48 research outputs found
Harmonic-Copuled Riccati Equations and its Applications in Distributed Filtering
The coupled Riccati equations are cosisted of multiple Riccati-like equations
with solutions coupled with each other, which can be applied to depict the
properties of more complex systems such as markovian systems or multi-agent
systems. This paper manages to formulate and investigate a new kind of coupled
Riccati equations, called harmonic-coupled Riccati equations (HCRE), from the
matrix iterative law of the consensus on information-based distributed
filtering (CIDF) algortihm proposed in [1], where the solutions of the
equations are coupled with harmonic means. Firstly, mild conditions of the
existence and uniqueness of the solution to HCRE are induced with collective
observability and primitiviness of weighting matrix. Then, it is proved that
the matrix iterative law of CIDF will converge to the unique solution of the
corresponding HCRE, hence can be used to obtain the solution to HCRE. Moreover,
through applying the novel theory of HCRE, it is pointed out that the real
estimation error covariance of CIDF will also become steady-state and the
convergent value is simplified as the solution to a discrete time Lyapunov
equation (DLE). Altogether, these new results develop the theory of the coupled
Riccati equations, and provide a novel perspective on the performance analysis
of CIDF algorithm, which sufficiently reduces the conservativeness of the
evaluation techniques in the literature. Finally, the theoretical results are
verified with numerical experiments.Comment: 14 pages, 4 figure
A Bayesian Circadian Hidden Markov Model to Infer Rest-Activity Rhythms Using 24-hour Actigraphy Data
24-hour actigraphy data collected by wearable devices offer valuable insights
into physical activity types, intensity levels, and rest-activity rhythms
(RAR). RARs, or patterns of rest and activity exhibited over a 24-hour period,
are regulated by the body's circadian system, synchronizing physiological
processes with external cues like the light-dark cycle. Disruptions to these
rhythms, such as irregular sleep patterns, daytime drowsiness or shift work,
have been linked to adverse health outcomes including metabolic disorders,
cardiovascular disease, depression, and even cancer, making RARs a critical
area of health research.
In this study, we propose a Bayesian Circadian Hidden Markov Model (BCHMM)
that explicitly incorporates 24-hour circadian oscillators mirroring human
biological rhythms. The model assumes that observed activity counts are
conditional on hidden activity states through Gaussian emission densities, with
transition probabilities modeled by state-specific sinusoidal functions. Our
comprehensive simulation study reveals that BCHMM outperforms frequentist
approaches in identifying the underlying hidden states, particularly when the
activity states are difficult to separate. BCHMM also excels with smaller
Kullback-Leibler divergence on estimated densities. With the Bayesian
framework, we address the label-switching problem inherent to hidden Markov
models via a positive constraint on mean parameters. From the proposed BCHMM,
we can infer the 24-hour rest-activity profile via time-varying state
probabilities, to characterize the person-level RAR. We demonstrate the utility
of the proposed BCHMM using 2011-2014 National Health and Nutrition Examination
Survey (NHANES) data, where worsened RAR, indicated by lower probabilities in
low-activity state during the day and higher probabilities in high-activity
state at night, is associated with an increased risk of diabetes
Consensus-Based Distributed Filtering with Fusion Step Analysis
For consensus on measurement-based distributed filtering (CMDF), through
infinite consensus fusion operations during each sampling interval, each node
in the sensor network can achieve optimal filtering performance with
centralized filtering. However, due to the limited communication resources in
physical systems, the number of fusion steps cannot be infinite. To deal with
this issue, the present paper analyzes the performance of CMDF with finite
consensus fusion operations. First, by introducing a modified discrete-time
algebraic Riccati equation and several novel techniques, the convergence of the
estimation error covariance matrix of each sensor is guaranteed under a
collective observability condition. In particular, the steady-state covariance
matrix can be simplified as the solution to a discrete-time Lyapunov equation.
Moreover, the performance degradation induced by reduced fusion frequency is
obtained in closed form, which establishes an analytical relation between the
performance of the CMDF with finite fusion steps and that of centralized
filtering. Meanwhile, it provides a trade-off between the filtering performance
and the communication cost. Furthermore, it is shown that the steady-state
estimation error covariance matrix exponentially converges to the centralized
optimal steady-state matrix with fusion operations tending to infinity during
each sampling interval. Finally, the theoretical results are verified with
illustrative numerical experiments
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
With the rise of artificial intelligence in recent years, Deep Neural
Networks (DNNs) have been widely used in many domains. To achieve high
performance and energy efficiency, hardware acceleration (especially inference)
of DNNs is intensively studied both in academia and industry. However, we still
face two challenges: large DNN models and datasets, which incur frequent
off-chip memory accesses; and the training of DNNs, which is not well-explored
in recent accelerator designs. To truly provide high throughput and energy
efficient acceleration for the training of deep and large models, we inevitably
need to use multiple accelerators to explore the coarse-grain parallelism,
compared to the fine-grain parallelism inside a layer considered in most of the
existing architectures. It poses the key research question to seek the best
organization of computation and dataflow among accelerators. In this paper, we
propose a solution HyPar to determine layer-wise parallelism for deep neural
network training with an array of DNN accelerators. HyPar partitions the
feature map tensors (input and output), the kernel tensors, the gradient
tensors, and the error tensors for the DNN accelerators. A partition
constitutes the choice of parallelism for weighted layers. The optimization
target is to search a partition that minimizes the total communication during
training a complete DNN. To solve this problem, we propose a communication
model to explain the source and amount of communications. Then, we use a
hierarchical layer-wise dynamic programming method to search for the partition
for each layer.Comment: To appear in the 2019 25th International Symposium on
High-Performance Computer Architecture (HPCA 2019
Us adolescent Rest-Activity Patterns: insights From Functional Principal Component analysis (Nhanes 2011-2014)
BACKGROUND: Suboptimal rest-activity patterns in adolescence are associated with worse health outcomes in adulthood. Understanding sociodemographic factors associated with rest-activity rhythms may help identify subgroups who may benefit from interventions. This study aimed to investigate the association of rest-activity rhythm with demographic and socioeconomic characteristics in adolescents.
METHODS: Using cross-sectional data from the nationally representative National Health and Nutrition Examination Survey (NHANES) 2011-2014 adolescents (N = 1814), this study derived rest-activity profiles from 7-day 24-hour accelerometer data using functional principal component analysis. Multiple linear regression was used to assess the association between participant characteristics and rest-activity profiles. Weekday and weekend specific analyses were performed in addition to the overall analysis.
RESULTS: Four rest-activity rhythm profiles were identified, which explained a total of 82.7% of variance in the study sample, including (1) High amplitude profile; (2) Early activity window profile; (3) Early activity peak profile; and (4) Prolonged activity/reduced rest window profile. The rest-activity profiles were associated with subgroups of age, sex, race/ethnicity, and household income. On average, older age was associated with a lower value for the high amplitude and early activity window profiles, but a higher value for the early activity peak and prolonged activity/reduced rest window profiles. Compared to boys, girls had a higher value for the prolonged activity/reduced rest window profiles. When compared to Non-Hispanic White adolescents, Asian showed a lower value for the high amplitude profile, Mexican American group showed a higher value for the early activity window profile, and the Non-Hispanic Black group showed a higher value for the prolonged activity/reduced rest window profiles. Adolescents reported the lowest household income had the lowest average value for the early activity window profile.
CONCLUSIONS: This study characterized main rest-activity profiles among the US adolescents, and demonstrated that demographic and socioeconomic status factors may shape rest-activity behaviors in this population
Artificial Light at Night and Social Vulnerability: an Environmental Justice analysis in the US 2012-2019
BACKGROUND: Artificial Light at Night (ALAN) is an emerging health risk factor that has been linked to a wide range of adverse health effects. Recent study suggested that disadvantaged neighborhoods may be exposed to higher levels of ALAN. Understanding how social disadvantage correlates with ALAN levels is essential for identifying the vulnerable populations and for informing lighting policy.
METHODS: We used satellite data from the National Aeronautics and Space Administration\u27s (NASA) Black Marble data product to quantify annual ALAN levels (2012-2019), and the Center for Disease Control and Prevention\u27s (CDC) Social Vulnerability Index (SVI) to quantify social disadvantage, both at the US census tract level. We examined the relationship between the ALAN and SVI (overall and domain-specific) in over 70,000 tracts in the Contiguous U.S., and investigated the heterogeneities in this relationship by the rural-urban status and US regions (i.e., Northeast, Midwest, South, West).
RESULTS: We found a significant positive relationship between SVI and ALAN levels. On average, the ALAN level in the top 20% most vulnerable communities was 2.46-fold higher than that in the 20% least vulnerable communities (beta coefficient (95% confidence interval) for log-transformed ALAN, 0.90 (0.88, 0.92)). Of the four SVI domains, minority and language status emerged as strong predictors of ALAN levels. Our stratified analysis showed considerable and complex heterogeneities across different rural-urban categories, with the association between greater vulnerability and higher ALAN primarily observed in urban cores and rural areas. We also found regional differences in the association between ALAN and both overall SVI and SVI domains.
CONCLUSIONS: Our study suggested ALAN as an environmental justice issue that may carry important public health implications. Funding National Aeronautics and Space Administration
Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets
Magnetic structure plays a pivotal role in the functionality of
antiferromagnets (AFMs), which not only can be employed to encode digital data
but also yields novel phenomena. Despite its growing significance, visualizing
the antiferromagnetic domain structure remains a challenge, particularly for
non-collinear AFMs. Currently, the observation of magnetic domains in
non-collinear antiferromagnetic materials is feasible only in MnSn,
underscoring the limitations of existing techniques that necessitate distinct
methods for in-plane and out-of-plane magnetic domain imaging. In this study,
we present a versatile method for imaging the antiferromagnetic domain
structure in a series of non-collinear antiferromagnetic materials by utilizing
the anomalous Ettingshausen effect (AEE), which resolves both the magnetic
octupole moments parallel and perpendicular to the sample surface. Temperature
modulation due to the AEE originating from different magnetic domains is
measured by the lock-in thermography, revealing distinct behaviors of octupole
domains in different antiferromagnets. This work delivers an efficient
technique for the visualization of magnetic domains in non-collinear AFMs,
which enables comprehensive study of the magnetization process at the
microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres