A view of database system performance measures

Summary of United States Human Proteome Organisation (HUPO) Symposium Entitled “Standardized Clinical Proteomics Platforms”

Optimizing directly the system performance metric is an important method for correcting wave-front distortions in adaptive optics (AO) systems. In extended object imaging, some classical image quality metrics are often used as system performance evaluation function, which is the optimized object of the control algorithm. But those metrics do not consider the existence of imaging noise. Practically, the observed object images are degraded not only by the atmospheric turbulence but also imaging system noise. The noise in image will affect the value of image quality criteria and further affect the correction capability of AO system. An AO system with Stochastic Parallel Gradient Descent (SPGD) algorithm and a 61-element deformable mirror is simulated to restore the image of a turbulence-degraded extended object and the gray level variance function acted the optimized object by control algorithm. The analysis results show that the correction capability is affected severely when the turbulence strength is relatively big (D/r0<10). This paper presents a method that the observed image is processed by the bilateral filter before the system performance metric is calculated, so that the effect of noise on the system performance metric can be mitigated. Numerical simulation results verify the method is effective and the image quality based on AO technique and the bilateral filter is much better than that of only using AO technique.

An advanced system for facial landmark detection and 3D facial animation rigging is proposed, utilizing deep learning algorithms to accurately detect key facial points, such as the eyes, mouth, and eyebrows. These landmarks enable precise rigging of 3D models, facilitating realistic and controlled facial expressions. The system enhances animation efficiency and realism, providing robust solutions for applications in gaming, animation, and virtual reality. This approach integrates cutting-edge detection techniques with efficient rigging mechanisms. The AI-assisted rigging process reduces manual effort and ensures precise, dynamic animations. The study evaluates the system's accuracy in facial landmark detection, the efficiency of the rigging process, and its performance in generating consistent emotional expressions across animations. Additionally, the system's computational efficiency, scalability, and system performance are assessed, demonstrating its practicality for real-time applications. Pilot testing, emotion recognition consistency, and performance metrics reveal the system's robustness and effectiveness in producing realistic animations while reducing production time. This work contributes to the advancement of animation and virtual environments, offering a scalable solution for realistic facial expression generation and character animation. Future research will focus on refining the system and exploring its potential applications in interactive media and real-time animation.

Performance of nearest neighbour metrics for pluvial flood nowcasts in urban catchments

In the digital era, protecting sensitive data has become an urgent necessity as incidents of identity theft and information breaches continue to rise. Transparent Data Encryption (TDE) offers a solution by enabling storage-level encryption without requiring changes to applications; however, PostgreSQL does not natively support TDE. Vendors such as EnterpriseDB and Cybertec have developed TDE implementations that can be integrated to address this security need. This study aims to explore the implementation of TDE on PostgreSQL and evaluate its impact on system performance and data security. An experimental approach was employed using the pgbench benchmarking tool. Performance tests were conducted on databases of varying sizes—from 50 to 5,000,000 rows under both low‑load and high‑load conditions. The primary metrics were transactions per second (TPS) and average transaction latency. Test results indicate that implementing TDE significantly enhances resistance to unauthorized access but incurs a notable performance penalty. The Cybertec non-TDE solution achieved the highest TPS and lowest latency, whereas Cybertec with TDE experienced an encryption overhead that reduced throughput by approximately 25–35%. Meanwhile, the EnterpriseDB non-TDE configuration demonstrated more stable performance, exhibiting a smaller standard deviation compared to the TDE implementation. In conclusion, adopting TDE on PostgreSQL substantially increases data protection, yet organizations must weigh this security benefit against the resultant performance decline. Future recommendations include optimizing encryption configurations and investigating hardware‑acceleration techniques to minimize overhead.

<title>Abstract</title> The terahertz (THz) band has received much attention as a potentially usable bandwidth for 6G. Near-Earth THz is susceptible to strong atmospheric interference, and direct links are vulnerable to obstacles, limiting the breadth of its applications. Emerging intelligent reflecting surfaces (IRS) with multi-input multi-output (MIMO) techniques can mitigate the effects of signal interference and atmospheric turbulence through non-line-of-sight (NLOS) links. To address these challenges, this paper introduces IRS to enhance the coverage and system performance of THz links, and thoroughly analyses the performance metrics of the IRS-assisted THz communication system. First, the atmospheric perturbation of the MIMO THz system is modelled to analyse the characteristics of the perturbation. Given that the turbulence intensity in THz wave transmission is positively correlated with the distance, M\'alaga and Gamma-Gamma distributions are used to simulate the atmospheric turbulence channels from the transmitter to the IRS and from the IRS to the receiver, respectively. Subsequently, pointing errors are considered for the potential displacement deviation of the receiving aperture and the NLOS link. Based on this, the probability density function of the channel state for the NLOS link is derived, and the equal-gain combination (EGC) and maximum-ratio combination (MRC) reception schemes are examined. The numerical results indicate that appropriate modulation techniques can improve performance, while pointing errors and turbulence effects have a significant impact on performance. Increasing the number of input and output ports also significantly improves system performance for all receiving schemes. Comparative analysis reveals that the MRC scheme offers a higher gain compared to the EGC scheme, typically achieving a 1-5 dB performance gain. Still, this advantage diminishes as the number of input ports increases. Meanwhile, the effects of phase noise and phase shift on the system performance are analysed. As phase noise and phase shift continue to decrease, their impact on system performance will gradually diminish and approach zero. Consequently, the performance effects due to phase shift and phase noise are weaker compared to the differences in system performance caused by variations in system parameters.

In this article, we investigate the performance behavior of the hierarchical storage architecture. To maximize the performance of the hierarchical storage system, the capacity and bandwidth of individual storage hierarchical needs to be well balanced. Another important factor which governs the performance of hierarchical storage system is the user access pattern. We use two orthogonal performance metrics, namely expected service time and system congestion and blocking probability to evaluate the server performance. We establish an analytical formulation for the given performance metrics and investigate how these performance metrics are affected by the parameter of the individual storage hierarchy. This analytical formulation enables us to investigate the effects of different configurations of the storage hierarchy and different data access patterns; it also provides a framework to determine the optimal configuration of storage hierarchies. The results of a simulation-based performance evaluation are also presented.

BackgroundClinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient’s clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them.ObjectiveOur objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured.MethodsThe proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called “EHRead Technology” (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard.ResultsThe application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs.ConclusionsWe showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.

Handheld thermal imaging cameras are an important tool for the first responder community. As their use becomes more prevalent, it will become important for a set of standard test metrics to be available to characterize the performance of these cameras. A major factor in the performance of the imagers is the quality of the image on a display screen. An imager may employ any type of display screen, but the results of this paper will focus on those using liquid crystal displays. First responders, especially firefighters, in the field rely on the performance of this screen to relay vital information during critical situations. Current research on thermal imaging camera performance metrics for first responder applications uses trained observer tests or camera composite output signal measurements. Trained observer tests are subjective and composite output tests do not evaluate the performance of the complete imaging system. It is the goal of this work to develop a non-nondestructive, objective method that tests the performance of the entire thermal imaging camera system, from the infrared destructive, sensor to the display screen. Application of existing display screen performance metrics to thermal imaging cameras requires additional consideration. Most display screen test metrics require a well defined electronic input, with either full black or white pixel input, often encompassing detailed spatial patterns and resolution. Well characterized thermal inputs must be used to obtain accurate, repeatable, and non-destructive display screen measurements for infrared cameras. For this work, a thermal target is used to correlate the measured camera output with the actual display luminance. A test method was developed to determine display screen luminance. A well characterized CCD camera and digital recording device were used to determine an electro-optical transfer function for thermal imaging cameras. This value directly relates the composite output signal to the luminance of the display screen, providing a realistic characterization of system performance.

Atmospheric turbulence and pointing errors are major hindrances behind the proliferation of free-space-optics (FSO) technology in next-generation fronthaul networks. This paper investigates the effect of a decode-and-forward relay in overcoming these impairments and how an additional source-to-relay millimeter-wave (mmWave) radio-frequency (RF) backup link improves the performance of the relay-aided FSO system further; i.e., we study the performance of a hybrid FSO/(FSO-FSO)/(RF-FSO) system. Generalized Málaga distribution is adopted to model atmospheric turbulence in FSO links, whereas the fading statistics of the mmWave RF link is modeled by Nakagami-mdistribution. The performance of the hybrid system is studied on the basis of analytical expressions derived for metrics such as outage probability, bit error rate, and ergodic capacity, and the results show the supremacy of the proposed hybrid FSO system. The improvement is, however, dependent on factors such as turbulence location and signal-to-noise ratio per bit, and not all performance metrics exhibit equal improvement.

&lt;p&gt;&lt;b&gt;Metric Learning has proved valuable in information retrieval and classification problems, with many algorithms using statistical formulations to their advantage. Optimal Transport, a powerful framework for computing distances between probability distributions, has seen significant uptake in Machine Learning and some applications in Metric Learning. The Bures-Wasserstein distance, a particular formulation of Optimal Transport, has seen limited application in Metric Learning despite being well suited to the field. In this thesis Linear Metric Learning algorithms incorporating the Bures-Wasserstein distance are developed, utilising its novel properties. The first set of algorithms we contribute use the Bures-Wasserstein distance for learning linear metrics using Cyclic Projections, Riemannian barycenters and gradient-based algorithms. The convergence properties and classification performance of these algorithms are then compared to benchmark Linear Metric Learning algorithms. Our algorithms achieve competitive classification performance on feature and image datasets and converge reliability.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;The second set of contributions extend Linear Metric Learning with the Bures-Wasserstein distance to a low-rank context to address issues related to scalability. An extension of the previous Cyclic Projections algorithm and a Riemannian optimisation method are developed. Convergence and classification experiments analogous to the full-rank case are conducted to examine how the reduced rank affects both of these performance metrics. Both algorithms converge reliably under a variety of synthetic conditions. Reduction in rank generally decreased the training time for algorithms until effects caused by the size of the training data dominated. Reducing rank either left classification error unchanged or increased it. With respect to other low-rank Linear Metric Learning algorithms our algorithms again performed competitively with respect to classification error. &lt;/p&gt; &lt;p&gt;The last set of contributions consider Linear Metric Learning on Symmetric Positive Definite (SPD) matrices. Some datasets are expressed as or are suited to a SPD representation. As such, Metric Learning algorithms that respect the structure of these matrices have found success when adapted to this kind of data. Another extension of the Cyclic Projections algorithm is developed to use the Generalised Bures-Wasserstein distance on SPD matrices. Two other methods based on a ground metric formulation of the Bures-Wasserstein distance for learning linear metrics on SPD data are also introduced. One ground metric formulation uses an approximation (inspired by the Sinkhorn distance) to increase its scalability. Convergence experiments on synthetic problems adapted to SPD matrices are conducted on the ground metric algorithms to establish their convergence properties. Classification experiments are also conducted with respect to several types of SPD data. While the Cyclic Projections method is competitive, the ground metric algorithms have poor performance. Despite their poor classification performance, the approximation method does evaluate faster than the Generalised Bures-Wasserstein distance, potentially offering some scalability improvements.&lt;/p&gt;

Systems deployed in mobile environments are typically characterized by intermittent connectivity and asynchronous sending/reception of data. To create effective mobile systems for such environments, it is essential to guarantee acceptable levels of timeliness between sending and receiving mobile users. In order to provide QoS guarantees in different application scenarios and contexts, it is necessary to model the system performance by incorporating the intermittent connectivity. Queueing Network Models (QNMs) offer a simple modeling environment, which can be used to represent various application scenarios, and provide accurate analytical solutions for performance metrics, such as system response time. In this paper, we provide an analytical solution regarding the end-to-end response time between users sending and receiving data by modeling the intermittent connectivity of mobile users with QNMs. We utilize the publish/subscribe (pub/sub) middleware as the underlying communication infrastructure for mobile users. To represent the user's connections/disconnections, we model and solve analytically an ON/OFF queueing system by applying a mean value approach. Finally, we validate our model using simulations with real-world workload traces. The deviations between the performance results foreseen by the analytical model and the ones provided by the simulator are shown to be less than 5% for a variety of scenarios.

A system of systems (SOS) is a coalition of independently operated and managed systems that function together as one unit to efficiently and effectively meet a specific need. Combining disparate systems can produce challenges across the entire system life-cycle. Specifically, system of systems introduces complications in performance management because of the autonomy of their individual system components and the complexity of their interactions as they function as a single unit. This paper examines the ways in which key performance metrics for the system's performance are affected by the autonomy of individual member systems. It assesses the impact the individual systems have on the system-of-systems. Included is a mapping of the system performance metrics to the system-of-system measures of effectiveness, and the development of an algorithm based on that mapping to determine the overall system effectiveness. The nonlinear objective function seeks to find the optimal system configuration while assessing all component systems simultaneously.

The effect of photodetector and erbium-doped fiber amplifier noise, together with coupler splitting and intrinsic losses, on the performance of an incoherent optical code-division multiple-access star network using optically orthogonal codes is studied using the bit-error probability as the performance metric. It is shown that the system performance is extremely sensitive to the parameter settings, such as amplifier gain and detector threshold that can have a much greater impact on system performance than the effect of the noise.

The SPEC benchmarks