Inherent Redundancy Research Articles

Myobolica: a stochastic approach to estimate physiological muscle control variability.

The inherent redundancy of the musculoskeletal systems is traditionally solved by optimizing a cost function. This approach may not be correct to model non-adult or pathological populations likely to adopt a "non-optimal" motor control strategy. Over the years, various methods have been developed to address this limitation, such as the stochastic approach. A well-known implementation of this approach, Metabolica, samples a wide number of plausible solutions instead of searching for a single one, leveraging Bayesian statistics and Markov Chain Monte Carlo algorithm, yet allowing muscles to abruptly change their activation levels. To overcome this and other limitations, we developed a new implementation of the stochastic approach (Myobolica), adding constraints and parameters to ensure the identification of physiological solutions. The aim of this study was to evaluate Myobolica, and quantify the differences in terms of width of the solution band (muscle control variability) compared to Metabolica. To this end, both muscle forces and knee joint force solutions bands estimated by the two approaches were compared to one another, and against (i) the solution identified by static optimization and (ii) experimentally measured knee joint forces. The use of Myobolica led to a marked narrowing of the solution band compared to Metabolica. Furthermore, the Myobolica solutions well correlated with the experimental data (R2 = 0.92, RMSE = 0.3 BW), but not as much with the optimal solution (R2 = 0.82, RMSE = 0.63 BW). Additional analyses are required to confirm the findings and further improve this implementation.

Gravitational edge mode in N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 1 Jackiw-Teitelboim supergravity

We study the gravitational edge mode in the N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 1 Jackiw-Teitelboim (JT) supergravity on the disk and its osp(2|1) BF formulation. We revisit the derivation of the finite-temperature Schwarzian action in the conformal gauge of the bosonic JT gravity through wiggling boundary and the frame fluctuation descriptions. Extending our method to N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 1 JT supergravity, we derive the finite-temperature super-Schwarzian action for the edge mode from both the wiggling boundary and the superframe field fluctuation. We emphasize the crucial role of the inversion of the super-Schwarzian derivative in elucidating the relation between the isometry and the OSp(2|1) gauging of the super-Schwarzian action. In osp(2|1) BF formulation, we discuss the asymptotic AdS condition. We employ the Iwasawa-like decomposition of OSp(2|1) group element to derive the super-Schwarzian action at finite temperature. We demonstrate that the OSp(2|1) gauging arises from inherent redundancy in the Iwasawa-like decomposition. We also discuss the path integral measure obtained from the Haar measure of OSp(2|1).

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

In real-world materials research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set. Traditional performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN’s significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN) as a case study for the perovskites dataset, and provide insights to improve their performance.

Timed fault-tolerant supervisory control

In Mulahuwaish,1–4 we investigated the problem of fault tolerance in the framework of untimed discrete-event systems (DES). This approach is different from the typical fault-tolerant methodology as the approach does not rely on detecting faults and switching to a new supervisor; it requires a supervisor to work correctly under normal and fault conditions. This is a passive approach that relies upon inherent redundancy in the system being controlled. In this paper we extend the work of Mulahuwaish1–4 to the timed DES (TDES) setting. We introduce our setting, and then provide a set of timed fault tolerant definitions designed to capture different types of fault scenarios and to ensure that our system remains controllable in each scenario. As the nonblocking property is the same for timed and untimed DES, the untimed fault-tolerant nonblocking properties and algorithms from Mulahuwaish1–4 can also be used in the timed setting without any changes. We then present algorithms to verify these properties followed by complexity analyses and correctness proofs of the algorithms. An example is then provided to illustrate our approach.

Photostasis and photosynthetic adaptation to polar life.

Photostasis is the light-dependent maintenance of energy balance associated with cellular homeostasis in photoautotrophs. We review evidence that illustrates how photosynthetic adaptation in polar photoautrophs such as aquatic green algae, cyanobacteria, boreal conifers as well as terrestrial angiosperms exhibit an astonishing plasticity in structure and function of the photosynthetic apparatus. This plasticity contributes to the maintenance of photostasis, which is essential for the long-term survival in the seemingly inhospitable Antarctic and Arctic habitats. However, evidence indicates that polar photoautrophic species exhibit different functional solutions for the maintenance of photostasis. We suggest that this reflects, in part, the genetic diversity symbolized by inherent genetic redundancy characteristic of polar photoautotrophs which enhances their survival in a thermodynamically challenging environment.

Efficient Recurrent Neural Networks via Importance-Based Sparsification

Embedded sensing devices that are battery-operated and resource-limited are increasingly utilizing recurrent neural networks (RNNs) to attain real-time perception of the complex environment. Nevertheless, the majority of RNNs typically handle substantial amounts of time-series data, resulting in computational intensity and high energy consumption. Hence, the development of energy-efficient RNN models is a crucial focus. This paper introduces IS-RNN, an energy-efficient RNN model featuring importance-based sparsification, aimed at enhancing the energy efficiency of RNN-based in-sensor inference. The principal motivation for this research arises from the recognition that time series data obtained from sensor sensing often exhibit inherent redundancy. Leveraging input sequence sparsification allows for the elimination of invalid input accesses and computations, thus optimizing efficiency. As a result, optimizing the sparsity of the input sequence through adaptive control allows us to improve inference performance while adhering to energy constraints. In particular, we build an importance score analysis method to quantify the contribution of each input toward the RNN inference performance. Building upon the proposed quantitative importance score analysis, we introduce a dynamic efficient RNN architecture, which enables the model to make decisions based on important data to obtain the highest accuracy under energy constraints. Moreover, we introduce an adaptive RNN inference method to enhance prediction accuracy under energy constraints by making a trade-off between computational cost and model performance. We evaluated IS-RNN on five datasets and the results show that it achieves a higher inference accuracy with the same amount of data processed compared to the state-of-the-art methods.

Motor learning in multijoint virtual arm movements with novel kinematics

Humans move their hands toward precise positions, a skill supported by the coordination of multiple joint movements, even in the presence of inherent redundancy. However, it remains unclear how the central nervous system learns the relationship between redundant joint movements and hand positions when starting from scratch. To address this question, a virtual-arm reaching task was performed in which participants were required to move a cursor corresponding to the hand of a virtual arm to a target. The joint angles of the virtual arm were determined by the heights of the participants’ fingers. The results demonstrated that the participants moved the cursor to the target straighter and faster in the late phase than they did in the initial phase of learning. This improvement was accompanied by a reduction in the amount of angular changes in the virtual limb joint, predominantly characterized by an increased reliance on the virtual shoulder joint as opposed to the virtual wrist joint. These findings suggest that the central nervous system selects a combination of multijoint movements that minimize motor effort while learning novel upper-limb kinematics.

Efficient DNA-based data storage using shortmer combinatorial encoding

Data storage in DNA has recently emerged as a promising archival solution, offering space-efficient and long-lasting digital storage solutions. Recent studies suggest leveraging the inherent redundancy of synthesis and sequencing technologies by using composite DNA alphabets. A major challenge of this approach involves the noisy inference process, obstructing large composite alphabets. This paper introduces a novel approach for DNA-based data storage, offering, in some implementations, a 6.5-fold increase in logical density over standard DNA-based storage systems, with near-zero reconstruction error. Combinatorial DNA encoding uses a set of clearly distinguishable DNA shortmers to construct large combinatorial alphabets, where each letter consists of a subset of shortmers. We formally define various combinatorial encoding schemes and investigate their theoretical properties. These include information density and reconstruction probabilities, as well as required synthesis and sequencing multiplicities. We then propose an end-to-end design for a combinatorial DNA-based data storage system, including encoding schemes, two-dimensional (2D) error correction codes, and reconstruction algorithms, under different error regimes. We performed simulations and show, for example, that the use of 2D Reed-Solomon error correction has significantly improved reconstruction rates. We validated our approach by constructing two combinatorial sequences using Gibson assembly, imitating a 4-cycle combinatorial synthesis process. We confirmed the successful reconstruction, and established the robustness of our approach for different error types. Subsampling experiments supported the important role of sampling rate and its effect on the overall performance. Our work demonstrates the potential of combinatorial shortmer encoding for DNA-based data storage and describes some theoretical research questions and technical challenges. Combining combinatorial principles with error-correcting strategies, and investing in the development of DNA synthesis technologies that efficiently support combinatorial synthesis, can pave the way to efficient, error-resilient DNA-based storage solutions.

Adversarial Robust Safeguard for Evading Deep Facial Manipulation

The non-consensual exploitation of facial manipulation has emerged as a pressing societal concern. In tandem with the identification of such fake content, recent research endeavors have advocated countering manipulation techniques through proactive interventions, specifically the incorporation of adversarial noise to impede the manipulation in advance. Nevertheless, with insufficient consideration of robustness, we show that current methods falter in providing protection after simple perturbations, e.g., blur. In addition, traditional optimization-based methods face limitations in scalability as they struggle to accommodate the substantial expansion of data volume, a consequence of the time-intensive iterative pipeline. To solve these challenges, we propose a learning-based model, Adversarial Robust Safeguard (ARS), to generate desirable protection noise in a single forward process, concurrently exhibiting a heightened resistance against prevalent perturbations. Specifically, our method involves a two-way protection design, characterized by a basic protection component responsible for generating efficacious noise features, coupled with robust protection for further enhancement. In robust protection, we first fuse image features with spatially duplicated noise embedding, thereby accounting for inherent information redundancy. Subsequently, a combination comprising a differentiable perturbation module and an adversarial network is devised to simulate potential information degradation during the training process. To evaluate it, we conduct experiments on four manipulation methods and compare recent works comprehensively. The results of our method exhibit good visual effects with pronounced robustness against varied perturbations at different levels.

Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature Refinement

The life stories of older adults encapsulate an array of personal experiences that reflect their care needs. However, due to inherent fuzzy features, fragmented natures, repetition, and redundancies, the practical application of the life story approach poses challenges for caregivers in acquiring and comprehending these narratives. Addressing this challenge, our study introduces a novel approach called Life Story Hierarchies with Graph-Enhanced Event Feature Refinement (LSH-GEFR). LSH-GEFR constructs a bilayer graph. Firstly, the event element map leverages intricate relationships between event elements to extract environmental features, providing a detailed context for understanding each event element. Secondly, the event map explores the complex web of relationships between the events themselves, allowing LSH-GEFR to generate a comprehensive understanding of each event and enhance its representation. Subsequently, we conducted experiments on different datasets and found that, in comparison with four advanced event tree generation methods, the proposed LSH-GEFR method outperformed them in terms of path coherence, branch reasonableness, and overall readability when generating life story hierarchies. Over 84.91% of the structured life narratives achieved readability, marking a 5.96% increase over the best-performing approach at the baseline.

An attenuation field network for dedicated cone beam breast CT with short scan and offset detector geometry

The feasibility of full-scan, offset-detector geometry cone-beam CT has been demonstrated for several clinical applications. For full-scan acquisition with offset-detector geometry, data redundancy from complementary views can be exploited during image reconstruction. Envisioning an upright breast CT system, we propose to acquire short-scan data in conjunction with offset-detector geometry. To tackle the resulting incomplete data, we have developed a self-supervised attenuation field network (AFN). AFN leverages the inherent redundancy of cone-beam CT data through coordinate-based representation and known imaging physics. A trained AFN can query attenuation coefficients using their respective coordinates or synthesize projection data including the missing projections. The AFN was evaluated using clinical cone-beam breast CT datasets (n = 50). While conventional analytical and iterative reconstruction methods failed to reconstruct the incomplete data, AFN reconstruction was not statistically different from the reference reconstruction obtained using full-scan, full-detector data in terms of image noise, image contrast, and the full width at half maximum of calcifications. This study indicates the feasibility of a simultaneous short-scan and offset-detector geometry for dedicated breast CT imaging. The proposed AFN technique can potentially be expanded to other cone-beam CT applications.

Realistic material property prediction using domain adaptation based machine learning

Scientists aim to predict properties for a known subset of related out-of-distribution materials rather than universally distributed samples. Introducing target material information enhances machine learning models.

Single-shot non-line-of-sight imaging based on chromato-axial differential correlography

Non-line-of-sight (NLOS) imaging is a challenging task aimed at reconstructing objects outside the direct view of the observer. Nevertheless, traditional NLOS imaging methods typically rely on intricate and costly equipment to scan and sample the hidden object. These methods often suffer from restricted imaging resolution and require high system stability. Herein, we propose a single-shot high-resolution NLOS imaging method via chromato-axial differential correlography, which adopts low-cost continuous-wave lasers and a conventional camera. By leveraging the uncorrelated laser speckle patterns along the chromato-axis, this method can reconstruct hidden objects of diverse complexity using only one exposure measurement. The achieved background stability through single-shot acquisition, along with the inherent information redundancy in the chromato-axial differential speckles, enhances the robustness of the system against vibration and colored stain interference. This approach overcomes the limitations of conventional methods by simplifying the sampling process, improving system stability, and achieving enhanced imaging resolution using available equipment. This work serves as a valuable reference for the real-time development and practical implementation of NLOS imaging.

Asymmetric DC-DC Converter for HVDC Heterogeneous Interconnections

Dc-dc converters are an important element for the future development of high voltage direct current (HVDC) grids. This paper presents a new dc-dc converter for the interconnection between different grid topologies. In particular, one elementary converter can interconnect an asymmetric monopole with a symmetric monopole. The use of two elementary converters allows the interconnection between a bipole and a symmetric monopole keeping the inherent redundancy of the bipole in case of a fault on that line. The converter is analyzed and sized for a proposed a case study. The converter is simulated in a Simulink-Matlab environment. The results prove the adequate performance of the control strategy during the power exchanges between the lines. A fault on the bipole is simulated showing the uninterrupted power transfer between the healthy pole and the symmetric monopole line bipole redundancy).

Video and Image Steganography

Video steganography, a technique for embedding secret information within video sequences, has gained prominence in the domain of covert communication. By exploiting the inherent redundancy and high capacity of video data, this approach enables secure data transmission without raising suspicion. This paper delves into the intricacies of AES-based video steganography, with a particular focus on the integration of the Advanced Encryption Standard (AES) algorithm. The AES algorithm, renowned for its robust encryption capabilities, serves as a cornerstone of this steganographic approach. By encrypting the secret data prior to embedding it within the video frames, an additional layer of security is introduced, safeguarding the confidentiality of the hidden information

MGen: A Framework for Energy-Efficient In-ReRAM Acceleration of Multi-Task BERT

Recently, multiple transformer models, such as BERT, have been utilized together to support multiple natural language processing (NLP) tasks in a system, also known as multi-task BERT. Multi-task BERT with very high weight parameters increases the area requirement of a processing in resistive memory (ReRAM) architecture, and several works have attempted to address this model size issue. Despite the reduced parameters, the number of multi-task BERT computations remains the same, leading to massive energy consumption in ReRAM-based deep neural network (DNN) accelerators. Therefore, we suggest a framework for better energy efficiency during the ReRAM acceleration of multi-task BERT. First, we analyze the inherent redundancies of multi-task BERT and the computational properties of the ReRAM-based DNN accelerator, after which we propose what is termed the model generator, which produces optimal BERT models supporting multiple tasks. The model generator reduces multi-task BERT computations while maintaining the algorithmic performance. Furthermore, we present task scheduler, which adjusts the execution order of multiple tasks, to run the produced models efficiently. As a result, the proposed framework achieves maximally 4.4× higher energy efficiency over the baseline, and it can also be combined with the previous multi-task BERT works to achieve both a smaller area and higher energy efficiency.

Accelerating CEST imaging using a model-based deep neural network with synthetic training data.

To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data. Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods. The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used. The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.

Power-Efficient and Aging-Aware Primary/Backup Technique for Heterogeneous Embedded Systems

One of the essential requirements of embedded systems is a guaranteed level of reliability. In this regard, fault-tolerance techniques are broadly applied to these systems to enhance reliability. However, fault-tolerance techniques may increase power consumption due to their inherent redundancy. For this purpose, power management techniques are applied, along with fault-tolerance techniques, which generally prolong the system lifespan by decreasing the temperature and leading to an aging rate reduction. Yet, some power management techniques, such as Dynamic Voltage and Frequency Scaling (DVFS), increase the transient fault rate and timing error. For this reason, heterogeneous multicore platforms have received much attention due to their ability to make a trade-off between power consumption and performance. Still, it is more complicated to map and schedule tasks in a heterogeneous multicore system. In this paper, for the first time, we propose a power management method for a heterogeneous multicore system that reduces power consumption and tolerates both transient and permanent faults through primary/backup technique while considering core-level power constraint, real-time constraint, and aging effect. Experimental evaluations demonstrate the efficiency of our proposed method in terms of reducing power consumption compared to the state-of-the-art schemes, together with guaranteeing reliability and considering the aging effect.

Hardware-aware approach to deep neural network optimization

Deep neural networks (DNNs) have been a pivotal technology in a myriad of fields, boasting remarkable achievements. Nevertheless, their substantial workload and inherent redundancies pose ongoing challenges for both practitioners and academia. While numerous researchers endeavor to optimize DNNs, the inherent parallelism features of hardware are generally underutilized, resulting in inefficient use of hardware resources. To address this deficit, the paper unveils a hardware-aware mechanism, IHSOpti, which incorporates hardware characteristics with software algorithms for DNN optimization. IHSOpti endeavors to exploit the full potential of modern hardware parallelism, with significant emphasis on pipelining mechanisms. Specifically, IHSOpti formulates an advanced sparse training algorithm Polar_HSPG which incorporates the newly-proposed layer-wise refined polarization regularizer (LWPolar), grounded on the half-space project gradient (HSPG). Subsequently, IHSOpti pioneeringly introduces the residual strategy for optimizing the layer-level redundancies of neural networks, capitalizing on the pipelining attributes inherent in current hardware. Experimental findings demonstrate that IHSOpti attains outstanding pruning ratios in both parameters and FLOPs. Specifically, IHSOpti achieves up to 96.90% and 82.73% pruning ratios with the accuracy of 93.34% for VGGBN, 97.69% and 95.24% pruning ratios with the accuracy of 94.69% for ResNet, 98.07% and 97.80% pruning ratios with the accuracy of 95.73% for the cutting-edge network RegNet, respectively. Notably, the running efficiency exhibits remarkable improvements with accelerations ranging from 3.63× to 8.20× for CPUs and 1.22× to 2.25× for GPUs, respectively. These outcomes surpass the latest advances in the field. Through the incorporation of specific hardware characteristics, IHSOpti provides a comprehensive and effective approach to harness the intrinsic parallelism of contemporary hardware platforms for DNNs.

Scalable frame resolution for efficient continuous sign language recognition

In this paper, we explore the spatial redundancy in continuous sign language recognition (CSLR), aiming to improve its efficiency. Despite recent advances in accuracy in CSLR, state-of-the-art CSLR methods typically require large amounts of computations and memory occupation, which are not friendly towards fast inference under limited computation/memory budgets. Based on a simple observation that not all frames are equally important for CSLR, we propose AdaSize to handle this problem by modeling the frame resolution decision as an end-to-end learnable task to save unnecessary computations. Specifically, a lightweight 2D convolutional neural network (CNN) is first used to quickly browse input frames under a low resolution (e.g., 112 × 112). These extracted coarse and cheap features are sent into a recurrent policy network to dynamically determine the desired resolution for each frame. Once the optimal resolution for each frame is decided, frames with different resolutions are fed into the following backbones to extract representative features. Finally, these features pass through a sequence of temporal modules and a classifier to predict sentences. Extensive experiments on four large-scale datasets, including PHOENIX14, PHOENIX14-T, CSL-Daily and CSL, demonstrate the effectiveness of AdaSize. AdaSize could consistently achieve comparable accuracy with state-of-the-art CSLR methods, with only 0.38× computations, 0.41× memory usage and 1.25× throughput. Comparisons with commonly-used lightweight backbones and other efficient methods verify the superiority of AdaSize under similar computational/memory budgets. We finally plot the frame resolution decisions for AdaSize, hoping to provide insightful analysis of the inherent spatial redundancy in videos.