A unified fused Lasso approach for sparse and blocky feature selection in regression and classification

In many applications, sparse and blocky coefficients often occur in regression and classification problems. The fused Lasso was designed to recover these sparse structured features especially when the design matrix encounters the situation of ultrahigh dimension. Quantile loss is well known as a robust loss function in regression and classification. In this paper, we combine quantile loss and fused Lasso penalty together to produce quantile fused Lasso which can achieve sparse and blocky feature selection in both regression and classification. Interestingly, our proposed model has the unified optimization formula for regression and classification. For ultrahigh dimensional collected data, we derive multi-block linearized alternating direction method of multipliers (LADMM) to deal with it. Moreover, we prove convergence and derive convergence rates of the proposed LADMM algorithm through an elegant method. Note that the algorithm can be easily extended to solve many existing fused Lasso models. Finally, we present some numerical results for several synthetic and real world examples, which illustrate the robustness, scalability, and accuracy of the proposed method

Risk factors for surgical site infection of pilon fractures

OBJECTIVES: Pilon fracture is a complex injury that is often associated with severe soft tissue damage and high rates of surgical site infection. The goal of this study was to analyze and identify independent risk factors for surgical site infection among patients undergoing surgical fixation of a pilon fracture. METHODS: The medical records of all pilon fracture patients who underwent surgical fixation from January 2010 to October 2012 were reviewed to identify those who developed a surgical site infection. Then, we constructed univariate and multivariate logistic regressions to evaluate the independent associations of potential risk factors with surgical site infection in patients undergoing surgical fixation of a pilon fracture. RESULTS: A total of 519 patients were enrolled in the study from January 2010 to October 2012. A total of 12 of the 519 patients developed a surgical site infection, for an incidence of 2.3%. These patients were followed for 12 to 29 months, with an average follow-up period of 19.1 months. In the final regression model, open fracture, elevated postoperative glucose levels (≥125 mg/dL), and a surgery duration of more than 150 minutes were significant risk factors for surgical site infection following surgical fixation of a pilon fracture. CONCLUSIONS: Open fractures, elevated postoperative glucose levels (≥125 mg/dL), and a surgery duration of more than 150 minutes were related to an increased risk for surgical site infection following surgical fixation of a pilon fracture. Patients exhibiting the risk factors identified in this study should be counseled regarding the possible surgical site infection that may develop after surgical fixation

Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder

In recent years, the field of 3D self-supervised learning has witnessed significant progress, resulting in the emergence of Multi-Modality Masked AutoEncoders (MAE) methods that leverage both 2D images and 3D point clouds for pre-training. However, a notable limitation of these approaches is that they do not fully utilize the multi-view attributes inherent in 3D point clouds, which is crucial for a deeper understanding of 3D structures. Building upon this insight, we introduce a novel approach employing a 3D to multi-view masked autoencoder to fully harness the multi-modal attributes of 3D point clouds. To be specific, our method uses the encoded tokens from 3D masked point clouds to generate original point clouds and multi-view depth images across various poses. This approach not only enriches the model's comprehension of geometric structures but also leverages the inherent multi-modal properties of point clouds. Our experiments illustrate the effectiveness of the proposed method for different tasks and under different settings. Remarkably, our method outperforms state-of-the-art counterparts by a large margin in a variety of downstream tasks, including 3D object classification, few-shot learning, part segmentation, and 3D object detection. Code will be available at: https://github.com/Zhimin-C/Multiview-MA

Detector optimization to reduce the cosmogenic neutron backgrounds in the TAO experiment

Short-baseline reactor antineutrino experiments with shallow overburden usually have large cosmogenic neutron backgrounds. The Taishan Antineutrino Observatory (TAO) is a ton-level liquid scintillator detector located at about 30 m from a core of the Taishan Nuclear Power Plant. It will measure the reactor antineutrino spectrum with high precision and high energy resolution to provide a reference spectrum for JUNO and other reactor antineutrino experiments, and provide a benchmark measurement to test nuclear databases. Background is one of the critical concerns of TAO since the overburden is just 10 meter-water-equivalent. The cosmogenic neutron background was estimated to be ~10% of signals. With detailed Monte Carlo simulations, we propose several measures in this work to reduce the neutron backgrounds, including doping Gadolinium in the buffer liquid, adding a polyethylene layer above the bottom lead shield, and optimization of the veto strategy. With these improvements, the neutron background-to-signal ratio can be reduced to ~2%, and might be further suppressed with pulse shape discrimination.Comment: 11 pages, 3 figure

Process, microstructure and mechanical properties

Funding Information: This work was supported by National Natural Science Foundation of China (Grant No. 51875168/52002112 ), Natural Science Foundation of Hebei Province (Grant No. E2019208089 ) and “Three-Three-Three Talent Project” Foundation of Hebei Province ( C20221022 ). Sichuan Province Science Funding for Distinguished Young Scholars ( 3NSFJQ0064 ). JPO acknowledges funding by national funds from FCT – Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020. Science and Technology Research Project of Colleges and Universities in Hebei Province (Grant No. BJK2022020 ). Publisher Copyright: © 2023 The AuthorsAiming to decouple the inherent relationship between mass transfer and heat transfer in traditional arc-based directed energy deposition, a novel heterogeneous multi-wire indirect arc directed energy deposition (DED) has been developed for in-situ synthesis of Al-Zn-Mg-Cu alloy components. Multi-wires (Al-Cu and Al-Mg) with a bypassing Zn wire have been used to replace the traditional homogeneous twin-wires. The process, microstructure and mechanical properties of the deposited Al-Zn-Mg-Cu alloy components obtained by multi-wire indirect arc DED were investigated. The results indicate that the wire feeding speed, current and angle between the two wires have a significant influence on the multi-wire indirect arc DED process. When the current was 200 A, the different wire feeding speeds could be used for both wires and the angle between them was 90°. The resulting indirect arc presented a ‘heart’ shape and allowed to obtain an Al-5.7Zn-3.4Mg-1.6Cu (wt%) alloy with a high deposition rate of 5.1 kg/h. The Al-5.7Zn-3.4Mg-1.6Cu alloy is mainly composed of α-Al, S (Al2CuMg), η (Mg (Al, Zn, Cu)2) and η′ phases. The composition and phases are in accordance with the 7xxx series aluminum alloys. The microstructure is dominated by columnar and equiaxed grains, and it has obvious periodic distribution along the building direction, which is related to the process thermal cycle. Fine second phases η′ are observed to precipitate during the manufacturing process. Furthermore, the average hardness, ultimate tensile strength and elongation of the fabricated material are 98.6 HV, 243.9 MPa and 5.9%, respectively. These mechanical properties are higher than those of as-cast 7050 aluminum alloy, thus showing the potential of this new process variant to fabricate high strength Al alloys in the as-deposited state. The fracture morphology exhibit features mainly associated to a ductile-like fracture, accompanied by some transgranular and partial cleavage fracture characteristics. This novel multi-wire indirect arc DED provides a new choice for arc-based directed energy deposition of Al-Zn-Mg-Cu alloys and shows great potential for the in-situ synthesis of other high-performance alloys.publishersversionpublishe