Bernardpena9893

By taking advantage of the particular structure of the Gaussian kernel model, a theoretical analysis on the convergence and rationality of the proposed method is also provided. Compared with the kernel algorithms with a fixed bandwidth, our novel learning framework can not only achieve adaptive learning results with a better prediction accuracy but also show performance that is more robust with a faster convergence speed. Encouraging numerical results are provided to demonstrate the advantages of our new method.In this article, we study the generalization performance of multitask learning (MTL) by considering MTL as a learning process of vector-valued functions (VFs). We will answer two theoretical questions, given a small size training sample 1) under what conditions does MTL perform better than single-task learning (STL)? And 2) under what conditions does MTL guarantee the consistency of all tasks during learning? In contrast to the conventional task-summation based MTL, the introduction of VF form enables us to detect the behavior of each task and the task-group relatedness in MTL. Specifically, the task-group relatedness examines how the success (or failure) of some tasks affects the performance of the other tasks. By deriving the specific deviation and symmetrization inequalities for VFs, we obtain a generalization bound for MTL to the upper bound of the joint probability that there is at least one task with a large generalization gap. To answer the first question, we discuss how the synergic relatedness between task groups affects the generalization performance of MTL and shows that MTL outperforms STL if almost any pair of complementary task groups is predominantly synergic. AP26113 Moreover, to answer the second question, we present a sufficient condition to guarantee the consistency of each task in MTL, which requires that the function class of each task should not have high complexity. In addition, our findings provide a strategy to examine whether the task settings will enjoy the advantages of MTL.Nontechnical losses (NTLs) are estimated to be considerable and increasing every year. Recently, high-resolution measurements from globally laid smart meters have brought deeper insights on users' consumption patterns that can be exploited potentially by NTL detection. However, consumption-pattern-based NTL detection is now facing two major challenges the inefficiency of harnessing high dimensionality and the severe lack of fraudulent samples. To overcome them, an NTL detection model based on deep learning and anomaly detection is proposed in this article, namely bidirectional Wasserstein GAN and support vector data description-based NTL detector (BSBND). Motivated by the powerful ability of generative adversarial networks (GANs) to learn deep representation from high-dimensional distributions of data, in the BSBND, we utilized a BiWGAN for feature extraction from high-dimensional raw consumption records, and a one-class classifier trained only on benign samples--SVDD--is adopted to map features into judgments. Moreover, a novel alternate coordinating algorithm is proposed to optimize the cooperation between the upstream BiWGAN and the downstream SVDD, and also, an interpreting algorithm is proposed to visualize the basis of each fraudulent judgment. Case studies have demonstrated the superiority of the BSBND over the state of the arts, the powerful feature extraction ability of BiWGAN, and also the effectiveness of the proposed coordinating and interpreting algorithms.A powerful feature of adaptive memory is its inherent flexibility. Alcohol and other addictive substances can remold neural circuits important for memory to reduce this flexibility. However, the mechanism through which pertinent circuits are selected and shaped remains unclear. We show that circuits required for alcohol-associated preference shift from population level dopaminergic activation to select dopamine neurons that predict behavioral choice in Drosophila melanogaster. During memory expression, subsets of dopamine neurons directly and indirectly modulate the activity of interconnected glutamatergic and cholinergic mushroom body output neurons (MBON). Transsynaptic tracing of neurons important for memory expression revealed a convergent center of memory consolidation within the mushroom body (MB) implicated in arousal, and a structure outside the MB implicated in integration of naïve and learned responses. These findings provide a circuit framework through which dopamine neuronal activation shifts from reward delivery to cue onset, and provide insight into the maladaptive nature of memory.Objective To present our initial experience with double-face augmentation urethroplasty for near-obliterative bulbar urethral strictures and analyze the short-term outcomes. Material and methods We retrospectively evaluated a prospectively maintained database of patients with near-obliterative bulbar urethral strictures (>2 cm), who underwent double-face augmentation urethroplasty. The patients' demographic characteristics, clinical data, and data regarding the investigations conducted were analyzed. Near-obliterative urethral stricture was defined as lumen less then 6 Fr. Double-face urethroplasty was performed using a ventral approach, during which dorsal inlay and ventral onlay buccal mucosal graft (BMG) augmentation were performed. A successful outcome was defined as normal voiding without the need for any instrumentation to improve the urinary flow rate. Results A total of 37 patients with a mean age of 50±11.7 years, who underwent this procedure were included in the study. The mean stricture length was 5.2±0.95 cm. The mean length of the dorsal inlay BMG augmentation was 3.1±0.5 cm and that of the ventral onlay BMG augmentation was 6.3±1.2 cm. Post-void dribbling (18.9%) was the most commonly reported complication. The maximum flow rates and symptom scores significantly improved in both groups compared with the preoperative parameters (p less then 0.001). The incidence of both erectile dysfunction and ejaculatory failure was reported in 6 (16.2%) patients; respectively. The overall success rate was 86.5% at a median follow-up period of 36 months (IQR 26.5-43). Conclusion Double-face augmentation urethroplasty is a safe and feasible option for near-obliterative bulbar urethral strictures, and our study showed satisfactory short-term outcomes for the same.