Multiscale Analysis of Information Dynamics for Linear Multivariate Processes

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using state-space (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors

Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections

Financial firms commonly process and store billions of time-series data, generated continuously and at a high frequency. To support efficient data storage and retrieval, specialized time-series databases and systems have emerged. These databases support indexing and querying of time-series by a constrained Structured Query Language(SQL)-like format to enable queries like "Stocks with monthly price returns greater than 5%", and expressed in rigid formats. However, such queries do not capture the intrinsic complexity of high dimensional time-series data, which can often be better described by images or language (e.g., "A stock in low volatility regime"). Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial. In this paper, we propose and demonstrate a framework to store multi-modal data for financial time-series in a lower-dimensional latent space using deep encoders, such that the latent space projections capture not only the time series trends but also other desirable information or properties of the financial time-series data (such as price volatility). Moreover, our approach allows user-friendly query interfaces, enabling natural language text or sketches of time-series, for which we have developed intuitive interfaces. We demonstrate the advantages of our method in terms of computational efficiency and accuracy on real historical data as well as synthetic data, and highlight the utility of latent-space projections in the storage and retrieval of financial time-series data with intuitive query modalities.Comment: Accepted to ICAIF 202

A Finite-Difference Method for Pseudo-Two-Dimensional Boundary Value Problems

A finite-difference method is presented for solving pseudo-two-dimensional boundary-value problems. The sparse and nearly block tridiagonal properties of the matrices generated by using the finite-difference method for problems of this type are fully utilized and maintained, which yields a method that is highly efficient in the use of storage space and computation. An example shows that the central process unit time required by the method is significantly less than that required by an alternative method

Convertible Codes: New Class of Codes for Efficient Conversion of Coded Data in Distributed Storage

Erasure codes are typically used in large-scale distributed storage systems to provide durability of data in the face of failures. In this setting, a set of k blocks to be stored is encoded using an [n, k] code to generate n blocks that are then stored on different storage nodes. A recent work by Kadekodi et al. [Kadekodi et al., 2019] shows that the failure rate of storage devices vary significantly over time, and that changing the rate of the code (via a change in the parameters n and k) in response to such variations provides significant reduction in storage space requirement. However, the resource overhead of realizing such a change in the code rate on already encoded data in traditional codes is prohibitively high. Motivated by this application, in this work we first present a new framework to formalize the notion of code conversion - the process of converting data encoded with an [n^I, k^I] code into data encoded with an [n^F, k^F] code while maintaining desired decodability properties, such as the maximum-distance-separable (MDS) property. We then introduce convertible codes, a new class of code pairs that allow for code conversions in a resource-efficient manner. For an important parameter regime (which we call the merge regime) along with the widely used linearity and MDS decodability constraint, we prove tight bounds on the number of nodes accessed during code conversion. In particular, our achievability result is an explicit construction of MDS convertible codes that are optimal for all parameter values in the merge regime albeit with a high field size. We then present explicit low-field-size constructions of optimal MDS convertible codes for a broad range of parameters in the merge regime. Our results thus show that it is indeed possible to achieve code conversions with significantly lesser resources as compared to the default approach of re-encoding

Multi-Modal Properties and Dynamics of the Gradient Echo Quantum Memory

We investigate the properties of a recently proposed Gradient Echo Memory (GEM) scheme for information mapping between optical and atomic systems. We show that GEM can be described by the dynamic formation of polaritons in k-space. This picture highlights the flexibility and robustness with regards to the external control of the storage process. Our results also show that, as GEM is a frequency-encoding memory, it can accurately preserve the shape of signals that have large time-bandwidth products, even at moderate optical depths. At higher optical depths, we show that GEM is a high fidelity multi-mode quantum memory.Comment: 4 pages 3 figure

Preparation and adsorbing sodium borohydride of porous hollow capsules

AbstractSodium borohydride, a solid material, has great attractive for its hydrogen storage and preparation properties. The solubility of sodium borohydride in water is up to 35% at room temperature. It can react with water for generating hydrogen. The merits of this reaction include high purity, mild reaction conditions and the high theoretical density of hydrogen generation. Complete hydrolysis of sodium borohydride can produce hydrogen and sodium metaborate, which can be recovered by advanced technology for sodium borohydride recycling. Porous hollow capsules containing nickel boride were prepared and used as storage and reaction space for sodium borohydride. The influences of the concentration of polymer solution, the ratio of the coagulation bath, the concentration and temperature on the porous structure of hollow capsule were investigated. The adsorption of porous hollow capsule was influenced and optimized by soaking time, adsorption conditions, the drying temperature and time. The best conditions of preparation of porous hollow capsule are: 15 wt% PVDF into capsule system configuration, with adding 15wt % attapulgite or 5 wt% PVP. The adsorption amount is up to 36%. The preparation method of porous hollow capsule is simple and easy to operate, low energy consumption, simple process only including dissolving, mixing, molding, adsorption and drying. The structure of porous hollow is stable and easy to storage and use. Hydrogen can be simple to release when mixed the adsorbed capsules with water

Machine learning and simulation for the optimisation and characterisation of electrodes in batterie

The performance of electrochemical energy storage (EES) and energy conversion (EC) technologies is closely related to their electrode microstrcuture. Thus, this work focuses on the development of two novel computational models for the characterisation and optimisation of electrodes for three devices: Redox Flow batteries (RFBs), Solid Oxide Fuel Cells (SOFCs), and Lithium-ion batteries (LIBs). The first method introduces a Pore Network Model (PNM) for simulating the coupled charge and mass transport processes within electrodes. This approach is implemented for a vanadium RFB using different commercially available carbon-based electrodes. The results from the PNM show non-uniformity in the concentration and current density distributions within the electrode, which leads to a fast discharge due to regions where mass-transport limitations are predominant. The second approach is based on the stochastic reconstruction of synthetic electrode microstructures. For this purpose, a deep convolutional generative adversarial network (DC-GAN) is implemented for generating three-dimensional n-phase microstructures of a LIB cathode and a SOFC anode. The results show that the generated data is able to represent the morphological properties and two-point correlation function of the real dataset. As a subsequent process, a generation-optimisation closed-loop algorithm is developed using Gaussian Process Regression and Bayesian optimisation for the design of microstructures with customised properties. The results show the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as an optimisation of these properties constrained by constant values of volume fraction. Overall, this work presents a comprehensive analysis of the effect of the electrode microstructure in the performance of different energy storage devices. The introduction of a PNM bridges the gap between volume-averaged continuum models and detailed the pore-scale models. The main advantage of this model is the ability to visually show the concentration and current distributions inside the electrode within a reasonably low computational time. Based on this, this work represents the first visual showcase of how regions limited by low convective flow affect the rate of discharge in an electrode, which is essential for the design of optimum electrode microstructures. The implementation of DC-GANs allows for the first time the fast generation of arbitrarily large synthetic microstructural volumes of n-phases with realistic properties and with periodic boundaries. The fact that the generator constitutes a virtual representation of the real microstructure allows the inclusion of the generator as a function of the input latent space in a closed-loop optimisation process. For the first time, a set of visually realistic microstructures of a LIB cathode with user-specified morphological properties were designed based on the optimisation of the generator’s latent space. The introduction of a closed-loop generation-optimisation approach represents a breakthrough in the design of optimised electrodes since it constitutes a first approach for evaluating the microstructure-performance correlation in a continuous forward and backward process.Open Acces

Dimension reduction of image and audio space

The reduction of data necessary for storage or transmission is a desirable goal in the digital video and audio domain. Compression schemes strive to reduce the amount of storage space or bandwidth necessary to keep or move the data. Data reduction can be accomplished so that visually or audibly unnecessary data is removed or recoded thus aiding the compression phase of the data processing. The characterization and identification of data that can be successfully removed or reduced is the purpose of this work. New philosophy, theory and methods for data processing are presented towards the goal of data reduction. The philosophy and theory developed in this work establish a foundation for high speed data reduction suitable for multi-media applications. The developed methods encompass motion detection and edge detection as features of the systems. The philosophy of energy flow analysis in video processing enables the consideration of noise in digital video data. Research into noise versus motion leads to an efficient and successful method of identifying motion in a sequence. The research of the underlying statistical properties of vector quantization provides an insight into the performance characteristics of vector quantization and leads to successful improvements in application. The underlying statistical properties of the vector quantization process are analyzed and three theorems are developed and proved. The theorems establish the statistical distributions and probability densities of various metrics of the vector quantization process. From these properties, an intelligent and efficient algorithm design is developed and tested. The performance improvements in both time and quality are established through algorithm analysis and empirical testing. The empirical results are presented