The past few years have seen the application of confocal and especially two-photon microscopy to the dynamic high-resolution imaging of lymphocytes and antigen presenting cells within organs such as ...lymph nodes and thymus. After summarizing some of the published results obtained to date using these methods, we describe our view of how this technology will develop and be applied in the near future. This includes its extension to a wide variety of non-lymphoid tissues, to the tracking of functional responses in addition to migratory behavior, to the analysis of molecular events previously studied only in vitro, to dissection of the interplay between hematopoietic and stromal elements, to visualization of a wider array of cell types including neutrophils, macrophages, NK cells, NKT cells and others, and to the interaction of the host with infectious agents. Reaching these goals will depend on a combination of new tools for genetic manipulations, novel fluorescent reporters, enhanced instrumentation, and better surgical techniques for the extended imaging of live animals. The end result will be a new level of understanding of how orchestrated cell movement and interaction contribute to the physiological and pathological activities of the immune system.
With the advancement in face manipulation technologies, the importance of face forgery detection in protecting authentication integrity becomes increasingly evident. Previous Vision Transformer ...(ViT)-based detectors have demonstrated subpar performance in cross-database evaluations, primarily because fully fine-tuning with limited Deepfake data often leads to forgetting pre-trained knowledge and over-fitting to data-specific ones. To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT). In FA-ViT, the vanilla ViT's parameters are frozen to preserve its pre-trained knowledge, while two specially designed components, the Local-aware Forgery Injector (LFI) and the Global-aware Forgery Adaptor (GFA), are employed to adapt forgery-related knowledge. our proposed FA-ViT effectively combines these two different types of knowledge to form the general forgery features for detecting Deepfakes. Specifically, LFI captures local discriminative information and incorporates these information into ViT via Neighborhood-Preserving Cross Attention (NPCA). Simultaneously, GFA learns adaptive knowledge in the self-attention layer, bridging the gap between the two different domain. Furthermore, we design a novel Single Domain Pairwise Learning (SDPL) to facilitate fine-grained information learning in FA-ViT. The extensive experiments demonstrate that our FA-ViT achieves state-of-the-art performance in cross-dataset evaluation and cross-manipulation scenarios, and improves the robustness against unseen perturbations.
Shadow removal aims at restoring the image content within shadow regions, pursuing a uniform distribution of illumination that is consistent between shadow and non-shadow regions. {Comparing to other ...image restoration tasks, there are two unique challenges in shadow removal:} 1) The patterns of shadows are arbitrary, varied, and often have highly complex trace structures, making ``trace-less'' image recovery difficult. 2) The degradation caused by shadows is spatially non-uniform, resulting in inconsistencies in illumination and color between shadow and non-shadow areas. Recent developments in this field are primarily driven by deep learning-based solutions, employing a variety of learning strategies, network architectures, loss functions, and training data. Nevertheless, a thorough and insightful review of deep learning-based shadow removal techniques is still lacking. In this paper, we are the first to provide a comprehensive survey to cover various aspects ranging from technical details to applications. We highlight the major advancements in deep learning-based single-image shadow removal methods, thoroughly review previous research across various categories, and provide insights into the historical progression of these developments. Additionally, we summarize performance comparisons both quantitatively and qualitatively. Beyond the technical aspects of shadow removal methods, we also explore potential future directions for this field.
9.1 Toward Automotive Surround-View Radars Hung, Chih-Ming; Lin, Alex TC; Peng, BC ...
2019 IEEE International Solid- State Circuits Conference - (ISSCC),
2019-Feb.
Conference Proceeding
The future of driving extends from physical mobility and enjoyment today to having more services enabled by wireless connectivity, clean and green technologies, secure transactions, and so forth. ...Whether the ownership is based on physical vehicles or services, or whether the drivers are human or robots, one demand that will never change is to have better safety at affordable cost. Among available sensor technologies for the advanced-driver-assistance-system (ADAS), radar is indispensable due to its unique capability in robustness against environmental impacts, long-range detection, sufficient range resolution, and simultaneous multi-depth detection. Those are very crucial since camera, Lidar and ultrasonic sensors perform poorly under severe weather conditions, and an autonomous vehicle would become partially blinded without radars. There are several automotive radar applications such as front radars responsible for autonomous cruise control and automatic emergency braking, as well as corner radars responsible for blind-spot detection (BSD), cross-traffic alert (CTA), and the like. A new class of applications comprehending ultra-short range sensing and 360° surround view for parking assistance, door-opening alert, etc. is emerging. In this paper, requirements of the new applications will be examined, which will be further broken into system and circuit specification. A new system including application-driven algorithm, hardware and software designs will be presented to fulfill the new demands.
Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, ...blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capability. To address these issues, we propose a novel Critical Forgery Mining (CFM) framework, which can be flexibly assembled with various backbones to boost their generalization and robustness performance. Specifically, we first build a fine-grained triplet and suppress specific forgery traces through prior knowledge-agnostic data augmentation. Subsequently, we propose a fine-grained relation learning prototype to mine critical information in forgeries through instance and local similarity-aware losses. Moreover, we design a novel progressive learning controller to guide the model to focus on principal feature components, enabling it to learn critical forgery features in a coarse-to-fine manner. The proposed method achieves state-of-the-art forgery detection performance under various challenging evaluation settings.
Aims
SOX
2 is a key regulatory gene in embryonic stem cells. Although it has been implicated in cancer progression, its role in breast carcinoma is poorly understood.
Materials and methods
...Fifty‐seven ductal carcinomas
in situ
(
DCIS
), 552 invasive breast carcinomas and 107 corresponding metastatic lymph nodes were evaluated immunohistochemically for the expression of
SOX
2. Its correlation with clinicopathological features, other biomarker profiles and patients’ outcomes were analysed.
Results
SOX
2 was detected in 19.0% (105 of 552) of invasive breast carcinomas and 12.3% (seven of 57) of
DCIS
. Expression correlated with larger tumour size (
P
=
0.005) and higher grade (
P
=
0.002). It was associated negatively with
ER
(
P
=
0.015) and
PR
(
P
=
0.046) expression, but positively with Ki67 index (
P
=
0.013). Interestingly, it was also associated with neuroendocrine marker expression (synpatophysin and chromogranin/synaptophysin,
P
=
0.048 and 0.028, respectively). Expression appeared to be independent from that of common stem cell markers, namely
CD
44,
CD
24 and aldehyde dehydrogenase 1 (
ALDH
1). Furthermore, a higher rate of expression was observed in metastatic lymph nodes than in the corresponding primary tumours (
P
=
0.034). High
SOX
2 expression was correlated with poor disease‐free survival (log‐rank=9.489,
P
=
0.012) and was an independent prognostic factor (
HR
=2.918,
P
=
0.015) in patients with high nodal stages.
Conclusions
In summary,
SOX
2 expression was related to adverse breast carcinoma profile and poor outcome in selected patient groups.
In this work, we used deep-molding manufacture of three kinds to fabricate micro pressure-swirl atomizers to promote their performance, and a phase doppler particle analyzer (PDPA) to measure the ...characteristic distributions of the spray flow field of these atomizers. The deep-molding techniques were X-ray LIGA process, ICP-LIGA process (inductive coupling plasma etching), and injection molding LIGA process. Parameters of atomizers examined here include configuration of flow channel, diameter of exit orifice, the ratio of diameters of swirl chamber and discharge orifice, and the thickness of atomizer. Experimental results showed that the manufacturing process combining injection molding with electroplating had large yields and that the technique is highly reliable; enable manufacture of an atomizer at small cost and great quality. Moreover, these microatomizers are assembled well with other components and can be readily applied. The results of PDPA diagnosis further revealed that the spray features are related to the design parameters of atomizer dimensions.
Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved ...significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency.
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