Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the Observation Sequence Duration of Hidden Markov Models for QRS Complex Detection in Single-Lead ECG Recordings

Hidden Markov models (HMM) are useful tools to characterize the dynamics of observation sequences. However, the duration of the observation sequence to characterize is often chosen empirically, based on a priori information. In this paper, we used a grid search approach to optimize two hyperparameters of an HMM-based QRS complex detector, namely, the duration of the observation sequence to be characterized and the adaptive decision threshold that compares the difference of log-likelihoods of observations from two competing HMM. To assure the reproducibility of the results, we have run the optimization process ten times using different random seeds per realization. Using the ECG signals from the MIT-BIH Arrhythmia database, we have found an optimal adaptive decision threshold of 60% but the optimal value of the observation sequence duration varies from 100 ms to 120 ms for different realizations of the optimization process. Consequently, the different parameters of the HMM-based QRS complex detector found in each realization do not always lead to the same detection performance. Using the optimal values, the detector achieves a sensitivity of 96.53% and a positive predictivity of 98.16%.
Novel Metric Using Laplacian Eigenmaps to Evaluate Ischemic Stress on the Torso Surface

The underlying pathophysiology of myocardial ischemia is incompletely understood, resulting in persistent difficulty of diagnosis. This limited understanding of underlying mechanisms encourages a data driven approach, which seeks to identify patterns in the ECG data that can be linked statistically to disease states. Laplacian Eigen-maps (LE) is a dimensionality reduction method popularized in machine learning that we have shown in large animal experiments to identify underlying ischemic stress both earlier in an ischemic episode, and more robustly, than typical clinical markers. We have now extended this approach to body surface potential mapping (BSPM) recordings acquired during acute, transient ischemia episodes from animal and human PTCA studies. Our previous studies, suggest that the LE approach is sensitive to the spatiotemporal electrocardiographic consequences of ischemia-induced stress within the heart and on the epicardial surface. In this study, we expand this technique to the body surface of animals and humans. Across 10 episodes of induced ischemia in animals and 200 human recordings during PTCA, the LE algorithm was able to detect ischemic events from BSPM as changes in the morphology of the resulting trajectories while maintaining the superior temporal performance the LE-metric has shown previously.
Role of Cardiac Microstructure Variability on Ventricular Arrhythmogenesis

The propagation of cardiac electrical excitation is influenced by tissue microstructure. Reaction-diffusion computational models of cardiac electrophysiology incorporating both dynamic action potential (AP) behaviour and image-based myocardial architecture provide an approach to study the complex organisation of excitation waves within variable myocardial structures. The role of tissue microstructure (cardiomyocyte and sheetlet orientations) on organ-scale arrhythmic excitations was investigated. Five healthy rat ventricle datasets were obtained using diffusion tensor MRI (DTI). The Fenton-Karma minimal AP model was modified to reproduce the rat AP duration and restitution. Re-entrant scroll waves were initiated in the five anatomical models at ten locations for three microstructure scenarios: (i) isotropic; (ii) anisotropic; and (iii) orthotropic. Variability in anatomy and microstructure caused simulated scroll waves to self-terminate, remain tachycardia-like, or degenerate into fibrillatory activity. Whilst inclusion of DTI-based microstructure increased total scroll wave filament length to differing extents between the five hearts, overall mean filament dynamics were quantitatively similar under anisotropic and orthotropic conditions. This study highlights the important role of inter-subject structural variability.
CPR Guideline Chest Compression Depths May Exceed Requirements for Optimal Physiological Response

A twelve-animal porcine study dataset was retrospectively analyzed to assess associations between chest compression (CC) depth, systolic blood pressure (SBP) and end-tidal carbon dioxide (EtCO2). Manual CCs were applied for 7 two-minute episodes, at CC depths between 10mm−55mm. A rolling 15s analysis window was applied to the continuous signals. Mean peak values were calculated for each window. Correlation analysis was applied to assess strength of association. Optimal CC depth to achieve physiological targets was determined via cut-off analysis. A total of 672 observations for each variable were available for analysis. Pearson correlations (95% confidence interval; p-value) between CC depth and both SBP and ETCO2 were 0.84 $(0.82, 0.86; p < 0.001)$ and 0.75 $(0.71, 0.78; p < 0.001)$ respectively. Optimal CC depth cutoff (sensitivity, specificity) to achieve $SBP \ge 100mmHg$ and $EtCO_{2}\ge 10mmHg$ was 33 mm (98.29%, 88.94%)and 20 mm (95.08%, 78.30%) respectively. A reasonable relationship between CC depth and physiological response was observed. Optimal SBP and EtCO2 cut-offs were achieved significantly below guideline depths. Furthermore, cut-off analysis suggests a disparity between CC depth and physiological targets.
Arousal Detection in Obstructive Sleep Apnea Using Physiology-Driven Features

Obstructive sleep apnea (OSA) is a condition in which a person repeatedly stops breathing during sleep due to closure of the upper airway, leading to a cycle of sleep fragmentation and intermittent hypoxia (oxygen deficiency). Conventional methods for detecting and quantifying OSA are largely based on physiological monitoring during sleep followed by manual labeling of sleep stages and arousals. Here there is scope for computerized methodologies that can efficiently and objectively perform this characterization of sleep. As part of the CinC/Physionet 2018 challenge to automatically detect arousals in a large, expert-annotated sleep dataset, we extracted 27 spectral and time domain features, chosen for their physiological relevance, from the available training set and implemented two contrasting methods, Generalized Linear Model (GLM) and Random Forest (RF), to classify arousals and non-arousals. We were able to achieve non-trivial classification accuracy, even in an imbalanced data set with far fewer arousals than non-arousals. This suggests that large machine learning problems can still benefit from physiology-informed feature selection, especially in the biomedical space.
Transient community detection and its application to data forwarding in delay tolerant networks

Community has received considerable attention because of its application to many practical problems in mobile networks. However, when considering temporal information associated with community (i.e., transient community), most existing community detection methods fail due to their aggregation of the contact information into a single weighted or unweighted network. In this paper, we propose a contact-burst-based clustering method to detect transient communities by exploiting the pairwise contact processes. In this method, we formulate each pairwise contact process as regular appearance of contact bursts, during which most contacts between the pair of nodes happen. Based on such formulation, we detect transient communities by clustering the pairs of nodes with similar contact bursts together. We also propose a new data forwarding strategy for delay tolerant networks in which transient communities serve as the data forwarding unit. Evaluation results show that our strategy can achieve much higher data delivery ratio than traditional community-based strategies with comparable network overhead.
Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.
Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates

High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.
Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurements of AGB may be a challenge because of their remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund Oceanium project that monitors 10,000 ha of mangrove plantations. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric support vector regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2, and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m, respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in young mangrove plantations.
Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. A year of 30 m Landsat-8 and 10 m Sentinel-2A AOD data retrieved using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities. Stringent selection criteria were used to select contemporaneous data - only satellite and AERONET data acquired within 10 minutes were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.
Crustal Deformation on the Northeastern Margin of the Tibetan Plateau from Continuous GPS Observations

We installed 10 continuous Global Positioning System (GPS) stations on the northeast margin of the Tibetan Plateau at the end of 2012, in order to qualitatively investigate strain accumulation across the Liupanshan Fault (LPSF). We integrated our newly built stations with 48 other existing GPS stations to provide new insights into three-dimensional tectonic deformation. We employed white plus flicker noise model as a statistical model to obtain realistic velocities and corresponding uncertainties in the ITRF2014 and Ordos-fixed reference frame. The total velocity decrease from northwest to southeast in the Longxi Block (LXB) was 5.3 mm/yr within the range of 200 km west of the LPSF on the horizontal component. The first-order characteristic of the vertical crustal deformation was uplift for the northeastern margin of the Tibetan Plateau. The uplift rates in the LXB and the Ordos Block (ORB) were 1.0 and 2.0 mm/yr, respectively. We adopted an improved spherical wavelet algorithm to invert for multiscale strain rates and rotation rates. Multiscale strain rates showed a complex crustal deformation pattern. A significant clockwise rotation of about 30 nradians/yr (10−9 radians/year) was identified around the Dingxi. Localized strain accumulation was determined around the intersectional region between the Haiyuan Fault (HYF) and the LPSF. The deformation pattern across the LFPS was similar to that of the Longmengshan Fault (LMSF) before the 2008 Wenchuan MS 8.0 earthquake. Furthermore, according to the distributed second invariant of strain rates at different spatial scale, strain partitioning has already spatially localized along the Xiaokou–Liupanshan–Longxian–Baoji fault belt (XLLBF). The tectonic deformation and localized strain buildup together with seismicity imply a high probability for a potential earthquake in this zone.
On the Complexity of non-recursive n-term Karatsuba Multiplier for Trinomials

In this paper, we continue the study of bit-parallel multiplier using a n-term Karatsuba algorithm (KA), recently introduced by Li et al. (IEEE Access 2018). Such a n-term KA is a generalization of the classic KA, which can multiply two n-term polynomials using O(n/2) scalar multiplications. Based on this observation, Li et al. developed an efficient bit-parallel multiplier scheme for a new special class of irreducible trinomial x + x + 1,m = nk. The lower bound of the space complexity of their proposal is about O( 2 2 + m). However, such a special type of trinomial does not always exist. In this contribution, we investigate the space and time complexity of Karatsuba multiplier for general trinomials, i.e., x + x + 1 where m > 2k. We use a new decomposition that m = n` + r, where r < n, r < `. Combined with shifted polynomial basis (SPB), a new approach other than Mastrovito approach is proposed to exploit the spatial correlation between different subexpressions. Explicit space and time complexity formulations are given to indicate the optimal choice of the decomposition. As a result, the optimal multiplier achieves nearly the same space complexity as x + x + 1,m = nk, but it is suitable to more general trinomials. Meanwhile, its time complexity matches or is at most 1TX higher than the similar KA multipliers, where TX is the delay of one 2-input XOR gate. Index Terms Bit-parallel multiplier, n-Karatsuba algorithm, shifted polynomial basis, optimal, trinomials.
Toha Key Hardened Function

TOHA is Key Hardened Function designed in the general spirit of sequential memoryhard function which based on secure cryptographic hash function, the idea behind its design is to make it harder for an attacker to perform some generic attacks and to make it costly as well, TOHA can be used for deriving keys from a master password or generating keys with length of 256-bit to be used in other algorithm schemes, general approach is to use a password and a salt like a normal scheme plus other parameters, and you can think of the salt as an index into a large set of keys derived from the same password, and of course you don’t need to hide the salt to operate.
CHES 2018 Side Channel Contest CTF - Solution of the AES Challenges

Alongside CHES 2018 the side channel contest ’Deep learning vs. classic profiling’ was held. Our team won both AES challenges (masked AES implementation), working under the handle AGSJWS. Here we describe and analyse our attack. We can solve the more difficult of the two challenges with 2 to 5 power traces, which is much less than was available in the contest. Our attack combines techniques from machine learning with classical techniques. The attack was superior to all classical and deep learning based attacks which we have tried. Moreover, it provides some insights on the implementation.
Why is Kemeny’s constant a constant?

Abstract In their 1960 book on finite Markov chains, Kemeny and Snell established that a certain sum is invariant. The value of this sum has become known as Kemeny’s constant. Various proofs have been given over time, some more technical than others. We give here a very simple physical justification, which extends without a hitch to continuous-time Markov chains on a finite state space. For Markov chains with denumerably infinite state space, the constant may be infinite and even if it is finite, there is no guarantee that the physical argument will hold. We show that the physical interpretation does go through for the special case of a birth-and-death process with a finite value of Kemeny’s constant.
The nonhomogeneous frog model on ℤ

Abstract We examine a system of interacting random walks with leftward drift on ℤ, which begins with a single active particle at the origin and some distribution of inactive particles on the positive integers. Inactive particles become activated when landed on by other particles, and all particles beginning at the same point possess equal leftward drift. Once activated, the trajectories of distinct particles are independent. This system belongs to a broader class of problems involving interacting random walks on rooted graphs, referred to collectively as the frog model. Additional conditions that we impose on our model include that the number of frogs (i.e. particles) at positive integer points is a sequence of independent random variables which is increasing in terms of the standard stochastic order, and that the sequence of leftward drifts associated with frogs originating at these points is decreasing. Our results include sharp conditions with respect to the sequence of random variables and the sequence of drifts that determine whether the model is transient (meaning the probability infinitely many frogs return to the origin is 0) or nontransient. We consider several, more specific, versions of the model described, and a cleaner, more simplified set of sharp conditions will be established for each case.