Recovery Threshold Research Articles

Photobiomodulation using low-level 808 nm diode laser rescues cochlear synaptopathy after acoustic overexposure in rat.

A certain degree of noise can cause hearing problems without a permanent change in the hearing threshold, which is called hidden hearing loss and results from partial loss of auditory synapses. Photobiomodulation (PBM) enhances neural growth and connections in the peripheral nervous systems. In this study, we assessed whether PBM could rescue cochlear synaptopathy after acoustic overexposure in rat. PBM was performed for 7 days after noise exposure. The auditory brainstem responses (ABRs) were acquired before and after noise exposure using a tone and a paired-click stimulus. Auditory response to paired click sound with short time interval was performed to evaluate auditory temporal processing ability. In the result, hearing threshold recovered 2 weeks after noise exposure in both groups. Peak wave 1 amplitude of the ABR and ABR recovery threshold did not recover in the noise only group, whereas it fully recovered in the noise + PBM group. The number of synaptic ribbons was significantly different in the control and noise only groups, while there was no difference between the control and noise + PBM group. These results indicate that PBM rescued peak wave 1 amplitude and maintained the auditory temporal processing ability resulting from a loss of synaptic ribbons after acoustic overexposure.

On rank awareness, thresholding, and MUSIC for joint sparse recovery

This letter establishes sufficient conditions for the sparse multiple measurement vector (MMV) or row-sparse matrix approximation problem for the Rank Aware Row Thresholding (RART) algorithm. Using the rank aware selection operator to define RART results in discrete MUltiple SIgnal Classification (MUSIC) from array signal processing. When the sensing matrix is drawn from the random Gaussian matrix ensemble, we establish that the rank of the row-sparse measurement matrix in the noiseless row-sparse recovery problem allows RART (MUSIC) to reduce the effect of the log ⁡ ( n ) penalty term that is present in traditional compressed sensing results and simultaneously provides a row-rank deficient recovery result for MUSIC. Empirical evidence shows that Thresholding closely matches RART in successful row-sparse approximation. The theoretical and empirical evidence provides further support for the conjecture that the thresholding operator in more sophisticated greedy algorithms is the source of their observed rank awareness.

An Adaptive Iterative Thresholding Algorithm for Distributed MIMO Radars

In this paper, a Block Iterative Method with Adaptive Thresholding for Sparse Recovery (BIMATSR) is proposed to recover the received signal in an under-sampled distributed multiple-input multiple-output radar. The BIMATSR scheme induces block sparsity with the aid of a signal-dependent thresholding operator which increases the accuracy of the target parameter estimation task. We have proved that under some sufficient conditions, the suggested scheme converges to a stable solution. Moreover, different simulation scenarios confirm that the BIMATSR algorithm outperforms its counterparts in terms of the target parameter estimation. This superiority is achieved at the expense of slightly more computational complexity. It is also shown that in this application, random sampling scheme offers better performance than random Gaussian matrix, while it has simpler structure.

Iterative hard thresholding for low-rank recovery from rank-one projections

A novel algorithm for the recovery of low-rank matrices acquired via compressive linear measurements is proposed and analyzed. The algorithm, a variation on the iterative hard thresholding algorithm for low-rank recovery, is designed to succeed in situations where the standard rank-restricted isometry property fails, e.g. in case of subexponential unstructured measurements or of subgaussian rank-one measurements. The stability and robustness of the algorithm are established based on distinctive matrix-analytic ingredients and its performance is substantiated numerically.

Dose/Volume histogram patterns in Salivary Gland subvolumes influence xerostomia injury and recovery

Xerostomia is a common consequence of radiotherapy in head and neck cancer. The objective was to compare the regional radiation dose distribution in patients that developed xerostomia within 6 months of radiotherapy and those recovered from xerostomia within 18 months post-radiotherapy. We developed a feature generation pipeline to extract dose volume histogram features from geometrically defined ipsilateral/contralateral parotid glands, submandibular glands, and oral cavity surrogates for each patient. Permutation tests with multiple comparisons were performed to assess the dose difference between injury vs. non-injury and recovery vs. non-recovery. Ridge logistic regression models were applied to predict injury and recovery using clinical features along with dose features (D10-D90) of the subvolumes extracted from oral cavity and salivary gland contours + 3 mm peripheral shell. Model performances were assessed by the area under the receiver operating characteristic curve (AUC) using nested cross-validation. We found that different regional dose/volume metrics patterns exist for injury vs. recovery. Compared to injury, recovery has increased importance to the subvolumes receiving lower dose. Within the subvolumes, injury tends to have increased importance towards D10 from D90. This suggests that different threshold for xerostomia injury and recovery. Injury is induced by the subvolumes receiving higher dose, and the ability to recover can be preserved by further reducing the dose to subvolumes receiving lower dose.

Reliability and Sensitivity Analysis of the Controllable Repair System with Warm Standbys under the Recovery Threshold Policy

Abstract This article performs reliability and sensitivity analyses of a controllable repair system with M operating units, S warm standby units, and an unreliable repair server under the recovery threshold policy. Failure times and service times of an operating unit or a standby unit are assumed to follow exponential distributions. When the repair server normally operates, it is subject to breakdowns; while a breakdown occurs, the repair server requires repair at another repair facility, and the repair of the breakdown server cannot be initiated until there are more than or equal to q failed units. The times to breakdown and repair times of the server are also assumed to be exponentially distributed. A server during a breakdown period can provide partial service with lower service rates. This article derives expressions for system reliability as well as the mean time to failure. The sensitivity analyses of both system performance measures are performed. Finally, some numerical experiments are designed and performed to demonstrate how the system performance measures are affected by the change of each system parameter.

Exact Recovery in Community Detection With Continuous-Valued Side Information

The community detection problem, as a special case of inference on graphs, has received much attention lately. However, in the presence of continuous-valued side information, the behavior of a sharp threshold for exact recovery has remained open, and is addressed in this letter. The new proof presented herein has the further advantage of closing the gap between necessary and sufficient conditions for exact recovery threshold that has existed in community detection under finite-alphabet side information.

Ecological condition of coral reef assemblages in the Cuban Archipelago

ABSTRACTThe condition of coral reefs in the Cuban Archipelago is poorly known. We aimed to analyse coral assemblages across 199 reef sites belonging to 12 localities. Crest and fore reefs were assessed using six metrics: species richness, density, coral cover, mortality, coral size and reef complexity. The condition of reefs varied across the archipelago from healthy to depleted reefs. The localities with best scores were Cienfuegos, Bahía de Cochinos and Cazones. These reefs have values of living coral cover (>20%) and complexity (>50 cm) similar to the best preserved Caribbean reefs. However, the majority of crest biotopes suffered important deterioration with old mortality of Acropora palmata populations and moderate coral cover (15%); although crest reefs still maintained their structural complexity. Despite moderate levels of coral cover in fore reefs (18%), their condition was alarming because 25% of the sites had cover below the recovery threshold of 10%, accumulated mortality and structural flattening. Compared with the 1980s, the species richness was roughly the same (42) for crest and fore reefs, although dominance has changed to widespread tolerant species. Coral reef assemblages varied at local and regional scales in similar magnitude, suggesting the combined effects of natural and anthropogenic drivers.

Secure Distributed Computing With Straggling Servers Using Polynomial Codes

In this paper, we consider a secure distributed computing scenario in which a master wants to perform matrix multiplication of confidential inputs with multiple workers in parallel. In such a setting, a master does not want to reveal information about the two input matrices to the workers in an information-theoretic sense. We propose a secure distributed computing scheme that can efficiently cope with straggling effects by applying polynomial codes on sub-tasks assigned to workers. The achievable recovery threshold, i.e., the number of workers that a master needs to wait for to get the final product, of our proposed scheme is revealed to be order-optimal to the number of workers. Moreover, we derive the achievable recovery threshold of the proposed scheme is within a constant multiplicative factor from information-theoretic lower bound. As a byproduct, we extend our strategy to secure distributed computing for convolution tasks on confidential data.

Erasure Coding for Distributed Matrix Multiplication for Matrices With Bounded Entries

Distributed matrix multiplication is widely used in several scientific domains. It is well recognized that computation times on distributed clusters are often dominated by the slowest workers (called stragglers). Recent work has demonstrated that straggler mitigation can be viewed as a problem of designing erasure codes. For matrices A and B, the technique essentially maps the computation of $\text {A}^{T} \text {B}$ into the multiplication of smaller (coded) submatrices. The stragglers are treated as erasures in this process. The computation can be completed as long as a certain number of workers (called the recovery threshold) complete their assigned tasks. We present a novel coding strategy for this problem when the absolute values of the matrix entries are sufficiently small. We demonstrate a tradeoff between the assumed absolute value bounds on the matrix entries and the recovery threshold. At one extreme, we are optimal with respect to the recovery threshold, and on the other extreme, we match the threshold of prior work. Experimental results on cloud-based clusters validate the benefits of our method.

Automated retrofit targeting of heat exchanger networks

The aim of this paper is to develop a novel heat exchanger network (HEN) retrofit method based on a new automated retrofit targeting (ART) algorithm. ART uses the heat surplus-deficit table (HSDT) in combination with the Bridge Retrofit concepts to generate retrofit bridges option, from which a retrofit design may be formulated. The HSDT is a tabular tool that shows potential for improved re-integration of heat source and sink streams within a HEN. Using the HSDT, retrofit bridges—a set of modifications that links a cooler to a heater to save energy—may be identified, quantified, and compared. The novel retrofit method including the ART algorithm has been successfully implemented in Microsoft ExcelTM to enable analysis of large-scale HENs. A refinery case study with 27 streams and 46 existing heat exchangers demonstrated the retrofit method’s potential. For the case study, the ART algorithm found 68903 feasible unique retrofit opportunities with a minimum 400 kW·unit–1 threshold for heat recovery divided by the number of new units. The most promising retrofit project required 3 new heat exchanger units to achieve a heat savings of 4.24 MW with a favorable annualised profit and a reasonable payback period.

Thresholds for severity, remission and recovery using the functioning assessment short test (FAST) in bipolar disorder

BackgroundDespite its importance, no distinction between none, mild, moderate and severe functional impairment is available. Categorization of functional impairment could help to better assess randomized controlled trials (RCT) and to study the correlates of functional impairment according to severity. The Functional Assessment Short Test (FAST) is one of the most widely used measures of functional impairment in bipolar disorder and related conditions, but to date no severity cut-offs have been determined for their use in clinical research and practice. MethodFAST and Global Functioning Assessment (GAF) ratings from 65 euthymic outpatients with bipolar disorder at the Hospital Clínic in Barcelona were analyzed. A linear regression was computed using the FAST as the independent variable and the GAF as the dependent variable. Cut-offs scores for the FAST were estimated taking into account the GAF scores as a reference. ResultsLinear regression analysis with GAF scores as the dependent variable yielded the following equation: GAF score = 91,41–1,031 * FAST score. The cut-off scores for the FAST scale derived from this equation were as follows: scores from 0 to 11 included patients with no impairment. Scores from 12 to 20, represented the category of mild impairment. Moderate impairment comprised scores from 21 to 40. Finally, scores above 40 represent severe functional impairment. Further, the 4 × 4 cross-tabulation resulted in a significant association of FAST and GAF severity gradation: (Chi2 = 95,095; df = 9; p < 0,001). Chance-corrected agreement was κ = 0,65 (p < 0.001). LimitationsIn the absence of a better alternative, the GAF, a broad clinical measure, was used as gold standard for establishing FAST categories according to severity. ConclusionThe categorization of functional impairment in four categories based on empirical data shows that 12, 20 and 40 represent clinically meaningful cut-offs of the FAST for mild, moderate, and severe functional impairment and for functional recovery, remission, and improvement. The proposed categories are suitable to be further implemented in clinical studies and RCTs.

Motion Adaptive Wavelet Thresholding for Recovery of Compressively Sampled Static and Dynamic MR Images

Iterative shrinkage algorithms like parallel coordinate descent and separable surrogate functional use wavelet thresholding with uniform and empirically selected threshold values to recover the under-sampled magnetic resonance (MR) images. In this paper, an adaptive thresholding parameter, for the recovery of static and dynamic MR images, is derived and used in wavelet domain shrinkage. A modified iterative shrinkage thresholding algorithm based on the derived parameter is also proposed. Simulation results show that adaptive wavelet thresholding yields significantly higher signal-to-noise ratio and correlation than the fixed thresholding value. The algorithm based on the adaptive threshold is experimentally tested for static and dynamic MR images with varying acceleration rates, and it has been shown that it outperforms the fixed thresholding value algorithm.

Hardware Implementation of Iterative Method With Adaptive Thresholding for Random Sampling Recovery of Sparse Signals

Sparse recovery methods find extensive applications in various fields such as image and audio processing, wireless communication, and spectral estimation. In this paper, a hardware architecture of iterative method with adaptive thresholding (IMAT) is presented to recover a sparse signal from its random samples. To demonstrate the effectiveness of IMAT, a comparison is performed between the IMAT algorithm and the compressive sensing recovery method, orthogonal matching pursuit, in terms of complexity and reconstruction quality. In the context of image reconstruction, as a practical case, simulation results show the advantages of IMAT in improving the performance of the signal reconstruction via peak signal-to-noise ratio and structural similarity index metrics. Since IMAT employs discrete transform as a principal operation in each iteration, using fast or fast approximate algorithms allows more efficient implementation. In this paper, two multiplication-free transforms, Walsh–Hadamard transform (WHT) and approximate discrete cosine transform (ADCT), are used to reduce computational complexity of IMAT implementation. The IMAT recovery algorithm is implemented on Virtex6 FPGA using two above transforms, and the results are compared with respect to the hardware resource utilization, power consumption, and recovery time. The results demonstrate that using WHT for IMAT implementation is more efficient than using ADCT in terms of hardware resources. Also, the comparison implies that the performance of the hardware implementation is very close to its floating point simulated counterpart. For a block size of $32 \times 32$ , the IMAT implementation using WHT provides the reconstruction time of $185~\mu \text{s}$ and the dynamic power of 123 mW.

Prediction of performance by heart rate-derived parameters in recreational runners

ABSTRACTWe investigated whether heart rate (HR)-derived parameters are accurate performance predictors in endurance recreational runners. One hundred thirty recreational athletes completed an incremental running test (4´running + 1´rest). After each stage, we recorded HR, % of maximum HR (%HRmax), and blood lactate. We also assessed HR after each recovery period, and calculated lactate and HR recovery thresholds and HR deflection point. We tested these parameters for associations with running performance, as measured by peak treadmill speed (PTS) and personal best International Association of Athletics Federations (IAAF) score. The %HRmax at 14.5 km·h−1 correlated with PTS (r = −0.92), and IAAF score (rho = −0.80). The magnitudes of the correlations of lactate-related parameters with PTS (|r| = 0.84 to 0.86) or IAAF score (|rho| = 0.70 to 0.77) in absolute values were slightly lower. The correlations detected between other HR-derived parameters and running performance were weaker (|r or rho| = 0.24 to 0.70). Regression models identified %HRmax at 14.5 km·h−1 as the strongest predictor of both PTS (β = −0.72) and IAAF score (β = −0.72). Consequently, tests based on %HRmax may provide a non-invasive and inexpensive alternate method for predicting the performance of these athletes.

The Panic of 1819 and the Second Bank of the United States

In 1822, the directors of the Second Bank of the United States contemplated the election of a new president of the bank. The policies of the new president would either continue those of the retiring incumbent, which had contracted the nation's money supply, restoring the bank's stability after a panic but also stoking outrage across the United States, or deviate from them to support national economic development by providing more credit and a sound paper currency usable throughout the country. The case frames the contest for control of the Second Bank that would ultimately determine the bank's role in the American economy. The case presents opportunities for financial analysis of the health and performance of the Second Bank, for assessment of the governance and management of the bank, and for consideration of the timing of a change in strategy regarding the bank. Excerpt UVA-F-1815 Rev. May 26, 2020 The Panic of 1819 and the Second Bank of the United States In the summer of 1822, the board of directors of the Second Bank of the United States began to contemplate the election of a new president of the bank. The retiring incumbent, Langdon Cheves, had served as president since 1819 and favored policies of austerity oriented toward stabilizing the bank after an episode of fraud and mismanagement. Cheves's policies contracted the nation's money supply, which stoked outrage across the United States and prompted personal vilification of Cheves. Although these policies restored the bank to a sound footing, contemporaries believed that they worsened the economic depression that followed the Panic of 1819. Cheves favored the appointment of William Meredith as his successor he believed that Meredith would continue the policies that had brought the bank to the threshold of recovery. Roswell Colt, a dissident investor who was personally acquainted with President James Monroe and Treasury Secretary William H. Crawford, instead nominated Nicholas Biddle. Biddle had served as a member of the bank's board of directors from 1819 to 1821 and had supported Cheves's election as president in 1819. However, in 1821, he broke with Cheves over the bank's strategy. Biddle asserted that the policy of austerity had gone too far and that the Second Bank should support national economic development by providing more credit and a sound paper currency usable throughout the country. (See Exhibit 1 for brief profiles.) The bank's board consisted of 25 directors, many of whom were loyal to Cheves but sensitive to the popular outrage generated by the bank's policies. Meredith and Biddle represented stark strategic alternatives. Which candidate should the board select? . . .

A Systematic Approach to the Dilemma between Flood Vulnerability and Resilience - Review and Concepts

The nature of "vulnerability" is that it not only exists when elements at risk are exposed to flood, but also hides in interactions between environmental, social and economic factors before the flood incident. While ecological resilience reflects the dynamic characteristics of resources, ecological vulnerability systematically describing aspects of stress was less researched. This paper develops a concept, modeling dynamic interactions between stress and resources taking into account of context, space, time, and stakeholder involvements, to provide direct causes and solutions for flood risk management. The paper reviews three major schools of thought concerning (i) vulnerability and resilience are two sides of the same coin, (ii) resilience is a part of vulnerability, and (iii) they are two separated concepts with some overlaps. Limitations of the schools of thought were recognized based on four main criteria: (i) vulnerability and resilience must reflect all social, economic and environmental aspects, (ii) they exist regardless of hazards, (iii) they are processes than outcomes, and (iv) they entail multiple objectives. Inspired by the flipping coin and separated concepts, a new framework has been developed. It acknowledges from outstanding points of the two that vulnerability and resilience are normalized as stress and resources of the same coin. Additionally, they can generate positive and/or negative outcomes. Therefore, their relationships are complex because resources, defined by stakeholders, may prevent system changes and consequently amplify stress. Depending on the ratio between stress and resources, a system can be characterized as a 'resistant', or a 'creative' regime. If incremental disturbances exceed the recovery threshold as defined by the available data and the stakeholders, the system might change regime. Decision makers can take advantage of this framework to prepare appropriate resources that increase the resilience of the community and anticipate adverse effects for incoming floods.

Quantized Spectral Compressed Sensing: Cramer–Rao Bounds and Recovery Algorithms

Efficient estimation of wideband spectrum is of great importance for applications such as cognitive radio. Recently, sub-Nyquist sampling schemes based on compressed sensing have been proposed to greatly reduce the sampling rate. However, the important issue of quantization has not been fully addressed, particularly for high-resolution spectrum and parameter estimation. In this paper, we aim to recover spectrally-sparse signals and the corresponding parameters, such as frequency and amplitudes, from heavy quantizations of their noisy complex-valued random linear measurements, e.g. only the quadrant information. We first characterize the Cramer-Rao bound under Gaussian noise, which highlights the trade-off between sample complexity and bit depth under different signal-to-noise ratios for a fixed budget of bits. Next, we propose a new algorithm based on atomic norm soft thresholding for signal recovery, which is equivalent to proximal mapping of properly designed surrogate signals with respect to the atomic norm that motivates spectral sparsity. The proposed algorithm can be applied to both the single measurement vector case, as well as the multiple measurement vector case. It is shown that under the Gaussian measurement model, the spectral signals can be reconstructed accurately with high probability, as soon as the number of quantized measurements exceeds the order of K log n, where K is the level of spectral sparsity and $n$ is the signal dimension. Finally, numerical simulations are provided to validate the proposed approaches.

Performance analysis of packet layer FEC codes and interleaving in FSO channels

The combination of forward-error-correction (FEC) and interleaving can be used to improve free-space optical (FSO) communication systems. Recent research has optimized the codeword length and interleaving depth under the assumption of a fixed buffering size, however, how the buffering size influences the system performance remains unsolved. This paper models the system performance as a function of buffering size and FEC recovery threshold, which allows system designers to determine optimum parameters in consideration of the overhead. The modelling is based on statistics of temporal features of correct data reception and burst error length through the measurement of the channel good time and outage time. The experimental results show good coherence with the theoretical values. This method can also be applied in other channels if a Continuous-Time-Markov-Chain (CTMC) model of the channel can be derived

S48 Axonal excitability, Schwann cells and the ‘Barrett-Barrett conductance’

In 1982, Ellen and John Barret published a paper on the depolarizing afterpotential in lizard axons which showed that it was due to passive spread of current from the nodal action potential to the internodal axolemma. The revolutionary electrical model of myelinated axons that they proposed was later found crucial to understanding the excitability changes recordable from human axons in the recovery cycle and threshold electrotonus. A key parameter in their new model was a resistive path across the Schwann cells, between the internodal periaxonal space and the external medium, and its conductance is known in their honour as the ‘Barrett-Barrett conductance’ (GBB). In recent years it has proved possible to understand quite well, by mathematical modelling,the changes in axonal excitability produced by changes in axonal membrane potential, and in the conductances of the major axonal voltage-dependent ion channels (sodium, fast and slow potassium, and HCN), such as in toxic and genetic channelopathies. Less well understood, however, are the complex excitability changes occurring in most demyelinating and other neuropathies that affect Schwann cells and the structure of myelinated axons. This review will bring together a few disparate genetic and auto-immune conditions in which modelling suggests that axonal excitability abnormalities are primarily due to Schwann cell dysfunction and an increase in the ‘Barrett-Barrett’ conductance. In some cases only, this increase appears to be accompanied by an increase in nodal fast potassium conductance, as might be expected for paranodal demyelination with exposure of juxtaparanodal potassium channels.