TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method

Gradient Boosting Machines (GBMs) are derived from Taylor expansion in functional space and have achieved state-of-the-art results on a variety of problems. However, there is a dilemma for GBMs to maintain a balance between performance and generality. Specifically, gradient descent-based GBMs employ the first-order Taylor expansion to make them appropriate for all loss functions. And Newton's method-based GBMs use the positive hessian information to achieve better performance at the expense of generality. In this paper, a generic Gradient Boosting Machine called Trust-region Boosting (TRBoost) is presented to maintain this balance. In each iteration, we apply a constrained quadratic model to approximate the objective and solve it by the Trust-region algorithm to obtain a new learner. TRBoost offers the benefit that we do not need the hessian to be positive definite, which generalizes GBMs to suit arbitrary loss functions while keeping up the good performance as the second-order algorithm. Several numerical experiments are conducted to confirm that TRBoost is not only as general as the first-order GBMs but also able to get competitive results with the second-order GBMs

A Wasserstein distributionally robust planning model for renewable sources and energy storage systems under multiple uncertainties

Nowadays, electricity markets and carbon trading mechanisms can promote investment in renewable sources but also generate new uncertainties in decision-making. In this paper, a two-stage Wasserstein distributionally robust optimization (WDRO) model is presented to determine the optimal planning strategy for renewable energy generators (REGs) and energy storage systems (ESSs) in the distribution network. This model considers supply-side and demand-side uncertainties in the distribution network and the interaction uncertainty from the main grid which are depicted by the ambiguity sets based on the Wasserstein metric and historical data. Meanwhile, both 1-norm and -norm Wasserstein metric constraints are considered to satisfy the decision-makers different preference. Furthermore, to solve this WDRO model, a systematic solution method with a three-step process is developed. Numerical results from a modified IEEE 33-node system and a 130-node system in the real world demonstrate the advantages of the two-stage WDRO model and the effectiveness of the solution method.</p

Controllable thioester-based hydrogen sulfide slow-releasing donors as cardioprotective agents

Hydrogen sulfide (H2S) is an important signaling molecule with promising protective effects in many physiological and pathological processes. However, the study of H2S has been impeded by the lack of appropriate H2S donors that could mimic its slow-releasing process in vivo. Herein, we report the rational design, synthesis, and biological evaluation of a series of thioester-based H2S donors. These cysteine-activated H2S donors release H2S in a slow and controllable manner. Most of the donors comprising an allyl moiety showed significant cytoprotective effects in H9c2 cellular models of oxidative damage. The most potent donor 5e decreased the mitochondrial membrane potential (MMP) loss and lactate dehydrogenase (LDH) release in H2O2-stimulated H9c2 cells. More importantly, donor 5e exhibited a potent cardioprotective effect in an in vivo myocardial infarction (MI) mouse model by reducing myocardial infarct size and cardiomyocyte apoptosis. Taken together, our studies demonstrated that these new allyl thioesters are potential cardioprotective agents by releasing H2S

Case report: A novel 5'-UTR-exon1-intron1 deletion in MLYCD in an IVF child with malonyl coenzyme A decarboxylase deficiency and literature review

The subject of the study is an 11-month old IVF baby girl with the typical clinical manifestation of malonyl coenzyme A decarboxylase deficiency, including developmental delay, limb weakness, cardiomyopathy, and excessive excretion of malonic acid and methylmalonic acid. Whole genome sequencing (WGS) revealed a novel heterozygous nonsense mutation (c.672delG, p.Trp224Ter) in the MLYCD gene of the proband and her father and a novel heterozygous deletion in 5'-UTR-exon1-intron1 of the MLYCD gene of the proband and her mother. The patient's cardiac function and limb weakness improved considerably after 3 months of a low-fat diet supplemented with L-carnitine. Furthermore, mapping of gene mutations and clinical manifestations was done by case collection

The structural modification of natural products for novel drug discovery

Introduction: Throughout history, natural products (NPs) have provided a rich source of compounds that have wide applications in the fields of medicine, health sciences, pharmacy and biology. Although naturally active substances are good lead compounds for the discovery of new drugs, most of them suffer from various deficiencies or shortcomings, such as complex structures, poor stability and solubility. Therefore, structural modification of NPs is needed to develop novel compounds with specific properties. Areas covered: This article presents an overview on the structural modifications of NPs in drug development. The application of multiple classes of NPs to the treatment of conditions such as cancers, infection, Alzheimer’s and diabetes are discussed. This article also reveals that modification of NPs is a versatile approach to explore their mode of actions, which may lead to the discovery of novel drugs. Expert opinion: NPs are usually described by structural diversity and complexity. The use of isolated NPs as scaffolds for modification is a good approach to drug discovery and development. Despite many limitations associated with NPs, the total synthesis, semisynthetic modification, SAR-based modification, sometimes even a single atom alteration, may lead to the discovery of a novel drug

MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection

In the field of monocular 3D detection, it is common practice to utilize scene geometric clues to enhance the detector's performance. However, many existing works adopt these clues explicitly such as estimating a depth map and back-projecting it into 3D space. This explicit methodology induces sparsity in 3D representations due to the increased dimensionality from 2D to 3D, and leads to substantial information loss, especially for distant and occluded objects. To alleviate this issue, we propose MonoNeRD, a novel detection framework that can infer dense 3D geometry and occupancy. Specifically, we model scenes with Signed Distance Functions (SDF), facilitating the production of dense 3D representations. We treat these representations as Neural Radiance Fields (NeRF) and then employ volume rendering to recover RGB images and depth maps. To the best of our knowledge, this work is the first to introduce volume rendering for M3D, and demonstrates the potential of implicit reconstruction for image-based 3D perception. Extensive experiments conducted on the KITTI-3D benchmark and Waymo Open Dataset demonstrate the effectiveness of MonoNeRD. Codes are available at https://github.com/cskkxjk/MonoNeRD.Comment: Accepted by ICCV 202

Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving

Multi-camera perception tasks have gained significant attention in the field of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot (LSS) in the multi-camera setting cannot produce suitable dense 3D features due to the projection nature and uncontrollable densification process. To resolve this problem, we propose to regulate intermediate dense 3D features with the help of volume rendering. Specifically, we employ volume rendering to process the dense 3D features to obtain corresponding 2D features (e.g., depth maps, semantic maps), which are supervised by associated labels in the training. This manner regulates the generation of dense 3D features on the feature level, providing appropriate dense and unified features for multiple perception tasks. Therefore, our approach is termed Vampire, stands for "Volume rendering As Multi-camera Perception Intermediate feature REgulator". Experimental results on the Occ3D and nuScenes datasets demonstrate that Vampire facilitates fine-grained and appropriate extraction of dense 3D features, and is competitive with existing SOTA methods across diverse downstream perception tasks like 3D occupancy prediction, LiDAR segmentation and 3D objection detection, while utilizing moderate GPU resources. We provide a video demonstration in the supplementary materials and Codes are available at github.com/cskkxjk/Vampire.Comment: Accepted by AAAI 202

Tunable ferroelectric topological defects on 2D topological surfaces: strain engineering skyrmion-like polar structures in 2D materials

Polar topological structures in ferroelectric thin films have recently drawn significant interest due to their fascinating physical behaviors and promising applications in high-density nonvolatile memories. However, most polar topological patterns are only observed in the perovskites superlattices. Here, we report the discovery of the tunable ferroelectric polar topological defective structures designed and achieved by strain engineering in two-dimensional PbX (X=S, Se, and Te) materials using multiscale computational simulations. First, the first-principles calculations demonstrate the strain-induced recoverable ferroelectric phase transition in such 2D materials. The unique polar topological vortex pattern is then induced by applied mechanical indentation, evidenced by molecular dynamics simulations based on a developed deep-learning potential. According to the strain phase diagram and applied complex strain loadings, the diverse polar topological structures, including antivortex structure and flux-closure structure, are predicted to be emergent through the finite-element simulations. We conclude that strain engineering is promising to tailor various designed reversible polar topologies in ultra-flexible 2D materials, which provide excellent opportunities for next-generation nanoelectronics and sensor devices.Comment: 36 pages, 6 figures for manuscript, 11 figures for supplementary informatio