Miracle-dz/UniFAR-FTUs huggingface dataset:
LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks

Digital pathology and microscope image analysis is widely used in comprehensive studies of cell morphology. Identification and analysis of leukocytes in blood smear images, acquired from bright field microscope, are vital for diagnosing many diseases such as hepatitis, leukaemia and acquired immune deficiency syndrome (AIDS). The major challenge for robust and accurate identification and segmentation of leukocyte in blood smear images lays in the large variations of cell appearance such as size, colour and shape of cells, the adhesion between leukocytes (white blood cells, WBCs) and erythrocytes (red blood cells, RBCs), and the emergence of substantial dyeing impurities in blood smear images. In this paper, an end‐to‐end leukocyte localization and segmentation method is proposed, named LeukocyteMask, in which pixel‐level prior information is utilized for supervisor training of a deep convolutional neural network, which is then employed to locate the region of interests (ROI) of leukocyte, and finally segmentation mask of leukocyte is obtained based on the extracted ROI by forward propagation of the network. Experimental results validate the effectiveness of the propose method and both the quantitative and qualitative comparisons with existing methods indicate that LeukocyteMask achieves a state‐of‐the‐art performance for the segmentation of leukocyte in terms of robustness and accuracy
Addiction Mindsets and Psychological Processes of Quitting Smoking

Abstract Background: Lay belief systems about the malleability of human attributes have been shown to impact behavior change in multiple domains. Addiction mindset—i.e., beliefs about the permanence (vs. malleability) of addiction — may affect cigarette smokers’ ability to quit, but this has never been examined. Objectives: The aims of the present research were to develop a measure of addiction mindset (study 1) and examine its associations with various psychological aspects of quitting smoking (study 2). Methods: In Study 1, using factor analysis of current smokers’ and nonsmokers’ (n = 600) responses to 22 items designed to measure addiction mindset, we developed a reliable six-item Addiction Mindset Scale (AMS). In Study 2, adult smokers (n = 200) completed the AMS, and measures of a number of psychological processes related to smoking. Results: Higher scores on the AMS, indicative of the belief that addiction is malleable (referred to as a growth mindset), were positively and significantly associated with greater motivation to quit, greater commitment to quitting, greater self-efficacy to abstain, less attribution of failure to lack of ability to change addiction, and fewer self-reported barriers to cessation (all p’s < .05). Conclusions: The results of this study show a relationship between the beliefs about the permanence of addiction and psychological processes relevant to quitting smoking. The findings underscore the potential of future research exploring how addiction mindsets relate to successful smoking cessation as well as other types of addictive behavior and how they can be applied to change people’s behavior.
The reality and evolutionary significance of human psychological sex differences

The aims of this article are: (i) to provide a quantitative overview of sex differences in human psychological attributes; and (ii) to consider evidence for their possible evolutionary origins. Sex differences were identified from a systematic literature search of meta‐analyses and large‐sample studies. These were organized in terms of evolutionary significance as follows: (i) characteristics arising from inter‐male competition (within‐sex aggression; impulsiveness and sensation‐seeking; fearfulness; visuospatial and object‐location memory; object‐centred orientations); (ii) those concerning social relations that are likely to have arisen from women's adaptations for small‐group interactions and men's for larger co‐operative groups (person‐centred orientation and social skills; language; depression and anxiety); (iii) those arising from female choice (sexuality; mate choice; sexual conflict). There were sex differences in all categories, whose magnitudes ranged from (i) small (object location memory; negative emotions), to (ii) medium (mental rotation; anxiety disorders; impulsivity; sex drive; interest in casual sex), to (iii) large (social interests and abilities; sociosexuality); and (iv) very large (escalated aggression; systemizing; sexual violence). Evolutionary explanations were evaluated according to whether: (i) similar differences occur in other mammals; (ii) there is cross‐cultural consistency; (iii) the origin was early in life or at puberty; (iv) there was evidence for hormonal influences; and (v), where possible, whether there was evidence for evolutionarily derived design features. The evidence was positive for most features in most categories, suggesting evolutionary origins for a broad range of sex differences. Attributes for which there was no sex difference are also noted. Within‐sex variations are discussed as limitations to the emphasis on sex differences.
Inferring Salient Objects from Human Fixations

Previous research in visual saliency has been focused on two major types of models namely fixation prediction and salient object detection. The relationship between the two, however, has been less explored. In this work, we propose to employ the former model type to identify salient objects. We build a novel Attentive Saliency Network (ASNet)11.Available at: https://github.com/wenguanwang/ASNet. that learns to detect salient objects from fixations. The fixation map, derived at the upper network layers, mimics human visual attention mechanisms and captures a high-level understanding of the scene from a global view. Salient object detection is then viewed as fine-grained object-level saliency segmentation and is progressively optimized with the guidance of the fixation map in a top-down manner. ASNet is based on a hierarchy of convLSTMs that offers an efficient recurrent mechanism to sequentially refine the saliency features over multiple steps. Several loss functions, derived from existing saliency evaluation metrics, are incorporated to further boost the performance. Extensive experiments on several challenging datasets show that our ASNet outperforms existing methods and is capable of generating accurate segmentation maps with the help of the computed fixation prior. Our work offers a deeper insight into the mechanisms of attention and narrows the gap between salient object detection and fixation prediction.
Smart Weather Alert System for dwellers of different Areas

With changing climate, heatstroke has proved to be disastrous for few countries especially. The dwellers of different areas are not warned of the consequences to come specifically in their areas as they are told the average of the whole city, while temperature varies at different altitudes and over short distances. The solution provided in the paper, is a smart weather station that not only monitor weather data but also predict it and generate instant alerts for dwellers of different areas, to help them be warned of the future hazard, using the combination of Internet of things and Machine Learning. It is deployed with different sensors that collect weather data from the environment, which are sent to cloud, where predictions are made, for which certain neural network models have been compared to find out which gives the most accurate results. Those values, as well as the real-time values can be displayed on the mobile Application 24/7. Also, alerts are generated in the form of Tweets, which are accessible to everyone, as shown in Figure-1. It is also discovered that Nonlinear Autoregressive Exogenous Neural Network (NARXNET) Algorithm is the best to be implemented for prediction of Weather, with the mean squared error of 0.084% in 1.55 seconds for training a model and producing predictions for next 24 hours.
Optimal Linear Detection for MIMO Systems With Finite Constellation Inputs

To realize the high data rates with low complexity in MIMO systems, linear detections are commonly used where the received signal vector is first transformed to a new vector and then detected on per symbol basis. However, the typical linear detectors such as zero forcing detector and minimum mean-square error detector are not optimal in terms of maximizing the achievable data rate of MIMO system under linear detection constraint. In this letter, we investigate the optimal linear transformer that can maximize such rate with finite constellation inputs. By capitalizing on the relationship between mutual information and the MMSE between channel inputs and outputs, the optimal linear detector (OLD) can be expressed as a fixed-point equation and be solved iteratively. Analytical results prove the consistence between the proposed OLD and the joint maximum likelihood detector (JMLD) in the low- and high-SNR region. The further simulation results verify the consistence, and moreover, at moderate SNR, OLD can significantly improve the achievable data rate and is close to that of JMLD.
Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing

This paper presents a new approach to wearable hand gesture recognition and finger angle estimation based on the modified barometric pressure sensing. Barometric pressure sensors were encased and injected with VytaFlex rubber such that the rubber directly contacted the sensing element allowing pressure change detection when the encasing rubber was pressed. A wearable prototype consisting of an array of ten modified barometric pressure sensors around the wrist was developed and validated with experimental testing for three different hand gesture sets and finger flexion/extension trials for each of the five fingers. The overall hand gesture recognition classification accuracy was 94%. Further analysis revealed that the most important sensor location was the underside of the wrist and that when reducing the sensor number to only five optimally placed sensors, classification accuracy was still 90%. For continuous finger angle estimation, aggregate ${R}^{\textsf {2}}$ values between actual and predicted angles were thumb: 0.81 ± 0.10, index finger: 0.85±0.06, middle finger: 0.77±0.08, ring finger: 0.77 ± 0.12, and pinkie finger: 0.75 ± 0.10, and the overall average was 0.79 ± 0.05. These results demonstrate that a modified barometric pressure wristband can be used to classify hand gestures and to estimate individual finger joint angles. This approach could serve to improve the clinical treatment for upper extremity deficiencies, such as for stroke rehabilitation, by providing objective patient motor control metrics to inform and aid physicians and therapists throughout the rehabilitation process.
Design, Optimization and Realization of Two Compact C-band Microstrip BPF Structures

In last two decades, several microstrip BPF topologies have been investigated to meet more than 100% fractional bandwidth requirement of Ultra-Wideband (UWB) systems with pass-band from 3.1-10.6 GHz. This paper presents two compact C-band microstrip BPF structures inspired from design techniques of UWB BPF. These C-band BPF are designed for center frequency f0 of 6 GHz and fractional bandwidth around 67% covering complete C-band (4-8 GHz). The first proposed BPF is realized by using a combination of step impedance low pass filter (LPF) and the optimum distributed short-circuited stubs high pass filter (HPF). The second filter is designed using pseudo-interdigital stepped impedance resonators (PIDT-SIRS. Both filters have been implemented on commercial printed circuit board (PCB) of RO4350B substrate and each occupy an area less than 20 mil × 10 mil. These filters require minimum pass band variation so that they can be integrated with a gain equalizer circuit (4-8 GHz) without significant degradation of overall gain treatment by later circuit. Measured results observe close agreement to simulated results. Insertion loss of first BPF is 0.85dB ±0.2 dB and second BPF is 1 dB±0.3 dB.
Hybrid sequencing reveals insight into heat sensing and signaling of bread wheat

Wheat (Triticum aestivum L.), a globally important crop, is challenged by increasing temperatures (heat stress, HS). However its polyploid nature, the incompleteness of its genome sequences and annotation, the lack of comprehensive HS-responsive transcriptomes and the unexplored heat sensing and signaling of wheat hinder our full understanding of its adaptations to HS. The recently released genome sequences of wheat, as well as emerging single-molecular sequencing technologies, provide an opportunity to thoroughly investigate the molecular mechanisms of the wheat response to HS. We generated a high-resolution spatio-temporal transcriptome map of wheat flag leaves and filling grain under HS at 0 min, 5 min, 10 min, 30 min, 1 h and 4 h by combining full-length single-molecular sequencing and Illumina short reads sequencing. This hybrid sequencing newly discovered 4947 loci and 70 285 transcripts, generating the comprehensive and dynamic list of HS-responsive full-length transcripts and complementing the recently released wheat reference genome. Large-scale analysis revealed a global landscape of heat adaptations, uncovering unexpected rapid heat sensing and signaling, significant changes of more than half of HS-responsive genes within 30 min, heat shock factor-dependent and -independent heat signaling, and metabolic alterations in early HS-responses. Integrated analysis also demonstrated the differential responses and partitioned functions between organs and subgenomes, and suggested a differential pattern of transcriptional and alternative splicing regulation in the HS response. This study provided comprehensive data for dissecting molecular mechanisms of early HS responses in wheat and highlighted the genomic plasticity and evolutionary divergence of polyploidy wheat.
An Optimal Greedy Algorithm for the Single Access Contention Resolution Problem

We present the greedy optimal algorithm for contention resolution (GOAL-CR), a greedy algorithm that solves a variant of the standard contention resolution problem where a set of nodes want to access a shared resource only once, and the objective is to minimize the time it takes for all the nodes to access the resource successfully. These assumptions hold, for instance, in event reporting applications or in the cluster formation phase of wireless sensor networks. We formally prove that the GOAL-CR computes access policies that minimize the expected contention resolution time. We also show, numerically, that the performance of the greedy policies is close to that of a protocol with complete information about the exact number of nodes that have not yet accessed the resource; this latter assumption is hard to fulfill in practice but allows the derivation of a lower bound for the problem. In addition, we show how to adapt the algorithm to scenarios where there is uncertainty in the initial number of nodes and to scenarios where nodes have very limited memory. Finally, we use simulations to show the robustness of the GOAL-CR against asynchronous starts.
A review of the influence of periodontal treatment in systemic diseases

The effects and consequences of periodontal diseases might not be confined to the oral cavity. A great body of evidence has arisen supporting the claim demonstrating an association with several systemic conditions and diseases. With different levels of evidence, an association between periodontal disease and cardiovascular disease, diabetes, psoriasis, rheumatoid arthritis, pregnancy outcomes and respiratory diseases has been established. However, the true nature of this association, if it is causal, still remains elusive. For a better understanding of the complex relationships linking different conditions, interventional studies now begin to focus on the possible outcomes of periodontal treatment in relation to the events, symptoms and biomarkers of several systemic disorders, assessing if periodontal treatment has any impact on them, hopefully reducing their severity or prevalence. Therefore, we proceeded to review the recent literature on the subject, attempting to present a brief explanation of the systemic condition or disease, what proposed mechanisms might give biological plausibility to its association with periodontal disease, and finally and more importantly, what data are currently available pertaining to the effects periodontal treatment may have. Raising awareness and discussing the possible benefits of periodontal treatment on overall systemic health is important, in order to change the perception that periodontal diseases are only limited to the oral cavity, and ultimately providing better and comprehensive care to patients.
Coordinated Target Assignment for Multi-Spacecraft Telescopic Missions

Space telescopic missions have been deployed for about half a century. Plenty of information about the characteristics of various asteroids and stars has been collected and analyzed through these missions. While there has been a lot of work done on the individual scheduling of space telescope missions, collaborative or coordinated scheduling of multiple space telescopes is relatively less explored. Coordinated scheduling is important to obtain scientific data in an efficient and robust manner. Therefore, this paper proposes a framework for coordinated target assignment of space telescopic missions that enables different satellites to share objectives of collection of scientific data. The proposed framework considers the cost involved in various scientific data collection activities with relevance to each individual space telescope. Satellite workload constraints and possibility of occurrence of faults have also been considered. Performance of the optimal policy calculated using the proposed model has been compared with a rule-based target assignment policy and superiority of the proposed approach has been demonstrated.
A 13-nW Voltage Reference with Orthogonal Trimming of Absolute Value and Temperature Coefficient

An all-MOSFET voltage reference generator (VRG) with innovative trimming that orthogonally adjusts V<inf>REF</inf> and temperature coefficient (TC) has been designed in 0. 18μm CMOS process. The circuit works from V<inf>DD</inf> = 0.5V, featuring V<inf>REF</inf>=239.9mV and TC =14.6ppm/°C. SPICE data reveal, for 0.5V ≤ VDD ≤ 1.5V, -40°C ≤ T ≤ 120°C and over all corners, post-trimming maximum spread of 1.63% and 34.5ppm/°C, respectively for ΔV<inf>REF</inf>/V<inf>REF</inf> and TC. Typically, consumption is only 13nW@120°C. Line regulation of 0.21%/V and rejection to supply-line noise of 115.5dB@100MHz are also predicted. Monte Carlo reveals post-trim 0.9% (3σ) accurate VREF.
Correcting for measurement error in fractional polynomial models using Bayesian modelling and regression calibration, with an application to alcohol and mortality

Exposure measurement error can result in a biased estimate of the association between an exposure and outcome. When the exposure–outcome relationship is linear on the appropriate scale (e.g. linear, logistic) and the measurement error is classical, that is the result of random noise, the result is attenuation of the effect. When the relationship is non‐linear, measurement error distorts the true shape of the association. Regression calibration is a commonly used method for correcting for measurement error, in which each individual's unknown true exposure in the outcome regression model is replaced by its expectation conditional on the error‐prone measure and any fully measured covariates. Regression calibration is simple to execute when the exposure is untransformed in the linear predictor of the outcome regression model, but less straightforward when non‐linear transformations of the exposure are used. We describe a method for applying regression calibration in models in which a non‐linear association is modelled by transforming the exposure using a fractional polynomial model. It is shown that taking a Bayesian estimation approach is advantageous. By use of Markov chain Monte Carlo algorithms, one can sample from the distribution of the true exposure for each individual. Transformations of the sampled values can then be performed directly and used to find the expectation of the transformed exposure required for regression calibration. A simulation study shows that the proposed approach performs well. We apply the method to investigate the relationship between usual alcohol intake and subsequent all‐cause mortality using an error model that adjusts for the episodic nature of alcohol consumption.
The association between prenatal alcohol exposure and protein expression in human placenta

The need for earlier recognition of children at risk for neurobehavioral problems associated with prenatal ethanol exposure (PAE) has prompted investigations of biomarkers prognostic for altered fetal development. Here, we examined whether PAE alters the expression of angiogenesis‐related proteins and cytokines in human placenta in subjects from an Ethanol, Neurodevelopment, Infant and Child Health prospective cohort.
Energy Optimisation in Wireless Sensor Network for Video Data Transmission

Wireless sensor network (WSN) is a network of large number of interconnected nodes that can independently sense, process and transmit data between themselves and to the base station. Video wireless sensor network (VWSN) is used for capturing and transmitting video data. In VWSN each node consists of camera, memory, processing unit, communication unit and battery. Due to the small size and limited battery life of the node, transmission of video data is a challenging task, as it requires larger bandwidth and more processing power. This paper primarily focuses on energy optimization which is achieved by compression of video at the first stage by using DCT lossless compression technique and then selection of the nodes for transmitting this video using 'Low Energy Adaptive Clustering Hierarchy' (LEACH) protocol. In LEACH protocol cluster head nodes are randomly selected in order to achieve even energy distribution.
Large Eddy Simulation of Flow over a New Type of Low-Wind-Pressure Conductor Using WALE Model

Associating with a grooved cylinder, our research group designs a new type of low-wind-pressure conductor. Based on the OpenFOAM v5.0 platform, five different types of three-dimensional low-wind-pressure conductors and the standard conductor are numerically simulated by using the Large Eddy Simulation with Wall-adapting local eddy-viscosity (WALE) SGS model at Re=1.4×105. Firstly, the flow around a circular cylinder is calculated to prove the reliability of computation in this paper. The calculation results are then analyzed for the comparison of the drag coefficient, flow field characteristics and surface pressure coefficient of the low-wind-pressure conductors and the standard conductor, respectively. The drag coefficient agrees well with the experimental value. The reason for drag reduction in low-wind-pressure conductors is proved to be the generation of small-scale vortices in the surface grooves, which delay flow separation and reduce pressure drag. By comparing the average drag coefficient of the five types of low-wind-pressure conductors, the rougher surface gives rise to a worse drag reduction effect.