Code Rate Research Articles (Page 1)

This is a correction notice that “Trends in delirium coding rates in older hospital inpatients in England and Scotland: full population data comprising 7.7M patients per year show substantial increases between 2012 and 2020” has been moved from its original journal of publication, Delirium Communications to Delirium . Communications has been merged into Delirium Journal and all of its articles now show Delirium Journal as the hosting/publishing journal, accordingly.

Spiking neural networks (SNNs) provide a biologically inspired, event-driven alternative to artificial neural networks (ANNs), potentially delivering competitive accuracy at substantially lower energy. This tutorial-study offers a unified, practice-oriented assessment that combines critical review and standardized experiments. We benchmark a shallow fully connected network (FCN) on MNIST and a deeper VGG7 architecture on CIFAR-10 across multiple neuron models (leaky integrate-and-fire (LIF), sigma–delta, etc.) and input encodings (direct, rate, temporal, etc.), using supervised surrogate-gradient training implemented in Intel Lava, SLAYER, SpikingJelly, Norse, and PyTorch. Empirically, we observe a consistent but tunable trade-off between accuracy and energy. On MNIST, sigma–delta neurons with rate or sigma–delta encodings achieve 98.1% accuracy (ANN baseline: 98.23%). On CIFAR-10, sigma–delta neurons with direct input reach 83.0% accuracy at just two time steps (ANN baseline: 83.6%). A GPU-based operation-count energy proxy indicates that many SNN configurations operate below the ANN energy baseline; some frugal codes minimize energy at the cost of accuracy, whereas accuracy-oriented settings (e.g., sigma–delta with direct or rate coding) narrow the performance gap while remaining energy-conscious—yielding up to threefold efficiency compared with matched ANNs in our setup. Thresholds and the number of time steps are decisive factors: intermediate thresholds and the minimal time window that still meets accuracy targets typically maximize efficiency per joule. We distill actionable design rules—choose the neuron–encoding pair according to the application goal (accuracy-critical vs. energy-constrained) and co-tune thresholds and time steps. Finally, we outline how event-driven neuromorphic hardware can amplify these savings through sparse, local, asynchronous computation, providing a practical playbook for embedded, real-time, and sustainable AI deployments.

Timely Documentation of CPR Codes and Medical Treatment Preferences in the EHRs of Nursing Homes.

A Simple but Accurate Approximation for Multivariate Gaussian Rate-Distortion Function and Its Application in Maximal Coding Rate Reduction

Distinguishing follicular thyroid carcinoma (FTC) from follicular adenoma (FTA) preoperatively remains a significant challenge in thyroid nodule management. This study aims to develop and validate a novel, interpretable deep learning model that explicitly leverages the critical diagnostic feature-tumor margins-to address this issue. A total of 577 patients, 435 females and 142 males (mean age, 51.05 8.31), were collected from two different centers. A total of 4358 thyroid US images were prospectively collected, with 3140 images from one center randomly divided into the training set and the validation set with a ratio of 4:1 for training the deep learning (DL) model, while 1218 images from the other center were viewed as a test dataset for the evaluation. We propose an end-to-end graph convolutional network that constructs a graph representation from ultrasound image patches, explicitly modeling the structural relationships between features, particularly at the tumor boundary. The model is optimized using a maximum code rate reduction (MCR2) loss to enhance feature discrimination. The overall prediction accuracy and AUC of the independent test set and validation dataset were 90.13%, 82.10%, 92.35%, and 87.36%, respectively, achieving significant and consistent improvement compared to other deep learning baselines. Our proposed model could diagnose FTC with good performance. By successfully incorporating domain knowledge and validating on multicenter data, this study represents a significant step toward reliable AI-assisted diagnosis of thyroid cancer.

Video-based point cloud compression (V-PCC) is a 3D point cloud compression standard that first projects the point cloud from 3D space onto 2D space, thereby generating geometric and attribute videos, and then encodes the geometric and attribute videos using high-efficiency video coding (HEVC). In the whole coding process, the coding of geometric videos is extremely time-consuming, mainly because the division of geometric video coding units has high computational complexity. In order to effectively reduce the coding complexity of geometric videos in video-based point cloud compression, we propose a fast segmentation algorithm based on the occupancy type of coding units. First, the CUs are divided into three categories—unoccupied, partially occupied, and fully occupied—based on the occupancy graph. For unoccupied CUs, the segmentation is terminated immediately; for partially occupied CUs, a geometric visual perception factor is designed based on their spatial depth variation characteristics, thus realizing early depth range skipping based on visual sensitivity; and, for fully occupied CUs, a lightweight fully connected network is used to make the fast segmentation decision. The experimental results show that, under the full intra-frame configuration, this algorithm significantly reduces the coding time complexity while almost maintaining the coding quality; i.e., the BD rate of D1 and D2 only increases by an average of 0.11% and 0.28% under the total coding rate, where the geometric video coding time saving reaches up to 58.71% and the overall V-PCC coding time saving reaches up to 53.96%.

The nervous system's capacity to process complex stimuli has long intrigued neuroscientists, with multiplexing now recognized as a fundamental neural coding strategy. Multiplexing refers to the simultaneous encoding of multiple stimulus features via vi distinct components of neuronal responses, such as firing rates and precise temporal spike patterns. This paper reviews the neural coding mechanisms underlying multiplexing, with a particular emphasis on the somatosensory system and its ability to represent tactile stimuli. The encoding of various sensory attributes, including vibration, texture, motion, and shape, is examined, highlighting the complementary roles of rate and temporal codes in capturing these features. The discussion further addresses how intrinsic and extrinsic noise, often viewed as detrimental, can facilitate multiplexed coding by supporting the concurrent encoding of both stimulus frequency and intensity. The relevance of multiplexing is also considered in translational contexts, such as the development of brain-machine interfaces. By synthesizing recent advances and integrating insights from empirical and theoretical studies, this review establishes multiplexing as a foundational principle in sensory neuroscience and identifies key directions for future research in both basic science and neuroengineering applications.

Semiconductor memories are the basic storage elements for advanced FPGAs. However, with technology scaling due to high packing densities, the temperature of the device rises drastically, creating temporary or permanent faults that manifest as errors in the stored data. Permanent errors cannot be corrected, but temporary errors can. If the data in the memory is critical, such as data used during satellite or missile launch, patient data, etc., there is a need for Error-Detecting and Correcting (EDAC) code. Memories are represented as a matrix that stores data in rows. EDAC codes correct random (errors at various distributed locations) and burst errors (a sequence of erroneous bits within a row). The Hamming code represents the basis for any EDAC code. This work focuses on a single code used to identify 8-bit erroneous data and correct for 6 and 7 random bit errors and 8 burst errors. The matrix code utilizes a memory representation and Hamming code to detect and correct errors, taking care to increase the code rate with less area and delay. In addition, a pipelining technique is used to reduce power dissipation, which also helps to increase the speed of the design. The codes were modeled in Verilog HDL and verified for the Zynq 7000 series FPGA using Xilinx Vivado 2023.2. The results were verified for technological parameters, such as area in terms of LUTs, critical path delay, power dissipation, etc., and for non-technological parameters such as code rate, bit overhead, detection capability, correction capability, etc. The proposed pipelined matrix code was better in most aspects compared to existing designs.

Effects of Legalizing Recreational Cannabis Sales on Cannabis Use and Cannabis-Related Disorder Among Presentations to a Psychiatric Emergency Service.

A photonic approach for generating dual-band dual-chirp waveforms with a programmable duty cycle based on a dual-drive Mach–Zehnder modulator (DDMZM) is proposed and experimentally demonstrated. By simultaneously applying a digital switching code, a predefined radio frequency (RF) signal, and a baseband chirped signal into the RF input ports of the DDMZM, dual-band dual-chirp signals with upconverted center frequencies are achieved, and their duty cycle can be fast switched. The switching speed is directly determined by the bit rate of the input code. Moreover, by appropriately adjusting the bias voltages, the generated signals can be anti-dispersion transmitted. Full experimental verification on the generation and anti-dispersion transmission of dual-band dual-chirp waveforms centered at 4.5 and 7.5 GHz over 10 or 20 km standard single-mode fibers with programmable duty cycle switching among 20%, 40%, 60%, and 80% is successfully carried out. The proposed approach features simple configuration, programmable switching capability, and immunity to power fading, which are highly desired in multifunction radar networks with optical fiber-based transmission.

A Simple but Accurate Approximation for Multivariate Gaussian Rate-Distortion Function and Its Application in Maximal Coding Rate Reduction

We present a fault-tolerant Bell-pair distillation scheme achieving constant overhead through high-rate quantum low-density parity-check (qLDPC) codes. Our approach maintains a constant distillation rate equal to the code rate while requiring no additional overhead beyond the physical qubits of the code. Full circuit-level analysis demonstrates fault-tolerance for input Bell-pair infidelities below a threshold ∼10%, readily achievable with near-term capabilities. Unlike previous proposals, our scheme keeps the output Bell pairs encoded in qLDPC codes at each node, eliminating unencoding overhead and enabling direct use in distributed quantum applications through recent advances in qLDPC computation. These results establish qLDPC-based distillation as a practical route toward resource-efficient quantum networks and distributed quantum computing.

The neural representations of acoustic features that differ in the location or timbre of the emitter elicit similar perceptions, suggesting the existence of a robust stimulus-response function between complex sounds and the activity of neural populations at all stages of the auditory system. This hypothesis is tested by decoding a random sound stream, using spike trains from a biophysical model of the auditory nerve and from large-scale recordings in the inferior colliculus, the auditory thalamus, and the auditory cortex of awake mice. At the level of individual neurons, the reliability of temporal and rate codes is found to decrease along the ascending auditory pathways. Rate coding is progressively favored with increasing independence of neuron frequency tuning. Firing in the auditory cortex is found to be synergistic, whereas that in subcortical areas is more redundant. Finally, combinatorial codes involving neural firing and neural silence within neuron pairs are shown to efficiently encode sound information, particularly in the auditory cortex. Overall, these findings reveal a progressive transformation of the neural code from an individual, redundant, and temporal code at the periphery to a more distributed rate-based code in the auditory cortex.

In the implementation of diagnosis-related groups (DRGs), hospitals respond to price changes by incorporating more patients into the more profitable DRGs, thereby providing evidence for upcoding. This study proposes a two-stage DRGs grouper (ML-DRG) to alleviate the risk of upcoding. The ML-DRG employs machine learning methods to build a predictive model of patients' clinical resource consumption and assigns the model output as the resource consumption index, which comprehensively considers various patients characteristics and is challenging to modify. We utilize the data from the Chengdu Healthcare Security Administration of China, covering the period from 2011 to 2018, to compare the performance of the proposed method with the 3 mainstream approaches. Our findings indicate that the intracranial hemorrhagic disease (BR1) group and respiratory infection/inflammation disease (ES2) group of ADRG were divided into 4 DRGs, with the coefficient of variation of each group being less than .8. Among the 4 grouping methods, ML-DRG demonstrated the best performance. These findings suggest that the application of ML-DRG may reduce the risk of upcoding by helping hospitals avoid selecting incorrect DRG codes for higher reimbursement rates.

Polar codes are the only error-correcting codes that have been mathematically proven to achieve the Shannon limit to date, playing a crucial role in the control channels of 5G mobile communication systems. For control channels, although the sphere decoding (SD) algorithm boasts excellent performance, its high computational complexity and significant latency present clear limitations in practical applications. In contrast, the list sphere decoding (LSD) algorithm strikes a balance between performance and complexity. This paper proposes a construction method that delays the decoding of specific information bits with the minimum row weight to mitigate the impact of error propagation. For scenarios where the total number of delay-decodable bits is limited, we introduce a segmented construction strategy. Through mathematical analysis, this strategy effectively increases the number of delay-decodable bits, thereby significantly reducing the impact of error propagation without changing the number of transmitted bits. Simulation results show that the decoding performance of the proposed algorithm is comparable to that of the SD algorithm at medium and low code rates (with a difference of less than 0.2 dB), but it abandons the concept of search radius in the SD algorithm and does not require a backtracking process. Furthermore, under high code rate conditions, such as P(32,20), the proposed algorithm also maintains excellent performance.

Foreign-born persons make up approximately 5% of the United States military active duty service members. Studies in the general population show that travelers visiting friends and relatives have an increased risk of malaria acquisition. We hypothesized a higher incidence of malaria within the Military Health System (MHS) for those with a familial connection to malaria-endemic countries. We performed a retrospective cohort study of all service members and their family members enrolled in the MHS between October 2012 and September 2018. Participants were allocated into 2 groups according to the service member's country of birth: malaria-endemic country or nonendemic country. Malaria cases were identified by International Classification of Diseases code and annual incidence rates were calculated based on service member's birth world region. Incidence rate ratios (IRRs) compared the 2 groups. Two hundred eighty-one cases of malaria were identified during the 6-year study period. The malaria-endemic group (n = 67) had 5 times the risk of malaria than the nonendemic group (n = 214) (IRR, 5.2 [95% confidence interval, 2.4-10.4]). Incidence remained higher for the malaria-endemic group even when stratified by service member and family member status. Those connected to the sub-Saharan Africa region had an average annual incidence rate of 42 cases per 100 000 persons, accounting for 86.6% of all malaria-endemic cases. Only 48% of all cases sought pretravel medical counseling. There was a >5 times higher incidence of malaria in the malaria-endemic group, highlighting their increased incidence of travel-related infectious diseases despite universal health insurance coverage and access to pretravel medical care.

Stably representing recurring visual scenes is crucial for behavior. However, previous studies report varying degrees of gradual neural activity changes over time in slow dynamic (1-5 seconds) firing rate code. Here we show that temporal codes, which capture structures in visually evoked fast (tens of milliseconds) spiking patterns, support the stability of visual representations. We tracked the spiking responses of the same visual cortical populations in male mice for 15 consecutive days using custom-developed, large-scale, ultraflexible electrode arrays. Across various stimuli, neurons exhibited different day-to-day stability in their firing rate-based tuning. The across day stability correlated with tuning reliability. Notably, temporal codes increased single neuron tuning stability, especially for less reliable neurons. Temporal coding further improved population representation discriminability and decoding accuracy. The stability of temporal codes was more correlated with network functional connectivity than rate coding. Thus, temporal coding may be essential in ensuring consistent sensory experiences over time.

Multiview data, characterized by rich features, are crucial in many machine learning applications. However, effectively extracting intraview features and integrating interview information present significant challenges in multiview learning (MVL). Traditional deep network-based approaches often involve learning multiple layers to derive latent. In these methods, the features of different classes are typically implicitly embedded rather than systematically organized. This lack of structure makes it challenging to explicitly map classes to independent principal subspaces in the feature space, potentially causing class overlap and confusion. Consequently, the capability of these representations to accurately capture the intrinsic structure of the data remains uncertain. In this article, we introduce an innovative multiview representation learning (MVRL) by maximizing two information-theoretic metrics: intraview coding rate reduction and interview mutual information. Specifically, in the intraview representation learning, we aim to optimize feature representations by maximizing the coding rate difference between the entire dataset and individual classes. This process expands the feature representation space while compressing the representations within each class, resulting in more compact feature representations within each viewpoint. Subsequently, we align and fuse these view-specific features through space transformation and cross-sample fusion to achieve consistent representation across multiple views. Finally, we maximize information transmission to maintain consistency and correlation among data representations across views. By maximizing mutual information between the consensus representations and view-specific representations, our method ensures that the learned representations capture more concise intrinsic features and correlations among different views, thereby enhancing the performance and generalization ability of MVL. Experiments show that the proposed methods have achieved excellent performance.

Robust data disclosure constitutes an essential problem for contemporary system communication, and the theory of coding becomes the key to maintaining data integrity. This paper discusses constructions and decoding of Bose–Chaudhuri–Hocquenghem (BCH) codes over Quasi-Galois Rings (QGRs) – generalization of classical Galois rings. The QGRs provide richer algebraic structures that lead to improved error correction capabilities, greater codes rates, and more codewords than their Galois counterparts. We provide an all-rounding theory of construction for BCH codes over QGRs, describe them in their construction process, and walk through an efficient decoding technique. Our findings demonstrate the ability of BCH codes under QGR to provide high reliability and performance for the communication systems which will make them a candidate for future use in data transmission and storage.

The performance of page-oriented holographic data storage systems, especially capacity, transmission speed, and symbol error rate, critically depends on the modulation code design. Under the constraint of maintaining approximately 20% code weight, this paper proposes an optimized 5:22 two-dimensional equal-weight sparse modulation code featuring elongated code block geometry. We theoretically evaluated the code rates and decoding efficiencies of various encoding formats with equivalent code weights, demonstrating that this design achieves optimal performance among existing binary encodings. Shape optimization analysis reveals that the elongated code blocks provide enhanced encoding flexibility, enabling the transformation of data pages from conventional square to more efficient circular configurations. Experimental results confirm that the 5:22 coding format achieves a 42.7% storage capacity improvement over the conventional 3:16 coding format while maintaining the raw symbol error rate at 10-4.