1,2-Bis[(3,6,9-trimethyl-3,12-ep­oxy-3,4,5,5a,6,7,8,8a,9,10,12,12a-dodeca­hydro­pyrano[4,3-j][1,2]benzodioxepin-4-yl)­oxy]ethane

The title compound, C32H50O10, prepared from a mixture of α- and β-dihydro­artemisinin, has two β-arteether moieties linked via an –OCH2CH2O– bridge, so that the mol­ecule is symmetric about the bridge. Each asymmetric unit contains a β-arteether moiety and an –OCH2 group, which is only one-half of the mol­ecule. The endo-peroxide bridges of the parent compounds have been retained in each half of the diol-bridged dimer. The rings exhibit chair and twist-boat conformations

Ultrafast Biomimetic Untethered Soft Actuators with Bone‐In‐Flesh Constructs Actuated by Magnetic Field

Soft actuators with unique mechanics have gained significant interests for unique capabilities and versatile applications. However, their actuation mechanisms (usually driven by light, heat, or chemical reactions) result in long actuation times. Reported magnetically actuated soft actuators can produce rapid and precise motions, yet their complex manufacturing processes may constrain their range of applications. Here, the “bone-in-flesh” is proposed that constructs combining rigid magnetic structures encapsulated within soft polymers to create untethered magnetic soft actuators. This approach enables these soft, impact-resistant, agile actuators with a significantly simplified fabrication process. As demonstration examples, multiple soft actuators are fabricated and tested, including actuators for auxetic properties, 2D–3D transformations, and multi-stable states. As such, this work offers a promising solution to challenges associated with soft actuators to potentially expand their applications in various domains

Surface-SOS:Self-Supervised Object Segmentation via Neural Surface Representation

Self-supervised Object Segmentation (SOS) aims to segment objects without any annotations. Under conditions of multi-camera inputs, the structural, textural and geometrical consistency among each view can be leveraged to achieve fine-grained object segmentation. To make better use of the above information, we propose Surface representation based Self-supervised Object Segmentation (Surface-SOS), a new framework to segment objects for each view by 3D surface representation from multi-view images of a scene. To model high-quality geometry surfaces for complex scenes, we design a novel scene representation scheme, which decomposes the scene into two complementary neural representation modules respectively with a Signed Distance Function (SDF). Moreover, Surface-SOS is able to refine single-view segmentation with multi-view unlabeled images, by introducing coarse segmentation masks as additional input. To the best of our knowledge, Surface-SOS is the first self-supervised approach that leverages neural surface representation to break the dependence on large amounts of annotated data and strong constraints. These constraints typically involve observing target objects against a static background or relying on temporal supervision in videos. Extensive experiments on standard benchmarks including LLFF, CO3D, BlendedMVS, TUM and several real-world scenes show that Surface-SOS always yields finer object masks than its NeRF-based counterparts and surpasses supervised single-view baselines remarkably.</p

Preparation of Material for Adsorption Ag(I) in the Solution

The application of silver in electronics, jewelry, catalytic and other industries often produces a large amount of silver-containing wastewater, which causes serious impact to the surrounding environment and human health, while silver has a certain economic value attached to it. Therefore, how to effectively treat and recover Ag(?) from the silver-containing wastewater is a hot topic of concern at present. In order to seek an efficient and environmentally friendly adsorbent, this paper compared the adsorption efficiency of purified, thermally modified, acid modified and thermally-acid modified Bentonite on silver, selected an economical and reasonable purified clay as a carrier, and then completed the preparation of modified Bentonite as well as the optimization of conditions with sodium silicate as a surfactant and 3-mercaptopropyltrimethoxysilane as a modifier. The experiments showed that under the conditions of sodium silicate dosage of 15% of Bentonite, Bentonite and modifier dosage of 1:1, solution pH of 9, temperature of 45 °C and modification time of 5 h, the synthesized sulfhydryl modified Bentonite has good adsorption performance on Ag(?), and its adsorption capacity can reach 293.7 mg·g-1

Learning To Rank Diversely At Airbnb

Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.Comment: Search ranking, Diversity, e-commerc

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.Comment: Accepted by KDD 202