Bias Reduction via End-to-End Shift Learning: Application to Citizen Science

Citizen science projects are successful at gathering rich datasets for various applications. However, the data collected by citizen scientists are often biased --- in particular, aligned more with the citizens' preferences than with scientific objectives. We propose the Shift Compensation Network (SCN), an end-to-end learning scheme which learns the shift from the scientific objectives to the biased data while compensating for the shift by re-weighting the training data. Applied to bird observational data from the citizen science project eBird, we demonstrate how SCN quantifies the data distribution shift and outperforms supervised learning models that do not address the data bias. Compared with competing models in the context of covariate shift, we further demonstrate the advantage of SCN in both its effectiveness and its capability of handling massive high-dimensional data

Model choice versus model criticism

The new perspectives on ABC and Bayesian model criticisms presented in Ratmann et al.(2009) are challenging standard approaches to Bayesian model choice. We discuss here some issues arising from the authors' approach, including prior influence, model assessment and criticism, and the meaning of error in ABC.Comment: This is a comment on the recent paper by Ratmann, Andrieu, Wiuf, and Richardson (PNAS, 106), submitted too late for PNAS to consider i

Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder

Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was motivated by two real-world applications in computational sustainability: one studies the spatial correlation among multiple bird species using the eBird data and the other models multi-dimensional landscape composition and human footprint in the Amazon rainforest with satellite images. We show that MEDL_CVAE captures rich dependency structures, scales better than previous methods, and further improves on the joint likelihood taking advantage of very large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall

CLR-DRNets: Curriculum Learning with Restarts to Solve Visual Combinatorial Games

We introduce a curriculum learning framework for challenging tasks that require a combination of pattern recognition and combinatorial reasoning, such as single-player visual combinatorial games. Our work harnesses Deep Reasoning Nets (DRNets) [Chen et al., 2020], a framework that combines deep learning with constraint reasoning for unsupervised pattern demixing. We propose CLR-DRNets (pronounced Clear-DRNets), a curriculum-learning-with-restarts framework to boost the performance of DRNets. CLR-DRNets incrementally increase the difficulty of the training instances and use restarts, a new model selection method that selects multiple models from the same training trajectory to learn a set of diverse heuristics and apply them at inference time. An enhanced reasoning module is also proposed for CLR-DRNets to improve the ability of reasoning and generalize to unseen instances. We consider Visual Sudoku, i.e., Sudoku with hand-written digits or letters, and Visual Mixed Sudoku, a substantially more challenging task that requires the demixing and completion of two overlapping Visual Sudokus. We propose an enhanced reasoning module for the DRNets framework for encoding these visual games We show how CLR-DRNets considerably outperform DRNets and other approaches on these visual combinatorial games

Adipose Tissue Distribution and Survival Among Women with Nonmetastatic Breast Cancer.

ObjectivePrevious studies of breast cancer survival have not considered specific depots of adipose tissue such as subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT).MethodsThis study assessed these relationships among 3,235 women with stage II and III breast cancer diagnosed between 2005 and 2013 at Kaiser Permanente Northern California and between 2000 and 2012 at Dana Farber Cancer Institute. SAT and VAT areas (in centimeters squared) were calculated from routine computed tomography scans within 6 (median: 1.2) months of diagnosis, covariates were collected from electronic health records, and vital status was assessed by death records. Hazard ratios (HRs) and 95% CIs were estimated using Cox regression.ResultsSAT and VAT ranged from 19.0 to 891 cm2 and from 0.484 to 454 cm2 , respectively. SAT was related to increased risk of death (127-cm2 increase; HR [95% CI]: 1.13 [1.02-1.26]), but no relationship was found with VAT (78.18-cm2 increase; HR [95% CI]: 1.02 [0.91-1.14]). An association with VAT was noted among women with stage II cancer (stage II: HR: 1.17 [95% CI: 0.99-1.39]; stage III: HR: 0.90 [95% CI: 0.76-1.07]; P interaction < 0.01). Joint increases in SAT and VAT were associated with mortality above either alone (simultaneous 1-SD increase: HR 1.19 [95% CI: 1.05-1.34]).ConclusionsSAT may be an underappreciated risk factor for breast cancer-related death

An fMRI study of affective perspective taking in individuals with psychopathy: imagining another in pain does not evoke empathy

While it is well established that individuals with psychopathy have a marked deficit in affective arousal, emotional empathy, and caring for the well-being of others, the extent to which perspective taking can elicit an emotional response has not yet been studied despite its potential application in rehabilitation. In healthy individuals, affective perspective taking has proven to be an effective means to elicit empathy and concern for others. To examine neural responses in individuals who vary in psychopathy during affective perspective taking, 121 incarcerated males, classified as high (n = 37; Hare psychopathy checklist-revised, PCL-R ≥ 30), intermediate (n = 44; PCL-R between 21 and 29), and low (n = 40; PCL-R ≤ 20) psychopaths, were scanned while viewing stimuli depicting bodily injuries and adopting an imagine-self and an imagine-other perspective. During the imagine-self perspective, participants with high psychopathy showed a typical response within the network involved in empathy for pain, including the anterior insula (aINS), anterior midcingulate cortex (aMCC), supplementary motor area (SMA), inferior frontal gyrus (IFG), somatosensory cortex, and right amygdala. Conversely, during the imagine-other perspective, psychopaths exhibited an atypical pattern of brain activation and effective connectivity seeded in the anterior insula and amygdala with the orbitofrontal cortex (OFC) and ventromedial prefrontal cortex (vmPFC). The response in the amygdala and insula was inversely correlated with PCL-R Factor 1 (interpersonal/affective) during the imagine-other perspective. In high psychopaths, scores on PCL-R Factor 1 predicted the neural response in ventral striatum when imagining others in pain. These patterns of brain activation and effective connectivity associated with differential perspective-taking provide a better understanding of empathy dysfunction in psychopathy, and have the potential to inform intervention programs for this complex clinical problem