The Dynamics of Productivity Changes in Agricultural Sector of Transition Countries

Relying on frontier production approach (e.g., Luenberger's shortage function), we investigated the performance of agricultural sector in transition countries and its changes over time, especially focusing on the dynamics of productivity changes. We found that; (i) CEE countries have improved their performance during the sample period whereas CIS have not; (ii) productivity changes in the last decade was attributable to the technical progress; (iii) overall performance was decelerated for the second 5-year sub-period (1997-2001) in both regions; (iv) agricultural reform has positive effects on the productivity and its components especially in CEE countries.transition countries, productivity, directional distance function, agricultural reform, Productivity Analysis,

Ubiquitous Computing Based Mobile Commerce : Toward the Ubiquitous Commerce

Mobile commerce is heading for advanced fourth generation (4G) mobile systems. However, rapid development of ubiquitous computing technology can implement and complement the 4G mobile systems. It enables anticipation that ubiquitous computing technology creates the new commerce, so called ubiquitous commerce. This paper analyzes the characteristics of mobile commerce service and ubiquitous computing service, and then extracts the core requirements for ubiquitous commerce. Moreover, this paper suggests technical requirements for the ubiquitous commerce. These requirements can lead the development of technology for the future and suggest the milestone that mobile commerce in present heads for

Lattice paramenter, lattice disorder and resistivity of carbohydrate doepd MgB2 and their correlation with the transition temperature

The change in the lattice parameters or the lattice disorder is claimed as a cause of the slight reduction in the transition temperature by carbon doping in MgB2. In this work, an extensive investigation on the effects of carbohydrate doping has been carried out. It is found that not only the a-axis but also the c-axis lattice parameter increases with the sintering temperature. A linear relation between the unit cell volume and the critical temperature is observed. Compared with the well known correlation between the lattice strain and the critical temperature, the X-ray peak broadening itself shows a closer correlation with the transition temperature. The residual resistivity and the critical temperature are linearly correlated with each other as well and its implication is further discussed.Comment: 3 pages. Accepted by Jouranl of nanoscience and Nanotechnology (JNN

Overcoming Catastrophic Forgetting by Neuron-level Plasticity Control

To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). While learning a new task, the proposed method preserves the knowledge for the previous tasks by controlling the plasticity of the network at the neuron level. NPC estimates the importance value of each neuron and consolidates important \textit{neurons} by applying lower learning rates, rather than restricting individual connection weights to stay close to certain values. The experimental results on the incremental MNIST (iMNIST) and incremental CIFAR100 (iCIFAR100) datasets show that neuron-level consolidation is substantially more effective compared to the connection-level consolidation approaches.Comment: 8 page

Weak-Lensing Detection of Intracluster Filaments in the Coma Cluster

Our concordance cosmological model predicts that galaxy clusters grow at the intersection of filaments structuring the cosmic web stretching tens of Mega parsecs. Although this hypothesis has been supported by the baryonic components, no observational study has detected the dark matter component of the intracluster filaments (ICFs), the terminal segment of the large-scale cosmic filaments at their conjunction with individual clusters. We report weak-lensing detection of ICFs in the Coma cluster field from the ~12 sq. deg Hyper Suprime-Cam imaging data. The detection is based on two methods: matched-filter technique and shear-peak statistic. The matched-filter technique (shear-peak statistic) yields detection significances of 6.6- (3.1) $\sigma$ and 3.6- (2.8) $\sigma$ for the northern and western ICFs at 110$^{\circ}$ and 340$^{\circ}$, respectively. Both ICFs are highly correlated with the overdensities in the WL mass reconstruction and are well-aligned with the known large-scale ($>10$ Mpc) cosmic filaments comprising the Coma supercluster.Comment: Accepted for publication by Nature Astronomy (2024). The current submission will differ from the published on

Binary Replacement Technique for Application Programming Interface Level Simulation

International audienceDesign of complex embedded software requires ingenious solutions to many architectural problems. One such solution that would be a crucial catalyst in designing scalable and customized embedded software, is developed by API (Application Programming Interface) level simulator. The use of API level simulator has been gaining wide acceptance due to its design and verification efficiency by enabling parallel development in multiple software layers. However, there are two major bottlenecks in realizing practical systems: source code modification and recompilation of the target software. The paper proposes a novel simulation technique to resolve these two critical issues. The proposed technique makes it possible to replace any part of the target binary without modifying its source code and recompiling it

Learning to Place Unseen Objects Stably using a Large-scale Simulation

Object placement is a fundamental task for robots, yet it remains challenging for partially observed objects. Existing methods for object placement have limitations, such as the requirement for a complete 3D model of the object or the inability to handle complex shapes and novel objects that restrict the applicability of robots in the real world. Herein, we focus on addressing the Unseen Object Placement (UOP}=) problem. We tackled the UOP problem using two methods: (1) UOP-Sim, a large-scale dataset to accommodate various shapes and novel objects, and (2) UOP-Net, a point cloud segmentation-based approach that directly detects the most stable plane from partial point clouds. Our UOP approach enables robots to place objects stably, even when the object's shape and properties are not fully known, thus providing a promising solution for object placement in various environments. We verify our approach through simulation and real-world robot experiments, demonstrating state-of-the-art performance for placing single-view and partial objects. Robot demos, codes, and dataset are available at https://gistailab.github.io/uop/Comment: 8 pages (main

PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation

The study addresses the foundational and challenging task of peg-in-hole assembly in robotics, where misalignments caused by sensor inaccuracies and mechanical errors often result in insertion failures or jamming. This research introduces PolyFit, representing a paradigm shift by transitioning from a reinforcement learning approach to a supervised learning methodology. PolyFit is a Force/Torque (F/T)-based supervised learning framework designed for 5-DoF peg-in-hole assembly. It utilizes F/T data for accurate extrinsic pose estimation and adjusts the peg pose to rectify misalignments. Extensive training in a simulated environment involves a dataset encompassing a diverse range of peg-hole shapes, extrinsic poses, and their corresponding contact F/T readings. To enhance extrinsic pose estimation, a multi-point contact strategy is integrated into the model input, recognizing that identical F/T readings can indicate different poses. The study proposes a sim-to-real adaptation method for real-world application, using a sim-real paired dataset to enable effective generalization to complex and unseen polygon shapes. PolyFit achieves impressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseen shapes in simulations, respectively. Real-world evaluations further demonstrate substantial success rates of 86.7% and 85.0%, highlighting the robustness and adaptability of the proposed method.Comment: 8 pages, 8 figures, 3 table