Miracle-dz/UniFAR-FTUs huggingface dataset:
SELF-DRIVING VEHICLES ’ IMPACTS ON AMERICANS ’ LONG-DISTANCE TRAVEL CHOICES

Fully autonomous cars (AVs) and shared autonomous vehicles (SAVs) may compete with airlines for long-distance trips increasing VMT and frequency throughout the United States in the future years. This research anticipates implications of these automated cars on long-distance (over 75miles one-way) passenger travel frequency, destination, mode, party size, and scheduling inside the US. Two national travel surveys are used to derive equations for such choices with and without AVs (and SAVs), using Poisson, negative binomial, zero-inflated negative binomial distributions, multinomial, and nested logit models. The results show that an increase in characteristics such as household licensed drivers and homeownership increases by one standard deviation increases car ownership by 39 and 18 percent, respectively. The same change in the number of cars in the home, the logarithm of household income per adult, and the respondent's education level increase frequency of long-distance journeys by more than 25%. Age and education significantly enhance the chances of long-distance leisure travel (like shopping, visiting friends and family, and dining). Long-distance travel party sizes decrease by 21% when the commute-purpose variable increases by 1 SD, and by 13.6 % when the business trip variable increases by 1 SD. Increasing worker density at the destination track increases party size by 15.5 percent. Summer and fall long-distance journeys include larger parties (relative to spring and winter trips). Younger, more educated, men, drivers, and full-time employees prefer AVs and SAVs for such excursions though air travel remains a dominant choice for travels above 500 miles.
Classification of digitalisation technologies for electric motor driven systems

One difficulty in assessing the policy measures required to increase efficiency through digitalisation technologies relates in particular to the definition and delimitation of these technologies. This report therefore provides a classification of digitalisation technologies that forms a basis for further analysis of energy effects and the elaboration of case studies and policy recommendations. The classification included the analysis, definition and description of digitalisation technologies in the field of motor systems and an estimation of the effects on energy consumption (as far as possible). The following technologies could be categorised by definition, description and application in motor systems: smart sensors, Internet of Things, smart control, motor and production level data analytics, real-time monitoring, digital twins, cloud based services, artificial intelligence and augmented reality. Drones, advanced robotics and additive manufacturing were also described. © Copyright IEA 4E Electric Motor Systems Annex, 2022 / All rights reserved.
Minimum Wage and Employer Variety ∗

Exploiting minimum wage variation within multi-state commuting zones, we document a negative relationship between minimum wages and establishment counts in the United States. To explain this finding, we construct a heterogeneous-firm model with a monopsonistic labor market and endogenous firm variety. The decentralized equilibrium underprovides the mass of firms compared to the outcome achieved by a welfare-maximizing planner. A binding minimum wage further reduces the mass of firms, exacerbating the distortion. Workers value employer variety, and thus, by reducing firm variety the minimum wage reduces workers’ welfare even if the average wage increases. Based on estimated elasticities, our model predicts that a 10 percent minimum wage hike reduces workers’ welfare by 1 . 87 percent. The long-term effects are even larger, with an estimated welfare loss of 4 . 14 percent after four years.
Extending Chapel to Support Fabric Attached Memory

This is a paper submitted to CUG-2022[12] and we intend to present the same content to CHIUW to seek feedback from the Chapel users group. Fabric Attached Memory (FAM) is of increasing interest in HPC clusters because it enables fast access to large datasets required in High Performance Data Analytics (HPDA) and Exploratory Data Analytics (EDA) [1]. Most approaches to handling FAM force programmers either to use low-level APIs, which are difficult to program, or to rely upon abstractions from file systems or key-value stores, which make accessing FAM less attractive than other levels in the memory model due to the overhead they bring. The Chapel language is designed to allow HPC programmers to use high-level programming constructs that are easy to use, while delegating the task of managing data and compute partitioning across the cluster to the Chapel compiler and runtime. This abstract summarizes the approach to integrate FAM access within the Chapel language described in the paper [12].
One Profile Fits All

Modern data center applications have multi-megabyte instruc- tion footprints that easily exhaust on-chip cache structures, which typically have a size of only a couple hundred kilobytes. Consequently, today's data center applications suffer from frequent Instruction cache (I-cache) and Instruction Transla- tion Lookaside Buffer (I-TLB) misses, causing performance losses worth millions of dollars. To make matters worse, the multi-megabyte instruction footprint of the Linux kernel pre- cipitates an undue burden on the performance of data center applications.
How to “buy” honest reviews. Experimental evidence of the impact of prices on online reviews∗

The paper documents the causal impact of transaction price on the subsequent product review submitted by the customer. The evidence is obtained using a Large Scale Randomized Field Experiment which introduced a random component into over 3 million nightly rates of short-term rentals. Using this variation, we show that exogenous 25% price decrease increases subsequent star rating by nearly one-fourth of standard deviation. Conversely, we also demonstrate that an exogenous increase of the most recent review by a standard deviation increases revenue from a subsequent transaction by at least 25%. These relationships imply that firms may invest in better reviews by lowering their price in addition to investing directly in product quality; conversely, firms may monetize good reviews by raising prices. We discuss implication of these results for optimal pricing and market efficiency. ∗We would like to thank Andrey Fradkin, Ganesh Iyer, and Miguel Villas-Boas for helpful discussion and suggestions. Both authors are co-founders and hold equity in Keybee Inc., the data supplier for this paper. Both authors received income from Keybee Inc. in excess of $10,000, which was unrelated to the research in this paper. Both authors and are on the board of directors of Keybee Inc. Konstantina Michelidaki is a CEO of Keybee Inc. †przemekj@berkeley.edu, UC Berkeley, Haas School of Business ‡nadia@keybeehosting.com, Keybee Inc.
Transcriptomic Profile Analysis of Streptococcus mutans Response to Acmella paniculata Flower Extracts

Background Acmella paniculata has been used as a traditional medicine to treat oral health diseases such as dental caries and periodontitis. Streptococcus mutans is a common bacterium that initiates dental caries at an early stage. Aim The aim of this study was to determine the mode of action of A. paniculata (extracts) against S. mutans growth. Methods Time-kill assay has been done to investigate the rate of kill and effectiveness of Acmella paniculata (AP) extracts against S. mutans growth. Phytochemical analysis was done to identify major compounds in AP extracts using gas chromatography mass spectrometry (GCMS). Scanning and transmission electron microscopy (SEM and TEM) have been done to observe the morphological changes of treated bacteria. Transcriptomic profile analysis has been done using Next Gene Sequencing. Results AP flower n-hexane (APFH) and AP flower dichloromethane (APFD) extracts acted as bactericidal agents after killing >3 log10 cfu/mL of S. mutans after 24 hours. Oleic and hexadecenoic acids were found to be the major compounds in APFD and APFH extracts, respectively. Photomicrographs from SEM and TEM of treated S. mutans show that the bacterial cell wall has been lysed and the cytoplasm content was decreased. Pathway analysis revealed that the APFD extract significantly affected biosynthesis peptidoglycan, gene expression, RNA processing, and macromolecule metabolism processes in S. mutans. Conclusion Data analysis revealed that multiple mechanisms of action were involved in antibacterial activity of A. paniculata extracts toward S. mutans.
Private Equity Characteristics , Earnings Management & Firm Value

The relationship among private equity investment, earnings management and enterprise value were studied using the data of small and medium-sized enterprises listed in Chinese Shenzhen Stock Exchanges from 2008 to 2012. This paper examines not only whether private equity affects the enterprise value, but also examines the influence degree of microscopic characteristics of private equity firms on the enterprise value. We find that, the involvement of private equity investment can promote enterprise value effectively and reduce earnings management behavior of enterprise management, accompanying the positive correlation from the private equity firms’ stake to the holding time to the performance of the enterprise value on the micro level. High reputation for private and foreign private equity is positively related to the enterprise value, while earnings management is negatively related to the firm value. Earnings management plays a partial mediating role during the process of private equity investment promoting enterprise value. ○C 2017IRABF All rights reserved.
Making a Gamble: Recruiting SE Participants on a Budget

Human studies in software engineering, especially those on a bud-get, often struggle to recruit participants. We investigate allocating a $100 budget for a remote program comprehension study to maximize the number of high-quality responses. We compare seven incentive structures, including various raffle-based and first-come-first-serve methods. We focus on computer science undergraduates, a common population in software engineering studies. Incentive structure does have significant effects on the number of participants and on data quality. We conclude with concrete guidelines for incentive allocation for online software engineering human studies.
ACADEMY OF FINANCIAL SERVICES OFFICERS

Based on our analyses of Survey of Consumer Finances datasets, the proportion of households owning a life insurance policy decreased from 72% in 1992 to 60% in 2016. We estimated logistic regressions on the likelihood of ownership of any, term, and cash value life insurance. We conclude that changes in household characteristics accounted for the decrease in term life insurance ownership, but not for the decreases in any and in cash value life insurance ownership. We also found a positive association between use of a financial planner and life insurance ownership. We discuss implications for financial planning. © 2020 Academy of Financial Services. All rights reserved. JEL classification: D12; D14; G22
A DASHBOARD FOR NURSING HOME COVID-19 IMPACT: A A DASHBOARD FOR NURSING HOME COVID-19 IMPACT: A DESIGN SCIENCE APPROACH DESIGN SCIENCE APPROACH

Using a design science approach, we developed a dashboard prototype (artifact) to better understand the factors related to the impact of COVID-19 within nursing homes (NHs). The artifact will allow stakeholders to make informed decisions related to potential spread of the coronavirus in NHs. Data integrated from disparate sources was used for building the design artifact. We used analytics techniques to customize the dashboard and enhance its visualization and interactive capabilities to help stakeholders get answers related to nursing home factors that impact the spread of COVID-19 in NHs. The viability of the dashboard will depend on the attributes possessed, such as the type of information conveyed, the level of detail, and reusability potential. The dashboard can inspire new policies to be developed for nursing home facilities across the country which will ultimately improve their situations in light of the pandemic.
SiamAVOT: A High-performance Siamese Network Tracker for Aerial Visual Object Tracking

Aerial vision object tracking technology plays an increasingly critical part in intelligent perception and task cognition when aircraft perform various reconnaissance missions. However, the particularity in object types and visual angles in aerial vision imagery render the tracking process more prone to deformation, scale variation, motion blur and occlusion, making this technique very challenging. A high-performance Siamese network tracker for aerial visual object tracking, SiamAVOT, is proposed to deal with this problem in this paper. First, we utilize the SiamCAR infrastructure to introduce the Bottleneck Attention Module (BAM) attention mechanism after its backbone network for fusion of low and high level features to improve recognition for deformation object features. Then, we change to Distance-IoU (DIoU) loss for the bounding box regression during the training process to improve the network’s ability to predict scale variation. Finally, a Kalman filter online learning module that integrates time and space trajectory information is designed to solve the object motion blur and occlusion disappearance problem in the inference process. The proposed SiamAVOT achieves leading performance on the UAV123 and AVOT40 aerial datasets and can run in real-time at 72 FPS.
Recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time–frequency diagrams as the input, which time–frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy.
Two-dimensional DOA estimation of coherent signals for MIMO Radar Based on tensor decomposition

This paper studies the two-dimensional DOA estimation of coherent targets. Subspace algorithm is usually used to solve this problem. Compared with subspace algorithm, tensor decomposition has significant advantages in estimation accuracy and has been widely used in the field of signal processing. However, the research on tensor decomposition algorithm mainly focuses on incoherent targets. Therefore, we study how to use the advantages of tensor decomposition to improve the estimation accuracy of coherent targets. Firstly, we construct the echo signal model corresponding to the “Sum array” of MIMO radar, and then use the spatial smoothing to process the data to solve the problem of covariance rank deficiency. Then a third-order tensor is constructed according to the position relationship of the data. Finally, the DOA can be estimated from the decomposed tensor according to the position relationship of the data. Simulation results show that proposed algorithm has significant advantages in estimation accuracy.
An Efficient Multi-dimensional Data Aggregation Scheme for Fog-Assisted Wireless Body Area Network

The rapid development of wireless body area network and monitoring technology has brought new opportunities for the later rehabilitation of patients. Due to the limited computing power and power of wireless body area network (WBAN) sensor nodes, combining WBAN with fog computing is one of the feasible solutions for remote monitoring. Wireless body area network can exchange medical information in real time, which is an effective way to improve medical services. The WBAN is inefficient and vulnerable to attacks, leading to leakage of user privacy information. This paper proposes an efficient data aggregation scheme for fog-assisted WBAN. The analysis shows that the scheme can resist the existing attack. The security analysis shows that the scheme meets the privacy and security requirements of data aggregation in WBAN. Compared with existing schemes, this scheme has an efficiency advantage.
A Multi-Scale Brain Tumor Segmentation Method based on U-Net Network

Brain tumor is a serious threat to human health. Because the size and shape of brain tumors can be uneven, irregular and unstructured. Automatic segmentation of tumors from magnetic resonance imaging (MRI) is a challenging task. Brain tumor segmentation using computer-aided diagnosis has important clinical significance for the prognosis and treatment of patients. The traditional U-Net network can not take full advantage of context information, which is easy to cause the loss of effective information of image. Therefore, we propose a multi-scale segmentation method for brain tumors based on U-Net network, in which a multi-scale module for feature extraction is added between down-sampling and up-sampling. The brain tumor public dataset BraTS2020 is used for testing. Dice coefficient was used as the evaluation index, and our experimental method made the average dice of the whole tumor area, core tumor area and enhanced tumor area reach 87.57%, 86.20% and 84.24%, respectively. The experimental results show that the proposed method has comparable precision with typical brain tumor segmentation methods.
Direction of arrival and target scale estimation based on split beamforming algorithm

Based on the theory of split beamforming algorithm and the experimental data collected by circular array, this paper analyzes the characteristics of DOA and target size estimation. In this paper, the signal receiving model of planar circular array and the split beamforming algorithm are studied. Then, based on the data collected from the lake experiment, the DOA estimation results of conventional beamforming algorithm, multiple signal classification algorithm and split beamforming algorithm are analyzed, and the target scale estimation results of split beamforming algorithm is analyzed. The experimental data analysis shows that the split beamforming algorithm has good direction finding stability, and the target scale estimation error based on the split beamforming algorithm is small.
Assessment of Water Availability for Rice Cultivation in South Lampung Regency

Climatic conditions influence the availability of water for rice cultivation. Climate change will significantly impact rice production in areas where water resource infrastructure is inadequate. South Lampung Regency is primarily a rain-fed rice production region. As a result, an assessment of water availability for rice cultivation is required for sustainable rice production. This study aims to provide recommendations to the government in carrying out the necessary infrastructure development. This study uses secondary data such as rainfall, temperature, solar radiation, evaporation, and humidity from 2010 to 2020. The study’s findings provide information on the sufficiency of water availability for rice cultivation in each sub-district in the South Lampung Regency, as well as scenarios for developing water resources infrastructure.
A Group Learning based Optimization Algorithm Applied to UWB Positioning

In dealing with optimization problems, metaheuristic algorithms have attracted much attention due to their simple structure and flexible characteristics. Inspired by the principle of teaching students in accordance with their aptitude, this paper proposed a novel metaheuristic algorithm——Group Learning based Optimization (GLBO) Algorithm, this algorithm is suitable for continuous optimization problems. The main idea of this method is to divide a class into three study groups according to their scores, and formulate different study strategies for them according to the characteristics of each group, so as to improve the scores of the whole class. To verify the performance of the algorithm, it is tested on the CEC21 Benchmark suit and applied to UWB positioning. The results show that the proposed method has excellent performance when dealing with continuous optimization problems.
A Design of Tunable Filter Integrated SPDT Switch Based on PIN Diode

In this paper, a tunable filter integrated SPDT switch based on PIN diode is proposed. The band-pass filter response is realized by using microstrip lines with different characteristic impedances, and the filter-integrated SPDT switch with tunable center frequency is designed by loading a varactor diode and PIN diodes. The measured results show that its filter response can be tuned in the range of 1.5 to 2.065 GHz, the insertion loss is 1.53-2.69 dB, and with well return loss and port isolation.
Single Image Super-Resolution Algorithm based on Fixed-Point Multi-Model Gaussian Process Regression

To solve the problem of large amount of computation in matrix inversion of Gaussian process regression model, a super-resolution algorithm based on local Gaussian process regression of fixed point multi-model is proposed. Firstly, the training samples are classified by Gaussian mixture model, and image patches are randomly selected as fixed points in each type of training samples, and its K nearest neighbor patch are searched. Secondly, local Gaussian process regression model by using its low-mid-frequency components and the corresponding high frequency components. Again, low resolution test image patch is classified, only the K nearest neighbor test image patch is searched during reconstruction. And then, find the nearest fixed point in each kind of image patch, and use the local Gaussian process regression model based on the fixed point to predict its corresponding high-frequency component. Finally, corresponding high frequency information is predicted by using this trained model, which improves the reconstruction efficiency. Experimental results have demonstrated that the proposed algorithm is superior in both quantitative and qualitative aspects against other algorithms.