Maximum gradient dimensionality reduction

We propose a novel dimensionality reduction approach based on the gradient of the regression function. Our approach is conceptually similar to Principal Component Analysis, however instead of seeking a low dimensional representation of the predictors that preserve the sample variance, we project onto a basis that preserves those predictors which induce the greatest change in the response. Our approach has the benefits of being simple and easy to implement and interpret, while still remaining very competitive with sophisticated state-of-the-art approaches

Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatialtemporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional ’zero-inflated’ model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using realworld datasets highlight STTD’s superiority in providing accurate predictions and precise confidence intervals, particularly in highresolution scenarios. GitHub code is available online

Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional 'zero-inflated' model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using real-world datasets highlight STTD's superiority in providing accurate predictions and precise confidence intervals, particularly in high-resolution scenarios.Comment: In proceeding of CIKM 2023. Doi: https://dl.acm.org/doi/10.1145/3583780.361521

Insights into the impact and solidification of metal droplets in ground-based investigation of droplet deposition 3D printing under microgravity

Droplet deposition 3D printing is an additive manufacturing technique offering a great potential for metal parts fabrication in space, of which some preliminary testing is usually performed on ground in the early research. The previous work mimicked the anti-gravity deposition of molten metal droplet through manipulating it into perpendicularly depositing on a vertical substrate. However, the ground-based simulation of droplet deposition 3D printing under microgravity remains an elusive goal, since the spreading and receding processes are still affected by gravity. To address this issue, the prevailing physical mechanisms of gravity effect on droplet impact and solidification are urgent to be defined. Here, we present the studies on the impact dynamics and transient solidification of the molten metal droplet deposited on vertical substrates through numerical modeling and experiments. It is observed that the spreading and retraction of the droplet are asymmetric, besides its solidification shape tilts to gravitational direction. The formation mechanisms of these undesired behaviors are further demonstrated. The results show that the asymmetrical spreading, retraction and solidification shape of the droplet originate from the interaction of gravity and solidification. Moreover, the tilt of the solidified droplet has a correlation with the critical process parameters, i.e. impact velocity, temperatures of droplet and substrate. With a larger impact inertia and a lower solidification rate, the undesired solidification shape can be effectively eliminated. This work provides a foundation for the further investigation of the ground-based physical simulation of outer space droplet deposition 3D printing

Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.Comment: Paper accepted at ISWC2023 In-Use trac

Damage of shotcrete under freeze-thaw loading

The freeze-thaw durability of shotcrete can be improved by adding an air-entraining agent in cold areas. The main focus of this paper is to investigate the changes in the internal pore structure of C25 ordinary shotcrete and shot­crete mixed with a RM-YQ air-entraining agent using computed tomography (CT) scanning technique during freeze-thaw cycles. The macroscopic tests were conducted, including mass loss, dynamic modulus of elasticity and ultrasonic wave velocity tests. Results were compared, and the freeze-thaw durability characteristics of shotcrete mixed with the air-entraining agent were revealed. Adding an air-entraining agent could reduce the number of pores largely that ranged mainly from 0.01 mm2 to 1.00 mm2 (excluding the pores or bubbles < 0.01 mm2 because of the precision of the CT scan­ning system), and could therefore improve the initial pore structure of the formed shotcrete. During first few freeze-thaw cycles, just few small pores formed. After cement mortar fragmentations appeared, the number of small pores (0.01 mm2 to 0.50 mm2) in ordinary shotcrete increased significantly. The pore structure deteriorated largely. However, this could be prevented effectively by adding an air-entraining agent. Therefore, the freeze–thaw durability of shotcrete was improved

Degradable hydrogel fibers encapsulate and deliver metformin and periodontal ligament stem cells for dental and periodontal regeneration

Human periodontal ligament stem cells (hPDLSCs) are promising cells for dental and periodontal regeneration. Objective: This study aimed to develop novel alginate-fibrin fibers that encapsulates hPDLSCs and metformin, to investigate the effect of metformin on the osteogenic differentiation of hPDLSCs, and to determine the regulatory role of the Shh/Gli1 signaling pathway in the metformin-induced osteogenic differentiation of hPDLSCs for the first time. Methodology: CCK8 assay was used to evaluate hPDLSCs. Alkaline phosphatase (ALP) staining, alizarin red S staining, and the expression of osteogenic genes were evaluated. Metformin and hPDLSCs were encapsulated in alginate-fibrinogen solutions, which were injected to form alginate-fibrin fibers. The activation of Shh/Gli1 signaling pathway was examined using qRT-PCR and western blot. A mechanistic study was conducted by inhibiting the Shh/Gli1 pathway using GANT61. Results: The administration of 50 μM metformin resulted in a significant upregulation of osteogenic gene expression in hPDLSCs by 1.4-fold compared to the osteogenic induction group (P &lt; 0.01), including ALP and runt-related transcription factor-2 (RUNX2). Furthermore, metformin increased ALP activity by 1.7-fold and bone mineral nodule formation by 2.6-fold (P&lt;0.001). We observed that hPDLSCs proliferated with the degradation of alginate-fibrin fibers, and metformin induced their differentiation into the osteogenic lineage. Metformin also promoted the osteogenic differentiation of hPDLSCs by upregulating the Shh/Gli1 signaling pathway by 3- to 6- fold compared to the osteogenic induction group (P&lt;0.001). The osteogenic differentiation ability of hPDLSCs were decreased 1.3- to 1.6-fold when the Shh/Gli1 pathway was inhibited, according to ALP staining and alizarin red S staining (P&lt;0.01). Conclusions: Metformin enhanced the osteogenic differentiation of hPDLSCs via the Shh/Gli1 signaling pathway. Degradable alginate-fibrin hydrogel fibers encapsulating hPDLSCs and metformin have significant potential for use in dental and periodontal tissue engineering applications. Clinical Significance: Alginate-fibrin fibers encapsulating hPDLSCs and metformin have a great potential for use in the treatment of maxillofacial bone defects caused by trauma, tumors, and tooth extraction. Additionally, they may facilitate the regeneration of periodontal tissue in patients with periodontitis