Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes

Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be guided. To this end, we introduce neural stethoscopes as a general purpose framework for quantifying the degree of importance of specific factors of influence in deep neural networks as well as for actively promoting and suppressing information as appropriate. In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. We apply neural stethoscopes to analyse the state-of-the-art neural network for stability prediction. We show that the baseline model is susceptible to being misled by incorrect visual cues. This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability. Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy. Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset. Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

Effects of strand and directional asymmetry on base-base coupling and charge transfer in double-helical DNA

Mechanistic models of charge transfer (CT) in macromolecules often focus on CT energetics and distance as the chief parameters governing CT rates and efficiencies. However, in DNA, features unique to the DNA molecule, in particular, the structure and dynamics of the DNA base stack, also have a dramatic impact on CT. Here we probe the influence of subtle structural variations on base-base CT within a DNA duplex by examining photoinduced quenching of 2-aminopurine (Ap) as a result of hole transfer (HT) to guanine (G). Photoexcited Ap is used as a dual reporter of variations in base stacking and CT efficiency. Significantly, the unique features of DNA, including the strandedness and directional asymmetry of the double helix, play a defining role in CT efficiency. For an (AT)(n) bridge, the orientation of the base pairs is critical; the yield of intrastrand HT is markedly higher through (A)n compared with (T)(n) bridges, whereas HT via intrastrand pathways is more efficient than through interstrand pathways. Remarkably, for reactions through the same DNA bridge, over the same distance, and with the same driving force, HT from photoexcited Ap to G in the 5' to 3' direction is more efficient and less dependent on distance than HT from 3' to 5'. We attribute these differences in HT efficiency to variations in base-base coupling within the DNA assemblies. Thus base-base coupling is a critical parameter in DNA CT and strongly depends on subtle structural nuances of duplex DNA