Environmental certification in a differentiated duopoly

The articleaims to explore the role of horizontal product differentiation in promoting/hindering firm’s participation in environmental certification. To this purpose, we consider a differentiated duopoly model where firms compete in both prices and environmental qualities. The result shows that when the level of horizontal differentiation relative to the degree of vertical differentiation is sufficiently high, only the symmetric equilibrium where both firms choose to or both choose not to certify their products exists. Asymmetric equilibrium (vertical dominance equilibrium) occurs when the level of horizontal differentiation relative to the degree of vertical differentiation is sufficiently low

Blockchain technology research and application: a systematic literature review and future trends

Blockchain, as the basis for cryptocurrencies, has received extensive attentions recently. Blockchain serves as an immutable distributed ledger technology which allows transactions to be carried out credibly in a decentralized environment. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability, security and other issues waiting to be overcome. This article provides a comprehensive overview of blockchain technology and its applications. We begin with a summary of the development of blockchain, and then give an overview of the blockchain architecture and a systematic review of the research and application of blockchain technology in different fields from the perspective of academic research and industry technology. Furthermore, technical challenges and recent developments are also briefly listed. We also looked at the possible future trends of blockchain

OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning

Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure. To achieve this, we propose OmniSeg3D, an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework, which is accomplished by two steps. Firstly, we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly, image features rendered from the 3D feature field are clustered at different levels, which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations, this framework yields a global consistent 3D feature field, which further enables hierarchical segmentation, multi-object selection, and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.Comment: To Appear in Proceedings of the IEE

Distributed Equivalent Substitution Training for Large-Scale Recommender Systems

We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features. DES introduces fully synchronous training to large-scale recommendation system for the first time by reducing communication, thus making the training of commercial recommender systems converge faster and reach better CTR. DES requires much less communication by substituting the weights-rich operators with the computationally equivalent sub-operators and aggregating partial results instead of transmitting the huge sparse weights directly through the network. Due to the use of synchronous training on large-scale Deep Learning Recommendation Models (DLRMs), DES achieves higher AUC(Area Under ROC). We successfully apply DES training on multiple popular DLRMs of industrial scenarios. Experiments show that our implementation outperforms the state-of-the-art PS-based training framework, achieving up to 68.7% communication savings and higher throughput compared to other PS-based recommender systems.Comment: Accepted by SIGIR '2020. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 202

Pengaruh Pendekatan Pembelajaran Matematika Realistik Terhadap Prestasi Belajar Matematika Ditinjau Dari Kemampuan Numerik Siswa Kelas VIII SMP Negeri 2 Amlapura

Penelitian ini bertujuan untuk mengetahui dan mendeskripsikan pengaruh pendekatan pembelajaran matematika realistik terhadap prestasi belajar matematika ditinjau dari kemampuan numerik siswa. Penelitian ini merupakan eksperimen semu dilaksanakan dengan menggunakan rancangan the post test only control group design. Populasinya adalah seluruh siswa kelas VIII SMP Negeri 2 Amlapura tahun pelajaran 2013-2014. Dari delapan kelas yang ada, empat kelas dipilih sebagai sampel yakni dua kelas sebagai kelas eksperimen dan dua kelas sebagai kelas kontrol yang diambil dengan teknik random. Data penelitian dikumpulkan menggunakan tes, yaitu tes kemampuan numerik dan tes prestasi belajar matematika. Data yang diperoleh dianalisis dengan analisis varians dua jalur dilanjutkan dengan uji Tukey. Berdasarkan hasil analisis data dan pembahasan, dapat disimpulkan, terdapat perbedaan yang signifikan prestasi belajar matematika antara siswa yang mengikuti pendekatan pembelajaran matematika realistik dengan siswa yang mengikuti pendekatan pembelajaran konvensional. Terdapat pengaruh interaksi antara pendekatan pembelajaran matematika realistik dan kemampuan numerik terhadap prestasi belajar matematika. Pada Siswa yang memiliki kemampuan numerik tinggi, prestasi belajar matematika siswa yang mengikuti pendekatan pembelajaran matematika realistik lebih baik daripada pendekatan konvensional. Pada siswa yang memiliki kemampuan numerik rendah, prestasi belajar matematika siswa yang mengikuti pendekatan pembelajaran matematika realistik tetap lebih tinggi dari siswa yang mengikuti pendekatan pembelajaran konvensional.Kata Kunci : pendekatan pembelajaran matematika realistik, kemampuan numerik, dan prestasi belajar matematika The study aimed at finding out and describing the contribution of realistic mathematic instructional approach towards mathematic learning achievement viewed from numeric skills. It was a quasi-experimental research by utilizing the post test only control group design. The study involved all students class VIII SMP Negeri 2 Amlapura in 2013-2014 as the population. Four classes of the students were chosen from eight parallel classes as the samples consisting of two classes as experimental and another two classes as control groups. They were determined based on random technique. The data were collected by testing, involving numeric ability and mathematic achievement tests. They were analysed based on two tailed variant analysis followed by Tukey-test. The results indicated that there was a significant difference between mathematic learning achievement of the students joining realistic mathematic instruction and those joining a conventional approach. There was an interactional contribution of realistic mathematic instructional approach and numeric ability towards mathematic learning achievement. The students having higher numeric skills, when joining realistic mathematic instruction approach their mathematic learning achievement was found better or higher than those joining a conventional approach. The students having lower numeric skills, when joining realistic mathematic instruction approach, their mathematic learning achievement was found better or higher than those joining a conventional approach

UHRF1 is required for basal stem cell proliferation in response to airway injury

Cellular senescence is a cell fate characterized by an irreversible cell cycle arrest, but the molecular mechanism underlying this senescence hallmark remains poorly understood. Through an unbiased search for novel senescence regulators in airway basal cells, we discovered that the epigenetic regulator ubiquitin-like with PHD and ring finger domain-containing protein 1 (UHRF1) is critical for regulating cell cycle progression. Upon injury, basal cells in the mouse airway rapidly induce the expression of UHRF1 in order to stimulate stem cell proliferation and tissue repair. Targeted depletion of Uhrf1 specifically in airway basal cells causes a profound defect in cell cycle progression. Consistently, cultured primary human basal cells lacking UHRF1 do not exhibit cell death or differentiation phenotypes but undergo a spontaneous program of senescence. Mechanistically, UHRF1 loss induces G1 cell cycle arrest by abrogating DNA replication factory formation as evidenced by loss of proliferating cell nuclear antigen (PCNA) puncta and an inability to enter the first cell cycle. This proliferation defect is partially mediated by the p15 pathway. Overall, our study provides the first evidence of an indispensable role of UHRF1 in somatic stem cells proliferation during the process of airway regeneration

Mutation screening of the SLC26A4 gene in a cohort of 192 Chinese patients with congenital hypothyroidism

ABSTRACT Objective: Pendred syndrome (PS) is an autosomal recessive disorder characterised by sensorineural hearing loss and thyroid dyshormonogenesis. It is caused by biallelic mutations in the SLC26A4 gene encoding for pendrin. Hypothyroidism in PS can be present from birth and therefore diagnosed by neonatal screening. The aim of this study was to examine the SLC26A4 mutation spectrum and prevalence among congenital hypothyroidism (CH) patients in the Guangxi Zhuang Autonomous Region of China and to establish how frequently PS causes hearing impairment in our patients with CH. Subjects and methods: Blood samples were collected from 192 CH patients in Guangxi Zhuang Autonomous Region, China, and genomic DNA was extracted from peripheral blood leukocytes. All exons of the SLC26A4 gene together with their exon-intron boundaries were screened by nextgeneration sequencing. Patients with SLC26A4 mutations underwent a complete audiological evaluation including otoscopic examination, audiometry and morphological evaluation of the inner ear. Results: Next generation sequencing analysis of SLC26A4 in 192 CH patients revealed five different heterozygous variations in eight individuals (8/192, 4%). The prevalence of SLC26A4 mutations was 4% among studied Chinese CH. Three of the eight were diagnosed as enlargement of the vestibular aqueduct (EVA), no PS were found in our 192 CH patients. The mutations included one novel missense variant p.P469S, as well as four known missense variants, namely p.V233L, p.M147I, p.V609G and p.D661E. Of the eight patients identified with SLC26A4 variations in our study, seven patients showed normal size/location of thyroid gland, and one patients showed a decreased size one. Conclusions: The prevalence of SLC26A4 pathogenic variants was 4% among studied Chinese patients with CH. Our study expanded the SLC26A4 mutation spectrum, provided the best estimation of SLC26A4 mutation rate for Chinese CH patients and indicated the rarity of PS as a cause of CH. Arch Endocrinol Metab. 2016;60(4):323-