Cognitive Deficit of Deep Learning in Numerosity

Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the CNN computational machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful for visual numerosity represents a minimum level of human intelligence.Comment: Accepted for presentation at the AAAI-1

Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation

News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability.Comment: 12 pages, 2 figure

Eucommia ulmoides extract attenuates angiotensin II-induced cardiac microvascular endothelial cell dysfunction by inactivating p53

Angiotensin II (AngII) causes endothelial dysfunction. Eucommia ulmoides extract (EUE) is documented to manipulate AngII, but its impact on cardiac microvascular endothelial cell (CMVEC) function remains unknown. This study determines the effects of EUE on AngII-treated CMVECs. CMVECs were treated with different concentrations of AngII or EUE alone and/or the p53 protein activator, WR-1065, before AngII treatment, followed by examinations of the apoptotic, migratory, proliferative, and angiogenic capacities and nitric oxide (NO), p53, von Willebrand factor (vWF), endothelin (ET)-1, endothelial NO synthase (eNOS), manganese superoxide dismutase (MnSOD), hypoxia-inducible factor (HIF)-1α, and vascular endothelial growth factor (VEGF) levels. AngII induced CMVEC dysfunction in a concentration-dependent manner. EUE enhanced the proliferative, migratory, and angiogenic capacities and NO, MnSOD, and eNOS levels but repressed apoptosis and vWF and ET-1 levels in AngII-induced dysfunctional CMVECs. Moreover, AngII increased p53 mRNA levels, p-p53 levels in the nucleus, and p53 protein levels in the cytoplasm and diminishes HIF-1α and VEGF levels in CMVECs; however, these effects were counteracted by EUE treatment. Moreover, WR-1065 abrogated the mitigating effects of EUE on AngII-induced CMVEC dysfunction by activating p53 and decreasing HIF-1α and VEGF expression. In conclusion, EUE attenuates AngII-induced CMVEC dysfunction by upregulating HIF-1α and VEGF levels via p53 inactivation

On the Importance of Backbone to the Adversarial Robustness of Object Detectors

Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. However, existing deep learning-based object detectors are vulnerable to adversarial attacks, which poses a significant challenge to their reliability and safety. Through experiments, we found that existing works on improving the adversarial robustness of object detectors have given a false sense of security. We argue that using adversarially pre-trained backbone networks is essential for enhancing the adversarial robustness of object detectors. We propose a simple yet effective recipe for fast adversarial fine-tuning on object detectors with adversarially pre-trained backbones. Without any modifications to the structure of object detectors, our recipe achieved significantly better adversarial robustness than previous works. Moreover, we explore the potential of different modern object detectors to improve adversarial robustness using our recipe and demonstrate several interesting findings. Our empirical results set a new milestone and deepen the understanding of adversarially robust object detection. Code and trained checkpoints will be publicly available.Comment: 12 page