Multiple Data Streams Research Articles

CYTOCON DB: A versatile database of human cell and molecule concentrations for accelerating model development

<scp>CYTOCON DB</scp>: A versatile database of human cell and molecule concentrations for accelerating model development

Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH): Physiological, Cognitive, and Biological Markers of Performance Decline in an Extreme Environment

Success in extreme environments comes with a cost of subtle performance decrements that if not mitigated properly can lead to lifethreatening consequences. Identification and prediction of performance decline could alleviate deleterious consequences and enhance success in challenging and high-risk operations. The Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH) project was designed to examine the cognitive, physiological, and biological markers of performance decline in the extreme environment of the Grand Canyon Rim-to-Rim (R2R) hike. The study utilized commercial off-the-shelf cognitive and physiological monitoring techniques, along with subjective self-assessments and hematologic measurements to determine subject performance and changes across the hike. The multiyear effort collected these multiple data streams in parallel on a large sample of participants hiking the R2R, leading to a rich and complex data set. This article describes the methodology and its evolution as devices and measurements were assessed after each data collection event. It also highlights a subset of the patterns of results found across the data streams. Subsequent work will draw on this data set to focus on building more sophisticated, predictive statistical models and dive deeper into specific analyses (such as the physiological and biological profiles of hikers who were left behind by their hiking partners).

GERONTOLOGIC BIOSTATISTICS: MERGING WITH DATA SCIENCE AND TOWARD PERSONALIZED MEDICINE

Abstract The sub-discipline of gerontologic biostatistics (GBS) was introduced in 2010 to emphasize the special challenges encountered in the design and analysis of research studies of older persons. These challenges center on the multifactorial nature of human aging, characterized by the parallel and progressive deterioration of diverse organ and cellular systems that eventually results in death. Ten years after the introduction of GBS, which initially focused on important aspects of design and analysis that ensure their statistical validity, we update how GBS has been enriched by evolving practices. We present individual sessions on three seminal developments in the practice of GBS: integration of data science and multiple streams of data, including those automated and or multidisciplinary in nature; enhanced methods of addressing the heterogeneity of treatment effects from health-related interventions for older patients; and how interactive visualization can help specific patients locate themselves along the continuum of individualized treatment effects. We conclude our presentation with a session that reviews three prominent trends in the validation of the heterogeneity inherent to the assessment of health among older adults. Reflecting this era of big gerontological data, we discuss several established modeling approaches for validation, the proliferation of signal intensive behavior phenotypes, and the deep characterization of phenotypes through OMICS studies and multimodal approaches. All talks discuss pitfalls and areas of future development and draw from published studies. We are submitting as an interest group collaborative panel submission between two interest groups: Epidemiology of Aging and Measurement, Statistics and Design.

Towards Discovering Digital Twins of Dementia Patients: Matching the Phases of Cognitive Decline.

Digital twins in healthcare is an emerging area. Twins are created from data of past patients. The objective is to facilitate data-driven decision support for clinicians for more precise and personalized dementia care. The project has three phases: (1) Twin discovering algorithm development from synthetic data; (2) Algorithm validation with real patient data; (3) Clinical implementation. The current work is related to Phase 1. Past subjects (n=30) showing cognitive decline (based on MMSE scores) mimicking a sample monitored over three years (i.e., 365 x 3 = 1,095 days) were synthetically generated. All past subjects show monotonically decreasing MMSE scores, and have had varying numbers of cognitive tests (between Three and Nine) across three years, unevenly spaced in time. Fig. 1 shows Three examples. Five present subjects (n=5) were also synthetically generated. Present subjects have had only their first two cognitive tests, unevenly spaced in time (i.e., 90 or 180 days-see Fig. 2). Since cognitive decline is traditionally modeled linearly, linear interpolation was used over present and past subjects to fill out missing data between cognitive tests. Post interpolation, an MMSE score is available for every day between a subject's first and last cognitive assessment. Then, for each present subject, the best matching past subject and their phase of cognitive decline-i.e., a digital twin from history that is closest to the present subject-were determined via Mean Absolute Percentage Error (MAPE) calculation between MMSE scores of a present subject and past subjects. Digital twin segments showing MAPE < 2%, i.e., 100 - MAPE > 98%, were identifiable for all Five present subjects. Table 1 shows the Five best matching digital twin segments for each present subject. Fig 3 graphically shows a matching. Results to discover digital twins via matching phases of cognitive decline are positive. This study looked at only the MMSE score as a one-dimensional metric, for proof of concept. The validity for such one-dimensional arrays, indicate generalizability for multi-dimensional data (i.e., a collection of multiple streams of one-dimensional data) collected over time, for discovering more comprehensive digital twins.

Scaling mmWave WLANs With Single RF Chain Multiuser Beamforming

Multi-user transmission in 60 GHz Wi-Fi can achieve data rates up to 100 Gbps by multiplexing multiple user data streams. However, a fundamental limit in the approach is that each RF chain is limited to supporting one stream or one user. In this paper, we scale multi-user 60 GHz WLAN data rate by overcoming this limit and propose SIngle RF chain Multi-user BeAmforming (SIMBA), a novel framework for multi-stream multi-user downlink transmission via a single RF chain. We build on single beamformed transmission via overlayed constellations to multiplex multiple users’ modulated symbols such that grouped users at different locations can share the same transmit beam from the AP. For this, we introduce user grouping and beam selection policies that span tradeoffs in data rate, training, and computation overhead. We implement a programmable WLAN testbed using software-defined radios and commercial 60 GHz transceivers and collect over-the-air measurements for different indoor WLAN deployments using a 12-element phased antenna array as well as horn antennas with varying beamwidth. We show that in comparison to single-user transmissions, SIMBA achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> improvement in aggregate rate and two-fold delay reduction for simultaneous transmission to four users.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.

Divergent seasonal responses of carbon fluxes to extreme droughts over China

Droughts affect the interannual variability of global land carbon fluxes and are expected to exert widespread impacts on the carbon cycle given a future climate with more intense and frequent droughts. Evidence indicates that the impacts of droughts on the carbon cycle vary in different seasons, but a quantitative examination of the seasonal differences is lacking. Here we ensemble multiple data streams for greenness indices and carbon fluxes, including those from remote sensing observations, flux tower upscaling, atmospheric inversions, and dynamic global vegetation models (DGVMs), to quantify the seasonal responses of vegetation to extreme droughts in China. We find that summer droughts cause the largest negative responses of leaf area index (LAI, with median standardized anomalies of -0.40), gross primary productivity (GPP, -0.55), and net ecosystem productivity (NEP, -0.74); notably, droughts in autumn largely suppress carbon uptake. The response patterns show a high degree of heterogeneity, and we identify the factors driving these spatial variations using the extreme gradient boosting (XGBoost) machine learning approach. Climate is the dominant driver of spring and autumn GPP responses while LAI predominantly drives summer GPP loss. Looking into the biotic factors, carry-over effects (previous-season vegetation growth affecting current-season growth) contribute substantially to the alleviation of drought stress, in that previous-season greening compensate vegetation loss during droughts. Our results not only quantify the seasonal response differences in carbon fluxes and greenness, but suggest that carbon fluxes respond more sensitively to drought than greenness. Also, we show seasonal differences in the degree to which factors contribute to drought impacts, which highlight that annual-scale drought analyses may mask spring and autumn vegetation response to droughts.

Intersymbol interference resilient interleaving architecture for multi‐stream interleaved frequency division multiple access system

SummaryA multi‐stream cyclic interleaving architecture for an interleaved frequency division multiple access (IFDMA) system is proposed. The proposed scheme exploits the full benefits of maximal length sequence ( ‐sequence), in contrast to single data stream transmission scheme proposed in earlier works, thereby allowing simultaneous transmission of multiple data streams by each user. The cyclic spreading allows the separation of multiple data streams for each user in addition to intersymbol interference (ISI) minimization. The assignment of orthogonal subcarriers to each user compensates multiuser interference (MUI). The increase in the number of data stream transmission improves the data rate of the system. The proposed scheme employs maximal ratio combining (MRC) to maximize the instantaneous signal‐to‐noise ratio (SNR) of the multipath signal. The performances of the proposed interleaving scheme are compared with the minimum mean square error frequency domain equalization (MMSE‐FDE)‐based conventional interleaving scheme. Despite the simultaneous transmission of multiple data streams, the proposed scheme retains the performance of single data stream transmission.

Design and Study of the Performance of Patch Antenna with Double C Slot for MIMO-OFDM Based Communications Systems for Media Technology

Mobile phones, server stacks, and other high-bitrate devices demand greater bandwidth from antennas. More bandwidth means higher bitrates and more data sent. MIMO antennas simultaneously deliver and receive data. MIMO antennas can broadcast data to unreachable places with little noise. Multiple data streams and multipath propagation boost performance and data quality. Many antennas allow MIMO systems to send and receive data at regular intervals. These antennas can perform beamforming and determine a signal's 3-D route. OFDM increases bitrates at the same frequency by multiplexing without guard bands. Multiple orthogonal carrier frequencies increase data in the same bandwidth and avoid carrier distortion. Air gaps, parasitic patches, and substrate modifications are methods to increase bandwidth and beam width for patch antennas. The conventional patch pattern has been modified and refined for maximum success. This article proposes a patch antenna with double C slots. This new design features a large bandwidth, wide beamwidth, and multidirectional transmission pattern. The gain, VSWR, and reflection coefficient of this patch antenna meets the MIMO-OFDM systems with 5G standard. Hence the proposed antenna design is recommended to employ in the MIMO-OFDM system for 5G applications.

Leveraging Multiple Data Streams for Prioritization of Mixtures for Hazard Characterization.

There is a growing need to establish alternative approaches for mixture safety assessment of polycyclic aromatic hydrocarbons (PAHs). Due to limitations with current component-based approaches, and the lack of established methods for using whole mixtures, a promising alternative is to use sufficiently similar mixtures; although, an established framework is lacking. In this study, several approaches are explored to form sufficiently similar mixtures. Multiple data streams including environmental concentrations and empirically and predicted toxicity data for cancer and non-cancer endpoints were used to prioritize chemical components for mixture formations. Air samplers were analyzed for unsubstituted and alkylated PAHs. A synthetic mixture of identified PAHs was created (Creosote-Fire Mix). Existing toxicity values and chemical concentrations were incorporated to identify hazardous components in the Creosote-Fire Mix. Sufficiently similar mixtures of the Creosote-Fire Mix were formed based on (1) relative abundance; (2) toxicity values; and (3) a combination approach incorporating toxicity and abundance. Hazard characterization of these mixtures was performed using high-throughput screening in primary normal human bronchial epithelium (NHBE) and zebrafish. Differences in chemical composition and potency were observed between mixture formation approaches. The toxicity-based approach (Tox Mix) was the most potent mixture in both models. The combination approach (Weighted-Tox Mix) was determined to be the ideal approach due its ability to prioritize chemicals with high exposure and hazard potential.

Detecting group concept drift from multiple data streams

Concept drift may lead to a sharp downturn in the performance of streaming in data-based algorithms, caused by unforeseeable changes in the underlying distribution of data. In this paper, we are mainly concerned with concept drift across multiple data streams, and in situations where the drift of each data stream cannot be detected in time, due to slight underlying distribution drifts. We call this group concept drift. When compared to the detection of concept drift for a single data stream, the challenges of detecting group concept drift arise from three aspects: first, the training data become more complex; second, the underlying distribution becomes more complex; and third, the correlations between data streams become more complex. To address these challenges, the key idea of our method is to construct a distribution free test statistic, free from any underlying distribution in multiple data streams. Then, for streaming data, we design an online learning algorithm to obtain this test statistic, thereby determining the concept drift caused by the hypothesis test. The experiment evaluations with both synthetic and real-world datasets prove that our method can accurately detect concept drift from multiple data streams.

H-DAC: discriminative associative classification in data streams

In this paper, we propose an efficient and highly accurate method for data stream classification, called discriminative associative classification. We define class discriminative association rules (CDARs) as the class association rules (CARs) in one data stream that have higher support compared with the same rules in the rest of the data streams. Compared to associative classification mining in a single data stream, there are additional challenges in the discriminative associative classification mining in multiple data streams, as the Apriori property of the subset is not applicable. The proposed single-pass H-DAC algorithm is designed based on distinguishing features of the rules to improve classification accuracy and efficiency. Continuously arriving transactions are inserted at fast speed and large volume, and CDARs are discovered in the tilted-time window model. The data structures are dynamically adjusted in offline time intervals to reflect each rule supported in different periods. Empirical analysis shows the effectiveness of the proposed method in the large fast speed data streams. Good efficiency is achieved for batch processing of small and large datasets, plus 0–2% improvements in classification accuracy using the tilted-time window model (i.e., almost with zero overhead). These improvements are seen only for the first 32 incoming batches in the scale of our experiments and we expect better results as the data streams grow.

To pre-filter, or not to pre-filter, that is the query: A multi-campus big data study

Library discovery platforms, which provide searchable user interfaces as their front-facing layer, aggregate tremendous amounts of metadata from multiple data streams describing a wide variety of print and electronic resources. Complicating the matter further, resources may differ in availability or delivery time depending not only on their media but also upon the source of the data stream describing them. How should libraries structure end users’ options for searching discovery platforms in light of the many options available? This study used a nonexperimental design and quantitative methods to analyze users’ revealed preferences for query type in twenty-four academic libraries in a data set containing metadata, sans queries, for over 64 million searches. Libraries studied were all located in California, used the same discovery layer software, and served similar user and faculty constituencies; however, the number of query types and pre-filtering options available differed between institutions. Results show that, when users were presented with the choice between search options, most conducted simple, more broad searches rather than complex and specific searches. When search options were highly constrained by the default choice architecture, but complex searches were possible, few users opted out of the default simple search. Implications for usability of discovery layers and the motivations of librarians in choice architecture are nontrivial and are discussed. The desires of librarians and “power user” faculty must be balanced with the fact that most users are novices and users of all abilities are largely habituated to commercial search products which emphasize post-search results filtering.

Alternate Data Stream Attack Framework to Perform Stealth Attacks on Active Directory Hosts

Microsoft’s file system, NTFS, is the most utilised file system by Windows OS versions XP, Vista, 7, and 10. These systems have a little-known file attribute feature known as alternate data streams (ADS) which allows each file in the NTFS file system to have multiple data streams. ADS cannot be removed from the NTFS operating systems. However, the presence of ADS is not inevitably an issue in the OS or file system. Valid instances can be found on systems if scanned and might be valid. Windows OS does not have any in-built tools or applications to determine and remove the presence of existing ADS. This research presents ADSA or alternate data stream attack framework to exploit the alternate data streams and perform cyberattacks on Microsoft operating systems. This research discusses the process of creating and searching alternate data streams with a standard file and an executable binary. The authors executed ADS-hidden executable binary in the ADS. The authors present methods to detect and perform a clean-up by deleting the alternate data stream.

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Double-coupling learning for multi-task data stream classification

Data stream classification methods exploiting cohesion in a single data stream have demonstrated promising performance. However, scenarios involving multiple data streams are indeed common in practice. They involve several correlated data streams and can be viewed as multi-task data streams. Instead of considering them separately as individual data streams, it is beneficial to leverage the correlations among the multi-task data streams in data stream modeling. In this regard, a novel classification method called double-coupling support vector machines (DC-SVM) is proposed to classify multiple data streams simultaneously, where both the external correlations between multiple data streams and the internal relationships within each individual data stream are considered. Experimental results on synthetic and real-world multi-task data streams show that the proposed method outperforms traditional data stream classification methods.

Physical Beam Sharing Multiuser Systems: Beamforming Designs and OTA Testings

This paper proposes a novel physical beam sharing system to use few radio-frequency (RF) chains to support more users. The concept of beam sharing is to design a beam pattern to simultaneously benefit all users. In this proposed system, every user can receive a number of data streams equal to the number of RF chains at the base station (BS) and achieve full multiplexing gain. A special design example is that the BS can support multiple users simultaneously even with only one RF chain. As a result, the computational and hardware costs of the BS can be significantly reduced due to the decreased RF chains in deploying a dense communications network. We first derive beamforming designs for this proposed system in a MISO channel with one data stream. Then, the results are further extended to MIMO channels with multiple data streams. Simulation results show that the proposed schemes with the derived solutions outperform conventional systems in several interested parameter settings, and have advantages in terms of fairness and complexity compared with NOMA (non-orthogonal multiple access) systems. Moreover, we also implemented 28 GHz hardware platforms with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> antenna array to verify the feasibility and advantages of the proposed schemes as well as measure the resulting beam patterns. The over-the-air (OTA) testings validate the concept of the proposed physical beam sharing and show that it outperforms conventional physical beamforming schemes.

Using piecewise regression to identify biological phenomena in biotelemetry datasets.

Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behaviour over time or respond to external stimuli. A wide variety of animal‐borne sensors can provide information on an animal's location, movement characteristics, external environmental conditions and internal physiological status.Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using multiple biotelemetry data streams. Different biological latent states can be inferred by partitioning a time‐series into multiple segments based on changes in modelled responses (e.g. their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval.We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short‐term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces; a physiological response (spike in heart‐rate) in a black bear Ursus americanus to a stressful stimulus (presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood‐rearing (i.e. hatching) for a breeding trumpeter swan.We implement analyses using the mcp package in R, which provides functionality for specifying and fitting a wide variety of user‐defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross‐validation measures.These simple modelling approaches are accessible to a wide audience and offer a straightforward means of assessing a variety of biologically relevant changes in animal behaviour.

Adaptive Driving Assistant Model (ADAM) for Advising Drivers of Autonomous Vehicles

Fully autonomous driving is on the horizon; vehicles with advanced driver assistance systems (ADAS) such as Tesla's Autopilot are already available to consumers. However, all currently available ADAS applications require a human driver to be alert and ready to take control if needed. Partially automated driving introduces new complexities to human interactions with cars and can even increase collision risk. A better understanding of drivers’ trust in automation may help reduce these complexities. Much of the existing research on trust in ADAS has relied on use of surveys and physiological measures to assess trust and has been conducted using driving simulators. There have been relatively few studies that use telemetry data from real automated vehicles to assess trust in ADAS. In addition, although some ADAS technologies provide alerts when, for example, drivers’ hands are not on the steering wheel, these systems are not personalized to individual drivers. Needed are adaptive technologies that can help drivers of autonomous vehicles avoid crashes based on multiple real-time data streams. In this paper, we propose an architecture for adaptive autonomous driving assistance. Two layers of multiple sensory fusion models are developed to provide appropriate voice reminders to increase driving safety based on predicted driving status. Results suggest that human trust in automation can be quantified and predicted with 80% accuracy based on vehicle data, and that adaptive speech-based advice can be provided to drivers with 90 to 95% accuracy. With more data, these models can be used to evaluate trust in driving assistance tools, which can ultimately lead to safer and appropriate use of these features.

ACDC: Online unsupervised cross-domain adaptation

We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces – a fully labeled source stream and an unlabeled target stream – are learned together. Unique characteristics and challenges such as covariate shift, asynchronous concept drifts, and contrasting data throughput arise. We propose ACDC, an adversarial unsupervised domain adaptation framework that handles multiple data streams with a complete self-evolving neural network structure that reacts to these defiances. ACDC encapsulates three modules into a single model: A denoising autoencoder that extracts features, an adversarial module that performs domain conversion, and an estimator that learns the source stream and predicts the target stream. ACDC is a flexible and expandable framework with little hyper-parameter tunability. Our experimental results under the prequential test-then-train protocol indicate an improvement in target accuracy over the baseline methods, achieving more than a 10% increase in some cases.