Human activity learning and segmentation using partially hidden discriminative models

Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised learning and generative models such as the hidden Markov models and its extensions. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support supervised training is often extremely expensive. In this paper, we propose a new approach based on semi-supervised training of partially hidden discriminative models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that these models allow us to incorporate both labeled and unlabeled data for learning, and at the same time, provide us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart, the partially hidden Markov model, even when a substantial amount of labels are unavailable.<br /

Hierarchical semi-markov conditional random fields for recursive sequential data

Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.<br /

Statistical evaluation of the geochemical data for prospecting polymetallic mineralization in the Suoi Thau – Sang Than region, Northeast Vietnam

In Northeast Vietnam, Suoi Thau-Sang Than is considered as a high potential area of polymetallic deposits. 1,720 geochemical samples were used to investigate polymetallic mineralization; thereby polymetallic ore occurrences in this study region were discovered and the statistical and multivariate analysis helps to define geochemical anomalies in some northeastern regions, namely Suoi Thau, Sang Than, and Ban Kep. The statistical method and cluster analysis of geochemical data indicate that the Cu, Pb, and Zn elements are good indicators, and most of them comply with the lognormal or gamma distribution. Based on the third-order threshold, the geochemical anomalies of the content of the Cu, Pb, and Zn elements reflect the concentration of copper forming ore bodies in the mineralized zone, and clearly show the concentration in three distinct zones. The trend surface analysis which was employed to determine spatial variations and relationships among these good indicator elements and anomalous areas revealed relative changes in the content of the indicator elements, and they can be considered as regular. Moreover, the goodness of fit obtained trend functions of Pb and Zn, and Cu elements is a third-degree trend surface model. These results indicate that the models can be useful in studying geochemical anomalies and analyzing the tendency of the concentration of indicator elements in the Suoi Thau-Sang Than region. Additionally, it is suggested that the statistical analysis shows a remarkable potential to use the bottom river sediments in the region to investigate polymetallic mineralization. Moreover, geochemical data can help to evaluate geochemical anomalies of the pathfinder elements and potential mineral mapping of the Suoi Thau-Sang Than region in Northeast Vietnam

MINH GIẢI TÀI LIỆU TRỌNG LỰC VÀ TỪ DỰ BÁO CẤU TRÚC TRIỂN VỌNG KHOÁNG SẢN RẮN KHU VỰC THỀM LỤC ĐỊA NAM - ĐÔNG NAM VIỆT NAM

The East Vietnam Sea is a marginal sea with complicated geological structures. The volcanic activities are quite strong after the sea-floor spreading in Cenozoic Era. There are the types of structure here favorable to the formation of solid minerals (manganese-iron aggregation). However, it is difficult to define their ranges and spatial locations. This paper presents the methods of reduction to the magnetic equator in low latitudes to bring out a better correlation between magnetic anomalies and their sources; High-frequency filtering is to separate gravity and magnetic anomalies as well as information about the solid minerals in the upper part of the Earth’s crust; 3D total gradient is to define the spatial location of high density and magnetic bodies. The potential structures of solid mineral are predicted by multi-dimensional correlation analysis between high frequency gravity and magnetic anomalies with weighted 3D total gradient.Biển Đông là một biển rìa có cấu kiến trúc phức tạp, hoạt động phun trào bazan núi lửa xảy ra khá mạnh mẽ ở thời kỳ sau tách giãn đáy. Ở đây, tồn tại các dạng địa hình thuận lợi cho việc hình thành cấu trúc chứa khoáng sản rắn (cụ thể là kết hạch sắt - mangan). Tuy nhiên việc xác định phạm vi, vị trí không gian của chúng gặp nhiều khó khăn bởi lớp nước dày và nguồn tài liệu khảo sát chưa được đầy đủ. Nghiên cứu này áp dụng phương pháp chuyển từ về xích đạo ở vĩ độ thấp nhằm tạo nên mối tương quan tốt hơn giữa dị thường và nguồn gây dị thường Từ; Phương pháp lọc trường tần số cao dùng để phân tách trường Trọng lực, Từ cũng như các thông tin về khoáng sản rắn ở phần trên của vỏ Trái đất; Phương pháp gradient toàn phần 3D xác định vị trí không gian các khối có mật độ, từ tính cao. Cấu trúc triển vọng khoáng sản rắn được dự báo bằng phép phân tích so sánh mối quan hệ đa chiều giữa trường Trọng lực và trường Từ tần số cao với trường trọng số gradient toàn phần 3D của chúng

Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL

In manufacturing practice, manufacturers always strive to achieve both quality and productivity targets simultaneously. In the first part, this study examines the relationship between input factors, including cutting speed, depth of cut, and feed rate, and the output response, which is surface roughness, when milling hardened SKD11 alloy steel under minimum coolant lubrication conditions using SiO2 nanofluid. The input parameters are divided into four levels to determine their influence on surface roughness and to find the optimal conditions for achieving the minimum surface roughness. The experimental design was conducted using an L16 array. A second-order regression model was developed to describe the relationship between the input variables and the output response. In the second part, multi-objective optimization was performed to simultaneously achieve the minimum surface roughness and the maximum material removal rate (MRR). The Response Surface Methodology (RSM) was employed in this study. The results indicated that to achieve the minimum surface roughness, machining should be performed at a cutting speed of 100&nbsp;m/min, a cutting depth of 0.2&nbsp;mm, and a feed rate of 0.01&nbsp;mm/tooth. With these settings, the predicted surface roughness could reach 0.0451&nbsp;µm. On the other hand, for the multi-objective optimization, to achieve the minimum surface roughness and the maximum MRR simultaneously, machining should be carried out at a cutting speed of 100&nbsp;m/min, a cutting depth of 0.36&nbsp;mm, and a feed rate of 0.0168&nbsp;mm/tooth. With this cutting condition, the predicted surface roughness could reach 0.1069&nbsp;µm, and the predicted MRR could reach 775.06&nbsp;mm3/mi

The Role of Serial NT-ProBNP Level in Prognosis and Follow-Up Treatment of Acute Heart Failure after Coronary Artery Bypass Graft Surgery

BACKGROUND: After coronary artery bypass graft (CABG) surgery, heart failure is still major problem. The valuable marker for it is needed. AIM: Evaluating the role of serial NT-proBNP level in prognosis and follow-up treatment of acute heart failure after CABG surgery. METHODS: The prospective, analytic study evaluated 107 patients undergoing CABG surgery at Ho Chi Minh Heart Institute from October 2012 to June 2014. Collecting data was done at pre- and post-operative days with measuring NT-proBNP levels on the day before operation, 2 hours after surgery, every next 24 h until the 5th day, and in case of acute heart failure occurred after surgery. RESULTS: On the first postoperative day (POD1), the NT-proBNP level demonstrated significant value for AHF with the cut-off point = 817.8 pg/mL and AUC = 0.806. On the second and third postoperative day, the AUC value of NT- was 0.753 and 0.751. It was statistically significant in acute heart failure group almost at POD 1 and POD 2 when analyzed by the doses of dobutamine, noradrenaline, and adrenaline (both low doses and normal doses). CONCLUSION: Serial measurement of NT-proBNP level provides useful prognostic and follow-up treatment information in acute heart failure after CABG surgery