Exploiting Trust Degree for Multiple-Antenna User Cooperation

For a user cooperation system with multiple antennas, we consider a trust degree based cooperation techniques to explore the influence of the trustworthiness between users on the communication systems. For the system with two communication pairs, when one communication pair achieves its quality of service (QoS) requirement, they can help the transmission of the other communication pair according to the trust degree, which quantifies the trustworthiness between users in the cooperation. For given trust degree, we investigate the user cooperation strategies, which include the power allocation and precoder design for various antenna configurations. For SISO and MISO cases, we provide the optimal power allocation and beamformer design that maximize the expected achievable rates while guaranteeing the QoS requirement. For a SIMO case, we resort to semidefinite relaxation (SDR) technique and block coordinate update (BCU) method to solve the corresponding problem, and guarantee the rank-one solutions at each step. For a MIMO case, as MIMO is the generalization of MISO and SIMO, the similarities among their problem structures inspire us to combine the methods from MISO and SIMO together to efficiently tackle MIMO case. Simulation results show that the trust degree information has a great effect on the performance of the user cooperation in terms of the expected achievable rate, and the proposed user cooperation strategies achieve high achievable rates for given trust degree.Comment: 15 pages,9 figures, to appear in IEEE Transactions on Wireless communication

Modeling Large-Scale Warfighter Cognitive Reasoning and Decision- Making Using Machine Learning (ML), Artificial Intelligence (AI), and Game Theory (GT)

NPS NRP Project PosterModeling Large-Scale Warfighter Cognitive Reasoning and Decision- Making Using Machine Learning (ML), Artificial Intelligence (AI), and Game Theory (GT)N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Modeling Large-Scale Warfighter Cognitive Reasoning and Decision- Making Using Machine Learning (ML), Artificial Intelligence (AI), and Game Theory (GT)

NPS NRP Executive SummaryModeling Large-Scale Warfighter Cognitive Reasoning and Decision- Making Using Machine Learning (ML), Artificial Intelligence (AI), and Game Theory (GT)N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Estimating Structured High-Dimensional Covariance and Precision Matrices: Optimal Rates and Adaptive Estimation

This is an expository paper that reviews recent developments on optimal estimation of structured high-dimensional covariance and precision matrices. Minimax rates of convergence for estimating several classes of structured covariance and precision matrices, including bandable, Toeplitz, sparse, and sparse spiked covariance matrices as well as sparse precision matrices, are given under the spectral norm loss. Data-driven adaptive procedures for estimating various classes of matrices are presented. Some key technical tools including large deviation results and minimax lower bound arguments that are used in the theoretical analyses are discussed. In addition, estimation under other losses and a few related problems such as Gaussian graphical models, sparse principal component analysis, factor models, and hypothesis testing on the covariance structure are considered. Some open problems on estimating high-dimensional covariance and precision matrices and their functionals are also discussed

Exploring the benefits of prenatal aspirin in patients at risk for preeclampsia at Boston Medical Center

BACKGROUND: Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality, affecting 2-8% of pregnancies worldwide. It is estimated that 76,000 women and 500,000 babies die from this disease each year globally. Preeclampsia is characterized as hypertension associated with the onset of proteinuria, maternal organ dysfunction or uteroplacental dysfunction occurring at or after 20 weeks of gestation. In addition to its effects on pregnancy, preeclampsia may also have long-term adverse effects on women who experience the disorder and their children later on in life. Currently, the only cure for preeclampsia is delivery, which is often associated with preterm birth, increasing the risk of neonatal death. Daily low-dose aspirin (81mg) has been shown to have a preventive effect on preeclampsia in women at high risk of developing the disorder. OBJECTIVE: To analyze patient data collected at Boston Medical Center to determine the effects of prescribed low dose prenatal aspirin (81 mg) on pregnancy outcomes. METHODS: There were 2648 obstetric deliveries at Boston Medical Center in the two-year span of 2017-2018. Using R, statistical analyses were performed to determine the difference in birth outcomes between the prenatal aspirin prescribed group and the non-aspirin prescribed group as well as the effect of prenatal aspirin on pregnancy outcomes. Logarithmic and linear models as well as basic statistical methods were employed for the analyses. RESULTS: The prenatal aspirin prescribed population had higher major and moderate risk factors as well as worse birth outcomes, Apgar scores and birthweight as compared with the non-prescribed population. However, prenatal aspirin may reduce the adverse effects of both major and moderate risk factors on birth outcomes. CONCLUSIONS: Prenatal aspirin may have beneficial effects on birth outcomes, and the pregnant population at Boston Medical Center may benefit from taking low-dose aspirin. This study was carried out retrospectively with a cohort that was not randomized, so this conclusion needs to be verified by future studies

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up. Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface. Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary. To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences. ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations. Project website: https://tonyzhaozh.github.io/aloha

Tests for conditional heteroscedasticity of functional data

Functional data objects derived from high-frequency financial data often exhibit volatility clustering. Versions of functional generalized autoregressive conditionally heteroscedastic (FGARCH) models have recently been proposed to describe such data, however so far basic diagnostic tests for these models are not available. We propose two portmanteau type tests to measure conditional heteroscedasticity in the squares of asset return curves. A complete asymptotic theory is provided for each test. We also show how such tests can be adapted and applied to model residuals to evaluate adequacy, and inform order selection, of FGARCH models. Simulation results show that both tests have good size and power to detect conditional heteroscedasticity and model mis-specification in finite samples. In an application, the tests show that intra-day asset return curves exhibit conditional heteroscedasticity. This conditional heteroscedasticity cannot be explained by the magnitude of inter-daily returns alone, but it can be adequately modeled by an FGARCH(1,1) model

Numerical analysis on global serviceability behaviours of tall Glulam frame buildings to the Eurocodes and UK National Annexes

Glued-laminated timber (Glulam) is an innovative engineered timber product and has been widely used for constructing spatial grand timber structures and tall timber buildings due to its exceptional natural attraction, easy processing, decent fire resistance and outstanding structural performance. However, global serviceability performances of tall timber buildings constructed from Glulam products for beams, columns and bracings and CLT products for lift core and floors under wind load are not well known yet though they are crucial in structural design and global analysis. In this study, finite element software SAP2000 is used to numerically simulate the global static and dynamic serviceability behaviours of a 105 m high 30-storey tall Glulam building with CLT lift core and floors assumed in Glasgow, Scotland, UK. The maximum horizontal storey displacement due to wind is 58.5% of the design limit and the maximum global horizontal displacement is 49.7% of the limit set to the Eurocodes. The first three lowest vibrational frequencies, modes and shapes of the building are obtained, with the fundamental frequency being 33.3% smaller than the code recommended value due to its low mass and stiffness. The peak acceleration of the building due to wind is determined to the Eurocodes and ISO 10137. The results show that the global serviceability behaviours of the building satisfy the requirements of the Eurocodes and other design standards. Parametric studies on the peak accelerations of the tall Glulam building are also conducted by varying timber material properties and building masses. Increasing the timber grade for CLT members, the generalised building mass and the generalised building stiffness can all be adopted to lower the peak accelerations at the top level of the building so as to reduce the human perceptions to the wind induced vibrations with respect to the peak acceleration