide range of applications, we particularly emphasize its significance for life sciences by introducing potential diagnosis and treatment techniques for diseases caused by dysfunction of intrabody nanonetworks.