Miracle-dz/UniFAR-FTUs huggingface dataset:
General Equilibrium Effects of Insurance Expansions: Evidence from Long-Term Care Labor Markets

Arrow (1963) hypothesized that demand-side moral hazard induced by health insurance leads to supply-side expansions in healthcare markets. Capturing these effects empirically has been challenging, as non-marginal insurance expansions are rare and detailed data on healthcare labor and capital is sparse. We combine administrative labor market data with the geographic variation in the rollout of a universal insurance program—the introduction of long-term care (LTC) insurance in Germany in 1995—to document a substantial expansion of the inpatient LTC labor market in response to insurance expansion. A 10 percentage point expansion in the share of insured elderly leads to 0.05 (7%) more inpatient LTC firms and four (13%) more workers per 1,000 elderly in Germany. Wages did not rise, but the quality of newly hired workers declined. We find suggestive evidence of a reduction in old-age mortality. Using a machine learning algorithm, we characterize counterfactual labor market biographies of potential inpatient LTC hires, finding that the reform moved workers into LTC jobs from unemployment and out of the labor force rather than from other sectors of the economy. We estimate that employing these additional workers in LTC is socially efficient if patients value the care provided by these workers at least at 25% of the market price for care. We show conceptually that, in the spirit of Harberger (1971), in a second-best equilibrium in which supply-side labor markets do not clear at perfectly competitive wages, subsidies for healthcare consumption along with the associated demand-side moral hazard can be welfare-enhancing. ∗We are grateful to Joshua Gottlieb and seminar participants at the NBER Summer Institute, the Junior Health Economics Summit, the American Society of Health Economists, the EALE-SOLE-AASLE World Conference, Harvard Health Policy Student Workshop, Stanford Eddie Lunch, and University of Toronto for helpful comments. Polyakova gratefully acknowledges grant support from the W.E. Upjohn Institute for Employment Research and from the National Institute on Aging (K01AG05984301). †University of California in Los Angeles, NBER, and CESifo ‡Institut für Arbeitsmarktund Berufsforschung (IAB) §Harvard University ¶Stanford University, NBER, and CESifo ‖Institut für Arbeitsmarktund Berufsforschung (IAB)
Structural empowerment and mental health in healthcare professionals: The role of civility

Quality of the work environment is a factor that promotes workers’ mental health and it’s a present concern for the European Agency for Safety and Health at Work. There is a need to understand the factors that can promote better mental health at work and under which circumstances structural empowerment represents a working environment that allows workers’ development, low rates of incivility and positive effects on mental health. The present study aims to verify in which conditions structural empowerment has a positive effect on workers’ mental health, through the mediating role of civility. To that purpose, we have applied the Portuguese versions of WCS, CWEQ-II and the mental health scale from MOS SF-36v2 to a sample of 303 health care professionals in a public-private hospital located in the Greater Lisbon. The results of this study reveal that structural empowerment has a direct positive effect both on workers’ mental health and on workplace civility. However, the indirect effect of structural empowerment on mental health through civility is only effective for health professionals with longer tenure.
GECNN for Weakly Supervised Semantic Segmentation of 3D Point Clouds

SUMMARY This paper presents a novel method for weakly supervised semantic segmentation of 3D point clouds using a novel graph and edge convolutional neural network (GECNN) towards 1% and 10% point cloud with labels. Our general framework facilitates semantic segmentation by encoding both global and local scale features via a parallel graph and edge aggregation scheme. More speciﬁcally, global scale graph structure cues of point clouds are captured by a graph convolutional neural network, which is propagated from pairwise a ﬃ nity representation over the whole graph established in a d -dimensional feature embedding space. We integrate lo-cal scale features derived from a dynamic edge feature aggregation convolutional neural networks that allows us to fusion both global and local cues of 3D point clouds. The proposed GECNN model is trained by us-ing a comprehensive objective which consists of incomplete, inexact, self-supervision and smoothness constraints based on partially labeled points. The proposed approach enforces global and local consistency constraints directly on the objective losses. It inherently handles the challenges of segmenting sparse 3D point clouds with limited annotations in a large scale point cloud space. Our experiments on the ShapeNet and S3DIS benchmarks demonstrate the e ﬀ ectiveness of the proposed approach for e ﬃ cient (within 20 epochs) learning of large scale point cloud semantics despite very limited labels.
Self-healing in power systems: an approach using islanding and rate of frequency decline-based load shedding

This paper provides a self-healing strategy to deal with catastrophic events when power system vulnerability analysis indicates that the system is approaching an extreme emergency state. In the authors' approach, the system is adaptively divided into smaller islands with consideration of quick restoration. Then, a load shedding scheme based on the rate of frequency decline is applied. The proposed scheme is tested on a 179-bus, 20-generator sample system and shows very good performance.
Nanoresonator Enhancement of Majorana-Fermion-Induced Slow Light in Superconducting Iron Chains

We theoretically investigate Fano resonance in the absorption spectrum of a quantum dot (QD) based on a hybrid QD-nanomechanical resonator (QD–NR) system mediated by Majorana fermions (MFs) in superconducting iron (Fe) chains. The absorption spectra exhibit a series of asymmetric Fano line shapes, which are accompanied by the rapid normal phase dispersion and induce the optical propagation properties such as the slow light effect under suitable parametric regimes. The results indicated that the slow light induced by MFs can be obtained under different coupling regimes and different detuning regimes. Moreover, we also investigated the role of the NR, and the NR behaving as a phonon cavity enhances the slow light effect.
Using Forecasting to Predict Long-term Resource Utilization for Web Services

Researchers have spent years understanding resource utilization to improve scheduling, load balancing, and system management through short-term prediction of resource utilization. Early research focused primarily on single operating systems; later, interest shifted to distributed systems and, finally, into web services. In each case researchers sought to more effectively use available resources. Since schedulers are required to manage the execution of multiple programs every second, short-term prediction has focused on time frames ranging from fractions of a second to several minutes. The recent increase in the number of research studies about web services has occurred because of the explosive growth and reliance on these services by most businesses. As demand has moved from static to dynamic content, the load on machine resources has grown exponentially, periodically resulting in temporary loss of service. To address these short-term denial-of-service issues, researchers have tried short-term prediction to manage scheduling of service requests. What researchers have not considered is that the same methods used for single step short-term prediction can also be used for long-term prediction if a coarse granularity of samples is used. Instead of using one or more samples per second, a coarser aggregate of minutes or hours more accurately emulates the long-term patterns. This research has shown that simple moving averages and exponential moving averages as a prediction technique can be used to more accurately predict hourly, daily, and weekly seasonal patterns of resource utilization for web servers. Additionally, this research provides a foundation where using a resource prediction within a confidence interval range could be more useful to an administrator or system software than a single prediction point. When the focus shifts to a range, a set of probabilities can establish normal function within that system. For distributed systems, it will provide the ability to notify other systems when resource utilization is no longer normal before that system is unable to issue a notice of overloading. For web systems it can be used to provide a warning, permitting the instantiation of a second system to begin load balancing during unscheduled heavy loads. In both cases, the availability of the system can be improved by predicting a resource utilization level and the confidence interval within which that resource use has historically fallen.
Teachers' persuasion to modify health behaviors among students of Bagmati Province, Nepal

Teachers encounter many of the unhealthier behaviors of students that need their support in improving such behaviors. Teachers employ various techniques including instruction, persuasion, and demonstration. They are role models to their students because they present opportunities for observational learning. Teachers can consciously attempt to influence students to change through persuasion. This cross-sectional descriptive survey of 344 teachers in 46 secondary schools aimed to identify the type of health degrading behaviors of the students modified by the teachers and the actions taken in modifying such behaviors, particularly including health persuasion. Teachers were found to involve in the act of persuasion, however, their action was mostly moving around the 'instruction' and 'advise' as the tools for modifying the student's health deteriorating behaviors. More effective persuasive techniques such as demonstrating healthy behaviors by the teachers are necessary to bring evident change.
A Novel Game-Based Intelligent Test for Detecting Elderly Cognitive Function Impairment

Purposes This research explores the game-based intelligent test (GBIT), predicts the possibilities of Mini-Mental State Examination (MMSE) scores and the risk of cognitive impairment, and then verifies GBIT as one of the reliable and valid cognitive assessment tools. Methods This study recruited 117 elderly subjects in Taiwan (average age is 79.92 ± 8.68, average height is 156.91 ± 8.01, average weight is 59.14 ± 9.67, and average MMSE score is 23.33 ± 6.16). A multiple regression model was used to analyze the GBIT parameters of the elderly's reaction, attention, coordination, and memory to predict their MMSE performance. The binary logistic regression was then utilized to predict their risk of cognitive impairment. The statistical significance level was set as α = 0.05. Results Multiple regression analysis showed that gender, the correct number of reactions, and the correct number of memory have a significantly positive predictive power on MMSE of the elderly (F = 37.60, R2 = 0.69, and p < 0.05). Binary logistic regression analysis noted that the correct average number of reactions falls by one question, and the ratio of cognitive dysfunction risk increases 1.09 times (p < 0.05); the correct average number of memory drops by one question, the ratio of cognitive dysfunction risk increases 3.76 times (p < 0.05), and the overall model predictive power is 88.20% (sensitivity: 84.00%; specificity: 92.30%). Conclusions This study verifies that GBIT is reliable and can effectively predict the cognitive function and risk of cognitive impairment in the elderly. Therefore, GBIT can be used as one of the feasible tools for evaluating older people's cognitive function.
Xerotolerance: A New Property in Exiguobacterium Genus

The highly xerotolerant bacterium classified as Exiguobacterium sp. Helios isolated from a solar panel in Spain showed a close relationship to Exiguobacterium sibiricum 255-15 isolated from Siberian permafrost. Xerotolerance has not been previously described as a characteristic of the extremely diverse Exiguobacterium genus, but both strains Helios and 255-15 showed higher xerotolerance than that described in the reference xerotolerant model strain Deinococcus radiodurans. Significant changes observed in the cell morphology after their desiccation suggests that the structure of cellular surface plays an important role in xerotolerance. Apart from its remarkable resistance to desiccation, Exiguobacterium sp. Helios strain shows several polyextremophilic characteristics that make it a promising chassis for biotechnological applications. Exiguobacterium sp. Helios cells produce nanoparticles of selenium in the presence of selenite linked to its resistance mechanism. Using the Lactobacillus plasmid pRCR12 that harbors a cherry marker, we have developed a transformation protocol for Exiguobacterium sp. Helios strain, being the first time that a bacterium of Exiguobacterium genus has been genetically modified. The comparison of Exiguobacterium sp. Helios and E. sibiricum 255-15 genomes revealed several interesting similarities and differences. Both strains contain a complete set of competence-related DNA transformation genes, suggesting that they might have natural competence, and an incomplete set of genes involved in sporulation; moreover, these strains not produce spores, suggesting that these genes might be involved in xerotolerance.
A Low-Latency Inference of Randomly Wired Convolutional Neural Networks on an FPGA

SUMMARY Convolutional neural networks (CNNs) are widely used for image processing tasks in both embedded systems and data centers. In data centers, high accuracy and low latency are desired for various tasks such as image processing of streaming videos. We propose an FPGA-based low-latency CNN inference for randomly wired convolutional neural networks (RWCNNs), whose layer structures are based on random graph models. Because RWCNNs have several convolution layers that have no direct dependencies between them, our architecture can process them e ﬃ ciently using a pipeline method. At each layer, we need to use the calculation re-sults of multiple layers as the input. We use an FPGA with HBM2 to enable parallel access to the input data with multiple HBM2 channels. We schedule the order of execution of the layers to improve the pipeline e ﬃ ciency. We build a conﬂict graph using the scheduling results. Then, we allocate the calculation results of each layer to the HBM2 channels by coloring the graph. Because the pipeline execution needs to be properly controlled, we developed an automatic generation tool for hardware functions. We implemented the proposed architecture on the Alveo U50 FPGA. We investigated a trade-o ﬀ between latency and recognition accuracy for the ImageNet clas-siﬁcation task by comparing the inference performances for di ﬀ erent input image sizes. We compared our accelerator with a conventional accelerator for ResNet-50. The results show that our accelerator reduces the latency by 2.21 times. We also obtained 12.6 and 4.93 times better e ﬃ ciency than CPU and GPU, respectively. Thus, our accelerator for RWCNNs is suitable for low-latency inference.
Practical considerations in the design of multisine test signals for system identification

The design of nonbinary multifrequency excitation signals for system identification is examined. The performance of such signals in parametric time-domain estimation and nonparametric frequency-domain estimation for noisy and nonlinear systems is assessed, and a comparison with the maximum length binary sequence is made. A nonbinary multifrequency signal composed of prime harmonics is proposed for frequency-domain estimation of nonlinear systems, and practical results are presented. Guidelines for test signal design are outlined. <<ETX>>
Embracing Channel Estimation in Multi-Packet Reception of ZigBee

As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Since ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computational complexity, which uses the estimated channel parameters modeling both signal amplitudes and phases. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed. Results show that CmZig achieves a bit error rate in the order of <inline-formula><tex-math notation="LaTeX">$10^{-3}$</tex-math><alternatives><mml:math><mml:msup><mml:mn>10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href="kong-ieq1-3131472.gif"/></alternatives></inline-formula> and more than 80% packet reception rate, which outperforms state-of-the-art mZig.
Automatic Image Characterization of Psoriasis Lesions

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.
Resource Threat versus Resource Loss and Emotional Well-Being of Ethnic Minorities during the COVID-19 Pandemic

This paper used Hobfoll’s conservation of resources theory as a theoretical framework to investigate which kinds of resource loss predicted the emotional well-being (EWB) of ethnic minorities and majority populations during a period of crisis. Data were collected from a national representative survey conducted by the Israel Central Bureau of Statistics during the COVID-19 pandemic. The sample included 1157 respondents, including 174 Israeli Palestinian citizens (ethnic minority) and 983 Israeli Jews (majority population). Measures of EWB, actual losses and threats of losses of economic, social, and health resources were examined. The results showed that the losses of economic, social, and health resources reduced the EWB of individuals. Negative effects of the actual losses of resources on EWB were greater than those of the perceived threats of loss. The largest effect was for economic resources. There were differences in effects between the ethnic minorities and the majority populations. The study revealed that for the ethnic minorities, who are less powerful and more disadvantaged than ethnic majorities, the depletion of already deficient resources during time of crisis is more important for predicting their EWB than for the majority populations.
Design of Ball-Beam Control System Based on Machine Vision

The nonlinear Ball-beam system combine with a CCD camera is studied in this paper. The images which include Ball-beam system and a ruler are collected by CCD sensor. The image is segmented using the adaptive image binarization threshold algorithm, and then the ruler, the ball position and the pointer position are extracted from the image. The ruler is scaled and the pointer position is also calculated. Finally, the value of pointer position is input into Ball-beam system as an expected ball balance position. A Fuzzy self-tuning PID controller and a BP neural network PID controller are designed for ball balance stable control. After experimental, the Ball-beam balance control in any position can be fulfilled using both of algorithms.
Lane changing assistance strategy based on an improved probabilistic model of dynamic occupancy grids

Lane changing assistance in autonomous vehicles is a popular research topic. Scene modeling of the driving area is a prerequisite for lane changing decision problems. A road environment representation method based on a dynamic occupancy grid is proposed in this study. The model encapsulates the data such as vehicle speed, obstacles, lane lines, and traffic rules into a form of spatial drivability probability. This information is compiled into a hash table, and the grid map is mapped into a hash map by means of hash function. A vehicle behavior decision cost equation is established with the model to help drivers make accurate vehicle lane changing decisions based on the principle of least cost, while considering influencing factors such as vehicle drivability, safety, and power. The feasibility of the lane changing assistance strategy is verified through vehicle tests, and the results show that the lane changing assistance system based on a probabilistic model of dynamic occupancy grids can provide lane changing assistance to drivers taking into consideration the dynamics and safety.
New constant-frequency current-mode control for power converters, stable for all values of duty ratio, and usable in all four quadrants

A constant-frequency, current-mode, pulse-width modulator (PWM) for switching power converters is presented. The PWM is stable for any value of the duty ration ( delta ) and is suitable for operation in one to four quadrants of the output current-voltage plane. The control system can be used with most DC-DC power-converter topologies. Due to its unique properties of delta range stability and four-quadrant operation, the control process is particularly useful in DC-AC conversion. The process is clearly advantageous compared to the other current-mode control method, the bang-bang process. It has all the advantages inherent to constant-frequency operation, including the possibility of paralleling different converters without low-frequency beating and the suppression of low-frequency subharmonics in DC-AC conversion. >