Crowdsourcing in the Digital Humanities: An Action Research on the Shengxuanhuai Manuscript Transcription

In recent years, there has been an emerging trend in the GLAMs (Galleries, Libraries, Archives and Museums) to leverage crowdsourcing to improve the collection, organization, and evaluation of valuable resources. Although a se-ries of notable crowdsourcing projects in the digital humanities have been launched worldwide, there are few academic studies on investigating the im-plementation and evaluation of such cases. To fill up the research gap, this study aims at conducting a field exploration on the real case called the Shengxuanhuai Manuscript Transcription Initiative (Transcribe Sheng for short). In this poster, action research will be carried out to explore the vari-ous stages of Transcribe Sheng project. Our attempts may shed light on the design and evaluation principles of the crowdsourcing in the digital humani-ties

Understanding Public Online Donations on Social Media during the Pandemic: A Social Presence Theory Perspective

The COVID-19 pandemic has had a huge impact on the global economy and health care, but online donations from the public on social media have increased significantly. However, the role of social presence in motivating people to donate online during the pandemic has been largely unexplored. This study examines the relationship between social presence on social media and online donation behavior during the pandemic using social presence theory. We explore the interplay between social presence, perceived threat, social properties of social media, and donation intentions. The results showed that social presence based on social media, perception of others and social interaction significantly affected social media online donation participation, and the perceived threat of COVID-19 significantly moderated online donation participation. Our research contributes to the understanding of online donation behavior during a pandemic crisis and provides insights into how social media can be leveraged for effective donation campaigns

The effects of filling patterns on the powder–binder separation in powder injection molding

AbstractThe powder–binder separation is a common difficulty during injection molding, which leads to the inhomogeneity in the debinding and sintering stages. Previous studies focus on the relationship between “final results” and “initial conditions”, while the dynamic filling process of feedstock and the evolution of powder–binder separation were ignored. This work investigated the effects of filling patterns on the powder–binder separation during powder injection molding. The mold filling model of PIM has been developed, based on the multiphase fluid theory and the viscosity model of feedstock. Parameters of the viscosity model were modified by the experimental data. Numerical simulations were compared with experiments with the same process parameters. The powder–binder separation phenomena in green bodies were detected by X-Ray computed tomography (CT). The experimental phenomena were explained clearly by the evolution of powder–binder separation obtained with numerical simulation method. A typical compacting filling pattern of PIM and filling mobility variable of the feedstock were proposed. A proper filling pattern was helpful to ensure the mobility of feedstock and the homogeneity of green body

The effect of Cu content on corrosion, wear and tribocorrosion resistance of Ti-Mo-Cu alloy for load-bearing bone implants

In this study, the effects of Cu content on wear, corrosion, and tribocorrosion resistance of Ti-10Mo-xCu alloy were investigated. Results revealed that hardness of Ti-10Mo-xCu alloy increased from 355.1 ± 15.2 HV to 390.8 ± 17.6 HV by increasing Cu content from 0 % to 5 %, much higher than CP Ti (106.6 ± 15.1 HV) and comparable to Ti64 (389.7 ± 13.9 HV). With a higher Cu content, wear and tribocorrosion resistance of Ti-10Mo-xCu alloys were enhanced, and corrosion resistance showed an initial increase with a subsequent decrease. Wear mechanisms under pure mechanical wear and tribocorrosion conditions of Ti-10Mo-xCu alloys were a combination of delamination, abrasion and adhesion wear

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

Design and performance evaluation of additively manufactured composite lattice structures of commercially pure Ti (CP-Ti)

Ti alloys with lattice structures are garnering more and more attention in the field of bone repair or regeneration due to their superior structural, mechanical, and biological properties. In this study, six types of composite lattice structures with different strut radius that consist of simple cubic (structure A), body-centered cubic (structure B), and edge-centered cubic (structure C) unit cells are designed. The designed structures are firstly simulated and analysed by the finite element (FE) method. Commercially pure Ti (CP–Ti) lattice structures with optimized unit cells and strut radius are then fabricated by selective laser melting (SLM), and the dimensions, microtopography, and mechanical properties are characterised. The results show that among the six types of composite lattice structures, combined BA, CA, and CB structures exhibit smaller maximum von-Mises stress, indicating that these structures have higher strength. Based on the fitting curves of stress/specific surface area versus strut radius, the optimized strut radius of BA, CA, and CB structures is 0.28, 0.23, and 0.30 mm respectively. Their corresponding compressive yield strength and compressive modulus are 42.28, 30.11, and 176.96 MPa, and 4.13, 2.16, and 7.84 GPa, respectively. The CP-Ti with CB unit structure presents a similar strength and compressive modulus to the cortical bone, which makes it a potential candidate for subchondral bone restorations

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

The Optimization of Ti Gradient Porous Structure Involves the Finite Element Simulation Analysis

Titanium (Ti) and its alloys are attracting special attention in the field of dentistry and orthopedic bioengineering because of their mechanical adaptability and biological compatibility with the natural bone. The dental implant is subjected to masticatory forces in the oral environment and transfers these forces to the surrounding bone tissue. Therefore, by simulating the mechanical behavior of implants and surrounding bone tissue we can assess the effects of implants on bone growth quite accurately. In this study, dental implants with different gradient pore structures that consisted of simple cubic (structure a), body centered cubic (structure b) and side centered cubic (structure c) were designed, respectively. The strength of the designed gradient porous implant in the oral environment was simulated by three-dimensional finite element simulation technique to assess the mechanical adaptation by the stress-strain distribution within the surrounding bone tissue and by examining the fretting of the implant-bone interface. The results show that the maximum equivalent stress and strain in the surrounding bone tissue increase with the increase of porosity. The stress distribution of the gradient implant with a smaller difference between outer and inner pore structure is more uniform. So, a-b type porous implant exhibited less stress concentration. For a-b structure, when the porosity is between 40 and 47%, the stress and strain of bone tissue are in the range of normal growth. When subject to lingual and buccal stresses, an implant with higher porosity can achieve more uniform stress distribution in the surrounding cancellous bone than that of low porosity implant. Based on the simulated results, to achieve an improved mechanical fixation of the implant, the optimum gradient porous structure parameters should be: average porosity 46% with an inner porosity of 13% (b structure) and outer porosity of 59% (a structure), and outer pore sized 500 μm. With this optimized structure, the bone can achieve optimal ingrowth into the gradient porous structure, thus provide stable mechanical fixation of the implant. The maximum equivalent stress achieved 99 MPa, which is far below the simulation yield strength of 299 MPa

Towards Learning Causal Representations from Multi-Instance Bags

Multi-instance learning (MIL) deals with objects represented as bags of instances and can predict instance labels from bag-level supervision. However, significant performance gaps exist between instance-level MIL algorithms and supervised learners since the instance labels are unavailable in MIL. Most existing MIL algorithms tackle the problem by treating multi-instance bags as harmful ambiguities and predicting instance labels by reducing the supervision inexactness. This work studies MIL from a new perspective by considering bags as important auxiliary information that can be utilized to identify invariant causal representations from bag-level weak supervision. We propose the TargetedMIL algorithm, which not only excels at instance label prediction but also is robust to distribution change by synergistically integrating MIL with identifiable variational autoencoder based on a practical and general assumption: the prior distribution over the instance latent representations belongs to the non-factorized exponential family conditioning on the multi-instance bags. Experiments on synthetic and real-world datasets demonstrate that our approach significantly outperforms various baselines on instance label prediction and out-of-distribution generalization tasks