Informal payments in developing countries' public health sector

In China and some other developing countries' public health sectors, many patients give their doctors a payment outside the official channel before a major treatment. This secret payment has been documented as informal payment in the literature. We argue that the fundamental cause for informal payments is that patients have more information about doctors' skill than the government does. The price, set by the government, for services offered by doctors cannot fully differentiate patients' various needs. As a consequence, informal payment rises as a tool for patients to compete for the skillful doctor. We study the welfare implications of different policies that can potentially be used to regulate such payments. Patient heterogeneity plays a central role in welfare implications of different policies: when patients' willingness-to-pay differs a lot, informal payments should be allowed and when it differs little, informal payments should be banned. Also we show that selling the right to choose physicians publicly always improves social welfare.informal payments; public health sector; welfare; efficiency

Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition

Open-set image recognition is a challenging topic in computer vision. Most of the existing works in literature focus on learning more discriminative features from the input images, however, they are usually insensitive to the high- or low-frequency components in features, resulting in a decreasing performance on fine-grained image recognition. To address this problem, we propose a Complementary Frequency-varying Awareness Network that could better capture both high-frequency and low-frequency information, called CFAN. The proposed CFAN consists of three sequential modules: (i) a feature extraction module is introduced for learning preliminary features from the input images; (ii) a frequency-varying filtering module is designed to separate out both high- and low-frequency components from the preliminary features in the frequency domain via a frequency-adjustable filter; (iii) a complementary temporal aggregation module is designed for aggregating the high- and low-frequency components via two Long Short-Term Memory networks into discriminative features. Based on CFAN, we further propose an open-set fine-grained image recognition method, called CFAN-OSFGR, which learns image features via CFAN and classifies them via a linear classifier. Experimental results on 3 fine-grained datasets and 2 coarse-grained datasets demonstrate that CFAN-OSFGR performs significantly better than 9 state-of-the-art methods in most cases

Recursive Counterfactual Deconfounding for Object Recognition

Image recognition is a classic and common task in the computer vision field, which has been widely applied in the past decade. Most existing methods in literature aim to learn discriminative features from labeled images for classification, however, they generally neglect confounders that infiltrate into the learned features, resulting in low performances for discriminating test images. To address this problem, we propose a Recursive Counterfactual Deconfounding model for object recognition in both closed-set and open-set scenarios based on counterfactual analysis, called RCD. The proposed model consists of a factual graph and a counterfactual graph, where the relationships among image features, model predictions, and confounders are built and updated recursively for learning more discriminative features. It performs in a recursive manner so that subtler counterfactual features could be learned and eliminated progressively, and both the discriminability and generalization of the proposed model could be improved accordingly. In addition, a negative correlation constraint is designed for alleviating the negative effects of the counterfactual features further at the model training stage. Extensive experimental results on both closed-set recognition task and open-set recognition task demonstrate that the proposed RCD model performs better than 11 state-of-the-art baselines significantly in most cases

Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition

Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision community. However, we empirically found that a typical vision transformer with the spatial self-attention mechanism could not learn accurate attention maps for distinguishing different categories of fine-grained images. To address this problem, motivated by the temporal attention mechanism in brains, we propose a spatial-temporal attention network for learning fine-grained feature representations, called STAN, where the features learnt by implementing a sequence of spatial self-attention operations corresponding to multiple moments are aggregated progressively. The proposed STAN consists of four modules: a self-attention backbone module for learning a sequence of features with self-attention operations, a spatial feature self-organizing module for facilitating the model training, a spatial-temporal feature learning module for aggregating the re-organized features via a Long Short-Term Memory network, and a context-aware module that is implemented as the forget block of the spatial-temporal feature learning module for preserving/forgetting the long-term memory by utilizing contextual information. Then, we propose a STAN-based method for open-set fine-grained recognition by integrating the proposed STAN network with a linear classifier, called STAN-OSFGR. Extensive experimental results on 3 fine-grained datasets and 2 coarse-grained datasets demonstrate that the proposed STAN-OSFGR outperforms 9 state-of-the-art open-set recognition methods significantly in most cases

Nanoporous ionic organic networks: from synthesis to materials applications

The past decade has witnessed the rapid progress in synthesizing nanoporous organic networks or polymer frameworks for various potential applications. Generally speaking, functionalization of porous networks to add extra properties and enhance materials performance could be achieved either during the pore formation (thus a concurrent approach) or post-synthetic modification (a sequential approach). Nanoporous organic networks which include ion pairs in a covalent manner are of special importance and possess extreme application profiles. Within these nanoporous ionic organic networks (NIONs), here with a pore size in the range from sub-1 nm to 100 nm, we observe a synergistic coupling of the electrostatic interaction of charges, the nanoconfinement within pores and the addressable functional units in soft matter resulting in a wide variety of functions and applications, above all catalysis, energy storage and conversion, as well as environmental operations. This review aims to highlight the recent progress in this area, and seeks to raise original perspectives that will stimulate future advancements at both the fundamental and applied level.Comment: 67 pages, 25 figures, Chemical Society Reviewers, 201

Informal payments in developing countries' public health sector

In China and some other developing countries' public health sectors, many patients give their doctors a payment outside the official channel before a major treatment. This secret payment has been documented as informal payment in the literature. We argue that the fundamental cause for informal payments is that patients have more information about doctors' skill than the government does. The price, set by the government, for services offered by doctors cannot fully differentiate patients' various needs. As a consequence, informal payment rises as a tool for patients to compete for the skillful doctor. We study the welfare implications of different policies that can potentially be used to regulate such payments. Patient heterogeneity plays a central role in welfare implications of different policies: when patients' willingness-to-pay differs a lot, informal payments should be allowed and when it differs little, informal payments should be banned. Also we show that selling the right to choose physicians publicly always improves social welfare

Informal payments in developing countries' public health sector

In China and some other developing countries' public health sectors, many patients give their doctors a payment outside the official channel before a major treatment. This secret payment has been documented as informal payment in the literature. We argue that the fundamental cause for informal payments is that patients have more information about doctors' skill than the government does. The price, set by the government, for services offered by doctors cannot fully differentiate patients' various needs. As a consequence, informal payment rises as a tool for patients to compete for the skillful doctor. We study the welfare implications of different policies that can potentially be used to regulate such payments. Patient heterogeneity plays a central role in welfare implications of different policies: when patients' willingness-to-pay differs a lot, informal payments should be allowed and when it differs little, informal payments should be banned. Also we show that selling the right to choose physicians publicly always improves social welfare

How cognitive and reactive fear circuits optimize escape decisions in humans

Flight initiation distance (FID), the distance at which an organism flees from an approaching threat, is an ecological metric of cost–benefit functions of escape decisions. We adapted the FID paradigm to investigate how fast- or slow-attacking “virtual predators” constrain escape decisions. We show that rapid escape decisions rely on “reactive fear” circuits in the periaqueductal gray and midcingulate cortex (MCC), while protracted escape decisions, defined by larger buffer zones, were associated with “cognitive fear” circuits, which include posterior cingulate cortex, hippocampus, and the ventromedial prefrontal cortex, circuits implicated in more complex information processing, cognitive avoidance strategies, and behavioral flexibility. Using a Bayesian decision-making model, we further show that optimization of escape decisions under rapid flight were localized to the MCC, a region involved in adaptive motor control, while the hippocampus is implicated in optimizing decisions that update and control slower escape initiation. These results demonstrate an unexplored link between defensive survival circuits and their role in adaptive escape decisions

Prompt Lambda(+)(c) baryons and D-0 meson production cross-section and nuclear modification in pPb collisions at root S-NN=5.02 TeV with the LHCb detector

A(c)(+) baryons and D-0 mesons are studied in pPb collisions at root S-NN = 5.02 TeV. The nuclear modification factor and forward-backward cross-section asymmetry are measured in order to study the cold nuclear matter effects. The prompt A(c)(+) production cross-section is compared to that of the prompt D-0 mesons, providing insights into the hadronisation mechanism of charmed hadrons.</p