Is ProtoPNet Really Explainable? Evaluating and Improving the Interpretability of Prototypes

ProtoPNet and its follow-up variants (ProtoPNets) have attracted broad research interest for their intrinsic interpretability from prototypes and comparable accuracy to non-interpretable counterparts. However, it has been recently found that the interpretability of prototypes can be corrupted due to the semantic gap between similarity in latent space and that in input space. In this work, we make the first attempt to quantitatively evaluate the interpretability of prototype-based explanations, rather than solely qualitative evaluations by some visualization examples, which can be easily misled by cherry picks. To this end, we propose two evaluation metrics, termed consistency score and stability score, to evaluate the explanation consistency cross images and the explanation robustness against perturbations, both of which are essential for explanations taken into practice. Furthermore, we propose a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and substantial discussions to uncover the interpretability of existing ProtoPNets. Experiments demonstrate that our method achieves significantly superior performance to the state-of-the-arts, under both the conventional qualitative evaluations and the proposed quantitative evaluations, in both accuracy and interpretability. Codes are available at https://github.com/hqhQAQ/EvalProtoPNet

Message-passing selection: Towards interpretable GNNs for graph classification

In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines. Unlike the most existing explanation methods, MSInterpreter provides a Message-passing Selection scheme(MSScheme) to select the critical paths for GNNs' message aggregations, which aims at reaching the self-explaination instead of post-hoc explanations. In detail, the elaborate MSScheme is designed to calculate weight factors of message aggregation paths by considering the vanilla structure and node embedding components, where the structure base aims at weight factors among node-induced substructures; on the other hand, the node embedding base focuses on weight factors via node embeddings obtained by one-layer GNN.Finally, we demonstrate the effectiveness of our approach on graph classification benchmarks.Comment: 6 pages, 1 figure

Twinning-assisted dynamic adjustment of grain boundary mobility

Grain boundary (GB) plasticity dominates the mechanical behaviours of nanocrystalline materials. Under mechanical loading, GB configuration and its local deformation geometry change dynamically with the deformation; the dynamic variation of GB deformability, however, remains largely elusive, especially regarding its relation with the frequently-observed GB-associated deformation twins in nanocrystalline materials. Attention here is focused on the GB dynamics in metallic nanocrystals, by means of well-designed in situ nanomechanical testing integrated with molecular dynamics simulations. GBs with low mobility are found to dynamically adjust their configurations and local deformation geometries via crystallographic twinning, which instantly changes the GB dynamics and enhances the GB mobility. This selfadjust twin-assisted GB dynamics is found common in a wide range of face-centred cubic nanocrystalline metals under different deformation conditions. These findings enrich our understanding of GB-mediated plasticity, especially the dynamic behaviour of GBs, and bear practical implication for developing high performance nanocrystalline materials through interface engineering

Association Between Pre-operative BUN and Post-operative 30-Day Mortality in Patients Undergoing Craniotomy for Tumors: Data From the ACS NSQIP Database

ObjectiveThere is limited evidence to clarify the specific relationship between pre-operative blood urea nitrogen (BUN) and post-operative 30-day mortality in patients undergoing craniotomy for tumors. Therefore, we aimed to investigate this relationship in detail.MethodsElectronic medical records of 18,642 patients undergoing craniotomy for tumors in the ACS NSQIP from 2012 to 2015 were subjected to secondary retrospective analysis. The principal exposure was pre-operative BUN. Outcome measures were post-operative 30-day mortality. We used binary logistic regression modeling to evaluate the association between them and conducted a generalized additive model and smooth curve fitting (penalized spline method) to explore the potential relationship and its explicit curve shape. We also conducted sensitivity analyses to ensure the robustness of the results and performed subgroup analyses.ResultsA total of 16,876 patients were included in this analysis. Of these, 47.48% of patients were men. The post-operative 30-day mortality of the included cases was 2.49% (420/16,876), and the mean BUN was 16.874 ± 6.648 mg/dl. After adjusting covariates, the results showed that pre-operative BUN was positively associated with post-operative 30-day mortality (OR = 1.020, 95% CI: 1.004, 1.036). There was also a non-linear relationship between BUN and post-operative 30-day mortality, and the inflection point of the BUN was 9.804. For patients with BUN < 9.804 mg/dl, a 1 unit decrease in BUN was related to a 16.8% increase in the risk of post-operative 30-day mortality (OR = 0.832, 95% CI: 0.737, 0.941); for patients with BUN > 9.804 mg/dl, a 1 unit increase in BUN was related to a 2.8% increase in the risk of post-operative 30-day mortality (OR = 1.028, 95% CI: 1.011, 1.045). The sensitivity analysis proved that the results were robust. The subgroup analysis revealed that all listed subgroups did not affect the relationship between pre-operative BUN and post-operative 30-day mortality (P > 0.05).ConclusionOur study demonstrated that pre-operative BUN (mg/dl) has specific linear and non-linear relationships with post-operative 30-day mortality in patients over 18 years of age who underwent craniotomy for tumors. Proper pre-operative management of BUN and maintenance of BUN near the inflection point (9.804 mg/dl) could reduce the risk of post-operative 30-day mortality in these cases

Airborne observations reveal elevational gradient in tropical forest isoprene emissions

Isoprene dominates global non-methane volatile organic compound emissions, and impacts tropospheric chemistry by influencing oxidants and aerosols. Isoprene emission rates vary over several orders of magnitude for different plants, and characterizing this immense biological chemodiversity is a challenge for estimating isoprene emission from tropical forests. Here we present the isoprene emission estimates from aircraft eddy covariance measurements over the Amazonian forest. We report isoprene emission rates that are three times higher than satellite top-down estimates and 35% higher than model predictions. The results reveal strong correlations between observed isoprene emission rates and terrain elevations, which are confirmed by similar correlations between satellite-derived isoprene emissions and terrain elevations. We propose that the elevational gradient in the Amazonian forest isoprene emission capacity is determined by plant species distributions and can substantially explain isoprene emission variability in tropical forests, and use a model to demonstrate the resulting impacts on regional air quality

Case studies of electrical characterisation of graphene by terahertz time-domain spectroscopy

Graphene metrology needs to keep up with the fast pace of developments in graphene growth and transfer. Terahertz time-domain spectroscopy (THz-TDS) is a non-contact, fast, and non-destructive characterization technique for mapping the electrical properties of graphene. Here we show several case studies of graphene characterization on a range of different substrates that highlight the versatility of THz-TDS measurements and its relevance for process optimization in graphene production scenarios

Using Cell Phone Location To Assess Misclassification Errors In Air Pollution Exposure Estimation

Air pollution epidemiologic and health impact studies often rely on home addresses to estimate individual subject\u27s pollution exposure. In this study, we used detailed cell phone location data, the call detail record (CDR), to account for the impact of spatiotemporal subject mobility on estimates of ambient air pollutant exposure. This approach was applied on a sample with 9886 unique simcard IDs in Shenzhen, China, on one mid-week day in October 2013. Hourly ambient concentrations of six chosen pollutants were simulated by the Community Multi-scale Air Quality model fused with observational data, and matched with detailed location data for these IDs. The results were compared with exposure estimates using home addresses to assess potential exposure misclassification errors. We found the misclassifications errors are likely to be substantial when home location alone is applied. The CDR based approach indicates that the home based approach tends to over-estimate exposures for subjects with higher exposure levels and under-estimate exposures for those with lower exposure levels. Our results show that the cell phone location based approach can be used to assess exposure misclassification error and has the potential for improving exposure estimates in air pollution epidemiology studies. Cell phone location-based exposure estimation has the potential for improving exposure estimates vs. home address-based approaches that are likely to have increased misclassification errors because it does not account for individual mobility

A Survey of Deep Learning for Low-Shot Object Detection

Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce due to the severe overfitting problem. Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task. Low-Shot Object Detection (LSOD) is an emerging research topic of detecting objects from a few or even no annotated samples, consisting of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and analyze them systematically, comprising some extensional topics of LSOD (semi-supervised LSOD, weakly-supervised LSOD and incremental LSOD). Then, we indicate the pros and cons of current LSOD methods with a comparison of their performance. Finally, we discuss the challenges and promising directions of LSOD to provide guidance for future works

The Effect of Ethanol on Abnormal Grain Growth in Copper Foils

Single-crystal Cu not only has high electrical and thermal conductivity, but can also be used as a promising platform for the epitaxial growth of two-dimensional materials. Preparing large-area single-crystal Cu foils from polycrystalline foils has emerged as the most promising technique in terms of its simplicity and effectiveness. However, the studies on transforming polycrystalline foil into large-area single-crystal foil mainly focus on the influence of annealing temperature and strain energy on the recrystallization process of copper foil, while studies on the effect of annealing atmosphere on abnormal grain growth behavior are relatively rare. It is necessary to carry out more studies on the effect of annealing atmosphere on grain growth behavior to understand the recrystallization mechanism of metal. Here, we found that introduction of ethanol in pure argon annealing atmosphere will cause the abnormal grain growth of copper foil. Moreover, the number of abnormally grown grains can be controlled by the concentration of ethanol in the annealing atmosphere. Using this technology, the number of abnormally grown grains on the copper foil can be controlled to single one. This abnormally grown grain will grow rapidly to decimeter-size by consuming the surrounding small grains. This work provides a new perspective for the understanding of the recrystallization of metals, and a new method for the preparation of large-area single-crystal copper foils