Search

Cloud object stores such as Amazon S3 are some of the largest and most cost-effective storage systems on the planet, making them an attractive target to store large data warehouses and data lakes. Unfortunately, their implementation as key-value stores makes it difficult to achieve ACID transactions and high performance: metadata operations such as listing objects are expensive, and consistency guarantees are limited. In this paper, we present Delta Lake, an open source ACID table storage layer over cloud object stores initially developed at Databricks. Delta Lake uses a transaction log that is compacted into Apache Parquet format to provide ACID properties, time travel, and significantly faster metadata operations for large tabular datasets (e.g., the ability to quickly search billions of table partitions for those relevant to a query). It also leverages this design to provide high-level features such as automatic data layout optimization, upserts, caching, and audit logs. Delta Lake tables can be accessed from Apache Spark, Hive, Presto, Redshift and other systems. Delta Lake is deployed at thousands of Databricks customers that process exabytes of data per day, with the largest instances managing exabyte-scale datasets and billions of objects.

Style transfer aims to generate images by combining the style of one image and the content of another. Though valuable efforts have been made in generating high-quality style transferred images, the resulting images are far from the distribution of real images. This greatly limits the application of style transfer such as improving the diversity of training set in computer vision task. We find that the reason of style transfer failing to generate realistic images is lack of reference targets and neglect of preserving data distribution. To solve the problem, we propose a Style-transfer Generative Adversarial Network with a weight-sharing strategy to make the stylized images be resemblance to the real images. The experimental results demonstrate that the proposed method can generate images with satisfying style transfers and high visual quality. Moreover, we apply our stylized images to augment the training set of object detection task, and improve the average precision faithfully. We believe that our method can enhance the performance of style transfer on computer vision tasks.

With the ubiquity of real-time data, organizations need streaming systems that are scalable, easy to use, and easy to integrate into business applications. Structured Streaming is a new high-level streaming API in Apache Spark based on our experience with Spark Streaming. Structured Streaming differs from other recent streaming APIs, such as Google Dataflow, in two main ways. First, it is a purely declarative API based on automatically incrementalizing a static relational query (expressed using SQL or DataFrames), in contrast to APIs that ask the user to build a DAG of physical operators. Second, Structured Streaming aims to support end-to-end real-time applications that integrate streaming with batch and interactive analysis. We found that this integration was often a key challenge in practice. Structured Streaming achieves high performance via Spark SQL's code generation engine and can outperform Apache Flink by up to 2x and Apache Kafka Streams by 90x. It also offers rich operational features such as rollbacks, code updates, and mixed streaming/batch execution. We describe the system's design and use cases from several hundred production deployments on Databricks, the largest of which process over 1 PB of data per month.

In this paper, we present two forward-secure signature schemes (FSS) with untrusted update. Our constructions use ideas from the work of Abdalla et al. [1] and Kozlov and Reyzin [2] in their forward-secure digital signature schemes. Our schemes are proven to be forward-secure based on the hardness of factoring in the random oraclemodel. Both schemes use the same method to encrypt the private key and are more efficient than Libert scheme [3] without adding any burden to the original forward-secure schemes.

According to the relevant criterion and principle for designing and evaluating various trusted computing chips, we have proposed a new trusted SoC chip, and have given the implementation of its basic functional modules. In detail, we have discussed the design of the trusted SoC security architecture and the main module functional modules such as microprocessor, cryptographic function module, security management module, input/output interface, along with the most important memory management unit. Moreover, we have discussed reliability of relevant parameters and transfer strategy for trusted root in chip development and application, together with the simulation and validation of corresponding functions. At last, we point out that one of the most important further research directions is the trusted measurement of dynamic data and software running in security environment.

A versatile coupling reaction for the preparation of polybutadiene–poly-(hexafluoropropylene oxide) (BF) diblock copolymers is described. Six diblock copolymers with different block lengths were characterized by nuclear magnetic resonance spectroscopy and size exclusion chromatography; all six had total molecular weights below 15,000. Microphase separation of the block copolymers in the bulk state was established by small-angle X-ray scattering (SAXS) and differential scanning calorimetry. SAXS data suggest that the diblocks are characterized by an unusually large Flory-Huggins interaction parameter, χ, on the order of 10. However, extraction of χ from the order–disorder transition gave large (order 1) but significantly different values, thereby suggesting that these copolymers are too small and too strongly interacting to be described by block copolymer mean-field theory. Dynamic light scattering was used to analyze dilute solutions of the title block copolymers in four selective organic solvents; the sizes of the micelles formed were solvent dependent. The micellar aggregates were large and nonspherical, and this is also attributed to the high degree of incompatibility between the two immiscible blocks. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 3685–3694, 2005

Spectra attacks proposed recently are more data efficient than algebraic attacks against stream cipher. They are also time-and-space efficient. A measurement of the security of a stream cipher against spectra attacks is spectral immunity, the lowest spectral weight of the annihilator of the key stream. We study both the annihilator and the spectral immunity. We obtain a necessary and sufficient condition for the existence of low spectral weight annihilator and find it is more difficult to decide the (non)existence of the low weight annihilator for spectra attacks than for algebraic attacks. We also give some basic properties of annihilators and find the probability of a periodic sequence to be the annihilator of another sequence of the same period is low. Finally we prove that the spectral immunity is upper bounded by half of the period of the key stream. As a result, to recover any key stream, the least amount of bits required by spectra attacks is at most half of its period.

This paper investigates oblivious transfer protocol based on privacy amplification that uses Renyi entropy of order a for any 1 < a < 2, the conditions under which the protocol is secure are given. In protocol it makes no assumptions about receiver's computing power, so under the given conditions the protocol is unconditionally-secure.