Building integrated representations through interleaved learning.

Inferring relationships that go beyond our direct experience is essential for understanding our environment. This capacity requires either building representations that directly reflect structure across experiences as we encounter them or deriving the indirect relationships across experiences as the need arises. Building structure directly into overlapping representations allows for powerful learning and generalization in neural network models, but building these so-called distributed representations requires inputs to be encountered in interleaved order. We test whether interleaving similarly facilitates the formation of representations that directly integrate related experiences in humans and what advantages such integration may confer for behavior. In a series of behavioral experiments, we present evidence that interleaved learning indeed promotes the formation of representations that directly link across related experiences. As in neural network models, interleaved learning gives rise to fast and automatic recognition of item relatedness, affords efficient generalization, and is especially critical for inference when learning requires statistical integration of noisy information over time. We use the data to adjudicate between several existing computational models of human memory and inference. The results demonstrate the power of interleaved learning and implicate the formation of integrated, distributed representations that support generalization in humans. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

When will AI misclassify? Intuiting failures on natural images.

Machine recognition systems now rival humans in their ability to classify natural images. However, their success is accompanied by a striking failure: a tendency to commit bizarre misclassifications on inputs specifically selected to fool them. What do ordinary people know about the nature and prevalence of such classification errors? Here, five experiments exploit the recent discovery of "natural adversarial examples" to ask whether naive observers can predict when and how machines will misclassify natural images. Whereas classical adversarial examples are inputs that have been minimally perturbed to induce misclassifications, natural adversarial examples are simply unmodified natural photographs that consistently fool a wide variety of machine recognition systems. For example, a bird casting a shadow might be misclassified as a sundial, or a beach umbrella made of straw might be misclassified as a broom. In Experiment 1, subjects accurately predicted which natural images machines would misclassify and which they would not. Experiments 2 through 4 extended this ability to how the images would be misclassified, showing that anticipating machine misclassifications goes beyond merely identifying an image as nonprototypical. Finally, Experiment 5 replicated these findings under more ecologically valid conditions, demonstrating that subjects can anticipate misclassifications not only under two-alternative forced-choice conditions (as in Experiments 1-4), but also when the images appear one at a time in a continuous stream-a skill that may be of value to human-machine teams. We suggest that ordinary people can intuit how easy or hard a natural image is to classify, and we discuss the implications of these results for practical and theoretical issues at the interface of biological and artificial vision.

Experimental Study on the Gmax Characteristics of the Sand-Silt Mixed Soil Materials Using Bender Element Testing.

To study the small strain shear modulus (Gmax) of saturated sand-silt mixed soil materials, a series of tests were conducted using the bender element apparatus, and the influences of fines content (FC), relative density (Dr), and effective confining pressure (σ'3c) were taken into consideration. The test results indicate that the Gmax of the mixed soil materials decreases first and then increases with the FC up to 100% with Dr = 35% and 50%, while the Gmax decreases with the increasing FC when Dr = 60%. Moreover, for a given Dr, the Gmax increases with the increasing σ'3c, and the increase rate keeps constant under various FCs. The Gmax of specimens under various FCs decreases with the increase of the void ratio (e). The decrease rate between the Gmax and e differs when the σ'3c is given, which is influenced by the FC. The Gmax of the mixed soil materials can be evaluated by the Hardin model when the FC is determined. The best-fitting parameter A of the Hardin model first decreases and then increases as FC increases. The revised Hardin model, considering the influence of FC, σ'3c, and e, can be used to evaluate the Gmax for different types of sand-silt mixed soil materials. The error between the evaluated and tested Gmax is less than 10%.

Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials.

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our algorithm is suitable for perspective cameras and nearby point light sources. Our data capture setup is simple, which consists of only a digital camera, some LED lights, and an optional automatic turntable. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. In experiments, we demonstrate our algorithm with two different setups: a studio setup for highest precision and a desktop setup for best usability. According to our experiments, under the studio setting, the captured shapes are accurate to 0.5 millimeters and the captured reflectance has a relative root-mean-square error (RMSE) of 9%. We also quantitatively evaluate state-of-the-art MVPS on a newly collected benchmark dataset, which is publicly available for inspiring future research.

[Temporal and spatial variations of impervious surface landscape pattern and the driving factors in Xiamen City, China]. / åŽ¦é—¨å¸‚ä¸é€æ°´é¢æ™¯è§‚æ ¼å±€æ—¶ç©ºå˜åŒ–åŠé©±åŠ¨åŠ›åˆ†æž.

Xiamen is one of China's five major special economic zones and is the core city of Haixi Economic Zone, with a high level of urbanization. Monitoring and driving force analysis of impervious surfaces can increase our understanding of urbanization process and have important significance for urban landscape pattern research and urban ecological environment construction. We used the Landsat remote sensing image data from 1978 to 2018 to reveal the temporal and spatial variation characteristics of the impervious surface landscape in Xiamen in the past 40 years, using the full-restricted least squares method, landscape pattern analysis, slope gradient analysis and correlation analysis. We further analyzed its relationship with social and economic factors. The results showed that, during 1978-2018, the impervious surface of Xiamen increased by 348.96 km2, with a mean annual increase of 8.72 km2. The impervious surface dynamics reached a maximum of 9.0% in 2005-2010. More than 86.6% of the impervious surface of Xiamen was distributed within 6° of slope, with a tendency to expand to a greater slope in 2010-2018. With the increases of slope, the proportion of impervious surface decreased, the density of plaque decreased with the shape tending to be regular and continuous, the degree of fragmentation of the impervious surface increased. The increases of impervious surface in Xiamen was significantly related to the regional economic aggregate and population. In the study period, the spatial pattern of impervious surface in Xiamen significantly altered. In the future urban planning process, the extent and speed of impervious surface expansion should be coordinated to avoid ecological problems caused by excessive impervious surface to meet the need for sustainable development of Xiamen.

Humans can decipher adversarial images.

Does the human mind resemble the machine-learning systems that mirror its performance? Convolutional neural networks (CNNs) have achieved human-level benchmarks in classifying novel images. These advances support technologies such as autonomous vehicles and machine diagnosis; but beyond this, they serve as candidate models for human vision itself. However, unlike humans, CNNs are "fooled" by adversarial examples-nonsense patterns that machines recognize as familiar objects, or seemingly irrelevant image perturbations that nevertheless alter the machine's classification. Such bizarre behaviors challenge the promise of these new advances; but do human and machine judgments fundamentally diverge? Here, we show that human and machine classification of adversarial images are robustly related: In 8 experiments on 5 prominent and diverse adversarial imagesets, human subjects correctly anticipated the machine's preferred label over relevant foils-even for images described as "totally unrecognizable to human eyes". Human intuition may be a surprisingly reliable guide to machine (mis)classification-with consequences for minds and machines alike.