Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between dynamic obesity and mortality in patients with first-ever ischemic stroke

Abstract Although obesity is an established risk factor of primary stroke, the association between obesity and post-stroke mortality remains unclear. The aim of this study was to investigate the association between dynamic obesity status and mortality in survivors of their first stroke in China. Of 775 patients with first-ever ischemic stroke included in a longitudinal study, 754 patients were included in this study and categorized into 4 categories of body mass index (BMI) (underweight, normal weight, overweight, and obese) and 2 categories of waist circumference (WC) (normal WC and abdominal obesity) according to standard Chinese criteria. The mortality information and obesity status were obtained via telephone follow-up every 3 months, beginning in 2010 through 2016. Time-dependent Cox proportional hazards models were used to estimate the unadjusted and adjusted hazard ratios (HRs) for the relationship between all-cause mortality and dynamic obesity status. Of 754 patients, 60.87% were male, and the overall mean age was 61.45 years. After adjusting for possible confounders, significant inverse associations were identified between BMI and WC and all-cause mortality. Compared with those with normal BMI or WC, those with abdominal obesity or overweight had a significantly lower risk of all-cause mortality (HR and 95% confidence intervals [CIs]: .521 [.303–.897] and 0.545 [.352–.845], respectively), whereas patients with underweight had the highest risk and those with obesity had lower risk of mortality, though it was not statistically significant (1.241 [.691–2.226] and .486 [.192–1.231], respectively). Overweight and abdominal obesity were paradoxically associated with reduced risk of mortality in patients who survived their first-ever ischemic stroke in China. Future prospective studies must look at evaluating the role of obesity in different stroke subtypes and devise appropriate weight-management strategies for optimal prognoses in secondary prevention in these survivors.
Influence of food availability and distribution on the movement patterns of a forest avian frugivore, the puff-throated bulbul (Alophoixus pallidus)

Abstract: Bulbul species (family Pycnonotidae) are important seed dispersers in Asian forests, but almost nothing is known of their movement patterns inside intact forest, which are likely to impact forest dynamics. We examined the movement patterns of the forest-dwelling puff-throated bulbul (Alophoixus pallidus) in relation to fruit productivity and distribution of fruiting trees/lianas in an evergreen forest in north-eastern Thailand. Movement patterns of 10 groups were precisely mapped by following colour-ringed individuals in each group 4 h mo−1 for 1 y. We evaluated fruit productivity and dispersion of fruiting trees/lianas based on monthly phenologies. There were clear seasonal fluctuations in fruit availability, which appeared to affect movement patterns, particularly distance moved between fruiting trees, time spent feeding and food selection. When fruit availability was low, bulbuls spent more time on average at a given food plant and moved longer distances between fruiting plants than compared with periods of higher fruit availability (low availability: 58 s, 83.2 m; high availability: 10 s, 43.4 m). This study points to the importance of seasonal availability of fruit resources on frugivore movement patterns. Seasonal dynamics of movement may be useful for understanding interactions between fruiting trees and their dispersers, and forest tree recruitment patterns.
The “ moment ” scalar product

Abstract The Fibonacci polynomials { Fn(x) } n≥0 have been studied in multiple ways, [1,7,8,10,14]. In this paper we study them by means of the theory of Heaps of Viennot [12,13]. In this setting our polynomials form a basis { Pn(x) } n≥0 with Pn(x) monic of degree n. This given, we are forced to set Pn(x) = Fn+1(x). The Heaps setting extends the Flajolet view [4] of the classical theory of orthogonal polynomials given by a three term recursion [3,11]. Thus with Heaps most of the identities for our Pn(x) ′s can be derived by combinatorial arguments. Using the present setting we derive a variety of new identities. We must mention that the theory of Heaps is presented here without restrictions. This is much more than needed to deal with the Fibonacci polynomials. We do this to convey a flavor of the power of Heaps. In the lecture notes [6] there is a chapter dedicated to Heaps where most of its contents are dedicated to applications of the theory. Introduction The sequence { Pn(x) } n≥0 is defined by the recursion
Chemotaxis in uncertain environments: Hedging bets with multiple receptor types

Eukaryotic cells are able to sense chemical gradients in a wide range of environments. We show that, if a cell is exposed to a highly variable environment, it may gain chemotactic accuracy by expressing multiple receptor types with varying affinities for the same signal, as found commonly in chemotaxing cells like Dictyostelium. As environment uncertainty is increased, there is a transition between cells preferring a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment. We predict the optimal receptor affinities given a particular environment. In chemotaxing, cells may also integrate their measurement over time. Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single type -- cells may extract orders of magnitude more chemotactic information than expected by naive time integration. Our results show when cells should express multiple receptor types to chemotax, and how cells can efficiently interpret the data from these receptors.
Alg\'ebricit\'e modulo p, s\'eries hyperg\'eom\'etriques et structures de Frobenius forte

This work is devoted to study of algebraicty modulo p of Siegel's G-fonctions. Our goal is emphasize the relevance of the notion of strong Frobenius structure, clasically studied in the theory of the p-adic diffential equations, for the study of a Adamczewski-Delaygue's conjecture concerning of the degree of algebraicity modulo p of G-fonctions. For this, we first make a Christol's result explicit by showing that if $f$ is a G-fonction which is solution of a differential operator in $ \mathbb{Q}(z)[d/dz]$ d'order $n$ which has a strong Frobenius structure of period $h$ for the prime number $ $ and that $f(z)$ belongs to coefficients in $\mathbb{Z}_{(p)}[[z]]$, then the reduction of $f$ modulo $ p $ is algebraic over $\mathbb F_p(z)$ and its degree of algebraicity is bounded by $p^{n^2h}$. By generalizing an approach introduced by Salinier, we then show that a Fuchsian operator with coefficients in $\mathbb{Q}(z)$, whose monodromy group is rigid and whose exponents are rational has for almost all prime number $p$ a strong Frobenius structure of period $h$, where $h$ is explicitly bounded and does not depend on $p$. A slightly different version of this result has been demonstrated recently by Crew following a different approach based on the $p$ -adic cohomology. We use these two results to solve the mentioned conjecture in the case of generalized hypergeometric series.
Tertiary regulation of cascaded run‐of‐the‐river hydropower in the islanded renewable power system considering multi‐timescale dynamics

To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run-of-the-river hydropower and volatile energies such as pv and wind are built around the world. In islanded operation mode, the primary and secondary frequency control, i.e., hydro governors and automatic generation control (AGC), ensure the frequency stability. However, due to limited water storage capacity of run-of-the-river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC with fixed participation factors cannot fully exploit the adjustability of cascaded hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. The timescales of the power system and river dynamics are very different. To unify the multi-timescale dynamics to establish a model predictive controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge over a time interval is approximated by a data-based second-order polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding-horizon manner, and load shedding is minimised. Simulation of a real-life system shows a significant improvement in the proposed method in terms of reducing load shedding.
POP: A Generic Framework for Real-Time Pose Estimation of Planar Objects

Accurate pose estimation of planar objects is a key computation in visual localization tasks, with recent studies showing remarkable progress on a handful of baseline datasets. Nonetheless, achieving similar performance on sequences in unconstrained environments is still an ongoing quest to be accomplished, largely due to the existence of several sources of errors, which are correlated but often only partly tackled in the literature. In this article, we propose POP, a generic real-time planar-object pose-estimation framework which is designed to handle the aforementioned types of errors while not losing generality to a specific choice of keypoint detection or tracking algorithm. The essence of POP lies in activating keypoint detection module in the background as well as adding several refinement steps in order to reduce correlated sources of errors within the pipeline. We provide extensive experimental evaluations against state-of-the-art planar object tracking algorithms on baseline and more challenging datasets, empirically demonstrating the effectiveness of the POP framework for scenes with large environmental variations.
Reactive caching of viral content in 5G networks

An efficient way to handle the excessive number of user requests by the existing infrastructure is network caching in which a popular content is cached at the edge of the network in order to decrease the repetitive downloads. In this paper, a non-Cooperative Hierarchical Reactive Caching (nCHRC) algorithm is proposed as an optimal multi-level cache content placement method. The main innovative idea of this work is using a parameter called virality which illustrates the impact of time on the popularity of the contents. Moreover, some parameters such as well-known popularity, virality and size of the file are efficiently aggregated as a single parameter named priority. The results show that our method successfully improves the cache hit rate, backhaul traffic and content access delay of the network.
Hierarchical Eye-Tracking Data Analytics for Human Fatigue Detection at a Traffic Control Center

Eye-tracking-based human fatigue detection at traffic control centers suffers from an unavoidable problem of low-quality eye-tracking data caused by noisy and missing gaze points. In this article, the authors conducted pioneering work by investigating the effects of data quality on eye-tracking-based fatigue indicators and by proposing a hierarchical-based interpolation approach to extract the eye-tracking-based fatigue indicators from low-quality eye-tracking data. This approach adaptively classified the missing gaze points and hierarchically interpolated them based on the temporal-spatial characteristics of the gaze points. In addition, the definitions of applicable fixations and saccades for human fatigue detection is proposed. Two experiments are conducted to verify the effectiveness and efficiency of the method in extracting eye-tracking-based fatigue indicators and detecting human fatigue. The results indicate that most eye-tracking parameters are significantly affected by the quality of the eye-tracking data. In addition, the proposed approach can achieve much better performance than the classic velocity threshold identification algorithm (I-VT) and a state-of-the-art method (U'n’Eye) in parsing low-quality eye-tracking data. Specifically, the proposed method attained relatively stable eye-tracking-based fatigue indicators and reported the highest accuracy in human fatigue detection. These results are expected to facilitate the application of eye movement-based human fatigue detection in practice.
Design of a Miniaturized Dual-Polarized Slot-Coupled Patch Antenna Element and Its Sub-array

A novel miniaturized, low profile, dual-polarized slot-coupled patch antenna and its sub-array for 5G massive (multi-input and multi-output) MIMO antenna is proposed in this letter. The antenna mainly consists of three parts: metal sheet (Radiator), feeding network and metal cover with cavity on it. The proposed antenna element is fed by two “H” type slots on the PCB board which are orthogonal to each other. The ports feed to the opposite of the slot through a power divider. The radiation sheet is fixed to the PCB board by plastic screws and the PCB board is fixed to the metal cover by metal screws. Then, three antenna elements are connected by one-three power dividers to achieve a sub-array. The Sub-array antenna has excellent impedance matching, low loss, easy assembly, good consistency and low cost. It can be widely used for 5G massive MIMO applications.
Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study

Opinion analysis or sentiment analysis is one of the most effective techniques these days in order to determine the sentiments or emotions of people regarding any event. This technique has come to the fore because of extensive use of social media platforms like Facebook, Twitter etc. by people to express their emotions regarding any event that has occurred or any event that is most likely to happen, be that the release of a movie or a political rally that is about to take place. People make sure to express their sentiments. Sentiment analysis proves very beneficial for any company selling a product to know how their product was received by people or by any political party to determine how people are reacting towards their running candidate. In order to analyze these sentiments two approaches of machine learning can be used – unsupervised or supervised. In this paper we have worked with Unsupervised Approach which is a rule based or lexical approach and can be done using the pre-built open source libraries like TextBlob, VADER.
Experimental Analysis of Structural Vibration Problems of a Biped Walking Robot

Over the past decade we have been able to vastly improve the control algorithms of our biped walking robot LOLA. Further enhancements, however, are limited by vibration problems caused by the dynamics of LOLA’s mechanical structure. In this work, we present small examples how structural dynamics limit our control design for walking control as well as low level position control of the joints. We also provide a procedure to identify weaknesses in the structural design of our biped using Experimental Modal Analysis. Using this method, we could successfully identify the structural modes of the system. Furthermore, we were able to use a closed-loop identification method to show a connection between the control loop resonances and the structural resonances of our robot.
Exploration and Comparison of Modern AI Algorithms to Predict Drug Efficacy

We compare various Machine Learning and Deep Learning algorithm variants and string embeddings to predict the pIC50 values against the JAK2 protein target. The paper works upon improving the existing predictor component in the ReLeaSE framework by comparing various models such as Random Forest Regressor and Convolutional Neural Network for prediction. The paper also works on using different SMILE string embeddings such as PPMI matrices and OpenBabel fingerprints for molecules to improve upon the current baseline accuracy. Using a data set consisting of an approximate of 2000 data points, the models were trained on 80:20 split. The comparison of various models provides us with insights on the robustness in using SMILES as a representation for molecules. The results show that the use of Random Forest Regressor along with OpenBabel fingerprinting to encode SMILES performed the best with an R2 score of 0.71, which improves the baseline predictor in ReLeaSE by a ΔR2 of 23%. Our models when trained on an augmented version of the JAK2 dataset gave an R2 score of 0.97
Simultaneous Wireless Power Transfer and Communications by Recycling Third Harmonics for WPAN Applications

Conventional signal splitting techniques applied in SWIPT, such as time-splitting and power-splitting, suffer from information loss or wasted energy. In this paper, a compact coupler-rectifier-receiver (CRR) system is proposed to address the problems inherent in these conventional techniques. Due to the intrinsic non-linearity of diodes, high-order harmonics are generated as part of the rectification process. Instead of eliminating these harmonics, the proposed structure couples the 3rd harmonic to its isolation port and used it for data recovery. The proposed method is validated by both simulation and measurement. The measured RF-to-DC conversion efficiency (PCE) is over 40% for input powers from - 11 dBm to - 1 dBm and reaches a maximum value of 57.3% at - 4 dBm. Simulation results on 3rd harmonic detection verify its potential for data recovery. Thus, this CRR system is feasible for SWIPT.
A Novel Articulated Soft Robot Capable of Variable Stiffness through Bistable Structure

Soft robot has demonstrated promise in unstructured and dynamic environments due to unique advantages, such as safe interaction, adaptiveness, easy to actuate, and easy fabrication. However, the highly dissipative nature of elastic materials results in small stiffness of soft robot which limits certain functions, such as force transmission, position accuracy, and load capability. In this paper, we present a novel articulated soft robot with variable stiffness. The robot is constructed by rigid joints and compliant bistable structures in series. Each joint can be independently locked through triggering the bistable structure to touch the mechanical constrain. Thus, the bending stiffness of the joint can be magnified which increases the stiffness of the articulated soft robot. Through this construction method, even driven by only one servomotor, the robot demonstrates variable workspace and stiffness which have the potential of dexterous manipulation and maintaining shape under tip load.
Global Visual And Semantic Observations for Outdoor Robot Localization

Most approaches to robot visual localization rely on local, global visual or semantic information as observation. In this paper, we use the combination of global visual and semantic information as landmark in the observation model of Bayesian filters. Introducing the improved Gaussian Process into observation models with visual information, we extend the GP-Localize algorithm to high dimensional data, which in this way the Bayesian filters can consider all the historical data with spatiotemporal correlation to achieve constant time and memory for persistent outdoor robot localization. Our another contribution is to combine the above parts into a system for robot visual localization and apply it to two real-world outdoor datasets including unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV). Through the analysis of experimental results, the features using global vision and semantic results will be more accurate than using single features. By using the combined features and improved Gaussian process approximation method in Bayes filters, our system is more robust and practical than existing localization systems such as ORB-SLAM.
Design and Analysis of Multi-Port SAW MEMS Resonators

Surface Acoustic Wave (SAW) resonators are considered very crucial elements in RF technology since they are CMOS compatible. Several designs of multiport SAW resonators are compared regarding their specifications in aim to find a trend between the number of ports and the simulated specifications such as quality factor, insertion loss, phase depth, temperature coefficient of frequency (TCF) and resonant frequency. Analysis is performed to find the lowest TCF value according to the application needed. The operating resonance frequency is obtained from COMSOL Multiphysics frequency domain analysis along with the (TCF). The remaining specifications are calculated from the Butterworth Van-Dyke (BVD) circuit model on Cadence Virtuoso.
Vision-Based Pose Optimization Using Learned Metrics

Camera pose optimization is the basis of geometric vision works, such as 3D reconstruction, structure from motion, and visual odometry. A pose optimization method based on learned metrics is proposed to improve the optimization convexity. The neural network was designed and trained based on the collected datasets, respectively. The network inputs pairwise patches and outputs the Euclidean distance of its center. This distance is involved in the residual calculation of Gauss-Newton, and the Jacobian corresponding to this distance can be analytically solved. The simulation verified convergence and generalization of the designed network. The accuracy and robustness of the proposed pose optimization compared with intensity- and feature-based optimizations are also verified.
Trajectory Tracking Nonlinear Model Predictive Control for an Overactuated MAV

This work presents a method to control omnidirectional micro aerial vehicles (OMAVs) for the tracking of 6-DoF trajectories in free space. A rigid body model based approach is applied in a receding horizon fashion to generate optimal wrench commands that can be constrained to meet limits given by the mechanical design and actuators of the platform. Allocation of optimal actuator commands is performed in a separate step. A disturbance observer estimates forces and torques that may arise from unmodeled dynamics or external disturbances and fuses them into the optimization to achieve offset-free tracking. Experiments on a fully overactuated MAV show the tracking performance and compare it against a classical PD-based controller.
Ultra-Thin Metasheet for Dual-Wide-Band Linear to Circular Polarization Conversion With Wide-Angle Performance

Satellite communication systems require dual-band circularly polarized (CP) waves to integrate receiving and transmit antennas into one dual-band terminal. Polarization converters play an important role in such a scenario to convert the incoming linearly polarized (LP) signal into the transmitted CP signal. Wide-band and wide-angle performance for the transmission based dual-band polarization converters has been a challenge. In this paper, a transmission-based linear polarization to circular polarization (LP-to-CP) converter is presented using a very simple and thin metasurface based structure. The proposed metasheet is uni-layered, patterned with a circular diagonal split ring enclosed in the square ring. The idea for dual-band polarization conversion is verified with an example in which x(y) polarization is converted into LHCP(RHCP) in Ku-band (15.25~18.9 GHz), and RHCP(LHCP) in Ka-band (29.7~36.7GHz). The converter performs polarization conversion for both bands over 21% of bandwidth which is very wide. Moreover, it maintains performance over wide-angled oblique incidences. Dual-band performance is analyzed theoretically, numerically, and experimentally. The equivalent circuit model is also presented for the unit cell. This metasheet has simple and low-cost design, wide polarization conversion bandwidths, polarization diversity, and wide-angle performance which are very promising for future communication systems.