Realtime Fewshot Portrait Stylization Based On Geometric Alignment

This paper presents a portrait stylization method designed for real-time mobile applications with limited style examples available. Previous learning based stylization methods suffer from the geometric and semantic gaps between portrait domain and style domain, which obstacles the style information to be correctly transferred to the portrait images, leading to poor stylization quality. Based on the geometric prior of human facial attributions, we propose to utilize geometric alignment to tackle this issue. Firstly, we apply Thin-Plate-Spline (TPS) on feature maps in the generator network and also directly to style images in pixel space, generating aligned portrait-style image pairs with identical landmarks, which closes the geometric gaps between two domains. Secondly, adversarial learning maps the textures and colors of portrait images to the style domain. Finally, geometric aware cycle consistency preserves the content and identity information unchanged, and deformation invariant constraint suppresses artifacts and distortions. Qualitative and quantitative comparison validate our method outperforms existing methods, and experiments proof our method could be trained with limited style examples (100 or less) in real-time (more than 40 FPS) on mobile devices. Ablation study demonstrates the effectiveness of each component in the framework.Comment: 10 pages, 10 figure

Permutation Classifier

We consider permutations of a given set of n different symbols. We are given two unordered training sets, T1 and T2, of such permutations that are each assumed to contain examples of permutations of the corresponding type, t1 and t2. Our goal is to train a classifier, C(q), by computing a statistical model from T1 and T2, which, when given a candidate permutation, q, decides whether q is of type t1 or t2. We discuss two versions of this problem. The ranking version focuses on the order of the symbols. Our Separation Average Distance Matrix (SADiM) solution expands on previously proposed ranking aggregation formulations. The grouping version focuses on contiguity of symbols and hierarchical grouping. We propose and compare two solutions: (1) The Population Augmentation Ratio (PAR) solution computes a PQ-tree for each training set and uses a novel measure of distance between these and q that is based on ratios of population counts (i.e., of numbers of permutations explained by specific PQ-trees). (2) The Difference of Positions (DoP) solution is computationally less expensive than PAR and is independent of the absolute population counts. Although DoP does not have the simple statistical grounding of PAR, our experiments show that it is consistently effective

OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis

Ultrasound (US) imaging is indispensable in clinical practice. To diagnose certain diseases, sonographers must observe corresponding dynamic anatomic structures to gather comprehensive information. However, the limited availability of specific US video cases causes teaching difficulties in identifying corresponding diseases, which potentially impacts the detection rate of such cases. The synthesis of US videos may represent a promising solution to this issue. Nevertheless, it is challenging to accurately animate the intricate motion of dynamic anatomic structures while preserving image fidelity. To address this, we present a novel online feature-decoupling framework called OnUVS for high-fidelity US video synthesis. Our highlights can be summarized by four aspects. First, we introduced anatomic information into keypoint learning through a weakly-supervised training strategy, resulting in improved preservation of anatomical integrity and motion while minimizing the labeling burden. Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator. Third, we adopted a multiple-feature discriminator to extract a comprehensive range of visual cues, thereby enhancing the sharpness and fine details of the generated videos. Fourth, we constrained the motion trajectories of keypoints during online learning to enhance the fluidity of generated videos. Our validation and user studies on in-house echocardiographic and pelvic floor US videos showed that OnUVS synthesizes US videos with high fidelity.Comment: 14 pages, 13 figures and 6 table

PIH1D3-knockout rats exhibit full ciliopathy features and dysfunctional pre-assembly and loading of dynein arms in motile cilia

Background: Recessive mutation of the X-linked gene, PIH1 domain-containing protein 3 (PIH1D3), causes familial ciliopathy. PIH1D3 deficiency is associated with the defects of dynein arms in cilia, but how PIH1D3 specifically affects the structure and function of dynein arms is not understood yet. To gain insights into the underlying mechanisms of the disease, it is crucial to create a reliable animal model. In humans, rats, and mice, one copy of the PIH1D3 gene is located on the X chromosome. Interestingly, mice have an additional, intronless copy of the Pih1d3 gene on chromosome 1. To develop an accurate disease model, it is best to manipulate the X-linked PIH1D3 gene, which contains essential regulatory sequences within the introns for precise gene expression. This study aimed to develop a tailored rat model for PIH1D3-associated ciliopathy with the ultimate goal of uncovering the intricate molecular mechanisms responsible for ciliary defects in the disease.Methods: Novel Pih1d3-knockout (KO) rats were created by using TALEN-mediated non-homologous DNA recombination within fertilized rat eggs and, subsequently, underwent a comprehensive characterization through a battery of behavioral and pathological assays. A series of biochemical and histological analyses were conducted to elucidate the identity of protein partners that interact with PIH1D3, thus shedding light on the intricate molecular mechanisms involved in this context.Results: PIH1D3-KO rats reproduced the cardinal features of ciliopathy including situs inversus, defects in spermatocyte survival and mucociliary clearance, and perinatal hydrocephalus. We revealed the novel function of PIH1D3 in cerebrospinal fluid circulation and elucidated the mechanism by which PIH1D3 deficiency caused communicating hydrocephalus. PIH1D3 interacted with the proteins required for the pre-assembly and uploading of outer (ODA) and inner dynein arms (IDA), regulating the integrity of dynein arm structure and function in cilia.Conclusion: PIH1D3-KO rats faithfully reproduced the cardinal features of ciliopathy associated with PIH1D3 deficiency. PIH1D3 interacted with the proteins responsible for the pre-assembly and uploading of dynein arms in cilia, and its deficiency led to dysfunctional cilia and, thus, to ciliopathy by affecting the pre-assembly and uploading of dynein arms. The resultant rat model is a valuable tool for the mechanistic study of PIH1D3-caused diseases

Tax Incentives, Common Institutional Ownership, and Corporate ESG Performance

Against the backdrop of sustainable development, enterprises, the general public, and regulatory bodies are exhibiting an escalating level of concern regarding the performance in environmental stewardship, social responsibility, and corporate governance collectively referred to as ESG (Environmental, Social Responsibility, and Corporate Governance). This research, from the vantage point of external fiscal policy, investigates the examination of the impact of tax incentives on corporate ESG performance. Drawing upon panel data spanning from 2010 to 2021 at the level of China's A-share listed companies and grounded in the context of accelerated depreciation policy for fixed assets, this study commitment to both identify and empirically test the presence of a significant positive correlation between tax incentives and corporate ESG performance. Our analysis of the financial mechanism and the Research and Development (R&D) mechanism reveals that tax incentives are instrumental in alleviating the financing constraints faced by corporations, thereby augmenting their financial performance. Furthermore, they serve to intensify R&D efforts, thereby fostering the generation of green innovations. In conclusion, our findings underscore that tax incentive policies significantly enhance the ESG performance of enterprises with common institutional shareholdings, an effect attributed to the presence of governance and synergy effect

Detergent-insoluble PFN1 inoculation expedites disease onset and progression in PFN1 transgenic rats

Accumulating evidence suggests a gain of elusive toxicity in pathogenically mutated PFN1. The prominence of PFN1 aggregates as a pivotal pathological hallmark in PFN1 transgenic rats underscores the crucial involvement of protein aggregation in the initiation and progression of neurodegeneration. Detergent-insoluble materials were extracted from the spinal cords of paralyzed rats afflicted with ALS and were intramuscularly administered to asymptomatic recipient rats expressing mutant PFN1, resulting in an accelerated development of PFN1 inclusions and ALS-like phenotypes. This effect diminished when the extracts derived from wildtype PFN1 transgenic rats were employed, as detergent-insoluble PFN1 was detected exclusively in mutant PFN1 transgenic rats. Consequently, the factor influencing the progression of ALS pathology in recipient rats is likely associated with the presence of detergent-insoluble PFN1 within the extracted materials. Noteworthy is the absence of disease course modification upon administering detergent-insoluble extracts to rats that already displayed PFN1 inclusions, suggesting a seeding rather than augmenting role of such extracts in initiating neuropathological changes. Remarkably, pathogenic PFN1 exhibited an enhanced affinity for the molecular chaperone DNAJB6, leading to the sequestration of DNAJB6 within protein inclusions, thereby depleting its availability for cellular functions. These findings shed light on a novel mechanism that underscores the prion-like characteristics of pathogenic PFN1 in driving neurodegeneration in the context of PFN1-related ALS

Segment Anything Model for Medical Images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. SAM has achieved impressive results on various natural image segmentation tasks. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and build a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.Comment: 23 pages, 14 figures, 12 table

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks