Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Masonry has superior fire resistance properties stemming from its inert characteristics, and slow degradation of mechanical properties. However, once exposed to fire conditions, masonry undergoes a series of physio-chemical changes. Such changes are often described via temperature-dependent material models. Despite calls for standardization of such models, there is a lack in such standardized models. As a result, available temperature-dependent material models vary across various fire codes and standards. In order to bridge this knowledge gap, this paper presents three methodologies, namely, regression-based, probabilistic-based, and the use of artificial neural (ANN) networks, to derive generalized temperature-dependent material models for masonry with a case study on the compressive strength property. Findings from this paper can be adopted to establish updated temperature-dependent material models of fire design and analysis of masonry structures

DisWOT: Student Architecture Search for Distillation WithOut Training

Knowledge distillation (KD) is an effective training strategy to improve the lightweight student models under the guidance of cumbersome teachers. However, the large architecture difference across the teacher-student pairs limits the distillation gains. In contrast to previous adaptive distillation methods to reduce the teacher-student gap, we explore a novel training-free framework to search for the best student architectures for a given teacher. Our work first empirically show that the optimal model under vanilla training cannot be the winner in distillation. Secondly, we find that the similarity of feature semantics and sample relations between random-initialized teacher-student networks have good correlations with final distillation performances. Thus, we efficiently measure similarity matrixs conditioned on the semantic activation maps to select the optimal student via an evolutionary algorithm without any training. In this way, our student architecture search for Distillation WithOut Training (DisWOT) significantly improves the performance of the model in the distillation stage with at least 180$\times$ training acceleration. Additionally, we extend similarity metrics in DisWOT as new distillers and KD-based zero-proxies. Our experiments on CIFAR, ImageNet and NAS-Bench-201 demonstrate that our technique achieves state-of-the-art results on different search spaces. Our project and code are available at https://lilujunai.github.io/DisWOT-CVPR2023/.Comment: Accepted by CVPR202

Context-Aware Personalized Point-of-Interest Recommendation System

The increasing volume of information has created overwhelming challenges to extract the relevant items manually. Fortunately, the online systems, such as e-commerce (e.g., Amazon), location-based social networks (LBSNs) (e.g., Facebook) among many others have the ability to track end users\u27 browsing and consumption experiences. Such explicit experiences (e.g., ratings) and many implicit contexts (e.g., social, spatial, temporal, and categorical) are useful in preference elicitation and recommendation. As an emerging branch of information filtering, the recommendation systems are already popular in many domains, such as movies (e.g., YouTube), music (e.g., Pandora), and Point-of-Interest (POI) (e.g., Yelp). The POI domain has many contextual challenges (e.g., spatial (preferences to a near place), social (e.g., friend\u27s influence), temporal (e.g., popularity at certain time), categorical (similar preferences to places with same category), locality of POI, etc.) that can be crucial for an efficient recommendation. The user reviews shared across different social networks provide granularity in users\u27 consumption experience. From the data mining and machine learning perspective, following three research directions are identified and considered relevant to an efficient context-aware POI recommendation, (1) incorporation of major contexts into a single model and a detailed analysis of the impact of those contexts, (2) exploitation of user activity and location influence to model hierarchical preferences, and (3) exploitation of user reviews to formulate the aspect opinion relation and to generate explanation for recommendation. This dissertation presents different machine learning and data mining-based solutions to address the above-mentioned research problems, including, (1) recommendation models inspired from contextualized ranking and matrix factorization that incorporate the major contexts and help in analysis of their importance, (2) hierarchical and matrix-factorization models that formulate users\u27 activity and POI influences on different localities that model hierarchical preferences and generate individual and sequence recommendations, and (3) graphical models inspired from natural language processing and neural networks to generate recommendations augmented with aspect-based explanations

Mechanism of feature learning in convolutional neural networks

Understanding the mechanism of how convolutional neural networks learn features from image data is a fundamental problem in machine learning and computer vision. In this work, we identify such a mechanism. We posit the Convolutional Neural Feature Ansatz, which states that covariances of filters in any convolutional layer are proportional to the average gradient outer product (AGOP) taken with respect to patches of the input to that layer. We present extensive empirical evidence for our ansatz, including identifying high correlation between covariances of filters and patch-based AGOPs for convolutional layers in standard neural architectures, such as AlexNet, VGG, and ResNets pre-trained on ImageNet. We also provide supporting theoretical evidence. We then demonstrate the generality of our result by using the patch-based AGOP to enable deep feature learning in convolutional kernel machines. We refer to the resulting algorithm as (Deep) ConvRFM and show that our algorithm recovers similar features to deep convolutional networks including the notable emergence of edge detectors. Moreover, we find that Deep ConvRFM overcomes previously identified limitations of convolutional kernels, such as their inability to adapt to local signals in images and, as a result, leads to sizable performance improvement over fixed convolutional kernels

Linear models, signal detection, and the Grassmann manifold

2014 Fall.Standard approaches to linear signal detection, reconstruction, and model identification problems, such as matched subspace detectors (MF, MDD, MSD, and ACE) and anomaly detectors (RX) are derived in the ambient measurement space using statistical methods (GLRT, regression). While the motivating arguments are statistical in nature, geometric interpretations of the test statistics are sometimes developed after the fact. Given a standard linear model, many of these statistics are invariant under orthogonal transformations, have a constant false alarm rate (CFAR), and some are uniformly most powerful invariant (UMPI). These properties combined with the simplicity of the tests have led to their widespread use. In this dissertation, we present a framework for applying real-valued functions on the Grassmann manifold in the context of these same signal processing problems. Specifically, we consider linear subspace models which, given assumptions on the broadband noise, correspond to Schubert varieties on the Grassmann manifold. Beginning with increasing (decreasing) or Schur-convex (-concave) functions of principal angles between pairs of points, of which the geodesic and chordal distances (or probability distribution functions) are examples, we derive the associated point-to-Schubert variety functions and present signal detection and reconstruction algorithms based upon this framework. As a demonstration of the framework in action, we implement an end-to-end system utilizing our framework and algorithms. We present results of this system processing real hyperspectral images