Consistent Prompting for Rehearsal-Free Continual Learning

Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt-based methods are inconsistent between training and testing, limiting their effectiveness. Two types of inconsistency are revealed. Test predictions are made from all classifiers while training only focuses on the current task classifier without holistic alignment, leading to Classifier inconsistency. Prompt inconsistency indicates that the prompt selected during testing may not correspond to the one associated with this task during training. In this paper, we propose a novel prompt-based method, Consistent Prompting (CPrompt), for more aligned training and testing. Specifically, all existing classifiers are exposed to prompt training, resulting in classifier consistency learning. In addition, prompt consistency learning is proposed to enhance prediction robustness and boost prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. Detailed analysis shows that improvements come from more consistent training and testing.Comment: Accepted by CVPR202

Malignant glomus tumor of prostate: A case report

We reported an 85-year-old patient with malignant glomus tumor (GT) of the prostate. He presented with urinary frequency for more than 2 years and gross hematuria for 7 days. Computed tomography scan showed that the prostate was markedly irregularly enlarged, and the boundary between the prostate and the posterior wall of the bladder was unclear. Bilateral kidneys and ureters were dilated. Biochemical examinations showed that the serum potassium was 7.24 mmol/L and the serum creatinine was 974.6 μmol/L. Transurethral diagnostic resection was performed after restoring homeostasis through several times of bedside blood filtration. The pathological diagnosis was malignant GT. The patient’s renal function recovered after bilateral nephrostomy, and he refused further treatment and was out of contact after 9 months. We summarize the clinical and histopathological features of malignant GT of the prostate in order to improve the early recognition of the disease by clinicians

Green resource allocation for mobile edge computing

We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems, where task offloading decisions, transmit power, and computation resource allocation are jointly optimized. The considered energy consumption minimization problem is a non-convex mixed-integer non-linear programming problem, which is challenging to solve. Therefore, we develop a joint search and Successive Convex Approximation (SCA) scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop, respectively. Specifically, in the inner loop, we solve the optimization problem with fixed task offloading decisions. Due to the non-convex objective function and constraints, this optimization problem is still non-convex, and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker conditions. In the outer loop, we optimize the offloading decisions through exhaustive search. However, the computational complexity of the exhaustive search method is greatly high. To reduce the complexity, a heuristic scheme is proposed to obtain a sub-optimal solution. Simulation results demonstrate the effectiveness of the developed schemes

Serum Antioxidant Parameters are Significantly Increased in Patients with Type 2 Diabetes Mellitus after Consumption of Chinese Propolis: A Randomized Controlled Trial Based on Fasting Serum Glucose Level

<p><b> </b><b>Article full text</b></p> <p><br></p> <p>The full text of this article can be found <a href="https://link.springer.com/article/10.1007/s13300-017-0341-9"><b>here</b>.</a></p> <p><br></p> <p><b>Provide enhanced content for this article</b></p> <p><br></p> <p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p> <p> </p> <p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p> <p><br></p> <p>Other enhanced features include, but are not limited to:</p> <p><br></p> <p>• Slide decks</p> <p>• Videos and animations</p> <p>• Audio abstracts</p> <p>• Audio slides</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p