Mobility of the Chinese Urban Poor - A Case Study of Hefei City

In a rapid economic development environment with rising income, escalating motorization, and growing urbanization, it is natural for government policies to focus on solving congestion related problems caused by the increased car ownership and usage. The mobility needs of the urban poor have been traditionally neglected in policy and in practice, particularly in developing countries. This paper addresses the mobility challenges the urban poor are facing based on a household travel survey in the City of Hefei in China. It first presents travel behaviors, transportation costs and commuting problems of the urban poor. It then discusses the urban transportation policy implications and examines the prevailing trends of urban transportation policies and plans in Chinese cities. Policy recommendations are suggested to improve the mobility needs of the urban poor.Urban transportation, poverty, mobility

A Compositional Analysis of Unbalanced Usages of Multiple Left-turn Lanes

Lane usage measures distribution of a specific traffic movement across multiple available lanes in a given time. Unbalanced lane usages decrease the capacity of subject segment. This paper took multiple left-turn lanes at signalized intersections as case study, and explored the influences of some factors on the lane usage balance. Lane usages were calculated from field collected lane volumes and the constant-sum constraint among them was explicitly considered in the statistical analysis. Classical and compositional analysis of variance was respectively conducted to identify significant influential factors. By comparing the results of compositional analysis and those of the classical one, the former ones have better interpretability. It was found that left-turn lane usages could be affected by parameter variance of geometric design or traffic control, such as length of turning curve, length of upstream segment, length of signal phase or cycle. These factors could make the lane usages achieve relative balance at different factor levels.</p

Rigid vortices in MgB2

Magnetic relaxation of high-pressure synthesized MgB$_2$ bulks with different thickness is investigated. It is found that the superconducting dia-magnetic moment depends on time in a logarithmic way; the flux-creep activation energy decreases linearly with the current density (as expected by Kim-Anderson model); and the activation energy increases linearly with the thickness of sample when it is thinner than about 1 mm. These features suggest that the vortices in the MgB$_2$ are rather rigid, and the pinning and creep can be well described by Kim-Anderson model.Comment: Typo corrected & reference adde

Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings

Despite the great success of neural visual generative models in recent years, integrating them with strong symbolic knowledge reasoning systems remains a challenging task. The main challenges are two-fold: one is symbol assignment, i.e. bonding latent factors of neural visual generators with meaningful symbols from knowledge reasoning systems. Another is rule learning, i.e. learning new rules, which govern the generative process of the data, to augment the knowledge reasoning systems. To deal with these symbol grounding problems, we propose a neural-symbolic learning approach, Abductive Visual Generation (AbdGen), for integrating logic programming systems with neural visual generative models based on the abductive learning framework. To achieve reliable and efficient symbol assignment, the quantized abduction method is introduced for generating abduction proposals by the nearest-neighbor lookups within semantic codebooks. To achieve precise rule learning, the contrastive meta-abduction method is proposed to eliminate wrong rules with positive cases and avoid less-informative rules with negative cases simultaneously. Experimental results on various benchmark datasets show that compared to the baselines, AbdGen requires significantly fewer instance-level labeling information for symbol assignment. Furthermore, our approach can effectively learn underlying logical generative rules from data, which is out of the capability of existing approaches

SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution

Advanced text-to-image models such as DALL-E 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential proliferation of unsafe content. This includes adult, violent, or deceptive imagery of political figures. Despite claims of rigorous safety mechanisms implemented in these models to restrict the generation of not-safe-for-work (NSFW) content, we successfully devise and exhibit the first prompt attacks on Midjourney, resulting in the production of abundant photorealistic NSFW images. We reveal the fundamental principles of such prompt attacks and suggest strategically substituting high-risk sections within a suspect prompt to evade closed-source safety measures. Our novel framework, SurrogatePrompt, systematically generates attack prompts, utilizing large language models, image-to-text, and image-to-image modules to automate attack prompt creation at scale. Evaluation results disclose an 88% success rate in bypassing Midjourney's proprietary safety filter with our attack prompts, leading to the generation of counterfeit images depicting political figures in violent scenarios. Both subjective and objective assessments validate that the images generated from our attack prompts present considerable safety hazards.Comment: 14 pages, 11 figure