BDEF : the behavioral design data exchange format

BDDB is a Behavioral Design Data Base that manages the design data produced and consumed by different behavioral synthesis tools. These different design tools retrieve design data from BDDB, manipulate the data, and then store the results back into the data base. BDDB thus needs to address the following two issues: (1) a design data exchange approach and (2) customized design data interfaces. To address the first issue, we have developed a textual description format for describing design data objects and relationships. This language, referred to as the Behavioral Design Data Exchange Format (BDEF), is used as common format for exchanging design data between BDDB and the design tools in the behavioral synthesis environment. To address the second issue, we have developed a behavioral object type description language (generally referred to as schema definition language) for describing the global data structures required by design tools as well as the desired design subviews of this global BDDB design information. One design view class, namely, BDEF, is the topic of this report.In this report we give a formal definition of the BDEF format. Then we describe a comprehensive example of applying BDEF to the behavioral synthesis domain. That is, we present the complete BDEF syntax for the Extended Control/Data Flow Graph Model (ECDFG), which is the design representation model used by most behavioral synthesis tools in the UCI CADLAB synthesis system. We also present several example descriptions of designs using this ECDFG model. A parser/graph compiler from BDEF into the generalized ECDFG design representation as well as a BDEF generator from the ECDFG data structures into the BDEF format have been implemented

On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201

Archiving multi-epoch data and the discovery of variables in the near infrared

We present a description of the design and usage of a new synoptic pipeline and database model for time series photometry in the VISTA Data Flow System (VDFS). All UKIRT-WFCAM data and most of the VISTA main survey data will be processed and archived by the VDFS. Much of these data are multi-epoch, useful for finding moving and variable objects. Our new database design allows the users to easily find rare objects of these types amongst the huge volume of data being produced by modern survey telescopes. Its effectiveness is demonstrated through examples using Data Release 5 of the UKIDSS Deep Extragalactic Survey (DXS) and the WFCAM standard star data. The synoptic pipeline provides additional quality control and calibration to these data in the process of generating accurate light-curves. We find that 0.6+-0.1% of stars and 2.3+-0.6% of galaxies in the UKIDSS-DXS with K<15 mag are variable with amplitudes \Delta K>0.015 magComment: 30 pages, 31 figures, MNRAS, in press Minor changes from previous version due to refereeing and proof-readin

Stochastic Development Regression on Non-Linear Manifolds

We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is based on stochastic development of Euclidean diffusion processes to the manifold. Defining the data distribution as the transition distribution of the mapped stochastic process, parameters of the model, the non-linear analogue of design matrix and intercept, are found via maximum likelihood. The model is intrinsically related to the geometry encoded in the connection of the manifold. We propose an estimation procedure which applies the Laplace approximation of the likelihood function. A simulation study of the performance of the model is performed and the model is applied to a real dataset of Corpus Callosum shapes