University of Rochester

About: University of Rochester is a education organization based out in Rochester, New York, United States. It is known for research contribution in the topics: Population & Laser. The organization has 63915 authors who have published 112762 publications receiving 5484122 citations. The organization is also known as: Rochester University.

International consensus on standardization of data collection for complications associated with esophagectomy: Esophagectomy Complications Consensus Group (ECCG)

Abstract: Introduction: Perioperative complications influence long- and short-term outcomes after esophagectomy. The absence of a standardized system for defining and recording complications and quality measures after esophageal resection has meant that there is wide variation in evaluating their impact on these outcomes. Methods: The Esophageal Complications Consensus Group comprised 21 high-volume esophageal surgeons from 14 countries, supported by all the major thoracic and upper gastrointestinal professional societies. Delphi surveys and group meetings were used to achieve a consensus on standardized methods for defining complications and quality measures that could be collected in institutional databases and national audits. Results: A standardized list of complications was created to provide a template for recording individual complications associated with esophagectomy. Where possible, these were linked to preexisting international definitions. A Delphi survey facilitated production of specific definitions for anastomotic leak, conduit necrosis, chyle leak, and recurrent nerve palsy. An additional Delphi survey documented consensus regarding critical quality parameters recommended for routine inclusion in databases. These quality parameters were documentation on mortality, comorbidities, completeness of data collection, blood transfusion, grading of complication severity, changes in level of care, discharge location, and readmission rates. Conclusions: The proposed system for defining and recording perioperative complications associated with esophagectomy provides an infrastructure to standardize international data collection and facilitate future comparative studies and quality improvement projects.

Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition

Abstract: Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that part localization (e.g., head of a bird) and fine-grained feature learning (e.g., head shape) are mutually correlated. In this paper, we propose a novel part learning approach by a multi-attention convolutional neural network (MA-CNN), where part generation and feature learning can reinforce each other. MA-CNN consists of convolution, channel grouping and part classification sub-networks. The channel grouping network takes as input feature channels from convolutional layers, and generates multiple parts by clustering, weighting and pooling from spatially-correlated channels. The part classification network further classifies an image by each individual part, through which more discriminative fine-grained features can be learned. Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way. MA-CNN does not need bounding box/part annotation and can be trained end-to-end. We incorporate the learned parts from MA-CNN with part-CNN for recognition, and show the best performances on three challenging published fine-grained datasets, e.g., CUB-Birds, FGVC-Aircraft and Stanford-Cars.

Selective cache ways: on-demand cache resource allocation

Abstract: Increasing levels of microprocessor power dissipation call for new approaches at the architectural level that save energy by better matching of on-chip resources to application requirements. Selective cache ways provides the ability to disable a subset of the ways in a set associative cache during periods of modest cache activity, while the full cache may remain operational for more cache-intensive periods. Because this approach leverages the subarray partitioning that is already present for performance reasons, only minor changes to a conventional cache are required, and therefore, full-speed cache operation can be maintained. Furthermore, the tradeoff between performance and energy is flexible, and can be dynamically tailored to meet changing application and machine environmental conditions. We show that trading off a small performance degradation for energy savings can produce a significant reduction in cache energy dissipation using this approach.