Fundamental Convergence Analysis of Sharpness-Aware Minimization

The paper investigates the fundamental convergence properties of Sharpness-Aware Minimization (SAM), a recently proposed gradient-based optimization method (Foret et al., 2021) that significantly improves the generalization of deep neural networks. The convergence properties including the stationarity of accumulation points, the convergence of the sequence of gradients to the origin, the sequence of function values to the optimal value, and the sequence of iterates to the optimal solution are established for the method. The universality of the provided convergence analysis based on inexact gradient descent frameworks (Khanh et al., 2023b) allows its extensions to the normalized versions of SAM such as VaSSO (Li & Giannakis, 2023), RSAM (Liu et al., 2022), and to the unnormalized versions of SAM such as USAM (Andriushchenko & Flammarion, 2022). Numerical experiments are conducted on classification tasks using deep learning models to confirm the practical aspects of our analysis.Comment: 21 page

Theoretical basis for reasonable population distribution in Tho Chu archipelago

Tho Chu archipelago is one of administrative units of Kien Giang province, Hon Nhan- one of its islands - is selected to become A1 base point of baseline for Vietnam territorial waters. If Tho Chau district is established, it will contribute to identifying sovereignty of Vietnam Southwest sea area following the United Nations Convention on the Law of the Sea, creating favourable conditions for islands’ socio-economic development, developing culture, enhancing effects of administration work and life quality of population in this island, firmly protecting sea border, securing island and sea sovereignty… However, the establishment of Tho Chu district appears in the context of streamlining administrative apparatus and limitation of capital for infrastructure construction in district level. This article focuses on the analysis of some factors affecting a reasonable population distribution in order to meet the requirement of building the Tho Chu into a district-level administrative unit in Kien Giang province

Impacts of κ- oligocarrageenan application on photosynthesis, nutrient uptake and bean yield of coffee (Coffea robusta)

κ-Oligocarrgeenan (OC) is well known as an effective and green plant growth promoter and elicitor. However, its effect on coffee plant has not been investigated so far. This study aimed to examine the impacts of OC on biophysical activity, vegetative growth and productivity of coffee plant (Coffea robusta). OC with average molecular weight (AMW) of 4.0 kDa was prepared by depolymerization of carrageenan using ascorbic acid. Field experiments were conducted by foliar spray four times per year at various OC concentrations (50, 100, 150, 200, and 250 ppm) for three years (2017-2019). The results showed that OC promoted growth of coffee plant in all tested concentrations, and an optimized concentration was found at 150 ppm which showed a significant increase compared to the control plant in total chlorophyll (24.79%), carotenoid (31.65%), uptakes of N (15.66%), P (15.81%), and K (22.25%) minerals in leaves, crop yield (19.80%) and bean size (13.10%). Therefore, OC is potentially promising to apply as a promoter to enhance yield of crops for sustainable coffee plantation

Search for High Energy Skimming Neutrinos at a Surface Detector Array

In the present study we propose a new method for detectionof high energy cosmological muon neutrinos by transition radiations at amedium interface. The emerging electro-magnetic radiations induced by earth-skimming heavy charged leptons are able to trigger a few of aligned neighboringlocal water Cherenkov stations at  a surface detector array similar tothe Pierre Auger Observatory. The estimation applied tothe model of Gamma Ray Burst induced  neutrino fluxes and the spherical earth surface shows a competitive rate of muonneutrino events in the energy range below the GZK cut-off

Design and Implementation of Fuzzy-based Fine-tuning PID Controller for Programmable Logic Controller

The Proportional-Integral-Derivative (PID) controller, already known for its stability, is widely used in industrial applications and integrated into many Programmable Logic Controllers (PLCs). However, most PLCs do not support the self-tuning mechanism for PID controller parameters. Therefore, users must manually adjust several times to achieve the desired outcomes. This manual adjustment is time-consuming and must be repeated as control object parameters change over time. This study proposed a fine-tuning mechanism for the PID controller’s parameters based on a fuzzy-PD controller. The mechanism was designed and simulated using MATLAB/Simulink on an identified plant, then converted into a Structured Control Language (SCL) code for implementation on the PLC programs. Experimental results on the Siemens S7-1200 PLC demonstrated the proposed mechanism’s effectiveness in stabilizing the thermal plant by adjusting the initial parameters of the integrated PID controller. The system response was more stable, and the overshoot was minimized in comparison with the built-in auto-tuning feature on the S7-1200. Specifically, overshoot decreased to 0.79% from 0.94%, and the setting error declined to 0.1 °C from 0.45 °C. The above results indicate the effectiveness of the proposed self-tuning mechanism when used to improve the quality of PID controllers in PLCs. In addition, due to its ability to self-tuning parameters, it helps users reduce the time required to design PID controllers

Utilisation agricole de plantes aquatiques, notamment en tant qu'amendement des sols, dans la province de Thua Thien Hue, Centre Vietnam. 1. Inventaire, abondance et caractérisation chimique des plantes aquatiques disponibles localement

Agricultura Use of Aquatic Plants, mainly as Soil Amendment, in the Thua Thien Hue Province, Central Vietnam. 1. Inventory, Abundance and Chemical Characterization of Collected Plants. The use of aquatic plants for various purposes, and notably as organic amendment for sandy soils with low inherent fertility is a frequent empirical practice in Central Vietnam. In the Thua Thien Hue Province, the Tam Giang lagoon covering 22,000 ha represents a source of exogenous biomass potentially important for agriculture. The present study makes an inventory of the submerged macrophytes and the algae occurring in the lagoon during the period of February-April 2005. Twelve species of macrophytes (belonging to the Potamogetonaceae, Najadaceae, Cymodoceaceae, Hydrocharitaceae, Ceratophyllaceae, and Haloragaceae families) and five of algae (belonging to the Ulvaceae, Cladophoraceae, Characeae, and Gracilariaceae families) were identified. Their abundance varies significantly following species and location in the lagoon. Indeed, the salt concentration, the water depth and the type of sediments in which the macrophytes are anchored are submitted to large variations depending on position in the lagoon. The highest values of fresh biomass measured for monospecific vegetal mats were observed for Vallisneria spiralis (3.1 kg.m-2), Najas indica (2.9 kg.m-2), Halodule tridentata (2.5 kg.m-2) and Cymodoceae rotundata (2.3 kg.m-2). The concentrations of main elements were determined in samples of all plant species. In the macrophytes, the following ranges of element concentrations (in % of dry matter) were found: N 1.0 to 3.5; P 0.08 to 0.45; K 1.0 to 4.2; Mg 0.3 to 1.4; Ca 0.7 to 2.8; Na 0.7 to 7.6. These variations indicate that the fertilization capacity of aquatic plants when they are used as soil amendment can vary to a large extent according to the species. Even more contrasted element concentrations were found for the algae. The Na concentrations in the collected plants can be partly explained by the salinity level met in the sampling areas

Vehicle Type Classification with Small Dataset and Transfer Learning Techniques

This study delves into the application of deep learning training techniques using a restricted dataset, encompassing around 400 vehicle images sourced from Kaggle. Faced with the challenges of limited data, the impracticality of training models from scratch becomes apparent, advocating instead for the utilization of pre-trained models with pre-trained weights. The investigation considers three prominent models—EfficientNetB0, ResNetB0, and MobileNetV2—with EfficientNetB0 emerging as the most proficient choice. Employing the gradually unfreeze layer technique over a specified number of epochs, EfficientNetB0 exhibits remarkable accuracy, reaching 99.5% on the training dataset and 97% on the validation dataset. In contrast, training models from scratch results in notably lower accuracy. In this context, knowledge distillation proves pivotal, overcoming this limitation and significantly improving accuracy from 29.5% in training and 20.5% in validation to 54% and 45%, respectively. This study uniquely contributes by exploring transfer learning with gradually unfreeze layers and elucidates the potential of knowledge distillation. It highlights their effectiveness in robustly enhancing model performance under data scarcity, thus addressing challenges associated with training deep learning models on limited datasets. The findings underscore the practical significance of these techniques in achieving superior results when confronted with data constraints in real-world scenario