Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Multiscale Entropy Analysis for the Detection of Cardiac Pathology

The study investigates the use of the Rényi entropy algorithm with variable threshold and a signal temporal multiscaling approach for analyzing RR interval signals. The study involved 8-minute ECG recordings of 90 participants from the PhysioNet database and grouped into three groups: normal sinus rhythm, cardiac arrhythmia, and congestive heart failure. A time coarse-graining algorithm was used to obtain different temporal scales of the original signal. Rényi entropy probabilities of each scale-factored signal were calculated using a method of density based on sequences of the RR interval time series. ANOVA and post-hoc t-test were used to determine significant differences between the multiscaled Rényi entropy measures of the different groups of RR interval signals. The novel multiscaled Rényi entropy analysis provided enhanced significant discrimination between healthy and pathological (NSR vs. ARR and NSR vs. CHF) signals at post hoc t-test probability values of $p < 5\mathrm{x}10^{-6}$ and within pathological signals (ARR vs. CHF) at $p < 5\mathrm{x}10^{-} 3$. The study concludes that applying a joint approach of cardiac signal temporal multiscaling and calculating its modified Rényi entropy with variable thresholding provides an optimized approach to identifying and separating healthy and pathological cardiac signals, and further supports the complex nature of the heart dynamics.
Origin and evolution of Guinea sorghums

On grounds of comparative morphology and present-day distribution patterns, the most likely progenitor of cultivated sorghum is considered to be the widely distributed verticilliflorum-aethiopicum wild complex that extends across the African savanna. A possible area of initial sorghum domestication is the savanna between western Ethiopia and eastern Chad. The first domesticated sorghums probably resembled race Bicolor. Selections from early Bicolor gave rise to races Guinea, Caudatum and Durra. While Durra may have originated either in north-eastern Africa or in India, Guinea sorghums originated in West Africa.
Fusion of Features with Neural Networks for Prediction of Secondary Neurological Outcome After Cardiac Arrest

As contribution to the 2023 George B. Moody challenge, we - team “BrAInstorm” - aimed for fusing semantic features based on medical knowledge with an end-to-end residual neural network to predict the secondary neurological outcome after successful resuscitation. More precisely, we fused numerical (e.g. age) and categorical (e.g. gender) information as well as features extracted from biosignals: We extracted absolute and relative power bands, coupling, and coherence from standard electroencephalography (EEG) frequency bands. To investigate the interplay between heart and brain, we computed deceleration capacity (DC) from electrocardiograms (ECGs). In contrast to these semantic features, we adapted a residual neural network based on agnostic features which are derived from the training data. The network architecture was originally developed for classification of ECGs and was adjusted to the challenge EEG data. The best metric scores were reached using only the neural network, demonstrating the complexity of outcome prediction and effectiveness of end-to-end methods. We received a challenge score of 0.57 ± 0.15 during 5-fold cross validation on training data and 0.448 on the hidden validation data. On the hidden test data we received a final score of 0.68 (rank 8 of 36).
An 18.8-to-40.4 GHz reconfigurable multi-mode VCO with wide-band common-mode resonance

This paper presents an 18.8-40.4 GHz voltage-controlled oscillator (VCO) for 5G FR2 band in 65-nm CMOS. This circuit uses two VCO cores to cover a wide tuning range. Each core is based on Class-B LC-VCO structure and equipped with 5-bit switched capacitors to extend its tuning range. The low Q wide-band common-mode resonance is used to optimize phase noise. The simulation results show that the proposed oscillator exhibits a 72.9% tuning range from 18.8 to 40.4 GHz and the phase noise performance is less than -105 dBc/Hz at 1-MHz offset. The figure of merit (FoM) is better than 186 dBc/Hz at 1-MHz offset in full frequency band. A state-of-the-art FoMT of 203.4-207.7 dBc/Hz at 1-MHz offset is achieved with power consumption of 4.63-6.86 mW. The variation range of FoM and FoMT is 3.7 dBc/Hz over the full frequency range.
Dynamic Data Partitioning in the WAFL File System

The WAFL file system leverages data partitioning to manage parallel processing of client and internal operations. Such operations are mapped to data partitions based on the data they touch. Changes in load over time can result in imbalances between data partitions, which can limit system parallelism. Ideally, the system would respond by periodically re-optimizing the partition mappings. However, there are a variety of challenges in making such changes on-the-fly without disrupting client accesses. In this paper, we present an approach that dynamically repartitions file system objects to balance partition load and increase parallelism. Our evaluation shows substantial performance gains across a variety of realistic workloads and configurations.
SDM: Sharing-Enabled Disaggregated Memory System with Cache Coherent Compute Express Link

Disaggregated memory has been gaining significant traction as a promising solution for scaling memory capacity and better utilizing memory resources in data centers. However, a disaggregated memory system that can simultaneously achieve high performance and user transparency is still not available. Although some modern interconnect technologies now feature hardware coherence protocols that can potentially enable data sharing among multiple computing nodes in a user-transparent manner, naively applying these technologies to disaggregated memory systems results in non-negligible performance overheads. In this work, we propose SDM, a sharing-enabled, cache-coherent disaggregated memory system that effectively utilizes modern interconnect technology. The key design principle of SDM is to implement a novel, dedicated control flow that efficiently enables data sharing among multiple computing nodes without the need to modify user applications, by leveraging the message types defined in the modern memory expansion standard, Compute Express Link (CXL). We also introduce resource management and speculative memory access mechanisms that do not interfere with normal memory transaction channels, thereby further improving the performance of disaggregated memory systems. We evaluate our design based on an in-house simulation framework with detailed analytical models that mimic a cache-coherent multi-node disaggregated memory system. The results show that SDM outperforms the optimized baseline system, which is similar to the one employing CXL 3.0, by $5.77\times$ and $2.65\times$ for two distinctive benchmark suites.
Demonstration of 5G-Underlay Signal Co-existence

Our demonstration showcases the proof-of-concept for a 5G compatible underlay within 5G standalone (SA) srsRAN software with underlay transmission at the gNodeB and reception at the User Equipment (UE). We used two links between gNodeB and User Equipment, i.e., a zeroMQ link to test and evaluate the functionality of underlay in controlled channel environments and an over-the-air link using software-defined radios to demonstrate the underlay 5G coexistence. Our demonstration shows that low-data rate resilient communication is possible through an underlay concurrent with other 5G NR channels (specifically, the PDSCH) without disrupting their transmissions. The proposed approach can be implemented by customizing the baseband processing at the gNodeB and the UE with very minimal modifications.
A Improved NonIntrusive Load Monitoing Method with Loads Multi-State Characterization

Aiming at the fact that traditional non-intrusive load monitoring and identification methods cannot accurately identify household appliances containing multiple operating states, this paper proposes an identification method for household loads based on multi-state feature information fusion. Firstly, the mRMR feature selection algorithm is used to select a set of optimal feature vectors from each operating state of the load as input, and then the SVM algorithm is used for identification, which achieves the classification effect of quickly and finely identifying multi-state confusable home appliances. Finally, a comparative analysis of the recognition effect considering multi-state feature information is carried out using the Plaid dataset, and the results show that the method proposed in this paper can effectively identify the confusing multi-state appliances and improve the recognition accuracy.
Intelligent Control System for Line Follower Robot: From Theory to Implementation on the Mechatronics Field

In this paper, it is investigated the design and implementation of a line follower robot. The research aims to create a durable and efficient chassis that is both lightweight and reliable. To achieve this, the methodology has developed a simple and streamlined electronic circuit printed on a circuit board and an advanced control system that enhances the robot's autonomy and adaptability. We aim to bridge the gap between theoretical concepts and practical applications in the field of mechatronics, and this line-follower robot will prove to be a valuable contribution to the field.
Security Risk Assessment of Metaverse Based Healthcare Systems Based on Common Vulnerabilities and Exposures (CVE)

Metaverse has been gaining the attention of industry and academia equally in the recent past. With the immense capabilities backed by cutting-edge technologies, it has the potential to revolutionize every domain, especially healthcare. Metaverse can help to give customized treatment by a specialist to a patient anywhere in the world. However, akin to any new technology, the adoption of a metaverse in healthcare is susceptible to security issues and vulnerabilities. A proper study of the weakness and potential vulnerabilities is required before the active integration of metaverse into healthcare. Therefore, in this paper, we analyze the existing applications of the metaverse and its enabling technologies in the healthcare domain and investigate the security issues that exist or are likely to arise. Through the literature analysis, we identified three essential metaverse components for future healthcare systems, namely, Extended Reality (XR), Blockchain, and Avatar. These components and their combinations in varying degrees will give different dimensions to metaverse-based healthcare solutions. Further, we analyzed the risks associated with these components and their combinations. Here, the overall risk assessment was based on the existing literature and vulnerabilities, new cyber attacks, and the current trend of cyber attacks in the healthcare domain. The vulnerabilities collected from the Common Vulnerabilities and Exposures (CVE) site were used for studying the risks associated with metaverse, healthcare and their integration.
Convolutional Neural Networks with Neural Memory Ordinary Differential Equations

Combining ordinary differential equations (ODEs) with neural networks (NNs) has emerged as a promising research direction in recent times. This integration allows for the development of deep neural networks based on neural memory ODEs, which have been designed and studied in the previous research. To further explore this direction, we have proposed a set of conjectures and designed corresponding network architectures for verification. Our experimental results, based on the CIFAR-10 dataset, reveal that simply linearly stacking nmODEs (neural memory ODEs) modules has a limited impact on the performance improvement of deep networks. However, a more effective approach involves replacing the linear operations in the nmODEs modules with convolution operations, leading to enhanced classification performance. Furthermore, incorporating shortcut architectures into the network design can further boost this performance. Overall, these findings demonstrate the great potential of combining ODEs with NNs and highlight the role that specific network architectures can play in enhancing overall performance.
Prediction of Functional Recovery Post-Cardiac Arrest Using an Ensemble of Extreme Gradient-Boosted Trees

Predicting the outcome of critically ill patients after cardiac arrest is a substantial clinical challenge. As part of the 2023 George B. Moody PhysioNet Challenge, we used an ensemble of extreme gradient boosted (XGB) trees, trained on electroencephalogram (EEG) data across three distinct levels of analysis to predict neurological outcomes. To this end, we preprocessed raw EEG recordings via filtering and motion correction and extracted several features, including connectivity, power spectral density (PSD), and coherence. The first step of model training and optimization occurred at the Recording level, where we trained XGB trees on each feature set (e.g., PSD) derived from every EEG recording. These models predicted the neurologic outcome for each recording. At the Patient level, we computed the median of each feature set for each patient, which we used to make patient-specific predictions. Finally, the Challenge level merged and optimized predictions from the previous levels, thus synthesizing patient and recording specific predictions. Our approach produced successful results on the test set, with Challenge Scores of 0.371 (12 hrs), 0.480 (24 hrs), 0.569 (48 hrs), and 0.678 (72 hrs), resulting in a final ranking of 9/36 (team: WesternUni). The approach showed particular promise at early timepoints, placing 2nd when only using EEG available 12-hours post-ictus.
Impact of Large-Scale PV-BESS on Dynamics of Power System Oscillatory Modes

The target of zero carbon emission from electric power generation results in the replacement of fossil-based energy generation by renewable sources. The increase of power electronically interfaced photovoltaic (PV) power plants into power systems causes severe stability problems due to lack of inertia and damping effect. The battery energy storage systems (BESS) at PV stations can be controlled by a virtual inertia controller (VIC) to provide virtual inertia and damping support while smoothing the power fluctuations of PV power plant. As BESS with VIC mimics the characteristics of a synchronous generator, the influence of BESS on low-frequency electro-mechanical oscillations (LEO) is significant. This research paper investigates the probabilistic distribution of power systems LEOs for various controller gain of VIC and stochastic variation of solar irradiance under the influence of phase-locked loop (PLL) dynamics. The modal interaction of PLL and virtual inertia controller with power system oscillatory modes are investigated by stochastic eigenvalue analysis using Monte Carlo simulation approach.
Prospects of Cuffless Pulse Pressure Estimation from a Chest-Worn Accelerometer Contact Microphone

This study explores the correlation between pulse pressure (PP) and heart sounds on the chest wall. The proposed framework leverages a sensitive accelerometer contact microphone (ACM) to record chest vibrations. A discrete wavelet transform (DWT) decomposes the chest vibration recordings into a set of sub-bands, from which several time-domain features are extracted. An extreme gradient boosting (XGBoost) regressor is trained on the feature space for PP estimation, and the estimated values are compared with PP readings from a standard cuff-based blood pressure monitor. The performance of the model is evaluated on 20 patients with cardiovascular diseases (CVDs). Average root mean square error (RMSE), mean absolute error (MAE), and accuracy of $11.41 (\pm 6.42) mmHg, 10.49 (\pm 6.73)\ mmHg$, and $77.14\% (\pm 19.09\%)$ are achieved, respectively, for a leave-subject-out validation. Additionally, the performance of the model is assessed through a 10-fold cross validation where an average accuracy of 95.68% is obtained, implying high consistency with ground-truth values. The most significant signal sub-bands for PP estimation are found to be high-frequency bands such as 1–2 kHz and 512-1,024 Hz, as well as medium-frequency bands of 32–64 Hz and 64–128 Hz. It is also demonstrated that the most contributive sub-band to PP estimation is 1–2 kHz.
Robust Learning for Autonomous Driving Perception Tasks in Cyber-Physical-Social Systems

Modern data-driven perception systems accomplish excellent performance in a simple, constrained environment but have unreliable detection in real, complex, and challenging weather situations. Therefore, robust perception performance in new and unanticipated domains is a crucial factor for autonomous driving. To this end, this paper proposes a novel robust learning scheme for arbitrary domain perturbations. In this paper, the model robustness is characterized by the anchor data and corresponding various domain shift directions. The robust learning problem is then formulated as a min-max optimization problem conjugated to the constrained exploration space defined by the perturbation model and the semantic parameter. The proposed robustify procedure solves the optimization problem considering the worst-case scenario, which improves the model resilience in multiple domain shift directions, especially for those variations in Cyber-Physical-Social Systems (CPSS).
Task-oriented Sequential Pose Motion Primitives

Deconstructing a task into multiple phases and connecting each phase in a sequence to achieve complex motion planning is the prevailing approach in the field of robotics research. However, this paradigm also faces the problem of how to increase the generalization capacity of each stage and ensure effective smooth transitions between adjacent stages. To address this problem, we propose task-oriented sequential postural motion primitives. A number of phases are partitioned based on task characteristics, and the pose movement primitives for each phase are parameterized in a data -driven manner to facilitate the acquisition of specific skills from multiple demonstration trajectories. Besides, the problem of transitioning between temporally sequential motion primitives is modeled as a tracking problem of a moving target in order to achieve a seamless merging of movements. Finally, experiments on the Sawyer robot demonstrate the effectiveness and feasibility of the proposed method.
A 32 Gb/s NRZ PI-Based Baud-Rate Digitial Clock and Data Recovery Circuit in 28nm CMOS Technology

The paper presents a novel baud rate Clock Data Recovery (CDR) architecture with a maximum rate of 32Gb/s based on a time-interleaved ADC-based Serdes designed in the 28nm process. The paper discusses the architectural details of the CDR, including the use of MMPD for early and late judgments, the design of the proportional-integral branch and the octagonal PI with 128 phases. It also analyzes the linearized model of the CDR and estimates the jitter tolerance. The simulation results show that the proposed CDR design effectively recovers the clock and maintains a stable BER at a data rate of 32Gb/s, while also having good jitter tolerance.
Estimating groundwater residence time and recharge patterns in a saline coastal aquifer

A detailed study using environmental tracers such as chloride (Cl−) and tritium (3H), deuterium (2H) and oxygen (18O) isotopes was performed in an alluvial coastal aquifer in two contrasting environments (urban and agricultural). These environmental tracers combined with a high‐resolution multi‐level sampling approach were used to estimate groundwater residence time and recharge patterns and to validate the hydrogeochemical conceptual model already proposed in previous studies. δ18O and δ2H combined with Cl− data proved that the hypersaline groundwater present in the deepest part of the aquifer was sourced from the underlying hypersaline aquitard via an upward flux. Both chemical and isotopic data were employed to calibrate a density‐dependent numerical model based on SEAWAT 4.0, where 3H and Cl− were helped quantifying solutes transport within the modelled aquifer. Model results highlighted the differences on estimated recharge in the two contrasting environments, with the urban one exhibiting concentrated recharge because of preferential infiltration associated to the storm water drains network, while scarce local recharge characterized the agriculture setting. In the urban field site, is still possible to recognize at 9 m b.g. l. the input of the atmospheric anthropogenic 3H generated by testing of thermonuclear weapons, while in the agricultural field site, the 3H peak has been washed out at 6 m b.g. l. because the groundwater circulation is restricted only to the upper fresh part of the aquifer, drained by the reclamation system. The presented approach that combined high‐resolution field monitoring, environmental tracers and numerical modelling, resulted effective in validating the conceptual model of the aquifer salinization. Copyright © 2016 John Wiley & Sons, Ltd.
NLAC: A Self-Maintained Trust Overlay for the XRP Ledger

This paper presents NLAC, a framework for creating, managing, and maintaining trusted Unique Node Lists (UNLs) for the XRP Ledger. NLAC consists of three modules that automate the generation of UNLs, including membership management, classification, and grouping. The framework uses layered Token-Curated Registries (TCRs) and an optimization algorithm to ensure the dispersion of authority within the UNL space. NLAC improves the safety and liveness of the XRP Ledger by achieving maximum trust in the UNL space.
IRIS-DMEM: Efficient Memory Management for Heterogeneous Computing

This paper proposes an efficient data memory management approach for the Intelligent RuntIme System (IRIS) heterogeneous computing framework along with new data transfer policies. IRIS provides a task-based programming model for extreme heterogeneous computing (e.g., CPU, GPU, DSP, FPGA) with support for today's most important programming languages (e.g., OpenMP, OpenCL, CUDA, HIP, OpenACC). However, the IRIS framework either forces the programmer to introduce data transfer commands for each task or relies on suboptimal memory management for automatic and transparent data transfers. The work described here extends IRIS with novel heterogeneous memory handling and introduces novel data transfer policies by employing the Distributed data MEMory handler (DMEM) for efficient and optimal movement of data among the various computing resources. The proposed approach achieves performance gains of up to 7× for tiled LU factorization and tiled DGEMM (i.e., matrix multiplication) benchmarks. Moreover, this approach also reduces data transfers by up to 71% when compared to previous IRIS heterogeneous memory management handlers. This work compares the performance results of the IRIS framework's novel DMEM with the StarPU runtime and MAGMA math library for GPUs. Experiments show a performance gain of up to 1.95× over StarPU and 2.1× over MAGMA.