Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence in male and female life expectancy: Direction, age pattern, and causes

BACKGROUND The cornerstone of mortalityand life-expectancy forecasting in developed nations, the Lee-Carter model relies on assumptions of there being a dominant singular value that captures most of the variance within a matrix of age-specific mortality rates over time and that the time trend captured by this lead singular value is constant. We revisit the model’s predictive ability and trends in mortality decline among developed nations since the end of the Cold War. OBJECTIVE To understand the predictive power of the Lee-Carter model with mortality trends since 1990. METHODS Mortality data were obtained from the Human Mortality Database. Forecasts were made using R with random walk forecasts using the package forecast. RESULTS While Lee-Carter forecasts of life expectancy for combined sexes were accurate, sexspecific forecasts tended to somewhat overestimate for females and significantly underestimate for males. Further investigation of the trend for males shows that the first singular value continues to capture the majority of the variation in mortality since 1990, with progress along this dimension moving at a constant rate. 1 Stanford University, School of Medicine. U.S.A. E-Mail:bensel@gmail.com. 2 Stanford University, Department of Statistics. U.S.A. 3 Stanford University, Department of Biology. U.S.A. http://www.demographic-research.org 1063 Seligman, Greenberg & Tuljapurkar: Convergence in male and female life expectancy CONCLUSIONS Lee-Carter forecasts have significantly underestimated gains in male life expectancy without major changes to the model’s assumptions. We believe this represents more rapid progress in tackling male mortality in the G7 countries without major changes to the age pattern of these gains. Curiously, this has not affected combined-sex forecasts, potentially being offset by slight overestimation of female mortality progress. CONTRIBUTION We show that the Lee-Carter model has made inaccurate forecasts of mortality rates unrelated to violations of its underlying assumptions.
Multiagent-based distributed control for operation cost minimization of droop controlled DC microgrid using incremental cost consensus

In this paper, a multiagent based distributed control is proposed for DC microgrid to minimize the operation cost. The power of each distributed generator (DG) is dispatched in a distributed manner in a multiagent system by means of voltage scheduling. Every DG unit is taken as an agent, and they share the load corresponding to the operation cost of all the units in the system with only communication with direct neighbors through incremental cost consensus. The power regulation according to the power reference generated by consensus is implemented through voltage scheduling in local primary controllers. Simulation verification shows that total operation cost of the DC microgrid is successfully reduced though the proposed method.
A Robust Iterative Super-Resolution Reconstruction of Image Sequences using a Lorentzian Bayesian Approach with Fast Affine Block-Based Registration

Although the topic of Super-Resolution Reconstruction (SRR) has recently received considerable attention within the traditional research community, the SRR estimations are based on LI or L2 statistical norm estimation. Therefore, these SRR methods are very sensitive to their assumed data and noise models, which limit their applications. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using LI or L2 norm may degrade the image sequence rather than enhance it. The robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes an alternate SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Lorentzian norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data. Tikhonov regularization is used to remove artifacts from the final result and improve the rate of convergence. In order to cope with real sequences and complex motion sequences, the fast affine block-based registration is used in the registration step of SRR. The experimental results show that the proposed reconstruction can be applied on real sequences such as Suzie sequence and confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on LI and L2 norm for several noise models such as AWGN, Poisson and Salt & Pepper noise.
A framework for human-machine interaction using Depthmap and compactness

In recent past research and development in the field of Human-computer/machine interaction (HMI) is on fast track because there has been enough advancement of technologies such as advanced image processing techniques and sensors etc. which provides ease to formulate a test bed to experiment various HMI. A device called Kinect has been introduced for controlling functionalities of windows operating system, which is being widely used in Xbox games by Microsoft. This paper illustrates one of the popular HMI application called mouse control using hand gesture recognition. The method is named as hand-mouse (HM). HM tracks the full body vital core point and the movement of palm controls the movement of mouse cursor and the opening and closing of palm controls a mouse click events. The HM system is using Kinect in order to avoid wearing of any sensors or gesture training dataset, which reduces the computational overhead and experimental results demonstrates a real time output with satisfactory results having higher true positive and lesser false negative.
Fibronectin is produced by human macrophages

Monocyte-enriched cultures were prepared from human blood mononuclear leukocytes by adherence to growth substratum. Synthesis and secretion of fibronectin was detected in these cultures concomitantly with morphological differentiation, starting on day 3--5. Production of fibronectin by macrophages was documented by metabolic labeling followed by immunoprecipitation and gel electrophoresis, radioimmunoassay specific for human fibronectin, and by indirect immunofluorescence. Fibronectin was detected mainly intracellularly but was also detected pericellularly only in minute amounts. No production of collagenous proteins was seen in these cultures. Macrophage fibronectin might act in vivo as a nonspecific opsonin and promote cell adhesion during macrophage migration in tissues.
SixthSense: Fast and Reliable Recognition of Dead Ends in MDPs

The results of the latest International Probabilistic Planning Competition (IPPC-2008) indicate that the presence of dead ends, states with no trajectory to the goal, makes MDPs hard for modern probabilistic planners. Implicit dead ends, states with executable actions but no path to the goal, are particularly challenging; existing MDP solvers spend much time and memory identifying these states. As a first attempt to address this issue, we propose a machine learning algorithm called SIXTHSENSE. SIXTHSENSE helps existing MDP solvers by finding nogoods, conjunctions of literals whose truth in a state implies that the state is a dead end. Importantly, our learned nogoods are sound, and hence the states they identify are true dead ends. SIXTHSENSE is very fast, needs little training data, and takes only a small fraction of total planning time. While IPPC problems may have millions of dead ends, they may typically be represented with only a dozen or two no-goods. Thus, nogood learning efficiently produces a quick and reliable means for dead-end recognition. Our experiments show that the nogoods found by SIXTHSENSE routinely reduce planning space and time on IPPC domains, enabling some planners to solve problems they could not previously handle.
Nitrogen Metabolism in Saccharomyces cerevisiae

INTRODUCTION In decaying fruit and vegetable matter, which form their wild habitat, yeast cells encounter a broad spectrum of compounds able to serve as nitrogen sources. These materials range from simple ammonia and amino acids to complex nucleic acids and their derivatives. In response to this heterogeneous environment, Saccharomyces cerevisiae has evolved equally broad degradative enzyme systems and sophisticated ways of regulating and integrating their operation.1 In the past, enteric bacteria have served as a model for viewing the control of nitrogen metabolism in yeast. As a starting point, these past paradigms have served us well. However, they are certain to be inadequate for the future. An important area of deficiency is their failure to involve the complex internal structure of a yeast cell in the control and integration of nitrogen metabolism. Ignoring such structure and its potential role in cellular homeostasis may mortgage our understanding of a yeast cell’s ability to cope with a constantly changing environment. A SUMMARY OF THE MAJOR CATABOLIC SYSTEMS As shown in Table 1, S. cerevisiae will grow fairly well on many amino acids, uracil, purine derivatives, urea, and ammonia. Sometimes the cells go through only one or two generations (this is indicated in Table 1) before growth ceases. In these cases, the conclusion that cells are able to use the compound effectively must be viewed skeptically. However, in other cases, steady-state growth can easily be maintained. Only a limited number of these compounds and their metabolic fates have been studied; they are briefly.
Online Learning of Robot Soccer Free Kick Plans Using a Bandit Approach

This paper presents an online learning approach for teams of autonomous soccer robots to select free kick plans. In robot soccer, free kicks present an opportunity to execute plans with relatively controllable initial conditions. However, the effectiveness of each plan is highly dependent on the adversary, and there are few free kicks during each game, making it necessary to learn online from sparse observations. To achieve learning, we first greatly reduce the planning space by framing the problem as a contextual multi-armed bandit problem, in which the actions are a set of pre-computed plans, and the state is the position of the free kick on the field. During execution, we model the reward function for different free kicks using Gaussian Processes, and perform online learning using the Upper Confidence Bound algorithm. Results from a physics-based simulation reveal that the robots are capable of adapting to various different realistic opponents to maximize their expected reward during free kicks.
A new approach to flexible multi-joint robot finger

This paper present a new approach to flexible multijoint robot finger mechanism, which consist of three links and similar to a human finger. The proposed mechanism is capable to joint as a multi-joint to realize as a soft gripper or robot hand. The advantage of proposed mechanism is obtaining more Degree-Of-Freedom (DOF) with less actuators and self-adaptive functionality under real life grasping. The structure of proposed mechanism is described and sensor feedback experiment is also given. The application of proposed mechanism to agriculture application is also discussed.
CPAIOR ’ 03 Solving the Maximum Clique Problem with Constraint Programming

This paper aims to show that Constraint Programming can be an efficient technique to solve a well-known combinatorial optimization problem: the search for a maximum clique in a graph. A clique of a graph G = (X, E) is a subset V of X, such that every two nodes in V are joined by an edge of E. The maximum clique problem consists of finding ω(G) the largest cardinality of a clique. We propose two new upper bounds of ω(G) and a new strategy to guide the search for an optimal solution. The interest of our approach is emphasized by the results we obtain for the DIMACS Benchmarks. Seven instances are solved for the first time and two better lower bounds for problems remaining open are found. Moreover, we show that the CP method we propose gives good results and quickly.
Deconvolution of the Two-Dimensional Bar Code Based on Binary Constraint

The density of the two-dimensional bar code is high, so the signal is easy to be blurred by the point spread function of the optical system. In order to deblur the image, a novel bar code deconvolution algorithm rooted in binary constraint is proposed in this paper. First, the bar code is rotated to be horizontal using the bilinear interpolation. After analyzing the waveform, the system identification is accomplished. At last, the bar code waveform is reconstructed based on binary constraint. The results show that the performance of the algorithm proposed here is excellent. It can achieve higher recognition rates than previous models.
An Analysis of Industrial Policy Support for the Indian Solar Photovoltaic Manufacturing Sector

National renewable energy policies often have many goals, and frequently an aspiration is the development of domestic renewable energy technology manufacturing capacity. The Jawaharlal Nehru National Solar Mission (NSM) in India is an example; besides targeting an installation of 20GW of grid-tied solar power capacity, it includes industrial policy to strengthen a photovoltaic solar manufacturing base. We develop and apply a framework to examine the likely efficiency and equity implications of the policy’s domestic content requirement (DCR), which requires 100% of crystalline silicon cells and modules in NSM plants to be sourced domestically. We suggest that any efficiency improvements would be driven largely by surplus gains by domestic manufacturers at the potentially large expense of electricity consumers. Our analysis reveals three observations that suggest this equity implication is unlikely to change throughout the NSM: (1) developers continue to favor substitute thin-film technology, bypassing the DCR, (2) the manufacturing base has become less competitive over time, and (3) ancillary domestic industries that could provide complementary skills are uncompetitive.
Decision Sciences and the New Case for Paternalism : a Critique

Several authors have recently claimed that empirical findings from distinct decision sciences provide compelling reasons in favour of paternalistic interference. Some advocate a so-called new case for paternalism, according to which the available behavioural and neuro-psychological findings enable paternalists to address traditional anti-paternalistic objections and reliably enhance the well-being of their target agents. In this paper, I combine insights from moral philosophy, decision-making research and evidence-based policy evaluation to assess the merits of this case. In particular, I articulate and defend three complementary arguments to demonstrate that, contrary to emerging consensus, the new paternalists have not put forward compelling reasons in favour of paternalistic interference. In doing so, I identify the main challenges faced by the new case for paternalism and explicate the implications of these challenges for the ongoing philosophical debate about the moral justifiability of paternalistic interference.
The Creation and Survival of an Academic Health Science Organization: Counter-Colonization Through a New Organizational Form?

This paper describes and analyses the creation and development of an Academic Health Science Centre (AHSC) as a major organizational innovation diffusing in the health knowledge economy, internationally. Drawing on an institutionalist model utilized in an earlier study of a failed merger in the USA (Kitchener, 2002), we explore empirically why the creation of this new AHSC in the UK produced very different organizational outcomes. Whereas institutionalist framing predicts ‘sedimented’ instability and repeated contest between managerialist and embedded (and ultimately, dominant) professional logics, the higher levels of clinical-academic engagement in our case exerted ‘upwards’ institutional pressure, creating a more stable, collaborative form. Our paper challenges and develops the institutionalist model, and explores the possibility of ’counter-colonization’ through a new organizational form invented in the academic-clinical domain.
Angular momentum evolution of low-mass pre-main sequence stars via extreme coronal mass ejections

The angular momentum evolution of cool stars during the pre-main sequence phase of stellar evolution remains a major outstanding problem. Multiple processes are likely involved in the transfer of mass and angular momentum within and out of the star+disk system. The role of coronal mass ejections (CMEs), energetic events which shed mass and magnetic flux in the Sun, has yet to be fully explored in the context of pre-main sequence stars. It is well established that young, solar-type stars exhibit X-ray activity levels up to four orders of magnitude higher than the present-day Sun, suggesting that CMEs associated with these extreme X-ray flares could be an important process for expelling mass and angular momentum. We present a novel approach to modeling the CMEs of low-mass pre-main sequence stars that uses a solar-calibrated CME model and observed X-ray flare rates for young stars. We derive mass loss rates via stellar CMEs and calculate their attendant angular momentum losses during the pre-main sequence phase. While we find the mass loss rates to be modest, ∼10 % of steady-state stellar wind values from the literature, the angular momentum losses can be substantial, potentially counteracting the effects of initial stellar spin-up due to contraction in tens of Myr.
Formulating Cost-Effective Monitoring Strategies for Service-Based Systems

When operating in volatile environments, service-based systems (SBSs) that are dynamically composed from component services must be monitored in order to guarantee timely and successful delivery of outcomes in response to user requests. However, monitoring consumes resources and very often impacts on the quality of the SBSs being monitored. Such resource and system costs need to be considered in formulating monitoring strategies for SBSs. The critical path of a composite SBS, i.e., the execution path in the service composition with the maximum execution time, is of particular importance in cost-effective monitoring as it determines the response time of the entire SBS. In volatile operating environments, the critical path of an SBS is probabilistic, as every execution path can be critical with a certain probability, i.e., its criticality. As such, it is important to estimate the criticalities of different execution paths when deciding which parts of the SBS to monitor. Furthermore, cost-effective monitoring also requires management of the trade-off between the benefit and cost of monitoring. In this paper, we propose CriMon, a novel approach to formulating and evaluating monitoring strategies for SBSs. CriMon first calculates the criticalities of the execution paths and the component services of an SBS and then, based on those criticalities, generates the optimal monitoring strategy considering both the benefit and cost of monitoring. CriMon has two monitoring strategy formulation methods, namely local optimisation and global optimisation. In-lab experimental results demonstrate that the response time of an SBS can be managed cost-effectively through CriMon-based monitoring. The effectiveness and efficiency of the two monitoring strategy formulation methods are also evaluated and compared.
Epoch extraction from allpass residual estimated using orthogonal matching pursuit

This paper presents an epoch extraction method from the phase spectrum of speech signals. The phase spectrum of speech is modelled as the response of an allpass (AP) system. The coefficients of AP system are estimated by imposing sparsity constraint on the input signal. The AP residual is estimated using orthogonal matching pursuit and it is found that the residual signal exhibits prominent peaks at epoch locations and negligible values elsewhere. A dynamic programming algorithm is employed to select appropriate peaks from AP residual corresponding to epoch locations. Epoch extraction performance of the proposed algorithm is evaluated on a subset of CMU Arctic database. The AP modelling based epoch extraction method is compared with two state-of-the-art epoch identification techniques, namely DYPSA and zero frequency resonator (ZFR). It is observed that the proposed algorithm delivers equivalent performance as that of ZFR for clean speech and significantly out-performs ZFR for telephone speech. Also the AP modelling based method provided slightly better epoch identification rate than DYPSA for clean as well as telephone speech.
Image processing based leukemia cancer cell detection

Microscopic pictures are reviewed visually by hematologists and the procedure is tedious and time taking which causes late detection. Therefore automatic image handling framework is required that can overcome related limitations in visual investigation which provide early detection of disease and also type of cancer. The proposed strategy is effectively connected to many numbers of pictures, demonstrating accurate results for changing image standard. Distinctive picture handling calculations, for example, Image enhancement, Clustering, Mathematical process and Labeling are executed utilizing MATLAB. Utilizing a portion of the productive image handling instruments we can recognize and section disease cell. The segmentation helps in knowing the precise size and shape of the cancer cell and the area. First we have utilized image enhancement strategies to improve the quality in terms of contrast and standardize the pixel values in the picture. After enhancement, segmentation is done to concentrate on area of interest; in this case it is nucleus. K-mean segmentation is used for segmentation. At that point we apply Feature extraction after that we have connected it to classifier to get the desired results as whether the cell is cancerous or not. The algorithm is been utilized on various pictures of the cancerous cell and has constantly given us the correct desired output.
Experiments with a Block Sorting Text Compression Algorithm

This report presents some preliminary work on a recently described “Block Sorting” lossless or text compression algorithm. While having little apparent relationship to established techniques, it has a performance which places it definitely among the bestknown compressors. The original paper did little more than present the algorithm, with strong advice for efficient implementation. Here, the algorithm is restated in data compression terms and various measurements are made on aspects of its operation. Consideration of the possible efficiency of text compression leads to the revival of ideas by Shannon as the basis of a text compressor and then to the classification of the Block Sorting compressor as an example of this “new” type. Finally, this work leads to a reconsideration of the meaning of escape codes in PPM-style compressors and a suggested technique for better estimating escape probabilities. This report is available by anonymous FTP from ftp.cs. auckland.ac. nz /out/peter-f/report111.ps Tech Rep 111 — Block Sort Compression 17 May 1995 Page 1