Eye View Research Articles

An algorithm for lane detection based on RIME optimization and optimal threshold

In order to address the challenges of low lane line detection rates caused by complex road conditions,we propose a novel algorithm that integrates frost and ice optimisation with optimal thresholding. A pre-processing model based on Retinex theory is used to reduce noise and preserve grey scale detail. The optimal OTSU threshold is determined for segmentation, which is enhanced by tent mapping. To further enhance the precision of the detection process, the binarized image is transformed into a bird’s-eye view, and the lane line pixel features are identified through the use of an adaptive sliding window. Ultimately, the RANSAC algorithm is utilized in conjunction with a parabolic model for lane line fitting. The experimental results demonstrate that, in comparison to similar image segmentation algorithms, the proposed method exhibits a notable advantage in terms of threshold calculation error and computational efficiency. Moreover, in comparison to analogous line detection algorithms, the detection accuracy rate reaches 93.87%, effectively reducing the impact of interference factors and demonstrating remarkable robustness that surpasses the traditional Hough Transform, which has an accuracy of 43.2%, and sliding window and Hough transform, with an accuracy of 89.16%. The code of our research work is publicly available at: https://github.com/zx2000430/rime.

A bird’s eye view of the SAJS through the lens of the ASSAf Executive Officer

A bird’s eye view of the SAJS through the lens of the ASSAf Executive Officer

Use of Basket Trials to Solve Sleep Problems in Patients with Rare Diseases

The need for sleep is universal, and the ability to meet this need impacts the quality of life for patients, families, and caregivers. Although substantial progress has been made in treating rare diseases, many patients have unmet medical sleep needs, and current regulatory policy makes it prohibitively difficult to address those needs medically. This opinion reviews the rare disease experience with sleep disorders and explores potential solutions. First, we provide case profiles for the rare diseases Wilson’s Disease, Angelman Syndrome, and Prader–Willi Syndrome. These profiles highlight challenges in rare disease diagnosis and barriers to pinpointing disease pathophysiology, including biomarkers that intersect with sleep disorders. Second, we transition to a bird’s eye view of sleep disorders and rare diseases by reporting input from a stakeholder discussion with the U.S. Food and Drug Administration regarding abnormal sleep patterns in various rare diseases. Last, in response to the profound unmet medical needs of patients with rare diseases and sleep disorders, we propose adapting and using the clinical trial design known as a “basket trial”. In this case, a basket trial would include patients with different rare diseases but the same debilitating symptoms. This research approach has the potential to benefit many rare disease patients who are otherwise left with profound unmet medical needs.

A Bird’s Eye View on the Integration of Artificial Intelligence (AI) In Ayurveda

The integration of Artificial Intelligence (AI) into Ayurveda marks a significant advancement in traditional medical practices. This review article explores AI's multifaceted uses and relevance in the conventional Healthcare industry and Ayurveda. It also highlights the transformative potential of AI in Ayurveda while acknowledging the importance of maintaining the holistic essence of traditional practices. By embracing AI, Ayurveda can evolve to meet contemporary healthcare demands, offering a synergistic approach that combines ancient wisdom with cutting-edge technology. The article concludes with a discussion about the future of Ayurveda in the era of Artificial Intelligence.

TDOcc: Exploit machine learning and big data in multi-view 3D occupancy prediction

With the advancement of machine learning and big data technologies, BEV(Bird’s Eye View)-based methodologies have recently achieved significant breakthroughs in multi-view 3D occupancy prediction tasks. However, BEV(Bird’s Eye View)-centric 3D occupancy prediction continues to grapple with feature representation and annotation costs when applied to complex open environments. In order to surmount these issues and further propel the evolution of 3D occupancy tasks, this study introduces a novel framework termed TDOcc. By leveraging multi-camera imagery, TDOcc executes 3D semantic occupancy prediction by directly learning from unprocessed 3D spaces, thereby maximizing information retention. TDOcc presents two notable advantages: firstly, it utilizes dense occupancy labels, which not only facilitate robust dense occupancy inference but also enable comprehensive object estimation within the scene. Secondly, the framework synthesizes historical feature information by adeptly aligning past and present features through temporal cues, thereby bolstering the efficacy of the feature fusion module. Additionally, with a view to address the ill-posed nature inherent in camera-based 3D occupancy prediction, we incorporate an enhancement module that operates within the 3D feature space. This module has been meticulously crafted for the training phase to amplify the model’s learning potential. Extensive experiments conducted on the widely recognized nuScenes dataset underscore the efficacy of our proposed approach. Compared to the most recent TPVFormer and OccFormer, our approach has achieved a significant improvement in mean Intersection over Union (mIoU) by 2.0 and 0.8 respectively, and has reached performance comparable to the state-of-the-art LiDAR-based methods.

Cartography of board games

ABSTRACT This paper focuses on the maps and map-like features in games. It discusses games with special topics or components, and it collects several titles of games named after a geographical object (city, land, etc.). Finally, it provides a proposed classification of the types of maps observed in the board games investigated. The popularity of board games has been increasing in an unstoppable way in the last years. Several modern board games work with maps or with map-like features. This article focuses on the maps and map-like features in games and attempts to classify the types of map in board games. The board games usually use maps on the main board. There are games that work with the map of the World in various map scales. These maps sometimes look as if they were old maps or they represented the present world. Some games' main board is similar to a map series or a classic atlas. Modular boards help to increase the replayability and give another experience game by game. Other games work with tiles (square, rectanglular or hexagonal shaped) representing a terrain type (forest, moorland, farmland, built in area, etc.). The tiles have to be placed next to each other, thereby the players can collect victory points. Fantasy maps depict fictional worlds: this helps the players to imagine better the fictional world and the characters. Fantasy maps often use partially orthogonal projection and partially bird's eye view.

Perspectives on drone journalism in Turkiye: Journalists' views on the use of drones

The subject of this study is the role of drone journalism as a new field within journalism. Drone journalism has emerged due to the rapid development of new communication technologies, making it crucial to define the role of drone technology in journalism. This study aims to investigate journalists' perspectives on drone technology and its effects on their professional lives. The research examines how journalists use drone technology, its contributions to their journalistic processes, and the challenges they face in detail. A qualitative paradigm was adopted for this study, and data were collected through semi-structured interviews within a phenomenological design. The data obtained were analyzed using thematic analysis with MAXQDA qualitative data analysis software. Due to resource and time constraints, the study was limited to 11 journalists working in Türkiye. The findings indicate that the use of drones in journalism is perceived positively for facilitating access, providing bird's-eye view imagery, enhancing aesthetic visuals, reducing physical and material risks in the news-gathering process, increasing the attractiveness of news, promoting objective journalism, and creating new employment opportunities. However, the findings also reveal concerns regarding the violation of personal data protection, privacy, and the boundaries between public and private domains. As a result of these findings, it can be concluded that there are debates about the meaning and use of drone journalism in the industry and that theoretical, practical and legal advances are needed. This study has limitations in terms of research methodology, sampling technique and size. Future research could be improved with new studies that are larger in scale and focus specifically on female drone journalists.

Three-Dimensional Outdoor Object Detection in Quadrupedal Robots for Surveillance Navigations

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects (e.g., cars) and frontal view objects (e.g., pedestrians), accommodating various types of 2D object detectors. We showcase the efficacy of our approach through the integration of YOLO3D, utilizing LiDAR point clouds on the KITTI dataset, to achieve real-time efficiency aligned with the demands of autonomous vehicle navigation. Our model selection process, tailored to the specific needs of quadrupedal robots, emphasizes considerations such as model complexity, inference speed, and customization flexibility, achieving an accuracy of up to 99.93%. This research represents a significant advancement in enabling quadrupedal robots to navigate complex and dynamic environments with heightened precision and safety.

Recent advances in responsive liquid crystal elastomer‐contained fibrous composites

AbstractResponsive polymers can react to surrounding environments by changing their physical and/or chemical properties. Among them, liquid crystal elastomers (LCEs) have emerged as one of the important branches in the field of applied polymer science due to their significant advantages in flexible mechanics and shape memory. Manufacturing LCE fibers with a large specific surface area and functional fillers has become a research hotspot in recent years. This type of LCE‐contained fibrous composite (LCEF) exhibits not only extremely high response sensitivity but also excellent axial mechanical strength and a high degree of deformation freedom. In this paper, we provide a bird's eye view of recent developments in LCEF, including structural designs, synthesis and forming methods, mechanical response principles and modes. Furthermore, we discuss recent advances of LCEF in artificial muscles, smart textiles, biomimetic systems, intelligent soft machines, followed by challenges and possible routes in fabrications and applications of LCEF. At the end, we aim to provide a perspective for an emerging field of stimulus‐responsive polymeric fiber composites.

A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing perspective of istradefylline towards major depressive disorder.

Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research. We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD). Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition). The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.

Rethinking the Non-Maximum Suppression Step in 3D Object Detection from a Bird’s-Eye View

In camera-based bird’s-eye view (BEV) 3D object detection, non-maximum suppression (NMS) plays a crucial role. However, traditional NMS methods become ineffective in BEV scenarios where the predicted bounding boxes of small object instances often have no overlapping areas. To address this issue, this paper proposes a BEV intersection over union (IoU) computation method based on relative position and absolute spatial information, referred to as B-IoU. Additionally, a BEV circular search method, called B-Grouping, is introduced to handle prediction boxes of varying scales. Utilizing these two methods, a novel NMS strategy called BEV-NMS is developed to handle the complex prediction boxes in BEV perspectives. This BEV-NMS strategy is implemented in several existing algorithms. Based on the results from the nuScenes validation set, there was an average increase of 7.9% in mAP when compared to the strategy without NMS. The NDS also showed an average increase of 7.9% under the same comparison. Furthermore, compared to the Scale-NMS strategy, the mAP increased by an average of 3.4%, and the NDS saw an average improvement of 3.1%.

A bird's-eye view of avian extinctions.

Conservation should consider species' functional and phylogenetic traits.

Bi-Interfusion: A bidirectional cross-fusion framework with semantic-guided transformers in LiDAR-camera fusion

Multi-sensor modal fusion has shown significant advantages in 3D object detection tasks. However, existing methods for fusing multi-modal features into the bird’s eye view (BEV) space often encounter challenges such as feature misalignment, underutilization of semantic information, and inaccurate depth estimation on the Z-axis, resulting in suboptimal performance. To address these issues, we propose Bi-Interfusion, a novel multi-modal fusion framework based on transformers. Bi-Interfusion incorporates a bidirectional fusion architecture, including components such as Pixel-wise Semantic Painting, Gaussian Depth Prior Distribution module, and Semantic Guidance Align module, to overcome the limitations of traditional fusion methods. Specifically, Bi-Interfusion employs a bidirectional cross-fusion strategy to merge image and LiDAR features, enabling the generation of multi-sensor BEV features. This approach leverages a refined Gaussian Depth Prior Distribution generated from LiDAR points, thereby improving the precision of view transformation. Additionally, we apply a pixel-wise semantic painting technique to embed image semantic information into LiDAR point clouds, facilitating a more comprehensive scene understanding. Furthermore, a transformer-based model is utilized to establish soft correspondences among multi-sensor BEV features, capturing positional dependencies and fully exploiting semantic information for alignment. Through experiments on nuScenes benchmark dataset, Bi-Interfusion demonstrates notable performance improvements, achieving a competitive performance of 72.6% mAP and 75.4% NDS in the 3D object detection task.

(Mis)representations of Asian Americans in Storybird: A Bird’s Eye View

This paper explores the misrepresentation of Asian Americans in Storybird, an online platform where users publish creative writing. Through a qualitative analysis of five stories featuring Asian characters, the study identifies recurring stereotypes, such as cultural conservatism, physical biases, and societal pressures. These misrepresentations often reduce complex identities into cultural caricatures, perpetuating misleading perceptions of Asian Americans. By shedding light on these biases, the paper advocates for increased cultural sensitivity and the promotion of more nuanced portrayals. Recommendations include engaging Asian American creators and promoting media literacy among young writers to challenge existing stereotypes and foster more inclusive content.

Dynamic Scene Representation for Docking in Urban Waters Using a Stereo Camera

Abstract To navigate and dock safely in urban waters, an autonomous ferry must assess its safe navigation paths by identifying navigable free space and potential obstacles. A deep understanding of its surroundings is crucial for situational awareness. Knowing which objects are static and dynamic is an indispensable ingredient for path planning algorithms and collision avoidance systems, and is especially useful for docking operations, as moving obstacles can interfere with the optimal path to the dock. To handle all types of obstacles, we present a novel dynamic scene representation for docking in urban waters using a short baseline stereo camera. Obstacles protruding from the water surface are represented with vertically oriented rectangles, known as stixels. The stixels are tracked over consecutive frames with dense optical flow. Together with stereo correspondences, their 3D motion is estimated. Stixels representing the same object are grouped together into line segments which are classified as either dynamic or static based on the motion of its associated stixels. Our dynamic representation presents objects in the 3D camera coordinate system as a bird’s eye view map. Additionally, stereo depth uncertainty can significantly impact depth estimates and is considered using a Kalman filter, providing smooth position and velocity estimates. We test our dynamic scene representation on a real docking scenario recorded with an autonomous ferry using a stereo camera. We evaluate the accuracy of the reconstructed 3D scene points and the velocity estimates of the dynamic objects using LiDAR and GNSS.

Pilot Study for Deciphering Post-Translational Modifications and Proteoforms of Tau Protein by Capillary Electrophoresis-Mass Spectrometry.

Abnormal accumulation of tau protein in the brain is one pathological hallmark of Alzheimer's disease (AD). Many tau protein post-translational modifications (PTMs) are associated with the development of AD, such as phosphorylation, acetylation, and methylation. Therefore, a complete picture of the PTM landscape of tau is critical for understanding the molecular mechanisms of AD progression. Here, we offered a pilot study of combining two complementary analytical techniques, capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) and reversed-phase liquid chromatography (RPLC)-MS/MS, for bottom-up proteomics of recombinant human tau-0N3R. We identified 50 phosphorylation sites of tau-0N3R in total, which is about 25% higher than that from RPLC-MS/MS alone. CZE-MS/MS provided more PTM sites (i.e., phosphorylation) and modified peptides of tau-0N3R than RPLC-MS/MS, and its predicted electrophoretic mobility helped improve the confidence of the identified modified peptides. We developed a highly efficient capillary isoelectric focusing (cIEF)-MS technique to offer a bird's-eye view of tau-0N3R proteoforms, with 11 putative tau-0N3R proteoforms carrying up to nine phosphorylation sites and lower pI values from more phosphorylated proteoforms detected. Interestingly, under native-like cIEF-MS conditions, we observed three putative tau-0N3R dimers carrying phosphate groups. The findings demonstrate that CE-MS is a valuable analytical technique for the characterization of tau PTMs, proteoforms, and even oligomerization.

Correction: A bird’s eye view of crime: assessing the effectiveness of mobile watchtowers on vehicle-related thefts

Correction: A bird’s eye view of crime: assessing the effectiveness of mobile watchtowers on vehicle-related thefts

Tanycytes from a bird’s eye view: gene expression profiling of the tanycytic region under different seasonal states in the Svalbard ptarmigan

Tanycytes from a bird’s eye view: gene expression profiling of the tanycytic region under different seasonal states in the Svalbard ptarmigan

Unraveling social undermining at the workplace: A systematic review of past achievements and future promises

AbstractIn the contemporary landscape of organizational dynamics, it is imperative to understand the nuance of social undermining behaviors in the workplace for effective strategy and decision‐making. In this regard, this study reviews, identifies, and critically evaluates the social undermining literature and offers new avenues for future research. The systematic literature review methodology has been used to select and critically review 65 studies on this topic. The analysis and findings of this review are presented in three sections, as follows, (i) research profiling—to highlight the current status of research on social undermining in management literature, (ii) literature synthesis—to highlight key theories and conceptions of social undermining as well as discuss the antecedents and outcomes of social undermining, and (iii) critical analysis—to highlight knowledge gaps in existing research and set future agenda. This review is the first comprehensive review on social undermining at the workplace and serves as a knowledge base by presenting a comprehensive literature review as well as extending further development of knowledge on this topic. A key contribution of our review is the proposal of a comprehensive framework of literature synthesis to provide a bird's eye view on this scholarly domain. Our study contributes to organizational behavior and strategic management discourse by offering crucial insights on human dynamics that shape organizational strategy, and change.

Enhancing 3D Rock Localization in Mining Environments Using Bird’s-Eye View Images from the Time-of-Flight Blaze 101 Camera

Enhancing 3D Rock Localization in Mining Environments Using Bird’s-Eye View Images from the Time-of-Flight Blaze 101 Camera