Feedback-based Approach Research Articles

Feedback-based quantum algorithm inspired by counterdiabatic driving

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems. Published by the American Physical Society 2024

Comparison of the effects of apprenticeship training by sandwich feedback and traditional methods on final-semester operating room technology students' perioperative competence and performance: a randomized, controlled trial.

Effective feedback is fundamental in clinical education, as it allows trainers to constantly diagnose the trainees' condition, determine their weaknesses, and intervene at proper times. Recently, different feedback-based approaches have been introduced in clinical training; however, the effectiveness of such interventions still needs to be studied extensively, especially in the perioperative field. Therefore, this study sought to compare the effects of apprenticeship training using sandwich feedback and traditional methods on the perioperative competence and performance of Operating Room (OR) technology students. Thirty final-semester undergraduate OR technology students taking the apprenticeship courses were randomly allocated into experimental (n = 15) and control (n = 15) groups through the stratified randomization approach. The students in the experimental group experienced Feedback-Based Learning (FBL) using a sandwich model, and the students in the control group participated in Traditional-Based Training (TBT) in six five-hour sessions weekly for three consecutive weeks. All students completed the Persian version of the Perceived Perioperative Competence Scale-Revised (PPCS-R) on the first and last days of interventions. Also, a blinded rater completed a checklist to evaluate all students' performance via Direct Observation of Procedural Skills (DOPS) on the last intervention day. Besides, the students in the FBL filled out a questionnaire regarding their attitude toward the implemented program. The mean total score of the PPCS-R was significantly higher in the FBL than in the TBT on the last intervention day (P < 0.001). Additionally, the increase in mean change of PPCS-R total score from the first to last days was significantly more in the FBL (P < 0.001). Likewise, the FBL students had higher DOPS scores than the TBT ones (P < 0.001). Most FBL students also had a good attitude toward the implemented program (n = 8; 53.3%). Apprenticeship training using a sandwich feedback-based approach was superior to the traditional method for enhancing perioperative competence and performance of final-semester OR technology students. Additional studies are required to identify the sustainability of the findings.

Designing and Manufacturing an Affordable and Easy to Use Visual Bio Feedback Device to Fix Forward Head Posture: A Pilot Study Involving Female Students

Forward Head Posture (FHP) is when the head leans forward due to factors such as heavy backpacks or poor computer ergonomics. FHP can lead to neck strain and discomfort as well as potential long-term issues such as arthritis. Treatment options include specialized exercises, orthopedic devices, manual therapy, physical exercises, and visual feedback techniques, along with guidance from specialists in physical medicine and rehabilitation. In this study, a visual feedback-based approach was used to address FHP in female students. The study spanned ten days and included a visual feedback group and a control group. The results showed significant improvements in maximum head angle deviation in the visual feedback group compared to the control group; however, there was no significant change in the DFA number, indicating stability in policy control by the central nervous system. The study demonstrated that visual feedback sessions led to immediate benefits, with participants progressively acquiring skills involving the maintenance of proper head positioning. The test results indicated that the neck angle decreased to less than 15 degrees, indicating a return to a normal state. The versatility of the developed affordable and easy-to-use device and the potential for using smartphone motion sensors for similar visual feedback systems are discussed in this paper as well. The study suggests the promising potential of visual feedback in healthcare, including remote monitoring and smartphone-based solutions.

Feedback-based approach in teaching medical interviewing

Educational feedback is a facilitating tool in improving medical interviewing skills through simulation practices. The simulated patient (SP) programme at the University of Pécs Medical School aims to provide efficient help for educators and students alike in language for medical purposes, communication and clinical courses. The constructivist feedback methodological approach ensures that students in history taking classes learn from role-playing in simulated scenarios and the feedback offered from the simulated patient. Effective assessment includes the learners’ own reflection and the patients’ non-judgmental observation on perceived interpersonal communication skills, observed professional misconduct, students’ ability to cope in emotionally challenging situations, and suggestions for improvement. Giving feedback has to be offered regularly to enable improvement in medical communication and nurture self-confidence. Our questionnaire survey of the students who attended history taking courses involved both self-reflection and instructor feedback based on their performance during simulated scenarios. The results of the study demonstrated substantial developments in relationship-building skills and self-confidence for students in post-course clinical practice.

A Control Theoretical Approach to Spectral Factorization is Unstable

Local stability analysis of a recently proposed recursive feedback-based approach to spectral factorization is performed. The method is found not to give stability guarantees. Interestingly enough, its global behavior often allows one to obtain reasonable approximations of spectral factorizations if a suitable stopping criterion is employed.

Multitask learning over shared subspaces.

This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broadly supported this hypothesis with either better performance on the second task if it shared the same subspace as the first, or positive correlations over task performance for shared subspaces. These empirical findings were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning and human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning.

Gradient-assisted focusing light through scattering media.

Focusing light through a scattering medium is a longstanding challenge in biomedical optics, to which wavefront shaping is a powerful solution. The state-of-the-art feedback-based approach is the widely used genetic algorithm method. However, it can only achieve relatively low enhancement of the focus, and the genetic algorithm is known to be time-consuming. To tackle those issues, we propose a gradient-assisted strategy for wavefront shaping. The proposed method conducts optimization in the function distribution space. Specifically, when optimizing the parameters along each iteration, the consequent function distribution changes within a distance as measured by the Kullback-Leibler divergence. Taking advantage of the gradient information, the proposed method is over 60× faster to obtain the same peak-to-background ratio (PBR) level. Compared with the genetic algorithm that is able to optimize a number of 64×64 phase segments, the proposed gradient strategy is able to optimize 256×256 phase segments, and gives 20× higher focus enhancement as quantified by the PBR.

Deadband feedback-based scheduling approach for networked control system with variable sampling period

Reasonable information scheduling strategies in the networked control system (NCS) can improve the quality of service of the network, reduce the conflict of information transmission in the network, and improve the overall performance of the NCS. In order to improve the performance of the NCS, a deadband feedback-based scheduling approach for the NCS with a variable sampling period is proposed. For the NCS with multi control loops, considering the limitation of network bandwidth resources, the dynamic real-time adjustment of a multi-loop sampling period is achieved through network utilization prediction, network bandwidth configuration and sampling period calculation. Furthermore, deadband feedback scheduling is combined with a variable sampling period algorithm. Deadband is set in the sensor and controller nodes to effectively adjust the information flow of the forward channel and the feedback channel. The proposed scheduling approach can reduce the impact of network conflict and network delay on system stability, make the network resources allocated reasonably, save network data traffic, and improve the overall performance of the NCS. A NCS with five control loops is used as the simulation object and carried out by True Time toolbox. The simulation results show that the proposed scheduling approach can improve output control performance of the system, reduce integral absolute error value of the control loops, and improve network utilization. The overall control performance of the system is improved.

Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.

Human-machine collaboration in online customer service \u2013 a long-term feedback-based approach

The rising expectations of customers have considerably contributed to the need for automated approaches supporting employees in online customer service. Since automated approaches still struggle to meet the challenge to fully grasp the semantics of texts, hybrid approaches combining the complementary strengths of human and artificial intelligence show great potential for assisting employees. While research in Case-Based Reasoning (CBR) already provides well-established approaches, they do not fully exploit the potential of CBR as hybrid intelligence. Against this background, we follow a design-oriented approach and develop an adapted textual CBR cycle that integrates employees’ feedback on semantic similarity, which is collected during the Reuse phase, into the Retrieve phase by means of long-term feedback methods from information retrieval. Using a real-world data set, we demonstrate the practical applicability and evaluate our approach regarding performance in online customer service. Our novel approach surpasses human-based, machine-based, and hybrid approaches in terms of effectiveness due to a refined retrieval of semantically similar customer problems. It is further favorable regarding efficiency, reducing the average time required to solve a customer problem.

Feedback-Based Resource Utilization for Smart Home Automation in Fog Assistance IoT-Based Cloud

In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.

Enhancing throughput in multi-radio cognitive radio networks

In recent years, cognitive radio networks (CRNs) have been widely investigated to solve the well-known spectrum scarcity problem through enhancing spectrum utilization. Another technique of enhancing spectrum utilization, which has already been well accepted, is to utilize multiple radios on a single node. Simultaneous usage of both these techniques is, therefore, expected to enhance the spectrum utilization further in road to improving overall network performance. However, little research efforts have been spent on investigating performance of the simultaneous usage through incorporating multiple radios in each node of a CRN. Existing studies in this regard propose several protocols for multi-radio cognitive radio networks (MRCRNs). However, none of them focuses on enhancing throughput in the network to the best of our knowledge. Nonetheless, increased network throughput should be a direct consequence of enhanced spectrum utilization through exploiting multiple radios in CRNs, even though an existing study (Khan et al., in: 2015 IEEE 11th international conference on wireless and mobile computing, networking and communications (WiMob), IEEE, pp 370–377, 2015) reports getting decreased network throughput while introducing multiple radios in each node of a CRN. Thus, a specialized treatment to the multiple radios in each node of a CRN is needed for enhancing network throughput. Accordingly, in this study, we propose a feedback-based multi-radio exploitation approach for MRCRNs, where information obtained from lower layers (Physical layer and Data Link layer) is incorporated in the process of decision making in an upper layer (Application layer) to enhance network throughput. We implement our proposed approach in the network simulator ns-3 to evaluate different performance metrics including network throughput, average end-to-end delay, and average packet drop ratio. We compare the performance against that of existing multi-radio exploitation approaches for CRNs. Our simulation results reveal that our proposed feedback-based approach always achieves substantially increased network throughput compared to the existing approaches, in parallel to achieving improved delay and packet drop-ratio in most of the cases.

A Feedback-Based Approach to Utilizing Embeddings for Clinical Decision Support

Clinical Decision Support (CDS) is widely seen as an information retrieval (IR) application in the medical domain. The goal of CDS is to help physicians find useful information from a collection of medical articles with respect to the given patient records, in order to take the best care of their patients. Most of the existing CDS methods do not sufficiently consider the semantic relation between texts, hence the potential in improving the performance in biomedical articles retrieval. This paper proposes a novel feedback-based approach which considers the semantic association between a retrieved biomedical article and a pseudo feedback set. Evaluation results show that our method outperforms the strong baselines and is able to improve over the best runs in the TREC CDS tasks.

Recreating the shading effects of ship wake induced turbidity to test acclimation responses in the seagrass Thalassia hemprichii

Elevated sediment delivery and resuspension in coastal waters from human activities such as shipping can have detrimental effects on seagrass health by limiting light penetration. Managing seagrasses requires knowledge of their light acclamatory abilities so guidelines for coastal activities (e.g. ship movements) that influence sediment dynamics can be created. Guidelines typically focus on ensuring that seagrasses are able to meet their minimal light requirements (MLR). MLRs can be achieved by different light regimes, but it remains unknown whether a chronically low yet stable light regime is less or more detrimental than a highly variable regime with periods of extreme low to no light. To test this, we compared the physiological and morphological responses of Thalassia hemprichii among three light regimes: an open control (30-40% ambient light), a shaded control with (11-15% ambient light), and a fluctuating shade (4-30% ambient light). The MLR for the T. hemprichii we studied was lower (4-10% ambient light) than previous reports (mean = 18%) illustrating enhanced light acclimation in Singapore's chronically turbid waters. Seagrass shoots in the shaded control, however, exhibited significantly more morphological stress symptoms, with reduced shoot growth and lower below ground biomass. These data suggest that for seagrass exposed to periods of acute light stress, energetic costs associated with photo-acclimation to more variable light regimes can be offset if the plant can meet its daily light requirements during periods of high light. Management of seagrass beds should incorporate regular light monitoring and move towards an adaptive feedback-based approach to ensure the long-term viability of these vulnerable ecosystems.

Extending the institutional repository to include undergraduate research

ABSTRACTWhile a primary strategy of scholarly communication initiatives has been to encourage faculty participation in institutional repositories (IRs), with some process and workflow customization, IR participation can be successfully extended to undergraduate students, with benefits to both the student and institution. Drawing observations from the University of New Hampshire Library's work collecting undergraduate honors theses and other student research, this article discusses customization strategies for creating an effective workflow for student self-deposit using an iterative, feedback-based approach, and the benefits, challenges, and potential concerns of encouraging undergraduate participation in institutional repositories.

Influence of the absorber dimensions on wavefront shaping based on volumetric optoacoustic feedback.

The recently demonstrated control over light distribution through turbid media based on real-time three-dimensional optoacoustic feedback has offered promising prospects to interferometrically focus light within scattering objects. Nevertheless, the focusing capacity of the feedback-based approach is strongly conditioned by the number of optical modes (speckle grains) enclosed in the volume that can be resolved with the optoacoustic imaging system. In this Letter, we experimentally tested the light intensity enhancement achieved with optoacoustic feedback measurements from different sizes of absorbing microparticles. The importance of the obtained results is discussed in the context of potential signal enhancement at deep locations within a scattering medium where the effective speckle grain sizes approach the minimum values dictated by optical diffraction.

Learning and Risk Management Decisions

Due to the lack of descriptive information about the effectiveness of risk management activities, decision-makers often have to rely on (their own) prior experience with these investments. Thus, we propose a novel, feedback-based approach to examine risk management decisions. We simulate individuals’ decisions over 50 time periods and analyze how distributional properties of different risk management instruments influence subjects’ propensity to invest in self-insurance or self-protection. Our results show that subjects act more risk averse over time because self-insurance take-up rates increase when learners gain more experience. Individuals’ risk management decisions are price-sensitive but we find only limited evidence for a complementary relationship between both risk management options. Our findings provide an alternative explanation for the low demand for risk management investments against low probability risks and can be used to predict developments in new insurance markets with inexperienced policyholders.

Shaping volumetric light distribution through turbid media using real-time three-dimensional opto-acoustic feedback.

Focusing light through turbid media represents a highly fascinating challenge in modern biophotonics. The unique capability of opto-acoustics for high-resolution imaging of light absorption contrast in deep tissues can provide a natural and efficient feedback to control light delivery in a scattering medium. While the basic feasibility of using opto-acoustic readings as a feedback mechanism for wavefront shaping has been recently reported, the suggested approaches may require long acquisition times, making them challenging to be translated into realistic tissue environments. In an attempt to significantly accelerate dynamic wavefront shaping capabilities, we present here a feedback-based approach using real-time three-dimensional opto-acoustic imaging assisted with genetic-algorithm-based optimization. The new technique offers robust performance in the presence of noisy measurements and can simultaneously control the scattered wave field in an entire volumetric region.

K-Anonymity for Crowdsourcing Database

In crowdsourcing database, human operators are embedded into the database engine and collaborate with other conventional database operators to process the queries. Each human operator publishes small HITs (Human Intelligent Task) to the crowdsourcing platform, which consists of a set of database records and corresponding questions for human workers. The human workers complete the HITs and return the results to the crowdsourcing database for further processing. In practice, published records in HITs may contain sensitive attributes, probably causing privacy leakage so that malicious workers could link them with other public databases to reveal individual private information. Conventional privacy protection techniques, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-Anonymity</i> , can be applied to partially solve the problem. However, after generalizing the data, the result of standard K-Anonymity algorithms may render uncontrollable information loss and affects the accuracy of crowdsourcing. In this paper, we first study the tradeoff between the privacy and accuracy for the human operator within data anonymization process. A probability model is proposed to estimate the lower bound and upper bound of the accuracy for general K-Anonymity approaches. We show that searching the optimal anonymity approach is NP-Hard and only heuristic approach is available. The second contribution of the paper is a general feedback-based K-Anonymity scheme. In our scheme, synthetic samples are published to the human workers, the results of which are used to guide the selection on anonymity strategies. We apply the scheme on Mondrian algorithm by adaptively cutting the dimensions based on our feedback results on the synthetic samples. We evaluate the performance of the feedback-based approach on U.S. census dataset, and show that given a predefined <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> , our proposal outperforms standard K-Anonymity approaches on retaining the effectiveness of crowdsourcing.

A Partial Histogram Maintenance Mode

Although histogram has been introduced into the optimizer of the relational database to assist cardinality estimates for more than thirty years, the issue of histogram maintenance is not addressed well. The application of the query feedback information provides a new way of thinking for the histogram maintenance. But it is not mature until now and inefficiency is the main problem. The query feedback-based histogram maintenance approach adopts the entire update mode and the whole histogram will be updated in each maintenance process. This mode brings huge computation for the maintenance process and becomes the most important reason for its inefficiency. Therefore, we propose the partial update mode in this paper to replace the entire update mode and try to improve the maintenance efficiency by limiting the updating range and decreasing the computation in each maintenance process.