Deep Saliency with Encoded Low level Distance Map and High Level Features

Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1X1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.Comment: Accepted by IEEE Conference on Computer Vision and Pattern Recognition(CVPR) 2016. Project page: https://github.com/gylee1103/SaliencyEL

Managerial Overconfidence And Going-Concern Modified Audit Opinion Decisions

We examine how auditors perceive managerial overconfidence during audit reporting by testing the relationship between managerial overconfidence and the likelihood of issuing a first-time going-concern modified audit opinion to financially distressed firms. After controlling for the factors affecting auditor’s going-concern modified audit opinion decision, we find that the likelihood of issuing a first-time going-concern modified audit opinion is positively associated with managerial overconfidence, suggesting that auditors adversely value overconfident management in financially distressed firms and thus tend to issue a first-time going-concern modified audit opinion to them. We also find that the positive association above is reinforced with capital market uncertainty

PHARMACODYNAMICS OF PYRONARIDINE IN COMBINATION WITH CURRENT ANTIMALARIALS IN A CYTOCIDAL MURINE MALARIA MODEL

While there has been a progress in understanding P. falciparum genomics, and exciting discoveries of promising leads in the antimalarial pipeline, the efficacies of existing artemisinin combination therapies are threatened by incorrect dosing and non-compliance with duration of dosing regimen, which might hasten the emergence of resistant parasites. Understanding of the effects of drug interactions on combination therapy with existing antimalarial agents may provide useful information such as their mechanism of action, and influence the development of new drug combination. A traditional way of evaluating antimalarial drug efficacy and combined drug interactions has been relying on in vitro drug sensitivity assays using cultured cells. This method measures the rate of suppression or inhibition of parasite growth during 48 – 72 hours in comparison to untreated controls. However, extrapolating in vitro results from cultured parasites to humans remains challenging as cytocidal activity of antimalarials (reduction in absolute parasite numbers) in patients is measured at a high asynchronous parasitemia at close to 1 trillion (per adult). In this thesis, we adapted a transgenic luciferase reporter, P. berghei ANKA to study cytocidal activity of tafenoquine and pyronaridine in mice for combinations of three current antimalarials, artesunate, amodiaquine, and azithromycin at clinically relevant dosage. We initiated each drug treatment at a high parasitemia (~10%) and followed for 30 days. Three metrics of measures we used in characterizing pharmacodynamic properties are 30-day survival, parasite log reduction over 48 hours, and recrudescent parasitemia (i.e., return to initial parasitemia). We adapted the parasite reduction ratio (PRR), or fractional log reduction in parasitemia as the PRR can be analogous to the killing rate, thus serving as a measure to compare the different intrinsic pharmacodynamic between the drugs in question. Pyronaridine when compared to tafenoquine at the same dose exhibited more potent cytocidal activity with approximately 1,000-fold more reduction in the number of parasite over the single 24-hour parasitic life cycle. Tafenoquine synergized with artesunate. In contrast, additive interactions were evident for pyronaridine in combination with artesunate. In addition, while pyronaridine synergized amodiaquine, differential interactions were shown with azithromycin, suggesting that synergy may only be achieved at certain dose ratios

Visual Style Prompting with Swapping Self-Attention

In the evolving domain of text-to-image generation, diffusion models have emerged as powerful tools in content creation. Despite their remarkable capability, existing models still face challenges in achieving controlled generation with a consistent style, requiring costly fine-tuning or often inadequately transferring the visual elements due to content leakage. To address these challenges, we propose a novel approach, \ours, to produce a diverse range of images while maintaining specific style elements and nuances. During the denoising process, we keep the query from original features while swapping the key and value with those from reference features in the late self-attention layers. This approach allows for the visual style prompting without any fine-tuning, ensuring that generated images maintain a faithful style. Through extensive evaluation across various styles and text prompts, our method demonstrates superiority over existing approaches, best reflecting the style of the references and ensuring that resulting images match the text prompts most accurately. Our project page is available https://curryjung.github.io/VisualStylePrompt/

Quetiapine Misuse and Abuse: Is It an Atypical Paradigm of Drug Seeking Behavior?

Recent case reports in medical literatures suggest that more and more second-generation atypical antipsychotics (AAs) have been prescribed for off-label use; quetiapine (Brand name: Seroquel®) showed increase in its trend for off-label use. Little is known about the reasons behind this trend, although historical sedative and hypnotic prescription patterns suggest that despite relatively superior safety profiles of quetiapine (especially for movement disorders), it may be used for treating substance abuse disorder. In addition, recent studies have shown a strong potential for misuse and abuse (MUA) of quetiapine beyond Food and Drug Administration-approved indications. This includes drug-seeking behaviors, such as feigning symptoms, motivated by quetiapine and use of quetiapine in conjunction with alcohol. Quetiapine appears to be the most documented AA with street values bartered illicitly on the street. A recent report from the Drug Abuse Warning Network has shown a high prevalence of quetiapine-related emergency department visits involving MUA. Several other case studies have found that quetiapine causes seeking behaviors observed in substance use disorder. In fact, the majority of quetiapine MUA involved patients diagnosed with substance use disorder. In the absence of a definitive mechanism of action of quetiapine\u27s reinforcing properties, it is imperative to gather robust evidence to support or refute increasing off-label use of AAs

3D-aware Blending with Generative NeRFs

Image blending aims to combine multiple images seamlessly. It remains challenging for existing 2D-based methods, especially when input images are misaligned due to differences in 3D camera poses and object shapes. To tackle these issues, we propose a 3D-aware blending method using generative Neural Radiance Fields (NeRF), including two key components: 3D-aware alignment and 3D-aware blending. For 3D-aware alignment, we first estimate the camera pose of the reference image with respect to generative NeRFs and then perform 3D local alignment for each part. To further leverage 3D information of the generative NeRF, we propose 3D-aware blending that directly blends images on the NeRF's latent representation space, rather than raw pixel space. Collectively, our method outperforms existing 2D baselines, as validated by extensive quantitative and qualitative evaluations with FFHQ and AFHQ-Cat.Comment: ICCV 2023, Project page: https://blandocs.github.io/blendner