MatRec: Matrix Factorization for Highly Skewed Dataset

Recommender systems is one of the most successful AI technologies applied in the internet cooperations. Popular internet products such as TikTok, Amazon, and YouTube have all integrated recommender systems as their core product feature. Although recommender systems have received great success, it is well known for highly skewed datasets, engineers and researchers need to adjust their methods to tackle the specific problem to yield good results. Inability to deal with highly skewed dataset usually generates hard computational problems for big data clusters and unsatisfactory results for customers. In this paper, we propose a new algorithm solving the problem in the framework of matrix factorization. We model the data skewness factors in the theoretic modeling of the approach with easy to interpret and easy to implement formulas. We prove in experiments our method generates comparably favorite results with popular recommender system algorithms such as Learning to Rank , Alternating Least Squares and Deep Matrix Factorization

Development and Validation of an Effective CRISPR/Cas9 Vector for Efficiently Isolating Positive Transformants and Transgene-Free Mutants in a Wide Range of Plant Species

The CRISPR/Cas9 technique is a highly valuable tool in creating new materials for both basic and applied researches. Previously, we succeeded in effectively generating mutations in Brassica napus using an available CRISPR/Cas9 vector pKSE401, while isolation of Cas9-free mutants is laborious and inefficient. Here, we inserted a fluorescence tag (sGFP) driven by the constitutive 35S promoter into pKSE401 to facilitate a visual screen of mutants. This modified vector was named pKSE401G and tested in several dicot plant species, including Arabidopsis, B. napus, Fragaria vesca (strawberry), and Glycine max (soybean). Consequently, GFP-positive plants were readily identified through fluorescence screening in all of these species. Among these GFP-positive plants, the average mutation frequency ranged from 20.4 to 52.5% in Arabidopsis and B. napus with stable transformation, and was 90.0% in strawberry and 75.0% in soybean with transient transformation, indicating that the editing efficiency resembles that of the original vector. Moreover, transgene-free mutants were sufficiently identified in Arabidopsis in the T2 generation and B. napus in the T1 generation based on the absence of GFP fluorescence, and these mutants were stably transmissible to next generation without newly induced mutations. Collectively, pKSE401G provides us an effective tool to readily identify positive primary transformants and transgene-free mutants in later generations in a wide range of dicot plant species

Operational performance of a novel fast-responsive heat storage/exchanging unit (HSEU) for solar heating systems

In order for a solar heating system to provide heat immediately after sunrise, a fast response is needed to the heat demand of a serviced space. The majority of existing solar heating systems have a slow response time due to the large volume of water stored in the heat storage/exchanger unit (HSEU). This leads to a slow heat delivery cycle, which results in discomfort for the occupants and thus creates a huge barrier to the wide deployment of solar heating systems. To overcome this critical issue, a novel interactive heat storage/exchanging unit (HSEU) employing a double-tank configuration was developed. Unlike conventional HSEUs, which have a single tank acting as the heat storage and exchanging unit, the new HSEU is comprised of a small tank for heat exchange combined with a large tank for heat storage. The small tank enables fast transfer of solar heat to the heating loop fluid without having to heat up the large volume of water in the entire HSEU tank, whilst the large tank is used to store and exchange heat between itself and the small tank using a temperature-oriented control mechanism. To test the proposed design, the heat transfer between the first (solar loop) and second (heating loop) fluids, the heat and mass transfer between the small and large tank and the associated operational strategy were investigated experimentally and theoretically for comparison. A conventional single tank HSEU requires around 120 min to deliver heat to a served space, whilst the new interactive double-tank HSEU can provide heat to the served space in around 20 min, thus creating a heating system which can respond significantly faster than traditional systems. The investigation of the heat exchange effect between the solar and heating loop fluids showed that the new HSEU achieved a convective heat transfer coefficient of as high as 391 W/m2·K, which is 551% higher than that of a conventional tank. As a result, the solar thermal efficiency of the solar panel-array in the new HSEU based system was increased by 7.5% compared to conventional HSEU based systems

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A novel scheme for domain-transfer problem in the context of sentiment analysis

In this work, we attempt to tackle domain-transfer problem by combining old-domain labeled examples with new-domain unlabeled ones. The basic idea is to use old-domain-trained classifier to label some informative unlabeled examples in new domain, and retrain the base classifier over these selected examples. The experimental results demonstrate that proposed scheme can significantly boost the accuracy of the base sentiment classifier on new domain