Data Objects Research Articles

Augmenting Circadian Biology Research With Data Science.

The nature of biological research is changing, driven by the emergence of big data, and new computational models to parse out the information therein. Traditional methods remain the core of biological research but are increasingly either augmented or sometimes replaced by emerging data science tools. This presents a profound opportunity for those circadian researchers interested in incorporating big data and related analyses into their plans. Here, we discuss the emergence of novel sources of big data that could be used to gain real-world insights into circadian biology. We further discuss technical considerations for the biologist interested in including data science approaches in their research. We conversely discuss the biological considerations for data scientists so that they can more easily identify the nuggets of biological rhythms insight that might too easily be lost through application of standard data science approaches done without an appreciation of the way biological rhythms shape the variance of complex data objects. Our hope is that this review will make bridging disciplines in both directions (biology to computational and vice versa) easier. There has never been such rapid growth of cheap, accessible, real-world research opportunities in biology as now; collaborations between biological experts and skilled data scientists have the potential to mine out new insights with transformative impact.

Data-Driven Performance Evaluation of Machine Learning for Velocity Estimation Based on Scan Artifacts from LiDAR Sensors

<div>Light detection and ranging (LiDAR) sensors are increasingly applied to automated driving vehicles. Microelectromechanical systems are an established technology for making LiDAR sensors cost-effective and mechanically robust for automotive applications. These sensors scan their environment using a pulsed laser to record a point cloud. The scanning process leads in the point cloud to a distortion of objects with a relative velocity to the sensor. The consecutive generation and processing of points offers the opportunity to enrich the measured object data from the LiDAR sensors with velocity information by extracting information with the help of machine learning, without the need for object tracking. Turning it into a so-called 4D-LiDAR. This allows object detection, object tracking, and sensor data fusion based on LiDAR sensor data to be optimized. Moreover, this affects all overlying levels of autonomous driving functions or advanced driver assistance systems. However, since such sensor-specific effects are rarely available in public datasets and the velocities of target objects are not included as ground truth in these datasets, it makes sense to enrich the limited real-world data with synthetic data. Therefore, this article discusses how such datasets can be created and combined to efficiently estimate velocities on real-world data using the novel method named VeloPoints.</div>

Detecting Influential Observations in Single-Index Fréchet Regression

Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook’s distance extending the original Cook’s distances to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook’s distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.

Impact of Charge on Strange Compact Stars in Rastall Theory

Within the framework of Rastall theory, we investigate the impact of charge on the structural development of different types of spherically symmetric anisotropic stars. To do so, we present modified field equations based upon the Finch–Skea metric potentials expressed in terms of three parameters (A,B,C). These constants are determined using suitable matching conditions and observational data for compact objects which include Her X-1, SAX J 1808.4-3658, PSR J038-0842, LMC X-4 and SMC X-1. The equation of state offered by the MIT bag model for quark–gluon plasma is used to investigate the inner structure and other characteristics of these compact objects. For a fixed bag constant, B=60MeV/fm3, and two sets of the Rastall and charge parameters, ζ=0.255,0.259 and Q˜=0.2,0.7, respectively, we analyze the consistency of the matter variables in the model and other physical parameters such as energy conditions, stellar mass, compactness, and surface redshift. In addition, we assess the stability of the constructed model through two different approaches. It is found that the obtained model is physically viable and stable.

Strategies to Increase Pipeline Status: A Case Study from Eclinical Data

In this paper we perform a case study regarding Eclinical data of Intelligent Medical Objects (IMO) which currently operates with eight pipeline statuses. It has been observed that the higher the pipeline status, the fewer consumers there tend to be. In this study, we aim to identify which factors significantly influence consumer presence at these advanced pipeline stages. Logistic regression is useful for predicting binary outcomes based on one or more independent variables. It estimates the probability of a particular outcome, allowing us to understand how different factors impact the likelihood of an event occurring. This method is widely used in fields like medicine, finance, and social sciences for classification problems and determining the significance of predictors, making it valuable for identifying key factors and making informed decisions based on probabilities. We apply logistic regression, using the probability of reaching the eighth status as our primary dependent variable. Of all the independent variables considered, only a select few are significant in explaining this outcome.

Ferry: Toward Better Understanding of Input/Output Space for Data Wrangling Scripts.

Understanding the input and output of data wrangling scripts is crucial for various tasks like debugging code and onboarding new data. However, existing research on script understanding primarily focuses on revealing the process of data transformations, lacking the ability to analyze the potential scope, i.e., the space of script inputs and outputs. Meanwhile, constructing input/output space during script analysis is challenging, as the wrangling scripts could be semantically complex and diverse, and the association between different data objects is intricate. To facilitate data workers in understanding the input and output space of wrangling scripts, we summarize ten types of constraints to express table space and build a mapping between data transformations and these constraints to guide the construction of the input/output for individual transformations. Then, we propose a constraint generation model for integrating table constraints across multiple transformations. Based on the model, we develop Ferry, an interactive system that extracts and visualizes the data constraints describing the input and output space of data wrangling scripts, thereby enabling users to grasp the high-level semantics of complex scripts and locate the origins of faulty data transformations. Besides, Ferry provides example input and output data to assist users in interpreting the extracted constraints and checking and resolving the conflicts between these constraints and any uploaded dataset. Ferry's effectiveness and usability are evaluated through two usage scenarios and two case studies, including understanding, debugging, and checking both single and multiple scripts, with and without executable data. Furthermore, an illustrative application is presented to demonstrate Ferry's flexibility.

Assessing the Impact of E-learning through Usage and Preference of E-resources

Aims: Any electronic device that delivers a collection of data, whether it be text referring to full text databases, electronic journals, photographs, other multimedia goods, or quantitative, visualizations, or time-based, is referred to as an electronic resource. These could be transmitted over the internet, tape, CD-ROM, tablets, smartphones, smart watches or another medium, these are now the basis of e-learning. Online searching has made it possible to get patent information more quickly, affordably, and conveniently than the traditional manual or CD-ROM based searching method. The ability to create and distribute documents in electronic form is now made possible by a number of established procedures and standards. So, in order to address the current problems, librarians are utilizing new media, particularly electronic resources, in their collection expansion makes the documentation of users better. As we can see, utilizing online resources is important in the modern world for a multitude of purposes. Because of this, it's important to understand the preferences, motives and usage of various ‘e- resources used by students who use online learning’. The aim of the present research paper was to examine the impact of e-resources using its usage and reading preferences. In this study, reasons such as time saving, more information, and busy schedule at college are considered. Methodology: Primary data was gathered from 250 students from Mumbai and Navi Mumbai who are using e-resources through the pre-structured questionnaire. The responses collected were recorded using the SPSS software for data analysis. In order to examine the link between causes, preferences, and the use of ‘e-resources’, a theoretical construct was developed grounded on a few assumptions. Statistical techniques like the chi-square test were used and data analysis was done using SPSS version 20 to examine the proposed construct. When doing the data analysis, the demographic profile, objectives, and hypothesis were all taken into consideration. Result: The average for each component that is time saving, more informative, and busy schedule at college was computed and was determined as 0.004, 0.004, and 0.000, correspondingly, for time saving, more informative content, and busy college schedule. As all of these values for all of the preferences under consideration are less than 0.05, it is clear that there is a connection between the usage of electronic resources and their underlying reasons and preferences. Conclusion: Hence, there is a substantial correlation between the reasons for using electronic resources and the different reading preferences, as well as between the two. Only three reasons namely time saving, more informative, and busy schedule at college are considered during this study. Data collection is done from Mumbai and Navi Mumbai region only.

IDENTIFICATION MANUSCRIPT AND CONTENT OF COMMUNITY KNOWLEDGE IN THE EAST COAST OF JAWA 18-21ST CENTURIES

Ancient manuscripts are considered to be the most important and authentic source of the intellectual output of our ancestors. It can also be said that manuscripts are a gift from the predecessors, with which later generations can connect the history of the past with the present. This article discusses the identification of ancient manuscripts and the knowledge content of local communities by focusing on the preservation and conservation patterns of ancient manuscripts. The eastern tip of Java was deliberately chosen as the locus of research and more specifically the Banyuwangi to Jember area, because in this region there are many kinds of manuscripts that are still 'alive' in the community. This research uses descriptive analysis method, the data source or object of this research is all manuscripts found. The data collection techniques used are interviews and literature studies. This research also uses a philological research flow consisting of manuscript inventory and manuscript description, and shows that from the findings of 40 manuscripts made from daluang, 241 made from European paper and 69 made from paper made by the Letjes factory, it can be identified the contents and titles or text identification, it can be seen that these manuscripts contain knowledge of Islamic teachings, for example some manuscripts contain songs that tell about the history of the prophet Muhammad SAW, the prophet Yusuf As, and Javanese shiiran patterns. Keywords: Ancient manuscripts, preservation and conservation, local community knowledge

STUDYING EFFICIENCY OF DATA CACHING STRATEGIES IN INFORMATION-CENTRIC NETWORKS

Today, the architecture of computer networks using IP addressing is focused on interaction between network devices with «binding» of data objects to specific sources, which leads to a decrease in bandwidth and network congestion. In information-centric networks (ICN), routing and caching of data objects on intermediate network nodes during transmission from source to recipient is performed using unique names. The purpose of the study is to analyze the performance of various caching strategies in terms of cache hit rate and request response time for data objects. Materials and methods. The following caching strategies were selected for the study: Leave Copy Everywhere, Leave Copy Down, Probabilistic Cache, random caching, Bernoulli probabilistic caching, symmetric hash routing scheme, asymmetric hash routing scheme, multicast scheme. The following metrics were calculated: cache hit rate, time delay in delivering the requested data object to the recipient, channel load when transmitting the data object, and the length of the data object’s path. The metrics were measured taking into account the following characteristics: asymmetry in the distribution of data object popularity, the total size of network caches of intermediate nodes as a share of the total number of data objects. The simulation was performed for three network topologies: GARR (Italian academic network), GEANT (European academic network), WIDE (Japanese academic network) using the Icarus 8.0 simulator. Results. The use of symmetric hash routing and multicast schemes allowed us to obtain the highest cache hit rate. The lowest cache hit rate in most tests is observed when using the asymmetric hash routing scheme, since the path of a data object to the recipient may not pass through the router responsible for caching. When using symmetric hash routing and multicast schemes, it was possible to obtain a lower delivery delay compared to other strategies. When using the Leave Copy Down caching strategy, the delivery delay with an increase in the data object popularity asymmetry indicator takes smaller values ​​than that of the hash routing schemes. The highest delay among the strategies under consideration is observed when using the asymmetric hash routing scheme. The use of the hash routing scheme leads to a relatively high value of the communication channel load parameter. The channel load indicator values ​​for such strategies as Leave Copy Everywhere (LCE), Leave Copy Down (LCD), Probabilistic Cache (ProbCache), random choice caching (RAND_CHOICE), Bernoulli caching (RAND_BERNOULLI) do not differ much from each other. The use of hash routing schemes leads to an increase in the length of the data delivery path, especially when using symmetric hash routing schemes and multicasting, since the path of the data object to the recipient may pass through the router responsible for caching, which is not always the shortest. Conclusions. Hash routing schemes with caching in some cases are a completely reasonable alternative to methods that are focused on caching along the request path and do not involve the use of hash functions to match the unique name of the data object and the router intended for caching. The practical significance of this work lies in the possibility of using the results of the study when choosing a particular caching strategy on intermediate network devices depending on the characteristics of the communication channel, in particular, the network topology and the degree of popularity of the transmitted data object.

‘Confederate agencies’: William Knight, document phenomenology and the ‘vibrant materiality’ of the nineteenth century museum lists at Marischal College, Aberdeen

ABSTRACT This paper uses Gorichanaz and Latham’s document phenomenology as a method for understanding early museum documentation, specifically, the collection or museum lists of William Knight of the Marischal College and University of Aberdeen. It seeks to provide an examination of the historical context of the production of these documents, the temporal and social contexts of their interpretation in the contemporary period as archival sources, and to develop scholarly interpretation and discourse regarding museum documentation history, theory and practice. In the museological literature, documents are typically relegated to being containers of data for objects, but this paper demonstrates that documents are instead an exemplary case of what Bennett terms ‘vibrant matter’; they are temporal, contingent, febrile, and evocative.

Adaptive micro partition and hierarchical merging for accurate mixed data clustering

Heterogeneous attribute data (also called mixed data), characterized by attributes with numerical and categorical values, occur frequently across various scenarios. Since the annotation cost is high, clustering has emerged as a favorable technique for analyzing unlabeled mixed data. To address the complex real-world clustering task, this paper proposes a new clustering method called Adaptive Micro Partition and Hierarchical Merging (AMPHM) based on neighborhood rough set theory and a novel hierarchical merging mechanism. Specifically, we present a distance metric unified on numerical and categorical attributes to leverage neighborhood rough sets in partitioning data objects into fine-grained compact clusters. Then, we gradually merge the current most similar clusters to avoid incorporating dissimilar objects into a similar cluster. It turns out that the proposed approach breaks through the clustering performance bottleneck brought by the pre-set number of sought clusters k and cluster distribution bias, and is thus capable of clustering datasets comprising various combinations of numerical and categorical attributes. Extensive experimental evaluations comparing the proposed AMPHM with state-of-the-art counterparts on various datasets demonstrate its superiority.

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory management, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. The framework includes tools to monitor the capacity and usage of individual data objects, routines that aggregate and convert this information into tier recommendations for the host platform, and mechanisms to enforce these recommendations according to user-selected policies. Moreover, the developed tools and techniques are fully automatic, work on standard Linux systems, and do not require modification or recompilation of existing software. Using this framework, this study evaluates and compares the impact of a variety of design choices for memory tiering, including different policies for prioritizes objects for fast memory tier as well as the frequency and timing of migration events. The results, collected on a modern Intel ® platform with conventional DDR4 SDRAM as well as Intel Optane NVRAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared to standard hardware- and software-directed data tiering strategies for a diverse set of memory-intensive workloads.

Analisis Kesalahan Morfologi dalam Penulisan Caption dan Komentar di Salah Satu Postingan Akun Instagram @official.kpk

The purpose of this study was to analyze language errors in the morphological category in captions and comments in one of the @official.kpk posts. The research method used is descriptive with a qualitative approach to describe the existence of errors or mistakes in writing captions and comments according to the procedures for analyzing language errors. The data source in this study is the @official.kpk Instagram post. on November 6, 2024. The data collection technique used was the purposive sampling technique to determine the data sample. Other data collection techniques used in this study were literature studies, documentation, and discourse analysis. The object data used was one post in the form of captions and comments. Data processing was carried out by collecting words that contained errors or mistakes, especially in the morphological category such as affixation or reduplication, then grouped. After grouping the data sources, the researcher conducted an analysis by analyzing using an analysis table and in accordance with applicable procedures. In the results and discussion section, it can be concluded that from one post that the researcher took, there were many errors and mistakes in the comments column, totaling 18 errors and mistakes.

Curved Geometric Networks for Visual Anomaly Recognition.

Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geometry constraints, posed in the embedding space, indicates that curved spaces might encode more structural information, leading to better discriminative power and hence richer representations. In this work, we investigate the benefits of the curved space for analyzing anomalous, open-set, or out-of-distribution (OOD) objects in data. This is achieved by considering embeddings via three geometry constraints, namely, spherical geometry (with positive curvature), hyperbolic geometry (with negative curvature), or mixed geometry (with both positive and negative curvatures). Three geometric constraints can be chosen interchangeably in a unified design, given the task at hand. Tailored for the embeddings in the curved space, we also formulate functions to compute the anomaly score. Two types of geometric modules (i.e., geometric-in-one (GiO) and geometric-in-two (GiT) models) are proposed to plug in the original Euclidean classifier, and anomaly scores are computed from the curved embeddings. We evaluate the resulting designs under a diverse set of visual recognition scenarios, including image detection (multiclass OOD detection and one-class anomaly detection) and segmentation (multiclass anomaly segmentation and one-class anomaly segmentation). The empirical results show the effectiveness of our proposal through consistent improvement over various scenarios. The code is made available at https://github.com/JHome1/GiO-GiT.

Topo-Geometric Analysis of Variability in Point Clouds Using Persistence Landscapes.

Topological data analysis provides a set of tools to uncover low-dimensional structure in noisy point clouds. Prominent amongst the tools is persistence homology, which summarizes birth-death times of homological features using data objects known as persistence diagrams. To better aid statistical analysis, a functional representation of the diagrams, known as persistence landscapes, enable use of functional data analysis and machine learning tools. Topological and geometric variabilities inherent in point clouds are confounded in both persistence diagrams and landscapes, and it is important to distinguish topological signal from noise to draw reliable conclusions on the structure of the point clouds when using persistence homology. We develop a framework for decomposing variability in persistence diagrams into topological signal and topological noise through alignment of persistence landscapes using an elastic Riemannian metric. Aligned landscapes (amplitude) isolate the topological signal. Reparameterizations used for landscape alignment (phase) are linked to a resolution parameter used to generate persistence diagrams, and capture topological noise in the form of geometric, global scaling and sampling variabilities. We illustrate the importance of decoupling topological signal and topological noise in persistence diagrams (landscapes) using several simulated examples. We also demonstrate that our approach provides novel insights in two real data studies.

Identification of Confounding Factors Related to Hypertension among Women in the Reproductive age Groups

Confounding is a major problem in epidemiological research during the analysis of epidemiological data. To identify the confounder in the relationship between disease and risk factor is of prime importance because confounders distorts the actual relationship of risk factor on the disease. Due to the influence of confounder, on the factor disease relationship, the study may not provide the representative results of the association of the study sample. In the present study an attempt has been made to identify different confounders in the risk factor diabetes on disease hypertension among women under reproductive age groups. To achieve the objectives of the study data from the fourth round of National Family Health Survey (2015-2016) are used. To search the different confounders which may influence the relationship between hypertension and diabetics, stratum specific odds ratios and Mantel-Haensel common odd ratios are calculated. It has been found that, the women who are Diabetic associated with an increased risk of hypertension with the odds ratio of 2.27 (95% CI: 2.21-2.32).Risk factors such as age, body mass index (BMI), smoking are found as a confounders.. KEYWORDS :Confounders, Hypertension, Mantel-Haensel (M-H) common odds ratio, Body mass index (BMI).

Проекционный метод определения аномальных датчиков в задачах виброакустики

A common problem in measurements using a set of vibration sensors in vibroacoustics is the lack of an accurate signal model on the receivers. Such uncertainty can lead to processing algorithms errors and an incorrect solution to the inverse problem. Due to a malfunction, vibration sensors data can also be bad, deteriorating the quality of measurements. In practice, the search for such anomalous sensors is a non-trivial task. In this paper, an original method related to projection algorithms is proposed for this problem. It is shown that the proposed method allows detecting anomalous sensors of various types. The method main feature is using both numerical finite element model of the object and experimental data. The efficiency of the proposed method is demonstrated in the localizing vibration sources problem. The new method can be used in other applications where the inverse problem is solved using a numerical or analytical model.

Penerapan Algoritma DBSCAN Untuk Clustering Penjualan di Supermarket

Supermarkets are shopping places that provide various daily necessities. Many customers visit supermarkets to buy necessities. The growth of supermarkets is increasing. Supermarkets have a variety of products with different brands, branches, and types of customers. To create a sales strategy, need to know the products that customers are interested in. In this research, supermarket product clustering was carried out based on sales data. The clustering algorithm used in this research is the DBSCAN algorithm. This algorithm is an algorithm for grouping data objects based on density, which is influenced by input parameters, namely the Eps and MinPts values. The data used in this research is secondary data consisting of 100 supermarket sales data, taking 2 attributes. The clustering results show that using the Eps parameter value = 6 and the MinPts value = 9, the product data is divided into 3 clusters, namely cluster 1 of products that are not in demand, cluster 2 of products that are in demand and cluster 3 of very popular products.

The impact of multiple exposures and movement on the fear response of poultry

Fear tests are a common research method to assess the affective state of an animal. This study aimed to assess: 1) the impact of repeated exposure to fear tests on fear response and, 2) how the addition of movement to fear stimuli during a novel object test impacts bird fear response. Over two trials, a total of 3,600 Ross 308 birds (1800 birds/trial) were raised until 42 days of age. At 23d, 30d, and 37d, three fear tests were performed, novel object, human approach, and response to observer tests. The novel object test was split into three movement treatments, stationary, intermittent, and continuous. The response to observer test was performed before and after the other tests. Data from the human approach test was analyzed for age effect by ANOVA. Novel object data was analyzed as repeated measures ANOVA for the effect of movement. Differences in response to observer were analyzed using a paired T-test. Repeated exposure to fear test within the same day decreased the fear response, with response to observer results before fear testing (66%) differing from after (42%, P<0.01). The fear response also decreased with multiple consecutive exposures. Both latency to human approach and latency to novel object interaction were longest on 23d, then 30d, and shortest on 37d (P<0.01). For both the human approach and the novel object test, at all-time points, the number of birds interested in the human or object was higher on 37d than 23d (P<0.01). The addition of movement to the novel object test increased the fear response at 23 days but decreased the fear response at 37 days. Overall, the repeated exposure of birds to fear tests reduced the bird's fear response, both for the repeated exposure to different fear tests on a single day and the repeated exposure to one type of fear test over the length of a flock. This impact of repeated exposure is important to recognize when designing experiments that utilize fear tests.

Robust Single Object Visual Tracking Using Yolo Object Detection and Adaptive Particle Filtering

Visual tracking is a fundamental technology in computer vision, with wide-ranging applications in fields such as robotics, autonomous driving, augmented reality, and security systems. A key challenge in these domains is ensuring stable object tracking in dynamic and complex environments. Tracking objects in real-world settings presents difficulties like occlusion, lighting changes, and sudden object movements, all of which demand the development of robust algorithms that can handle such variability effectively. This research introduces a novel single object visual tracking technique that leverages the YOLO (You Only Look Once) object detection model, combined with an adaptive particle filter. By integrating YOLO's precise object detection capabilities with the continuous state estimation offered by particle filters, this approach achieves stable object tracking, even in challenging environments. At the heart of the proposed algorithm is a dynamic fusion of YOLO detection results with image feature-based particle weight updates. When YOLO successfully detects an object, the position and size data of the object are used to adjust the state and weights of the particles, resulting in improved tracking accuracy and quick recovery from events such as sudden movements or occlusions. Conversely, in situations where YOLO detection is unavailable, the algorithm seamlessly transitions to a pure particle filter mode. In this fallback mode, visual features, such as image color histograms, are used to update particle weights, ensuring continuous and robust tracking even during temporary detection failures. The object’s dynamic state is modeled using an 8-dimensional vector, which includes information about position, velocity, size, and size-change rate. This comprehensive state representation enables precise tracking of various object motions and size transformations. Additionally, a Bhattacharyya distance-based weight calculation method is employed to assess particle similarity more effectively. Experimental results demonstrate that the proposed method excels in various challenging scenarios, including those with sudden object movements, partial occlusions, and lighting variations. The algorithm consistently delivers stable tracking, underscoring its robustness and adaptability. The contributions of this research have potential applications in fields such as autonomous driving, SLAM (Simultaneous Localization and Mapping), and augmented reality. Specifically, this technique could enhance pedestrian and vehicle tracking in autonomous driving systems and improve the accuracy of dynamic object tracking and environmental mapping in SLAM. It is also likely to play a key role in real-world applications like person tracking in security systems, player motion analysis in sports, and focused tracking in medical imaging. Future research directions include expanding the method to handle multi-object tracking and optimizing its real-time performance, broadening its applicability. Additionally, further improvements in tracking accuracy and robustness may be achieved by incorporating deep learning-based feature extraction and integrating data from various sensors.