Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding

On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists. On the other hand, the dehazing task should take into account the subjective factor, which is to give the user selectable dehazed images rather than a single result. Therefore, this paper proposes a multi-output dehazing network by introducing illumination controllable ability, called IC-Dehazing. The proposed IC-Dehazing can change the illumination intensity by adjusting the factor of the illumination controllable module, which is realized based on the interpretable Retinex theory. Moreover, the backbone dehazing network of IC-Dehazing consists of a Transformer with double decoders for high-quality image restoration. Further, the prior-based loss function and unsupervised training strategy enable IC-Dehazing to complete the parameter learning process without the need for paired data. To demonstrate the effectiveness of the proposed IC-Dehazing, quantitative and qualitative experiments are conducted on image dehazing, semantic segmentation, and object detection tasks. Code is available at https://github.com/Xiaofeng-life/ICDehazing

Fooling the Image Dehazing Models by First Order Gradient

The research on the single image dehazing task has been widely explored. However, as far as we know, no comprehensive study has been conducted on the robustness of the well-trained dehazing models. Therefore, there is no evidence that the dehazing networks can resist malicious attacks. In this paper, we focus on designing a group of attack methods based on first order gradient to verify the robustness of the existing dehazing algorithms. By analyzing the general purpose of image dehazing task, four attack methods are proposed, which are predicted dehazed image attack, hazy layer mask attack, haze-free image attack and haze-preserved attack. The corresponding experiments are conducted on six datasets with different scales. Further, the defense strategy based on adversarial training is adopted for reducing the negative effects caused by malicious attacks. In summary, this paper defines a new challenging problem for the image dehazing area, which can be called as adversarial attack on dehazing networks (AADN). Code and Supplementary Material are available at https://github.com/Xiaofeng-life/AADN Dehazing.Comment: This paper is accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Study of brain network alternations in non-lesional epilepsy patients by BOLD-fMRI

ObjectiveTo investigate the changes of brain network in epilepsy patients without intracranial lesions under resting conditions.MethodsTwenty-six non-lesional epileptic patients and 42 normal controls were enrolled for BOLD-fMRI examination. The differences in brain network topological characteristics and functional network connectivity between the epilepsy group and the healthy controls were compared using graph theory analysis and independent component analysis.ResultsThe area under the curve for local efficiency was significantly lower in the epilepsy patients compared with healthy controls, while there were no differences in global indicators. Patients with epilepsy had higher functional connectivity in 4 connected components than healthy controls (orbital superior frontal gyrus and medial superior frontal gyrus, medial superior frontal gyrus and angular gyrus, superior parietal gyrus and paracentral lobule, lingual gyrus, and thalamus). In addition, functional connectivity was enhanced in the default mode network, frontoparietal network, dorsal attention network, sensorimotor network, and auditory network in the epilepsy group.ConclusionThe topological characteristics and functional connectivity of brain networks are changed in in non-lesional epilepsy patients. Abnormal functional connectivity may suggest reduced brain efficiency in epilepsy patients and also may be a compensatory response to brain function early at earlier stages of the disease

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Impact performance optimization of a YDC valve-type double action hydraulic hammer

A YDC type hydraulic hammer is a new valve-type double action hydraulic hammer suitable for oil and gas well drilling. It is hard to find out the optimal matching relationship among various factors based on experience and experiments, for the matching relationships of inner pressure is complex and the impact performance is influenced by many factors. In this paper, the operating principle of a YDC type hydraulic hammer was investigated, the force applied to the main moving components (valve core and hammer) was analyzed and a dynamic model of valve core and hammer in each operating stage was established. Then, a hydraulic hammer performance optimization design software was developed on the Matlab software platform, and the performance parameters calculated by the software were compared with the laboratory test results. The following research results were obtained. Firstly, single impact energy, impact frequency and impact power increase with the increase of pump displacement or the decrease of flow bean diameter, and they increase firstly and then decrease with the increase of area difference between the upper and lower chambers. Secondly, with the increase of hammer weight, single impact energy and impact power increase, but the impact frequency decreases slowly. Thirdly, with the increase of hammer travel, single impact energy presents an increasing trend, impact frequency presents a decreasing trend and impact power basically remains unchanged. Fourthly, with the increase of valve core weight, single impact energy presents an increasing trend, while both impact frequency and impact power decrease. Fifthly, the parameter combination corresponding to the optimal single impact energy and impact power is A5B1C5D4E3F2, and the effect of displacement on single impact energy and impact power is the greatest. It is concluded that under the existing displacement and pressure of drilling pumps, the impact performance of the hydraulic hammer can be increased effectively by improving the structure of the hydraulic hammer and thus increasing its work displacement. Keywords: Hydraulic hammer, Hammer, Valve core, Displacement, Force analysis, Dynamic model software, Impact performance, Influence rule, Parameter combinatio