A regioselectivity switch in Pd-catalyzed hydroallylation of alkynes.

By exploiting the reactivity of a vinyl-Pd species, we control the regioselectivity in hydroallylation of alkynes under Pd-hydride catalysis. A monophosphine ligand and carboxylic acid combination promotes 1,5-dienes through a pathway involving isomerization of alkynes to allenes. In contrast, a bisphosphine ligand and copper cocatalyst favor 1,4-dienes via a mechanism that involves transmetalation. Our study highlights how to access different isomers by diverting a common organometallic intermediate

Formation and growth of sub-3 nm particles in megacities : impact of background aerosols

New particle formation (NPF) occurs frequently in various atmospheric environments and contributes majorly to the aerosol number budget. In megacities, the high concentrations of gaseous precursors and background aerosols add complexity to this process. Based on long-term measurements (373 days) in urban Beijing, we examine the formation and growth of sub-3 nm particles under the effects of background aerosols, as indicated by the condensation sink (CS) or the Fuchs surface area. The median CS and the median PM2.5 mass concentration for the days with NPF events were 0.03 s(-1) and 34 mu g m(-3), respectively. The high loss rates of both molecular clusters and sub-3 nm particles to background aerosols reduce their atmospheric residence time and suppress their survival. As the key clusters for H2SO4-base nucleation, sulfuric acid dimer and trimer concentrations in Beijing decrease significantly when CS increases and the scavenging becomes stronger. The occurrence of NPF events and the formation of sub-3 nm particles in Beijing is governed by CS. 95% of the observed NPF days occurred with CS values below 0.03 s(-1). During NPF events, high concentrations of sub-3 nm particles were formed and they mostly ranged from 10(3) to 10(5) cm(-3) with a median value of 6.2 x 10(3) cm(-3). Driven by the fast H2SO4-base nucleation, the daily maximum formation rate of 1.5 nm particles in Beijing has a mean value of 77 cm(-3) s(-1) and is much higher than that in clean environments. However, the mean growth rate of sub-3 nm particles in Beijing was only 2.6 nm h(-1), not significantly different from that in clean environments. The relatively low growth rate and the high level of scavenging by background aerosols result in low survival of newly formed particles. The analyses also reinforce prior results on the need to correct conventional methods to adequately quantify the formation and growth rates when analyzing data from megacities with strong coagulation scavenging due to background aerosols. The conventional balance formula underestimates the formation rate of 1.5 nm particles, while the conventional appearance time method overestimates the growth rate of sub-3 nm particles. These findings highlight the governing role of background aerosols in urban NPF.Peer reviewe

LATR: 3D Lane Detection from Monocular Images with Transformer

3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird's eye view) built from front-view image features and camera parameters. However, the depth ambiguity in monocular images inevitably causes misalignment between the constructed surrogate feature map and the original image, posing a great challenge for accurate lane detection. To address the above issue, we present a novel LATR model, an end-to-end 3D lane detector that uses 3D-aware front-view features without transformed view representation. Specifically, LATR detects 3D lanes via cross-attention based on query and key-value pairs, constructed using our lane-aware query generator and dynamic 3D ground positional embedding. On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance lane information. On the other hand, 3D space information is injected as positional embedding from an iteratively-updated 3D ground plane. LATR outperforms previous state-of-the-art methods on both synthetic Apollo, realistic OpenLane and ONCE-3DLanes by large margins (e.g., 11.4 gain in terms of F1 score on OpenLane). Code will be released at https://github.com/JMoonr/LATR .Comment: Accepted by ICCV2023 (Oral

Relativistic hyperpolarizabilities for atomic H, Li, and Be+^+ systems

The hyperpolarizability of an atom is a property that describes the nonlinear interaction between an atom and an external electric field leading to a higher-order Stark shift. Accurate evaluations of these coefficients for various systems are crucial to improve experimental precision in advanced atom-based clocks. However, there is a dearth of reports on atomic hyperpolarizabilities, particularly regarding relativistic hyperpolarizabilities. Thus, in this paper, we use fourth-order perturbation theory to establish a universal formula for the hyperpolarizability and calculate the relativistic hyperpolarizabilities of low-lying states for the monovalent electronic atomic systems H, Li, and Be$^+$. The highly accurate results given here for the H atom could serve as benchmarks for other theoretical methods.Comment: 12 pages; 1 figur

M^2-3DLaneNet: Multi-Modal 3D Lane Detection

Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. In this work, we propose the M^2-3DLaneNet, a Multi-Modal framework for effective 3D lane detection. Aiming at integrating complementary information from multi-sensors, M^2-3DLaneNet first extracts multi-modal features with modal-specific backbones, then fuses them in a unified Bird's-Eye View (BEV) space. Specifically, our method consists of two core components. 1) To achieve accurate 2D-3D mapping, we propose the top-down BEV generation. Within it, a Line-Restricted Deform-Attention (LRDA) module is utilized to effectively enhance image features in a top-down manner, fully capturing the slenderness features of lanes. After that, it casts the 2D pyramidal features into 3D space using depth-aware lifting and generates BEV features through pillarization. 2) We further propose the bottom-up BEV fusion, which aggregates multi-modal features through multi-scale cascaded attention, integrating complementary information from camera and LiDAR sensors. Sufficient experiments demonstrate the effectiveness of M^2-3DLaneNet, which outperforms previous state-of-the-art methods by a large margin, i.e., 12.1% F1-score improvement on OpenLane dataset

OrthoClust: An Orthology-Based Network Framework for Clustering Data Across Multiple Species

Increasingly, high-dimensional genomics data are becoming available for many organisms.Here, we develop OrthoClust for simultaneously clustering data across multiple species. OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing the orthology relationships of genes between species. It outputs optimized modules that are fundamentally cross-species, which can either be conserved or species-specific. We demonstrate the application of OrthoClust using the RNA-Seq expression profiles of Caenorhabditis elegans and Drosophila melanogaster from the modENCODE consortium. A potential application of cross-species modules is to infer putative analogous functions of uncharacterized elements like non-coding RNAs based on guilt-by-association