532 research outputs found
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 =450 kA (), and electron density
below a critical value of
. 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
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