Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables to discover similar temporal summaries (e.g., recurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea

Markov modeling of peptide folding in the presence of protein crowders

We use Markov state models (MSMs) to analyze the dynamics of a $\beta$-hairpin-forming peptide in Monte Carlo (MC) simulations with interacting protein crowders, for two different types of crowder proteins [bovine pancreatic trypsin inhibitor (BPTI) and GB1]. In these systems, at the temperature used, the peptide can be folded or unfolded and bound or unbound to crowder molecules. Four or five major free-energy minima can be identified. To estimate the dominant MC relaxation times of the peptide, we build MSMs using a range of different time resolutions or lag times. We show that stable relaxation-time estimates can be obtained from the MSM eigenfunctions through fits to autocorrelation data. The eigenfunctions remain sufficiently accurate to permit stable relaxation-time estimation down to small lag times, at which point simple estimates based on the corresponding eigenvalues have large systematic uncertainties. The presence of the crowders have a stabilizing effect on the peptide, especially with BPTI crowders, which can be attributed to a reduced unfolding rate $k_\text{u}$, while the folding rate $k_\text{f}$ is left largely unchanged.Comment: 18 pages, 6 figure

Writer Identification for chinese handwriting

Abstract Chinese handwriting identification has become a hot research in pattern recognition and image processing. In this paper, we present overview of relevant papers from the previous related studies until to the recent publications regarding to the Chinese Handwriting Identification. The strength, weaknesses, accurateness and comparison of well known approaches are reviewed, summarized and documented. This paper provides broad spectrum of pattern recognition technology in assisting writer identification tasks, which are at the forefront of forensic and biometrics based on identification application

Prioritized experince deep deterministic policy gradient method for dynamic systems

In this thesis, the problem of learning to control a dynamic system through reinforcement learning is taken up. There are two important problems in learning to control dynamic systems under this framework: correlated sample space and curse of dimensionality: The first problem means that samples sequentially taken from the plant are correlated, and fail to provide a rich data set to learn from. The second problem means that plants with a large state dimension are untractable if states are quantized for the learning algorithm. Recently, these problems have been attacked by state-of-the-art algorithm called Deep Deterministic Policy Gradient method (DDPG). In this thesis, we propose a new algorithm Prioritized Experience DDPG (PE-DDPG) that improves the sample efficiency of DDPG, through a Prioritized Experience Replay mechanism integrated into the original DDPG. It allows the agent experience some samples more frequently depending on their novelty. PE-DDPG algorithm is tested on OpenAI Gym's Inverted Pendulum task. The results of experiment show that the proposed algorithm can reduce training time and it has lower variance which implies more stable learning process

Big Data Preprocessing for Multivariate Time Series Forecast

Big data platforms alleviate collecting and organizing large datasets of varying content. A downside of this is the heavy preprocessing required to analyze their data by conventional analysis techniques. Especially time series data is found challenging to transform from platform-provided raw format into tables of feature and target values, required by supervised machine learning models. This thesis presents an experiment of preprocessing a data-platform-extracted collection of multivariate time series and forecasting it by machine learning models such as neural networks and support vector machines. Reviewed techniques of data preprocessing and time series analysis literature are utilized, but also custom solutions such as log level-based target variable, and valuedistribution-based feature elimination are developed. No significant forecasting accuracies are achieved, which indicates the difficulty of modelling big data. The expected reason for this is the inadequate validation of model parameters and preprocessing decisions, which would require excessive testing to improve.Big data -alustat helpottavat isojen datamäärien talletusta ja hallintaa. Niiden haittapuolena on kuitenkin laaja data-analyysiin vaadittava esikäsittelyn tarve, mikäli halutaan käyttää tavanomaisia analyysimenetelmiä. Erityisen haastavaksi todetaan aikasarjojen muuntaminen alustan tarjoamasta muodosta ohjatun koneoppimisen vaatimaan taulumuotoon, koostuen ennustettavasta kohdemuuttujasta sekä muista ominaisuusmuuttujista. Tässä tutkielmassa tutkitaan usean muuttujan aikasarjadatan esikäsittelyä, sekä käsitellyn datan ennustamista koneoppimismenetelmillä, kuten neuroverkoilla ja tukivektorimallinnuksella. Tutkimusmenetelmät perustuvat kirjallisuuteen datan esikäsittelystä ja aikasarja-analyysistä, mutta myös uusia menetelmiä kehitetään, kuten lokitasoon perustuva kohdemuuttuja sekä muuttujien arvojakaumaan perustuva karsiminen. Ennustustulokset jättävät kuitenkin toivomisen varaa, mikä kertoo big datan mallinnuksen vaikeudesta. Epäiltyinä syinä ovat liian vähäinen malliparametrien ja esikäsittelyvalintojen optimointi, joiden täydentäminen vaatisi resursseihin nähden liian kattavaa testausta

Synergy of Physics-based Reasoning and Machine Learning in Biomedical Applications: Towards Unlimited Deep Learning with Limited Data

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by training-data incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data

A Robotic System for Learning Visually-Driven Grasp Planning (Dissertation Proposal)

We use findings in machine learning, developmental psychology, and neurophysiology to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually-driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a gripper, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up such important issues as observation sparsity in high-dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well-established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for projections of high-dimensional data sets that capture task invariants. We also pursue the following problem: how can we use human expertise and insight into grasping to train a system to select both appropriate hand preshapes and approaches for a wide variety of objects, and then have it verify and refine its skills through trial and error. To accomplish this learning we propose a new class of Density Adaptive reinforcement learning algorithms. These algorithms use statistical tests to identify possibly interesting regions of the attribute space in which the dynamics of the task change. They automatically concentrate the building of high resolution descriptions of the reinforcement in those areas, and build low resolution representations in regions that are either not populated in the given task or are highly uniform in outcome. Additionally, the use of any learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not damage itself. We address this by the use of an instrumented, compliant robot wrist that controls impact forces