Dust in Active Galactic Nuclei: Anomalous Silicate to Optical Extinction Ratios?

Dust plays a central role in the unification theory of active galactic nuclei (AGNs). However, little is known about the nature (e.g., size, composition) of the dust which forms a torus around the AGN. In this Letter we report a systematic exploration of the optical extinction (A_V) and the silicate absorption optical depth (\Delta\tau9.7) of 110 type 2 AGNs. We derive A_V from the Balmer decrement based on the Sloan Digital Sky Survey data, and \Delta\tau9.7 from the Spitzer/Infrared Spectrograph data. We find that with a mean ratio of A_V/\Delta\tau9.7 ~ 5.5, the optical-to-silicate extinction ratios of these AGNs are substantially lower than that of the Galactic diffuse interstellar medium (ISM) for which A_V/\Delta\tau9.7 ~ 18.5. We argue that the anomalously low A_V/\Delta\tau9.7 ratio could be due to the predominance of larger grains in the AGN torus compared to that in the Galactic diffuse ISM.Comment: ApJL, 792, L9, in prin

MMF3: Neural Code Summarization Based on Multi-Modal Fine-Grained Feature Fusion

Background: Code summarization automatically generates the corresponding natural language descriptions according to the input code. Comprehensiveness of code representation is critical to code summarization task. However, most existing approaches typically use coarse-grained fusion methods to integrate multi-modal features. They generally represent different modalities of a piece of code, such as an Abstract Syntax Tree (AST) and a token sequence, as two embeddings and then fuse the two ones at the AST/code levels. Such a coarse integration makes it difficult to learn the correlations between fine-grained code elements across modalities effectively. Aims: This study intends to improve the model's prediction performance for high-quality code summarization by accurately aligning and fully fusing semantic and syntactic structure information of source code at node/token levels. Method: This paper proposes a Multi-Modal Fine-grained Feature Fusion approach (MMF3) for neural code summarization. We introduce a novel fine-grained fusion method, which allows fine-grained fusion of multiple code modalities at the token and node levels. Specifically, we use this method to fuse information from both token and AST modalities and apply the fused features to code summarization. Results: We conduct experiments on one Java and one Python datasets, and evaluate generated summaries using four metrics. The results show that: 1) the performance of our model outperforms the current state-of-the-art models, and 2) the ablation experiments show that our proposed fine-grained fusion method can effectively improve the accuracy of generated summaries. Conclusion: MMF3 can mine the relationships between crossmodal elements and perform accurate fine-grained element-level alignment fusion accordingly. As a result, more clues can be provided to improve the accuracy of the generated code summaries.Comment: 12 pages, 5 figure

Consensus-based construction of high-dimensional free energy surface

One essential problem in quantifying the collective behaviors of molecular systems lies in the accurate construction of free energy surfaces (FESs). The main challenges arise from the prevalence of energy barriers and the high dimensionality. Existing approaches are often based on sophisticated enhanced sampling methods to establish efficient exploration of the full-phase space. On the other hand, the collection of optimal sample points for the numerical approximation of FESs remains largely under-explored, where the discretization error could become dominant for systems with a large number of collective variables (CVs). We propose a consensus sampling-based approach by reformulating the construction as a minimax problem which simultaneously optimizes the function representation and the training set. In particular, the maximization step establishes a stochastic interacting particle system to achieve the adaptive sampling of the max-residue regime by modulating the exploitation of the Laplace approximation of the current loss function and the exploration of the uncharted phase space; the minimization step updates the FES approximation with the new training set. By iteratively solving the minimax problem, the present method essentially achieves an adversarial learning of the FESs with unified tasks for both phase space exploration and posterior error-enhanced sampling. We demonstrate the method by constructing the FESs of molecular systems with a number of CVs up to 30

Construction of coarse-grained molecular dynamics with many-body non-Markovian memory

We introduce a machine-learning-based coarse-grained molecular dynamics (CGMD) model that faithfully retains the many-body nature of the inter-molecular dissipative interactions. Unlike common empirical CG models, the present model is constructed based on the Mori-Zwanzig formalism and naturally inherits the heterogeneous state-dependent memory term rather than matching the mean-field metrics such as the velocity auto-correlation function. Numerical results show that preserving the many-body nature of the memory term is crucial for predicting the collective transport and diffusion processes, where empirical forms generally show limitations

Understanding the Role of Pathways in a Deep Neural Network

Deep neural networks have demonstrated superior performance in artificial intelligence applications, but the opaqueness of their inner working mechanism is one major drawback in their application. The prevailing unit-based interpretation is a statistical observation of stimulus-response data, which fails to show a detailed internal process of inherent mechanisms of neural networks. In this work, we analyze a convolutional neural network (CNN) trained in the classification task and present an algorithm to extract the diffusion pathways of individual pixels to identify the locations of pixels in an input image associated with object classes. The pathways allow us to test the causal components which are important for classification and the pathway-based representations are clearly distinguishable between categories. We find that the few largest pathways of an individual pixel from an image tend to cross the feature maps in each layer that is important for classification. And the large pathways of images of the same category are more consistent in their trends than those of different categories. We also apply the pathways to understanding adversarial attacks, object completion, and movement perception. Further, the total number of pathways on feature maps in all layers can clearly discriminate the original, deformed, and target samples

Examining The Impact of a DSP Project Through a Comparative Adult Education Lens: A Snapshot of Principal Professional Development for Education Internationalization in Beijing, China

Through the lenses of comparative adult education and international educational leadership development, this study explores the learning experiences of local school principals after they participated in a professional development program named Domestic Study Program (DSP) in Beijing. A qualitative narrative inquiry was applied and four school principals who self-reported as experiencing personal and professional improvement through the DSP program were interviewed. Their lived learning experiences as adult learners through the DSP project were sorted, categorized, grouped, and regrouped following the qualitative research data analysis protocols suggested by Rossman and Rallis (2003) and Creswell (2014). The research indicates that the four local principals experienced major changes in the areas of self-perception, ways of thinking, and ways of doing. The findings are interpreted through the lenses of comparative adult education and international educational leadership development