Non-adaptive Version Research Articles

Subjective listening effort

When investigating listening effort the individual subjective feedback is important to learn about and take into account the situations in which hearing impaired suffer in terms of listening effort and the relationship of listening effort to speech intelligibility. Here, we present results from a daily life questionnaire study and results from a new tool to evaluate listening effort subjectively under controlled conditions in the lab. A research questionnaire was used to determine the individual subjective listening effort in daily life in a systematic way. The questionnaire with 29 well described situations was used in a multi-center study in Denmark, Germany, and USA with 112 subjects to identify the most relevant effortful situations (e.g., “watching news in TV” or “talking at the kitchen table”). As a lab procedure we developed an adaptive categorical listening effort scaling procedure. It is based on a non-adaptive version that turned out to be a sensitive lab method in which subjects rate listening effort at predefined signal to noise ratios. The new method automatically finds the SNRs corresponding to subjective ratings from “extreme effort” to “no effort.” First results show the relationship between listening effort and intelligibility for different SNRs and background noises.

Running Errands in Time: Approximation Algorithms for Stochastic Orienteering

In the stochastic orienteering problem, we are given a finite metric space, where each node contains a job with some deterministic reward and a random processing time. The processing time distributions are known and independent across nodes. However the actual processing time of a job is not known until it is completely processed. The objective is to compute a nonanticipatory policy to visit nodes (and run the corresponding jobs) so as to maximize the total expected reward, subject to the total distance traveled plus the total processing time being at most a given budget of B. This problem combines aspects of the stochastic knapsack problem with uncertain item sizes as well as the deterministic orienteering problem. In this paper, we consider both nonadaptive and adaptive policies for Stochastic Orienteering. We present a constant-factor approximation algorithm for the nonadaptive version and an O(log log B)-approximation algorithm for the adaptive version. We extend both these results to directed metrics and a more general sequence orienteering problem. Finally, we address the stochastic orienteering problem when the node rewards are also random and possibly correlated with the processing time and obtain an O(log n log B)-approximation algorithm; here n is the number of nodes in the metric. All our results for adaptive policies also bound the corresponding “adaptivity gaps”.

Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra.

Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.

QOS-aware error recovery in wireless body sensor networks using adaptive network coding.

Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

No evidence for true training and transfer effects after inhibitory control training in young healthy adults.

Recent studies reported that training of working memory may improve performance in the trained function and beyond. Other executive functions, however, have been rarely or not yet systematically examined. The aim of this study was to test the effectiveness of inhibitory control (IC) training to produce true training-related function improvements in a sample of 122 healthy adults using a randomized, double-blind pretest/posttest/follow-up design. Two groups performed either adaptive (training group) or nonadaptive (active control) versions of go/no-go and stop-signal tasks for 3 weeks. Training gains as well as near-transfer to an untrained Stroop task and far-transfer to psychometric fluid intelligence were explored. Although the adaptive group could substantially improve overall IC task performance after training, no differences to the active control group occurred, neither at posttest nor at follow-up testing. A large decrease in response latency from pre- to posttest (and from pretest to 4 months' follow-up testing) was found when the training group was compared to the passive control group, which, however, does not sufficiently control for possible confounds. Thus, no conclusive evidence was found that this performance increase mirrors a true increase in IC function. The fact that training improvement was mainly related to response latency may indicate that individuals were more focused on performance gains in the prepotent go trials but less on the stop trials to meet the requirements of the tasks as well as possible. The challenges for response inhibition training studies are extensively discussed.

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

BackgroundExtracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes.MethodsWe present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case.ResultsExtracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min −1 (0.3 min −1) and -0.7 bpm (1.7 bpm) (compared to -0.2 min −1 (0.4 min −1) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time.ConclusionsIt is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.

An adaptive invasion-based model for distributed Differential Evolution

A novel adaptive model for a recently devised distributed Differential Evolution algorithm is introduced. The distributed algorithm, following the stepping-stone model, is characterized by a migration model inspired by the phenomenon known as biological invasion. The adaptive model is endowed with three updating schemes to randomly set the mutation and the crossover parameters. These schemes are here tied to the migration and are guided by a performance measure between two consecutive migrations. The proposed adaptive model is tested on a set of classical benchmark functions over the different setting schemes. To evaluate its performance, the model is compared against the original non-adaptive version with a fixed parameter setting, and against a well-known distributed Differential Evolution algorithm equipped with the same schemes for the control parameter updating. The experimental study shows that the method results in high effectiveness in terms of solutions detected and convergence speed on most of the benchmark problems and for the majority of the setting schemes investigated. Finally, to further estimate its effectiveness, the proposed approach is also compared with several state-of-the-art Differential Evolution frameworks endowed with different randomized or self-adaptive parameter setting strategies. This comparison shows that our adaptive model allows obtaining the best performance in most of the tests studied.

Adaptive Maximum Likelihood Sequence Detection for QPSK Coherent Optical Communication System

A novel Viterbi-type adaptive maximum likelihood sequence detection algorithm is developed and investigated in coherent optical communication systems in the presence of both laser phase noise and nonlinear phase noise. Compared with the nonadaptive version, it can self-adjust the effective memory length of the phase estimator and automatically deliver the optimum bit-error rate performance without requiring any prior statistical knowledge of the optical channel. The phase error variance of the estimator is also analytically investigated.

On Zhao-Woodroofe's condition for martingale approximation

The Zhao-Woodroofe condition is a necessary and sufficient condition for the existence of a martingale approximation of a causal stationary process. Here, a nonadapted version is given and the convergence of Cesaro averages is replaced by a convergence of a subsequence. The nonadapted version is of a different form than in other cases, e.g. of Wu-Woodroofe or Maxwell-Woodroofe conditions.

An Evaluation of a Working Memory Training Scheme in Older Adults

Working memory is a cognitive process that is particularly vulnerable to decline with age. The current study sought to evaluate the efficacy of a working memory training scheme in improving memory in a group of older adults. A 5-week online training scheme was designed to provide training in the main components of Baddeley’s (2000) working memory model, namely auditory and visuospatial short-term and working memory. A group of older adults aged between 64 and 79 were randomly assigned to a trainee (n = 19) or control (n = 17) group, with trainees engaging in the adaptive training scheme and controls engaging in a non-adaptive version of the program. Before and after training and at 3- and 6-month follow-up sessions, trainees and controls were asked to complete measures of short-term and working memory, long-term episodic memory, subjective ratings of memory, and attention and achievement of goals set at the beginning of training. The results provided evidence of an expansion of auditory short-term memory span, which was maintained 6 months later, and transfer to long-term episodic memory but no evidence of improvement in working memory capacity per se. A serendipitous and intriguing finding of a relationship between time spent training, psychological stress, and training gains provided further insight into individual differences in training gains in older adults.

Working memory training in survivors of pediatric cancer: a randomized pilot study

Survivors of pediatric brain tumors and acute lymphoblastic leukemia (ALL) are at increased risk for neurocognitive deficits, but few empirically supported treatment options exist. We examined the feasibility and preliminary efficacy of a home-based, computerized working memory training program, CogmedRM, with survivors of childhood cancer. Survivors of brain tumors or ALL (n = 20) with identified deficits in attention and/or working memory were randomized to either the success-adapted computer intervention or a non-adaptive, active control condition. Specifically, children in the adaptive condition completed exercises that became more challenging with each correct trial, whereas those in the non-adaptive version trained with exercises that never increased in difficulty. All participants were asked to complete 25 training sessions at home, with weekly, phone-based coaching support. Brief assessments were completed pre-intervention and post-intervention; outcome measures included both performance-based and parent-report measures of working memory and attention. Eighty-five percent of survivors were compliant with the intervention, with no adverse events reported. After controlling for baseline intellectual functioning, survivors who completed the intervention program evidenced significant post-training improvements in their visual working memory and in parent-rated learning problems compared with those in the active control group. No differences in verbal working memory functioning were evident between groups, however. Home-based, computerized cognitive training demonstrates good feasibility and acceptability in our sample. Children with higher intellectual functioning at baseline appeared to benefit more from the training, although further study is needed to clarify the strength, scope, and particularly the generalizability of potential treatment effects.

Regularized ${\rm RLS}$-$\lambda$ and DHOBE: An Adaptive Feedforward for a Solenoid Valve

To allow a stable and fast acting hydraulic pressure control on a continuously variable transmission (CVT) for road vehicles, an adaptive feedforward strategy is used. The Dasgupta-Huang outer bounding ellipsoid (DHOBE) and recursive least squares (RLS) with exponential forgetting factor(RLS-λ) adaptation algorithms are compared to the non-adaptive feedforward. The experiments show a clear advantage for the adaptive over the non-adaptive version by compensating for the slow drift of the valve pressure gain during the warm-up period of the transmission. Because of highly correlated input data, the adaptation algorithms offer deceiving performances with oscillating identified parameters. A regularization procedure is added to both adaptation algorithms, giving the rRLS-λ and rDHOBE. The regularized algorithms offer significantly better performances and stability than their non-regularized counterparts. Because of its implicit parametric uncertainty calculation while keeping an equivalent convergence rate, and a lower number of updates, the rDHOBE algorithm is regarded as the best solution for the application. By adapting a simple linear model, the rDHOBE adaptive feedforward succeeds in responding to an abrupt change of the external pressure setpoint with no added actuation delay while keeping the pressure error under 0.5 bar.

Computerized training of non-verbal reasoning and working memory in children with intellectual disability

Children with intellectual disabilities show deficits in both reasoning ability and working memory (WM) that impact everyday functioning and academic achievement. In this study we investigated the feasibility of cognitive training for improving WM and non-verbal reasoning (NVR) ability in children with intellectual disability. Participants were randomized to a 5-week adaptive training program (intervention group) or non-adaptive version of the program (active control group). Cognitive assessments were conducted prior to and directly after training and 1 year later to examine effects of the training. Improvements during training varied largely and amount of progress during training predicted transfer to WM and comprehension of instructions, with higher training progress being associated with greater transfer improvements. The strongest predictors for training progress were found to be gender, co-morbidity, and baseline capacity on verbal WM. In particular, females without an additional diagnosis and with higher baseline performance showed greater progress. No significant effects of training were observed at the 1-year follow-up, suggesting that training should be more intense or repeated in order for effects to persist in children with intellectual disabilities. A major finding of this study is that cognitive training is feasible in this clinical sample and can help improve their cognitive performance. However, a minimum cognitive capacity or training ability seems necessary for the training to be beneficial, with some individuals showing little improvement in performance. Future studies of cognitive training should take into consideration how inter-individual differences in training progress influence transfer effects and further investigate how baseline capacities predict training outcome.

Adaptive Tests for Detecting Gene-Gene and Gene-Environment Interactions

There has been an increasing interest in detecting gene-gene and gene-environment interactions in genetic association studies. A major statistical challenge is how to deal with a large number of parameters measuring possible interaction effects, which leads to reduced power of any statistical test due to a large number of degrees of freedom or high cost of adjustment for multiple testing. Hence, a popular idea is to first apply some dimension reduction techniques before testing, while another is to apply only statistical tests that are developed for and robust to high-dimensional data. To combine both ideas, we propose applying an adaptive sum of squared score (SSU) test and several other adaptive tests. These adaptive tests are extensions of the adaptive Neyman test [Fan, 1996], which was originally proposed for high-dimensional data, providing a simple and effective way for dimension reduction. On the other hand, the original SSU test coincides with a version of a test specifically developed for high-dimensional data. We apply these adaptive tests and their original nonadaptive versions to simulated data to detect interactions between two groups of SNPs (e.g. multiple SNPs in two candidate regions). We found that for sparse models (i.e. with only few non-zero interaction parameters), the adaptive SSU test and its close variant, an adaptive version of the weighted sum of squared score (SSUw) test, improved the power over their non-adaptive versions, and performed consistently well across various scenarios. The proposed adaptive tests are built in the general framework of regression analysis, and can thus be applied to various types of traits in the presence of covariates.

Adaptive Multilevel Methods with Local Smoothing for $H^1$- and $H^{\mathrm{curl}}$-Conforming High Order Finite Element Methods

A multilevel method on adaptive meshes with hanging nodes is presented, and the additional matrices appearing in the implementation are derived. Smoothers of overlapping Schwarz type are discussed; smoothing is restricted to the interior of the subdomains refined to the current level; thus it has optimal computational complexity. When applied to conforming finite element discretizations of elliptic problems and Maxwell equations, the method's convergence rates are very close to those for the nonadaptive version. Furthermore, the smoothers remain efficient for high order finite elements. We discuss the implementation in a general finite element code using the example of the deal.II library.

Self-Adaptation of Mutation Operator and Probability for Permutation Representations in Genetic Algorithms

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one "optimal choice"; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via self-adaptation, thus removing this nontrivial task from the GA user and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Self-adaptation has been proven successful for mutation step sizes in the continuous domain, and for the probability of applying bitwise mutation to binary encodings; here we examine whether this can translate to the choice and parameterisation of mutation operators for permutation encodings. We examine one method for adapting the choice of operator during runtime, and several different methods for adapting the rate at which the chosen operator is applied. In order to evaluate these algorithms, we have used a range of benchmark TSP problems. Of course this paper is not intended to present a state of the art in TSP solvers; rather, we use this well known problem as typical of many that require a permutation encoding, where our results indicate that self-adaptation can prove beneficial. The results show that GAs using appropriate methods to self-adapt their mutation operator and mutation rate find solutions of comparable or lower cost than algorithms with "static" operators, even when the latter have been extensively pretuned. Although the adaptive GAs tend to need longer to run, we show that is a price well worth paying as the time spent finding the optimal mutation operator and rate for the nonadaptive versions can be considerable. Finally, we evaluate the sensitivity of the self-adaptive methods to changes in the implementation, and to the choice of other genetic operators and population models. The results show that the methods presented are robust, in the sense that the performance benefits can be obtained in a wide range of host algorithms.

Effects of Adaptive Lane Departure Warning System on Driver Response to a Surprise Event

A lane departure warning (LDW) system monitors the current lane position of a vehicle and presents a driver alert when one of the vehicle's front tires crosses a threshold, for example, the nearest lane line. The primary intent of such warning systems is to prevent or mitigate road departures and related crashes caused by driver distraction or drowsiness. The present evaluation compared adaptive and nonadaptive versions of an LDW system. The adaptive version adapted to the driver's state, whereas the nonadaptive version did not. The adaptive LDW system alerted the driver only if a driver state monitor (DSM) indicated that the driver was looking away from the road ahead for 2 s or longer at about the time when a lane line was crossed. Forty volunteers drove a high-fidelity, moving-base driving simulator in a study to compare driver responses to a surprise lane departure when they used a nonadaptive LDW system and then an adaptive LDW system or vice versa. The results indicated that in the adaptive LDW mode, 13 subjects (34%) either experienced delayed activation of the LDW alert or received no LDW alert at all when they should have, primarily because of both the 2-s rule in the adaptive LDW algorithm and DSM registration issues. The adaptive LDW resulted in significantly larger lane excursions at the onset of the LDW alert compared with those that occurred in the non-adaptive LDW mode. These results highlight the dependence of the performance effects of adaptive systems on system hardware, algorithms, and algorithm parameters.

Adaptive training leads to sustained enhancement of poor working memory in children

Working memory plays a crucial role in supporting learning, with poor progress in reading and mathematics characterizing children with low memory skills. This study investigated whether these problems can be overcome by a training program designed to boost working memory. Children with low working memory skills were assessed on measures of working memory, IQ and academic attainment before and after training on either adaptive or non-adaptive versions of the program. Adaptive training that taxed working memory to its limits was associated with substantial and sustained gains in working memory, with age-appropriate levels achieved by the majority of children. Mathematical ability also improved significantly 6 months following adaptive training. These findings indicate that common impairments in working memory and associated learning difficulties may be overcome with this behavioral treatment.

Designing high-level decision making systems based on fuzzy if–then rules for a point-to-point car racing game

In this paper, fuzzy rule-based systems are applied to a point-to-point car racing game. In the point-to-point car racing game, two car agents compete with each other for taking waypoints. There are three waypoints in the car racing field, each of which is assigned a number that indicates the order to take. The control process of car agents is modeled as a non-holonomic system where there are two input variables (acceleration and steering) for controlling the position, angle and velocity of the car agents. Fuzzy rule-based systems are used to make a high-level decision where the target waypoint to take is determined. Since a fuzzy rule-based system for the high-level decision making is generated in the manner of supervised learning, a set of training patterns should be given for the construction of the fuzzy rule-based systems. In this paper we examine two methods to obtain such a set of training patterns. We also examine two representations of input vectors for the fuzzy rule-based systems. We discuss the effect of obtained training patterns and the input representation on the performance of the fuzzy rule-based systems. After discussing and analyzing the experimental results, we present an adaptive framework of fuzzy rule-based systems. The performance of adaptive fuzzy rule-based systems is then examined based on the results of their non-adaptive version. A series of computational experiments are performed to show the learning ability of the adaptive fuzzy rule-based systems.

Adaptive Bayesian network classifiers

This paper is concerned with adaptive learning algorithms for Bayesian network classifiers in a prequential (on-line) learning scenario. In this scenario, new data is available over time. An efficient supervised learning algorithm must be able to improve its predictive accuracy by incorporating the incoming data, while optimizing the cost of updating. However, if the process is not strictly stationary, the target concept could change over time. Hence, the predictive model should be adapted quickly to these changes. The main contribution of this work is a proposal of an unified, adaptive prequential framework for supervised learning called AdPreqFr4SL, which attempts to handle the cost-performance trade-off and deal with concept drift. Starting with the simple Naive Bayes, we scale up the complexity by gradually increasing the maximum number of allowable attribute dependencies, and then by searching for new dependences in the extended search space. Since updating the structure is a costly task, we use new data to primarily adapt the parameters. We adapt the structure only when is actually necessary. The method for handling concept drift is based on the Shewhart P-Chart. We experimentally prove the advantages of using the AdPreqFr4SL in comparison with its non-adaptive versions.