What are the potential factors when the fingerprint data delayed to transfer to the custody?5 answersFactors contributing to delays in transferring fingerprint data to custody include procedural inefficiencies, incomplete information, lack of incentives and familiarity with academic practices among data suppliers, and unresponsiveness from faculty members. In Delay Tolerant Networks (DTNs), custody transfer plays a crucial role in ensuring reliable data transfer, especially in scenarios with intermittent connectivity and long delays. The custody transfer mechanism in DTNs faces challenges such as duplicate message fragments and limited storage resources, hindering traffic flow despite available connections. To address delays and enhance robustness, strategies like adding synchronization fingerprints and utilizing relative delays have been proposed to combat desynchronization attacks in fingerprint embedding algorithms. These factors collectively impact the timely and secure transfer of fingerprint data to custody in various network environments.
Which measures for transfer distance have been developed so far?4 answersVarious measures for transfer distance have been developed to assess the transferability of models across different systems. One such measure is the transfer distance (TD), which was introduced in the context of phylogenetic tree matching. Additionally, the concept of transfer distance has been applied in classical machine learning approaches to address non-stationarity and enable knowledge sharing between systems. Moreover, a novel method called adapted transfer has been proposed, combining distance learning and transfer learning to find suitable combinations of chemical distance measures for predicting biological activity based on chemical structure. These measures play a crucial role in quantifying the transferability of models and enhancing predictive capabilities across various domains.
What can be transferred in transfer learning for time series?5 answersTransfer learning for time series involves transferring knowledge or features from one domain to another to improve forecasting performance or fault detection. Specifically, in the context of time series forecasting, transfer learning aims to alleviate the limitation of data scarcity by aligning transferable knowledge from cross-domain datasets. This can be achieved by decomposing the original features into components such as seasonal and trend features, which are easier to transfer. In the case of fault detection, transfer learning involves transferring early stage series to their corresponding serious stage for fault feature enhancement, and training a transferred fault mode recognition model using transferred normal series. Transfer learning has been shown to enhance the performance of time series segmentation models in terms of accuracy and training speed, particularly when the source and target data are closely related and the number of target training data samples is low.
What is delay in article 1170?4 answersThe concept of delay is mentioned in multiple articles. Mao and Sabanis propose a model driven by a delay geometric Brownian motion (DGBM) in the context of financial instruments, where the delay effect is shown to be robust and not too sensitive to time lag changes. Dewhurst discusses the delay in implementing the Proposed Council Directive on Equal Treatment, which has been in the pipeline since 2008, and highlights the detrimental impact it has on non-discrimination protection in the EU. Dibadj argues for delayering corporate law to strip it down to its essence and suggests concrete reforms to address the hollow core of corporate law, which has been further complicated by additional layers of laws and regulations. Wolfe describes a thermochromic ink display on a beverage container that reveals images in a stepped time delay sequence when a hot drink is poured into the container. Gaultier emphasizes the importance of stratifying individual risk and organizing a process for receiving calls during the delay period in the context of coronary disease and the need for timely medical intervention.
What is a frequency count in statistics?5 answersA frequency count in statistics refers to the process of determining the number of times a particular value or category appears in a dataset. It is a common statistic used in various applications, such as text analysis, graph analysis, and log analysis. Frequency counts can be used to analyze the distribution of values within a dataset and can provide insights into patterns and trends. Different techniques and structures, such as approximate counting structures and count tables, can be used to efficiently compute frequency distributions. The goal of frequency counting is to accurately capture the occurrence of values or categories in a dataset, allowing for further analysis and interpretation of the data.
How do you get the phase data when having time delay?4 answersTo obtain phase data when having time delay, one approach is to convert the time domain delay into phase detection and eliminate ambiguity through frequency scanning. This can be followed by data processing to obtain the desired result. Another method involves incorporating quadrature demodulation into the software processing phase data, allowing for the acquisition of both amplitude and phase data. In linear controlled systems, the delay can be derived from the control loop and identified using the frequency-response function and harmonic fitting of experimental data. Additionally, a time delay phase shifter can be used to control the phase of a broadband signal digitally by utilizing switches, elements with inductance components, and an on-off driving unit. Another option is to use a true time delay (TTD) device combined with amplifiers and a combiner to modify and combine the signals while maintaining the phase relationship.