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Recent progress in generative AI, primarily through diffusion models, presents significant challenges for real-world deepfake detection. The increased realism in image details, diverse content, and widespread accessibility to the general public complicates the identification of these sophisticated deepfakes. Acknowledging the urgency to address the vulnerability of current deepfake detectors to this evolving threat, our paper introduces two extensive deepfake datasets generated by state-of-the-art diffusion models as other datasets are less diverse and low in quality. Our extensive experiments also showed that our dataset is more challenging compared to the other face deepfake datasets. Our strategic dataset creation not only challenge the deepfake detectors but also sets a new benchmark for more evaluation. Our comprehensive evaluation reveals the struggle of existing detection methods, often optimized for specific image domains and manipulations, to effectively adapt to the intricate nature of diffusion deepfakes, limiting their practical utility. To address this critical issue, we investigate the impact of enhancing training data diversity on representative detection methods. This involves expanding the diversity of both manipulation techniques and image domains. Our findings underscore that increasing training data diversity results in improved generalizability. Moreover, we propose a novel momentum difficulty boosting strategy to tackle the additional challenge posed by training data heterogeneity. This strategy dynamically assigns appropriate sample weights based on learning difficulty, enhancing the model's adaptability to both easy and challenging samples. Extensive experiments on both existing and newly proposed benchmarks demonstrate that our model optimization approach surpasses prior alternatives significantly., Comment: 28 pages including Supplementary material

Let $Y$ be a prequantization bundle over a closed spherically monotone symplectic manifold $\Sigma$. Adapting an idea due to Diogo and Lisi, we study a split version of Rabinowitz Floer homology for $Y$ in the following two settings. First, $\Sigma$ is a symplectic hyperplane section of a closed symplectic manifold $X$ satisfying a certain monotonicity condition; in this case, $X \setminus \Sigma$ is a Liouville filling of $Y$. Second, the minimal Chern number of $\Sigma$ is greater than one, which is the case where the Rabinowitz Floer homology of the symplectization $\mathbb{R} \times Y$ is defined. In both cases, we construct a Gysin-type exact sequence connecting the Rabinowitz Floer homology of $X\setminus\Sigma$ or $\mathbb{R} \times Y$ and the quantum homology of $\Sigma$. As applications, we discuss the invertibility of a symplectic hyperplane section class in quantum homology, the isotopy problem for fibered Dehn twists, the orderability problem for prequantization bundles, and the existence of translated points. We also provide computational results based on the exact sequence that we construct., Comment: 80 pages, 7 figures. Comments welcome! v2: minor revision, mistakes in Section 6.2.3 corrected

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found here., Comment: CVPR 2020 Workshop

The resistive switching phenomena of HfO2 films grown by metalorganic chemical vapor deposition was studied for the application of ReRAM devices. In the fabricated Pt/HfO2/TiN memory cells, the bipolar resistive switching characteristics were observed, and the set and reset states were measured to be as low as 7 uA and 4 uA, respectively, at VREAD = 1 V. Regarding the resistive switching performance, the stable RS performance was observed under 40 repetitive dc cycling test with the small variations of set/reset voltages and currents, and good retention characteristics over 105 s in both LRS and HRS. These results show the possibility of MOCVD grown HfO2 films as a promising resistive switching materials for ReRAM applications.

In this study, we proposed and demonstrated a self-rectifying property of silicon nitride (Si3N4)-based resistive random access memory device by employing p-type silicon (p-Si) as bottom electrode. The RRAM devices consisted of Al/Si3N4/p-Si are fabricated by a low presure chemical vapor deposition and exhibited an intrinsic diode property with non-linear current-voltage (I-V) behavior. In addition, compared to conventional metal/insulator/metal (MIM) structure of Al/Si3N4/Ti RRAM cells, operating current in whole bias regions for proposed metal/insulator/semiconductor (MIS) cells has been dramatically lowered because introduced p-Si bottom electrode efficiently suppresses the current in both low and high resistive states. As a result, the results mean that by employing p-Si as bottom electrode the Si3N4-based RRAM cells can be applied to selector-free RRAM cells.