Barrier Inhomogeneity of Schottky Diode on Nonpolar AlN Grown by Physical Vapor Transport

An aluminum nitride (AlN) Schottky barrier diode (SBD) was fabricated on a nonpolar AlN crystal grown on tungsten substrate by physical vapor transport. The Ni/Au-AlN SBD features a low ideality factor n of 3.3 and an effective Schottky barrier height (SBH) of 1.05 eV at room temperature. The ideality factor n decreases and the effective SBH increases at high temperatures. The temperature dependences of n and SBH were explained using an inhomogeneous model. A mean SBH of 2.105 eV was obtained for the Ni-AlN Schottky junction from the inhomogeneity analysis of the current-voltage characteristics. An equation in which the parameters have explicit physical meanings in thermionic emission theory is proposed to describe the current-voltage characteristics of inhomogeneous SBDs.Comment: 6 pages, 6 figure

The rising of livestream business model: Insights fromthe case study of TikTok in the UK

Consumer purchasing behaviours have experienced a revolution from offline shopping malls to social media Livestream during the last decade (Sorescu et al., 2011). This study adopts the case-study approach and an online focus group with the managers of TikTok trading business in the UK to examine how TikTok's retailing path can help brands or sellers build a close tie with consumers. This paper concludes with how the TikTok Livestream commercialization pathway impacts the conventional business model. Three conclusions are drawn from this study. Firstly, a video creator who holds the Intellectual Property of a TikTok account plays an essential role as the Livestream video influencer. They have created value for their followers and drive consumers' intention of purchasing behaviour. The livestream of video creators provides opportunities for consumers to interact with the video creator and other consumers. Secondly, Livestream video shopping breaks the limitations of offline physical space and attracts consumers efficiently. Thirdly, social e-commerce empowered by artificial intelligence technologies including image analysis and text analysis plays an essential role in targeting consumers and sustaining the operation of the Livestream shopping platform

Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers

Query expansion has been proved to be effective in improving recall and precision of first-stage retrievers, and yet its influence on a complicated, state-of-the-art cross-encoder ranker remains under-explored. We first show that directly applying the expansion techniques in the current literature to state-of-the-art neural rankers can result in deteriorated zero-shot performance. To this end, we propose GFF, a pipeline that includes a large language model and a neural ranker, to Generate, Filter, and Fuse query expansions more effectively in order to improve the zero-shot ranking metrics such as nDCG@10. Specifically, GFF first calls an instruction-following language model to generate query-related keywords through a reasoning chain. Leveraging self-consistency and reciprocal rank weighting, GFF further filters and combines the ranking results of each expanded query dynamically. By utilizing this pipeline, we show that GFF can improve the zero-shot nDCG@10 on BEIR and TREC DL 2019/2020. We also analyze different modelling choices in the GFF pipeline and shed light on the future directions in query expansion for zero-shot neural rankers

Towards Disentangling Relevance and Bias in Unbiased Learning to Rank

Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention recently. A popular ULTR approach for real-world applications uses a two-tower architecture, where click modeling is factorized into a relevance tower with regular input features, and a bias tower with bias-relevant inputs such as the position of a document. A successful factorization will allow the relevance tower to be exempt from biases. In this work, we identify a critical issue that existing ULTR methods ignored - the bias tower can be confounded with the relevance tower via the underlying true relevance. In particular, the positions were determined by the logging policy, i.e., the previous production model, which would possess relevance information. We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation. We then propose three methods to mitigate the negative confounding effects by better disentangling relevance and bias. Empirical results on both controlled public datasets and a large-scale industry dataset show the effectiveness of the proposed approaches

RD-Suite: A Benchmark for Ranking Distillation

The distillation of ranking models has become an important topic in both academia and industry. In recent years, several advanced methods have been proposed to tackle this problem, often leveraging ranking information from teacher rankers that is absent in traditional classification settings. To date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide range of tasks and datasets make it difficult to assess or invigorate advances in this field. This paper first examines representative prior arts on ranking distillation, and raises three questions to be answered around methodology and reproducibility. To that end, we propose a systematic and unified benchmark, Ranking Distillation Suite (RD-Suite), which is a suite of tasks with 4 large real-world datasets, encompassing two major modalities (textual and numeric) and two applications (standard distillation and distillation transfer). RD-Suite consists of benchmark results that challenge some of the common wisdom in the field, and the release of datasets with teacher scores and evaluation scripts for future research. RD-Suite paves the way towards better understanding of ranking distillation, facilities more research in this direction, and presents new challenges.Comment: 15 pages, 2 figures. arXiv admin note: text overlap with arXiv:2011.04006 by other author

Learning List-Level Domain-Invariant Representations for Ranking

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space. Although this method is studied extensively and applied on classification and regression problems, its adoption on ranking problems is sporadic, and the few existing implementations lack theoretical justifications. This paper revisits invariant representation learning for ranking. Upon reviewing prior work, we found that they implement what we call item-level alignment, which aligns the distributions of the items being ranked from all lists in aggregate but ignores their list structure. However, the list structure should be leveraged, because it is intrinsic to ranking problems where the data and the metrics are defined and computed on lists, not the items by themselves. To close this discrepancy, we propose list-level alignment -- learning domain-invariant representations at the higher level of lists. The benefits are twofold: it leads to the first domain adaptation generalization bound for ranking, in turn providing theoretical support for the proposed method, and it achieves better empirical transfer performance for unsupervised domain adaptation on ranking tasks, including passage reranking.Comment: NeurIPS 2023. Comparison to v1: revised presentation and proof of Corollary 4.

Novel sequences of subgroup J avian leukosis viruses associated with hemangioma in Chinese layer hens

<p>Abstract</p> <p>Background</p> <p>Avian leukosis virus subgroup J (ALV-J) preferentially induces myeloid leukosis (ML) in meat-type birds. Since 2008, many clinical cases of hemangioma rather than ML have frequently been reported in association with ALV-J infection in Chinese layer flocks.</p> <p>Results</p> <p>Three ALV-J strains associated with hemangioma were isolated and their proviral genomic sequences were determined. The three isolates, JL093-1, SD09DP03 and HLJ09MDJ-1, were 7,670, 7,670, and 7,633 nt in length. Their gag and pol genes were well conserved, with identities of 94.5-98.6% and 97.1-99.5%, respectively, with other ALV-J strains at the amino acid level (aa), while the env genes of the three isolates shared a higher aa identity with the env genes of other hemangioma strains than with those of ML strains. Interestingly, two novel 19-bp insertions in the U3 region in the LTR and 5' UTR, most likely derived from other retroviruses, were found in all the three isolates, thereby separately introducing one E2BP binding site in the U3 region in the LTR and RNA polymerase II transcription factor IIB and core promoter motif ten elements in the 5' UTR. Meanwhile, two binding sites in the U3 LTRs of the three isolates for NFAP-1 and AIB REP1 were lost, and a 1-base deletion in the E element of the 3' UTR of JL093-1 and SD09DP03 introduced a binding site for c-Ets-1. In addition to the changes listed above, the rTM of the 3' UTR was deleted in each of the three isolates.</p> <p>Conclusion</p> <p>Our study is the first to discovery the coexistence of two novel insertions in the U3 region in the LTR and the 5' UTR of ALV-J associated with hemangioma symptoms, and the transcriptional regulatory elements introduced should be taken into consideration in the occurrence of hemangioma.</p

Concurrence of multiple aneurysms, extreme coiling of the extracranial internal carotid artery and ipsilateral persistent primitive hypoglossal artery: A case report and literature review

BackgroundThe primitive hypoglossal artery (PHA) is an anastomotic vessel of the carotid-basilar artery system that is prevalent only transiently during the embryonic period. Persistent primitive hypoglossal artery (PPHA) is a rare vessel variation in which PHA exists persistently in adulthood and occurs in approximately 0.02–0.1% of the population. Tortuosity of the extracranial internal carotid artery (ICA) is relatively common, impacting 10–43% of the population, and is caused by either congenital or acquired factors. It is still unknown whether PPHA and tortuosity of extracranial ICA are associated. Here, we present a case report of the concurrence of three types of pathologies of the carotid artery: extreme coiling of the extracranial internal carotid artery, multiple aneurysms and persistent primitive hypoglossal artery.Case descriptionA 66-year-old woman suffered intermittent headaches, dizziness and numbness of the right eyelid for 5 years. Magnetic resonance angiography performed in a local hospital reported an aneurysm of the posterior communicating artery segment of the left ICA and a left PPHA. Digital subtraction angiography conducted after admission showed a PPHA originating from the left cervical ICA and an extremely coiling segment of the ICA distal to the beginning of PPHA. Except for the aneurysm of the posterior communicating artery segment of the left ICA, multiple aneurysms were found at the coiling segment of the ICA.ConclusionTo the best of our knowledge, this is the first report of PPHA accompanied by an adjacent, extremely coiling ICA. There are no reports of similar tortuous ICAs to this extent or at this position. Including aneurysms, three types of pathologies suggest their congenital origin, and a review of the literature infers the probable association of these lesions

Requirements and process analysis for ports and waterways open BIM ISO standards development

Purpose Defining building information modelling (BIM) standards for the infrastructure domain is a central issue to the successful implementation of BIM in civil engineering domains. To this end, this paper aims to present a requirements and process analysis for the ports and waterways domain to address the lack of BIM standards development, using the information delivery manual (IDM) approach and the ethos of openBIM standards. Design/methodology/approach This research uses the IDM approach. This involves the definition of use cases, process maps, exchange scenarios and subsequent exchange requirements. All these developments were sourced and validated by a series of international industry consultations. Findings The paper identifies 30 domain relevant use cases collated from existing sources and new cases. An overview and detailed ports and waterways process map (defining actors, activities and data exchanges). The process maps highlighted 38 exchange scenarios between various activities. Various exchange requirements were defined and are discussed in the context of the required information exchange model and the extensions required to fulfil the needs of the domain. The analysis provides the core information for the next steps of development for a substantial extension to the Industry Foundation Classes and the supporting data dictionary standards. Research limitations/implications Because of the international scope of the research, the outcomes can be applied by any stakeholders in the domain of ports and waterways. Therefore, some variation is expected at a national and organizational level. This research has the potential to accelerate the adoption of openBIM standards within the ports and waterways domain leading to increases in efficiency, collaborative working. Originality/value This paper reviews the requirements of an identified gap in the provision of openBIM standards relevant and applicable to the domain of ports and waterways

Autonomous concrete crack semantic segmentation using deep fully convolutional encoder-decoder network in concrete structures inspection

Structure health inspection is the way to ensure that structures stay in optimum condition. Traditional inspection work has many disadvantages in dealing with the large workload despite using remote image-capturing devices. This research focuses on image-based concrete crack pattern recognition utilizing a deep convolutional neural network (DCNN) and an encoder–decoder module for semantic segmentation and classification tasks, thereby lightening the inspectors’ workload. To achieve this, a series of contrast experiments have been implemented. The results show that the proposed deep-learning network has competitive semantic segmentation accuracy (91.62%) and over-performs compared with other crack detection studies. This proposed advanced DCNN is split into multiple modules, including atrous convolution (AS), atrous spatial pyramid pooling (ASPP), a modified encoder–decoder module, and depthwise separable convolution (DSC). The advancement is that those modules are well-selected for this task and modified based on their characteristics and functions, exploiting their superiority to achieve robust and accurate detection globally. This application improved the overall performance of detection and can be implemented in industrial practices