A Compact and Low Profile Loop Antenna with Six Resonant Modes for LTE Smart phone

In this paper, a novel six-mode loop antenna covering 660-1100 MHz, 1710-3020 MHz, 3370-3900 MHz, and 5150-5850 MHz has been proposed for the application of Long Term Evolution (LTE) including the coming LTE in unlicensed spectrum (LTE-U) and LTE-Licensed Assisted Access (LTE-LAA). Loop antennas offer better user experience than conventional Planar Inverted-F Antennas (PIFA), Inverted-F Antennas (IFA), and monopole antennas because of their unique balanced modes (1?, 2?, …). However, the bandwidth of loop antennas is usually narrower than that of PIFA/IFA and monopole antennas due to these balanced modes. To overcome this problem, a novel monopole/dipole parasitic element, which operates at an unbalanced monopole-like 0.25? mode and a balanced dipole-like 0.5? mode, is first proposed for loop antennas to cover more frequency bands. Benefiting from the balanced mode, the proposed parasitic element is promising to provide better user experience than conventional parasitic elements. To the authors’ knowledge, the balanced mode for a parasitic element is reported for the first time. The proposed antenna is able to provide excellent user experience while solving the problem of limited bandwidth in loop antennas. To validate the concept, one prototype antenna with the size of 75×10×5 mm3 is designed, fabricated and measured. Both simulations and experimental results are presented and discussed. Good performance is achieved

FoxM1B transcriptionally regulates vascular endothelial growth factor expression and promotes the angiogenesis and growth of glioma cells.

We previously found that FoxM1B is overexpressed in human glioblastomas and that forced FoxM1B expression in anaplastic astrocytoma cells leads to the formation of highly angiogenic glioblastoma in nude mice. However, the molecular mechanisms by which FoxM1B enhances glioma angiogenesis are currently unknown. In this study, we found that vascular endothelial growth factor (VEGF) is a direct transcriptional target of FoxM1B. FoxM1B overexpression increased VEGF expression, whereas blockade of FoxM1 expression suppressed VEGF expression in glioma cells. Transfection of FoxM1 into glioma cells directly activated the VEGF promoter, and inhibition of FoxM1 expression by FoxM1 siRNA suppressed VEGF promoter activation. We identified two FoxM1-binding sites in the VEGF promoter that specifically bound to the FoxM1 protein. Mutation of these FoxM1-binding sites significantly attenuated VEGF promoter activity. Furthermore, FoxM1 overexpression increased and inhibition of FoxM1 expression suppressed the angiogenic ability of glioma cells. Finally, an immunohistochemical analysis of 59 human glioblastoma specimens also showed a significant correlation between FoxM1 overexpression and elevated VEGF expression. Our findings provide both clinical and mechanistic evidence that FoxM1 contributes to glioma progression by enhancing VEGF gene transcription and thus tumor angiogenesis

All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison

Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which the media shape opinion is via the strategic inclusion or omission of partisan events that may support one side or the other. We develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. Our experiments first validate the existence of partisan event selection, and then show that article alignment and cross-document comparison detect partisan events and article ideology better than competitive baselines. Our results reveal the high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship. Our codebase and dataset are available at https://github.com/launchnlp/ATC.Comment: EMNLP'23 Main Conferenc

HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling

In this work, we tackle the challenging problem of learning-based single-view 3D hair modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using synthetic data to provide prior knowledge for real domain becomes a leading solution. This unfortunately introduces the challenge of domain gap. Due to the inherent difficulty of realistic hair rendering, existing methods typically use orientation maps instead of hair images as input to bridge the gap. We firmly think an intermediate representation is essential, but we argue that orientation map using the dominant filtering-based methods is sensitive to uncertain noise and far from a competent representation. Thus, we first raise this issue up and propose a novel intermediate representation, termed as HairStep, which consists of a strand map and a depth map. It is found that HairStep not only provides sufficient information for accurate 3D hair modeling, but also is feasible to be inferred from real images. Specifically, we collect a dataset of 1,250 portrait images with two types of annotations. A learning framework is further designed to transfer real images to the strand map and depth map. It is noted that, an extra bonus of our new dataset is the first quantitative metric for 3D hair modeling. Our experiments show that HairStep narrows the domain gap between synthetic and real and achieves state-of-the-art performance on single-view 3D hair reconstruction.Comment: CVPR 2023 Highlight, project page: https://paulyzheng.github.io/research/hairstep

DSCA-PSPNet: Dynamic spatial-channel attention pyramid scene parsing network for sugarcane field segmentation in satellite imagery

Sugarcane plays a vital role in many global economies, and its efficient cultivation is critical for sustainable development. A central challenge in sugarcane yield prediction and cultivation management is the precise segmentation of sugarcane fields from satellite imagery. This task is complicated by numerous factors, including varying environmental conditions, scale variability, and spectral similarities between crops and non-crop elements. To address these segmentation challenges, we introduce DSCA-PSPNet, a novel deep learning model with a unique architecture that combines a modified ResNet34 backbone, the Pyramid Scene Parsing Network (PSPNet), and newly proposed Dynamic Squeeze-and-Excitation Context (D-scSE) blocks. Our model effectively adapts to discern the importance of both spatial and channel-wise information, providing superior feature representation for sugarcane fields. We have also created a comprehensive high-resolution satellite imagery dataset from Guangxi’s Fusui County, captured on December 17, 2017, which encompasses a broad spectrum of sugarcane field characteristics and environmental conditions. In comparative studies, DSCA-PSPNet outperforms other state-of-the-art models, achieving an Intersection over Union (IoU) of 87.58%, an accuracy of 92.34%, a precision of 93.80%, a recall of 93.21%, and an F1-Score of 92.38%. Application tests on an RTX 3090 GPU, with input image resolutions of 512 × 512, yielded a prediction time of 4.57ms, a parameter size of 22.57MB, GFLOPs of 11.41, and a memory size of 84.47MB. An ablation study emphasized the vital role of the D-scSE module in enhancing DSCA-PSPNet’s performance. Our contributions in dataset generation and model development open new avenues for tackling the complexities of sugarcane field segmentation, thus contributing to advances in precision agriculture. The source code and dataset will be available on the GitHub repository https://github.com/JulioYuan/DSCA-PSPNet/tree/main

Characterization of physicochemical properties of ivy nanoparticles for cosmetic application

Background Naturally occurring nanoparticles isolated from English ivy (Hedera helix) have previously been proposed as an alternative to metallic nanoparticles as sunscreen fillers due to their effective UV extinction property, low toxicity and potential biodegradability. Methods This study focused on analyzing the physicochemical properties of the ivy nanoparticles, specifically, those parameters which are crucial for use as sunscreen fillers, such as pH, temperature, and UV irradiation. The visual transparency and cytotoxicity of ivy nanoparticles were also investigated comparing them with other metal oxide nanoparticles. Results Results from this study demonstrated that, after treatment at 100°C, there was a clear increase in the UV extinction spectra of the ivy nanoparticles caused by the partial decomposition. In addition, the UVA extinction spectra of the ivy nanoparticles gradually reduced slightly with the decrease of pH values in solvents. Prolonged UV irradiation indicated that the influence of UV light on the stability of the ivy nanoparticle was limited and time-independent. Compared to TiO2 and ZnO nanoparticles, ivy nanoparticles showed better visual transparency. Methylthiazol tetrazolium assay demonstrated that ivy nanoparticles exhibited lower cytotoxicity than the other two types of nanoparticles. Results also suggested that protein played an important role in modulating the three-dimensional structure of the ivy nanoparticles. Conclusions Based on the results from this study it can be concluded that the ivy nanoparticles are able to maintain their UV protective capability at wide range of temperature and pH values, further demonstrating their potential as an alternative to replace currently available metal oxide nanoparticles in sunscreen applications

Characterization of physicochemical properties of ivy nanoparticles for cosmetic application

Background Naturally occurring nanoparticles isolated from English ivy (Hedera helix) have previously been proposed as an alternative to metallic nanoparticles as sunscreen fillers due to their effective UV extinction property, low toxicity and potential biodegradability. Methods This study focused on analyzing the physicochemical properties of the ivy nanoparticles, specifically, those parameters which are crucial for use as sunscreen fillers, such as pH, temperature, and UV irradiation. The visual transparency and cytotoxicity of ivy nanoparticles were also investigated comparing them with other metal oxide nanoparticles. Results Results from this study demonstrated that, after treatment at 100°C, there was a clear increase in the UV extinction spectra of the ivy nanoparticles caused by the partial decomposition. In addition, the UVA extinction spectra of the ivy nanoparticles gradually reduced slightly with the decrease of pH values in solvents. Prolonged UV irradiation indicated that the influence of UV light on the stability of the ivy nanoparticle was limited and time-independent. Compared to TiO2 and ZnO nanoparticles, ivy nanoparticles showed better visual transparency. Methylthiazol tetrazolium assay demonstrated that ivy nanoparticles exhibited lower cytotoxicity than the other two types of nanoparticles. Results also suggested that protein played an important role in modulating the three-dimensional structure of the ivy nanoparticles. Conclusions Based on the results from this study it can be concluded that the ivy nanoparticles are able to maintain their UV protective capability at wide range of temperature and pH values, further demonstrating their potential as an alternative to replace currently available metal oxide nanoparticles in sunscreen applications. doi:10.1186/1477-3155-11-

RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural Network

Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph snapshot by correctly and incrementally refining the output features of the previous graph snapshot and also enable regular accesses of vertices\u27 input features. Through traversing the graph on the fly, RACE identifies the vertices that are not affected by graph updates between successive snapshots to reuse these vertices\u27 states (i.e., their output features) of the previous snapshot for the processing of the latest snapshot. The vertices affected by graph updates are also tracked to incrementally recompute their new states using their neighbors\u27 input features of the latest snapshot for correctness. In this way, the processing and accessing of many graph data that are not affected by graph updates can be correctly eliminated, enabling smaller redundant computation and memory access overhead. Besides, the input features, which are accessed more frequently, are dynamically identified according to graph topology and are preferentially resident in the on-chip memory for less off-chip communications. Experimental results show that RACE achieves on average 1139× and 84.7× speedups for DGNN inference, with average 2242× and 234.2× energy savings, in comparison with the state-of-the-art software DGNN running on Intel Xeon CPU and NVIDIA A100 GPU, respectively. Moreover, for DGNN inference, RACE obtains on average 13.1×, 11.7×, 10.4×, and 7.9× speedup and 14.8×, 12.9×, 11.5×, and 8.9× energy savings over the state-of-the-art Graph Neural Network accelerators, i.e., AWB-GCN, GCNAX, ReGNN, and I-GCN, respectively