The effects of local voids and imperfections of surrounding rock on the performance of existing tunnel lining

Local voids and imperfections may exist around the tunnel due to reasons such as inadequate back infill behind the lining, insufficient local lining thickness, ground water erosion, and other imperfect construction related activities. Such local voids and imperfections generally will lead to local contact loss and discontinuity in the ground-lining interaction. This paper evaluates the effect of local voids and imperfections developing around the tunnel vault area on the mechanical performance of tunnel lining. Based on field investigation results, a series of voids and imperfections with different geometries are defined to reflect cases resulting from different causes. Numerical parametric analyses were performed to investigate how those voids and imperfections influence the internal force and the safety factor of the lining, and the reinforced concrete lining were modelled with the smeared crack model to examine the development of cracking directions and patterns. Furthermore, the numerical approach was verified by comparing with field investigations and measurements. This study aims to investigate the most unsafe situation due to local voids and imperfections around the tunnel, and the modelled cracking feature shows a way to preliminary evaluate the possible local voids and imperfections behind tunnel lining based on field observation

Automatic Structured Menu Extraction from Menu Photographs

Restaurant menu images can be utilized to automatically obtain structured data about dish names, prices, etc. However, the raw optical character recognition (OCR) output suffers from low quality and OCR techniques do not have sufficient ability to adapt to the diversity in language and design of restaurant menus. A language model can be used together with OCR to identify dish names and other content through a named entity recognition (NER) process. However, this is not scalable due to the requirement of a large, labeled dataset across languages and countries. This disclosure describes the use of a multimodal large language model (LLM) to automatically generate digital structured menus from restaurant menu photographs. The use of a multimodal large language model enables automatic creation of structured digital menus that include price, description, ingredients, etc. without the requirement of a large amount of labeled data and can also overcome difficulties associated with low quality photographs. The capabilities of multimodal LLMs are leveraged by formulating the task of menu understanding from the user-provided photos as a multimodal information extraction or a visual question answering task which fits naturally with the framework of multimodal pretrained large models

Timing Recovery for Point-to-Multi-Point Coherent Passive Optical Networks

We propose a timing recovery for point-to-multi-point coherent passive optical networks. The results show that the proposed algorithm has low complexity and better robustness against the residual chromatic dispersion.Comment: The artical have been submitted to SPPCom conferenc

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements

Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep learning models or training models on large datasets), vanilla Bayesian optimization (BO) is typically computationally infeasible. To alleviate this issue, Hyperband (HB) utilizes the early stopping mechanism to speed up configuration evaluations by terminating those badly-performing configurations in advance. This leads to two kinds of quality measurements: (1) many low-fidelity measurements for configurations that get early-stopped, and (2) few high-fidelity measurements for configurations that are evaluated without being early stopped. The state-of-the-art HB-style method, BOHB, aims to combine the benefits of both BO and HB. Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only. However, the scarcity of high-fidelity measurements greatly hampers the efficiency of BO to guide the configuration search. In this paper, we present MFES-HB, an efficient Hyperband method that is capable of utilizing both the high-fidelity and low-fidelity measurements to accelerate the convergence of HPO tasks. Designing MFES-HB is not trivial as the low-fidelity measurements can be biased yet informative to guide the configuration search. Thus we propose to build a Multi- Fidelity Ensemble Surrogate (MFES) based on the generalized Product of Experts framework, which can integrate useful information from multi-fidelity measurements effectively. The empirical studies on the real-world AutoML tasks demonstrate that MFES-HB can achieve 3.3-8.9x speedups over the state-of-the-art approach - BOHB

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

SRA Inhibition Improves Antitumor Potency of Antigen-Targeted Chaperone Vaccine

We Have Previously Demonstrated that Scavenger Receptor a (SRA) Acts as an Immunosuppressive Regulator of Dendritic Cell (DC) Function in Activating Antitumor T Cells. Here We Investigate the Potential of Inhibiting SRA Activity to Enhance DC-Targeted Chaperone Vaccines Including One that Was Recently Evaluated in Melanoma Patients. We Show that Short Hairpin RNA-Mediated SRA Silencing Significantly Enhances the Immunogenicity of DCs that Have Captured Chaperone Vaccines Designed to Target Melanoma (I.e., Hsp110-Gp100) and Breast Cancer (I.e., Hsp110-HER/Neu-ICD). SRA Downregulation Results in Heightened Activation of Antigen-Specific T Cells and Increased CD8+ T Cell-Dependent Tumor Inhibition. Additionally, Small Interfering RNA (SiRNA) Complexed with the Biodegradable, Biocompatible Chitosan as a Carrier Can Efficiently Reduce SRA Expression on CD11c+ DCs in Vitro and in Vivo. Our Proof-Of-Concept Study Shows that Direct Administration of the Chitosan-SiRNA Complex to Mice Promotes Chaperone Vaccine-Elicited Cytotoxic T Lymphocyte (CTL) Response, Culminating in Improved Eradication of Experimental Melanoma Metastases. Targeting SRA with This Chitosan-SiRNA Regimen Combined with the Chaperone Vaccine Also Leads to Reprogramming of the Tumor Environment, Indicated by Elevation of the Cytokine Genes (I.e., Ifng, Il12) Known to Skew Th1-Like Cellular Immunity and Increased Tumor Infiltration by IFN-Γ+CD8+ CTLs as Well as IL-12+CD11c+ DCs. Given the Promising Antitumor Activity and Safety Profile of Chaperone Vaccine in Cancer Patients, Further Optimization of the Chitosan-SiRNA Formulation to Potentially Broaden the Immunotherapeutic Benefits of Chaperone Vaccine is Warranted

Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems

Ensuring the reliability of cloud systems is critical for both cloud vendors and customers. Cloud systems often rely on virtualization techniques to create instances of hardware resources, such as virtual machines. However, virtualization hinders the observability of cloud systems, making it challenging to diagnose platform-level issues. To improve system observability, we propose to infer functional clusters of instances, i.e., groups of instances having similar functionalities. We first conduct a pilot study on a large-scale cloud system, i.e., Huawei Cloud, demonstrating that instances having similar functionalities share similar communication and resource usage patterns. Motivated by these findings, we formulate the identification of functional clusters as a clustering problem and propose a non-intrusive solution called Prism. Prism adopts a coarse-to-fine clustering strategy. It first partitions instances into coarse-grained chunks based on communication patterns. Within each chunk, Prism further groups instances with similar resource usage patterns to produce fine-grained functional clusters. Such a design reduces noises in the data and allows Prism to process massive instances efficiently. We evaluate Prism on two datasets collected from the real-world production environment of Huawei Cloud. Our experiments show that Prism achieves a v-measure of ~0.95, surpassing existing state-of-the-art solutions. Additionally, we illustrate the integration of Prism within monitoring systems for enhanced cloud reliability through two real-world use cases.Comment: The paper was accepted by the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Image Sharpness-Based System Design for Touchless Palmprint Recognition

Currently, many palmprint acquisition devices have been proposed, but how to design the systems are seldom studied, such as how to choose the imaging sensor, the lens, and the working distance. This chapter aims to find the relationship between image sharpness and recognition performance and then utilize this information to direct the system design. In this chapter, firstly, we introduce the development of recent palmprint acquisition systems and abstract their basic frameworks to propose the key problems needed to be solved when designing new systems. Secondly, the relationship between the palm distance in the field of view (FOV) and image pixels per inch (PPI) is studied based on the imaging model. Suggestions about how to select the imaging sensor and camera lens are provided. Thirdly, image blur and depth of focus (DOF) are taken into consideration; the recognition performances of the image layers in the Gaussian scale space are analyzed. Based on this, an image sharpness range is determined for optimal imaging. The experiment results are obtained using different algorithms on various touchless palmprint databases collected using different kinds of devices. They could be references for new system design

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

The development and continuity of consistent long-term data records from similar overlapping satellite observations is critical for global monitoring and environmental change assessments. We developed an empirical approach for inter-calibration of satellite microwave brightness temperature (Tb) records over land from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Microwave Scanning Radiometer 2 (AMSR2) using overlapping Tb observations from the Microwave Radiation Imager (MWRI). Double Differencing (DD) calculations revealed significant AMSR2 and MWRI biases relative to AMSR-E. Pixel-wise linear relationships were established from overlapping Tb records and used for calibrating MWRI and AMSR2 records to the AMSR-E baseline. The integrated multi-sensor Tb record was largely consistent over the major global vegetation and climate zones; sensor biases were generally well calibrated, though residual Tb differences inherent to different sensor configurations were still present. Daily surface air temperature estimates from the calibrated AMSR2 Tb inputs also showed favorable accuracy against independent measurements from 142 global weather stations (R2 ≥ 0.75, RMSE ≤ 3.64 °C), but with slightly lower accuracy than the AMSR-E baseline (R2 ≥ 0.78, RMSE ≤ 3.46 °C). The proposed method is promising for generating consistent, uninterrupted global land parameter records spanning the AMSR-E and continuing AMSR2 missions