Target Error Rate Research Articles

Effects of error rates and target sizes on neck and shoulder biomechanical loads during augmented reality interactions

Augmented reality (AR) interactions have been associated with increased biomechanical loads on the neck and shoulders. To provide a better understanding of the factors that may impact such biomechanical loads, this repeated-measures laboratory study evaluated the effects of error rates and target sizes on neck and shoulder biomechanical loads during two standardized AR tasks (omni-directional pointing and cube placing). Twenty participants performed the two AR tasks with different error rates and target sizes. During the tasks, angles, moments, and muscle activity in the neck and shoulders were measured. The results showed that the target sizes and error rates significantly affected angles, moments, and muscle activity in the neck and shoulder regions. Specifically, the presence of errors increased neck extension, shoulder flexion angles and associated moments. Muscle activity in the neck (splenius capitis) and shoulder (anterior and medial deltoids) also increased when the errors were introduced. Moreover, interacting with larger targets resulted in greater neck extension moments and shoulder abduction angles along with higher muscle activity in the splenius capitis and upper trapezius muscles. These findings indicate the importance of reducing errors and incorporating appropriate target sizes in the AR interfaces to minimize risks of musculoskeletal discomfort and injuries in the neck and shoulders.

Avionic Touchscreen Interaction under Vibration: Supported versus Freehand Target Selection in Cockpit Conditions

With touchscreens being installed in aircraft flight decks, reach-and-turbulence-related challenges arise. Using the ISO 9241-411 multidirectional selection task (a 2D Fitts’ task), we quantified the impact of vibration on touchscreen target selection throughput (a performance score combining both speed and accuracy) and error rate in a cockpit layout. 24 participants completed the task under 2 vibration levels (helicopter level flight versus static), 2 hand support methods (using the thumb, while holding onto the screen’s edge, versus using the index finger freehand), 4 touchscreen types (two avionic and two consumer touchscreens), 2 touchscreen positions (main instrument panel versus pedestal), and 4 target sizes (0.8, 1, 1.5 and 2 cm). We found average throughput values of 6.5 bits per second (bps) in static conditions, versus 5.7 bps under vibration, and average error rates of 10.3% in static conditions, versus 16.6% under vibration. Similar to prior work, we found an exponential increase in error rate with decreasing target size. Larger target sizes helped mitigate the impact of vibration. We did not find evidence of a benefit to anchoring the hand on the touchscreen’s bezel edge, compared to the freehand baseline, under vibration or static conditions. Under vibration, the pedestal outperformed the main instrument panel position, with higher throughput and lower error rate. In static conditions, the two positions performed similarly. This work contributes to vibration mitigation methods when interacting with touchscreens in the aviation context.

Rare-Event Analysis of Packet-Level Coding for URLLC via Virtual Queue

In this paper, we propose a method to analyze the performance of ultra-reliable low-latency communication (URLLC) with packet-level coding when no feedback from a receiver is available. Unlike conventional methods of analyzing URLLC performance using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average</i> error rate, we focus on the events of burst or clustered (reception) errors <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> as they can be fatal for certain real-time wireless control systems. In the proposed method, to see the impact of clustered errors on the performance, a virtual queue is considered, where the reception error sequence is regarded as the arrival process and the departure process is characterized by the target error rate. This virtual queue allows to find how often the events of clustered errors of certain sizes occur using large deviations theory. For packet-level channels modeled by an independent and identically distributed process and a two-state Markov chain, the quality-of-service exponents are derived, and the asymptotic probability of buffer overflow is obtained, which agrees with simulation results. This demonstrates that the proposed method using virtual queue is useful to find the probability of system failure due to clustered errors and allows to determine the values of key parameters of URLLC for a certain probability of system failure under given conditions.

Fault-Tolerant Cooperative Signal Detection for Petahertz Short-Range Communication With Continuous Waveform Wideband Detectors

Motivated by unique scattering properties at petahertz frequencies, we present a novel statistical model of fault-tolerant cooperative signal detection for short-range optical petahertz wireless communications, where a single-scattering assumption holds valid. We characterize the received non-line-of-sight (NLOS) signal by a fault-tolerant continuous waveform wideband detector with a non-zero failure probability. To better reflect the physical properties of the petahertz communication, both signal-independent and signal-dependent noise sources are considered in characterizing the received signal. The distribution of the received signal is quantified with each scattered path following the Málaga distribution. We leverage the location flexibility of randomly distributed collaborative users, considering each user experiences an independent channel condition. Utilizing the Neyman-Pearson criterion, we develop the binary hypothesis testing problem and subsequently derive the likelihood ratio for the test statistics. Moreover, to quantify the performance, a framework is developed for the average area under the receiver operating characteristic (ROC) curve for both single user and cooperative scenarios. An optimal decision fusion with a majority rule for fault-tolerant signal detection is applied to exploit the maximum spectrum opportunity. It is found that with the optimal voting rule and for a given target error rate, the network requires fewer collaborative secondary users than the total number of users available in an optical network. However, in the limiting case, it is shown that, as the cost function approaches its minimum or maximum value within its allowable range, the optimal number of collaborative users becomes independent of the failure probabilities. With the realistic assumption of fault-tolerant users, it is found that the false alarm probability increases faster than the detection probability.

The Application of Artificial Intelligence Technology in the Asset Management of Start-Ups in the Context of Deep Learning

With the coninuous improvement and development of artificial intelligence (AI) technology, this technology has been used in the asset management of companies. To improve the asset management level of Chinese start-ups, firstly, back-propagation neural network (BPNN) has been studied in depth, and an evaluation system of the company's asset quality has been established. Secondly, the BPNN is integrated with the evaluation indicators of asset quality, and an evaluation model of asset quality based on BPNN is constructed. Next, start-up A is taken as the experimental object; the evaluation score of the asset quality of A company is input into the model, which proves that there is still a certain gap between the asset management level of start-ups and mature companies. Finally, to find out the problems of the company's asset quality, the traditional financial analysis method is used to carry out a specific microanalysis of the evaluation indicators of its asset quality. In view of the existing problems, suggestions are put forward for prudent investment, improve inventory operation efficiency, increase investment in R&D and innovation, improve the quality of sales outlets, and increase the proportion of high-quality intangible assets. The asset quality evaluation system for start-ups established here includes 19 evaluation indicators. The BPNN-based asset quality evaluation model selects 5 mature companies in the same industry as sample companies. The scores of the evaluation indicators of asset quality of the 5 sample companies in the past three years are normalized and input into the model. The model contains 19 nodes of the input layer, 39 nodes of the hidden layer, and 1 node of the output layer. The target error rate is 0.001, the learning rate is 0.1, the number of training times is 1000, and the training function is the trainlm function. This research has a certain reference for the application of AI technology in the asset management of start-ups.

Deep-Learning for Breaking the Trapping Sets in Low-Density Parity-Check Codes

In the low-error rate regime, message-passing (MP) decoding for low-density parity-check (LDPC) codes is known to have performance degradation due to trapping sets (TSs), which often limits the use of LDPC codes for applications with low target error rates like storage devices. This work proposes a novel deep-learning based decoding algorithm which is tailored for breaking TSs. In particular, when MP decoding fails due to TSs, there exist pairs of unsatisfied check nodes (CNs) which are connected through paths only with error variable nodes (VNs), i.e., VNs with erroneous hard-decision results. The proposed algorithm efficiently identifies the paths with error VNs between unsatisfied CNs with the aid of deep-learning techniques. Then, the decoding failures are resolved by repeating the MP decoding after re-initializing the channel outputs for the error VNs in the identified paths. In addition, by analyzing the behaviors of the deep-learning based algorithm, we propose a low-complexity algorithm, called adaptive-error-path (AEP) detector. Simulation results show that the proposed algorithms efficiently break the TSs and significantly improve the error-floor performance in the low error-rate regime.

Near-Optimal Coding for Many-User Multiple Access Channels

This paper considers the Gaussian multiple-access channel (MAC) in the asymptotic regime where the number of users grows linearly with the code length. We propose efficient coding schemes based on random linear models with approximate message passing (AMP) decoding and derive the asymptotic error rate achieved for a given user density, user payload (in bits), and user energy. The tradeoff between energy-per-bit and achievable user density (for a fixed user payload and target error rate) is studied, and it is demonstrated that in the large system limit, a spatially coupled coding scheme with AMP decoding achieves near-optimal tradeoffs for a wide range of user densities. Furthermore, in the regime where the user payload is large, we also study the tradeoff between energy-per-bit and spectral efficiency and discuss methods to reduce decoding complexity.

Efficient Secure NOMA Schemes Based on Chaotic Physical Layer Security for Wireless Networks

Non-orthogonal multiple access (NOMA) is considered by the 3GPP as a potential technology for beyond 5G wireless networks to support massive connectivity with robust reliability. However, the massive increase in connected users critically leads to data security problems owing to the high possibility of eavesdropping. In this paper, uplink secure NOMA (sNOMA) schemes based on chaotic physical layer security (PLS) are proposed to achieve two-fold secrecy of integrated channel coding and secret key approaches. Different code-domain and power-domain techniques are employed for sNOMA designs over realistic fading channel environments. Equal power allocation strategy is used for the transmitted chaotic signals in code-domain sNOMA (CD-sNOMA), whereas dynamic power control is employed in power-domain sNOMA (PD-sNOMA) and the hybrid code-power-domain sNOMA (CPD-sNOMA) approaches. The former design adopts joint maximum Likelihood (ML) signal detection while the later scenarios utilize integrated receiver designs based on successive interference cancellation (SIC) and ML techniques. For the proposed sNOMA schemes, power control algorithms are presented to optimize the system performance over constrained total received power and target error rate. Numerical results validate the effectiveness of sNOMA designs compared with the benchmark systems under the worst-case secrecy of unauthorized receiver with complete knowledge of the transmission scheme and associated channel,. Valuable tradeoffs are demonstrated between the achieved error rate, connectivity, security gap, and complexity. Moreover, the utilized chaotic signals offer robust and cost-effective PLS solutions with a huge key-space to combat the most powerful brute-force eavesdropping attacks.

Analysis and Research on the Impact of Physical Exercise on Residents' Health Based on the Improved BP Neural Network Model.

With the rapid improvement of social economy and the enhancement of people's health awareness, it is necessary to make an in-depth analysis of the rationality of physical exercise and the physical quality of residents. Hence, this study aims to explore the algorithm optimization of the improved BP model to analyze the effect of exercise intervention on improving public sports effect. K-clustering and Levenberg–Marquardt algorithm were used to construct an improved BP neural network model to determine the sample clustering center, as well as the weight and threshold of the indicators, so as to optimize the analysis algorithm of improving public sports effect. MATLAB simulation shows that under the target error conditions of 0.01, 0.005, 0.001, and 0.0001, the target error rate and iteration times of the improved BP model are better than the standard BP model, and the time consumption is shorter, which can be conducive to more accurately analyzing the changes of improving public sports effect under exercise intervention. Therefore, the improved BP model can effectively solve the problems of data clustering and result error rate adjustment in the process of improving public sports effect analysis and improve the analysis speed and accuracy.

The effects of target size and error rate on the cognitive demand and stress during augmented reality interactions

This study investigated the effects of target size and error rate on cognitive demand during augmented reality (AR) interactions. In a repeated-measures laboratory study, twenty participants performed two AR tasks (omni-directional pointing and cube placing) with different target sizes and error rates. During the AR tasks, we measured cerebral oxygenation using functional near-infrared spectroscopy (fNIRS), perceived workload using the NASA-TLX questionnaire, stress using the Short Stress State Questionnaire, and task performance (task completion time). The results showed that the AR tasks with more interaction errors increased cerebral oxygenation, perceived workload, and task completion time while the target size significantly affected physical demand and task completion time. These results suggest that appropriate target sizes and low system errors may reduce potential cognitive demand in AR interactions.

The cost of being absent: Is meta-awareness of mind-wandering related to depression symptom severity, rumination tendencies and trauma intrusions?

BackgroundDeng, Li and Tang (2014) reported that depression symptom severity is negatively associated with dispositional mindfulness and importantly, positively associated with zone-outs (mind-wandering without meta-awareness). We replicated and extended their study by exploring possible explanations for these relationships, and by also investigating whether mind-wandering is related to (1) trait rumination subtype—brooding, depressive or reflective, and (2) trauma intrusions—a hallmark PTSD symptom, since both rumination and trauma intrusions strongly correlate with depression. We also explored if dispositional mindfulness—the opposing construct of mind-wandering—mediated these relationships. MethodTwo hundred participants completed mindfulness tendency and depression severity measures, counterbalanced with the Sustained Attention to Response Task (SART)—including thought probes to index behavioral mind-wandering (target-error frequency), subjective mind-wandering and meta-awareness—then the rumination style and trauma intrusion frequency measures. ResultsDepression scores positively correlated with mind-wandering with and without meta-awareness and with SART target-error rates, and negatively correlated with dispositional mindfulness. Further, trait brooding positively correlated with mind-wandering without meta-awareness. Dispositional mindfulness mediated the relationships between brooding and depression, and depression and mind-wandering, and also negatively correlated with trauma intrusion frequency. LimitationsLimitations include measurement and mind-wandering definitions, and an inability to make causal claims. ConclusionsPeople experiencing greater depression symptomology, and/or who have a greater tendency to brood, mind-wandered more often. Further, people who experience more trauma intrusions tend to be less mindful. These results point to potential harmful effects of mind-wandering through people's reduced propensity to be mindful, facilitating a negative self-referenced cognitive loop that may maintain or increase depression.

SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness.

SafePredict is a novel meta-algorithm that works with any base prediction algorithm for online data to guarantee an arbitrarily chosen correctness rate, 1-ϵ, by allowing refusals. Allowing refusals means that the meta-algorithm may refuse to emit a prediction produced by the base algorithm so that the error rate on non-refused predictions does not exceed ϵ. The SafePredict error bound does not rely on any assumptions on the data distribution or the base predictor. When the base predictor happens not to exceed the target error rate ϵ, SafePredict refuses only a finite number of times. When the error rate of the base predictor changes through time SafePredict makes use of a weight-shifting heuristic that adapts to these changes without knowing when the changes occur yet still maintains the correctness guarantee. Empirical results show that (i) SafePredict compares favorably with state-of-the-art confidence-based refusal mechanisms which fail to offer robust error guarantees; and (ii) combining SafePredict with such refusal mechanisms can in many cases further reduce the number of refusals. Our software is included in the supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPAMI.2019.2932415.

Balancing Queueing and Retransmission: Latency-Optimal Massive MIMO Design

One fundamental challenge in 5G URLLC is how to optimize massive MIMO systems for achieving low latency and high reliability. A natural design choice to maximize reliability and minimize retransmission is to select the lowest allowed target error rate. However, the overall latency is the sum of queueing latency and retransmission latency, hence choosing the lowest target error rate does not always minimize the overall latency. In this paper, we minimize the overall latency by jointly designing the target error rate and transmission rate adaptation, which leads to a fundamental tradeoff point between queueing and retransmission latency. This design problem can be formulated as a Markov decision process, which is theoretically optimal, but its complexity is prohibitively high for real-system deployments. We managed to develop a low-complexity closed-form policy named Large-arraY Reliability and Rate Control (LYRRC), which is proven to be asymptotically latency-optimal as the number of antennas increases. In LYRRC, the transmission rate is twice of the arrival rate, and the target error rate is a function of the antenna number, arrival rate, and channel estimation error. With simulated and measured channels, our evaluations find LYRRC satisfies the latency and reliability requirements of URLLC in all the tested scenarios.

Exploration of End-to-End Framework for Code-Switching Speech Recognition Task: Challenges and Enhancements

The end-to-end (E2E) framework has emerged as a viable alternative to conventional hybrid systems in automatic speech recognition (ASR) domain. Unlike the monolingual case, the challenges faced by an E2E system in code-switching ASR task include (i) the expansion of target set to account for multiple languages involved, (ii) the requirement of a robust target-to-word (T2W) transduction, and (iii) the need for more effective context modeling. In this paper, we aim to address those challenges for reliable training of the E2E ASR system on a limited amount of code-switching data. The main contribution of this work lies in the E2E target set reduction by exploiting the acoustic similarity and the proposal of a novel context-dependent T2W transduction scheme. Additionally, a novel textual feature has been proposed to enhance the context modeling in the case of code-switching data. The experiments are performed on a recently created Hindi-English code-switching corpus. For contrast purposes, the existing combined target set based system is also evaluated. The proposed system outperforms the existing one and yields a target error rate of 18.1% along with a word error rate of 29.79%.

基于双阈值-非极大值抑制的Faster R-CNN改进算法

According to the problems of target missed detection and repeated detection in the object detection algorithm, this paper proposes an improved Faster R-CNN algorithm based on dual threshold-non-maximum suppression. The algorithm first uses the deep convolutional network architecture to extract the multi-layer convolution features of the targets, and then proposes the dual threshold-non-maximum suppression (DT-NMS) algorithm in the RPN(region proposal network). The phase extracts the deep information of the target candidate regions, and finally uses the bilinear interpolation method to improve the nearest neighbor interpolation method in the original RoI pooling layer, so that the algorithm can more accurately locate the target on the detection dataset. The experimental results show that the DT-NMS algorithm effectively balances the relationship between the single-threshold algorithm and the target missed detection problem, and reduces the probability of repeated detection. Compared with the soft-NMS algorithm, the repeated detection rate of the DT-NMS algorithm in PASCAL VOC2007 is reduced by 2.4%, and the target error rate of multiple detection is reduced by 2%. Compared with the Faster R-CNN algorithm, the detection accuracy of this algorithm on the PASCAL VOC2007 is 74.7%, the performance is improved by 1.5%, and the performance on the MSCOCO dataset is improved by 1.4%. At the same time, the algorithm has a fast detection speed, reaching 16 FPS.

Modeling of Ultra-Long Span Bidirectional Raman Transmission Link Using Three-Segment Hybrid Fiber Core Structure

Ultra-long span unrepeatered systems using distributed Raman amplification are cost-effective solutions for bridging moderate transmission distances. However, there are two major limiting factors: nonlinear Kerr effect-induced nonlinear signal distortion and optical signal-to-noise ratio degradation due to spontaneous Raman noise. In this report, we proposed a model of three-segment hybrid fiber effective core area structure and developed a model covering: (1) generalized mathematical formulations, (2) analysis of three-segment Raman amplified link, and (3) simulation model of data transmission. The proposed model showed an improvement of the Raman gain profile, a reduction of the negative impact of the nonlinear Kerr effect, and an enhancement of the optical signal-to-noise ratio. A numerical simulation of the transmission performance of the three-segment hybrid structure was compared to conventional single-segment single fiber core structure on 80 Gb/s differential quadrature phase-shift keying (DQPSK) modulated data signals over a propagation distance of 390 km. The required optical signal-to-noise ratio was reduced by 2.71 dB to achieve the target error rate without using forward error correction. The numerical model and simulation of various data rates up to 100 Gb/s consistently showed that an improvement in transmission performance could be achieved by using three-segment hybrid fiber effective core area structure.

On Power and Rate Allocation for Coded Uplink NOMA in a Multicarrier System

In this paper, we propose a simple power and rate allocation scheme for uplink non-orthogonal multiple access based on a multicarrier system when a practical modulation and coding scheme is employed at each user. Since information-theoretic approaches with asymptotic settings are not considered, the proposed power and rate allocation scheme is able to provide a guaranteed performance with any practical block codes of finite length in terms of the probability of code word error. For the performance prediction in the power and rate allocation with given channel realizations, we employ non-asymptotic approaches for bounds on the probability of code word error. Through simulation results, we can confirm that the probability of code word error can be lower than or close to the target error rate using the proposed power and rate allocation.

Phase II study of enzalutamide monotherapy with radiation therapy for intermediate risk prostate cancer.

58 Background: Castrating androgen deprivation therapy (ADT) is standardly prescribed in combination with radiation therapy (RT) for intermediate or high-risk prostate cancer (PCa). ADT is associated with multiple side effects including weight gain, loss of libido, hot flashes and muscle atrophy. In contrast, enzalutamide monotherapy is associated with much fewer side effects. Methods: At Dana-Farber/ Harvard Cancer Center we performed an open label phase II study of enzalutamide for 6 months as neo- and adjuvant treatment for intermediate risk PCa patients (NCCN criteria) receiving RT. The primary endpoint was the proportion of patients achieving a PSA response of ≤0.2 . This endpoint is predictive of long term PSA response in a similar risk-group of patients treated with RT and ADT. PSA values were obtained at baseline and monthly on 6 cycles of enzalutamide (160mg/day). 79.2 Gy in 44 fractions of IMRT was started between 6 and 10 weeks after the initiation of enzalutamide. Quality of life questionnaires, hormone levels and anthropomorphic measurements were obtained. Results: 45 of 60 evaluable patients had a PSA ≤0.2 at the end of 6 months of enzalutamide. With a sample size of 64 evaluable patients, if the number achieving a PSA level is ≤0.2 is 44 or more, the null hypothesis is rejected with a target error rate of alpha = 0.10. Also 54 of 60 evaluable patients had a PSA of ≤0.5ng/ml. Importantly, less than half of the participants experienced erectile dysfunction or decreased libido and these were predominantly grade I. Less than a quarter of patients reported hot flashes (all grade I). Waist circumference did not change with therapy. The most frequent grade 2 or greater events were hypertension and gynecomastia. Testosterone and free testosterone levels rose significantly on enzalutamide therapy. Conclusions: Enzalutamide monotherapy with RT may be as effective as castrating ADT, and associated with fewer side effects. Larger, randomized trials are needed to further evaluate enzalutamide monotherapy, instead of ADT, to be used in combination with RT. Clinical trial information: NCT0208988.

Optimization of Scalable Broadcast for a Large Number of Antennas

In this paper, for a system incorporating a large number of antennas, we address the optimal space–time coding of multimedia scalable sources, which require unequal target error rates in their bitstream. First, in terms of the number of antennas, we analyze the behavior of the crossover point of the outage probability curves for the vertical Bell Laboratories space–time (V-BLAST) architecture with a linear or a maximum-likelihood receiver, and orthogonal space–time block codes (OSTBCs). We prove that, as the number of antennas increases with the transmission data rate fixed, the crossover point in outage probability monotonically decreases. This holds for any data rate employed by the system and is valid over propagation channels such as spatially correlated Rayleigh or Rician fading channels, as well as independent and identically distributed Rayleigh channels. We next show that, over such propagation channels with a large number of antennas, those analytical results can be used to simplify the computational complexity involved with the optimal space–time coding of a sequence of scalable packets, with no performance degradation.

Low Cost Endurance Test-pattern Generation for Multi-level Cell Flash Memory

A new endurance test-pattern generation on NAND-flash memory is proposed to improve test cost. We mainly focus on the correlation between the data-pattern and the device error-rate during endurance testing. The novelty is the development of testing method using quasi-random pattern based on device architectures in order to increase the test efficiency during time-consuming endurance testing. It has been proven by the experiments using the commercial 32 nm NAND flash-memory. Using the proposed method, the error-rate increases up to 18.6% compared to that of the conventional method which uses pseudo-random pattern. Endurance testing time using the proposed quasi-random pattern is faster than that of using the conventional pseudorandom pattern since it is possible to reach the target error rate quickly using the proposed one. Accordingly, the proposed method provides more lowcost testing solutions compared to the previous pseudo-random testing patterns.