Dynamic Background Research Articles

Constraints of the Yanshanian dynamic background in the northeastern Hunan: Chronology and geochemical characteristics of granitic intrusive rocks in the Jinjing area

Constraints of the Yanshanian dynamic background in the northeastern Hunan: Chronology and geochemical characteristics of granitic intrusive rocks in the Jinjing area

Moving Object Detection in Freely Moving Camera via Global Motion Compensation and Local Spatial Information Fusion.

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds.

Real‐time Neural Rendering of Dynamic Light Fields

AbstractSynthesising high‐quality views of dynamic scenes via path tracing is prohibitively expensive. Although caching offline‐quality global illumination in neural networks alleviates this issue, existing neural view synthesis methods are limited to mainly static scenes, have low inference performance or do not integrate well with existing rendering paradigms. We propose a novel neural method that is able to capture a dynamic light field, renders at real‐time frame rates at 1920× resolution and integrates seamlessly with Monte Carlo ray tracing frameworks. We demonstrate how a combination of spatial, temporal and a novel surface‐space encoding are each effective at capturing different kinds of spatio‐temporal signals. Together with a compact fully‐fused neural network and architectural improvements, we achieve a twenty‐fold increase in network inference speed compared to related methods at equal or better quality. Our approach is suitable for providing offline‐quality real‐time rendering in a variety of scenarios, such as free‐viewpoint video, interactive multi‐view rendering, or streaming rendering. Finally, our work can be integrated into other rendering paradigms, e.g., providing a dynamic background for interactive scenarios where the foreground is rendered with traditional methods.

Exploring the effects of E-Banking Products and Services on Conventional Banking Transactions in Nepalese Commercial Banks

This research investigates the growing scenery of E-Banking Products and Services within Nepalese Commercial Banks and their impact on conventional banking transactions. In the context of global financial sector transformations driven by technology, this study seeks to provide insights into the specific implications for Nepal's banking industry.
 To achieve this, the research employs a comprehensive framework, analyzing the range of E-Banking Products and Services currently offered by Nepalese Commercial Banks. It describes e-banking as the ATM banking, Internet banking and Mobile banking and their impacts on conventional banking transactions within Nepalese Commercial Banks. A mixed-methods approach is applied for this research to understand how E-Banking products and services affect conventional banking transactions in Nepalese Commercial Banks.
 Ultimately, the objective of the research is to offer valuable insights into the complex interplay between E-Banking and conventional banking transactions in Nepalese Commercial Banks. By enhancing our understanding of this dynamic background, the study intends to provide practical recommendations and strategies for banks to influence the benefits of E-Banking while mitigating potential drawbacks. This research contributes to the broader discussion on e-banking technology adoption and its implications in emerging economies and banking practices.

Progressive colapse efect in reinforced concrete buildings

Progressive collapse is defined as the spread of an initial failure from element to element, eventually resulting in the collapse of an entire structure or a disproportionately large part of it according to ASCE (2002). It is a chain phenomenon where structural damage occurs in an amplified way due to the action of gravity or caused by some dynamic factor such as explosions. Collapses of slabs on upper floors are very common caused by some dynamic fator or failures on reinforcements. The present paper aims to analyze the progressive collapse of concrete structures in commercial and residential buildings. In this work, the dynamic background of progressive collapse are defined, the pathological aspects existing in buildings, as well as the calculation parameters used by Brazilian and international codes to prevent such pathologies. Real cases that occurred around the world are analyzed and how progressive collapse has been treated in recent decades. It is also suggested for cases in which the structure allows recovery, which procedures can be used.

AI Enhancement Automated Movement Detection

This paper introduces an innovative artificial intelligence (AI) method aimed at tackling the challenges associated with detecting and tracking moving objects in video surveillance systems. By utilizing self-organization through artificial neural networks, our approach effectively manages scenes with dynamic backgrounds and gradual changes in lighting, ensuring robust detection across different types of videos recorded by stationary cameras. In the realm of moving object detection, our method leverages the adaptability of neural networks, enabling precise detection in complex visual environments. For object tracking, we propose a combination of Kalman filtering techniques and a sophisticated matching model based on Multiple Hypothesis Testing, ensuring accurate and consistent tracking across frames. Through experimental validation using various color video sequences, we demonstrate the effectiveness and reliability of our approach, highlighting its potential to enhance the performance of surveillance systems in real-world scenarios.

Robust and efficient FISTA-based method for moving object detection under background movements

Moving object detection is a fundamental task in many video processing applications, such as video surveillance. The robustness and efficiency of background subtraction make it one of the most common methods for detecting moving objects from a video stream. However, adapting background models with moving cameras remains challenging. Major issues include maintaining the model given viewpoint alterations and compensating motions considering depth variations. Moreover, gradual illumination changes, dynamic backgrounds, and complex motions intensify over time in moving camera scenarios, further complicating background model maintenance. In this context, this paper proposes a novel Robust and Online Tensor-based model named ROTAB that incorporates a more implicit consideration of the relationship between sequential frames than the previous methods, allowing for better adaptation to background changes. Moreover, we propose an Improved version of FISTA named IFISTA that employs two strategies to reduce oscillatory behavior and minimize iterations, improving stability and efficiency. Practically, the combination of IFISTA and ROTAB (IFISTA-ROTAB) demonstrates suitable performance for real-time applications. Quantitative and qualitative experiments are conducted on three large-scale datasets, namely CDnet 2014, BMC 2012 and LASIESTA showing the superiority of IFISTA-ROTAB with a gain from two up to seven percent in average.

Experimental saturation characteristics of the PMT and adaptive compensation algorithm for OWC

Orthogonal frequency division multiplexing (OFDM) utilizes numerous sub-carriers to achieve high transmission data rates. The frequency selectivity of the channel becomes a crucial factor influencing the communication performance of OFDM-based systems. In optical wireless communication (OWC) systems, the photomultiplier tube (PMT) may experience saturation when the incident optical power approaches its saturation threshold. This paper, for the first time, characterizes the saturation levels of a high-speed PMT based on the measured amplitude in the time domain and the output response of the PMT in the frequency domain. Additionally, an adaptive optical saturation compensation algorithm, leveraging an electronically controlled variable optical attenuator, is proposed to realize a reliable OWC system. Experimental results demonstrate that the proposed saturation compensation method achieves a higher tolerance to large dynamic signal and background radiation compared with that without compensation, while maintaining a satisfactory bit error rate.

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking.

The Development of a Feature-Driven Analytical Approach for Gamma-Ray Spectral Analysis

Gamma-ray spectroscopy is an essential tool in nuclear science, nuclear security, and environmental monitoring. However, challenges arise in interpreting spectral data due to the presence of low counts, multiple sources, and dynamic backgrounds. To address these issues, a novel feature-driven analytical approach for gamma-ray spectral analysis using machine-learning techniques is developed. The method utilizes a series of random forest models for in-distribution (ID) multi-label classification, and the model-derived feature importance values to guide the out-of-distribution (OOD) detection task. The performance of this approach is quantitatively evaluated across various spectral parameters, including acquisition time, number of sources, energy of an OOD source, and background composition. Increasing the acquisition time from 1 s to 100 s leads to improved performance for multi-label classification, with 22 sources achieving F1-scores ≥ 0.9 after 50 s acquisitions for a CLLBC handheld detector and a standoff distance of 30 cm. The feature-driven analytical approach also demonstrates robustness when handling complex source mixtures. Furthermore, it provides contextual energetic information for OOD detection. The results presented here highlight the interpretability of the approach, establishing clear links between the spectral features and underlying physics. Moreover, the approach effectively distinguishes overlapping spectral signatures of different ID gamma-ray sources, enhancing human reliability in machine learning-based gamma-ray spectral analysis. The feature-driven analytical approach offers a promising solution to automate gamma-ray spectral analysis by addressing existing limitations and providing insights into performance across diverse spectral parameters.

Context reinstatement requires a schema relevant virtual environment to benefit object recall.

How does our environment impact what we will later remember? Early work in real-world environments suggested that having matching encoding/retrieval contexts improves memory. However, some laboratory-based studies have not replicated this advantageous context-dependent memory effect. Using virtual reality methods, we find support for context-dependent memory effects and examine an influence of memory schema and dynamic environments. Participants (N = 240) remembered more objects when in the same virtual environment (context) as during encoding. This traded-off with falsely "recognizing" more similar lures. Experimentally manipulating the virtual objects and environments revealed that a congruent object/environment schema aids recall (but not recognition), though a dynamic background does not. These findings further our understanding of when and how context affects our memory through a naturalistic approach to studying such effects.

Ponds and Wetlands Landscapes of Flood Management in the Cities of the Lower Yellow River Floodplain—The Case of Huaiyang, China

As interest has increased in green infrastructure and nature-based solutions, traditional approaches to managing landscapes have emerged as providing useful models for sustainable water and land management. From local gazetteers, archives, oral histories, maps, and satellite images, we documented the historical landscapes of ponds and wetlands back to the 17th century in the historic city of Huaiyang on the lower Yellow River floodplain. Like neighboring cities, Huaiyang was protected by a ring levee. As the floodplain rapidly aggraded, cities within the levee became topographically lower than the surrounding landscape. In this context, ponds and wetlands were essential for flood and storm water retention in the low-lying city. These seasonal waterbodies alternated between drying and wetting, providing a dynamic and diverse background for native habitats and human uses. CORONA satellite images ca. 1960s show ponds and wetlands shrinking in the dry season to 35.6% of their wet season extent, while the farmed area expanded 5.3 times. The multiple uses of wetlands included dry-season farming, harvesting wetland plants, and fishing, each use adapted to the localized topographic and hydrologic conditions of the wetlands. The late 20th century saw massive transformations for modern agriculture and urbanization. Understanding the historical evolution of this landscape can provide inspiration for developing green infrastructure and resilient designs that preserve cultural diversity and sustainably manage water in an urbanizing landscape.

Exploring phononlike interactions in one-dimensional Bose-Fermi mixtures

With the objective of simulating the physical behavior of electrons in a dynamic background, we investigate a cold atomic Bose-Fermi mixture confined in an optical lattice potential solely affecting the bosons. The bosons, residing in the deep superfluid regime, inherit the periodicity of the optical lattice, subsequently serving as a dynamic potential for the polarized fermions. Owing to the atom-phonon interaction between the fermions and the condensate, the coupled system exhibits a Berezinskii-Kosterlitz-Thouless transition from a Luttinger liquid to a Peierls phase. However, under sufficiently strong Bose-Fermi interaction, the Peierls phase loses stability, leading to either a collapsed or a separated phase. We find that the primary function of the optical lattice is to stabilize the Peierls phase. Furthermore, the presence of a confining harmonic trap induces a diverse physical behavior, surpassing what is observed for either bosons or fermions individually trapped. Notably, under attractive Bose-Fermi interaction, the insulating phase may adopt a fermionic wedding-cake-like configuration, reflecting the dynamic nature of the underlying lattice potential. Conversely, for repulsive interaction, the trap destabilizes the Peierls phase, causing the two species to separate. Published by the American Physical Society 2024

Late Neoproterozoic tectonic evolution of the northern Tarim block: New insights from integrated detrital zircon and rutile geochronology and trace element geochemistry

In the Neoproterozoic era, the breakup of the Rodinia supercontinent had a profound impact on different aspects of Earth; yet, the precise driving mechanism behind this event has been a source of ongoing controversy. The Tarim block, as a crucial component, presents an opportunity to gain valuable insights into the breakup process through its tectonic-sedimentary evolution history and dynamic background. However, the comprehension of basin evolution is limited due to the presence of an ambiguous prototype basin during the late Neoproterozoic and its intricate interconnections with surrounding blocks. In this contribution, we integrate detailed field observations, U-Pb dating of detrital zircon and rutile, and trace element analysis of detrital rutile to elucidate the source-to-sink relationships and the tectonic setting of Ediacaran sedimentary strata in the Aksu area (northern Tarim block, China). Detrital zircon age spectra exhibit consistent distributions with ages between ∼850 and ∼600 Ma and between ∼2100 and ∼1800 Ma. Rutile age groups however exhibit an abrupt transition between the lower member of the Sugatbrak Formation and the succeeding upper member to the Qigebrak Formation in the basin, with ages between ∼1800 and ∼900 Ma and nearly identical to those between ∼2100 and ∼1800 Ma. Calculated Zr-in-rutile temperatures and Cr-Nb compositions imply that the majority of the detrital rutiles were sourced from amphibolite facies metapelites, with an abrupt transition from amphibolitic schist to amphibolite-granulite facies for the ∼1800 Ma group grains. Provenance tracing shows that the detrital sediments of the lower Sugatbrak Formation are sourced from both the Central Tianshan and the Tarim blocks, and that the overlying strata have a single source in the Tarim block. A corresponding change from syn-rift detrital facies to a shallow-marine environment in on a passive continental margin is suggested in terms of depositional setting. In light of prior published evidence from petrology, geochronology and paleomagnetism, we propose that the Neoproterozoic northern Tarim margin was affected by a protracted subduction zone retreat. Associated back-arc spreading eventually was responsible for the opening of the southern Tianshan Ocean and rifting of the Central Tianshan block from the Tarim block in the late Ediacaran. This development ultimately prevented detrital material from the Central Tianshan block from reaching the northern Tarim depocenters as the latter evolved into a passive continental margin.

CSR in India – A Vision for the Sustainable Future

CSR has emerged as a critical component of modern business practices, especially in the Indian context. The dynamic background of CSR in India reflects a transformation in the role of corporations from mere profit-making entities to responsible corporate citizens contributing to societal welfare. With a rich history deeply intertwined with philanthropy and social causes, India has witnessed a significant shift in the way businesses perceive and execute their social responsibilities. In this backdrop, the present paper puts an attempt to comprehensively analyze the role of CSR in contributing sustainable development and challenges within the Indian CSR ecosystem. Further, the article concludes with strategics to address these challenges, advocating for collaborative efforts and policy reforms to shape the future of CSR in India.

Demonstration of quantum-enhanced rangefinding robust against classical jamming.

We demonstrate a quantum-enhanced lidar capable of performing confident target detection and rangefinding in the presence of strong, time-varying classical noise whilst operating with over five orders of magnitude separation between signal and background levels and target reflectivities down to -52 dB. We use a log-likelihood-based framework to introduce a new protocol for dynamic background tracking, verifying resilience of our system to both fast- and slow-modulation jamming in regimes where a classical illumination-based system fails to find a target. These results demonstrate the advantage of exploiting quantum correlations for lidar applications, providing a clear route to implementation in real-world scenarios.

Three-Stage Deep Learning Framework for Video Surveillance

The escalating use of security cameras has resulted in a surge in images requiring analysis, a task hindered by the inefficiency and error-prone nature of manual monitoring. In response, this study delves into the domain of anomaly detection in CCTV security footage, addressing challenges previously encountered in analyzing videos with complex or dynamic backgrounds and long sequences. We introduce a three-stage deep learning architecture designed to detect abnormalities in security camera videos. The first stage employs a pre-trained convolutional neural network to extract features from individual video frames. Subsequently, these features are transformed into time series data in the second stage, utilizing a blend of bidirectional long short-term memory and multi-head attention to analyze short-term frame relationships. The final stage leverages relative positional embeddings and a custom Transformer encoder to interpret long-range frame relationships and identify anomalies. Tested on various open datasets, particularly those with complex backgrounds and extended sequences, our method demonstrates enhanced accuracy and efficiency in video analysis. This approach not only improves current security camera analysis but also shows potential for diverse application settings, signifying a significant advancement in the evolution of security camera monitoring and analysis technologies.

3D convolutional long short-term encoder-decoder network for moving object segmentation

Moving object segmentation (MOS) is one of the important and wellstudied computer vision tasks that is used in a variety of applications, such as video surveillance systems, human tracking, self-driving cars, and video compression. While traditional approaches to MOS rely on hand-crafted features or background modeling, deep learning methods using Convolution Neural Networks (CNNs) have been shown to be more effective in extracting features and achieving better accuracy. However, most deep learning-based methods for MOS offer scene-dependent solutions, leading to reduced performance when tested on previously unseen video content. Because spatial features are insufficient to represent the motion information, the spatial and temporal features should be used together to succeed in unseen videos. To address this issue, we propose the MOS-Net deep framework, an encoder-decoder network that combines spatial and temporal features using the flux tensor algorithm, 3D CNNs, and ConvLSTM in its different variants. MOS-Net 2.0 is an enhanced version of the base MOS-Net structure, where additional ConvLSTM modules are added to 3D CNNs for extracting long-term spatiotemporal features. In the final stage of the framework the output of the encoder-decoder network, the foreground probability map, is thresholded for producing a binary mask where moving objects are in the foreground and the rest forms the background. In addition, an ablation study has been conducted to evaluate different combinations as inputs to the proposed network, using the ChangeDetection2014 (CDnet2014) which includes challenging videos such as those with dynamic backgrounds, bad weather, and illumination changes. In most approaches, the training and test strategy are not announced, making it difficult to compare the algorithm results. In addition, the proposed method can be evaluated differently as video-optimized or video-agnostic. In video-optimized approaches, the training and test set is obtained randomly and separated from the overall dataset. The results of the proposed method are compared with competitive methods from the literature using the same evaluation strategy. It has been observed that the introduced MOS networks give highly competitive results on the CDnet2014 dataset. The source code for the simulations provided in this work is available online.

Effects of Transformational Leadership on Organizational Identification and Adaptive Performance in Health Care Industry

In consideration of global economy, business management should adapt to such environmental changes. Under such dynamic background, it is emergent to seek for excellent change leaders. Based on such background, researchers propose the idea of transformational leadership, stressing on how such a leadership style effectively manages employees and induces employees’ advanced requirements to create larger value for the business, satisfy employees’ personal needs, as well as realize effective coordinated development between employees’ individual performance and organizational goals. Cross-regional health care related enterprises in Shandong Province are selected as the research samples for this study. Total 300 participants, standing for various roles, backgrounds, and experiences in modern enterprises, are invited to provide rich and diverse data for this study. Mature domestic and international scales are adopted for the research. The results reveal positive effects of transformational leadership on employees’ adaptive performance, definite and significant effects of transformational leadership on organizational identification, as well as definite and remarkable effects of organizational identification on adaptive performance. Finally, corresponding suggestions in management are proposed, expecting that both leaders and employees, when facing complicated environment and work requirements, could contribute to the long-term development of the organizations.

Abstract C013: Clinical biomarkers based on PK/PD modeling to guide the development for a first-in-class, highly selective SMARCA2 (BRM) degrader, PRT3789

Abstract Background: The SWI/SNF family of chromatin-remodeling complexes is frequently dysregulated in multiple tumor types, resulting in aberrant expression of genes. Damaging mutations resulting in loss of function of one of its core catalytic subunits SMARCA4 (BRG1) occur in multiple tumor types, including in 5-10% of non-small cell lung cancer (NSCLC). SMARCA4-deleted cells become highly dependent on the other catalytic subunit, SMARCA2 (BRM), for their survival, therefore selective degradation of SMARCA2 has therapeutic potential in these cancer types. PRT3789 is a first-in-class, potent and selective SMARCA2 degrader that is currently under evaluation in a Phase 1 study in patients with advanced solid tumors with loss of SMARCA4 (NCT05639751). We deeply characterized the degradation kinetic parameters of PRT3789 based on preclinical PK/PD studies, which enabled dose-selection. Based on this modeling, we developed a suite of biomarker assays that can be used to assess target engagement in this Phase 1 study. Methods: A sensitive and quantitative plate-based immunoassay utilizing the MSD® platform was developed for the measurement of SMARCA2 protein in human PBMCs following treatment with PRT3789. A secondary, qPCR-based assay was developed for assessment of SMARCA2 transcriptional targets in peripheral blood that may be associated with response to SMARCA2 degradation by PRT3789. Additionally, an immunohistochemistry (IHC) assay for the assessment of SMARCA2 and SMARCA4 protein expression in tumor tissue was developed on the Leica BOND III platform using commercially available antibodies. Results: Detailed in vitro enzymology characterized the degradation kinetic parameters of PRT3789 and was successfully used to predict the PRT3789 effects observed in PK/PD and xenograft efficacy studies in mice. The MSD® assay demonstrated a wide dynamic range of detection for SMARCA2 with a lower level of quantification of 23 pg/mL, as well as exhibiting acceptable spike recovery and minimal cross-reactivity to SMARCA4. Based on the qPCR assay, we developed a differential gene expression (DGE) score based on expression of an 8-gene panel that correlates with SMARCA2 protein degradation in PBMCs. The IHC assay demonstrated discrete and robust nuclear tumor staining with a dynamic intensity range and limited cytoplasmic background for both SMARCA2 and SMARCA4. A panel of NSCLC tumor samples with known SMARCA4 mutations were further evaluated in this assay. Loss of SMARCA4 protein expression in tumor cells by IHC was associated with nonsense and frameshift mutations in the SMARCA4 gene. Conclusions: We demonstrate the sensitive assessment of SMARAC2 protein degradation and effects on downstream gene expression in PBMCs, which will be used to assess target engagement following treatment with PRT3789 in a Phase 1 clinical study. Citation Format: Min Wang, Andrew Moore, Alexander Grego, Miles Cowart, Joseph Rager, Gina Paris, Koichi Ito, Naveen Babbar, Sandy Geeganage, Peegy Scherle, Neha Bhagwat. Clinical biomarkers based on PK/PD modeling to guide the development for a first-in-class, highly selective SMARCA2 (BRM) degrader, PRT3789 [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C013.