4,304 research outputs found
Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks
The Convolutional Neural Network (CNN) is a widely used deep learning
architecture for computer vision. However, its black box nature makes it
difficult to interpret the behavior of the model. To mitigate this issue, AI
practitioners have explored explainable AI methods like Class Activation Map
(CAM) and Grad-CAM. Although these methods have shown promise, they are limited
by architectural constraints or the burden of gradient computing. To overcome
this issue, Score-CAM and Ablation-CAM have been proposed as gradient-free
methods, but they have longer execution times compared to CAM or Grad-CAM based
methods, making them unsuitable for real-world solution though they resolved
gradient related issues and enabled inference mode XAI. To address this
challenge, we propose a fast gradient-free Reciprocal CAM (Recipro-CAM) method.
Our approach involves spatially masking the extracted feature maps to exploit
the correlation between activation maps and network predictions for target
classes. Our proposed method has yielded promising results, outperforming
current state-of-the-art method in the Average Drop-Coherence-Complexity (ADCC)
metric by to , excluding VGG-16 backbone. Moreover,
Recipro-CAM generates saliency maps at a similar rate to Grad-CAM and is
approximately times faster than Score-CAM. The source code for
Recipro-CAM is available in our data analysis framework
Improved Rate Capability and Thermal Stability of LiNi0.5Co0.2Mn0.3O2 Cathode Materials via Nanoscale SiP2O7 Coating
Battery Science and TechnologyIn order to overcome the inherent problems of LiNiO2, many method, such as coating and doping have been investigated. However, none of previous studies have not been reported to solve both rate capability at higher rates and thermal stability of the Ni-based cathode materials simultaneously. Here, we report the LiδPyOzâcoated LiNi0.5Co0.2Mn0.3O2 cathode materials doped with P and Si ions which possesses both higher rates and thermal stability. It was prepared by direction reaction of LiOH and SiP2O7-coated Ni0.5Co0.2Mn0.3(OH)2 precursors. The coated cathodes exhibited quite impressive results; rate capability was improved by almost 100% at a 7C rate compared to pristine LiNi0.5Co0.2Mn0.3O2. Furthermore, the amount of heat generation at 4.5V charge cut-off as a result of the evolution of oxygen was reduced by 79%, compared to pristine LiNi0.5Co0.2Mn0.3O2 sample. Overall, this coating method is also applicable to other bulk cathodes, such as LiMn2O4 and LiCoO2 which need to improve electrochemical properties both at room and elevated temperatures.ope
Double resonance of Raman transitions in a degenerate Fermi gas
We measure momentum-resolved Raman spectra of a spin-polarized degenerate
Fermi gas of Yb atoms for a wide range of magnetic fields, where the
atoms are irradiated by a pair of counterpropagating Raman laser beams as in
the conventional spin-orbit coupling scheme. Double resonance of first- and
second-order Raman transitions occurs at a certain magnetic field and the
spectrum exhibits a doublet splitting for high laser intensities. The measured
spectral splitting is quantitatively accounted for by the Autler-Townes effect.
We show that our measurement results are consistent with the spinful band
structure of a Fermi gas in the spatially oscillating effective magnetic field
generated by the Raman laser fields.Comment: 7 pages, 6 figure
The full repertoire of Drosophila gustatory receptors for detecting an aversive compound.
The ability to detect toxic compounds in foods is essential for animal survival. However, the minimal subunit composition of gustatory receptors required for sensing aversive chemicals in Drosophila is unknown. Here we report that three gustatory receptors, GR8a, GR66a and GR98b function together in the detection of L-canavanine, a plant-derived insecticide. Ectopic co-expression of Gr8a and Gr98b in Gr66a-expressing, bitter-sensing gustatory receptor neurons (GRNs) confers responsiveness to L-canavanine. Furthermore, misexpression of all three Grs enables salt- or sweet-sensing GRNs to respond to L-canavanine. Introduction of these Grs in sweet-sensing GRNs switches L-canavanine from an aversive to an attractive compound. Co-expression of GR8a, GR66a and GR98b in Drosophila S2 cells induces an L-canavanine-activated nonselective cation conductance. We conclude that three GRs collaborate to produce a functional L-canavanine receptor. Thus, our results clarify the full set of GRs underlying the detection of a toxic tastant that drives avoidance behaviour in an insect
Do Financial Analysts Facilitate Investorsâ Assessment Of Earnings?: Evidence From The Korean Stock Market
This paper seeks to enhance our understanding of financial analysts in assisting market investorsâ use of accounting earnings in the Korean stock market. We examine whether stock returns differentially reflect earnings information for firms with analyst coverage. We propose that the role of analysts as external monitors as well as information intermediaries enhances the market investorsâ valuation of earnings. We find that market valuation of earnings is higher for firms with analyst following. Furthermore, market investorsâ valuation of earnings increases (or decreases) with the number of analysts (or with the dispersion of analystsâ forecasts). This suggests that the beneficial effect of analysts arises through the quantity and quality of analystsâ information. This study contributes to the literature by investigating the important role of analysts in emerging market
Continuous Facial Motion Deblurring
We introduce a novel framework for continuous facial motion deblurring that
restores the continuous sharp moment latent in a single motion-blurred face
image via a moment control factor. Although a motion-blurred image is the
accumulated signal of continuous sharp moments during the exposure time, most
existing single image deblurring approaches aim to restore a fixed number of
frames using multiple networks and training stages. To address this problem, we
propose a continuous facial motion deblurring network based on GAN (CFMD-GAN),
which is a novel framework for restoring the continuous moment latent in a
single motion-blurred face image with a single network and a single training
stage. To stabilize the network training, we train the generator to restore
continuous moments in the order determined by our facial motion-based
reordering process (FMR) utilizing domain-specific knowledge of the face.
Moreover, we propose an auxiliary regressor that helps our generator produce
more accurate images by estimating continuous sharp moments. Furthermore, we
introduce a control-adaptive (ContAda) block that performs spatially deformable
convolution and channel-wise attention as a function of the control factor.
Extensive experiments on the 300VW datasets demonstrate that the proposed
framework generates a various number of continuous output frames by varying the
moment control factor. Compared with the recent single-to-single image
deblurring networks trained with the same 300VW training set, the proposed
method show the superior performance in restoring the central sharp frame in
terms of perceptual metrics, including LPIPS, FID and Arcface identity
distance. The proposed method outperforms the existing single-to-video
deblurring method for both qualitative and quantitative comparisons
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