Local Conditional Controlling for Text-to-Image Diffusion Models

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level structure controls, e.g., edge and depth maps, to manipulate the generation process together with text prompts to obtain desired images. This controlling process is globally operated on the entire image, which limits the flexibility of control regions. In this paper, we explore a novel and practical task setting: local control. It focuses on controlling specific local region according to user-defined image conditions, while the remaining regions are only conditioned by the original text prompt. However, it is non-trivial to achieve local conditional controlling. The naive manner of directly adding local conditions may lead to the local control dominance problem, which forces the model to focus on the controlled region and neglect object generation in other regions. To mitigate this problem, we propose Regional Discriminate Loss to update the noised latents, aiming at enhanced object generation in non-control regions. Furthermore, the proposed Focused Token Response suppresses weaker attention scores which lack the strongest response to enhance object distinction and reduce duplication. Lastly, we adopt Feature Mask Constraint to reduce quality degradation in images caused by information differences across the local control region. All proposed strategies are operated at the inference stage. Extensive experiments demonstrate that our method can synthesize high-quality images aligned with the text prompt under local control conditions

Performance of Multi-Armed Bandit Algorithms in Dynamic vs. Static Environments: A Comparative Analysis

This paper conducts a comparative analysis of Multi-Armed Bandit (MAB) algorithms, particularly the Upper Confidence Bound (UCB) and Thompson Sampling (TS) algorithms, and focuses on the performance of these algorithms in both static and dynamic environments. Multi-armed bandit algorithms are instrumental in optimizing decision-making problems. While these algorithms have been studied in a static environment where the reward distribution is constant throughout the problem, real-world issues often have an unstable reward distribution, where the reward distribution may change throughout the process. This paper simulates both static and dynamic environments to evaluate the performance of UCB and TS algorithms by using the MovieLens 1M database. The paper demonstrates that the TS algorithm consistently outperforms the UCB algorithm in both static and dynamic environments. However, both algorithm shows a significantly higher cumulative regret in a dynamic environment compared with a static environment, which is due to the challenges of adapting to changing reward distribution over time. These results provide valuable insight into the application of Multi-Armed Bandit algorithms in real-world environments and highlight the need for further advancement in dynamic adaption for algorithms

Assessing Safety Efficiency in China’s Provincial Construction Industry: Trends, Influences, and Implications

Ensuring safety is crucial for promoting the sustainable growth of the construction industry. Assessing safety efficiency is of significant importance for optimizing safety management processes and improving the safety environment. However, the current mainstream methods for evaluating safety efficiency have limitations such as ignoring non-desired outputs and slack variables, the efficiency values being limited to the (0, 1) range, and a narrow perspective. To address these shortcomings, this study focuses on the characteristics of the construction industry and introduces the Super-SBM model and Malmquist index into the assessment of safety efficiency in the construction industry. The study analyzes the evolution characteristics of safety efficiency from both static and dynamic perspectives. Furthermore, using panel quantile regression models, the study identifies the factors influencing safety efficiency and analyzes their heterogeneity. Analyzing panel data from 30 provinces in China from 2015 to 2021, the results show that the overall safety efficiency of the construction industry in China is relatively low, with noticeable spatial clustering characteristics. Provinces in the eastern and central regions exhibit higher levels of construction safety efficiency. The Malmquist index demonstrates a declining trend, with technical efficiency being the primary factor limiting the improvement of safety efficiency in construction. Factors such as per capita GDP, urbanization rate, committed contract amounts, and the number of professionals engaged in survey and design, as well as engineering supervision, have an impact on construction safety efficiency, and the effects of these variables vary across different quantile levels of safety efficiency. This research can assist decision-makers in gaining a better understanding of the safety conditions in different regions of the construction industry. It can also assist in developing customized policies to enhance the health and safety environment, thereby promoting the stable development of the construction industry

Influence of machining parameters in longitudinaltorsional ultrasonic vibration milling titanium alloy for milling force

Abstract To study the effect of different parameters on milling force for longitudinal torsional ultrasonic vibration milling (LTUM) of titanium alloy, the kinematic theory of LTUM is combined with the model of milling transient cutting thickness to establish the milling force equation, and experiment was carried out. The experimental results showed that: Milling force was positively correlated with cutting speed, cutting depth, feed per tooth (milling force increased by about 40% in increasing the cutting speed from 40m/min to 100m/min, 300% in increasing the depth of cut from 0.1mm to 0.4mm, and 25% in increasing the feed per tooth from 0.01mm to 0.04mm). Milling force was negatively correlated with ultrasonic amplitude, tool helix angle (milling force reduced by about 22% in increasing the ultrasonic amplitude from 1µm to 4µm, and 23% in increasing the tool helix angle from 30° to 45°). The milling force was minimized when the ultrasonic amplitude was 4 µm, cutting speed was 60 m/min, cutting depth was 0.1 mm, feed per tooth was 0.01 mm/z, and tool helix angle was 40°. In further, the empirical model of milling force was established and accuracy was verified by experimental data.</jats:p

Drinking coffee may help accelerate orthodontic tooth movement

Introduction: Developing new methods to enhance orthodontic tooth movement and shorten the duration of treatment has always been desired. However, to date, no therapies have been widely used in clinics. Recent studies and feedback information from patients have shown that drinking coffee may accelerate orthodontic tooth movement. The Hypothesis: Drinking coffee, as a daily habit of many people, can be an effective accelerator of tooth movement with little side effect for caffeine can break the calcium balance in bone tissue and directly inhibit the development of osteoblasts, leading to temporary decreased bone mineral density and consequently inducing faster orthodontic tooth movement. Evaluation of the Hypothesis: Much effort has been made to explore therapies to shorten orthodontic treatment period with limited success. Daily coffee consumption may be a promising approach to enhance orthodontic tooth movement for its reversible effect on bone mineral density and calcium balance