Unsupervised BLSTM-Based Electricity Theft Detection With Training Data Contaminated

Electricity theft can cause economic damage and even increase the risk of outage. Recently, many methods have implemented electricity theft detection on smart meter data. However, how to conduct detection on the dataset without any label still remains challenging. In this article, we propose a novel unsupervised two-stage approach under the assumption that the training set is contaminated by attacks. Specifically, the method consists of two stages: (1) a Gaussian mixture model is employed to cluster consumption patterns with respect to different habits of electricity usage, and with the goal of improving the accuracy of the model in the posterior stage; (2) an attention-based bidirectional long short-term memory encoder-decoder scheme is employed to improve the robustness against the non-malicious changes in usage patterns leveraging the process of encoding and decoding. Quantifying the similarity of consumption patterns and reconstruction errors, the anomaly score is defined to improve detection performance. Experiments on a real dataset show that the proposed method outperforms the state-of-the-art unsupervised detectors

Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG)

Objective: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. Methods: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. Results: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. Conclusion: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. Significance: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.</p

Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG)

Objective: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. Methods: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. Results: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. Conclusion: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. Significance: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.</p

The neutrophil-to-lymphocyte ratio is associated with mild cognitive impairment in community-dwelling older women aged over 70 years: a population-based cross-sectional study

BackgroundThe neutrophil-to-lymphocyte ratio (NLR) is a marker of inflammation that can be obtained quickly, conveniently, and cheaply from blood samples. However, there is no research to explore the effects of sex and age on the relationship between the NLR and mild cognitive impairment (MCI) in community-dwelling older adults.MethodsA total of 3,169 individuals aged over 60 years in Shanghai were recruited for face-to-face interviews, and blood samples were collected. MCI was assessed by the Mini-Mental State Examination (MMSE) and the Instrumental Activities of Daily Living (IADL) scale, and neutrophil count and lymphocyte counts were measured in fasting blood samples. The NLR was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count.ResultsIn females, the NLR in the MCI group was significantly higher than that in the cognitively normal group (2.13 ± 0.94 vs. 1.85 ± 0.83, p &lt; 0.001) but not in men. Logistic regression showed that a higher NLR was an independent risk factor for MCI in women [odds ratio (OR) = 1.28; 95% confidence interval (CI) = 1.09–1.49]. In addition, the elevated NLR quartile was associated with an increased risk of MCI, especially in women older than 70 years (p-value for trend = 0.011).ConclusionCompared with males, female MCI patients had a significantly higher NLR than cognitively normal controls. In addition, elevated NLR was found to be significantly associated with MCI risk in women older than 70 years. Therefore, elderly Chinese women with a higher NLR value may be the target population for effective prevention of MCI

Feasibility of Surgeon-Delivered Audit and Feedback Incorporating Peer Surgical Coaching to Reduce Fistula Incidence following Cleft Palate Repair: A Pilot Trial

Background: Improving surgeons\u27 technical performance may reduce their frequency of postoperative complications. The authors conducted a pilot trial to evaluate the feasibility of a surgeon-delivered audit and feedback intervention incorporating peer surgical coaching on technical performance among surgeons performing cleft palate repair, in advance of a future effectiveness trial. Methods: A nonrandomized, two-arm, unblinded pilot trial enrolled surgeons performing cleft palate repair. Participants completed a baseline audit of fistula incidence. Participants with a fistula incidence above the median were allocated to an intensive feedback intervention that included selecting a peer surgical coach, observing the coach perform palate repair, reviewing operative video of their own surgical technique with the coach, and proposing and implementing changes in their technique. All others were allocated to simple feedback (receiving audit results). Outcomes assessed were proportion of surgeons completing the baseline audit, disclosing their fistula incidence to peers, and completing the feedback intervention. Results: Seven surgeons enrolled in the trial. All seven completed the baseline audit and disclosed their fistula incidence to other participants. The median baseline fistula incidence was 0.4 percent (range, 0 to 10.5 percent). Two surgeons were unable to receive the feedback intervention. Of the five remaining surgeons, two were allocated to intensive feedback and three to simple feedback. All surgeons completed their assigned feedback intervention. Among surgeons receiving intensive feedback, fistula incidence was 5.9 percent at baseline and 0.0 percent following feedback (adjusted OR, 0.98; 95 percent CI, 0.44 to 2.17). Conclusion: Surgeon-delivered audit and feedback incorporating peer coaching on technical performance was feasible for surgeons