A fine-grained computational interpretation of Girard’s intuitionistic proof-nets

This paper introduces a functional term calculus, called pn, that captures the essence of the operational semantics of Intuitionistic Linear Logic Proof-Nets with a faithful degree of granularity, both statically and dynamically. On the static side, we identify an equivalence relation on pn-terms which is sound and complete with respect to the classical notion of structural equivalence for proof-nets. On the dynamic side, we show that every single (exponential) step in the term calculus translates to a different single (exponential) step in the graphical formalism, thus capturing the original Girard’s granularity of proof-nets but on the level of terms. We also show some fundamental properties of the calculus such as confluence, strong normalization, preservation of β-strong normalization and the existence of a strong bisimulation that captures pairs of pn-terms having the same graph reduction.

Peramalan Stock Barang Dagangan Menggunakan Metode Single Exponential Smoothing

Peramalan bisa digunakan dibidang manapun yang mana membutuhkan sebuah prediksi akan keberadaan data di masa yang mendatang. Forecasting bisa diterapkan salah satunya untuk membantu anggaran penjulan ke periode berikutnya. Data time series diperoleh dari data penjualan selama periode tertentu penjualan suatu produk bisa digunakan sebagai dasar forecasting-nya. Restock barang yang berlebih tidak baik untuk sebuah toko, karena ada kemungkinan barang yang dibeli tidak laku kedepannya. Perlu adanya proses kontrol yang baik untuk restock barang, salah satunya yang bisa digunakan adalah menggunakan prediksi restock barang dagangan menggunakan single exponential smoothing (SES). Data penjulan yang digunakan ada dua macam yaitu Beras Koi 5kg-an dan Minyak Bimoli 900ml-an. Dari hasil perhitungan SES diperoleh nilai alpha yang bagus untuk peramalan Beras Koi 5kg-an adalah 0,46. Sedangkan nilai alpha untuk Minyak Bimoli 900ml-an adalah 0,704. Nilai alpha tersebut diperoleh dari perhitungan nilai MSE yang terkecil. Hasil prediksinya menunjukkan pada periode berikutnya (15-30 September 2021) menunjukkan adanya penurunan jumlah penjualan barang dari kedua produk tersbut, sehingga pemilik toko bisa mengurangi jatah belanjanya.

Fast and robust single-exponential decay recovery from noisy fluorescence lifetime imaging

The preprint has been already submitted to an IEEE journal for peer review.

Fast and robust single-exponential decay recovery from noisy fluorescence lifetime imaging

The preprint has been already submitted to an IEEE journal for peer review.

Demand Forecasting of The Automobile Sales Using Least Square, Single Exponential Smoothing and Double Exponential Smoothing

Sales strategies require the right managerial in marketing products with the development of technology and communication, the decision making in product sales supported by complete data and can be analyzed into intelligence business solutions. The research discussed and provided solutions about how to forecast future demand targets from a set of data history by making a predictive model of product demand in the real case. The research study was obtained from automobile sales, which the company probably set the strategy from the forecast result of automobile sales by the system in the future. The research used forecasting methods such as Least Square, Single Exponential Smoothing, and Double Exponential Smoothing to achieve a small percentage of prediction error. The dataset was collected from Mitsubishi Motors Corporation which obtained 60 samples of popular product types such as Pajero, FE and L300 from 2014 to 2018 over a period of months. The experimental results reported that Double Exponential Smoothing has given a better performance than other methods. The forecasting result of Pajero reached the MAPE of 3.26%, FE reached the MAPE of 3.24%, and L300 reached the MAPE of 3.37%. This study indicates that the selection of the forecasting method depends on the actual data pattern and the adjustment of the parameters in predicting future points.

Comparing the Performance of Four Sinc Methods for Numerical Indefinite Integration

Aims/ Objectives: To compare the performance of four Sinc methods for the numerical approximation of indefinite integrals with algebraic or logarithmic end-point singularities. Methodology: The first two quadrature formulas were proposed by Haber based on the sinc method, the third is Stengers Single Exponential (SE) formula and Tanaka et al.s Double Exponential (DE) sinc method completes the number. Furthermore, an application of the four quadrature formulas on numerical examples, reveals convergence to the exact solution by Tanaka et al.s DE sinc method than by the other three formulae. In addition, we compared the CPU time of the four quadrature methods which was not done in an earlier work by the same author. Conclusion: Haber formula A is the fastest as revealed by the CPU time.

Negative Binomial Time Series Regression – Random Forest Ensemble in Intermittent Data

Intermittent dataset is a unique data that will be challenging to forecast. Because the data is containing a lot of zeros. The kind of intermittent data can be sales data and rainfall data. Because both sometimes no data recorded in a certain period. In this research, the model is created to overcome the problem. The approach that is used in this research is the ensemble method. Mostly the intermittent data comes from the Negative Binomial because the variance is over the mean. We use two datasets, which are rainfall and sales data. So, our approach is creating the base model from the time series regression with Negative Binomial based, and then we augmented the base model with a tree-based model which is random forest. Furthermore, we compare the result with the benchmark method which is The Croston method and Single Exponential Smoothing (SES). As the result, our approach can overcome the benchmark based on metric value by 1.79 and 7.18.