Significant wave height forecasting based on the hybrid EMD-SVM method

1957-1962Prediction of significant wave height (SWH) is considered an effective method in marine engineering and prevention of marine disasters. Support vector machine (SVM) model has limitations in processing nonlinear and non-stationary SWH time series. Fortunately, empirical mode decomposition (EMD) can effectively deal with the complicated series. So, the SWH prediction method based on EMD and SVM is proposed by combining the advantages of both methods. A statistical analysis was carried out to compare the results of two models i.e., between the hybrid EMD-SVM and SVM. In addition, two models are used for forecasting SWH with 3, 6, 12 and 24 hours lead times, respectively. A high R value of different prediction times for the hybrid model. Results indicate that SWH prediction of the hybrid EMD-SVM model is superior to the SVM model

Comparison of gemcitabine/carboplat in versus paclitaxel/cisplatin for the management of non small cell lung cancer

Purpose: To determine the comparative efficacy and toxicity of gemcitabine/carboplatin and paclitaxel/cisplatin in patients with completely resected stage IIa - IIIa non-small cell lung cancer (NSCLC). Methods: Sixty eligible NSCLC patients treated in Funan County People's Hospital were enrolled and assigned to two groups by randomization (n = 30 each). One group (CG group) received the combination of gemcitabine and carboplatin, while the second group (CP group) received a combination of cisplatin and paclitaxel. Efficacy was assessed based on 2-year progression-free survival, while adverse reactions were recorded to assess the toxicity of the chemotherapy treatments. Results: No marked difference was found in the 2-year relapse-free survival in the two groups with similar clinical baseline characteristics after follow-up (60 % in CG group vs. 56.67 % in CP group, p = 0.826). Specifically, no significant difference was found between the two groups with regard to incidence of local metastases, distant metastases, or brain tissue metastases within 2 years, and there were no treatment-related deaths. CG group was more likely to develop leukopenia (93.33 % vs. 63.33 % for CP group, p = 0.04), but no significant difference was observed for other adverse effects such as anemia, vomiting, and nausea. Conclusion: This study shows that adjuvant treatment using carboplatin and gemcitabine produces the same therapeutic efficacy as cisplatin and paclitaxel, but exhibits higher toxicity levels than the latter

MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation

Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures i.e., Vestibular Schwannoma (VS) and Cochlea on high-resolution T2 images. First, a segmentation-enhanced contrastive unpaired image translation module is designed for image-level domain adaptation from source T1 to target T2. Next, multi-scale deep supervision and consistency regularization are introduced to a mean teacher network for self-ensemble learning to further close the domain gap. Furthermore, self-training and intensity augmentation techniques are utilized to mitigate label scarcity and boost cross-modality segmentation performance. Our method demonstrates promising segmentation performance with a mean Dice score of 83.8% and 81.4% and an average asymmetric surface distance (ASSD) of 0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.Comment: Accepted by BrainLes MICCAI proceedings (5th solution for MICCAI 2022 Cross-Modality Domain Adaptation (crossMoDA) Challenge

InstructCoder: Empowering Language Models for Code Editing

Code editing encompasses a variety of pragmatic tasks that developers deal with daily. Despite its relevance and practical usefulness, automatic code editing remains an underexplored area in the evolution of deep learning models, partly due to data scarcity. In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring. To facilitate this, we introduce InstructCoder, the first dataset designed to adapt LLMs for general-purpose code editing, containing highdiversity code-editing tasks. It consists of over 114,000 instruction-input-output triplets and covers multiple distinct code editing scenarios. The dataset is systematically expanded through an iterative process that commences with code editing data sourced from GitHub commits as seed tasks. Seed and generated tasks are used subsequently to prompt ChatGPT for more task data. Our experiments demonstrate that open-source LLMs fine-tuned on InstructCoder can edit code correctly based on users' instructions most of the time, exhibiting unprecedented code-editing performance levels. Such results suggest that proficient instruction-finetuning can lead to significant amelioration in code editing abilities. The dataset and the source code are available at https://github.com/qishenghu/CodeInstruct

Giant Enhancement of Magnonic Frequency Combs by Exceptional Points

With their incomparable time-frequency accuracy, frequency combs have significantly advanced precision spectroscopy, ultra-sensitive detection, and atomic clocks. Traditional methods to create photonic, phononic, and magnonic frequency combs hinge on material nonlinearities which are often weak, necessitating high power densities to surpass their initiation thresholds, which subsequently limits their applications. Here, we introduce a novel nonlinear process to efficiently generate magnonic frequency combs (MFCs) by exploiting exceptional points (EPs) in a coupled system comprising a pump-induced magnon mode and a Kittel mode. Even without any cavity, our method greatly improves the efficiency of nonlinear frequency conversion and achieves optimal MFCs at low pump power. Additionally, our novel nonlinear process enables excellent tunability of EPs using the polarization and power of the pump, simplifying MFC generation and manipulation. Our work establishes a synergistic relationship between non-Hermitian physics and MFCs, which is advantages for coherent/quantum information processing and ultra-sensitive detection.Comment: 7 pages, 4 figure

KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections

Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions.Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training.Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification method

Variation of Helicoverpa armigera symbionts across developmental stages and geographic locations

Cotton bollworm (Helicoverpa armigera) poses a global problem, causing substantial economic and ecological losses. Endosymbionts in insects play crucial roles in multiple insect biological processes. However, the interactions between H. armigera and its symbionts have not been well characterized to date. We investigated the symbionts of H. armigera in the whole life cycle from different geographical locations. In the whole life cycle of H. armigera, Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria were the dominant bacteria at the phylum level, while Enterococcus, Enterobacter, Glutamicibacter, and Bacillus were the four dominant bacteria at the genus level. Furthermore, high similarity in symbiotic bacterial community was observed in different stages of H. armigera, which were dominated by Enterococcus and Enterobacter. In fields, the dominant bacteria were Proteobacteria and Bacteroidetes, whereas, in the laboratory, the dominant bacteria were Proteobacteria. At the genus level, the dominant bacteria in cotton bollworm eggs of wild populations were Enterobacter, Morganella, Lactococcus, Asaia, Apibacter, and Enterococcus, and the subdominant bacteria were Bartonella, Pseudomonas, and Orbus. Moreover, the symbionts varied with geographical locations, and the closer the geographical distance, the more similar the microbial composition. Taken together, our study identifies and compares the symbiont variation along with geographical gradients and host development dynamic and reveals the high flexibility of microbiome communities in H. armigera, which probably benefits for the successful survival in a complicated changing environment

Human Performance Modeling and Rendering via Neural Animated Mesh

We have recently seen tremendous progress in the neural advances for photo-real human modeling and rendering. However, it's still challenging to integrate them into an existing mesh-based pipeline for downstream applications. In this paper, we present a comprehensive neural approach for high-quality reconstruction, compression, and rendering of human performances from dense multi-view videos. Our core intuition is to bridge the traditional animated mesh workflow with a new class of highly efficient neural techniques. We first introduce a neural surface reconstructor for high-quality surface generation in minutes. It marries the implicit volumetric rendering of the truncated signed distance field (TSDF) with multi-resolution hash encoding. We further propose a hybrid neural tracker to generate animated meshes, which combines explicit non-rigid tracking with implicit dynamic deformation in a self-supervised framework. The former provides the coarse warping back into the canonical space, while the latter implicit one further predicts the displacements using the 4D hash encoding as in our reconstructor. Then, we discuss the rendering schemes using the obtained animated meshes, ranging from dynamic texturing to lumigraph rendering under various bandwidth settings. To strike an intricate balance between quality and bandwidth, we propose a hierarchical solution by first rendering 6 virtual views covering the performer and then conducting occlusion-aware neural texture blending. We demonstrate the efficacy of our approach in a variety of mesh-based applications and photo-realistic free-view experiences on various platforms, i.e., inserting virtual human performances into real environments through mobile AR or immersively watching talent shows with VR headsets.Comment: 18 pages, 17 figure

A Prospective Randomized Study of the Radiotherapy Volume for Limited-stage Small Cell Lung Cancer: A Preliminary Report

Background and objective Controversies exists with regard to target volumes as far as thoracic radiotherapy (TRT) is concerned in the multimodality treatment for limited-stage small cell lung cancer (LSCLC). The aim of this study is to prospectively compare the local control rate, toxicity profiles, and overall survival (OS) between patients received different target volumes irradiation after induction chemotherapy. Methods LSCLC patients received 2 cycles of etoposide and cisplatin (EP) induction chemotherapy and were randomly assigned to receive TRT to either the post- or pre-chemotherapy tumor extent (GTV-T) as study arm and control arm, CTV-N included the positive nodal drainage area for both arms. One to 2 weeks after induction chemotherapy, 45 Gy/30 Fx/19 d TRT was administered concurrently with the third cycle of EP regimen. After that, additional 3 cycles of EP consolidation were administered. Prophylactic cranial irradiation (PCI) was administered to patients with a complete response. Results Thirty-seven and 40 patients were randomly assigned to study arm and control arm. The local recurrence rates were 32.4% and 28.2% respectively (P=0.80); the isolated nodal failure (INF) rate were 3.0% and 2.6% respectively (P=0.91); all INF sites were in the ipsilateral supraclavicular fossa. Medastinal N3 disease was the risk factor for INF (P=0.02, OR=14.13, 95%CI: 1.47-136.13). During radiotherapy, grade I, II weight loss was observed in 29.4%, 5.9% and 56.4%, 7.7% patients respectively (P=0.04). Grade 0-I and II-III late pulmonary injury was developed in 97.1%, 2.9% and 86.4%, 15.4% patients respectively (P=0.07). Median survival time was 22.1 months and 26.9 months respectively. The 1 to 3-year OS were 77.9%, 44.4%, 37.3% and 75.8%, 56.3%, 41.7% respectively (P=0.79). Conclusion The preliminary results of this study indicate that irradiant the post-chemotherapy tumor extent (GTV-T) and positive nodal drainage area did not decrease local control and overall survival while radiation toxicity was reduced. But the current sample size has not met designed requirements, and further investigation is warranted before final conclusions could be drawn