Show, Recall, and Tell: Image Captioning with Recall Mechanism

Generating natural and accurate descriptions in image cap-tioning has always been a challenge. In this paper, we pro-pose a novel recall mechanism to imitate the way human con-duct captioning. There are three parts in our recall mecha-nism : recall unit, semantic guide (SG) and recalled-wordslot (RWS). Recall unit is a text-retrieval module designedto retrieve recalled words for images. SG and RWS are de-signed for the best use of recalled words. SG branch cangenerate a recalled context, which can guide the process ofgenerating caption. RWS branch is responsible for copyingrecalled words to the caption. Inspired by pointing mecha-nism in text summarization, we adopt a soft switch to balancethe generated-word probabilities between SG and RWS. Inthe CIDEr optimization step, we also introduce an individualrecalled-word reward (WR) to boost training. Our proposedmethods (SG+RWS+WR) achieve BLEU-4 / CIDEr / SPICEscores of 36.6 / 116.9 / 21.3 with cross-entropy loss and 38.7 /129.1 / 22.4 with CIDEr optimization on MSCOCO Karpathytest split, which surpass the results of other state-of-the-artmethods.Comment: Published in AAAI 202

Electrostatic Force Microscopy on Oriented Graphite Surfaces: Where Insulating and Conducting Behaviors Coexist

We present measurements of the electric potential fluctuations on the surface of highly oriented pyrolytic graphite using electrostatic force and atomic force microscopy. Micrometric domain-like potential distributions are observed even when the sample is grounded. Such potential distributions are unexpected given the good metallic conductivity of graphite because the surface should be an equipotential. Our results indicate the coexistence of regions with metallic and insulating behaviors showing large potential fluctuations of the order of 0.25V. We discuss the implications of these measurements in the disorder structure of graphite.Comment: 14 pages, 5 figure

A preclinical animal study to evaluate the operability and safety of domestic one-way endobronchial valves

PurposeTo evaluate the operability and safety of bronchoscopic domestic one-way endobronchial valves (EBV) on animals.MethodsNine pigs were randomly assigned (2:1) to receive domestic one-way EBV (the experimental group, n = 6) and Zephyr® EBV (the control group, n = 3). Routine blood tests, arterial blood gases, and CT scans of the lungs were performed 1 day pre-procedure in addition to 1 week and 1 month post-procedure to assess changes in blood markers and lung volumes. At 1 month post-procedure, the animals were sacrificed, followed by removal of all valves via bronchoscopy. Pathological examinations of critical organs were subsequently performed.ResultsA total of 15 valves were placed in the experimental group and 6 valves were placed in the control group, without serious complications. Routine blood tests and arterial blood gas examinations at 1 day pre-procedure, 1 week post-procedure, and 1 month post-procedure did not differ significantly in both groups. No EBV displacement was noted under bronchoscopy, and the valve was smoothly removable by bronchoscope at 1 month post-procedure. At 1 week post-procedure, varying degrees of target lung lobe volume reduction were observed on lung CT in both groups. Lung volume reduction was achieved at 1 month post-procedure in both groups, without significant statistical difference. Although 3 cases in the experimental group and 1 case in the control group developed varying degrees of pneumonia, the inflammatory response did not increase over time during the experimental period. Pathological examination revealed no significant abnormal changes in the critical organs for both groups.ConclusionOur results demonstrate that domestic EBV is safe and reliable for endobronchial application in general-grade laboratory white pigs. The safety of domestic EBV is similar to that of Zephyr® EBV, with good ease of use and operability. This kind of domestic EBV can meet the safety evaluation requirements for animal testing

Neuroanatomical Circuitry Associated with Exploratory Eye Movement in Schizophrenia: A Voxel-Based Morphometric Study

Schizophrenic patients present abnormalities in a variety of eye movement tasks. Exploratory eye movement (EEM) dysfunction appears to be particularly specific to schizophrenia. However, the underlying mechanisms of EEM dysfunction in schizophrenia are not clearly understood. To assess the potential neuroanatomical substrates of EEM, we recorded EEM performance and conducted a voxel-based morphometric analysis of gray matter in 33 schizophrenic patients and 29 well matched healthy controls. In schizophrenic patients, decreased responsive search score (RSS) and widespread gray matter density (GMD) reductions were observed. Moreover, the RSS was positively correlated with GMD in distributed brain regions in schizophrenic patients. Furthermore, in schizophrenic patients, some brain regions with neuroanatomical deficits overlapped with some ones associated with RSS. These brain regions constituted an occipito-tempro-frontal circuitry involved in visual information processing and eye movement control, including the left calcarine cortex [Brodmann area (BA) 17], the left cuneus (BA 18), the left superior occipital cortex (BA 18/19), the left superior frontal gyrus (BA 6), the left cerebellum, the right lingual cortex (BA 17/18), the right middle occipital cortex (BA19), the right inferior temporal cortex (BA 37), the right dorsolateral prefrontal cortex (BA 46) and bilateral precentral gyri (BA 6) extending to the frontal eye fields (FEF, BA 8). To our knowledge, we firstly reported empirical evidence that gray matter loss in the occipito-tempro-frontal neuroanatomical circuitry of visual processing system was associated with EEM performance in schizophrenia, which may be helpful for the future effort to reveal the underlying neural mechanisms for EEM disturbances in schizophrenia

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network

The solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution process is complicated. Due to the mapping ability of the neural network, the use of neural networks to solve robot inverse kinematics problems has attracted widespread attention. However, it has slow convergence speed and low accuracy. This paper proposes the FOA optimized BP neural network algorithm to solve inverse kinematics. It overcomes the shortcomings of low convergence accuracy, slow convergence speed, and easy to fall into local minima when using BP neural network to solve inverse kinematics. The experimental results show that using the trained FOA optimized BP neural network to solve the inverse kinematics, the maximum error range of the output joint angle is [−0.04686, 0.1271]. The output error of the FOA optimized BP neural network algorithm is smaller than that of the ordinary BP neural network algorithm and the PSO optimized BP neural network algorithm. Using the FOA optimized BP neural network algorithm to solve the robot kinematics can improve the control accuracy of the robot