Hand neuroprostheses restore voluntary movement in people with paralysis through neuromodulation protocols. There are a variety of strategies to control hand neuroprostheses, which can be based on residual body movements or brain activity. There is no universally superior solution, rather the best approach may vary from patient to patient. Here, we propose a protocol based on an immersive virtual reality (VR) environment that simulates the use of a hand neuroprosthesis to allow patients to experience and familiarize themselves with various control schemes in clinically relevant tasks and choose the preferred one. We used our VR environment to compare two alternative control strategies over 5 days of training in four patients with C6 spinal cord injury: (a) control via the ipsilateral wrist, (b) control via the contralateral shoulder. We did not find a one-fits-all solution but rather a subject-specific preference that could not be predicted based only on a general clinical assessment. The main results were that the VR simulation allowed participants to experience the pros and cons of the proposed strategies and make an educated choice, and that there was a longitudinal improvement. This shows that our VR-based protocol is a useful tool for personalization and training of the control strategy of hand neuroprostheses, which could help to promote user comfort and thus acceptance.

Background: Document forgery poses a significant challenge to the integrity of sensitive documents and necessitates the development of advanced anti-forgery techniques. This study explored the integration of piezoelectric nanomaterials, specifically zinc oxide (ZnO) nanowires and barium titanate (BaTiO3) nanoparticles, into document fibers for enhanced forgery detection. Methods: This review examines the superior mechanical properties of ZnO nanowires and the high dielectric constant, piezoelectricity, and ferroelectricity of BaTiO3 nanoparticles, making them suitable for embedding within document fibers. Advanced scanning probe techniques, such as piezoresponse force microscopy (PFM), scanning Kelvin probe microscopy (SKPM), and electrostatic force microscopy (EFM), have been used to characterize the electrical properties and structural integrity of nanomaterials. Results: Embedding piezoelectric nanomaterials within document fibers enables the detection of tampering through changes in electrical properties, providing a non-invasive and highly sensitive forgery detection method. This study highlights the importance of optimizing the manufacturing processes, developing rigorous measurement protocols, and exploring alternative piezoelectric nanomaterials. Conclusions: The integration of piezoelectric nanomaterials into document fibers is a promising approach for addressing the persistent challenges of document forgery. Future research should focus on refining manufacturing techniques, improving measurement accuracy, and expanding the applications to various sensitive documents.

Within few-shot learning, in-context learning (ICL) has become a potential method for leveraging contextual information to improve model performance on small amounts of data or in resource-constrained environments where training models on large datasets is prohibitive. However, the quality of the selected sample in a few shots severely limits the usefulness of ICL. The primary goal of this paper is to enhance the performance of evaluation metrics for in-context learning by selecting high-quality samples in few-shot learning scenarios. We employ the chi-square test to identify high-quality samples and compare the results with those obtained using low-quality samples. Our findings demonstrate that utilizing high-quality samples leads to improved performance with respect to all evaluated metrics.

Deep Neural Networks (DNNs) have gained considerable traction in recent years due to the unparalleled results they gathered. However, the cost behind training such sophisticated models is resource intensive, resulting in many to consider DNNs to be intellectual property (IP) to model owners. In this era of cloud computing, high-performance DNNs are often deployed all over the internet so that people can access them publicly. As such, DNN watermarking schemes, especially backdoor-based watermarks, have been actively developed in recent years to preserve proprietary rights. (Add that no backdoor watermark guarantee). Nonetheless, there lies much uncertainty on the robustness of existing backdoor watermark schemes, towards both adversarial attacks and unintended means such as fine-tuning neural network models. In this paper, we extensively evaluate the persistence of recent backdoor-based watermarks within neural networks in the scenario of fine-tuning, we propose/develop a novel data-driven idea to restore watermark after fine-tuning without exposing the trigger set. Our empirical results show that by solely introducing training data after fine-tuning, the watermark can be restored if model parameters do not shift dramatically during fine-tuning. Depending on the types of trigger samples used, trigger accuracy can be reinstated to up to 100%. Our study further explores how the restoration process works using loss landscape visualization, as well as the idea of introducing training data in fine-tuning stage to alleviate watermark vanishing.

A document by Mahmood Mohammadzadeh. Click on the document to view its contents.

Due to malfunction of network devices and surge in physical layer impairments, the quality of transmission (QoT) in backbone optical networks may degrade. If the cause of the degradation is not timely diagnosed and addressed adequately, it may deteriorate into a hard failure. In this study, we consider the external cavity laser (ECL) malfunction-, erbium-doped fiber amplifier (EDFA) malfunction-, and nonlinear interference-related soft failures. We propose a software-defined optical network (SDON) based soft failure detection and identification strategy using a cascaded deep learning model. Time series QoT data of normal and degraded lightpaths obtained through the optical performance monitoring equipment is used to train the proposed cascaded deep learning model. In the first stage, a long shortterm memory-based autoencoder (LSTM-AE) model is used as a binary classifier to identify the anomalous time series sequences. Subsequently, a multiclass LSTM-based classifier is employed to identify the type of soft failure. Our proposed approach shows an accuracy of 99.70%.

Quantum approximate optimization algorithm (QAOA) with layer depth p is promising near-optimum performance and low complexity for NP-hard maximum-likelihood (ML) detection in n × n multi-input multi-output (MIMO) systems. Experimental challenges for ML detection on Noisy Intermediate-Scale Quantum (NISQ) computers arise from accumulated errors with large p and n. Recursive QAOA (RQAOA) is promising with small p by reducing complexity over n steps. In this article, we modify RQAOA for p n with cost sorting and post-selection in m n steps, and then integrate it with majority voting (MV) and successive interference cancellation (SIC) into the QAOA-MVSIC algorithm to tackle experimental challenges. We truncate QAOA circuits to further improve experimental feasibility. Simulations with n = 24 and 12 for BPSK and QPSK modulations, respectively, show near-optimum bit-error rate (BER) with p = 1 and m ≤ 4. Truncated version requires O(m n p) quantum and O(m n2 ) classical operations with low complexity. We experimentally implement QAOA combined with MV (QAOA-MV) for n ∈ [17, 64] in IBM Eagle processor by observing superior performance of QAOA-MV over QAOA and reducing problem dimensions by at least n/4. We generalize QAOA as cost-restricted uniform sampling (CRUS) oracle and approximately simulate for n ≤ 128 to obtain comparison benchmark for future QAOA experiments.

In conventional energy systems, the synchronization of power generation and consumption is largely handled by thermal power plants. The increasing share of volatile renewable energy generation which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term "system friendliness" which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of "system friendliness" and develop comprehensive and holistic related indicators. Our findings demonstrate that today's regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers.

The aim of this study, therefore, is to reinvent the future of computing systems in terms of performance, efficiency, and adaptability by identifying the "frontier" of AI-driven innovations in CPU design. * This document surveys front-end optimizations driven by AI, new architectural ideas, and upcoming paradigms that might revolutionize CPU technology through a thorough literature review as well as empirical evaluations. * With the help of advanced artificial intelligence methods like as machine learning, deep learning, and reinforcement learning, the exploration of new CPU architectures and optimization techniques permits the unleashing of computational powers beyond anything previously imagined. * AI methods on CPU architecture and address significant concerns such as scalability, energy efficiency, security, and reliability have been systematically explored. Furthermore, the transformative ability of AI-driven CPUs is also illustrated through real-world-case applications in several fields, e.g. autonomous systems, healthcare informatics, edge computing. This paper helps clear the roadmap for the next system of creative revolution in AI-imbued computing, defining potential directions for additional research and cooperation.

Unlike the universal approximation theorems for functions mapping from a real-valued (RV) vector to a RV number or from a complex-valued (CV) vector to a CV number, in the field of electromagnetism, we need to approximate functions mapping from a RV vector to a CV number when we consider the electric field as a function of the spatial coordinate in the frequency domain. Typically, CV numbers contain phase information. When such phase information is handled properly, the performance of the neural networks (NNs) can be improved. This work derives a universal approximation theorem for functions mapping from a RV vector to a CV number. A deep NN, named as HV-DL, is designed accordingly, which consists of a RV input layer, a RV module containing two branches, a CV module and a CV output layer. The proposed universal approximation theorem is verified by numerical experiments on the HV-DL solution of the two-dimensional (2-D) electric field integral equation (EFIE). To integrate the underlying physics of electromagnetic scattering into the proposed HV-DL, the loss function is computed according to the EFIE.

The fast advancement of low-field MRI (magnetic resonance imaging) has generated a high demand for cost-effective and versatile consoles for MRI scanners. MaRCoS (MAgnetic Resonance COntrol System) is such an open-source system that has been well-tested on various low-field systems. However, due to limitations of the basic hardware, MaRCoS is constrained in its ability to support a wide range of field strengths and RF (radio-frequency) channels. In this study, we aim to port the MaRCoS console to high-field MRI systems and increase the number of RF receive channels, enabling phased-array coils and/or active EMI (electromagnetic interference) elimination techniques. A series of implementations were conducted across 0.11-, 0.5-, and 1.5-Telsa MRI systems, in order to evaluate its compatibility and performance. Promising results indicate that the extended console not only matches but, to some extent, surpasses the performance of a commercial spectrometer, particularly in terms of flexibility and accessibility. It is hoped that this study could effectively expand the scope of open-source MRI technology, making MRI scans more accessible and affordable.

In recent years, the rapid evolution of technology has been astounding. This got me thinking: what if there was a way to visualize our thoughts and share them with others? We've all experienced moments where explaining our ideas becomes a challenge, especially in critical situations. That's where Project Eye comes in. This concept emerged from the idea of visualizing our thoughts as a "third eye," allowing others to see what we're thinking. It bridges the gap between our minds and communication. It comes under the field of Human Computer Interaction. I truly believe that even if it is not becoming entirely popular now, with time and proper implementation it would be one of the most valuable assets one might hold.

This paper introduces a new construct called Human Factors (HF) Respect as a proposed measure to characterize and quantify assistance provided by humans to autonomous systems. Technological advancements are allowing autonomous systems (or agents) to take on roles previously performed by human teammates. Utilizing autonomous systems as team members might require assistance (or respectful behaviors) from human teammates in order for these autonomous systems to realize their full potential. We believe this forms the basis of the construct of HF Respect, which has not been well studied within the context of human autonomy teaming. We performed a literature review to establish a definition and structure for the HF Respect construct by considering adjacent constructs such as respect seen in human teams, trust, cooperation, and compliance. We defined HF Respect as the opportunity cost (e.g., time) incurred by humans when partnering with autonomous agents to improve the combined team’s performance and assist them in overcoming their limitations. We further defined and categorized this assistance as consideration, cooperation, compliance, and correction. HF Respect and proposed metrics can be used to characterize and quantify assistance to autonomous systems to improve the performance of human-autonomy teams. The construct of HF Respect introduces a new perspective to human-autonomy teaming. Researchers should consider this perspective when investigating human-autonomy teams to better describe human-AI team interactions and to determine if humans who demonstrate high HF Respect improve human-autonomy team performance.

This study explores the application of the YOLOv8 segmentation model in automated dental diagnostics through deep learning techniques, focusing on its effectiveness in detecting and classifying various dental features from X-ray images. The research emphasizes methodological rigor in dataset configuration, training with a pre-trained model, and employing advanced data augmentation strategies. Visualizations including bar charts and density plots illustrate the distribution and spatial characteristics of dental instances, highlighting prevalent categories like fillings and caries. The integration of deep learning in dental imaging not only enhances diagnostic precision but also supports personalized medicine by adapting diagnostic tools to individual patient data. This advancement holds promise for improving efficiency in disease detection and treatment planning, potentially transforming dental care delivery through enhanced automation and accuracy.

In this paper, we introduce a framework for detecting changes in strategy of an adversarial cognitive radar in the inverse learning context. We model the cognitive radar as a constrained utility maximizer and formulate the problem of detection of change in strategy as a changepoint detection problem in the revealed preference setting. We address the revealed preference strategy changepoint detection problem in a Bayesian setting, wherein we model the utility of cognitive radar as a random direction vector, that follows the von Mises-Fisher distribution with unknown parameters. The main contribution of the paper is the development of Bayesian changepoint detection algorithm that detects the changes in strategy of an adversarial cognitive radar, in an inverse learning context under stochastic revealed preference framework. The main advantages of the proposed method are (i) The proposed method uses a Hamiltonian Monte Carlo sampling algorithm that exploits the Afriat's theorem as well as a subsetting structure that arises in the posterior, and hence is devoid of the computational burden of solving optimization problems in existing techniques. (ii) Numerical results demonstrate the ability to determine the changes in the beam allocation strategy of a cognitive radar in noise free and noisy adversarial settings. The proposed approach inherently resists observation noise compared to the existing naive approaches. (iii) The proposed changepoint detection performs, on an average, five times faster compared to a naive, Generalized Likelihood Ratio approach for changepoint detection. Further, we also demonstrate that the proposed method generalizes existing changepoint detection under revealed preference setting and can be used for applications such as social media and finance. The changepoint detection framework, in the inverse learning context, is useful in the design of both electronic counter measures and electronic counter-counter measures.

It is commonly known that both different brain regions and brainwaves are associated with different activities. This paper attempts to determine and analyze the differences in region activation and brainwave manifestation between a subject with Autism Spectrum Disorder (ASD) and a neurotypical subject regarding the working memory for spatial information. I hypothesized that variances in temporal and frontal lobe activation as well as beta and gamma wave activation. Data from a spatial memory test was captured, purified, plotted, and analyzed through Muse, MATLAB, and EEGLAB. I found inverse activation patterns between the frontal and temporal regions of the neurotypical and ASD brain. The ASD brain presented cumulatively lower-level delta, theta, alpha, and gamma waves. The neurotypical brain featured significantly more powerful beta waves. Increasing the subject pool and the number of electrodes would better reinforce the results of this study.

This paper presents a novel Graph-Convolutional-Network Based Deep-Learning (GCDL) approach to effectively determine estrogen-receptor-status (ERS) in patients with invasive breast cancer, from their Hematoxylin-and-Eosin (H&E) stained Tissue Microarray (TMA) images. Exploring previously overlooked correlation between ERS and nuclei spatial properties from H&E images, we use a set of novel methods to 1) construct two level cell-graphs from breast cancer H&E-stained tissue images and a novel graph node pooling, 2) extract two-level graph-metrics, 3) find the feature distribution statistics on low-level graph, 4) combine the features from 2) and 3) with conventional nuclei morphometric features to form composite node embeddings on high-level subgraph, and 5) associate a single-layer graph convolution with a stack of several nonlinear dense layers to create a GCDL classifier for enhanced performance. The proposed GCDL approach is tested and compared with a popular residual based Convolutional-Neural-Network (ResNet) method on a dataset of 960 patients with their ERSs diagnosed by pathologists. The test results show that the GCDL approach outperforms ResNet, achieving the best accuracy by far in ERS classification using a single H&E-stained TMA image per patient. Moreover, the GCDL approach requires much less training data and training time to achieve competitive results.

As RISC-V processors become more widely distributed, security issues arise in them. To this end, security verification techniques for processors should be incorporated into the design process to ensure the processor specifications, security requirements, and its actual implementation are consistent. Among the verification techniques, assertionbased verification has emerged as one of the most promising techniques. Although assertions are widely used for functional verification, there is limited effort in applying assertions for security verification. Thus, in this work, a novel security assertionbased technique is introduced for verifying the invulnerability of the processors against attacks. The method works in three main steps, first, collecting the security properties related to RISC-V, second, systematically identifying characteristics of instruction set architecture for each security property. Third, with the guide of input from the previous step, a security-based assertion miner is proposed to automatically extract a set of security assertions for the RISC-V. The experiments show that the proposed method can automatically generate security assertions in a very short amount of time and detect all the inserted Hardware Trojans in the processor, thereby accurately verifying the security of the processor.