Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Workers Willing to Pay to Join a Better Team?

Using data from the Major League Baseball free‐agent market, this study is the first to show that the productivity expected of the team a worker will join produces a significant, negative compensating wage differential. The younger workers in the sample drive this result, trading 25% of their wages to join teams with an expected productivity one standard deviation higher. This investment can be recouped if a reasonable increase in human capital occurs. These results are robust to contract length‐wage simultaneity and indicate that investment in human capital motivates the observed tradeoff, suggesting a new pathway through which human capital accumulation can affect wages. Reliable measures of workers' own past productivity and the productivity expected of a worker's future team provide key advantages to identifying these effects. (JEL J31, J24, M54)
Quantifying soil organic carbon in complex landscapes: an example of grassland undergoing encroachment of woody plants

The invasion of woody plants into grass‐dominated ecosystems has occurred worldwide during the past century with potentially significant impacts on soil organic carbon (SOC) storage, ecosystem carbon sequestration and global climate warming. To date, most studies of tree and shrub encroachment impacts on SOC have been conducted at small scales and results are equivocal. To quantify the effects of woody plant proliferation on SOC at broad spatial scales and to potentially resolve inconsistencies reported from studies conducted at fine spatial scales, information regarding spatial variability and uncertainty of SOC is essential. We used sequential indicator simulation (SIS) to quantify spatial uncertainty of SOC in a grassland undergoing shrub encroachment in the Southern Great Plains, USA. Results showed that both SOC pool size and its spatial uncertainty increased with the development of woody communities in grasslands. Higher uncertainty of SOC in new shrub‐dominated communities may be the result of their relatively recent development, their more complex above‐ and belowground architecture, stronger within‐community gradients, and a greater degree of faunal disturbance. Simulations of alternative sampling designs demonstrated the effects of spatial uncertainty on the accuracy of SOC estimates and enabled us to evaluate the efficiency of sampling strategies aimed at quantifying landscape‐scale SOC pools. An approach combining stratified random sampling with unequal point densities and transect sampling of landscape elements exhibiting strong internal gradients yielded the best estimates. Complete random sampling was less effective and required much higher sampling densities. Results provide novel insights into spatial uncertainty of SOC and its effects on estimates of carbon sequestration in terrestrial ecosystem and suggest effective protocol for the estimating of soil attributes in landscapes with complex vegetation patterns.
Variable structure control combined with adaptive iterative learning control for motion tracking of a piezoelectric microgripper

This paper proposes a novel controller to compensate for both the vibration and nonlinear hysteresis of a microgripper driven by piezoelectric actuators. The proposed control strategy includes two parts. The first part is an adaptive algorithm based on iterative learning control, and the second one is a feedback controller constructed by discrete-time sliding mode control. ILC has great properties to regulate repeating disturbance from previous steps, and it can also compensate for the error caused by hysteresis. In addition, the switching type of reaching law for the variable structure control of the discrete-time system is developed to realize the motion control of the piezo-driven gripper. The proposed control algorithm has been experimentally tested to verify its effectiveness on enhancing the performance in precision position control in comparison with conventional control schemes for the microgripper.
Experience Implementing Electronic Health Records in Three East African Countries

INTRODUCTION Efficient use of health care resources in low-income countries by providers and local and national managers requires timely access to patient data. OBJECTIVE To implement electronic health records (EHRs) in HIV clinics in Kenya, Tanzania, and Uganda. RESULTS We initially developed and implemented an EHR in Kenya through a mature academic partnership. The EHR was then implemented in six HIV clinics in Tanzania and Uganda in collaboration with their National AIDS Control Programmes. All implementations were successful, but the system's use and sustainability varied depending on who controlled clinic funding. CONCLUSIONS Successful EHR use and sustainability were enhanced by local control of funds, academic partnerships (mainly by leveraging research funds), and in-country technology support.
Inflation Targeting in the United States?

The paper begins by tracing the origins of the case for inflation targeting in postwar US monetary history. It describes five aspects of inflation targeting practiced implicitly by the Greenspan Fed. It argues that (1) low long run inflation should be an explicit priority for monetary policy, (2) as a practical matter it is not desirable for the Fed to vary its short run inflation target (3) strict inflation targeting can be regarded as effcient constrained countercyclical stabilization policy. Finally, the paper suggests that the Fed publicly acknowledge its implicit priority for low long run inflation, that Congress recognize that priority, and that in return representatives of the FOMC consider participating in a monetary policy forum to better inform the public and congressional oversight committees about current monetary policy.
EBG ME-dipole antenna with enhanced gain for intelligent radio-over-fiber systems

An electromagnetic band-gad (ERG) magneto-electric (ME) dipole antenna with enhanced gain is proposed for intelligent radio-over-fiber systems. With the ERG etched around the radiator patch, gain enhancement is achieved without deteriorating the previous impedance bandwidth of more than 33% from 50 to 70 GHz. The maximum gain is increased from 7.7 to 11.7 dBi. Many advantages, such as low cost, easy fabrication, wide bandwidth and high gain are presented.
Joint Tracking and Classification of Moving Objects at Intersection Using a Single-Row Laser Range Scanner

Analyzing and monitoring traffic behavior at intersection is considered very important to reduce accidents, solve traffic jam and improve its accessibility. However such a data is not easy to obtain, as the visual-based systems require high sensor setting conditions, which could not be met easily in many traffic environments. In this research, we propose a novel system of monitoring and collecting a traffic data at an intersection using a single-row laser range scanner, which is set on a roadside, horizontally profiles the moving objects at the intersection at an elevation about 40cm from the ground. A method of joint tracking and classification is developed by spatially and temporally processing on laser scan data, where moving objects are classified into 0-axis object (e.g. pedestrians), 1-axis object (e.g. bicycles), 2-axis object (e.g. cars, trucks, buses). An object model is defined by picking up the typical appearances (Markov states) of an object in an instant laser measurement, and modeling each traffic flow as the transition along a chain of the Markov states (Markov chain). An accurate estimation to the shape of the object, e.g. width and length, side vectors, corner points, etc., and kinematic parameters, e.g. position, direction, speed is achieved by integrating such a partially (at each Markov State) but continuously (transitions along a Markov chain) collected knowledge to the object. An experiment is conducted in an intersection, where a SICK LMS291 is applied to collect laser data for tracking and classifying of the moving objects at the intersection, a video image is recorded to make a better understanding to the results. A ten-minute data is processed, where a successful ratio of above 95% is found by examining the results on both laser data and video images. In this experiment, although the data are processed in an off-line mode, a low computation cost (less than half of the scanning period is cost for the processing of each frame) is found of the method, which proved the possibility for a on-line system
Building a safe care-providing robot

A service robot especially a care-providing robot, works in the vicinity of a human body and is sometimes even in direct contact with it. Conventional safety methods and precautions in industrial robotics are not applicable to such robots. This paper presents a safety approach for designing the safe care-providing robot FRIEND. The approach is applied in each step of design iteratively to identify and assess the potential hazards during design. The steps are explained briefly in this work. The main contribution of this paper is verification of safety requirements using the Ramadge-Wonham (RW) framework. The greater complexity of the tasks the robot will perform, the more complex is the identification of safety requirements. Use of this framework led us to analyze the requirements and verify them formally, systematically and on a modular basis. In our approach human-robot interaction (HRI) is also modeled by a set of uncontrolled events that may happen any time during operation. Subsequently the safety requirements are modified to consider these interactions. As a result the safety module behaves like a controller, running in parallel with the system, which maintains the system safe and works according to the safety requirements by enabling the admissible sequences of events.
Entity Disambiguation and Linking over Queries using Encyclopedic Knowledge

Literature has seen a large amount of work on entity recognition and semantic disambiguation in text but very limited on the effect in noisy text data. In this paper, we present an approach for recognizing and disambiguating entities in text based on the high coverage and rich structure of an online encyclopedia. This work was carried out on a collection of query logs from the Bridgeman Art Library. As queries are noisy unstructured text, pure natural language processing as well as computational techniques can create problems, we need to contend with the impact noise and the demands it places on query analysis. In order to cope with the noisy input, we use machine learning method with statistical measures derived from Wikipedia. It provides a huge electronic text from the Internet, which is also noisy. Our approach is an unsupervised approach and do not need any manual annotation made by human experts. We show that data collection from Wikipedia can be used statistically to derive good performance for entity recognition and semantic disambiguation over noisy unstructured text. Also, as no natural language specific tool is needed, the method can be applied to other languages in a similar manner with little adaptation.
Distance metric learning for feature-agnostic place recognition

The recent focus on performing visual navigation and place recognition in changing environments has resulted in a large number of heterogeneous techniques each utilizing their own learnt or hand crafted visual features. This paper presents a generally applicable method for learning the appropriate distance metric by which to compare feature responses from any of these techniques in order to perform place recognition under changing environmental conditions. We implement an approach which learns to cluster images captured at spatially proximal locations under different conditions, separated from frames captured at different places. The formulation is a convex optimization, guaranteeing the existence of a global solution. We evaluate the general applicability of our method on two benchmark change datasets using three typical image pre-processing and feature types: GIST, Principal Component Analysis and learnt Convolutional Neural Network features. The results demonstrate that the distance metric learning approach uniformly improves single-image-based visual place recognition performance across all feature types. Furthermore, we demonstrate that this performance improvement is maintained when the sequence-based algorithm SeqSLAM is applied to the single-image place recognition results, leading to state-of-the-art performance.
Sparse matrix permutations to a block triangular form in a distributed environment

Arranging the sparse circuit matrix into a diagonal block upper triangular form is the first step of the KLU algorithm. This paper presents the two steps of the parallel algorithm, running in a distributed environment, that performs unsymmetric and symmetric permutations of the matrix's rows. First, using the [Duff] maximum transversal algorithm and performing asymmetrical permutations, the matrix is shaped to achieve a zero free diagonal. Then, searching the strongly connected components of the associated matrix's graph, and performing symmetric permutation, the sparse matrix is shaped in a diagonal block upper triangular form. Both algorithm and architecture are presented.
Rath & Strong's Six Sigma Leadership Handbook

Foreword. Preface. About the Company-Rath & Strong: A Distinguished History. Acknowledgments. Introduction. What Is Six Sigma? Why Six Sigma? Six Sigma and Its Application in Different Industries and Functions. Infrastructure and Roles. The Non-Delegable Role of Executives. Lean and Six Sigma. Work-Out and Six Sigma. Organization Culture and Six Sigma. The Customer Connection. Process Improvement: DMAIC. Design for Six Sigma. Process Management. Managing with Dashboards. Preparaing for Six Sigma. Launching Six Sigma. Cross-Cultural Aspects of Deploying Six Sigma. Stabilizing, Extending, and Integrating Six Sigma. Measuring the Effectiveness of Your Six Sigma Deployment. Change Management and Communication. Black Belt Selection and Development. Project Selection. project Reviews. Replicating Results and Managing Knowledge. Measuring and Auditing Results. Developing Change Leadership Capacity. Appendix A: Basic Six Sigma Concepts. Appendix B: Case Study: Six Sigma in Small and Medium Enterprises. Appendix C: DFSS Case Study. Notes. Index. About the Contributors.
Introducing a Framework for Automatically Differentiating Witness Accounts of Events from Social Media

Crowdsourcing information about events from social networks such as Twitter is recognised as an opportunity to harvest detailed real-time information, for example enhancing situational awareness for emergency response and management [18] and creating news summaries of large sporting spectacles [19]. However, these opportunities come with many problems to solve, including detecting the fraction of relevant micro-blogs, and assessing the credibility and location of the micro-bloggers who posted them. This research makes unique contributions by proposing a framework towards distinguishing those micro-blogs which are Witness Accounts (WA) of events. WA are defined as those micro-blogs which contain an observation of the event or its effects [17], for example a statement I see the bushfire smoke! or an image conveying the same information. The micro-blogger who posted the WA is considered a potential Witness to the event, and it can be inferred they are on-the-ground (OTG) [15], that is they in close proximity to the event [17]. Impact Accounts (IA) are defined for those micro-blogs which do no contain an observation of the event, but from which it can also be inferred that the micro-blogger who posted it is OTG. IA statements may be as explicit as I’m being evacuated from my home due to the bushfire. Formally modelling the witnessing fundamentals of observation and spatial relationship separately enables a generic model for a range of event types including unpredicted natural disasters to scheduled events broadcast live from dedicated venues, such as the case study presented in this paper. All micro-bloggers who post observations of the event whether viewed direct from the grandstands or via television are by definition Witnesses. The research in this paper questions whether it is possible to differentiate those Witnesses which are physically at the event from those watching a broadcast. Such differentiation is supported by micro-blogs with geotags, but typically they are present in only a fraction of micro-blogs, for example 1% [1]. This research demonstrates that including the text content and linked images as evidence, the sample of micro-blogs posted from the event location can be increased significantly from those identified by geotags alone. Additionally, this research questions whether text content and linked images can be automatically categorised, and used to test whether they corroborate
Characterisation of circular objects based on the hough transform

This paper presents a new image analysis approach based on the Hough Transform, for the characterisation of natural objects of approximately circular shape in real situations. To accomplish this purpose, an innovative interpretation and filtering methodology of the three dimensional Hough Transform (HT) accumulation space for circles was conceived and implemented, which proved to be crucial when dealing with shape and texture irregularities of natural objects. Some results that demonstrate the robustness of the developed approach under real testing conditions are also presented, namely concerning its application into a vision system that automates the volume measurement of piles of timber logs, allowing the evaluation of their diameter distributions.
A Note on Log-Concavity Matthias Seeger

This is a small observation concerning scale mixtures and their log-concavity. A function f(x) ≥ 0, x ∈ Rn is called log-concave if f (λx + (1 − λ)y) ≥ f(x)f(y) (1) for all x,y ∈ Rn, λ ∈ [0, 1]. Log-concavity is important in applied Bayesian Statistics, since a distribution with a log-concave density is easy to treat with many different approximate inference techniques. For example, log-concavity implies unimodality. Log-concave distributions over few variables can be sampled from using a generic Markov chain Monte Carlo technique called adaptive rejection sampling [3]. For certain approximate inference techniques such as expectation propagation [5, 6], log-concavity of all sites means that the algorithm can be implemented in a numerically stable manner and tends to converge quickly, while in the absense of log-concavity it can fail badly. Many well-known densities are log-concave, for example the Gaussian or the Gamma ∝ xeI{x>0}, the latter for a ≥ 1. In the Bayesian context it is important to note that exponential family densities are in general log-concave in their natural parameters (but not necessarily in their data argument). A very important result concerning log-concave functions has been given by Prékopa (see [1], Sect. 1.8). Namely, if f : Rn1 × Rn2 → R is (jointly) log-concave, so is g(x) = ∫ f(x,y)dy , where the integral is over all of Rn2 . In this note, we are interested in scale mixture distributions [4] P (x) = E[N(x|h, s)] with some mixing distribution P (s) over the variance. We give a sufficient condition for P (x) to be log-concave, and show that the condition is not necessary. Since concavity is preserved under linear transformations, we can assume w.l. o.g. that h = 0. We have that
Shortest Paths for a Robot With Nonholonomic and Field-of-View Constraints

This paper presents a complete characterization of shortest paths to a goal position for a robot with unicycle kinematics and an on-board camera with limited field-of-view (FOV), which must keep a given feature in sight. Previous work on this subject has shown that the search for a shortest path can be limited to simple families of trajectories. In this paper, we provide a complete optimal synthesis for the problem, i.e., a language of optimal control words, and a global partition of the motion plane induced by shortest paths, such that a word in the optimal language is univocally associated with a region and completely describes the shortest path from any starting point in that region to the goal point. An efficient algorithm to determine the region in which the robot is at any time is also provided.
Systems Design, Fabrication, and Testing of a High-Speed Miniature Motor for Cryogenic Cooler

The long-term storage of liquid hydrogen for space missions is of considerable interest to NASA. To this end, the Reverse Turbo-Brayton Cryocooler (RTBC) is considerably lighter than conventional designs and a potentially viable and attractive solution for NASA's long-term Zero-Boil-off (ZBO) hydrogen storage system for future space missions. We present the systems design, fabrication, and performance evaluation of the Permanent Magnet Synchronous Motor (PMSM) powering a cryocooler capable of removing 20 W of heat at 18 K with a COP of 0.005 and driven by two 2-kW permanent magnet synchronous motors operating at 200 000 rpm and at room temperature and 77 K. Structural, thermal, and rotordynamic aspects of system design are considered.
Coverage Under Dead Reckoning Errors : A Hybrid Approach

Coverage is a task, where a robot is to move about a given a target area until every point in it is visited. Many efficient coverage algorithms cannot be used in practice, because they assume accurate movements by the robot; unfortunately, real robots have navigational errors. A standard costly solution is to utilize a robot that continuously localizes, so as to make course corrections. In this work we present TRIM SAIL , a novel hybrid coverage algorithm that takes as input an exact-movement coverage algorithm, and a maximal dead-reckoning error bound. It optimizes use of the exact-movement algorithm, so as to execute its coverage plan while minimizing movement and localization costs. T RIM SAIL guarantees complete coverage, even under deadreckoning errors. We present several variants of TRIM SAIL and demonstrate their efficacy in experiments using data collected from real robots.