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Objective: To assess the effect of overall, between- and within-day subjectively rated fluctuations in motor and non-motor symptoms in people with functional motor disorder (FMD) on the health-related quality of life (HRQoL). Background: FMD is a complex condition characterized by fluctuating motor and non-motor symptoms that may negatively impact HRQoL. Methods: Seventy-seven patients (54 females, mean age 45.4 (SD 10.4) years) with a clinically established diagnosis of FMD, including weakness, completed symptom diaries, rating the severity of motor and non-motor symptoms (i.e., pain, fatigue, mood, cognitive difficulties) on a 10-point numerical scale three times daily for seven consecutive days. HRQoL was assessed using the SF-36 questionnaire. For the analysis, fluctuation magnitude was defined in terms of the variability in self-reported symptom scores. Results: The mental component of SF-36 was jointly predicted by the overall severity scores (P<0.001) and overall general fluctuations (P=0.004). The physical SF-36 was found to be related only to the overall symptom severity scores (P<0.001), but not to the overall fluctuations. The assessment of the impact of different components showed that the mental component of SF-36 was significantly influenced by the combined effect of average fatigue (P<0.001), between-day cognitive symptoms fluctuations (P=0.002), and within-day mood fluctuations (P=0.015). Conclusions: This study demonstrated the impact of self-reported symptom fluctuations across multiple motor and non-motor domains on mental but not physical HRQoL in FMD and highlighted the importance of assessing and managing fluctuations in clinical practice., Comment: 21 pages, 3 figures

We present HUP-3D, a 3D multi-view multi-modal synthetic dataset for hand-ultrasound (US) probe pose estimation in the context of obstetric ultrasound. Egocentric markerless 3D joint pose estimation has potential applications in mixed reality based medical education. The ability to understand hand and probe movements programmatically opens the door to tailored guidance and mentoring applications. Our dataset consists of over 31k sets of RGB, depth and segmentation mask frames, including pose related ground truth data, with a strong emphasis on image diversity and complexity. Adopting a camera viewpoint-based sphere concept allows us to capture a variety of views and generate multiple hand grasp poses using a pre-trained network. Additionally, our approach includes a software-based image rendering concept, enhancing diversity with various hand and arm textures, lighting conditions, and background images. Furthermore, we validated our proposed dataset with state-of-the-art learning models and we obtained the lowest hand-object keypoint errors. The dataset and other details are provided with the supplementary material. The source code of our grasp generation and rendering pipeline will be made publicly available., Comment: https://conferences.miccai.org/2024/en/

High-throughput screening, in which multiwell plates are used to test large numbers of compounds against specific targets, is widely used across many areas of the biological sciences and most prominently in drug discovery. We propose a statistically principled approach to these screening experiments, using the machinery of supersaturated designs and the Lasso. To accommodate limitations on the number of biological entities that can be applied to a single microplate well, we present a new class of row-constrained supersaturated designs. We develop a computational procedure to construct these designs, provide some initial lower bounds on the average squared off-diagonal values of their main-effects information matrix, and study the impact of the constraint on design quality. We also show via simulation that the proposed constrained row screening method is statistically superior to existing methods and demonstrate the use of the new methodology on a real drug-discovery system., Comment: Additional supplementary materials can be found at https://sites.miamioh.edu/byran-smucker/research-2/

Domain wall (DW) devices have garnered recent interest for diverse applications including memory, logic, and neuromorphic primitives; fast, accurate device models are therefore imperative for large-scale system design and verification. Extant DW motion models are sub-optimal for large-scale system design either over-consuming compute resources with physics-heavy equations or oversimplifying the physics, drastically reducing model accuracy. We propose a DW model inspired by the phenomenological similarities between motions of a DW and a classical object being acted on by forces like air resistance or static friction. Our proposed phenomenological model predicts DW motion within 1.2% on average compared with micromagnetic simulations that are 400 times slower. Additionally our model is seven times faster than extant collective coordinate models and 14 times more accurate than extant hyper-reduced models making it an essential tool for large-scale DW circuit design and simulation. The model is publicly posted along with scripts that automatically extract model parameters from user-provided simulation or experimental data to extend the model to alternative micromagnetic parameters.

Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals, leading to new bursts of discovery. Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, reproducibility, and automation. Drawing inspiration from advancements in the software industry, we present a roadmap to enhance the reliability and scalability of scientific operations for diverse research teams tackling large and complex projects. We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations in projects ranging from small-scale exploratory studies to large-scale, multi-disciplinary research endeavors. Achieving higher levels of operational maturity necessitates the adoption of new, technology-enabled methodologies, which we refer to as SciOps. This concept is derived from the DevOps methodologies that have revolutionized the software industry. SciOps involves digital research environments that seamlessly integrate computational, automation, and AI-driven efforts throughout the research cycle-from experimental design and data collection to analysis and dissemination, ultimately leading to closed-loop discovery. This maturity model offers a framework for assessing and improving operational practices in multidisciplinary research teams, guiding them towards greater efficiency and effectiveness in scientific inquiry., Comment: 16 pages, two figures

A screening experiment attempts to identify a subset of important effects using a relatively small number of experimental runs. Given the limited run size and a large number of possible effects, penalized regression is a popular tool used to analyze screening designs. In particular, an automated implementation of the Gauss-Dantzig selector has been widely recommended to compare screening design construction methods. Here, we illustrate potential reproducibility issues that arise when comparing screening designs via simulation, and recommend a graphical method, based on screening probabilities, which compares designs by evaluating them along the penalized regression solution path. This method can be implemented using simulation, or, in the case of lasso, by using exact local lasso sign recovery probabilities. Our approach circumvents the need to specify tuning parameters associated with regularization methods, leading to more reliable design comparisons. This article contains supplementary materials including code to implement the proposed methods., Comment: 48 pages, 28 figures

Conventional semiconductor-based integrated circuits are gradually approaching fundamental scaling limits. Many prospective solutions have recently emerged to supplement or replace both the technology on which basic devices are built and the architecture of data processing. Neuromorphic circuits are a promising approach to computing where techniques used by the brain to achieve high efficiency are exploited. Many existing neuromorphic circuits rely on unconventional and useful properties of novel technologies to better mimic the operation of the brain. One such technology is single flux quantum (SFQ) logic -- a cryogenic superconductive technology in which the data are represented by quanta of magnetic flux (fluxons) produced and processed by Josephson junctions embedded within inductive loops. The movement of a fluxon within a circuit produces a quantized voltage pulse (SFQ pulse), resembling a neuronal spiking event. These circuits routinely operate at clock frequencies of tens to hundreds of gigahertz, making SFQ a natural technology for processing high frequency pulse trains. Prior proposals for SFQ neural networks often require energy-expensive fluxon conversions, involve heterogeneous technologies, or exclusively focus on device level behavior. In this paper, a design methodology for deep single flux quantum neuromorphic networks is presented. Synaptic and neuronal circuits based on SFQ technology are presented and characterized. Based on these primitives, a deep neuromorphic XOR network is evaluated as a case study, both at the architectural and circuit levels, achieving wide classification margins. The proposed methodology does not employ unconventional superconductive devices or semiconductor transistors. The resulting networks are tunable by an external current, making this proposed system an effective approach for scalable cryogenic neuromorphic computing.

Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency. Conventional approaches store synaptic weights in non-volatile memory devices with analog resistance states, permitting in-memory computation of neural network operations while avoiding the costs associated with transferring synaptic weights from a memory array. However, the use of analog resistance states for storing weights in neuromorphic systems is impeded by stochastic writing, weights drifting over time through stochastic processes, and limited endurance that reduces the precision of synapse weights. Here we propose and experimentally demonstrate neuromorphic networks that provide high-accuracy inference thanks to the binary resistance states of magnetic tunnel junctions (MTJs), while leveraging the analog nature of their stochastic spin-transfer torque (STT) switching for unsupervised Hebbian learning. We performed the first experimental demonstration of a neuromorphic network directly implemented with MTJ synapses, for both inference and spike-timing-dependent plasticity learning. We also demonstrated through simulation that the proposed system for unsupervised Hebbian learning with stochastic STT-MTJ synapses can achieve competitive accuracies for MNIST handwritten digit recognition. By appropriately applying neuromorphic principles through hardware-aware design, the proposed STT-MTJ neuromorphic learning networks provide a pathway toward artificial intelligence hardware that learns autonomously with extreme efficiency.

This paper will present a multi-fidelity, data-adaptive approach with a Long Short-Term Memory (LSTM) neural network to estimate ship response statistics in bimodal, bidirectional seas. The study will employ a fast low-fidelity, volume-based tool SimpleCode and a higher-fidelity tool known as the Large Amplitude Motion Program (LAMP). SimpleCode and LAMP data were generated by common bi-modal, bi-directional sea conditions in the North Atlantic as training data. After training an LSTM network with LAMP ship motion response data, a sample route was traversed and randomly sampled historical weather was input into SimpleCode and the LSTM network, and compared against the higher fidelity results., Comment: Presented at the 22nd Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT) in Drubeck, Germany on May 25th, 2023

Nanostructured electronic devices, such as those based on graphene, are typically grown on top of the insulator SiO2. Their exposure to a flux of small size-selected silver nanoparticles has revealed remarkably selective adhesion: the graphene channel can be made fully metallized while the insulating substrate remains coverage-free. This conspicuous contrast derives from the low binding energy between the metal nanoparticles and a contaminant-free passivated silica surface. In addition to providing physical insight into nanoparticle adhesion, this effect may be of value in applications involving deposition of metallic layers on device working surfaces: it eliminates the need for masking the insulating region and the associated extensive and potentially deleterious pre- and postprocessing., Comment: 29 pages