Dynamic Configuration Research Articles

Optimizing Kubernetes Environments: Best Practices for Configuring and Managing Admission Webhooks

Abstract: Kubernetes has emerged as the leading platform for container orchestration, offering a robust framework for automating the deployment, scaling, and management of containerized applications. As its adoption increases, managing diverse workloads across various environments becomes increasingly complex, necessitating mechanisms for policy enforcement, compliance assurance, and dynamic configuration management. In Kubernetes, admission webhooks provide a means to intercept requests, enabling the mutation and validation of these requests to enforce custom policies. This paper presents comprehensive guidelines for configuring and operating admission webhooks to enhance security, consistency, and operational efficiency in Kubernetes environments. Through this paper, readers will gain hands-on experience in implementing best practices, thereby optimizing the use of admission webhooks to effectively maintain organizational policies and standards.

An Energy-Efficient Dynamic Feedback Image Signal Processor for Three-Dimensional Time-of-Flight Sensors

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices.

The self-organized structure of glioma oncostreams and the disruptive role of passive cells

Oncostreams are self-organized structures formed by spindle-like, elongated, self-propelled cells recently described in glioblastomas and especially in gliosarcomas. Cells within these structures either move as large clusters in one main direction, flocks, or as linear, intermingling collections of cells advancing in opposite directions, streams. Round, passive cells are also observed, either inside or segregated from the oncostreams. Here we generalize a recently formulated particle-field approach to investigate the genesis and evolution of these structures, first showing that, in systems consisting only of identical self-propelled cells, both flocks and streams emerge as self-organized dynamic configurations. Flocks are the more stable configurations, while streams are transient and usually originate in collisions between flocks. Stream degradation is easier at low self-propulsion speeds. In systems consisting of both motile and passive cells, the latter block stream formation and accelerate their degradation and flock stabilization. Since the flock appears to be the most effective invasive structure, we thus argue that a phenotype mixture (motile and passive cells) may favor glioblastoma invasion. hlBy relating cellular properties to the observed outcome, our model shows that oncostreams are self-organized structures that result from the interplay between speed, shape, and steric repulsion.

Boosting the evolutionary picture of Cl 0024+17 and MS 0451-03: A case study at intermediate-redshift

Abstract In this work we improve the dynamic-evolutionary framework of two massive clusters at intermediate redshifts: Cl 0024+17 at z ∼ 0.4 and MS 0451-03 at z ∼ 0.5. The spectroscopic galaxy members were selected from Moran et al. (2007a), which combine optical and UV imaging with spectroscopy. Using a set of dynamic estimators with different approaches, our results show that both Cl 0024+17 and MS 0451-03 are non-relaxed systems with distinct dynamical configurations. Cl 0024+17 exhibits a disturbed kinematics, displaying significant gaps and a velocity dispersion profile suggesting a merger. This is confirmed by the presence of previously reported substructures and new ones identified in this study. MS 0451-03 appears less disturbed than Cl 0024+17, indicating by the significant segregation between late and early-type galaxies, with the latter occupying more central regions of the projected phase-space. However, five previously unobserved substructures and non-Gaussianity in the velocity distribution indicate that MS 0451-03 is also out of equilibrium. In both clusters, there are substructures infalling onto the systems, indicating key moments in their assembly histories and potential effects on the pre-processing of galaxies within these subgroups. This is suggested by the high percentage of early-type galaxies outside R200 (approximately $83\%$) in the case of CL 0024+17. This work reinforces the importance of more detailed dynamical analysis of clusters to better characterize their evolutionary picture.

Quantum theory of the spin dynamics excited by ultrashort THz laser pulses in rare earth antiferromagnets. DyFeO3

A quantum theory of spin dynamics in the rare-earth orthoferrites excited by terahertz laser pulses is developed. The study demonstrates that dynamic magnetic configurations, triggered by a light pulse, exhibit stability even after the excitation source is ceased. The magnitude of post-excitation oscillations is linked to the ratio between the frequency of rare-earth ion excitations and the frequency of the external source. According to the analysis presented, dynamic response is significantly amplified when the system is exposed to ultrashort terahertz pulses. The physical characteristics of the oscillations emerging after the pulse are determined, and the factors governing their amplitude and phase are identified. The response signal is found to be dependent on the initial part of the pulse, specifically the half-period of the ultrashort light wave, while the subsequent part of the pulse contributes minimally to post-pulse magnetization dynamics. The findings highlight that in DyFeO3, terahertz dynamics primarily result from the influence of the magnetic field of the light, leading to excitations of electrons from the ground state to low-lying electronic levels of Dy3+ions. Additionally, the dynamic magnetoelectric effect excited by the electric field of the pulse is explored, revealing the emergence of odd magnetic modes.

Conceptualizing Global Trends

Numerous publications contain high-quality descriptions of causes, processes, and effects of global trends. However, very few of these publications are theoretically guided or aim at theory building. This is a handicap preventing most publications on global trends from developing high-quality explanations and prognostications. This article contains an attempt at developing a conceptual framework for resolving the issue. The attempt is based on the paradigm of social interaction and includes arguments in favor of four global trends: upgrading the rationality of organizations, individualization, spread of instrumental activism, and homogenization of culture. The decomposition of the trends makes it possible to identify their structures and dynamics. The analyzed examples usually refer to constructive processes and effects. In reality, the global trends are internally controversial and bring about both constructive and destructive processes and effects. The most challenging task for future studies concerns the interplay of the internal dynamics of each global trend with the dynamics of the configuration of global trends. The conceptualization of the dynamic configuration of interacting global trends is the core of the diagnosis of the global situation.

Transitioning trends into action: A simulation-based Digital Twin architecture for enhanced strategic and operational decision-making

In the dynamic realm of nowadays manufacturing, integrating digital technologies has become paramount for enhancing operational efficiency and decision-making processes. This article presents a novel system architecture that integrates a Simulation-based Digital Twin (DT) with emerging trends in manufacturing to enhance decision-making, accompanied by a detailed technical approach encompassing protocols and technologies for each component. The DT leverages advanced simulation techniques to model, monitor, and optimize production processes in real time, facilitating both strategic and operational decision-making. Complementing the DT, trending technologies such as artificial intelligence, additive manufacturing, collaborative robots, autonomous vehicles, and connectivity advancements are strategically integrated to enhance operational efficiency and facilitate the adoption of the Manufacturing as a Service (MaaS) paradigm. A case study within a MaaS supplier context, deployed in an industrial laboratory with advanced robotic systems, demonstrates the practical application of optimizing dynamic job-shop configurations using Simulation-based DT, showcasing strategies to improve operational efficiency and resource utilization. The results of the industrial experiment were highly encouraging, underscoring the potential for extension to more intricate industrial systems, with particular emphasis on incorporating sustainability and remanufacturing principles.

Light-induced magnetization from magnonic rectification.

Rectification describes the conversion of an oscillating field or current into a quasi-static one and the most basic example of a rectifier is an AC/DC converter in electronics. This principle can be translated to nonlinear light-matter interactions, where optical rectification converts the oscillating electric field component of light into a quasi-static polarization and phononic rectification converts a lattice vibration into a quasi-static structural distortion. Here, we present a rectification mechanism for magnetism that we call magnonic rectification, where a spin precession is converted into a quasi-static magnetization through the force exerted by a coupled chiral phonon mode. The transiently induced magnetic state resembles that of a canted antiferromagnet, opening an avenue toward creating dynamical spin configurations that are not accessible in equilibrium.

Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.

DAMO: A Deep Solver for Arbitrary Marker Configuration in Optical Motion Capture

Marker-based optical motion capture (mocap) systems are increasingly utilized for acquiring 3D human motion, offering advantages in capturing the subtle nuances of human movement, style consistency, and ease of obtaining desired motion. Motion data acquisition via mocap typically requires laborious marker labeling and motion reconstruction, recent deep-learning solutions have aimed to automate the process. However, such solutions generally presuppose a fixed marker configuration to reduce learning complexity, thereby limiting flexibility. To overcome the limitation, we introduce DAMO, an end-to-end deep solver, proficiently inferring arbitrary marker configurations and optimizing pose reconstruction. DAMO outperforms state-of-the-art like SOMA and MoCap-Solver in scenarios with significant noise and unknown marker configurations. We expect that DAMO will meet various practical demands such as facilitating dynamic marker configuration adjustments during capture sessions, processing marker clouds irrespective of whether they employ mixed or entirely unknown marker configurations, and allowing custom marker configurations to suit distinct capture scenarios. DAMO code and pretrained models are available at https://github.com/CritBear/damo

Machine Learning Structure for Controlling the Speed of Variable Reluctance Motor via Transitioning Policy Iteration Algorithm

This paper investigated a new speed regulator using an adaptive transitioning policy iteration learning technique for the variable reluctance motor (VRM) drive. A transitioning strategy is used in this unique scheme to handle the nonlinear behavior of the VRM by using a series of learning centers, each of which is an individual local learning controller at linear operational location that grows throughout the system’s nonlinear domain. This improved control technique based on an adaptive dynamic programming algorithm is developed to derive the prime solution of the infinite horizon linear quadratic tracker (LQT) issue for an unidentified dynamical configuration with a VRM drive. By formulating a policy iteration algorithm for VRM applications, the speed of the motor shows inside the machine model, and therefore the local centers are directly affected by the speed. Hence, when the speed of the rotor changes, the parameters of the local centers grid would be updated and tuned. Additionally, a multivariate transition algorithm has been adopted to provide a seamless transition between the Q-centers. Finally, simulation and experimental results are presented to confirm the suggested control scheme’s efficacy.

Spontaneous acts of everyday co-creation: somaesthetic study of affective placemaking in the case of Durga Puja, Kolkata

ABSTRACT Everyday participatory acts can lead to the co-creation of urban spaces even without the specific intention of dominant stakeholders. Such spontaneous co-creation is often triggered by macro-level urban events with significant affective value in placemaking. We explore the Durga Puja festival in Kolkata as a prominent example of this phenomenon. Utilising Richard Shusterman’s somaesthetic framework, we analyse how space is dynamically co-created by diverse ‘sentient, purposive bodies’, or somas. Our fieldwork and interviews map affective placemaking, examining somatic associations within dynamic spatial configurations. This is illustrated through examples of spontaneous community involvements that result in the co-creation of new types of places, without adhering to a pre-planned, predictable course. Two types of co-creation emerge: the first, more dominant, where everyday elements recede into the background through intrusive design motifs, and the second, where the everyday is the sole inspiration behind affective placemaking. The dominant model of co-creation highlights overarching themes, while the second type, explored through detailed case studies, offers a nuanced understanding of a place’s historicity, geographical context, and cultural significance. Strategies for blending into surroundings, rather than standing out, have significant implications for sustainable urban design that values affective placemaking.

Hybrid Teamwork: What We Know and Where We Can Go From Here.

Hybrid teamwork, which describes any combination of one's work time spent across organizational and other (typically domestic) work settings, has become a critical aspect of modern work environments. However, despite the rising prevalence and technological support for hybrid teamwork, there is limited understanding of its impact at the team level. Although we still lack research that addresses the dynamic geographic configurations inherent to hybrid teamwork, we believe that much of the extant literature on virtual teamwork can inform our understanding and guide future research. Accordingly, this paper aims to advance knowledge on hybrid teamwork by defining its unique characteristics and critically reviewing three broad classes of theory from the virtual teams literature and their implications for understanding hybrid teamwork. Based on both contributions and limitations of these three theory classes, we conclude this paper by mapping out pressing questions to guide future research.

Near‐Full‐Spectrum Emission Realized in a Single Lead Halide Perovskite across the Visible‐Light Region

AbstractThe engineering of tunable photoluminescence (PL) in single materials with a full‐spectrum emission represents a highly coveted objective but poses a formidable challenge. In this context, the realization of near‐full‐spectrum PL emission, spanning the visible light range from 424 to 620 nm, in a single‐component two‐dimensional (2D) hybrid lead halide perovskite, (ETA)2PbBr4 (ETA+=(HO)(CH2)2NH3+), is reported, achieved through high‐pressure treatment. A pressure‐induced phase transition occurs upon compression, transforming the crystal structure from an orthorhombic phase under ambient conditions to a monoclinic structure at high pressure. This phase transition driven by the adaptive and dynamic configuration changes of organic amine cations enables an effective and continuous narrowing of the band gap in this halide crystal. The hydrogen bonding interactions between inorganic layers and organic amine cations (N−H⋅⋅⋅Br and O−H⋅⋅⋅Br hydrogen bonds) efficiently modulate the organic amine cations penetration and the octahedral distortion. Consequently, this phenomenon induces a phase transition and results in red‐shifted PL emissions, leading to the near‐full‐spectrum emission. This work opens a possibility for achieving wide PL emissions with coverage across the visible light spectrum by employing high pressure in single halide perovskites.

Grapher: A Reconfigurable Graph Computing Accelerator with Optimized Processing Elements

In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP.

General heterogeneous real-time baseband backend system scheme applied to QTT based on RFSoC+GPU

This paper proposes a heterogeneous real-time baseband astronomical backend solution centered on RFSoC and combined GPU. The solution implemented the integration of the high-performance, completely configurable RF signal link to digitize, process, and transmit increasingly massive radio astronomy signals and provided a highly flexible and scalable scheme for the QTT backend. The RFSoC-based integration RF pre-processing front-end directly digitizes dual-polarization signals at the sampling rate of 2048 MHz and converts them to dual-16-channel data streams for parallel mixing and polyphase decimation filtering. The generated baseband data with 256 MHz center frequency and 100 MHz bandwidth is packaged and output to the GPU server through the 100GbE Interface. Complete processes of direct RF sampling, pre-processing, and high-speed transmission are implemented at the receiver. The GPU server executes real-time post-processing at the remote end. The dynamic configuration of two observation modes can be achieved with frequency resolutions of 3.051 and 0.763 kHz. The H2CO absorption line observations with the Nanshan 26-m radio telescope verified the system’s performance and the design’s rationality. This paper explores and confirms the RFSoC’s applicability on the backend. The flexible and efficient QTT backend scheme was planned based on the proposed real-time baseband pattern by expanding multi-RFSoC and GPU computing nodes.

Genetic-epigenetic interplay in the determination of plant 3D genome organization.

The 3D chromatin organization plays a major role in the control of gene expression. However, our comprehension of the governing principles behind nuclear organization remains incomplete. Particularly, the spatial segregation of loci with similar repressive transcriptional states in plants poses a significant yet poorly understood puzzle. In this study, employing a combination of genetics and advanced 3D genomics approaches, we demonstrated that a redistribution of facultative heterochromatin marks in regions usually occupied by constitutive heterochromatin marks disrupts the 3D genome compartmentalisation. This disturbance, in turn, triggers novel chromatin interactions between genic and transposable element (TE) regions. Interestingly, our results imply that epigenetic features, constrained by genetic factors, intricately mold the landscape of 3D genome organisation. This study sheds light on the profound genetic-epigenetic interplay that underlies the regulation of gene expression within the intricate framework of the 3D genome. Our findings highlight the complexity of the relationships between genetic determinants and epigenetic features in shaping the dynamic configuration of the 3D genome.

Supervisory Control for Dynamic Feature Configuration in Product Lines

In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory controller synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. The solutions are exemplified in the CIF toolset using a model of a coffee machine. A use case of the much larger Body Comfort System product line is performed to showcase feasibility for industrial-sized systems.

Tracking the extensive three-dimensional motion of single ions by an engineered point-spread function

Three-dimensional (3D) imaging of individual atoms is a critical tool for discovering new physical phenomena and developing new technologies in microscopic systems. However, the current single-atom-resolved 3D imaging methods are limited to static circumstances or a shallow detection range. Here, we demonstrate a generic dynamic 3D imaging method to track the extensive motion of single ions by exploiting the engineered point-spread function (PSF). We show that the image of a single ion can be engineered into a helical PSF, thus enabling single-snapshot acquisition of the position information of the ion in the trap. A preliminary application of this technique is demonstrated by recording the 3D motion trajectory of a single trapped ion and reconstructing the 3D dynamical configuration transition between the zig and zag structures of a 5-ion crystal. This work opens the path for studies on single-atom-resolved dynamics in both trapped-ion and neutral-atom systems.

Swifts Form V-Shaped Wings While Dipping in Water to Fine-Tune Balance.

Swifts, a distinctive avian cohort, have garnered widespread attention owing to their exceptional flight agility. While their aerial prowess is well documented, the challenge swifts encounter while imbibing water introduces an intriguing complexity. The act of water uptake potentially disrupts their flight equilibrium, yet the mechanisms enabling these birds to maintain stability during this process remain enigmatic. In this study, we employed high-speed videography to observe swifts' water-drinking behavior. Notably, we observed that the swift adopts a dynamic V-shaped wing configuration during water immersion with the ability to modulate the V-shaped angle, thereby potentially fine-tuning their balance. To delve deeper, we utilized a three-dimensional laser scanner to meticulously construct a virtual 3D model of swifts, followed by computational fluid dynamics simulations to quantitatively assess the mechanical conditions during foraging. Our model indicates that the adoption of V-shaped wings, with a variable wing angle ranging from 30 to 60 degrees, serves to minimize residual torque, effectively mitigating potential flight instability. These findings not only enhance our comprehension of swifts' flight adaptability but also hold promise for inspiring innovative, highly maneuverable next-generation unmanned aerial vehicles. This research thus transcends avian biology, offering valuable insights for engineering and aeronautics.