Power system stability scanning and security assessment using machine learning

Future grids planning requires a major departure from conventional power system planning, where only a handful of the most critical scenarios is analyzed. To account for a wide range of possible future evolutions, scenario analysis has been proposed in many industries. As opposed to the conventional power system planning, where the aim is to find an optimal transmission and/or generation expansion plan for an existing grid, the aim in future grids scenario analysis is to analyze possible evolution pathways to inform power system planning and policy making. Therefore, future grids’ planning may involve large amount of scenarios and the existing planning tools may no longer suitable. Other than the raised future grids’ planning issues, operation of future grids using conventional tools is also challenged by the new features of future grids such as intermittent generation, demand response and fast responding power electronic plants which lead to much more diverse operation conditions compared to the existing networks. Among all operation issues, monitoring stability as well as security of a power system and action with deliberated preventive or remedial adjustment is of vital important. On- line Dynamic Security Assessment (DSA) can evaluate security of a power system almost instantly when current or imminent operation conditions are supplied. The focus of this dissertation are, for future grid planning, to develop a framework using Machine Learning (ML) to effectively assess the security of future grids by analyzing a large amount of the scenarios; for future grids operation, to propose approaches to address technique issues brought by future grids’ diverse operation conditions using ML techniques. Unsupervised learning, supervised learning and semi-supervised learning techniques are utilized in a set of proposed planning and operation security assessment tools

Point-PC: Point Cloud Completion Guided by Prior Knowledge via Causal Inference

Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles. Many approaches utilize a partial-complete paradigm in which missing parts are directly predicted by a global feature learned from partial inputs. This makes it hard to recover details because the global feature is unlikely to capture the full details of all missing parts. In this paper, we propose a novel approach to point cloud completion called Point-PC, which uses a memory network to retrieve shape priors and designs an effective causal inference model to choose missing shape information as additional geometric information to aid point cloud completion. Specifically, we propose a memory operating mechanism where the complete shape features and the corresponding shapes are stored in the form of ``key-value'' pairs. To retrieve similar shapes from the partial input, we also apply a contrastive learning-based pre-training scheme to transfer features of incomplete shapes into the domain of complete shape features. Moreover, we use backdoor adjustment to get rid of the confounder, which is a part of the shape prior that has the same semantic structure as the partial input. Experimental results on the ShapeNet-55, PCN, and KITTI datasets demonstrate that Point-PC performs favorably against the state-of-the-art methods

Trade Privacy for Utility: A Learning-Based Privacy Pricing Game in Federated Learning

To prevent implicit privacy disclosure in sharing gradients among data owners (DOs) under federated learning (FL), differential privacy (DP) and its variants have become a common practice to offer formal privacy guarantees with low overheads. However, individual DOs generally tend to inject larger DP noises for stronger privacy provisions (which entails severe degradation of model utility), while the curator (i.e., aggregation server) aims to minimize the overall effect of added random noises for satisfactory model performance. To address this conflicting goal, we propose a novel dynamic privacy pricing (DyPP) game which allows DOs to sell individual privacy (by lowering the scale of locally added DP noise) for differentiated economic compensations (offered by the curator), thereby enhancing FL model utility. Considering multi-dimensional information asymmetry among players (e.g., DO's data distribution and privacy preference, and curator's maximum affordable payment) as well as their varying private information in distinct FL tasks, it is hard to directly attain the Nash equilibrium of the mixed-strategy DyPP game. Alternatively, we devise a fast reinforcement learning algorithm with two layers to quickly learn the optimal mixed noise-saving strategy of DOs and the optimal mixed pricing strategy of the curator without prior knowledge of players' private information. Experiments on real datasets validate the feasibility and effectiveness of the proposed scheme in terms of faster convergence speed and enhanced FL model utility with lower payment costs.Comment: Accepted by IEEE ICC202

Value of imaging examinations in diagnosing lumbar disc herniation: A systematic review and meta-analysis

PurposeTo systematically review the clinical value of three imaging examinations (Magnetic Resonance Imaging, Computed Tomography, and myelography) in the diagnosis of Lumbar Disc Herniation.MethodsDatabases including PubMed, Embase, The Cochrane Library, Web of Science, CBM, CNKI, WanFang Data, and VIP were electronically searched to collect relevant studies on three imaging examinations in the diagnosis of Lumbar Disc Herniation from inception to July 1, 2021. Two reviewers using the Quality Assessment of Diagnostic Accuracy Studies-2 tool independently screened the literature, extracted the data, and assessed the risk of bias of included studies. Then, meta-analysis was performed by using Meta-DiSc 1.4 software and Stata 15.0 software.ResultsA total of 38 studies from 19 articles were included, involving 1,875 patients. The results showed that the pooled Sensitivity, pooled Specificity, pooled Positive Likelihood Ratio, pooled Negative Likelihood Ratio, pooled Diagnostic Odds Ratio, Area Under the Curve of Summary Receiver Operating Characteristic, and Q* were 0.89 (95%CI: 0.87–0.91), 0.83 (95%CI: 0.78–0.87), 4.57 (95%CI: 2.95–7.08), 0.14 (95%CI: 0.09–0.22), 39.80 (95%CI: 18.35–86.32), 0.934, and 0.870, respectively, for Magnetic Resonance Imaging. The pooled Sensitivity, pooled Specificity, pooled Positive Likelihood Ratio, pooled Negative Likelihood Ratio, pooled Diagnostic Odds Ratio, Area Under the Curve of Summary Receiver Operating Characteristic, and Q* were 0.82 (95%CI: 0.79–0.85), 0.78 (95%CI: 0.73–0.82), 3.54 (95%CI: 2.86–4.39), 0.19 (95%CI: 0.12–0.30), 20.47 (95%CI: 10.31–40.65), 0.835, and 0.792, respectively, for Computed Tomography. The pooled Sensitivity, pooled Specificity, pooled Positive Likelihood Ratio, pooled Negative Likelihood Ratio, pooled Diagnostic Odds Ratio, Area Under the Curve of Summary Receiver Operating Characteristic, and Q* were 0.79 (95%CI: 0.75–0.82), 0.75 (95%CI: 0.70–0.80), 2.94 (95%CI: 2.43–3.56), 0.29 (95%CI: 0.21–0.42), 9.59 (95%CI: 7.05–13.04), 0.834, and 0.767 respectively, for myelography.ConclusionThree imaging examinations had high diagnostic value. In addition, compared with myelography, Magnetic Resonance Imaging had a higher diagnostic value

Optical rotatory power of polymer-stabilized blue phase liquid crystals

Macroscopically, a polymer-stabilized blue phase liquid crystal (BPLC) is assumed to be an optically isotropic medium. Our experiment challenges this assumption. Our results indicate that the optical rotatory power (ORP) of some nano-scale double-twist cylinders in a BPLC composite causes the polarization axis of the transmitted light to rotate a small angle, which in turn leaks through the crossed polarizers. Rotating the analyzer in azimuthal direction to correct this ORP can greatly improve the contrast ratio. A modified De Vries equation based on a thin twisted-nematic layer is proposed to explain the observed phenomena

Observation of plateau regions for zero bias peaks within 5% of the quantized conductance value 2e2/h2e^2/h

Probing an isolated Majorana zero mode is predicted to reveal a tunneling conductance quantized at $2e^2/h$ at zero temperature. Experimentally, a zero-bias peak (ZBP) is expected and its height should remain robust against relevant parameter tuning, forming a quantized plateau. Here, we report the observation of large ZBPs in a thin InAs-Al hybrid nanowire device. The ZBP height can stick close to $2e^2/h$, mostly within 5% tolerance, by sweeping gate voltages and magnetic field. We further map out the phase diagram and identify two plateau regions in the phase space. Our result constitutes a step forward towards establishing Majorana zero modes.Comment: Raw data and processing codes within this paper are available at https://doi.org/10.5281/zenodo.654697

Influenza A virus preferentially snatches noncoding RNA caps

Influenza A virus (IAV) lacks the enzyme for adding 5\u27 caps to its RNAs and snatches the 5\u27 ends of host capped RNAs to prime transcription. Neither the preference of the host RNA sequences snatched nor the effect of cap-snatching on host processes is completely defined. Previous studies of influenza cap-snatching used poly(A)-selected RNAs from infected cells or relied on annotated host genes to define the snatched host RNAs, and thus lack details on many noncoding host RNAs including snRNAs, snoRNAs, and promoter-associated capped small (cs)RNAs, which are made by paused Pol II during transcription initiation. In this study, we used a nonbiased technique, CapSeq, to identify host and viral-capped RNAs including nonpolyadenylated RNAs in the same samples, and investigated the substrate-product correlation between the host RNAs and the viral RNAs. We demonstrated that noncoding host RNAs, particularly U1 and U2, are the preferred cap-snatching source over mRNAs or pre-mRNAs. We also found that csRNAs are highly snatched by IAV. Because the functions of csRNAs remain mostly unknown, especially in somatic cells, our finding reveals that csRNAs at least play roles in the process of IAV infection. Our findings support a model where nascent RNAs including csRNAs are the preferred targets for cap-snatching by IAV and raise questions about how IAV might use snatching preferences to modulate host-mRNA splicing and transcription

Targeted disruption of MCPIP1/Zc3h12a results in fatal inflammatory disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141347/1/imcb201311.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141347/2/imcb201311-sup-0001.pd