Search

Gradient descent (GD) and stochastic gradient descent (SGD) have been widely used in a large number of application domains. Therefore, understanding the dynamics of GD and improving its convergence speed is still of great importance. This paper carefully analyzes the dynamics of GD based on the terminal attractor at different stages of its gradient flow. On the basis of the terminal sliding mode theory and the terminal attractor theory, four adaptive learning rates are designed. Their performances are investigated in light of a detailed theoretical investigation, and the running times of the learning procedures are evaluated and compared. The total times of their learning processes are also studied in detail. To evaluate their effectiveness, various simulation results are investigated on a function approximation problem and an image classification problem., Comment: 8 pages, 4 figures

With the robust uptick in the applications of Bayesian external data borrowing, eliciting a prior distribution with the proper amount of information becomes increasingly critical. The prior effective sample size (ESS) is an intuitive and efficient measure for this purpose. The majority of ESS definitions have been proposed in the context of borrowing control information. While many Bayesian models can be naturally extended to leveraging external information on the treatment effect scale, very little attention has been directed to computing the prior ESS in this setting. In this research, we bridge this methodological gap by extending the popular ELIR ESS definition. We lay out the general framework, and derive the prior ESS for various types of endpoints and treatment effect measures. The posterior distribution and the predictive consistency property of ESS are also examined. The methods are implemented in R programs available on GitHub: https://github.com/squallteo/TrtEffESS.

Vision-and-Language Navigation (VLN) is a challenging task that requires a robot to navigate in photo-realistic environments with human natural language promptings. Recent studies aim to handle this task by constructing the semantic spatial map representation of the environment, and then leveraging the strong ability of reasoning in large language models for generalizing code for guiding the robot navigation. However, these methods face limitations in instance-level and attribute-level navigation tasks as they cannot distinguish different instances of the same object. To address this challenge, we propose a new method, namely, Instance-aware Visual Language Map (IVLMap), to empower the robot with instance-level and attribute-level semantic mapping, where it is autonomously constructed by fusing the RGBD video data collected from the robot agent with special-designed natural language map indexing in the bird's-in-eye view. Such indexing is instance-level and attribute-level. In particular, when integrated with a large language model, IVLMap demonstrates the capability to i) transform natural language into navigation targets with instance and attribute information, enabling precise localization, and ii) accomplish zero-shot end-to-end navigation tasks based on natural language commands. Extensive navigation experiments are conducted. Simulation results illustrate that our method can achieve an average improvement of 14.4\% in navigation accuracy. Code and demo are released at https://ivlmap.github.io/.

Rapid signal fluctuations due to blockage effects cause excessive handovers (HOs) and degrade mobility performance. By reconfiguring line-of-sight (LoS) Links through passive reflections, intelligent reflective surface (IRS) has the potential to address this issue. Due to the lack of introducing blocking effects, existing HO analyses cannot capture excessive HOs or exploit enhancements via IRSs. This paper proposes an LoS state transition model enabling analysis of mobility enhancement achieved by IRS-reconfigured LoS links, where LoS link blocking and reconfiguration utilizing IRS during user movement are explicitly modeled as stochastic processes. Specifically, the condition for blocking LoS links is characterized as a set of possible blockage locations, the distribution of available IRSs is thinned by the criteria for reconfiguring LoS links. In addition, BSs potentially handed over are categorized by probabilities of LoS states to enable HO decision analysis. By projecting distinct gains of LoS states onto a uniform equivalent distance criterion, mobility enhanced by IRS is quantified through the compact expression of HO probability. Results show the probability of dropping into non-LoS decreases by 70% when deploying IRSs with the density of 93/km$^2$, and HOs decrease by 67% under the optimal IRS distributed deployment parameter., Comment: 13 pages, 11 figures, submitted to IEEE

Owning to the reflection gain and double path loss featured by intelligent reflecting surface (IRS) channels, handover (HO) locations become irregular and the signal strength fluctuates sharply with variations in IRS connections during HO, the risk of HO failures (HOFs) is exacerbated and thus HO parameters require reconfiguration. However, existing HO models only assume monotonic negative exponential path loss and cannot obtain sound HO parameters. This paper proposes a discrete-time model to explicitly track the HO process with variations in IRS connections, where IRS connections and HO process are discretized as finite states by measurement intervals, and transitions between states are modeled as stochastic processes. Specifically, to capture signal fluctuations during HO, IRS connection state-dependent distributions of the user-IRS distance are modified by the correlation between measurement intervals. In addition, states of the HO process are formed with Time-to-Trigger and HO margin whose transition probabilities are integrated concerning all IRS connection states. Trigger location distributions and probabilities of HO, HOF, and ping-pong (PP) are obtained by tracing user HO states. Results show IRSs mitigate PPs by 48% but exacerbate HOFs by 90% under regular parameters. Optimal parameters are mined ensuring probabilities of HOF and PP are both less than 0.1%., Comment: 13 pages, 12 figures, submitted to IEEE

In High-definition (HD) maps, lane elements constitute the majority of components and demand stringent localization requirements to ensure safe vehicle navigation. Vision lane detection with LiDAR position assignment is a prevalent method to acquire initial lanes for HD maps. However, due to incorrect vision detection and coarse camera-LiDAR calibration, initial lanes may deviate from their true positions within an uncertain range. To mitigate the need for manual lane correction, we propose a patch-wise lane correction network (PLCNet) to automatically correct the positions of initial lane points in local LiDAR images that are transformed from point clouds. PLCNet first extracts multi-scale image features and crops patch (ROI) features centered at each initial lane point. By applying ROIAlign, the fix-sized ROI features are flattened into 1D features. Then, a 1D lane attention module is devised to compute instance-level lane features with adaptive weights. Finally, lane correction offsets are inferred by a multi-layer perceptron and used to correct the initial lane positions. Considering practical applications, our automatic method supports merging local corrected lanes into global corrected lanes. Through extensive experiments on a self-built dataset, we demonstrate that PLCNet achieves fast and effective initial lane correction.

To use historical controls for indirect comparison with single-arm trials, the population difference between data sources should be adjusted to reduce confounding bias. The adjustment is more difficult for time-to-event data with a cure fraction. We propose different adjustment approaches based on pseudo observations and calibration weighting by entropy balancing. We show a simple way to obtain the pseudo observations for the cure rate and propose a simple weighted estimator based on them. Estimation of the survival function in presence of a cure fraction is also considered. Simulations are conducted to examine the proposed approaches. An application to a breast cancer study is presented.

Peak sidelobe level reduction (PSLR) is crucial in the application of large-scale array antenna, which directly determines the radiation performance of array antenna. We study the PSLR of subarray level aperiodic arrays and propose three array structures: dislocated subarrays with uniform elements (DSUE), uniform subarrays with random elements (USRE), dislocated subarrays with random elements (DSRE). To optimize the dislocation position of subarrays and random position of elements, the improved Bat algorithm (IBA) is applied. To draw the comparison of PSLR effect among these three array structures, we take three size of array antennas from small to large as examples to simulate and calculate the redundancy and peak sidelobe level (PSLL) of them. The results show that DSRE is the optimal array structure by analyzing the dislocation distance of subarray, scanning angle and applicable frequency. The proposed design method is a universal and scalable method, which is of great application value to the design of large-scale aperiodic array antenna.

Use of historical control data to augment a small internal control arm in a randomized control trial (RCT) can lead to significant improvement of the efficiency of the trial. It introduces the risk of potential bias, since the historical control population is often rather different from the RCT. Power prior approaches have been introduced to discount the historical data to mitigate the impact of the population difference. However, even with a Bayesian dynamic borrowing which can discount the historical data based on the outcome similarity of the two populations, a considerable population difference may still lead to a moderate bias. Hence, a robust adjustment for the population difference using approaches such as the inverse probability weighting or matching, can make the borrowing more efficient and robust. In this paper, we propose a novel approach integrating propensity score for the covariate adjustment and Bayesian dynamic borrowing using power prior. The proposed approach uses Bayesian bootstrap in combination with the empirical Bayes method utilizing quasi-likelihood for determining the power prior. The performance of our approach is examined by a simulation study. We apply the approach to two Acute Myeloid Leukemia (AML) studies for illustration.

Researchers have used NMR to measure multi-phase fluid saturation and distribution inside porous media of natural rock. However, the NMR signal amplitude suffers reduction with the increase of temperature. The main reason is the Transverse Overhauser Effect, where heating increases the freedom for ionic motion, affecting spinning behavior by having two spins go in two opposite directions to form the Dipolar Coupling. We approach solving NMR thermal effects correction by melding experimentation and numerical simulation method. We use NMR for Cretaceous carbonate rock multi-phase flow research. We conduct time step in-situ temperature measurement for four different sections of the flooding system at the inlet, center, and outlet along the flooding path. In addition, we conduct a temperature measurement at the NMR device radial axis, representing the permanent magnet temperature. We build a 3D cylindrical heat transfer model for the numerical simulator that simulates thermal effect distribution on the NMR for optimally generating the correction model. The insight provided by the simulator improved the understanding of the thermal distribution at the natural rock core plug to produce a better thermal correction model that meld experimentation and simulation, a method we call Th-CENS.