The Sequence of Magmatic-Tectonic Events and Orogenic Processes of the Yanshan Belt, North China

This paper emphasizes that the interactive constraints of geology and isotopic dating is the best approach to construct the geological event sequence, and has compiled 106 data of reasonable isotopic ages for the igneous rocks of the Yanshan belt. We propose a sequence of mgmatic-tectonic events in the Jurassic-Cretaceous Yanshan orogen of North China. Five orogenic episodes are divided, (1) pre-and initial orogenic episode (Early Jurassic); (2) early orogenic episode (Middle Jurassic); (3) peak orogenic episode (Late Jurassic); (4) late orogenic episode (early Early Cretaceous), and (5) post-orogenic episode. Each episode is a short cycle, all of the orogenic processes construct a longer cycle, and they, in general, followed a counter-clockwise (ccw) PTt path. Finally, it is suggested that the Yanshanian movement was so intensive that the magmatism and tectonic deformation had involved all the lithosphere thickness and the late-Achaean-formed cratonic lithosphere had been significantly reworked.

Human activity recognition based on HMM by improved PSO and event probability sequence

This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model （HMM） parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence （EPS） which consists of a series of events is computed to describe the unique characteristic of human activities. The anatysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.

Event-Based Anomaly Detection for Non-Public Industrial Communication Protocols in SDN-Based Control Systems

As the main communication mediums in industrial control networks,industrial communication protocols are always vulnerable to extreme exploitations,and it is very difficult to take protective measures due to their serious privacy.Based on the SDN(Software Defined Network)technology,this paper proposes a novel event-based anomaly detection approach to identify misbehaviors using non-public industrial communication protocols,and this approach can be installed in SDN switches as a security software appliance in SDN-based control systems.Furthermore,aiming at the unknown protocol specification and message format,this approach first restructures the industrial communication sessions and merges the payloads from industrial communication packets.After that,the feature selection and event sequence extraction can be carried out by using the N-gram model and K-means algorithm.Based on the obtained event sequences,this approach finally trains an event-based HMM(Hidden Markov Model)to identify aberrant industrial communication behaviors.Experimental results clearly show that the proposed approach has obvious advantages of classification accuracy and detection efficiency.

An efficient algorithm to generate candidates in discovering frequent episodes

One of the important steps in mining event sequences is to find frequent episodes. Once the frequent episodes are discovered, rules about temporal relationships can he derived. In this paper, an cfficient algorithm for discovering frequent episodes is presented based on the level-wise search algorithm WINEPI. The proposed algorithm gains hetter candidate generation quality by introducing a new Lemma to help to target the combinations of episodes that are interesting in the next level and thins reduces the execution time. Experimental results on artificial and real data show the enhanced efficiency of the algorithm.

Comparative visual analytics for assessing medical records with sequence embedding

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare.Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence.However,such analysis is not straightforward due to the characteristics of medical records:high dimensionality,irregularity in time,and sparsity.To address this challenge,we introduce a method for similarity calculation of medical records.Our method employs event and sequence embeddings.While we use an autoencoder for the event embedding,we apply its variant with the self-attention mechanism for the sequence embedding.Moreover,in order to better handle the irregularity of data,we enhance the self-attention mechanism with consideration of different time intervals.We have developed a visual analytics system to support comparative studies of patient records.To make a comparison of sequences with different lengths easier,our system incorporates a sequence alignment method.Through its interactive interface,the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records.We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

A visual analytics design for studying rhythm patterns from human daily movement data

Human’s daily movements exhibit high regularity in a space-time context that typically forms circadian rhythms.Understanding the rhythms for human daily movements is of high interest to a variety of parties from urban planners,transportation analysts,to business strategists.In this paper,we present an interactive visual analytics design for understanding and utilizing data collected from tracking human’s movements.The resulting system identifies and visually presents frequent human movement rhythms to support interactive exploration and analysis of the data over space and time.Case studies using real-world human movement data,including massive urban public transportation data in Singapore and the MIT reality mining dataset,and interviews with transportation researches were conducted to demonstrate the effectiveness and usefulness of our system.