Limited Feedback Research Articles

Towards inferring positioning of straight cochlear-implant electrode arrays during insertion using real-time impedance sensing.

Cochlear-implant electrode arrays (EAs) are currently inserted with limited feedback, and impedance sensing has recently shown promise for EA localisation. We investigate the use of impedance sensing to infer the progression of an EA during insertion. We show that the access resistance component of bipolar impedance sensing can detect when a straight EA reaches key anatomical locations in a plastic cochlea and when each electrode contact enters/exits the cochlea. We also demonstrate that dual-sided electrode contacts can provide useful proximity information and show the real-time relationship between impedance and wall proximity in a cadaveric cochlea for the first time. The access resistance component of bipolar impedance sensing has high potential for estimating positioning information of EAs relative to anatomy during insertion. Main limitations of this work include using saline as a surrogate for human perilymph in ex vivo models and using only one type of EA.

Understanding the role of antibiotic-associated adverse events in influencing antibiotic decision-making.

To (1) understand the role of antibiotic-associated adverse events (ABX-AEs) on antibiotic decision-making, (2) understand clinician preferences for ABX-AE feedback, and (3) identify ABX-AEs of greatest clinical concern. Focus groups. Academic medical center. Medical and surgical house staff, attending physicians, and advanced practice practitioners. Focus groups were conducted from May 2022 to December 2022. Participants discussed the role of ABX-AEs in antibiotic decision-making and feedback preferences and evaluated the prespecified categorization of ABX-AEs based on degree of clinical concern. Thematic analysis was conducted using inductive coding. Four focus groups were conducted (n = 15). Six themes were identified. (1) ABX-AE risks during initial prescribing influence the antibiotic prescribed rather than the decision of whether to prescribe. (2) The occurrence of an ABX-AE leads to reassessment of the clinical indication for antibiotic therapy. (3) The impact of an ABX-AE on other management decisions is as important as the direct harm of the ABX-AE. (4) ABX-AEs may be overlooked because of limited feedback regarding the occurrence of ABX-AEs. (5) Clinicians are receptive to feedback regarding ABX-AEs but are concerned about it being punitive. (6) Feedback must be curated to prevent clinicians from being overwhelmed with data. Clinicians generally agreed with the prespecified categorizations of ABX-AEs by degree of clinical concern. The themes identified and assessment of ABX-AEs of greatest clinical concern may inform antibiotic stewardship initiatives that incorporate reporting of ABX-AEs as a strategy to reduce unnecessary antibiotic use.

Design and analysis of quantized feedback based user-antenna joint scheduling scheme for ongoing 5G and beyond multi-user massive MIMO FDD communication systems

For the efficient user scheduling (US) and interference mitigation, channel state information (CSI) is mandatory at the base station (BS), particularly for the frequency division duplexing (FDD) systems where the users need to provide some CSI back to the BS for performing the scheduling process. As the feedback overhead increases drastically with increase of antennas as well as users, so, US with limited/reduced feedback is essential. Deploying massive multiple-input multiple-output frequency division duplexing (mMIMO FDD) systems remains an open area for research with major difficulties of associated uplink CSI feedback load as the downlink and uplink channels are not reciprocal. Therefore, smart balancing is required between feedback data and achievable throughput while scheduling the users. In order to handle the above mentioned issue, we employed a selective 4-bit quantized CSI feedback based user-antenna paired/joint scheduling scheme for the multi user (MU) FDD mMIMO systems. The key idea involves in this process is that, only a limited set of users qualifying a predefined selection threshold feed back the 4-bit quantized signal-to-interference-plus-noise ratio (SINR) values based on angle-of-departure (AoD) along with the antenna indices for the scheduling process. The BS schedules the users with respective best antenna. Furthermore, using this scheme the multi user diversity gain is also achieved. This dynamic adjustment of antenna/user selection and collision free antenna sharing between the users majorly helps in improving the throughput of the system with this selective 4-bit quantized limited feedback. The mathematical sum rate performance analysis has been demonstrated along with simulation results for different system parameters.

Machine Learning for Ultra High Throughput Screening of Organic Solar Cells: Solving the Needle in the Haystack Problem

AbstractOver the last two decades the organic solar cell community has synthesized tens of thousands of novel polymers and small molecules in the search for an optimum light harvesting material. These materials are often crudely evaluated simply by measuring the current–voltage (JV) curves in the light to obtain power conversion efficiencies (PCEs). Materials with low PCEs are quickly disregarded in the search for higher efficiencies. More complex measurements such as frequency/time domain characterization that could explain why the material performed as it is often not performed as they are too time consuming/complex. This limited feedback forced the field to advance using a more or less random walk of material development and has significantly slowed progress. Herein, a simple technique based on machine learning that can quickly and accurately extract recombination time constants and charge carrier mobilities as a function of light intensity simply from light/dark JV curves alone. This technique reduces the time to fully analyze a working cell from weeks to seconds and opens up the possibility of not only fully characterizing new devices as they are fabricated, but also data mining historical data sets for promising materials the community has overlooked.

Performance Evaluation of Spectral Efficiency Hybrid Precoding and Combining Algorithm for Millimeter Wave -MIMO Systems

Multiple input multiple output (MIMO) system with Millimeter Wave spectrum is currently used in most wireless applications and all cellular system to provides high data rates. with using large antenna array which is possible by decrease the wavelength to achieve high beamforming gain and improve the spectral efficiency. in this paper, used low complexity with hybrid precoding at the transmitting side and combining at the receiver side with limited feedback system, by using the concept of orthogonal matching pursuit (OMP) in single and multi-user cases, and compared the results with analog only beasmstring. The results of simulation showed that when used Minimum Mean Square Error (MMSE) precoders performed better than other hybrid precoding approaches, in addition the MMSE hybrid precoding /combining technique offers higher spectral efficiency compared with analog only beamstring.

Mechanisms of octopus arm search behavior without visual feedback

The octopus coordinates multiple, highly flexible arms with the support of a complex distributed nervous system. The octopus’s suckers, staggered along each arm, are employed in a wide range of behaviors. Many of these behaviors, such as foraging in visually occluded spaces, are executed under conditions of limited or absent visual feedback. In coordinating unseen limbs with seemingly infinite degrees of freedom across a variety of adaptive behaviors, the octopus appears to have solved a significant control problem facing the field of soft-bodied robotics. To study the strategies that the octopus uses to find and capture prey within unseen spaces, we designed and 3D printed visually occluded foraging tasks and tracked arm motion as the octopus attempted to find and retrieve a food reward. By varying the location of the food reward within these tasks, we can characterize how the arms and suckers adapt to their environment to find and capture prey. We compared these results to simulated experimental conditions performed by a model octopus arm to isolate the primary mechanisms driving our experimental observations. We found that the octopus relies on a contact-based search strategy that emerges from local sucker coordination to simplify the control of its soft, highly flexible limbs.

Use of the Faculty Handbook and Student Feedback to Intentionally Improve Teaching

Making informed, reflective changes to improve teaching can be challenging when there is a limited feedback available about one’s teaching. This Quick Fix article presents an approach that I used, informed by my university’s faculty handbook, to solicit, organize, and reflect upon student feedback about my teaching. The process improved my ability to make intentional changes to become a more effective teacher.

Edge Video Analytics With Adaptive Information Gathering: A Deep Reinforcement Learning Approach

With growing popularity of enormous public safety and transportation infrastructure cameras, there are increasing demands for automatic mobile video analytics. The emerging multi-access edge computing (MEC) technology has been recently applied to improve the accuracy-latency tradeoff of mobile video analytics. In this paper, we study an MEC-enabled multi-device video analytics system and formulate the problem as a Markov decision process (MDP) to meet two practical challenges: i) the absence of ground truth in real-time and ii) the content-varying degradation-accuracy relation. In particular, we aim to design an online joint frame degradation and bandwidth allocation algorithm with the time-varying function and limited feedback from each device. Thanks to the MDP formulation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -step return technique, the long-term goal offers adaptive information gathering and thus improves the average accuracy and latency. For sample efficiency, we decompose the MDP problem into discrete degradation adaptation subproblems and continuous bandwidth allocation subproblems. Based on the decomposition, we propose a deep reinforcement learning (DRL) based framework, referred to as DBAG, to solve the decomposed subproblems. DBAG integrates model-based optimization and model-free DRL to solve the MDP problem with a discrete-continuous hybrid action space. Under various network setups and public datasets, DBAG greatly improves the accuracy-latency tradeoff.

Deposition and bioconversion law of β-carotene in laying hens after long-term supplementation under adequate vitamin A status in the diet

β-Carotene, because it is the precursor of vitamin A and has versatile biological roles, has been applied as a feed additive in the poultry industry for a long time. In this study, we investigated the deposition and bioconversion of β-carotene in laying hens. A total of 600 Hy-line brown laying hens at 40 wk of age were randomly divided into 5 dietary treatments, each group's dietary supplemental levels of β-carotene were 0, 15, 30, 60, 120 mg/kg feed, and the vitamin A levels were all 8,000 IU/kg. After 14-wk trial, samples were collected, then carotenoids and different forms of vitamin A were detected using the novel method developed by our laboratory. We found that dietary β-carotene treatment had no significant effects on laying hens' production performance and egg quality (P > 0.05), except the yolk color. The deposition of β-carotene in the body gradually increased (P < 0.01) with the supplemental dose, whereas the contents of lutein and zeaxanthin decreased (P < 0.05). When the β-carotene supplemental level was above 30 mg/kg in the diet, the different forms of vitamin A in in serum, liver, ovary, and yolks were increased compared to the control group (P < 0.05). However, these indicators decreased when the additional dose was 120 mg/kg. Moreover, the mRNA levels of the genes involved in β-carotene absorption, bioconversion, and negative feedback regulation in duodenal mucosa and liver were upregulated after long-term feeding (P < 0.05). Histological staining of the ovaries indicated that the deposition of β-carotene led to a lower rate of follicle atresia (P < 0.05), and this positive effects may be related to the antioxidant function of β-carotene, which caused a reduction of oxidation products in the ovary (P < 0.05). Altogether, β-carotene could accumulate in laying hens intactly and exert its biological functions in tissue. Meanwhile, a part of β-carotene could also be converted into vitamin A but this bioconversion has an upper limit and negative feedback regulation.

Stereoscopic calibration for augmented reality visualization in microscopic surgery.

Middle and inner ear procedures target hearing loss, infections, and tumors of the temporal bone and lateral skull base. Despite the advances in surgical techniques, these procedures remain challenging due to limited haptic and visual feedback. Augmented reality (AR) may improve operative safety by allowing the 3D visualization of anatomical structures from preoperative computed tomography (CT) scans on real intraoperative microscope video feed. The purpose of this work was to develop a real-time CT-augmented stereo microscope system using camera calibration and electromagnetic (EM) tracking. A 3D printed and electromagnetically tracked calibration board was used to compute the intrinsic and extrinsic parameters of the surgical stereo microscope. These parameters were used to establish a transformation between the EM tracker coordinate system and the stereo microscope image space such that any tracked 3D point can be projected onto the left and right images of the microscope video stream. This allowed the augmentation of the microscope feed of a 3D printed temporal bone with its corresponding CT-derived virtual model. Finally, the calibration board was also used for evaluating the accuracy of the calibration. We evaluated the accuracy of the system by calculating the registration error (RE) in 2D and 3D in a microsurgical laboratory setting. Our calibration workflow achieved a RE of 0.11 ± 0.06mm in 2D and 0.98 ± 0.13mm in 3D. In addition, we overlaid a 3D CT model on the microscope feed of a 3D resin printed model of a segmented temporal bone. The system exhibited small latency and good registration accuracy. We present the calibration of an electromagnetically tracked surgical stereo microscope for augmented reality visualization. The calibration method achieved accuracy within a range suitable for otologic procedures. The AR process introduces enhanced visualization of the surgical field while allowing depth perception.

Reinforcement-Learning-Based Overhead Reduction for Online Fountain Codes With Limited Feedback

We investigate the application of reinforcement learning (RL) on online fountain codes, and propose two schemes to reduce the full-recovery overhead with limited feedback. First, we use RL in determining the optimal degree of coded symbols for a given number of feedback, and propose the RL-based degree determination (RL-DD), with the help of theoretical analysis of the relationship between recovery rate and buffer occupancy. Then we propose online fountain codes with no build-up phase using sectioned distribution (OFCNB-SD), where the encoder sends symbols whose degrees are sampled from different sections of an overall distribution, and the decoder is improved to utilize coded symbols that are not immediately decodable. We present theoretical analysis of OFCNB-SD, and introduce RL-based sectioned distribution (RL-SD) scheme where the sectioning of the overall distribution is optimized with RL. Simulation results show that our proposed schemes could achieve lower full-recovery overhead with limited feedback compared to existing schemes.

Channel Customization for Limited Feedback in RIS-Assisted FDD Systems

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

Analog Beamforming Using ESPAR for Single-RF Precoding Systems

A novel analog beamforming technique that utilizes single radio frequency (single-RF) electronically steerable parasitic array radiator (ESPAR) antennas is proposed. The ESPAR beamforming techniques are aimed at precoding in multiple antenna wireless communication systems using the single or hybrid RF approach. One advantage of ESPAR analog beamforming compared to conventional analog beamforming using phase shifters is that arbitrary patterns can be straightforwardly formed. Therefore, ESPAR analog beamforming can provide performance almost identical to digital beamforming. More importantly, ESPAR analog beamforming does not require phase shifters and power splitters, overcoming implementation challenges of conventional analog beamforming. It also has a more compact size compared to conventional beamforming using uniform antenna arrays. We analyze and optimize ESPAR analog beamforming and propose a channel approach based on beamspace pilot transmission to acquire channel state information (CSI). In addition, we propose limited feedback ESPAR analog beamforming based on a beamforming selection strategy. Simulation results for average gain show that ESPAR analog beamforming has almost identical performance to digital beamforming and better performance than analog beamforming using phase shifters. Energy efficiency simulations show that the technique has the highest efficiency among all the beamforming techniques. Therefore, ESPAR analog beamforming has near optimal performance, high energy efficiency, low circuit complexity, low cost, compact size and can be an effective alternative to conventional analog beamforming.

HaptGlove-Untethered Pneumatic Glove for Multimode Haptic Feedback in Reality-Virtuality Continuum.

Novel haptics technologies are urgently needed to bridge the gap between entirely physical world and fully digital environment to render a more realistic and immersive human-computer interaction. Current virtual reality (VR) haptic gloves either deliver limited haptic feedback or are bulky and heavy. The authors develop a haptic glove or HaptGlove, an untethered and lightweight pneumatic glove, that allows users to "physically" interact in a VR environment and enables both kinesthetic and cutaneous sensations naturally and realistically. Integrated with five pairs of haptic feedback modules and fiber sensors, HaptGlove provides variable stiffness force feedback and fingertip force and vibration feedback, allowing users to touch, press, grasp, squeeze, and pull various virtual objects and feel the dynamic haptic changes. Significant improvements in VR realism and immersion areobserved in a user study with participants achieving 78.9% accuracy in sorting six virtual balls of different stiffnesses. Importantly, HaptGlove facilitates VR training, education, entertainment, and socialization in a reality-virtuality continuum.

What would you like to ask your audience? Enhancing feedback value in PM&R grand rounds through customizable evaluation forms with presenter-generated questions.

Feedback and evaluation are important in the professional development of academic physiatrists. Yet, physical medicine and rehabilitation (PM&R) learners giving academic presentations receive limited narrative feedback through generic evaluation forms. To assess whether customizable evaluation forms that integrate a presenter's specific questions would be associated with an increase in quantity and quality of narrative feedback received from the audience. Separate samples pre-post intervention study. A large academic PM&R department's grand rounds. PM&R faculty and trainees attending grand rounds (10-50 attendees with one presenter per session). The study included 20 presentations pre intervention (across 1 year) and 38 presentations post intervention (across about 3 years). A customizable evaluation form that integrates a presenter's own questions into a tailored evaluation form comprising both standardized and presenter-built questions. Narrative feedback quantity was defined as the mean percentage and number of evaluation forms per presentation with at least one comment. Narrative feedback quality included three metrics: mean percentage and number of evaluation forms per presentation with comments that (1) contained ≥8 words, (2) referenced something specific, and (3) offered an actionable suggestion. Compared to preintervention, presentations in the postintervention period had a greater mean percentage of evaluation forms containing at least one comment (pre = 33.4%, post =74.7%, p < .001), a comment that contained ≥8 words (pre = 20.2%, post = 44.2%, p < .001), a comment that referenced something specific (pre = 19.6%, post = 55.1%, p < .001), and a comment that offered an actionable suggestion (pre = 10.2%, post = 22.2%, p < .001). Use of a customizable evaluation form in PM&R grand rounds that integrates a presenter's own questions was associated with a greater mean percentage of evaluation forms containing comments as well as comments meeting quality metrics related to length, specificity, and actionability.

Glioma Metabolic Feedback In Situ: A First-In-Human Pharmacodynamic Trial of Difluoromethylornithine + AMXT-1501 Through High-Molecular Weight Microdialysis.

No new drug has improved survival for glioblastoma since temozolomide in 2005, due in part to the relative inaccessibility of each patient's individualized tumor biology and its response to therapy. We have identified a conserved extracellular metabolic signature of enhancing high-grade gliomas enriched for guanidinoacetate (GAA). GAA is coproduced with ornithine, the precursor to protumorigenic polyamines through ornithine decarboxylase (ODC). AMXT-1501 is a polyamine transporter inhibitor that can overcome tumoral resistance to the ODC inhibitor, difluoromethylornithine (DFMO). We will use DFMO with or without AMXT-1501 to identify candidate pharmacodynamic biomarkers of polyamine depletion in patients with high-grade gliomas in situ . We aim to determine (1) how blocking polyamine production affects intratumoral extracellular guanidinoacetate abundance and (2) the impact of polyamine depletion on the global extracellular metabolome within live human gliomas in situ. DFMO, with or without AMXT-1501, will be administered postoperatively in 15 patients after clinically indicated subtotal resection for high-grade glioma. High-molecular weight microdialysis catheters implanted into residual tumor and adjacent brain will be used for postoperative monitoring of extracellular GAA and polyamines throughout therapeutic intervention from postoperative day (POD) 1 to POD5. Catheters will be removed on POD5 before discharge. We anticipate that GAA will be elevated in tumor relative to adjacent brain although it will decrease within 24 hours of ODC inhibition with DFMO. If AMXT-1501 effectively increases the cytotoxic impact of ODC inhibition, we expect an increase in biomarkers of cytotoxicity including glutamate with DFMO + AMXT-1501 treatment when compared with DFMO alone. Limited mechanistic feedback from individual patients' gliomas hampers clinical translation of novel therapies. This pilot Phase 0 study will provide in situ feedback during DFMO + AMXT-1501 treatment to determine how high-grade gliomas respond to polyamine depletion.

Challenges Of a Hard of Hearing Learner in Online Language Education

While inclusive education (IE) is embraced at the conceptual level and desired at the classroom level, successful IE implementation remains highly complex. COVID-19 (Coronavirus Disease 2019) outbreak which necessitated a quick switch to remote instruction made IE even harder to put into practice. Despite several studies regarding inclusion during face-to-face education, there has been quite limited research on online education and inclusive practices. This exploratory case study of qualitative nature took place at a state university in the southeast of Turkey with three participants: a hard of hearing (HH) English as a foreign language learner who is a freshman enrolled at the English Language Education Department and his two instructors. Data were collected by means of semi-structured interviews, journals, and narrative reports. Interviews were conducted with the student about the challenges he had and instructors’ practices during distance education, and with instructors about their general opinions towards IE and their online education practices regarding the inclusion of that student. Data were analyzed through thematic analysis. The analyses of student data revealed one major theme as “rechallenge” of an already challenging life and subthemes as nature of online education, instructors’ practices, delayed/ limited feedback, and lack of interaction. Instructors’ data revealed themes as beliefs and attitudes towards IE and need for training. Findings presented the challenges of online education for a HH pupil with significant implications, suggesting many solutions to the problems HH learners face, and raising public awareness on IE.

Covert Communication in Uplink NOMA Systems Under Channel Distribution Information Uncertainty

It is challenging to covert communication under channel distribution information (CDI) uncertainty caused by limited feedback and rapid-changing channels. This letter investigates covert communication in uplink non-orthogonal multiple access (NOMA) systems under CDI uncertainty, where a covert user (CU) and reliable user (RU) transmit messages to a legitimate receiver (Bob) threatened by a warden (Willie). RU not only transmits its public messages but also helps CU to send private messages against Willie’s detection. To protect CU’s transmission with imperfect CDI, we first study the worst case for NOMA users and obtain a lower bound of the worst-case detection error probability (DEP). In addition, both closed-form expressions for RU and CU’s connection outage probabilities are derived. Constrained by the DEP and connection outage probability, we maximize the expected covert rate (ECR) and derive the optimal power settings for RU and CU under imperfect CDI. Numerical results show that the proposed robust power setting scheme achieves a positive maximum ECR despite CDI uncertainty causing the reduction of covertness performance.

Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

A Recursive System Identification With Non-Uniform Temporal Feedback Under Coprime Collaborative Sensing

Abstract We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback.