Model for Anisotropic Directed Percolation

We propose a simulation model to study the properties of directed percolation in two-dimensional (2D) anisotropic random media. The degree of anisotropy in the model is given by the ratio $\mu$ between the axes of a semi-ellipse enclosing the bonds that promote percolation in one direction. At percolation, this simple model shows that the average number of bonds per site in 2D is an invariant equal to 2.8 independently of $\mu$. This result suggests that Sinai's theorem proposed originally for isotropic percolation is also valid for anisotropic directed percolation problems. The new invariant also yields a constant fractal dimension $D_{f} \sim 1.71$ for all $\mu$, which is the same value found in isotropic directed percolation (i.e., $\mu = 1$).Comment: RevTeX, 9 pages, 3 figures. To appear in Phys.Rev.

Super-Poissonian noise in a Coulomb blockade metallic quantum dot structure

The shot noise of the current through a single electron transistor (SET), coupled capacitively with an electronic box, is calculated, using the master equation approach. We show that the noise may be sub-Poissonian or strongly super-Poissonian, depending mainly on the box parameters and the gate. The study also supports the idea that not negative differential conductance, but charge accumulation in the quantum dot, responds for the super-Poissonian noise observed.Comment: 4 Pages, 3 Figure

The Current Adoption of Dry-Direct Seeding Rice (DDSR) in Thailand and Lessons Learned for Mekong River Delta of Vietnam

The paper documents the joint study trip, organized by CCAFS Southeast Asia for Vietnamese rice researchers, extension workers, as well as local decision makers, to visit Thailand in April 2018. The goal of the study trip was to observe and learn the experience of Thai farmers on the large-scale adoption process of dry-direct seeding rice (DDSR), a viable alternative to address regional scarcity of fresh water in irrigation caused by the drought and salinity intrusion in the Mekong River Delta

Spies, Allies, and Murder?: The Ominous Origins of the Tet Offensive

Although the Tet Offensive represented a major turning point in the Vietnam War, much of North Vietnam’s decision making surrounding the offensive remains unclear. Based on recently declassified materials from Vietnam, this paper reveals how North Vietnamese domestic politics and foreign relations influenced Hanoi’s strategy deliberation for the 1968 offensive.Ohio State University. Mershon Center for International Security Studies.Event Web Page, MP4 Video, Photo

Statistical analysis of the impact of class imbalance on model performance

Abstract. This work studies the impact of class imbalance in data distribution on models and performance metrics. With the increasing amount of data available, new challenges arise, and one of them is unbalanced data. This problem occurs when one class in a dataset is underrepresented compared to others. The under-represented class is called the minority, while the dominant class is called the majority. In an unbalanced data problem, the number of instances in the minority class is much fewer than that in the majority class, with a ratio such as 1:99. The thesis begins with a comprehensive overview of the challenges and solutions associated with unbalanced data in machine learning. Different types of class imbalances have been defined and discussion of challenges from this issue. Next the thesis discusses various techniques for dealing with unbalanced data, including resampling and more complex algorithms such as XGboost. The thesis also presents an empirical study and results on the Caravan insurance dataset. This dataset was provided in the Coil Challenge in 2000, where participants competed to develop the best model to predict whether a customer would buy insurance for their caravan. The Caravan dataset shows that only around six percent of customers will buy insurance, making it heavily unbalanced. With this distribution, the dataset is a good candidate to study the impact of imbalance on model performance and performance metrics. Unbalanced data can cause problems in various industries, from fraud detection to credit risk management. Therefore, understanding how to deal with this issue is crucial for developing accurate machine learning models that can be used effectively across different domains. This work provides valuable insights into how to address this challenge by presenting empirical evidence and discussing commonly applied solutions. It is a helpful resource for anyone interested in machine learning and data science. Using a real business data set for empirical test, there are three findings from this research. Firstly, performance metrics such as accuracy may not be suitable for heavily unbalanced data. Secondly, most common modeling method, logistic regressions may not provide best results for minority class since performance metrics are dominated by majority class. Finally, to address imbalance issue, both resampling techniques to balance data before modelling and ensemble modelling methods such as XGBoost can produce good results.Tilastollisen aineiston todennäköisyysjakaumien luokittaisen epätasapainon vaikutuksia tilastollisten mallien ennustekykyyn. Tiivistelmä. Pro gradu -tutkielmani tavoitteena on tarkastella tilastollisen aineiston todennäköisyysjakaumien luokittaisen epätasapainon vaikutuksia tilastollisten mallien ennustekykyyn. Liiketaloudelliseen aineistoon perustuvan empiirisen tutkimuksen tuloksena saatiin kolme keskeistä tulosta. Ensiksi tilastollisten mallien arvioinnissa ennustekyvyn mittarit eivät sovellu luokittaisesti epätasapainoisiin aineistoihin. Toiseksi yleisimpiin käytössä oleviin tilastollisiin malleihin kuuluva logistinen regressioanalyysi aliarvioi havaintomäärältään pienimpien osajoukkojen esiintymisten todennäköisyyttä. Kolmanneksi luokittaisesta epätapainosta aiheutuvaa tilastollisen mallin ennustetarkkuuteen liittyvää virhettä voidaan merkittävästi parantaa XGBoostmallinnustekniikalla ja resampling-menetelmillä