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Although there have been numerous studies on the heritage attributes, characteristics, and values of the historic garden as a special category of cultural heritage, the question is why a comprehensive review combining mainstream historic garden conservation with ways of understanding the garden in a landscape context has not been conducted. Landscape is an integrative concept that combines physical features and the diversity of functions with social and ecological processes throughout the scales of time and space. Therefore, this landscape context means applying the landscape approach to explore the organic connection between the scale of evolution and the architectonic elements in relation to each other. To elaborate, instead of viewing the garden as an object in one specific temporal-spatial frame, such an approach focuses on the evolution of the site in order to identify persistent structures and other values. The method used in this study involved paper coding as qualitative analysis combined with bibliometric visualization software. We reviewed 162 studies to explore the interconnections between the historic garden and landscape approach. The result is that there are three correspondences between landscape approaches and different stages of the historic garden’s conservation and development: studies identifying the historic garden’s characteristics using landscape mapping, studies demonstrating historic gardens’ conservation based on landscape planning, and studies exploring the potential of development and reuse through landscape design. Finally, we discuss the research gaps and outline an action framework for the conservation and development of heritage gardens in a landscape context., Landscape Architecture, Spatial Planning and Strategy

Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-aided pathological diagnosis, has rarely been studied due to the limitations in datasets and model training efficacy. In this paper, we propose a new paradigm Subtype-guided Masked Transformer (SGMT) for pathological captioning based on Transformers, which treats a WSI as a sequence of sparse patches and generates an overall caption sentence from the sequence. An accompanying subtype prediction is introduced into SGMT to guide the training process and enhance the captioning accuracy. We also present an Asymmetric Masked Mechansim approach to tackle the large size constraint of pathological image captioning, where the numbers of sequencing patches in SGMT are sampled differently in the training and inferring phases, respectively. Experiments on the PatchGastricADC22 dataset demonstrate that our approach effectively adapts to the task with a transformer-based model and achieves superior performance than traditional RNN-based methods. Our codes are to be made available for further research and development.

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of components, leading significant human effort. This paper proposes TraceDiag, an end-to-end RCA framework that addresses the challenges for large-scale microservice systems. It leverages reinforcement learning to learn a pruning policy for the service dependency graph to automatically eliminates redundant components, thereby significantly improving the RCA efficiency. The learned pruning policy is interpretable and fully adaptive to new RCA instances. With the pruned graph, a causal-based method can be executed with high accuracy and efficiency. The proposed TraceDiag framework is evaluated on real data traces collected from the Microsoft Exchange system, and demonstrates superior performance compared to state-of-the-art RCA approaches. Notably, TraceDiag has been integrated as a critical component in the Microsoft M365 Exchange, resulting in a significant improvement in the system's reliability and a considerable reduction in the human effort required for RCA.

High-quality human transcription is essential for training and improving Automatic Speech Recognition (ASR) models. Recent study~\cite{libricrowd} has found that every 1% worse transcription Word Error Rate (WER) increases approximately 2% ASR WER by using the transcriptions to train ASR models. Transcription errors are inevitable for even highly-trained annotators. However, few studies have explored human transcription correction. Error correction methods for other problems, such as ASR error correction and grammatical error correction, do not perform sufficiently for this problem. Therefore, we propose HTEC for Human Transcription Error Correction. HTEC consists of two stages: Trans-Checker, an error detection model that predicts and masks erroneous words, and Trans-Filler, a sequence-to-sequence generative model that fills masked positions. We propose a holistic list of correction operations, including four novel operations handling deletion errors. We further propose a variant of embeddings that incorporates phoneme information into the input of the transformer. HTEC outperforms other methods by a large margin and surpasses human annotators by 2.2% to 4.5% in WER. Finally, we deployed HTEC to assist human annotators and showed HTEC is particularly effective as a co-pilot, which improves transcription quality by 15.1% without sacrificing transcription velocity., Comment: 13 pages, 4 figures, 11 tables, AMLC 2023

In this thesis, we explore efficient machine learning techniques for calcium imaging based neural decoding in two directions: first, techniques for pruning neural network models to reduce computational complexity and memory cost while retaining high accuracy; second, new techniques for converting graph-based input into low-dimensional vector form, which can be processed more efficiently by conventional neural network models. Neural decoding is an important step in connecting brain activity to behavior --- e.g., to predict movement based on acquired neural signals. Important application areas for neural decoding include brain-machine interfaces and neuromodulation. For application areas such as these, real-time processing of neural signals is important as well as high quality information extraction from the signals. Calcium imaging is a modality that is of increasing interest for studying brain activity. Miniature calcium imaging is a neuroimaging modality that can observe cells in behaving animals with high spatial and temporalresolution, and with the capability to provide chronic imaging. Compared to alternative modalities, calcium imaging has potential to enable improved neural decoding accuracy. However, processing calcium images in real-time is a challenging task as it involves multiple time-consuming stages: neuron detection, motion correction, and signal extraction. Traditional neural decoding methods, such as those based on Wiener and Kalman filters, are fast; however, they are outperformed in terms of accuracy by recently-developed deep neural network (DNN) models. While DNNs provide improved accuracy, they involve high computational complexity, which exacerbates the challenge of real-time processing. Addressing the challenges of high-accuracy, real-time, DNN-based neural decoding is the central objective of this research. As a first step in addressing these challenges, we have developed the NeuroGRS system. NeuroGRS is designed to explore design spaces for

Wei Chen,1 Tao Wei,1 Yinghua Chen,2 Lan Yang,3 Xiaomin Wu1 1Department of Oncology, People’s Hospital of Huadu District, Guangzhou 510800, People’s Republic of China; 2Department of Histology and Embryology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, People’s Republic of China; 3Department of Laser Beauty, People’s Hospital of Huadu District, Guangzhou 510800, People’s Republic of ChinaCorrespondence: Wei ChenDepartment of Oncology, People’s Hospital of Huadu District, Xinhua Road #48, Huadu District, Guangzhou 510800, People’s Republic of ChinaTel +86 20 86832256Email 13926260691@163.comIntroduction: Interleukin-1 receptor-associated kinase 1 (IRAK1) was shown to contribute to a variety of cancer-related processes. However, the function of IRAK1 in hepatocellular carcinoma (HCC) pathogenesis has not been investigated in detail.Methods: IRAK1 expression in HCC was examined by immunohistochemistry, qRT-PCR, and Western blot assays. In addition, Huh7 and Hep3B cells were transfected with IRAK1 siRNAs and/or a NOD-like receptor family pyrindomain containing 3 (NLRP3) plasmid. Western blot, EdU staining, and Transwell assays were performed to determine changes of apoptosis, proliferation, migration, and invasion in HCC cells. Moreover, changes in the expression of proteins involved in the MAPKs/NLRP3/IL-1β pathway were confirmed by Western blotting.Results: IRAK1 was found to be highly upregulated in HCC tissues and cells. Knockdown of IRAK1 signaling prevented the proliferation, invasion, migration, epithelial–mesenchymal transition (EMT) of HCC cells. Mechanistically, we found that activation of the MAPKs/NLRP3/IL-1β pathway could be markedly suppressed by IRAK1 knockdown in HCC cells. Furthermore, our data showed that NLRP3 could partially reverse the reduced aggressive biological behaviors of HCC cells which were caused by RAK1 knoc

RationaleAtrial fibrillation (AF) is the most common arrhythmia, and advanced age is an inevitable and predominant AF risk factor. However, the mechanisms that couple aging and AF propensity remain unclear, making targeted therapeutic interventions unattainable.ObjectiveTo explore the functional role of an important stress response JNK (c-Jun N-terminal kinase) in sarcoplasmic reticulum Ca2+ handling and consequently Ca2+-mediated atrial arrhythmias.Methods and resultsWe used a series of cutting-edge electrophysiological and molecular techniques, exploited the power of transgenic mouse models to detail the molecular mechanism, and verified its clinical applicability in parallel studies on donor human hearts. We discovered that significantly increased activity of the stress response kinase JNK2 (JNK isoform 2) in the aged atria is involved in arrhythmic remodeling. The JNK-driven atrial proarrhythmic mechanism is supported by a pathway linking JNK, CaMKII (Ca2+/calmodulin-dependent kinase II), and sarcoplasmic reticulum Ca2+ release RyR2 (ryanodine receptor) channels. JNK2 activates CaMKII, a critical proarrhythmic molecule in cardiac muscle. In turn, activated CaMKII upregulates diastolic sarcoplasmic reticulum Ca2+ leak mediated by RyR2 channels. This leads to aberrant intracellular Ca2+ waves and enhanced AF propensity. In contrast, this mechanism is absent in young atria. In JNK challenged animal models, this is eliminated by JNK2 ablation or CaMKII inhibition.ConclusionsWe have identified JNK2-driven CaMKII activation as a novel mode of kinase crosstalk and a causal factor in atrial arrhythmic remodeling, making JNK2 a compelling new therapeutic target for AF prevention and treatment.

RationaleAtrial fibrillation (AF) is the most common arrhythmia, and advanced age is an inevitable and predominant AF risk factor. However, the mechanisms that couple aging and AF propensity remain unclear, making targeted therapeutic interventions unattainable.ObjectiveTo explore the functional role of an important stress response JNK (c-Jun N-terminal kinase) in sarcoplasmic reticulum Ca2+ handling and consequently Ca2+-mediated atrial arrhythmias.Methods and resultsWe used a series of cutting-edge electrophysiological and molecular techniques, exploited the power of transgenic mouse models to detail the molecular mechanism, and verified its clinical applicability in parallel studies on donor human hearts. We discovered that significantly increased activity of the stress response kinase JNK2 (JNK isoform 2) in the aged atria is involved in arrhythmic remodeling. The JNK-driven atrial proarrhythmic mechanism is supported by a pathway linking JNK, CaMKII (Ca2+/calmodulin-dependent kinase II), and sarcoplasmic reticulum Ca2+ release RyR2 (ryanodine receptor) channels. JNK2 activates CaMKII, a critical proarrhythmic molecule in cardiac muscle. In turn, activated CaMKII upregulates diastolic sarcoplasmic reticulum Ca2+ leak mediated by RyR2 channels. This leads to aberrant intracellular Ca2+ waves and enhanced AF propensity. In contrast, this mechanism is absent in young atria. In JNK challenged animal models, this is eliminated by JNK2 ablation or CaMKII inhibition.ConclusionsWe have identified JNK2-driven CaMKII activation as a novel mode of kinase crosstalk and a causal factor in atrial arrhythmic remodeling, making JNK2 a compelling new therapeutic target for AF prevention and treatment.

In this paper, a practical wireless transmission scheme is proposed to transmit confidential messages to the desired user securely and precisely by the joint use of multiple techniques including artificial noise (AN) projection, phase alignment (PA)/beamforming, and random subcarrier selection (RSCS) based on OFDM, and directional modulation (DM), namely RSCS-OFDM-DM. This RSCS-OFDM-DM scheme provides an extremely low-complexity structures for the transmitter and desired receiver and makes the secure and precise wireless transmission realizable in practice. For illegal eavesdroppers, the receive power of confidential messages is so weak that their receivers cannot intercept these confidential messages successfully once it is corrupted by AN. In such a scheme, the design of phase alignment/beamforming vector and AN projection matrix depend intimately on the desired direction angle and distance. It is particularly noted that the use of RSCS leads to a significant outcome that the receive power of confidential messages mainly concentrates on the small neighboring region around the desired receiver and only small fraction of its power leaks out to the remaining large broad regions. This concept is called secure precise transmission. The probability density function of real-time receive signal-to-interference-and-noise ratio (SINR) is derived. Also, the average SINR and its tight upper bound are attained. The approximate closed-form expression for average secrecy rate is derived by analyzing the first-null positions of SINR and clarifying the wiretap region., Comment: 14 pages, 7 figures

This chapter presents: (i) a layered peer-to-peer Cloud provisioning architecture; (ii) a summary of the current state-of-the-art in Cloud provisioning with particular emphasis on service discovery and load-balancing; (iii) a classification of the existing peer-to-peer network management model with focus on extending the DHTs for indexing and managing complex provisioning information; and (iv) the design and implementation of novel, extensible software fabric (Cloud peer) that combines public/private clouds, overlay networking and structured peer-to-peer indexing techniques for supporting scalable and self-managing service discovery and load-balancing in Cloud computing environments. Finally, an experimental evaluation is presented that demonstrates the feasibility of building next generation Cloud provisioning systems based on peer-to-peer network management and information dissemination models. The experimental test-bed has been deployed on a public cloud computing platform, Amazon EC2, which demonstrates the effectiveness of the proposed peer-to-peer Cloud provisioning software fabric.

Summary: With the strikingly fast development of industrial applications and research projects, control systems have become more and more complex than ever. Intelligent control techniques, featuring their being more robust and their availability when system mathematical models are unknown, have proven to be one of the most attractive and highlighted areas in the automatic control arena. This thesis concentrates first on the design of a laser tracking system. A standard design procedure of Fuzzy Logic Controllers (FLCs) is followed, which is then realized in a PC-based environment in the design. An essential issue in this thesis study is the auto tuning of the Fuzzy Logic Controller. An efficient tuning method, mu-law functions, which can adjust both the shape and scaling gain of fuzzy controller's decision table is adopted. Also a search process called Downhill Simplex Search is chosen. Combining these two methods, a Simplex-mu-law auto-tuning algorithm that fits our application is applied to tune the FLC for the laser tracking system. Another issue covered in this research is to modify the Fuzzy Logic Controller structure by changing the distribution of the membership functions. Based on the analysis of the real time error histogram of the system, a novel method is proposed in the thesis for the modification of the membership functions To assess the effectiveness of the methods proposed in this thesis, a prototype laser tracking system is constructed at the FAU Robotics Center. The control strategy proposed in this thesis is tested extensively by simulations and experimentations on the prototype system., College of Engineering and Computer Science, Collection: FAU Electronic Theses and Dissertations Collection, Thesis (M.S.)--Florida Atlantic University, 1999.