An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion

Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. This technique will prevent radiologists switch back and forth between different images and save lots of time in the diagnostic process. In this paper, we introduce a novel Dilated Residual Attention Network for the medical image fusion task. Our network is capable to extract multi-scale deep semantic features. Furthermore, we propose a novel fixed fusion strategy termed Softmax-based weighted strategy based on the Softmax weights and matrix nuclear norm. Extensive experiments show our proposed network and fusion strategy exceed the state-of-the-art performance compared with reference image fusion methods on four commonly used fusion metrics.Comment: 8 pages, 8 figures, 3 table

Design and research into the nonlinear main vibration spring in double-mass high energy vibration milling

Due to the shortcomings of one - mass vibration mill such as inefficiency, high energy consumption and big noise, a double - mass high energy vibration mill, in which transient high vibration intensity is produced, is investigated by applying the non - linear vibration theory. The nonlinear hard - feature variable-pitch spring i0s used in the main vibration system which has the characteristic of the stiffness that can be varied along with the dynamic load. In this way, the goals of operation stabilization and energy saving will be achieved. Results from the field test show that the efficiency is obviously improved, i.e. a 28% increase in the vibration intensity, 10% decrease in energy consumption and 4% decrease in noise. That verifies the correctness of the main vibration system construction. This system can be used by others as a reference design for this field

Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive review

In the digital era, human-robot interaction is rapidly expanding, emphasizing the need for social robots to fluently understand and communicate in multiple languages. It is not merely about decoding words but about establishing connections and building trust. However, many current social robots are limited to popular languages, serving in fields like language teaching, healthcare and companionship. This review examines the AI-driven language abilities in social robots, providing a detailed overview of their applications and the challenges faced, from nuanced linguistic understanding to data quality and cultural adaptability. Last, we discuss the future of integrating advanced language models in robots to move beyond basic interactions and towards deeper emotional connections. Through this endeavor, we hope to provide a beacon for researchers, steering them towards a path where linguistic adeptness in robots is seamlessly melded with their capacity for genuine emotional engagement

Dynamics of cold pulses induced by super-sonic molecular beam injection in the EAST tokamak

Evolution of electron temperature, electron density and its fluctuation with high spatial and temporal resolutions are presented for the cold pulse propagation induced by super-sonic molecular beam injection (SMBI) in ohmic plasmas in the EAST tokamak. The non-local heat transport occurs for discharges with plasma current $I_p$=450 kA ($q_{95}\sim5.55$), and electron density $n_{e0}$ below a critical value of $(1.35\pm0.25)\times10^{19}~\mathrm{m^{-3}}$. In contrary to the response of core electron temperature and electron density (roughly 10 ms after SMBI), the electron density fluctuation in the plasma core increases promptly after SMBI and reaches its maximum around 15 ms after SMBI. The electron density fluctuation in the plasma core begins to decrease before the core electron temperature reaches its maximum (roughly 30 ms). It was also observed that the turbulence perpendicular velocity close to the inversion point of the temperature perturbation changes sign after SMBI

Stimulating the Dorsolateral Prefrontal Cortex Decreases the Asset Bubble: A tDCS Study

Many studies have discussed the neural basis of asset bubbles. They found that the dorsolateral prefrontal cortex (DLPFC) played an important role in bubble formation, but whether a causal relationship exists and the mechanism of the effect of the DLPFC on bubbles remains unsettled. Using transcranial direct current stimulation (tDCS), we modulated the activity of the DLPFC and investigated the causal relationship between the DLPFC and the asset bubble in the classical learning-to-forecast experiment. 126 subjects were randomly divided into three groups and received different stimulations (left anodal/right cathodal, right anodal/left cathodal, or sham stimulation), respectively. We also conducted a 2-back task before and after stimulation to measure changes in subjects’ cognitive abilities and explore in detail the cognitive mechanism of the effect of DLPFC stimulation on asset bubbles. Based on our results, we found that the bubble of the left anodal/right cathodal stimulation group was significantly smaller than that of the sham stimulation group. In the meantime, subjects performed significantly better in the 2-back task after left anodal/right cathodal stimulation but not right anodal/left cathodal or sham stimulation, which is consistent with their performance in the learning-to-forecast experiment, supporting the cognitive mechanism to some extent. Furthermore, we examined different forecasting rules across individuals and discovered that the left anodal/right cathodal stimulation group preferred the adaptive learning rule, while the sham and right anodal/left cathodal stimulation groups adopted a pure trend-following rule that tended to intensify market volatility aggressively

Generalized gradient approximation for solids and their surfaces

Successful modern generalized gradient approximations (GGA) are biased toward atomic energies. Restoration of the first-principles gradient expansion for the exchange energy over a wide range of density gradients eliminates this bias. We introduce PBEsol, a revised Perdew-Burke-Ernzerhof GGA that improves equilibrium properties for many densely-packed solids and their surfaces.Comment: 4pages, 2figures,2table

Multimarket Contact in Italian Retail Banking: Competition and Welfare

This paper studies banks' competitive behavior on the deposit side of the Italian retail banking industry. We use a structural model to estimate demand for deposit services and test several supply models. We find that both the competitive, differentiated product Bertrand and the perfectly collusive models are rejected against partially collusive models with coalitions based on the participants' market contact. In the best fitting collusive model, the coalition includes 8 banks with at least 19 overlapped regions. Banks with extensive multi-market contacts tend to be less competitive and behave as if they were maximizing their profit jointly, taking into account the competitive fringe of smaller banks