Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Two Design Principles of Unpowered Ankle-Foot Orthoses for Supporting Push-Off: A Case Study

Ankle propulsion is essential for efficient human walking. In recent years, several working principles have been investigated and applied to ankle-foot orthoses (AFOs) to enhance the work of the plantarflexor muscles and achieve proper propulsion during gait. Comparing the performance and effectiveness of different designs is difficult because researchers do not have a standardized set of criteria and procedures to follow. This leads to a wide range of tests being conducted, with variations in important factors such as walking speed and assistance provided, which greatly affect users' kinematics and kinetics. In this work, we investigate the possibilities and potential benefits of two of the most important design principles for supporting ankle propulsion with unpowered AFOs. To this end, we present and evaluate two AFO prototypes with springs parallel to the Achilles tendon based on: (i) a linear compression spring, and (ii) a customized leaf spring-cam transmission with a non-linear ankle torque-angle curve. The effects of both AFOs are reported for a case study with one healthy participant using both prototypes at two walking speeds under the same experimental conditions. Large reductions in muscular activity were found when the user received assistance, and ankle kinematics were influenced by the different assistance approaches. This case study was intended as a first step to provide insights on how two promising principles can passively support push-off during gait.
Performance Modeling and Estimation of a Configurable Output Stationary Neural Network Accelerator

Neural network accelerators are designed to process Neural Networks (NN) optimizing three Key Performance Indicators (KPIs): latency, power, and chip area. This work is based on the study of Gemini, an industrial prototype near memory computing inference accelerator designed using a high-level synthesis technique. Gemini is an output stationary configurable accelerator that achieves its performance based on two structural parameters. The measurement of the KPIs requires simulations that are time-consuming and resource-intensive. This paper presents a high-level practical estimator that can instantly predict the KPIs depending on the NN and the Gemini configuration. The latency is accurately derived using an analytical model based on the architecture, the operators scheduling and the NN characteristics. The power and the chip area are computed analytically and the models are calibrated using simulations. Finally, we show how to use the estimator to derive Pareto optima for choosing the best Gemini configurations for a VGG-like NN.
Lateral interaction between metamorphic core complexes and less-extended, tilt-block domains: the Alpujarras strike-slip transfer fault zone (Betics, SE Spain)

The Alpujarras area in southeastern Spain exhibits one of the scant documented examples of extension related strike-slip faults bordering core complexes in the world. Faults of the Alpujarras fault zone define a regional-scale complex ENE-striking transfer zone that marks the boundary among the Sierra Nevada elongated dome, a highly-extended, constricted core-complex, and a less-extended domain formed by large-scale tiltblocks. Detailed mapping and structural analysis show that the Alpujarras fault zone is an integral part of the WSW-directed normal fault systems that thinned the Betic hinterland during the middle Miocene to Recent time. Fault patterns and palaeostress analysis both indicate that dextral movement along the strike-slip faults was induced by a local stress field with a sub-horizontal E–W- to ESE–WNW-trending maximum principal stress axis, which is synchronous with the regional stress field driving the normal fault systems. Palaeostress analysis also indicates subsequent variations in the stress field with a sub-horizontal NW–SE- to N–S-trending maximum principal stress axis, thus producing both the tectonic inversion of the northern fault of the Alpujarras system and shortening of the unloaded extensional detachment footwall. A simplified kinematic model for the tectonic evolution of the Alpujarras area from the middle Miocene to Recent emphasizes the kinematic coupling of normal faults and strike-slip transfer zones in the extensional process.
What 2‐D multichannel seismic and multibeam bathymetric data tell us about the North Sumatra wedge structure and coseismic response

Recent large earthquakes have prompted studies to reevaluate seismicity and rupture propagation behavior along the world's major subduction margins. Our study area covers the entire fore arc from northwest of northern Sumatra to west of Simeulue Island, the southern portion of the 2004 tsunamigenic earthquake rupture zone. The accretionary prism width is up to ~180 km, water depths between ~4.5 km near the Sunda Trench and <1 km on fore‐arc high. The wedge consists of a steep outer slope (5–12°), a plateau ~100–120 km wide with anticlinal folds spaced 2–15 km apart, and a steep inner slope adjacent to the Aceh Basin. Analysis of seismic profiles and bathymetry reveal three main structural zones consistent along‐strike, from the trench landward: (1) predominantly landward vergent folds, (2) mixed vergent folds, and (3) predominantly seaward vergent folds. This paper uses those zones to propose a geometry of an underlying rigid backstop. This backstop is seaward dipping and extends from under the Aceh Basin to beneath the mixed vergence zone. A dynamic backstop possibly exists seaward of the rigid backstop and is responsible for the steep slope of the outer prism. Indurated accreted sediments form the landward vergence zone. Along with the possible dynamic backstop beneath the outer wedge, and the rigid backstop in the inner wedge, all behave as a solid block coseismically. This block allows great earthquake rupture to propagate farther seaward toward the Sunda Trench, with resultant hazardous tsunamigenic potential.
Solving Inverse Problems in Compressive Imaging with Score-Based Generative Models

Snapshot Compressive Imaging (SCI) is a technique for capturing high-dimensional data through snapshot measurements using a two-dimensional (2D) detector. This approach is accomplished via coded aperture compressive temporal imaging (CACTI), which involves applying a temporally variant mask to spatially encode each sequential signal before aggregating the encoded information into a single compressed measurement. The objective of our work is to develop algorithms capable of reconstructing each video frame as a 3D data cube from its 2D measurement. To achieve this goal, we introduce multiple approaches that utilize unconditional and pre-trained 2D score models for video frame reconstruction. Our method involves modeling both the forward perturbation process of the data distribution and its reverse process as stochastic differential equations (SDEs). We also employ score-based deep learning models to estimate the scores of the data distribution across different time steps. Differing from many applications, our sampling process relies on the observed measurement, which directly corresponds to pixel values, rather than class labels. We demonstrate that employing traditional score-based generative methods with 2D score models in SCI, or integrating them into the plug-and-play framework as a deep generative prior, presents challenges. Furthermore, we propose ideas to address these limitations for future research.
Enhanced performance of LED-based photoacoustic imaging using short-lag spatial coherence beamforming: comparative analysis with laser-based imaging

Short-lag spatial coherence beamforming (SLSC) has shown promise in improving the signal-to-noise ratio (SNR) of photoacoustic (PA) images acquired under conditions of low optical fluence. However, the performance of this technique has not yet been evaluated with LED-based PA imaging (PAI). Therefore, the objective of this study was to compare the resulting image quality when applying SLSC and a conventional amplitude-based (FFT) beamformer. The influence of both LED and laser illumination was investigated using the same PAI system on a tissue-mimicking phantom and in ex vivo experiments. In both experimental conditions and illumination strategies, the SLSC method exhibited superior performance compared to FFT. Lowering the LED PAI frame rate, by increasing frame averaging, resulted in more significant SNR improvement for amplitude-based PAI compared to SLSC. For example, in the ex vivo experiment, the SNR values with SLSC exhibited enhancements of 13.6 dB and 11.1 dB for LED and laser illumination at 10 Hz, respectively. Meanwhile, at a frame rate of 0.25 Hz, the improvements were less pronounced, specifically 11.2 dB and 7.4 dB for LED and laser illumination, respectively. In conclusion, our results demonstrate that SLSC can improve SNR of LED-based PAI, particularly in conditions of low-SNR.
Technical Analysis of Stock Market Trends using LSTM for Price Prognosis

This study proposes the utilization of Long Short Term Memory (LSTM) brain networks for specialized analysis of stock exchange patterns and trends to anticipate future stock prices. In this particular study, a comparison is done with three other Machine Learning techniques to show that LSTM is the best implementation. The LSTM model is trained on the previous stock market data, including prices and trading volumes, to learn complex patterns and relationships with stock price trends. The model's accuracy in predicting future stock prices is evaluated, and the results show that LSTM is an effective tool for technical analysis, outperforming traditional time-series forecasting methods. LSTM's ability to capture long term dependencies and unpredictable relationships in the dataset makes it a valuable tool for traders and investors seeking to make informed decisions based on technical analysis of stock market trends. The study demonstrates the potential of LSTM in stock market forecasting, improving investment decision making with accurate price predictions.
Practical Tie-Breaking for Parallel/Distributed Simulations

In this paper, we discuss a tie-breaking strategy based on a bitwise comparison of event payload that allows parallel and distributed discrete-event simulations to observe a deterministic order in the execution of events, even in the presence of event ties. This approach provides practical usability whenever model-assisted tie-breaking is unavailable, thus ensuring that multiple simulation executions provide deterministic behaviour and repeatable results. Moreover, it ensures that the selected order of events is also consistent with sequential executions. We discuss the theory behind this strategy and experimentally show that the performance drop is imputable to event queue management when relying on tie-breaking strategies like the ones discussed in this work.
Kirigami Integrated Yagi-Uda Antenna for Strain Sensing in Biomedical Applications

This paper presents a low-profile flexible microstrip Yagi- Uda array antenna with strain sensing capability assisted by a Kirigami structure and design. The Yagi-Uda array was integrated on a PET substrate with a thickness of $100\mu{\mathrm{m}}$, demonstrating strain-induced changes in the antenna's resonant frequency and impedance bandwidth. Operating based on the concept of mutual impedance, displacing the director elements of the antenna by an applied force (i.e., strain), affects the mutual coupling of array elements, and consequently impacts the antenna parameters. The unstrained antenna operated at 5.32 GHz, showing a 1.7% fractional bandwidth with a measured estimated gain of 5.86 dBi. For variations in the range of 3–7 cm from the initial length, equivalent to 27-63% strain, the observed shift in the resonant frequency was $140\sim 4$ MHz, respectively. This antenna array with both communication and strain-sensing capabilities is a proof of concept for multi-functional, wearable/flexible microwave devices in biomedical applications.
Design of a Neural Helmet for the Treatment of People With Dementia

This research presents the design and control of a neural helmet for the treatment of people with dementia. The helmet design integrates sensors, electrodes, and advanced algorithms to record and stimulate brain activity that include person comfort, precise sensor placement, and safety. The development of the project enables the collection of data on brain activity and specific modulation of neural function. The control and design of the proposed system will provide improvement of cognitive function including memory, attention and executive function, in people with dementia.
Detection of Natural Pulse Waves (PWs) for Anisotropy characterization: an in vitro Study

Several studies showed that the propagation of natural Mechanical Waves can be used to characterize the elastic properties of the myocardium. The closure of Aortic and Mitral valves generates a shear wave that can be used to estimate tissue stiffness. Similarly, the Atrial Kick (AK) generates a wave comparable to Pulse Waves (PWs) in the arteries, which can also be used to characterize tissue stiffness. However, little is known about the possibility of estimating tissue anisotropy from PWs propagation. In this work we tried to answer this question by studying the effect of anisotropy on PWs propagation in an experimental setup. Two anisotropic tube phantoms were manufactured using Polyvinyl Alcohol (PVA). After two freeze-thaw cycles, axial anisotropy was induced in one tube by stretching the material, whereas helical anisotropy was induced in the other tube by stretching and twisting. The phantoms were placed in a water tank and connected to a peristaltic pump to induce PWs. Beamformed data were acquired for 2 seconds using a Vantage 256 scanner equipped with L7-4 probe. In the axial anisotropy case, the estimated time-of-arrival is uniform over the cross-section (delay < 1ms). In the helical anisotropy case, the estimated time-of-arrival increases along the anticlockwise direction (0 to 10 ms). These results illustrate that PW propagation is altered by anisotropy in the material and that it can be estimated using conventional tissue elasticity imaging methods.
Unraveling the Potential of Heart Rate Variability for Type 1 Diabetes Prediction

Type 1 diabetes (T1D) is an irremediable chronic disease with reduced metabolic functions of pancreatic $\beta$-cells, causing insulin diminution and elevated blood glucose concentration. Automatic diagnosis of T1D is crucial in early clinical interventions followed by appropriate management initiatives. This work explores different machine learning (ML) techniques and the ensemble of ML to non-invasively detect T1D patients exploiting the heart rate variability (HRV) captured from ECG. The ensemble classifiers are formed by assigning weights to each ML model, where the area under the receiver operating characteristics curve (AUC) is used as weights, while soft voting generates classification results. An extensive experiment has been carried out to find the appropriate hyperparameters for each ML model through grid search, while AUC is selected as the performance metric due to unbiasedness to class imbalance. A total of 115 ECG recordings (36 diabetics, 79 healthy) from 29 subjects (20 healthy, 9 T1D patients) of the D1NAMO dataset are used in the experiments. The highest classification performance is achieved using the k-nearest neighbor (KNN) classifier achieving an AUC of $0.750\pm 0.090$. The outcome of this work will enhance the understanding of distinct HRV features of diabetic and healthy individuals in T1D diagnosis and characterize the impacts of T1D on cardiac asynchrony.
Symbolic Fixpoint Algorithms for Logical LTL Games

Two-player games are a fruitful way to represent and reason about several important synthesis tasks. These tasks include controller synthesis (where one asks for a controller for a given plant such that the controlled plant satisfies a given temporal specification), program repair (setting values of variables to avoid exceptions), and synchronization synthesis (adding lock/unlock statements in multi-threaded programs to satisfy safety assertions). In all these applications, a solution directly corresponds to a winning strategy for one of the players in the induced game. In turn, logically-specified games offer a powerful way to model these tasks for large or infinite-state systems. Much of the techniques proposed for solving such games typically rely on abstraction-refinement or template-based solutions. In this paper, we show how to apply classical fixpoint algorithms, that have hitherto been used in explicit, finite-state, settings, to a symbolic logical setting. We implement our techniques in a tool called GENSys-LTL and show that they are not only effective in synthesizing valid controllers for a variety of challenging benchmarks from the literature, but often compute maximal winning regions and maximally-permissive controllers. We achieve 46.38X speed-up over the state of the art and also scale well for non-trivial LTL specifications.
Influence of Amorphous State Local Structure in Tinicu Alloys on Structure and Phase Transformations during Crystallization

The study of TiNiCu alloy samples in the initial state after quenching by EXAFS revealed the dependence of the local atomic structure of the alloys on the copper content. Studies of the structure of alloys by X-ray diffraction (XRD) using synchrotron radiation with a wavelength of 0.8 Å showed that in the initial state after quenching, the alloys are in an amorphous state. After isothermal crystallization of alloys at a temperature of 500° C for 300 s, it was found that samples of alloys with a copper content of more than 30 at. % after crystallization became so brittle that they were unsuitable for further research. This is a consequence of the peculiarities of the local atomic structure of the alloys identified by the EXAFS method. Temperature intervals and the nature of alloy phase martensitic transformations (MT) were determined by differential scanning calorimetry (DSC).
Influence of Image Discretization and Patch Size on ULM Localization Precision

To obtain super-resolved maps of the microvasculature using ultrasound localization microscopy, the localization precision of the microbubbles (MB) is essential. However, previous studies showed some counterintuitive results across different localization methods and image discretizations. Therefore, this study systematically analyzes the influence of image discretization and patch size on localization precision to derive guidelines for a good choice of parameters. For this purpose, we simulated MB images with Gaussian point-spread functions (PSF) of varying width σ at randomly generated subpixel positions. Rician distributed noise was added. The MB localizations were carried out on patches of different sizes (number of pixels N × N). We investigated two localization techniques: a parametric Gaussian fit and the radial symmetry method. For the Gaussian fit, the localization error is Gaussian distributed around zero and achieved standard deviations (STD) of about 0.03 times the pixel size Δ at its optimum performance. However, as the ratio of PSF width σ to pixel size Δ decreases to σ⁄Δ ≈ 0. 4, the error distribution becomes bimodal across all patch sizes, resulting in an increased localization STD of around 0. 1Δ. With even smaller ratios, the STD deteriorates completely. For too small patches, the STD also increases, because only the peak region of the PSF is used for localization. The same findings apply to the radial symmetry method, albeit with slightly lower precision. Based on our results, we conclude that for precise MB localization, the necessary pixel (voxel) size Δ in each direction (axial, lateral, and elevational in case of 3D) is determined only indirectly by the wavelength but mainly by the width σ of the PSF. The ratio of PSF width to pixel size should be σ⁄Δ ≥ 0.6. Regarding the patch size, the rule N ≥ 2σ⁄Δ + 3 is recommended.
Towards in-Home Quantitative Gait Assessment Using Millimeter-wave Radar

Recent findings suggest that gait assessment is a useful tool for the early detection and prediction of neurode-generative diseases (such as Alzheimer's and Parkinson's) and fall risk in the elderly. This work proposed a method based on millimeter-wave radar for in-home quantitative gait assessment. The results obtained from experimental studies demonstrated the high capability of the millimeter wave radar for capturing instantaneous gait change in real-life environments. Particularly, the developed method gives high accuracy in the measurement of step time, which is a key gait parameter for predicting fall risk and neurodegenerative diseases in the elderly.
Unfolding temporal networks through statistically significant graph evolution rules

Understanding and extracting knowledge from temporal networks is crucial to understand their dynamic nature and gain insights into their evolutionary characteristics Existing approaches to network growth often rely on single-parameterized mechanisms, neglecting the diverse and heterogeneous behaviors observed in contemporary techno-social networks. To overcome this limitation, methods based on graph evolution rules (GER) mining have proven promising GERs capture interpretable patterns describing the transformation of a small subgraph into a new subgraph, providing valuable insights into evolutionary behaviors However, current approaches primarily focus on estimating subgraph frequency, neglecting the evaluation of rule significance. To address this gap, we propose a tailored null model integrated into the GERM algorithm, the first and most stable graph evolution rule mining method. Our null model preserves the graph’s static structure while shuffling timestamps, maintaining temporal distribution, and introducing randomness to event sequences By employing a z-score test, we identify statistically significant rules deviating from the null model We evaluate our methodology on three temporal networks representing co-authorship and mutual online message exchanges Our results demonstrate that the introduction of the null model affects the evaluation and interpretation of identified rules, revealing the prevalence of under-represented rules and suggesting that temporal factors and other mechanisms may impede or facilitate evolutionary paths. These findings provide deeper insights into the dynamics and mechanisms driving temporal networks, highlighting the importance of assessing the significance of the evolution patterns in understanding network evolution.
Application of Optimization Algorithm in Meniscus-Constrained Electrodeposition of Three-Dimensional Metal Microstructure

Precision metallic micro-nano structures fabricated cost-effective by meniscus-confined electrodeposition (MCED) have potential applications in sensors and electronic devices. The tuning of deposition quality and deposition rate is of great importance for the MCED of micro-nano structured devices. Currently, accurately predicting the combination of process parameters during deposition is an effective way to regulate the quality and rate of deposited structures. Therefore, in this study, the process parameters of the MCED are analyzed and data sources are obtained through simulation, and the MCED process parameters are optimized by optimization algorithm to obtain high-performance metal microstructures. In addition, experiments with the optimized parameters on MCED equipment were performed with a maximum error of 7.1% relative to the target value electrochemical deposition rate. The processing of process parameters by optimization algorithms can regulate the quality and speed of deposited metal microstructures, and the results of the study are essential guidance for the actual deposition.