DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Targeting to understand the underlying explainable factors behind observations and modeling the conditional generation process on these factors, we connect disentangled representation learning to Diffusion Probabilistic Models (DPMs) to take advantage of the remarkable modeling ability of DPMs. We propose a new task, disentanglement of (DPMs): given a pre-trained DPM, without any annotations of the factors, the task is to automatically discover the inherent factors behind the observations and disentangle the gradient fields of DPM into sub-gradient fields, each conditioned on the representation of each discovered factor. With disentangled DPMs, those inherent factors can be automatically discovered, explicitly represented, and clearly injected into the diffusion process via the sub-gradient fields. To tackle this task, we devise an unsupervised approach named DisDiff, achieving disentangled representation learning in the framework of DPMs. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of DisDiff.Comment: Accepted by NeurIPS 202

An adaptive reclosing scheme based on phase characteristics for MMC-HVDC systems

To improve the reliability of power supply, reclosing schemes are required after transient faults, which commonly occur in overhead line based high voltage DC (HVDC) systems. However, in the event of permanent faults, the auto-reclosing scheme may cause a severe strike. To avoid the severe impacts caused by permanent faults, the fault type should be discriminated before activating the reclosing scheme. Therefore, an adaptive reclosing scheme based on phase characteristics is proposed in this paper. Firstly, the modulation of a periodic voltage by actively controlling the hybrid DC circuit breaker (DCCB) is introduced. Then, a cascaded π equivalent model and its decoupling algorithm are presented to analyze the frequency-domain characteristics of the measured impedance of the coupled overhead lines. From the frequency-domain characteristics, the frequency of the periodic detecting voltage is determined to analyze the phase features of the measured impedance at primary frequency. The permanent or transient faults can thus be accurately identified by using these different phase characteristics, with negligible influence on the healthy lines. In addition, the proposed scheme is robust to various fault resistances, leading to improved reliability. The effectiveness of the proposed scheme is verified in PSCAD/EMTDC

An integrated control and protection scheme based on FBSM-MMC active current limiting strategy for DC distribution network

DC faults can easily lead to overcurrent in DC distribution networks; these faults pose serious threats to the safe operation of the system. The blocking of modular multilevel converters based on the full-bridge sub-modules (FBSM-MMC) is mostly utilized to cut off the fault current. However, the blocking causes short-term blackouts in the entire DC distribution network and there are presently no effective solutions to address this problem. In this study, an integrated control and protection scheme based on the FBSM-MMC active current limiting strategy is proposed. The project includes three stages: first, MMC active current limiting strategy is used to limit the output current of the converter to about 1.2 p.u. after the occurrence of the fault (Stage 1); next, faulty lines are identified based on the asynchronous zero-crossing features of the DC currents of the two ends of the line (Stage 2); then, a fault isolation scheme based on the cooperation of converters, DC circuit breakers, and high-speed switches is proposed to isolate the faulty line (Stage 3). The distribution network can restart quickly via control of the converters. Finally, the simulation of a four-terminal flexible DC distribution network in PSCAD/EMTDC demonstrates the effectiveness of the proposed integrated scheme

Modelling of the DC Fault Response of Modular Multilevel Converters with Fault Control Capability Considering Converter Saturation

Modular multilevel converters (MMC) with fault control capability, such as full-bridge and hybrid MMC, can maintain the operation of DC system by actively limiting the fault current. However, converters with these capabilities present complex fault characteristics, complicating the DC protection design. Although fault features can be analyzed by simulations, short-circuit current calculations are desirable as they give analytical expressions, which simplify the analysis of factors affecting the fault response and are suitable for large-scale applications. However, calculations become challenging when MMC arm voltage reaches the limitation sum sub-module capacitor voltage, a condition known as converter saturation, which is hard to represent with transfer functions. To clarify the fault characteristics during converter saturation, a DC fault current calculation method for converters with fault control capability is proposed. The features under fault control are analyzed and divided into different stages. Then the basic analytical equation based on control and fault circuit is deduced and the saturation impact is considered by adopting a short-time numerical calculation. The proposed method is validated in Simulink and a statistical similarity check shows 91% similarity between the proposed model and simulation results, versus 23% similarity when using a transfer function model that does not consider saturation effects.</p

Spectral-based mesh segmentation

In design and manufacturing, mesh segmentation is required for FACE construction in boundary representation (BRep), which in turn is central for featurebased design, machining, parametric CAD and reverse engineering, among others -- Although mesh segmentation is dictated by geometry and topology, this article focuses on the topological aspect (graph spectrum), as we consider that this tool has not been fully exploited -- We preprocess the mesh to obtain a edgelength homogeneous triangle set and its Graph Laplacian is calculated -- We then produce a monotonically increasing permutation of the Fiedler vector (2nd eigenvector of Graph Laplacian) for encoding the connectivity among part feature submeshes -- Within the mutated vector, discontinuities larger than a threshold (interactively set by a human) determine the partition of the original mesh -- We present tests of our method on large complex meshes, which show results which mostly adjust to BRep FACE partition -- The achieved segmentations properly locate most manufacturing features, although it requires human interaction to avoid over segmentation -- Future work includes an iterative application of this algorithm to progressively sever features of the mesh left from previous submesh removal

Enhanced Cascade Prostate Cancer Classifier in mp-MRI Utilizing Recall Feedback Adaptive Loss and Prior Knowledge-Based Feature Extraction

Prostate cancer is the second most common cancer in males worldwide, and mpMRI is commonly used for diagnosis. However, interpreting mpMRI is challenging and requires expertise from radiologists. This highlights the urgent need for automated grading in mpMRI. Existing studies lack integration of clinical prior information and suffer from uneven training sample distribution due to prevalence. Therefore, we propose a solution that incorporates prior knowledge, addresses the issue of uneven medical sample distribution, and maintains high interpretability in mpMRI. Firstly, we introduce Prior Knowledge-Based Feature Extraction, which mathematically models the PI-RADS criteria for prostate cancer as diagnostic information into model training. Secondly, we propose Adaptive Recall Feedback Loss to address the extremely imbalanced data problem. This method adjusts the training dynamically based on accuracy and recall in the validation set, resulting in high accuracy and recall simultaneously in the testing set.Thirdly, we design an Enhanced Cascade Prostate Cancer Classifier that classifies prostate cancer into different levels in an interpretable way, which refines the classification results and helps with clinical intervention. Our method is validated through experiments on the PI-CAI dataset and outperforms other methods with a more balanced result in both accuracy and recall rate

Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features

Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection. However, this lacks an explanation of the specific information in the subband, and these features also lose information such as phase. Inspired by the mechanism of synthetic speech, the fundamental frequency (F0) information is used to improve the quality of synthetic speech, while the F0 of synthetic speech is still too average, which differs significantly from that of real speech. It is expected that F0 can be used as important information to discriminate between bonafide and fake speech, while this information cannot be used directly due to the irregular distribution of F0. Insteadly, the frequency band containing most of F0 is selected as the input feature. Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately. Finally, the results of F0, real and imaginary spectrogram features are fused. Experimental results on the ASVspoof 2019 LA dataset show that our proposed system is very effective for the audio deepfake detection task, achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all systems

Update on the Clinical Effect of Acupuncture Therapy in Patients with Gouty Arthritis: Systematic Review and Meta-Analysis

Objective. The aim of this study is to evaluate the clinical efficacy and safety of acupuncture therapy in the treatment of acute gouty arthritis. Methods. A literature search of PubMed, EMBASE, ISI Web of Science, CENTRAL, and CNKI was conducted from the inception date of each database up to October 2015. Two investigators screened each article independently and were blinded to the findings of the other reviewer. Data was extracted according to the predetermined collection form. Meta-analysis was performed. Results. We analyzed data from 28 RCTs involving 2237 patients with gouty arthritis. Compared with conventional pharmacological treatments acupuncture was more effective in rendering patients free from symptoms after 24 hours, lowering serum urate, alleviating pain associated with gouty arthritis, and decreasing the ESR; regarding CRP, no statistically significant difference was found. In addition, the frequency of adverse events in acupuncture treatment was lower than that in control group. Conclusion. Based on the findings of our study, we cautiously suggest that acupuncture is an effective and safe therapy for patients with gouty arthritis. However, the potential beneficial effect of acupuncture might be overstated due to the methodological deficiency of included studies. High quality RCTs with larger scale are encouraged

Drug and therapeutics committee interventions in managing irrational drug use and antimicrobial stewardship in China

Aim: This study aimed to investigate the key points in the transformation of the functions of the Drug and Therapeutics Committee (DTC) of the Shandong Provincial Third Hospital and how to provide full authority to its role in the control of rational drug use, especially in the management of antibiotic use.Method: A prescription review management group, antimicrobial stewardship group, and rational drug use service group were established under the DTC. From January 2016 to December 2021, each group played a role in promoting rational drug use and antimicrobial stewardship. In addition, we performed statistics on typical management cases, irrational drug use, bacterial resistance rate, and drug costs from 2015 to 2021 to evaluate the effect of management by the DTC.Results: Intervention by the DTC led to a significant reduction in prescribing errors (71.43%, p &lt; 0.05), the intervention acceptance rate increased by 16.03%, and the problem solved rate increased by 32.41% (p &lt; 0.05). Resistance rates of general spectrum antibiotics were reduced remarkably after the intervention. The quality of drug treatment was improved and patient drug expenses was continuously reduced.Conclusion: Giving full play to the functions of the DTC can significantly improve the level of drug treatment and reduce unreasonable drug use to save unnecessary drug expenses and slow the development of drug resistance

UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities

Entity Set Expansion (ESE) aims to identify new entities belonging to the same semantic class as a given set of seed entities. Traditional methods primarily relied on positive seed entities to represent a target semantic class, which poses challenge for the representation of ultra-fine-grained semantic classes. Ultra-fine-grained semantic classes are defined based on fine-grained semantic classes with more specific attribute constraints. Describing it with positive seed entities alone cause two issues: (i) Ambiguity among ultra-fine-grained semantic classes. (ii) Inability to define "unwanted" semantic. Due to these inherent shortcomings, previous methods struggle to address the ultra-fine-grained ESE (Ultra-ESE). To solve this issue, we first introduce negative seed entities in the inputs, which belong to the same fine-grained semantic class as the positive seed entities but differ in certain attributes. Negative seed entities eliminate the semantic ambiguity by contrast between positive and negative attributes. Meanwhile, it provide a straightforward way to express "unwanted". To assess model performance in Ultra-ESE, we constructed UltraWiki, the first large-scale dataset tailored for Ultra-ESE. UltraWiki encompasses 236 ultra-fine-grained semantic classes, where each query of them is represented with 3-5 positive and negative seed entities. A retrieval-based framework RetExpan and a generation-based framework GenExpan are proposed to comprehensively assess the efficacy of large language models from two different paradigms in Ultra-ESE. Moreover, we devised three strategies to enhance models' comprehension of ultra-fine-grained entities semantics: contrastive learning, retrieval augmentation, and chain-of-thought reasoning. Extensive experiments confirm the effectiveness of our proposed strategies and also reveal that there remains a large space for improvement in Ultra-ESE.Comment: Initial Versio