Learning: Does Organization Ownership Matter? Structure and Performance in For-profit, Nonprofit and Local Government Nursing Homes*

We compare the structure and performance of for-profit (FP), nonprofit (NP) and local government (LG) organizations. These organizations differ in their ownership structure, objectives and agency relations. We conjecture that, compared to NP and LG, FP firms (a) delegate less decision-making power to employees, (b) provide more incentives and fewer fringe benefits, (c) monitor less, and (d) rely less on social networks to recruit employees. We also hypothesize that, relative to NP and LG, FP firms (i) are more efficient, (ii) provide similar levels of service elements that observable to their customers, (iii) provide lower levels of less-well observable elements, and (iv) provide less of the relational elements. Differences in structure and performance are likely to be tempered by regulation, market competition and institutional pressures for similarity. We study detailed performance outcomes for all the 369 Minnesota nursing homes included in federal and state datasets, and organization structure for a subsample of 105 homes that responded to our survey. Our empirical investigation generally supports our hypotheses. In particular, we find that FP homes serve more residents than NP and LG, after controlling for quality differences. However, FP homes provide lower quality services on a large array of attributes, especially those that are less observable by nursing home residents and their families. The differences among different types of organization are small, but significant.

Lavish Returns on Cheap Talk: Non-binding Communication in a Trust Experiment

We let subjects interact with anonymous partners in trust (investment) games with and without one of two kinds of pre-play communication: numerical (tabular) only, and verbal and numerical. We find that either kind of pre-play communication increases trusting, trustworthiness, or both, in inter-subject comparisons, but that the inclusions of verbal communication generates both a larger effect and one that is robust across both inter-subject and intra-subject comparisons. In all conditions, trustors earn more when they invest more of their endowment, trustors and trustees gravitate to "fair and efficient" interactions, and the majority of trustees adhere to their commitments, whether explicit or implicit. Finally, we study trusting and trustworthiness in the sense of adhering to agreements, and we find that both are enhanced when the parties can use words, and especially when an agreement is reached with words and not only with the exchange of numerical proposals.

Advanced Three-Dimensional Echocardiography

__Abstract__ During the development of echocardiography, 3D echocardiography imaging represents a major innovation in cardiovascular ultrasound (Figure 1). Advancements in computer and transducer technologies permit real-time 3D acquisition and presentation of cardiac structures from any view. An important milestone in the history of real-time 3D echocardiography was reached shortly after the year 2000, with the development of fully sampled matrix-array transducers (Figure 2). These transducers provide excellent real-time imaging of the beating heart in three dimensions and require significant technological developments in both hardware and software

Burst Denoising with Kernel Prediction Networks

We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.Comment: To appear in CVPR 2018 (spotlight). Project page: http://people.eecs.berkeley.edu/~bmild/kpn

Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding

Curriculum learning and self-paced learning are the training strategies that gradually feed the samples from easy to more complex. They have captivated increasing attention due to their excellent performance in robotic vision. Most recent works focus on designing curricula based on difficulty levels in input samples or smoothing the feature maps. However, smoothing labels to control the learning utility in a curriculum manner is still unexplored. In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner. In ULS and SVLS, a bigger smoothing factor value enforces a heavy smoothing penalty in the true label and limits learning less information. Therefore, we design the curriculum by label smoothing (CBLS). We set a bigger smoothing value at the beginning of training and gradually decreased it to zero to control the model learning utility from lower to higher. We also designed a confidence-aware pacing function and combined it with our CBLS to investigate the benefits of various curricula. The proposed techniques are validated on four robotic surgery datasets of multi-class, multi-label classification, captioning, and segmentation tasks. We also investigate the robustness of our method by corrupting validation data into different severity levels. Our extensive analysis shows that the proposed method improves prediction accuracy and robustness. The code is publicly available at https://github.com/XuMengyaAmy/P-CBLS. Note to Practitioners —The motivation of this article is to improve the performance and robustness of deep neural networks in safety-critical applications such as robotic surgery by controlling the learning ability of the model in a curriculum learning manner and allowing the model to imitate the cognitive process of humans and animals. The designed approaches do not add parameters that require additional computational resources