Improving Lane Detection Generalization: A Novel Framework using HD Maps for Boosting Diversity

Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable results, lane detection algorithms must have robust generalization performance in various road environments. However, despite the significant performance improvement of deep learning-based lane detection algorithms, their generalization performance in response to changes in road environments still falls short of expectations. In this paper, we present a novel framework for single-source domain generalization (SSDG) in lane detection. By decomposing data into lane structures and surroundings, we enhance diversity using High-Definition (HD) maps and generative models. Rather than expanding data volume, we strategically select a core subset of data, maximizing diversity and optimizing performance. Our extensive experiments demonstrate that our framework enhances the generalization performance of lane detection, comparable to the domain adaptation-based method.Comment: 6 pages, 5 figure

A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis

Match me if you can: Semantic Correspondence Learning with Unpaired Images

Recent approaches for semantic correspondence have focused on obtaining high-quality correspondences using a complicated network, refining the ambiguous or noisy matching points. Despite their performance improvements, they remain constrained by the limited training pairs due to costly point-level annotations. This paper proposes a simple yet effective method that performs training with unlabeled pairs to complement both limited image pairs and sparse point pairs, requiring neither extra labeled keypoints nor trainable modules. We fundamentally extend the data quantity and variety by augmenting new unannotated pairs not primitively provided as training pairs in benchmarks. Using a simple teacher-student framework, we offer reliable pseudo correspondences to the student network via machine supervision. Finally, the performance of our network is steadily improved by the proposed iterative training, putting back the student as a teacher to generate refined labels and train a new student repeatedly. Our models outperform the milestone baselines, including state-of-the-art methods on semantic correspondence benchmarks.Comment: 12 page

Targeted Degradation of Transcription Coactivator SRC-1 through the N-Degron Pathway

Aberrantly elevated steroid receptor coactivator-1 (SRC-1) expression and activity are strongly correlated with cancer progression and metastasis. Here we report, for the first time, the development of a proteolysis targeting chimera (PROTAC) that is composed of a selective SRC-1 binder linked to a specific ligand for UBR box, a unique class of E3 ligases recognizing N-degrons. We showed that the bifunctional molecule efficiently and selectively induced the degradation of SRC-1 in cells through the N-degron pathway. Importantly, given the ubiquitous expression of the UBR protein in most cells, PROTACs targeting the UBR box could degrade a protein of interest regardless of cell types. We also showed that the SRC-1 degrader significantly suppressed cancer cell invasion and migration in vitro and in vivo. Together, these results demonstrate that the SRC-1 degrader can be an invaluable chemical tool in the studies of SRC-1 functions. Moreover, our findings suggest PROTACs based on the N-degron pathway as a widely useful strategy to degrade disease-relevant proteins.N

The Long-Term Effect of Preterm Birth on Renal Function: A Meta-Analysis

The preterm-born adult population is ever increasing following improved survival rates of premature births. We conducted a meta-analysis to investigate long-term effects of preterm birth on renal function in preterm-born survivors. We searched PubMed and EMBASE to identify studies that compared renal function in preterm-born survivors and full-term-born controls, published until 2 February 2019. A random effects model with standardized mean difference (SMD) was used for meta-analyses. Heterogeneity of the studies was evaluated using Higgin’s I2 statistics. Risk of bias was assessed using the Newcastle–Ottawa quality assessment scale. Of a total of 24,388 articles screened, 27 articles were finally included. Compared to full-term-born controls, glomerular filtration rate and effective renal plasma flow were significantly decreased in preterm survivors (SMD −0.54, 95% confidence interval (CI), −0.85 to −0.22, p = 0.0008; SMD −0.39, 95% CI, −0.74 to −0.04, p = 0.03, respectively). Length and volume of the kidneys were significantly decreased in the preterm group compared to the full-term controls (SMD −0.73, 95% CI, −1.04 to −0.41, p < 0.001; SMD −0.82, 95% CI, −1.05 to −0.60, p < 0.001, respectively). However, serum levels of blood urea nitrogen, creatinine, and cystatin C showed no significant difference. The urine microalbumin to creatinine ratio was significantly increased in the preterm group. Both systolic and diastolic blood pressures were also significantly elevated in the preterm group, although the plasma renin level did not differ. This meta-analysis demonstrates that preterm-born survivors may be subject to decreased glomerular filtration, increased albuminuria, decreased kidney size and volume, and hypertension even though their laboratory results may not yet deteriorate

Design of a Broadband Transition from a Coaxial Cable to a Reduced-Height Rectangular Waveguide

For miniaturization, rectangular waveguides with a reduced height are often required, along with a coaxial transition for signal launching. We present a simulation-based design of a broadband transition from a coaxial cable to a rectangular waveguide with the height(b)-to-width(a) ratio b/a ranging from 0.125 to 0.375. The proposed transition consists of a coaxial probe with a cylindrical head or a disk and two symmetrically placed tuning posts. To extend the operating frequency range, three sections of the rectangular waveguide are employed with properly chosen dimensions. Design examples are presented for the WR75 waveguide transition with a b/a of 0.125, 0.25, and 0.375, having a bandwidth of 83.4%, 92.7%, and 84.4%, respectively. Compared with previous works, our design offers the largest bandwidth in a right-angle coaxial-to-rectangular waveguide transition employing the aforementioned structure

Display Visibility Improvement Through Content and Ambient Light-Adaptive Image Enhancement

An image in a display device under strong illuminance can be perceived as darker than the original due to the nature of the human visual system (HVS). In order to alleviate this degradation in terms of software, existing schemes employ global luminance compensation or tone mapping. However, since such approaches focus on restoring luminance only, it has a fundamental drawback that chrominance cannot be sufficiently restored. Also, the previous approaches seldom provide acceptable visibility because it does not consider the content of an input image. Furthermore, because they focus mainly on global image quality, they may show unsatisfactory image quality for certain local areas. This paper introduces VisibilityNet, a neural network model designed to restore both chrominance and luminance. By leveraging VisibilityNet, we generate an optimally enhanced dataset tailored to the ambient light conditions. Furthermore, employing the generated dataset and a convolutional neural network (CNN), we estimate weighted piece-wise linear enhancement curves (WPLECs) that take into account both ambient light and image content. These WPLECs effectively enhance global contrast by addressing both luminance and chrominance aspects. Ultimately, through the utilization of a salient object detection algorithm that emulates the HVS, visibility enhancement is achieved not only for the overall region but also for visually salient areas. We verified the performance of the proposed method by comparing it with five existing approaches in terms of two quantitative metrics for a dataset we built ourselves. Experimental findings substantiate that the proposed method surpasses alternative approaches by significantly improving visibility