A data preparation approach for cloud storage based on containerized parallel patterns

In this paper, we present the design, implementation, and evaluation of an efficient data preparation and retrieval approach for cloud storage. The approach includes a deduplication subsystem that indexes the hash of each content to identify duplicated data. As a consequence, avoiding duplicated content reduces reprocessing time during uploads and other costs related to outsource data management tasks. Our proposed data preparation scheme enables organizations to add properties such as security, reliability, and cost-efficiency to their contents before sending them to the cloud. It also creates recovery schemes for organizations to share preprocessed contents with partners and end-users. The approach also includes an engine that encapsulates preprocessing applications into virtual containers (VCs) to create parallel patterns that improve the efficiency of data preparation retrieval process. In a study case, real repositories of satellite images, and organizational files were prepared to be migrated to the cloud by using processes such as compression, encryption, encoding for fault tolerance, and access control. The experimental evaluation revealed the feasibility of using a data preparation approach for organizations to mitigate risks that still could arise in the cloud. It also revealed the efficiency of the deduplication process to reduce data preparation tasks and the efficacy of parallel patterns to improve the end-user service experience.This research was supported by "Fondo Sectorial de Investigación para la Educación";, SEP-CONACyT Mexico, through projects 281565 and 285276

The Massive and Distant Clusters of WISE Survey V: Extended Radio Sources in Massive Galaxy Clusters at z~1

We present the results from a pilot study with the Karl G. Jansky Very Large Array (JVLA) to determine the radio morphologies of extended radio sources and the properties of their host-galaxies in 10 massive galaxy clusters at z~1, an epoch in which clusters are assembling rapidly. These clusters are drawn from a parent sample of WISE-selected galaxy clusters that were cross-correlated with the VLA Faint Images of the Radio Sky at Twenty-Centimeters survey (FIRST) to identify extended radio sources within 1$^{\prime}$ of the cluster centers. Out of the ten targeted sources, six are FR II sources, one is an FR I source, and three sources have undetermined morphologies. Eight radio sources have associated Spitzer data, 75% presenting infrared counterparts. A majority of these counterparts are consistent with being massive galaxies. The angular extent of the FR sources exhibits a strong correlation with the cluster-centric radius, which warrants further investigation with a larger sample.Comment: accepted to Ap

ZnO/BiOI heterojunction photoanodes with enhanced photoelectrochemical water oxidation activity

ZnO/BiOI heterojunction photoanode thin films were prepared by aerosol-assisted chemical vapour deposition, and the impact of growth temperature and film thickness on the water oxidation functionality was systematically investigated. A top ZnO layer with a thickness of 120 nm (deposited at 350 °C) and a 390 nm thick BiOI layer (deposited at 300 °C) were found to achieve the best photoelectrochemical performance of the heterojunction. The ZnO/BiOI heterojunction exhibited a significant increase in photoelectrochemical activity, with a photocurrent of 0.27 mA·cm−2 observed at 1.1 VRHE (350 nm, 2.58 mW·cm−2), which is ~ 2.2 times higher than that of single-layer ZnO and far higher than that of BiOI. Photoluminescence spectroscopy and transient absorption spectroscopy measurements showed that there was effective charge transfer across the heterojunction which spatially separated charge carriers and increased their lifetime and ability to drive photoelectrochemical water oxidation

Optimizing LLM Queries in Relational Workloads

Analytical database providers (e.g., Redshift, Databricks, BigQuery) have rapidly added support for invoking Large Language Models (LLMs) through native user-defined functions (UDFs) to help users perform natural language tasks, such as classification, entity extraction, and translation, inside analytical workloads. For instance, an analyst might want to extract customer sentiments on millions of product reviews. However, LLM inference is highly expensive in both computational and economic terms: for example, an NVIDIA L4 GPU running Llama2-7B can only process 6 KB of text per second. In this paper, we explore how to optimize LLM inference for analytical workloads that invoke LLMs within relational queries. We show that relational queries present novel opportunities for accelerating LLM inference, including reordering rows to maximize key-value (KV) cache reuse within the LLM inference engine, reordering columns within a row to further increase cache reuse, and deduplicating redundant inference requests. We implement these optimizations in Apache Spark, with vLLM as the model serving backend and achieve up to 4.4x improvement in end-to-end latency on a benchmark of diverse LLM-based queries on real datasets. To the best of our knowledge, this is the first work to explicitly address the problem of optimizing LLM invocations within SQL queries

The Stellar Mass Components of Galaxies: Comparing Semi-Analytical Models with Observation

We compare the stellar masses of central and satellite galaxies predicted by three independent semianalytical models with observational results obtained from a large galaxy group catalogue constructed from the Sloan Digital Sky Survey. In particular, we compare the stellar mass functions of centrals and satellites, the relation between total stellar mass and halo mass, and the conditional stellar mass functions, which specify the average number of galaxies of stellar mass M_* that reside in a halo of mass M_h. The semi-analytical models only predict the correct stellar masses of central galaxies within a limited mass range and all models fail to reproduce the sharp decline of stellar mass with decreasing halo mass observed at the low mass end. In addition, all models over-predict the number of satellite galaxies by roughly a factor of two. The predicted stellar mass in satellite galaxies can be made to match the data by assuming that a significant fraction of satellite galaxies are tidally stripped and disrupted, giving rise to a population of intra-cluster stars in their host halos. However, the amount of intra-cluster stars thus predicted is too large compared to observation. This suggests that current galaxy formation models still have serious problems in modeling star formation in low-mass halos.Comment: 12 pages, 6 figures, accepted for publication in Ap

A new way of valorizing biomaterials: the use of sunflower protein for 1 a-tocopherol microencapsulation

Biopolymer based microparticles were efficiently prepared from sunflower protein (SP) wall material and a-tocopherol (T) active core using a spray-drying technique. Protein enzymatic hydrolysis and/or N-acylation were carried out to make some structural modifications to the vegetable protein. Native and hydrolyzed SP were characterized by Asymmetrical Flow Field-Flow Fractionation (AsFlFFF). Results of AsFlFFF confirmed that size of proteinic macromolecules was influenced by degree of hydrolysis. The effect of protein modifications and the influence of wall/core ratio on both emulsions and microparticle properties were evaluated. Concerning emulsion properties, enzymatic hydrolysis involved a decrease in viscosity, whereas acylation did not significantly affect emulsion droplet size and viscosity. Microparticles obtained with hydrolyzed SP wall material showed lower retention efficiency (RE) than native SP microparticles (62-80% and 93% respectively). Conversely, acylation of both hydrolyzed SP and native SP allowed a higher RE to be reached (up to 100%). Increasing T concentration increased emulsion viscosity, emulsion droplet size, microparticle size, and enhanced RE. These results demonstrated the feasibility of high loaded (up to 79.2% T) microparticles