Non-adaptive Schemes Research Articles

Automated Adaptive Absolute Binding Free Energy Calculations.

Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a fully automated ABFE workflow based on the automated selection of λ windows, the ensemble-based detection of equilibration, and the adaptive allocation of sampling time based on inter-replicate statistics. We find that the automated selection of intermediate states with consistent overlap is rapid, robust, and simple to implement. Robust detection of equilibration is achieved with a paired t-test between the free energy estimates at initial and final portions of a an ensemble of runs. We determine reasonable default parameters for all algorithms and show that the full workflow produces equivalent results to a nonadaptive scheme over a variety of test systems, while often accelerating equilibration. Our complete workflow is implemented in the open-source package A3FE (https://github.com/michellab/a3fe).

Anti‐windup compensation for model reference adaptive control schemes

AbstractActuator constraints, particularly saturation limits, are an intrinsic and long‐standing problem in the implementation of most control systems. Model reference adaptive control (MRAC) is no exception and it may suffer considerably when actuator saturation is encountered. With this in mind, this paper proposes an anti‐windup strategy for model reference adaptive control schemes subject to actuator saturation. A prominent feature of the proposed compensator is that it has the same architecture as well‐known nonadaptive schemes, namely model recovery anti‐windup, which rely on the assumption that the system model is known accurately. Since, in the adaptive case, the model is largely unknown, the proposed approach uses an “estimate” of the system matrices for the anti‐windup formulation and modifies the adaptation laws that update the controller gains; if the (unknown) ideal control gains are reached, the model recovery anti‐windup formulation is recovered. The main results provide conditions under which, if the ideal control signal eventually lies within the control constraints, then the system states will converge to those of the reference model, that is, the tracking error will converge to zero asymptotically. The article deals with open‐loop stable linear systems and highlights the main challenges involved in the design of anti‐windup compensators for model‐reference adaptive control systems, demonstrating its success via a flight control application.

Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes.

With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.

Performance of cascaded gain saturated and fixed gain optical amplifier FSO communication systems limited by scintillation and pointing error

A major limitation to achieving acceptable system performance in free space optical (FSO) communication systems is the presence of atmospheric turbulence (AT), which results in received signal power fluctuations. In addition to the signal power fluctuations at the receiving end, pointing errors (PEs) can also significantly impair system performance. While achieving reliable communication over long distances is desirable, it is challenging because the AT effects and PEs are more pronounced at longer communication distances. Using Monte Carlo simulation methods, the impact of amplified spontaneous emission noise, AT, PE and geometric spread on cascaded gain saturated and fixed gain optical amplifier (OA) FSO communication systems is investigated in this paper. Results are obtained for different AT regimes, normalised PE standard deviations and normalised beam widths employing non-adaptive and adaptive decision thresholding schemes at the receiver. Results obtained show that increasing the size of the receiver aperture does not have any impact on the average bit error rate (BER) when the PEs are minimal. Also, the BER performances obtained with systems using a smaller receiver aperture are better than those obtained with systems using a larger receiver aperture when PEs are significant. Results obtained also show that even though unacceptable BER performances were obtained under moderate and strong atmospheric regimes when the decision threshold of the receiver is non-adaptive, the advantage of using gain saturated OAs is still evident as gain saturated OAs performed better than fixed gain OAs when the decision threshold of the receiver is non-adaptive.

A Theory for Why Even Simple Covariance Localization Is So Useful in Ensemble Data Assimilation

Abstract Covariance localization has been the key to the success of ensemble data assimilation in high dimensional problems, especially in global numerical weather prediction. We review and synthesize optimal and adaptive localization methods that are rooted in sampling error theory and that are defined by optimality criteria, e.g., minimizing errors in forecast covariances or in the Kalman gain. As an immediate result, we note that all optimal localization methods follow a universal law and are indeed quite similar. We confirm the similarity of the various schemes in idealized numerical experiments, where we observe that all localization schemes we test—optimal and nonadaptive schemes—perform quite similarly in a wide array of problems. We explain this perhaps surprising finding with mathematical rigor on an idealized class of problems, first put forward by Bickel and others to study the collapse of particle filters. In combination, the numerical experiments and the theory show that the most important attribute of a localization scheme is the well-known property that one should dampen spurious long-range correlations. The details of the correlation structure, and whether or not these details are used to construct the localization, have a much smaller effect on posterior state errors. Significance Statement Covariance localization has been the key to the success of ensemble data assimilation (DA) in global numerical weather prediction. In this paper, we synthesize a large body of literature on optimal localization and then report and explain a number of surprising observations.

EARLY MALADAPTIVE SCHEMAS AS A RISK FACTOR FOR BREAST CANCER IN ALGERIAN WOMEN

The study aimed to identify whether maladaptive early schemas are a risk factor for breast cancer or not. To investigate if early maladaptive charts are a risk factor for breast cancer. Active schemas were also evaluated in women with breast cancer, considering that their effectiveness may stimulate psychological problems that lead to a negative prognosis in the trajectory of the disease. The study was conducted on a sample consisting of (78 women with breast cancer and 79 women non-affected) the researchers used the descriptive approach and the scale of early maladaptive charts of Jeffrey Young, the abbreviated version. After verifying the psychometric characteristics of an exploratory sample, the research reached the following findings: There are no statistically significant differences between breast cancer patients and non-patients in early and maladaptive schemas of various types, and the second result is that most of the early maladaptive schemas are activated in women with breast cancer in an exaggerated manner. The active schemas are sacrifice, high demands, control, neglect, lack of interest, lack of self-control, excessive impulse control and merging relationships. isolation and aversion. Therefore, the study does not confirm that early maladaptive schemas are a risk factor for the onset of breast cancer but confirms the activity of a group of early maladaptive schemas in women with breast cancer, which is probably a risk factor for a destitute prognosis for breast cancer. According to the variable of the early, non-adaptive schemes, the current study drew a psychological allusive for women with breast cancer in Algerian society, This is what leads and encourages us to conduct other studies aimed at psychological support for breast cancer patients, and to open the door to preventive psychological programs that may limit the factors of the risk of breast cancer. Keywords: Risk Factors, Early Maladaptive Schemas, Breast Cancer DOI： https://doi.org/10.35741/issn.0258-2724.58.3.53

Energy efficiency aware dynamic rate and power adaptation in carrier sensing based WLANs under Rayleigh fading and shadowing

We consider the problem of energy efficiency aware dynamic adaptation of data transmission rate and transmission power of the users in carrier sensing based Wireless Local Area Networks (WLANs) in the presence of path loss, Rayleigh fading and log-normal shadowing. For a data packet transmission, we formulate an optimization problem, solve the problem, and propose a rate and transmission power adaptation scheme with a restriction methodology of data packet transmission for achieving the optimal energy efficiency. In the restriction methodology of data packet transmission, a user does not transmit a data packet if the instantaneous channel gain of the user is lower than a threshold. To evaluate the performance of the proposed scheme, we develop analytical models for computing the throughput and energy efficiency of WLANs under the proposed scheme considering a saturation traffic condition. We then validate the analytical models via simulation. We find that the proposed scheme provides better throughput and energy efficiency with acceptable throughput fairness if the restriction methodology of data packet transmission is included. By means of the analytical models and simulations, we demonstrate that the proposed scheme provides significantly higher throughput, energy efficiency and fairness index than a traditional non-adaptive scheme and an existing most relevant adaptive scheme. Throughput and energy efficiency gains obtained by the proposed scheme with respect to the existing adapting scheme are about 75% and 103%, respectively, for a fairness index of 0.8. We also study the effect of various system parameters on throughput and energy efficiency and provide various engineering insights.

Gyrokinetic simulations of turbulence and zonal flows driven by steep profile gradients using a delta-f approach with an evolving background Maxwellian

Long global gyrokinetic turbulence simulations are particularly challenging in situations where the system deviates strongly from its initial state and when fluctuation levels are high, for example, in strong gradient regions. For particle-in-cell simulations, statistical sampling noise accumulation from large marker weights due to large deviations from the control variate of a delta-f scheme makes such simulations often impractical. An adaptive control variate in the form of a flux-surface-averaged Maxwellian with a time-dependent temperature profile is introduced in an attempt to alleviate the former problem. Under simplified collisionless physics, this adaptive delta-f scheme is shown to reduce noise accumulation in the zonal flows and the simulated heat flux in a quasi-steady turbulent state. The method also avoids the collapse of the signal-to-noise ratio, which occurs in the standard non-adaptive scheme, and, therefore, allows one to reach numerically converged results even with lower marker numbers.

On the Recursive Structure of Multigrid Cycles

A new non-adaptive recursive scheme for multigrid algorithms is introduced. Governed by a positive parameter $\kappa$ called the cycle counter, this scheme generates a family of multigrid cycles dubbed $\kappa$-cycles. The well-known $V$-cycle, $F$-cycle, and $W$-cycle are shown to be particular members of this rich $\kappa$-cycle family, which satisfies the property that the total number of recursive calls in a single cycle is a polynomial of degree $\kappa$ in the number of levels of the cycle. This broadening of the scope of fixed multigrid cycles is shown to be potentially significant for the solution of some large problems on platforms, such as GPU processors, where the overhead induced by recursive calls may be relatively significant. In cases of problems for which the convergence of standard $V$-cycles or $F$-cycles (corresponding to $\kappa=1$ and $\kappa=2$, respectively) is particularly slow, and yet the cost of $W$-cycles is very high due to the large number of recursive calls (which is in exponential in the number of levels), intermediate values of $\kappa$ may prove to yield significantly faster run-times. This is demonstrated in examples where $\kappa$-cycles are used for the solution of rotated anisotropic diffusion problems, both as a stand-alone solver and as a preconditioner. Moreover, a simple model is presented for predicting the approximate run-time of the $\kappa$-cycle, which is useful in pre-selecting an appropriate cycle counter for a given problem on a given platform. Implementing the $\kappa$-cycle requires making just a small change in the classical multigrid cycle.

Smart pooling: AI-powered COVID-19 informative group testing

Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

Adaptive AFM imaging based on object detection using compressive sensing

Atomic force microscopy (AFM) is a kind of high-precision nanoscale instrument to measure the surface morphology of various samples. Nevertheless, the standard AFM scanning process takes a very long time to obtain high-resolution images. Compressive sensing (CS) can be used to achieve fast AFM imaging. But, the traditional CS-AFM imaging is difficult to balance the image quality of each local area, resulting in poor quality in the object area at low sampling rate. Therefore, a novel imaging scheme of adaptive CS-AFM is proposed. The fast scanning is first used to generate a low resolution image in a short time, and then bicubic interpolation is performed to obtain a high resolution image. Afterwards, an advanced detection algorithm is used to realize the accurate detection and positioning of the objects. Furthermore, the supplementary scanning is carried out to achieve adaptive sampling on the objects. After sampling, the measurement matrix corresponding to the measurement points is constructed. Finally, Total Variation Minimization by Augmented Lagrangian and Alternating Direction Algorithm (TVAL3) is used to reconstruct the whole AFM image. The imaging quality of the sample is analyzed and assessed by image evaluation metrics (PSNR and SSIM) and visual effect. Compared with two non-adaptive imaging schemes, the proposed scheme is characterized by high automation, short time, and high quality.

Utility Maximization for Multihop Wireless Networks Employing BATS Codes

BATS (BATched Sparse) codes are a class of efficient random linear network coding variation that has been studied for multihop wireless networks mostly in scenarios of a single communication flow. Towards sophisticated multi-flow network communications, we formulate a network utility maximization (NUM) problem that jointly optimizes the BATS code parameters of all the flows and network scheduling. The NUM problem adopts a batch-wise packet loss model that can be obtained from the network local statistics without any constraints on packet loss patterns. Moreover, the NUM problem allows a different number of recoded packets to be transmitted for different batches in a flow, which is called adaptive recoding. Due to both the probably nonconcave objective and the BATS code-related variables, the algorithms developed for the existing flow optimization problems cannot be applied directly to solve our NUM problem. We introduce a two-step algorithm to solve our NUM problem, where the first step solves the problem with nonadaptive recoding schemes, and the second step optimizes adaptive recoding hop-by-hop from upstream to downstream in each flow. We perform various numerical evaluations and simulations to verify the effectiveness and efficiency of the algorithm.

On the Non-Adaptive Zero-Error Capacity of the Discrete Memoryless Two-Way Channel.

We study the problem of communicating over a discrete memoryless two-way channel using non-adaptive schemes, under a zero probability of error criterion. We derive single-letter inner and outer bounds for the zero-error capacity region, based on random coding, linear programming, linear codes, and the asymptotic spectrum of graphs. Among others, we provide a single-letter outer bound based on a combination of Shannon’s vanishing-error capacity region and a two-way analogue of the linear programming bound for point-to-point channels, which, in contrast to the one-way case, is generally better than both. Moreover, we establish an outer bound for the zero-error capacity region of a two-way channel via the asymptotic spectrum of graphs, and show that this bound can be achieved in certain cases.

Adaptive quantum state tomography with iterative particle filtering

Several Bayesian estimation-based heuristics have been developed to perform quantum state tomography (QST). Their ability to quantify uncertainties using region estimators and include a priori knowledge of the experimentalists makes this family of methods an attractive choice for QST. However, specialized techniques for pure states do not work well for mixed states and vice versa. In this paper, we present an adaptive particle filter (PF)-based QST protocol which improves the scaling of fidelity compared to nonadaptive Bayesian schemes for arbitrary multi-qubit states. This is due to the protocol’s unabating perseverance to find the states’ diagonal bases and more systematic handling of enduring problems in popular PF methods relating to the subjectivity of informative priors and the invalidity of particles produced by resamplers. Numerical examples and implementation on IBM quantum devices demonstrate improved performance for arbitrary quantum states and the application readiness of our proposed scheme.

Secure physical layer short-packet communication for 5G

Wireless short-packet communication is one of the key technologies for realizing 5G Internet of Things (IoT) applications such as massive machine type communication (mMTC) and ultra-reliable low latency communication (uRLLC). For applications such as the industrial IoT and the Internet of Vehicles, the security of wireless communication is of paramount importance. This paper investigates the secure transmission from the perspective of physical layer security for short-packet communication systems under slow fading channels. A secrecy outage probability (SOP) is defined based on the conclusion of secure short-packet communication information theory, while the reliable throughput with the SOP restriction is established as the secure communication performance metric of the short-packet communication system. On this basis, adaptive and non-adaptive encoder schemes are designed, reliable throughputs under these two encoder schemes can be maximized by designing coding rates and optimizing transmission thresholds with the explicit/partial instantaneous channel state information (CSI) of the legitimate channel, respectively. Whats more, the impacts of main system parameters on the optimal throughput are further analyzed. Numerical results verify the effectiveness of transmission schemes, and verify impacts of main system parameters on the secure performance of the transmission systems with SOP constraints.

Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment

This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel. For a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs the adaptive sensing vectors sequentially based on the available information so far at the base station (BS). By recognizing that the AoA posterior distribution is a sufficient statistic for solving the initial access problem, we use the posterior distribution as the input to the proposed DNN for designing the adaptive sensing strategy. However, computing the posterior distribution can be computationally challenging when the channel fading coefficient is unknown. To address this issue, this paper proposes to use an estimate of the fading coefficient to compute an approximation of the posterior distribution. Further, this paper shows that the proposed DNN can deal with practical beamforming constraints such as the constant modulus constraint. Numerical results demonstrate that compared to the existing adaptive and non-adaptive beamforming schemes, the proposed DNN-based adaptive sensing strategy achieves a significantly better AoA acquisition performance.

Low-Latency Network-Adaptive Error Control for Interactive Streaming

We introduce a novel network-adaptive algorithm that is suitable for alleviating network packet losses for low-latency interactive communications between a source and a destination. Our network-adaptive algorithm estimates in real-time the best parameters of a recently proposed streaming code that uses forward error correction (FEC) to correct both arbitrary and burst losses, which cause a crackling noise and undesirable jitters, respectively in audio. In particular, the destination estimates appropriate coding parameters based on its observed packet loss pattern and sends them back to the source for updating the underlying code. Besides, a new explicit construction of practical low-latency streaming codes that achieve the optimal tradeoff between the capability of correcting arbitrary losses and the capability of correcting burst losses is used. Simulation evaluations based on statistical losses and real-world packet loss traces reveal the following: (i) Our proposed network-adaptive algorithm combined with our optimal streaming codes can achieve significantly higher performance compared to uncoded and non-adaptive FEC schemes over UDP (User Datagram Protocol); (ii) Our explicit streaming codes can significantly outperform traditional MDS (maximum-distance separable) streaming schemes when they are used along with our network-adaptive algorithm. In addition, we study different factors that can affect the performance of our network-adaptive algorithm.

Autoencoder-bank based design for adaptive channel-blind robust transmission

The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to 10^{-4}, our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

A new adaptive control chart for the simultaneous monitoring of the mean and variability of multivariate normal processes

This paper considers adaptive schemes for the simultaneous monitoring of the mean and variability of a multivariate normal quality characteristic. At first, we extend an already existing bivariate non-adaptive simultaneous control chart to a multivariate one. Then, we develop several adaptive schemes, which will cover both previously bivariate and newly multivariate charts. After having designed adaptive schemes for the multivariate chart, eight performance measures are computed based on the run length, time to signal, number of observations to signal and number of switches to signal and evaluated using a new Markov chain model. With the developed performance measures, non-adaptive and adaptive schemes under different mean, variability, simultaneous shift sizes, and different number of quality characteristics are compared. Our scheme is also compared to one of the best methods available in the literature. A numerical example is also provided in order to demonstrate how the adaptive scheme can be implemented in practice.

Adaptive streaming of HD and 360[formula omitted] videos over software defined radios

In this paper we study and implement real-time adaptation schemes for video encoding and channel selection that work in tandem to facilitate HD and 360° video streaming for secondary users in a dynamic spectrum access network. Out-of-band feedbacks on instantaneous pathloss of the signal between the transmitter and the receiver, the received signal strength indicator (RSSI) at the receiver, and the quality of the reconstructed video, are used to continuously determine the best possible encoding parameters. Similarly, the radio transmitter continuously adjusts the channel parameters (i.e., center frequency and channel bandwidth) based on the transmission activities of the primary users who are prioritized on these channels. We consider the physical limitations of the encoder and the channel statistics to determine when to change the encoder parameters and when to switch to a new channel. We propose a multi-level threshold based mechanism to find the optimal number of encoding bit rates. We also propose a threshold based algorithm to find the best available channel between the transmitter–receiver pair. We validate our theoretical propositions on an indoor testbed using universal software radio peripherals (USRPs) and GNU Radio. Live videos captured with a web-cam and a 360° VR camera were encoded with open source H.264 software libraries and streamed using GStreamer. The signal was transmitted over the 915 MHz ISM bands with omni-directional antennas. We used two B210s (from Ettus Research) for the transmitter and the receiver. Another B210 was programmed to sense the energy levels on all the channels in order to detect the presence of primary user transmissions. GNU Radio was used to build the initial flow-graph of all the signal processing modules, both at the transmitter and the receiver. We used Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to measure the video quality. Experimental results show that (i) the video encoder and the USRP transmitter–receiver pair were able to adapt to the changing RF conditions, (ii) the adaptation schemes yielded better video quality than non-adaptive schemes, and (iii) the USRPs could switch the channels fast enough allowing uninterrupted video streaming even when the primary users preempted the secondary user’s transmission. We also present a simulation study to show that the proposed encoder and channel adaptation algorithms can be scaled to larger networks.