A separation logic for negative dependence

Formal reasoning about hashing-based probabilistic data structures often requires reasoning about random variables where when one variable gets larger (such as the number of elements hashed into one bucket), the others tend to be smaller (like the number of elements hashed into the other buckets). This is an example of negative dependence , a generalization of probabilistic independence that has recently found interesting applications in algorithm design and machine learning. Despite the usefulness of negative dependence for the analyses of probabilistic data structures, existing verification methods cannot establish this property for randomized programs. To fill this gap, we design LINA, a probabilistic separation logic for reasoning about negative dependence. Following recent works on probabilistic separation logic using separating conjunction to reason about the probabilistic independence of random variables, we use separating conjunction to reason about negative dependence. Our assertion logic features two separating conjunctions, one for independence and one for negative dependence. We generalize the logic of bunched implications (BI) to support multiple separating conjunctions, and provide a sound and complete proof system. Notably, the semantics for separating conjunction relies on a non-deterministic , rather than partial, operation for combining resources. By drawing on closure properties for negative dependence, our program logic supports a Frame-like rule for negative dependence and monotone operations. We demonstrate how LINA can verify probabilistic properties of hash-based data structures and balls-into-bins processes.

The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies.

PENGALAMAN KERJA, PELATIHAN KERJA, IKLIM OGANISASI DAN PENGARUHNYA TERHADAP KINERJA KARYAWAN PT. TRENGGINAS JAYA

Thiss study aims to determine work experience, job training, organizational climate, and its influence on employee performance at PT. Trengginas Jaya either partially or simultaneously. The sample used as many as 30 employees. This research uses descriptive and verification methods. Hypothesis testing techniques T-test and F-test and data analysis techniques for Multiple Linear Regression using SPSS v25 for Windowss software. The result of this study indicate that there is a significant effect of work experience, job training, and organizational climate on employee performance simultaneously of 26.907. In addition, work experience, job training, and organizational climate also partially have a significant effect on employe performance. Among these variables, partially Work Experience has a significantly greater level than the other three variables, which is 3.778

Exploring the Usefulness of Downscaling Free Forecasts from the Warn-on-Forecast System

The National Severe Storms Lab (NSSL) Warn-on-Forecast System (WoFS) is an experimental real-time rapidly-updating convection-allowing ensemble that provides probabilistic short-term thunderstorm forecasts. This study evaluates the impacts of reducing the forecast model horizontal grid spacing Δx from 3 km to 1.5 km on the WoFS deterministic and probabilistic forecast skill, using eleven case days selected from the 2020 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE). Verification methods include (i) subjective forecaster impressions; (ii) a deterministic object-based technique that identifies forecast reflectivity and rotation track storm objects as contiguous local maxima in the composite reflectivity and updraft helicity fields, respectively, and matches them to observed storm objects; and (iii) a recently developed algorithm that matches observed mesocyclones to mesocyclone probability swath objects constructed from the full ensemble of rotation track objects. Reducing Δx fails to systematically improve deterministic skill in forecasting reflectivity object occurrence, as measured by critical success index (CSIDET), a metric that incorporates both probability of detection (PODDET) and false alarm ratio (FARDET). However, compared to the Δx = 3 km configuration, the Δx = 1.5 km WoFS shows improved mid-level mesocyclone detection, as evidenced by its statistically significant (i) higher CSIDET for deterministic mid-level rotation track objects and (ii) higher normalized area under the performance diagram curve (NAUPDC) score for probability swath objects. Comparison between Δx = 3 km and Δx = 1.5 km reflectivity object properties reveals that the latter have 30% stronger mean updraft speeds, 17% stronger median 80-m winds, 67% larger median hail diameter, and 28% higher median near-storm-maximum 0-3 km storm-relative helicity.

Pengaruh Self Image Congruence Terhadap Keputusan Pembelian Dimediasi Oleh Brand Trust Pada Maskapai Penerbangan Air Asia

The Influence of Self Image Congruence on Purchasing Decisions Mediated by Brand Trust (Survey on Air Asia Airlines). The aviation industry is an industry engaged in air transportation that is needed by the public. The development of the aviation industry in Indonesia, especially domestic airlines, is increasingly booming and growing rapidly with the existence of many airlines that have sprung up since the enactment of Law No. 15 of 1992. The purpose of this study was to see the effect of Self Image Congruence on Purchasing Decisions Mediated by Brand Trust on Air Asia Airlines. This research was conducted using descriptive and verification methods. The sampling technique of this research is probability sampling using Hair formula. The types of data used in this study are primary data and secondary data. The data were processed using path analysis. The results of this study prove that Self Image Congruence has a positive and significant effect on Brand Trust which has an impact on Purchasing Decisions on Air Asia Airlines.

Overview of spatial verification methods and their application to ensemble forecasting

State-of-the-art high-resolution NWP models simulate mesoscale systems with a high degree of detail, with large amplitudes and high gradients of fields of weather variables. Higher resolution leads to the spatial and temporal error growth and to a well-known double penalty problem. To solve this problem, the spatial verification methods have been developed over the last two decades, which ignore moderate errors (especially in the position), but can still evaluate the useful skill of a high-resolution model. The paper refers to the updated classification of spatial verification methods, briefly describes the main methods, and gives an overview of the international projects for intercomparison of the methods. Special attention is given to the application of the spatial approach to ensemble forecasting. Popular software packages are considered. The Russian translation is proposed for the relevant English terms. Keywords: high-resolution models, verification, double penalty, spatial methods, ensemble forecasting, object-based methods

FAKTOR-FAKTOR PENDUKUNG AKUNTABILITAS PENGELOLAAN KEUANGAN DESA

ABSTRAKPenelitian ini bertujuan untuk menganalisis pengaruh kualitas sumber daya manusia, pengawasan Badan Permusyawaratan Desa dan partisipasi masyarakat terhadap akuntabilitas pengelolaan keuangan desa dengan sistem keuangan desa sebagai variabel intervening. Penelitian ini menggunakan metode deskriptif dan verifikatif. Populasi dalam penelitian ini adalah pemerintah desa se-Kecamatan Talaga dan Kecamatan Maja. Teknik analisis data menggunakan analisis regresi linear berganda. Hasil penelitian ini menunjukkan bahwa kualitas sumber daya manusia (SDM), pengawasan Badan Permusyawaratan Daerah (BPD), partisipasi masyarakat, dan sistem keuangan desa secara parsial berpengaruh positif terhadap akuntabilitas pengelolaan keuangan desa. Kualitas SDM dan pengawasan BPD berpengaruh positif terhadap akuntabilitas pengelolaan keuangan desa sedangkan partispasi masyarakat tidak berpengaruh terhadap sistem keuangan desa. Sistem keuangan desa mampu memediasi kualitas SDM, pengawasan BPD dan partisipasi masyarakat terhadap akuntabilitas pengelolaan keuangan desa. Implikasi dari penelitian ini untuk mewujudkan asas pengelolaan keuangan desa yang akuntabel diperlukan SDM yang berkualitas, Badan Permusyawartan Desa bertugas mengawasi secara baik dan selalu melibatkan masyarakat dalam rapat perencanaan pembangunan maupun pelaksanaan pembangunan desa jangka pendek maupun jangka panjang dan mengungkapkan penggunaan dana secara akuntabel dan transparan. ABSTRACTThis study aims to analyze the influence of the quality of human resources, supervision of the Village Consultative Body and community participation on the accountability of village financial management with the village financial system as an intervening variable. This research uses descriptive and verification methods. The population in this study is the village government in Talaga and Maja sub-districts. The data analysis technique used multiple linear regression analysis. The results of this study indicate that the quality of human resources (HR), supervision of the Regional Consultative Body (BPD), community participation, and the village financial system partially have a positive effect on village financial management accountability. The quality of human resources and supervision of the BPD has a positive effect on the accountability of village financial management, while community participation has no effect on the village financial system. The village financial system is able to mediate the quality of human resources, BPD supervision and community participation in the accountability of village financial management. The implication of this research is that to realize the principle of accountable village financial management, quality human resources are needed. The Village Consultative Body is tasked with supervising properly and always involving the community in development planning meetings and implementing short and long-term village development and disclosing the use of funds in an accountable and transparent manner.

Research and Application of the 3D Model of Beijing in Urban Planning Based on Oblique Photogrammetry

In recent years, with the accelerated development of the urbanization and the continuous emergence of the smart cities, the new requirements and the challenges have been raised for the urban governance and the urban planning. Based on the oblique aerial photogrammetry technology and the 3D automatic modelling technology, this study constructs the high-precision basic data of the 3D urban model of Beijing and verifies the 3D model’s precision comprehensively by using two verification methods: the point accuracy assessment and the plane accuracy assessment. And then, using the validated 3D model data and taking the demolition of the illegal buildings in urban planning as an example, an application of the 3D model is studied in the simulated environmental scenes of the urban planning, which also provides a reference for the development of the smart cities in the future.

Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines

Deep neural networks are widely used in the field of image processing for micromachines, such as in 3D shape detection in microelectronic high-speed dispensing and object detection in microrobots. It is already known that hyperparameters and their interactions impact neural network model performance. Taking advantage of the mathematical correlations between hyperparameters and the corresponding deep learning model to adjust hyperparameters intelligently is the key to obtaining an optimal solution from a deep neural network model. Leveraging these correlations is also significant for unlocking the “black box” of deep learning by revealing the mechanism of its mathematical principle. However, there is no complete system for studying the combination of mathematical derivation and experimental verification methods to quantify the impacts of hyperparameters on the performances of deep learning models. Therefore, in this paper, the authors analyzed the mathematical relationships among four hyperparameters: the learning rate, batch size, dropout rate, and convolution kernel size. A generalized multiparameter mathematical correlation model was also established, which showed that the interaction between these hyperparameters played an important role in the neural network’s performance. Different experiments were verified by running convolutional neural network algorithms to validate the proposal on the MNIST dataset. Notably, this research can help establish a universal multiparameter mathematical correlation model to guide the deep learning parameter adjustment process.