Miracle-dz/UniFAR-FTUs huggingface dataset:
Constellation optimization for Visible Light Communication under Ergodic Optical Channel

Visible light communication (VLC) has gradually gained increasing attention in recent years, but new researches are still needed for conventional technologies in the VLC system, as the visible light signal must be real-valued and non-negative, and it is transmitted over the specific ergodic channel. In this paper, the definition of ergodic optical channel is firstly proposed, and the coded modulation average mutual information (CMAMI) of the VLC system is investigated under both fixed and ergodic optical channels. The gradient descent (GD) algorithm for constellation optimization of amplitude-phase modulation (APM) symbols is adopted, while the improved method for GD based on central symmetry is also proposed to accelerate the convergence for higher order constellations. The numerical simulation proves that the VLC system with optimized constellations can reach larger CM-AMI based on asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) compared with quadrature amplitude modulation (QAM) systems, and the influence of the ergodic optical channel is also evaluated.
Activity-aware Attributes for Zero-Shot Driver Behavior Recognition

In real-world environments, such as the vehicle cabin, we have to deal with novel concepts as they arise. To this end, we introduce ZS-Drive&Act – the first zero-shot activity classification benchmark specifically aimed at recognizing previously unseen driver behaviors. ZS-Drive&Act is unique due to its focus on fine-grained activities and presence of activity-driven attributes, which are automatically derived from a hierarchical annotation scheme. We adopt and evaluate multiple off-the-shelf zero-shot learning methods on our benchmark, showcasing the difficulties of such models when moving to our application-specific task. We further extend the prominent method based on feature generating Wasserstein GANs with a fusion strategy for linking semantic attributes and word vectors representing the behavior labels. Our experiments demonstrate the effectiveness of leveraging both semantic spaces simultaneously, improving the recognition rate by 2.79%.
Effectiveness of Perioperative Auricular Therapy on Postoperative Pain after Total Hip Arthroplasty: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Auricular therapy (AT) is a conventional therapy in traditional Chinese medicine. However, the effectiveness of perioperative AT in pain treatment after total hip arthroplasty (THA) is still controversial. Nine randomised controlled trials (RCTs) involving 605 patients who have undergone THA with or without AT from inception to March 2018 were collected and included in this study by searching more than 12 databases (e.g., PubMed, Excerpta Medica Database, and Cochrane Library). A random-effects model that pooled seven articles showed that the visual analogue scale (VAS) in the AT group was lower than that of the control group at each postoperative time point in patients after THA, except at the time points of 6 and 36 h. The intraoperative body mass-adjusted fentanyl amount in the AT group was also lower than that of the control group in two trials. The other outcomes (time to first analgesic request and incidence of postoperative nausea and vomiting, perioperative bradycardia, and transitory hypotension) showed insignificant difference. Then, subgroup analysis showed similar results to those of the total articles with the term “VAS”. Regression analysis found that the prolonged time after the operation decreased the difference in VAS between the two groups. Although all the outcomes were assessed as very low to low in the GRADE system, evidence on the effectiveness of perioperative AT in pain treatment after total hip replacement was positive.
Statistical Based Algorithm for Reducing Residual Error in Embedded Systems Implemented Using the Controller Area Network

In this paper, a new approach is introduced to reduce bit stuffing and consequently residual error probability in the controller area network (CAN). The proposed method is based on XOR masking. Unlike the XOR method, the proposed approach does not use a fixed mask for all IDs. Using statistical parameters of data, a proper mask for each CAN ID is generated and applied to the messages before transmitting. The performance of the method to reduce bit stuffing and residual error probability has been evaluated by considering a real data set. Results show that the method can significantly reduce bit stuffing and residual error probability. A comparison has been also conducted with previously reported methods. The results show the superiority of the SMC method in reducing residual error without payload and data transfer rate reduction.
Advanced Fabrication Techniques of Microengineered Physiological Systems

The field of organs-on-chips (OOCs) has experienced tremendous growth over the last decade. However, the current main limiting factor for further growth lies in the fabrication techniques utilized to reproducibly create multiscale and multifunctional devices. Conventional methods of photolithography and etching remain less useful to complex geometric conditions with high precision needed to manufacture the devices, while laser-induced methods have become an alternative for higher precision engineering yet remain costly. Meanwhile, soft lithography has become the foundation upon which OOCs are fabricated and newer methods including 3D printing and injection molding show great promise to innovate the way OOCs are fabricated. This review is focused on the advantages and disadvantages associated with the commonly used fabrication techniques applied to these microengineered physiological systems (MPS) and the obstacles that remain in the way of further innovation in the field.
Proportional-based Resource Allocation Control with QoS Adaptation for IEEE 802.11ax

One consequence of the upcoming increase of machine-to-machine communication is saturation of the present wireless networks. The latest version of Wi-Fi, IEEE 802.11ax, has been designed to improve the efficiency and performance of wireless local area networks. To overcome the problem of dense networks with regard to IEEE 802.11ax, orthogonal frequency-division multiple access (OFDMA) was introduced. The protocol was designed with a flexible standard around the radio allocation scheme for OFDMA. The flexible specification brings the opportunity to develop a radio channel scheduler. In this paper, a novel resource unit (RU) allocation scheduler is proposed for scheduling access in OFDMA. The proposed scheduler includes a closed-loop feedback controller with proportional gain to simultaneously consider priority and fairness. The combined information of quality of service and buffered data in the stations is used to allocate the data flows more efficiently in dense network conditions. Simulations show that the proposed scheduler provides efficient RU allocation in IEEE 802.11ax networks.
Invariant Sets for Integrators and Quadrotor Obstacle Avoidance

Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. However, finding analytical expressions for maximal invariant sets, so as to maximize the operational freedom of the system without compromising safety, is notoriously difficult for high-dimensional systems with input constraints. Here we present a generic method for characterizing invariant sets of nth-order integrator systems, based on analyzing roots of univariate polynomials. Additionally, we obtain analytical expressions for the orders n ≤ 4. Using differential flatness we subsequently leverage the results for the n =4 case to the problem of obstacle avoidance for quadrotor UAVs. The resulting controller has a light computational footprint that showcases the power of finding analytical expressions for control-invariant sets.
An Experimental Platform of Impulse UWB Radar for Through-Wall Imaging based on FPGA

In this paper, we present a new development architecture of impulse UWB (Ultra-Wide-Band) radar, this radar is geared toward to see through-wall imaging which is able to scan automatically the scene by using SP8T switch that facilitates obtain a 2D images of person with high resolution in cross-range direction. The experiment to see through the wall is achieved based on RF components, FPGA (Field Programmable Gate Array) board for UWB signal generation and high sampling oscilloscope for signal acquisition. Experimental results are encouraging and show that the system can obtain images with a high resolution of the person behind a wall in different locations.
The macroecology of the COVID-19 pandemic in the Anthropocene

Severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease 2019 (COVID-19), has expanded rapidly throughout the world. Thus, it is important to understand how global factors linked with the functioning of the Anthropocene are responsible for the COVID-19 outbreak. We tested hypotheses that the number of COVID-19 cases, number of deaths and growth rate of recorded infections: (1) are positively associated with population density as well as (2) proportion of the human population living in urban areas as a proxies of interpersonal contact rate, (3) age of the population in a given country as an indication of that population’s susceptibility to COVID-19; (4) net migration rate and (5) number of tourists as proxies of infection pressure, and negatively associated with (5) gross domestic product which is a proxy of health care quality. Data at the country level were compiled from publicly available databases and analysed with gradient boosting regression trees after controlling for confounding factors (e.g. geographic location). We found a positive association between the number of COVID-19 cases in a given country and gross domestic product, number of tourists, and geographic longitude. The number of deaths was positively associated with gross domestic product, number of tourists in a country, and geographic longitude. The effects of gross domestic product and number of tourists were non-linear, with clear thresholds above which the number of COVID-19 cases and deaths increased rapidly. The growth rate of COVID-19 cases was positively linked to the number of tourists and gross domestic product. The growth rate of COVID-19 cases was negatively associated with the mean age of the population and geographic longitude. Growth was slower in less urbanised countries. This study demonstrates that the characteristics of the human population and high mobility, but not population density, may help explain the global spread of the virus. In addition, geography, possibly via climate, may play a role in the pandemic. The unexpected positive and strong association between gross domestic product and number of cases, deaths, and growth rate suggests that COVID-19 may be a new civilisation disease affecting rich economies.
A Critical Review of LIS Literature on First-Generation Students

abstract:This review offers a critical analysis of the library and information science (LIS) literature on first-generation students (FGS) from the last 40 years. This literature demonstrates an interest in understanding the needs of FGS to serve them better, but it is often grounded in a deficit model of education that focuses on what first-generation students lack instead of what they have. This review identifies four predominant themes in the literature: FGS as outsiders, as a problem, as reluctant library users, and as capable students. Then it suggests possible avenues of future research, such as using a "funds of knowledge" approach to build on the learning and skills that students bring from their families and communities.
Characterising the proximal patellar tendon attachment and its relationship to skeletal maturity in adolescent ballet dancers

tendon inferior the patella, and 2 centimetres distally. in (greyscale echopattern) in and peri-PHV participants. to Adolescence may be a critical time for the formation of normal tendon attachment. Level of evidence: IIb individual cohort study. varied of the in (ballet across attachment using
Transcriptome Response of Metallicolous and a Non-Metallicolous Ecotypes of Noccaea goesingensis to Nickel Excess

Root transcriptomic profile was comparatively studied in a serpentine (TM) and a non-metallicolous (NTM) population of Noccaea goesingensis in order to investigate possible features of Ni hyperaccumulation. Both populations were characterised by contrasting Ni tolerance and accumulation capacity. The growth of the TM population was unaffected by metal excess, while the shoot biomass production in the NTM population was significantly lower in the presence of Ni in the culture medium. Nickel concentration was nearly six- and two-fold higher in the shoots than in the roots of the TM and NTM population, respectively. The comparison of root transcriptomes using the RNA-seq method indicated distinct responses to Ni treatment between tested ecotypes. Among differentially expressed genes, the expression of IRT1 and IRT2, encoding metal transporters, was upregulated in the TM population and downregulated/unchanged in the NTM ecotype. Furthermore, differences were observed among ethylene metabolism and response related genes. In the TM population, the expression of genes including ACS7, ACO5, ERF104 and ERF105 was upregulated, while in the NTM population, expression of these genes remained unchanged, thus suggesting a possible regulatory role of this hormone in Ni hyperaccumulation. The present results could serve as a starting point for further studies concerning the plant mechanisms responsible for Ni tolerance and accumulation.
A Truncated Matrix Decomposition for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution addresses the problem of fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to produce a high-resolution hyperspectral image (HR-HSI). In this paper, we propose a novel fusion approach for hyperspectral image super-resolution by exploiting the specific properties of matrix decomposition, which consists of four main steps. First, an endmember extraction algorithm is used to extract an initial spectral matrix from LR-HSI. Then, with the initial spectral matrix, we estimate the spatial matrix, i.e., the spatial-contextual information, from the degraded observations of HR-HSI. Third, the spatial matrix is further utilized to estimate the spectral matrix from LR-HSI by solving a least squares (LS)-based problem. Finally, the target HR-HSI is constructed by combing the estimated spectral and spatial matrixes. In particular, two models are proposed to estimate the spatial matrix. One is a simple case that involves a LS-based problem, and the other is an elaborate case that consists of two fidelity terms and a spatial regularizer, where the spatial regularizer aiming to restrain the range of solutions is achieved by exploiting the superpixel-level low-rank characteristics of HR-HSI. Experiment results conducted on both synthetic and real data sets demonstrate the effectiveness of the proposed approach as compared to other hyperspectral image super-resolution methods.
Nasal microbiome in chronic rhinosinusitis.

INTRODUCTION Chronic rhinosinusitis (CRS) is defined as an inflammatory disorder of the paranasal sinuses and of the nasal mucosa that lasts 12 weeks or longer. In CRS microbes contribute to the disease pathogenesis. Clinical microbiology is focused on finding single pathogens that causes the disease and the main goal is the use of antibiotics to kill bacteria. Efforts to achieve a better understanding of CRS include the study of the sinus microbiome, and to evaluate the ability of probiotics to augment homeostasis and modulate the immune response of the host mucosa. OBJECTIVE This review provides an update on the role of the microbiome in CRS. METHODS This review was conducted using two databases: PubMed and Science Direct. We searched for articles in English that matched the review topic. We first used the abstracts of articles to assess whether they met the inclusion criteria. We also reviewed the references of the selected articles and read those with titles that might be of interest. RESULTS Several studies have shown that endogenous microbiome dysbiosis can impact mucosa health and disease severity. Some bacterial species presenting protective or pathogenic effect. Antimicrobial agents can create a similar disruption and impact the nasal microbiome balance. On the other hand, probiotics offers a promising avenue for developing systemic and topical therapies geared towards strategic manipulation of the biological host load, thereby augmenting immune homeostasis. CONCLUSIONS A better comprehension of sinus-nasal microbiome in healthy and in CRS patients and the link with different CRS phenotype can help in developing new prognostics, diagnostics, and therapeutics strategies. Going forward, the use of probiotics can restore the native sinus ecology with significant therapeutic and preventive implications.
Dynamic Joint Probabilistic Data Association Framework for Target Tracking with Ground Robots

With the rise in use for ground robots, there is a need for efficient detection and tracking to improve SLAM performed by each such agent. This paper analyses the effectiveness of the Dynamic Joint Probabilistic Data Association (DJPDA) framework for target tracking in dense environments by creating a test bed with a fleet of ground robots. DJPDA is a framework that utilizes tensor decomposition, a commonly used technique to tackle "curse of dimensionality", to handle the exponential growth in the binary non-competing joint association events (or feasible events) in Joint Probabilistic Data Association (JPDA) filter. The number of feasible events in JPDA is reduced by utilizing the "core" tensor, a result of the tensor decomposition, as a surrogate for the input of JPDA instead of the complete set of measurements. The test bed created for this experiment consists of 5 Kobuki ground robots. The laser scan data from the on-board Xbox Kinect sensor is collected for each time step by using one of these robots as the observer. Finally, the collected point cloud data is passed to the DJPDA framework for offline computation of the tracks to compare these predicted tracks with the true tracks obtained from the odometry data for each ground robot.
Use of a Smartphone App for Weight Loss Versus a Paper-Based Dietary Diary in Overweight Adults: Randomized Controlled Trial

Background Mobile health (mHealth) tools may be useful platforms for dietary monitoring and assessment. Objective This study aims to evaluate the effectiveness of a mobile dietary self-monitoring app for weight loss versus a paper-based diary among adults with a BMI of 23 kg/m2 or above. Methods A total of 33 men and 17 women aged 18-39 years participated in a 6-week randomized controlled trial. We randomly assigned participants to one of two groups: (1) a smartphone app group (n=25) or (2) a paper-based diary group (n=25). The smartphone app group recorded foods and dietary supplements that they consumed and received immediate dietary feedback using Well-D, a dietary self-monitoring app developed by our team. The paper-based diary group was instructed to record foods or supplements that they consumed using a self-recorded diary. The primary outcomes were weight, BMI, waist circumference, body fat mass, and skeletal muscle mass. We also examined changes in nutrient intake, including energy, carbohydrate, protein, fat, dietary fiber, vitamins, and minerals, using 3-day 24-hour recalls. Differences in changes between the two groups were analyzed using independent t tests or Wilcoxon Mann-Whitney tests. All of the data were analyzed using intent-to-treat analysis. Results The mean number of days recorded was 18.5 (SD 14.1) for the app group and 15.5 (SD 10.1) for the paper-based diary group. The differences in changes in weight, BMI, and waist circumference were not significantly different between the app group and paper-based diary group (P=.33, .34, and .70, respectively). Similarly, changes in body fat mass or skeletal muscle mass did not differ between the two groups (P=.71 and .054, respectively). Although energy intake was reduced in both groups, there was no significant difference in changes in energy intake between the two groups (P=.98). Conclusions There were no differences in changes in anthropometric measures and nutrient intake between the app group and the paper-based diary group. Both mobile dietary self-monitoring app and paper-based diary may be useful for improving anthropometric measures. Trial Registration Clinical Research Information Service KCT0003170; https://cris.nih. go.kr/cris/search/search_result_st01_en. jsp? seq=11642&ltype=&rtype=
Towards Real-time Traffic Movement Count and Trajectory Reconstruction Using Virtual Traffic Lanes

In this paper, we discuss our framework and observations for AI City Challenge Track 1: Vehicle Counts by Class at Multiple Intersections. The framework we propose utilizes creating virtual traffic lanes for the movements of interest. Using a Python Graphical User Interface (GUI), the entry polygons for the movements of interest are identified. This leads to labeling the trajectories for the vehicles that have been first detected entering the region of interest via those entry polygons. Those vehicles, forming what we refer to as "virtual traffic lanes" inside the region ofinterest, are then used as identifiers for other vehicles detected further downstream using a nearest neighbors search. The framework we propose can run as an additional layer to any multi-object tracker with minimal additional computation. Our results and evaluation for the challenge track indicate the high potential of our proposed framework, and showcase the momentous value of incorporating domain knowledge in computer-vision applications.
Explicit Iterative Algorithms for Continuous Coupled Lyapunov Matrix Equations

In this article, two iterative algorithms are proposed to solve the coupled Lyapunov matrix equations associated with continuous-time Markovian jump linear systems. First, an explicit iterative algorithm is presented to solve the considered matrix equations via introducing a tuning parameter. A necessary and sufficient condition is established for the proposed algorithm to be convergent. Furthermore, the optimal value of the tuning parameter achieving the fastest convergence rate is given explicitly. Second, a modified version of the previous explicit algorithm is established by using a new information update scheme. Similarly, a necessary and sufficient condition is provided for the modified explicit iterative algorithm. Based on this convergence condition, some easily verifiable convergence results are further derived, and the optimal tuning parameter is given explicitly. Finally, two examples are presented to validate the obtained theoretical results.