TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting

Time-series forecasting has caught increasing attention in the AI research field due to its importance in solving real-world problems across different domains, such as energy, weather, traffic, and economy. As shown in various types of data, it has been a must-see issue to deal with drastic changes, temporal patterns, and shapes in sequential data that previous models are weak in prediction. This is because most cases in time-series forecasting aim to minimize $L_p$ norm distances as loss functions, such as mean absolute error (MAE) or mean square error (MSE). These loss functions are vulnerable to not only considering temporal dynamics modeling but also capturing the shape of signals. In addition, these functions often make models misbehave and return uncorrelated results to the original time-series. To become an effective loss function, it has to be invariant to the set of distortions between two time-series data instead of just comparing exact values. In this paper, we propose a novel loss function, called TILDE-Q (Transformation Invariant Loss function with Distance EQuilibrium), that not only considers the distortions in amplitude and phase but also allows models to capture the shape of time-series sequences. In addition, TILDE-Q supports modeling periodic and non-periodic temporal dynamics at the same time. We evaluate the effectiveness of TILDE-Q by conducting extensive experiments with respect to periodic and non-periodic conditions of data, from naive models to state-of-the-art models. The experiment results indicate that the models trained with TILDE-Q outperform those trained with other training metrics (e.g., MSE, dynamic time warping (DTW), temporal distortion index (TDI), and longest common subsequence (LCSS)).Comment: 9 pages paper, 2 pages references, and 7 pages appendix. Submitted as conference paper to ICLR 202

Visualization and Analysis of Sensory Data

Recently, California has suffered a severe drought, making water a scarce resource to its population. Many viticulturists are based in this area who rely on heavy irrigation to produce a better grape and a better wine. Not just in California, but throughout the nation, irrigation must be applied intelligently for efficient use of water and funding. By taking measurements of physical characteristics of a vineyard over time, one may be able to visualize trends in the data which lend itself to describing preferred growing methods. Wireless sensors can be used to take measurements including moisture, temperature, sunlight, and more. Sensors have been installed at multiple locations about a vineyard. A framework has been put in place to capture, adjust, and calibrate the data as well as store it for future retrieval. The data are visualized over time to see the effects of techniques in the long term. These are helpful for suggesting irrigation strategy that will lead to the best yield. Sensors are cheap and effective, but are prone to malfunction and transmission errors. When these problems occur, the faulty time-series data can be cleaned by correlating with similar time-series data in the same time span. The data system will be a necessity for competitive viticulturists, reducing cost of irrigation and improving quality of wine. In the future, the tool could be applied to other crops. Also, if any new important values must be derived or measured, the system can be extended to include them

Visualization of the growth and production of grapes through analysis of sensory data

Grapes used in the wine industry have been one of the highest value crops in the United States. However, with unpredictable weather changes and recent drought in the Western United States, vineyard owners and grape growers have faced difficulties on producing good quality grapes suited for wine making. Therefore, a technology that would keep record of environmental data and incorporate the data to support agricultural decisions will help the growers to produce quality grapes even in extreme conditions. As such, this research focuses on developing an interactive system that uses sensory data and visual analytics to facilitate vineyard management and agricultural decisions (such as choosing irrigation strategy and deciding harvesting date) through predictive analysis and historical comparisons. The system visualizes the data gathered by data loggers at vineyard sites to aid growers in decision making. The current system incorporates a stack zooming graph of historical temperature data from different sites and depths with annotation of important dates like bud break and harvest. This stack zooming graph can also be used to check for any erroneous data and implement database cleaning to fix these errors. Some analysis of agricultural characteristics such as soil type and moisture relationship and collective effects of different weather components are currently being done. As this is an ongoing project, integrating new features with better predictive analysis and more visuals will be necessary for the growers to rely on this system

ZeVis: A Visual Analytics System for Exploration of a Larval Zebrafish Brain in Serial-Section Electron Microscopy Images

The automation and improvement of nano-scale electron microscopy imaging technologies have expanded a push in neuroscience to understand brain circuits at the scale of individual cells and their connections. Most of this research effort, called 'connectomics', has been devoted to handling, processing, and segmenting large-scale image data to reconstruct graphs of neuronal connectivity. However, connectomics datasets contain a wealth of high-resolution information about the brain that could be leveraged to understand its detailed anatomy beyond just the connections between neurons, such as cell morphologies and distributions. This study introduces a novel visualization system, ZeVis, for the interactive exploration of a whole larval zebrafish brain using a terabyte-scale serial-section electron microscopy dataset. ZeVis combines 2D cross-sectional views and 3D volumetric visualizations of the input serial-section electron microscopy data with overlaid segmentation results to facilitate the analyses of various brain structures and their interpretations. The system also provides a graph-based data processing interface to generate subsets of feature segmentation data easily. The segmentation data can be filtered by morphological features or anatomical constraints, allowing statistical analysis and comparisons across regions. We applied ZeVis to actual data of a terabyte-scale whole-brain larval zebrafish and analyzed cell nucleus distributions in several anatomical regions

Aided decision-making through visual analytics systems for large multivariate, spatiotemporal, hierarchical and network data

As technologies have advanced, various types of data been produced in science and industry, and extracting actionable information for making effective decisions becomes increasingly difficult for analysts and decision makers. The main reasons causing such difficulty are two-fold; 1) the overwhelming amount of data prevents users understand the data in the exploration, and 2) the complexity of the multiple data characteristics (multi-variate, spatial, temporal or/and networked) needs an integrated data presentation for finding any pattern, trend, anomaly for decision-making. To overcome the analysts\u27 information overload and enable effective visual presentation for efficient analysis and decision making, an interactive visual exploration and analysis environment is needed since traditional machine learning and big data analytics alone are insufficient. This dissertation presents an integrated visual analytics and data exploration framework that allows users to effectively explore and analyze multi-variate, spatio-temporal, and network data. We design and incorporate new visual representations and visualization techniques and apply our work to real world data sets, including sales data and economic impact data, as well as flight delay data across US airports. Our framework helps users to answer hypotheses by visualizing a large table data with pixel-based visualization. In order to present a maximum amount of multi-variate data to a given available screen space, our framework extends the pixel-based matrices and provides interaction methods, including a magnification lens. In addition, our framework incorporates forecasting algorithms (e.g., ARIMA) to present trends on the data of interest. In this way, our framework effectively supports users who constantly explore and analyze the business sector data (e.g., market share analysts) and need to verify future trends and anomalies. In order to enable effective exploration of high-dimensional multi-variate network data exploration, we design and implement two novel visual representations, Petals and Threads. Then, this dissertation describes case studies on US airport flight delay network to demonstrate how our framework can be applied to real world problems. Lastly, for evaluation we present a user study result of the petals and threads and feedback from a domain expert

Applying mobile device soft keyboards to collaborative multitouch tabletop displays: design and evaluation

We present an evaluation of text entry methods for tabletop displays given small display space allocations, an increasingly important design constraint as tabletops become collaborative platforms. Small space is already a requirement of mobile text entry methods, and these can often be easily ported to tabletop settings. The purpose of this work is to determine whether these mobile text entry methods are equally useful for tabletop displays, or whether there are unique aspects of text entry on large, horizontal surfaces that influence design. Our evaluation consists of two studies designed to elicit differences between the mobile and tabletop domains. Results show that standard soft keyboards perform best, even at small space allocations. Furthermore, occlusion-reduction methods like Shift do not yield significant improvements to text entry; we speculate that this is due to the low ratio of resolution per surface units (i.e., DPI) for current tabletops