SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar

The 4D Millimeter wave (mmWave) radar is a promising technology for vehicle sensing due to its cost-effectiveness and operability in adverse weather conditions. However, the adoption of this technology has been hindered by sparsity and noise issues in radar point cloud data. This paper introduces spatial multi-representation fusion (SMURF), a novel approach to 3D object detection using a single 4D imaging radar. SMURF leverages multiple representations of radar detection points, including pillarization and density features of a multi-dimensional Gaussian mixture distribution through kernel density estimation (KDE). KDE effectively mitigates measurement inaccuracy caused by limited angular resolution and multi-path propagation of radar signals. Additionally, KDE helps alleviate point cloud sparsity by capturing density features. Experimental evaluations on View-of-Delft (VoD) and TJ4DRadSet datasets demonstrate the effectiveness and generalization ability of SMURF, outperforming recently proposed 4D imaging radar-based single-representation models. Moreover, while using 4D imaging radar only, SMURF still achieves comparable performance to the state-of-the-art 4D imaging radar and camera fusion-based method, with an increase of 1.22% in the mean average precision on bird's-eye view of TJ4DRadSet dataset and 1.32% in the 3D mean average precision on the entire annotated area of VoD dataset. Our proposed method demonstrates impressive inference time and addresses the challenges of real-time detection, with the inference time no more than 0.05 seconds for most scans on both datasets. This research highlights the benefits of 4D mmWave radar and is a strong benchmark for subsequent works regarding 3D object detection with 4D imaging radar

Systemic treatments for breast cancer brain metastasis

Breast cancer (BC) is the most common cancer in females and BC brain metastasis (BCBM) is considered as the second most frequent brain metastasis. Although the advanced treatment has significantly prolonged the survival in BC patients, the prognosis of BCBM is still poor. The management of BCBM remains challenging. Systemic treatments are important to maintain control of central nervous system disease and improve patients’ survival. BCBM medical treatment is a rapidly advancing area of research. With the emergence of new targeted drugs, more options are provided for the treatment of BM. This review features currently available BCBM treatment strategies and outlines novel drugs and ongoing clinical trials that may be available in the future. These treatment strategies are discovered to be more efficacious and potent, and present a paradigm shift in the management of BCBMs

Systematic review and perspective on the progress of algal biofuels

In recognition of the increasing demand of energy and the worsening environmental problems linked with fossil fuels usage, algal biofuel has been proposed as one of the alternative energy sources. It has become one of the hottest topics in renewable energy field in the new century, especially over the past decade. In this review, we summarized the characteristics of different types of algae biofuels. Besides, an in-depth evaluation of the systematic cultivation and practical application of algae have been conducted. Although algal biofuel has a great potential, its unacceptably high cost limits the large-scale industrialization. In order to resolve such restrictions, feasible methods of improving the large scale production and practical application of algal biofuels are proposed. Future efforts should be focused not only on the cost reduction and innovation techniques, but also towards high value by-products to maximize economic benefits. Our results are dedicated to provide valuable references for subsequent research and guidelines on algae biofuels field

SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar

The 4D millimeter-Wave (mmWave) radar is a promising technology for vehicle sensing due to its costeffectiveness and operability in adverse weather conditions. However, the adoption of this technology has been hindered by sparsity and noise issues in radar point cloud data. This paper introduces spatial multi-representation fusion (SMURF), a novel approach to 3D object detection using a single 4D imaging radar. SMURF leverages multiple representations of radar detection points, including pillarization and density features of a multidimensional Gaussian mixture distribution through kernel density estimation (KDE). KDE effectively mitigates measurement inaccuracy caused by limited angular resolution and multipath propagation of radar signals. Additionally, KDE helps alleviate point cloud sparsity by capturing density features. Experimental evaluations on View-of-Delft (VoD) and TJ4DRadSet datasets demonstrate the effectiveness and generalization ability of SMURF, outperforming recently proposed 4D imaging radarbased single-representation models. Moreover, while using 4D imaging radar only, SMURF still achieves comparable performance to the state-of-the-art 4D imaging radar and camera fusion-based method, with an increase of 1.22% in the mean average precision on bird’s-eye view of TJ4DRadSet dataset and 1.32% in the 3D mean average precision on the entire annotated area of VoD dataset. Our proposed method demonstrates impressive inference time and addresses the challenges of real-time detection, with the inference time no more than 0.05 seconds for most scans on both datasets. This research highlights the benefits of 4D mmWave radar and is a strong benchmark for subsequent works regarding 3D object detection with 4D imaging radar

Encoding Word Order in Complex Embeddings

Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or precedence) between individual word positions. We present a novel and principled solution for modeling both the global absolute positions of words and their order relationships. Our solution generalizes word embeddings, previously defined as independent vectors, to continuous word functions over a variable (position). The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions. Hence, word representations in different positions can correlate with each other in a continuous function. The general solution of these functions is extended to complex-valued domain due to richer representations. We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the first work in NLP to link imaginary numbers in complex-valued representations to concrete meanings (i.e., word order).Comment: 15 pages, 3 figures, ICLR 2020 spotlight paper. A typo on Ablation Table was revised thanks to Jingquan Zeng from SCU

Quantification of Asian monsoon variability from 68 ka BP through pollen-based climate reconstruction

The glacial-interglacial variability of precipitation and its driving mechanism in monsoonal regions has long been a subject of debate. However, there are few records of quantitative climate reconstruction dating to the last glacial cycle in areas dominated by the Asian summer monsoon. Here, using a pollen-based quantitative climate reconstruction based on three sites in areas exposed to the Asian summer monsoon, we demonstrate that climate has undergone great variability over the past 68 ka. The differences between the last glacial and the Holocene optimum could have been as much as 35%-51% for precipitation, and 5-7 °C for mean annual temperature. Our findings also reveal regional heterogeneity during the abrupt climate events of Heinrich Event 1 and Younger Dryas, that drove drier conditions in southwestern China dominated by the Indian summer monsoon, and a wetter climate in central eastern China. The pattern of variation in reconstructed precipitation, exhibiting strong glacial-interglacial variability, is broadly consistent with the stalagmite d18O records from Southwest China and South Asia. Our results of reconstruction quantify the sensitivity of the MIS3 precipitation to orbital insolation changes, and highlight the prominent influence of interhemispheric temperature gradients on Asian monsoon variability. Comparison with transient simulations and major climate forcings has shown that the mode of precipitation variability during the transition from the last glacial maximum to the Holocene has been significantly modulated by weak or collapsed Atlantic meridional overturning circulation events in addition to insolation forcing

Superstable Magnetic Nanoparticles in Conjugation with Near-Infrared Dye as a Multimodal Theranostic Platform

Near-infrared (NIR) dyes functionalized magnetic nanoparticles (MNPs) have been widely applied in magnetic resonance imaging (MRI), NIR fluorescence imaging, drug delivery, and magnetic hyperthermia. However, the stability of MNPs and NIR dyes in water is a key problem to be solved for long-term application. In this study, a kind of superstable iron oxide nanoparticles was synthesized by a facile way, which can be used as <i>T</i>₁ and <i>T</i>₂ weighted MRI contrast agent. IR820 was grafted onto the surface of nanoparticles by 6-amino hexanoic acid to form IR820-CSQ-Fe conjugates. Attached IR820 showed increased stability in water at least for three months and an enhanced ability of singlet oxygen production of almost double that of free dyes, which will improve its efficiency for photodynamic therapy. Meanwhile, the multispectral optoacoustic tomography (MSOT) and NIR imaging ability of IR820-CSQ-Fe will greatly increase the accuracy of disease detection. All of these features will broaden the application of this material as a multimodal theranostic platform