Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-Oriented Mobility-Aware Caching Strategies in D2D Networks With Delay Constraint

Pre-caching popular files at mobile users with the aid of device-to-device (D2D) communications can offload the data traffic to low-cost D2D links and reduce the network transmission cost. This leads to additional cache leasing cost brought by the lease of storage from mobile users. Besides, newly-emerging video-related applications also pose strict requirement on the network delay. Thus, it is of great significance to design caching strategies considering the transmission cost, the cache leasing cost and the delay. As the movement of mobile users can improve the communication opportunities among different users and increase the cache hit ratio, in this paper, mobility-aware caching strategies are designed to minimize the network cost including both the transmission cost and the cache leasing cost with the delay constraint. In specific, by characterizing the user mobility as an inter-contact model, analytical expressions of the average network cost and the average file delivery delay are derived and a cost-oriented mobility-aware caching problem is formulated. To handle this mixed integer nonlinear programming (MINLP) problem, we first relax the binary cache placement indicator as a continuous one and prove that both the average network cost and the average file delivery delay are convex. Hence, an iterative caching algorithm is proposed with the successive convex approximation method. Moreover, to lower the complexity, combinatorial optimization method is adopted. Firstly, to make the caching problem tractable, the average file delivery delay constraint is implicitly added in the cost objective function as a penalty term. Then, the reformulated objective function is proved to have the non-monotone submodular property and thus a modified low-complexity greedy caching strategy is proposed. Simulation results show that, compared with the most popular caching strategy, our proposed mobility-aware caching strategy can reduce the average cost by 46% when the user speed is high.
Non-invasive estimation of whole body averaged SAR by the personal RF dosimeter measurements

The Personal Dosimeter is a portable device that allows measurement of electromagnetic field levels. In this work we propose a new method for non-invasive evaluation of the whole body averaged body specific absorption rate of the human body (${SAR}_{wba}$) from only the personal dosimeter measurement. This new method is based on linear regression to reduce errors due to the presence of the human body on the measurement. The results show a SAR assessment with very low error rates in the order of 5%, a reduction in the average error of 5 times for a morphology of a middle adult man and 9 times for a morphology of a child of 10 years old.
Adaptive Treatment Assisting System for Patients Using Machine Learning

Machine learning techniques have achieved a lot of success in many healthcare related aspects including personalized treatment, medical imaging, diagnostic systems etc. In present work, we propose a medicine reminder system which can assist patients in their treatment process at home. In case of treatment at home of patients with different physical and/or mental disabilities, a key challenge is the choice of the proper message for each specific patient taking into account his/her needs. To this aim, the proposed system can use a variety of messages (i.e audio, visual and textual) to communicate with patients taking into account their specific physical and/or mental disabilities. We apply Upper Confidence Bound method which enables the guidance system to adapt according to a patient‘s mental and physical skills. To demonstrate the feasibility of the proposed treatment assisting system, a sample treatment plan are implemented in an simulated experiment. The results show that our proposed system through interactions with the patient of simulated physical and mental skills is capable of selecting right mode of message which will lead the patient to the right medicine box.
Reliability Analysis of Assembly Processes Performed by Human-Robot Interaction

Together with innovations in robot technologies and the emergence of the fourth industrial revolution called Industry 4.0, collaborative robot applications have improved. Collaborative robots are the robots that provide the safe handling of many processes such as assembly, pick and place, palletizing in factories. The reliability analysis of the system has great importance in order to determine the failure conditions and failure possibilities that will occur in the systems enabling these operations. In this study, reliability analysis of assembly processes performed by human-robot interaction is presented. The reliability block diagram of the system is created and the reliability value of the system over time is determined. Then, Markov model indicating the fault conditions in the system is created and the probability of occurrence of these fault conditions is determined. The results show that if the system operates continuously for five years, the reliability will drop below 10% and the reasons that decrease the reliability are discussed in the last section.
Towards Robotic Picking of Targets with Background Distractors using Deep Reinforcement Learning

Robotic manipulation using vision-based learning algorithms has benefits many tasks where the robot needs to interact with a desired target. However, when a desired target is placed with other objects, there remains several technical challenges. Firstly, the target might be occluded by other objects in terms of the camera field of view. Secondly, prioritizing the selection of the objects changes the polices and rewards of the learning algorithm which might deteriorate the successful rates. Thirdly, the occlusion by other objects increases the difficulty of manipulation as it requires complex synergies between pushing and grasping. Therefore, we propose a value-based deep reinforcement learning with Mask R-CNN to address the issues of robotic manipulation for multiple object. In the proposed three rewards are proposed, namely: 1) success of grasping the desired target; 2) removal of the distractors; and 3) effective pushes. This method aims to enable the manipulation to grasp a desired target among distractors. Simulation has been conducted to demonstrate the effectiveness of the proposed method. The results show that the robot is able to effectively pick the desired targets out despite the physical and optical occlusion by other objects. All tasks are completed with 100% successful rate within 30 episodes.
Multi-Source Medical Image Fusion Based on Wasserstein Generative Adversarial Networks

In this paper, we propose the medical Wasserstein generative adversarial networks (MWGAN), an end-to-end model, for fusing magnetic resonance imaging (MRI) and positron emission tomography (PET) medical images. Our method establishes two adversarial games between a generator and two discriminators to generate a fused image with the details of soft tissue structures in organs from MRI images and the functional and metabolic information from PET images. Different information from source images can be effectively adjusted with a specifically designed loss function. In addition, we use WGAN instead of the traditional generative adversarial networks to make the training process more stable and allow our architecture to deal with source images of different resolutions. Qualitative and quantitative comparisons on publicly available datasets demonstrate the superiority of MWGAN over the state-of-the-art networks. Furthermore, our MWGAN is applied to the fusion of MRI and computed tomography images of different resolutions, achieving a satisfactory performance.
Identification of Indoor Harmful Gas to Human Respiratory System using Support Vector Machines

Today, it is among widely studied subjects to determine the air quality of living spaces and take appropriate actions. In this study, indoor air quality was continuously monitored using an array of various gas sensors. The system is modelled to detect any harmful gas among different gasses and give a warning. Support Vector Machines was used in the model, and it was trained by sample gas data that was created using a group of harmful and harmless gasses to human health. Tests on the trained model showed that the proposed model was able to classify gasses as harmful or harmless with a 100% success rate. The time it takes for the system to give a response with such an accuracy rate was significantly short. The speed of the system is also an important contribution of this study. This study showed that Support Vector Machines can be used with high accuracy-high sensitivity and high specificity- in determining gasses that are harmful or harmless to human health while measuring indoor air quality.
TAP: A High-Precision Network Timing Method Over Air Interface Based on Physical-Layer Signals

Absolute time synchronization (timing) is a constant demand in various fields. There are many timing techniques at present. They are mainly based on dedicated equipments such as GNSS or specific protocols such as NTP, PTP, SIB16, etc. Different timing methods are applied according to the scenario, cost and demands for timing accuracy. Recently, the timing demands for some wireless scenarios have become increasingly urgent, like power IoT and auto-driving. However, due to the instability and complexity of radio link, the current methods can hardly meet the demands of high-precision and low cost simultaneously in mobile network. Here we propose a timing method over air interface based on physical layer signals (TAP). Periodic physical layer signals in both downlink and uplink channel are considered to reduce the impact of radio link instability on timing. We implemented the proposed method on an open source LTE software defined radio platform named OpenAirInterface and conducted a series of tests. Our tests prove that TAP can provide microsecond-level timing over the air interface, and it is more stable and precise than PTP. Further simulation shows that using TAP in 5G NR can improve the timing accuracy.
Enhancement of Reliability in Condition Monitoring Techniques in Wind Turbines

The majority of electrical failures in wind turbines occur in the semiconductor components (IGBTs) of converters. To increase reliability and decrease the maintenance costs associated with this component, several health-monitoring methods have been proposed in the literature. Many laboratory-based tests have been conducted to detect the failure mechanisms of the IGBT in their early stages through monitoring the variations of thermo-sensitive electrical parameters. The methods are generally proposed and validated with a single-phase converter with an air-cored inductive or resistive load. However, limited work has been carried out considering limitations associated with measurement and processing of these parameters in a three-phase converter. Furthermore, looking at just variations of the module junction temperature will most likely lead to unreliable health monitoring as different failure mechanisms have their own individual effects on temperature variations of some, or all, of the electrical parameters. A reliable health monitoring system is necessary to determine whether the temperature variations are due to the presence of a premature failure or from normal converter operation. To address this issue, a temperature measurement approach should be independent from the failure mechanisms. In this paper, temperature is estimated by monitoring an electrical parameter particularly affected by different failure types. Early bond wire lift-off is detected by another electrical parameter that is sensitive to the progress of the failure. Considering two separate electrical parameters, one for estimation of temperature (switching off time) and another to detect the premature bond wire lift-off (collector emitter on-state voltage) enhance the reliability of an IGBT could increase the accuracy of the temperature estimation as well as premature failure detection.
Limitation of complementary resources affects colony growth, foraging behavior, and reproduction in bumble bees.

Resource availability has been disturbed for many organisms in agricultural landscapes including pollinator species. Abundance and diversity in flower availability benefit bee populations, however, little is known about which of protein or carbohydrate resources may limit their growth and reproductive performance. Here, we test the hypothesis of complementary resource limitation using a supplemental feeding approach. We applied this assumption with bumble bees (Bombus terrestris), assuming that colony growth and reproductive performance should depend on the continuous supply of carbohydrates and proteins, through the foraging for nectar and pollen respectively. We placed wild-caught bumble bee colonies along a landscape gradient of semi-natural habitats, and monitored the colonies' weight, foraging activity and reproductive performance during the whole colony cycle. We performed supplemental feeding as an indicator of landscape resource limitation, using a factorial design consisting of the addition of sugar-water (carbohydrate, supplemented or not) crossed by pollen (protein, supplemented or not). Bumble bee colony dynamics showed a clear seasonal pattern with a period of growth followed by a period of stagnation. Higher abundance of semi-natural habitats resulted in reducing the proportion of pollen foragers relative to all foragers in both periods, and in improving the reproductive performance of bumble bees. Interestingly, the supplemental feeding of sugar-water positively affected the colony weight during the stagnation period, while the supplemental feeding of pollen mitigated the landscape effect on pollen collection investment. Single and combined supplementation of sugar-water and pollen increased the positive effect of semi-natural habitats on reproductive performance. This study reveals a potential co-limitation in pollen and nectar resources affecting foraging behavior and reproductive performance in bumble bees, and indicates that even in mixed agricultural landscapes with higher proportions of semi-natural habitats, bumble bee populations face resource limitations. We conclude that the seasonal management of floral resources must be considered in conservation to support bumble bee populations and pollination services in farmlands.
Time-series single-cell RNA-seq data reveal auxin fluctuation during endocycle.

Appropriate cell cycle regulation is crucial for achieving coordinated development and cell differentiation in multicellular organisms. In Arabidopsis, endoreduplication is often observed in terminally differentiated cells, and several reports have shown its molecular mechanisms. Auxin is a key factor for the mode transition from mitotic cell cycle to endocycle; however, it remains unclear if and how auxin maintains the endocycle mode. Here, we reanalyzed root single-cell transcriptome data and reconstructed cell cycle trajectories of the mitotic cell cycle and endocycle. With progression of the endocycle, genes involved in auxin synthesis, influx, and efflux were induced at the specific cell phase, suggesting that auxin concentration fluctuated dynamically. Such induction of auxin-related genes was not observed in the mitotic cell cycle, suggesting that the auxin fluctuation plays some roles in maintaining the endocycle stage. In addition, the expression level of CYCB1;1, which is required for cell division in the M phase, coincided with the expected amount of auxin and cell division. Our analysis also provided a set of genes expressed in specific phases of the cell cycle. Taking these findings together, reconstruction of single-cell transcriptome data enables us to identify properties of the cell cycle more accurately.
Innate immunity to malaria—The role of monocytes

Monocytes are innate immune cells essential for host protection against malaria. Upon activation, monocytes function to help reduce parasite burden through phagocytosis, cytokine production, and antigen presentation. However, monocytes have also been implicated in the pathogenesis of severe disease through production of damaging inflammatory cytokines, resulting in systemic inflammation and vascular dysfunction. Understanding the molecular pathways influencing the balance between protection and pathology is critical. In this review, we discuss recent data regarding the role of monocytes in human malaria, including studies of innate sensing of the parasite, immunometabolism, and innate immune training. Knowledge gained from these studies may guide rational development of novel antimalarial therapies and inform vaccine development.
Osteophyte volume calculation using dissimilarity‐excluding Procrustes registration of archived bone models from healthy volunteers

Osteophytes are associated with later stage osteoarthritis and are most commonly described using semiquantitative radiographic grading systems. A detailed understanding of osteophyte formation is, in part, limited by the ability to quantify bone pathology. Osteophytes can be quantified relative to pre‐osteoarthritic bone, or to the contralateral bone if it is healthy; however, in many cases, neither are available as references. We present a method for computing three‐dimensional (3D) osteophyte models using a library of healthy control bones. An existing data set containing the computed tomography scans of 90 patients with first carpometacarpal osteoarthritis (OA) and 46 healthy subjects were utilized. A healthy bone that best fit each OA subject's bone was determined using a dissimilarity‐excluding Procrustes registration technique (DEP) that minimized the influence of dissimilar features (ie, osteophytes). The osteophyte model was then computed through Boolean subtraction of the reference bone model from the OA bone model. DEP reference bones conformed significantly better to the OA bones (P < .0001) than by finite difference iterative closest point registration (root mean squared distances, 0.33 ± 0.05 and 0.41 ± 0.16 mm, respectively). The effect of library size on dissimilarity measure was investigated by leave‐k‐out cross‐validation randomly reducing k from 46 to 1. A library of n ≥ 31 resulted in less than 10% difference from the theoretical minimum value. The proposed method enables quantification of osteophytes when the disease‐free bone or the healthy contralateral bone is not available for any 3D data set. Quantifying osteophyte formation and growth may aid in understating the associated mechanisms in OA.
Immunity against sexual stage Plasmodium falciparum and Plasmodium vivax parasites

The efficient spread of malaria from infected humans to mosquitoes is a major challenge for malaria elimination initiatives. Gametocytes are the only Plasmodium life stage infectious to mosquitoes. Here, we summarize evidence for naturally acquired anti‐gametocyte immunity and the current state of transmission blocking vaccines (TBV). Although gametocytes are intra‐erythrocytic when present in infected humans, developing Plasmodium falciparum gametocytes may express proteins on the surface of red blood cells that elicit immune responses in naturally exposed individuals. This immune response may reduce the burden of circulating gametocytes. For both P. falciparum and Plasmodium vivax, there is a solid evidence that antibodies against antigens present on the gametocyte surface, when co‐ingested with gametocytes, can influence transmission to mosquitoes. Transmission reducing immunity, reducing the burden of infection in mosquitoes, is a well‐acknowledged but poorly quantified phenomenon that forms the basis for the development of TBV. Transmission enhancing immunity, increasing the likelihood or intensity of transmission to mosquitoes, is more speculative in nature but is convincingly demonstrated for P. vivax. With the increased interest in malaria elimination, TBV and monoclonal antibodies have moved to the center stage of malaria vaccine development. Methodologies to prioritize and evaluate products are urgently needed.
Mindfulness and cognitive behavior therapy for provoked vestibulodynia: Mediators of treatment outcome and long-term effects.

OBJECTIVE Provoked vestibulodynia (PVD) is a chronic vulvo-vaginal pain condition affecting 8% of premenopausal women. Cognitive-behavioral therapy (CBT) is effective in managing pain and associated sexual and psychological symptoms, and a recent study found group mindfulness-based cognitive therapy (MBCT) to be equivalent. Our goal was to examine the long-term outcomes of these treatments and to explore mediators of change. METHOD Participants were 130 women diagnosed with PVD who had participated in a clinical trial comparing 8 weeks of group CBT to 8 weeks of group MBCT. Data were collected at pretreatment, posttreatment, and at 6- and 12-month follow-up periods. Outcomes focused on (a) pain with vaginal penetration, (b) pain elicited with a vulvalgesiometer, and (c) sex-related distress. Mediators of interest included pain acceptance (both pain willingness and activities engagement), self-compassion, self-criticism, mindfulness, decentering, and pain catastrophizing. RESULTS All improvements in the 3 outcomes were retained at 12-month follow-up, with no group differences. Pain catastrophizing, decentering, and chronic pain acceptance (both scales) were mediators of improvement common to both MBCT and CBT. Changes in mindfulness, self-criticism, and self-compassion mediated improvements only in the MBCT group. CONCLUSIONS Both MBCT and CBT are effective for improving symptoms in women with PVD when assessed 12 months later. The findings have implications for understanding common and potentially distinct pathways by which CBT and MBCT improve pain and sex-related distress in women with PVD. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Protein structure from experimental evolution

Natural evolution encodes rich information about the structure and function of biomolecules in the genetic record. Previously, statistical analysis of co-variation patterns in natural protein families has enabled the accurate computation of 3D structures. Here, we explored whether similar information can be generated by laboratory evolution, starting from a single gene and performing multiple cycles of mutagenesis and functional selection. We evolved two bacterial antibiotic resistance proteins, β-lactamase PSE1 and acetyltransferase AAC6, and obtained hundreds of thousands of diverse functional sequences. Using evolutionary coupling analysis, we inferred residue interactions in good agreement with contacts in the crystal structures, confirming genetic encoding of structural constraints in the selected sequences. Computational protein folding with contact constraints yielded 3D structures with the same fold as that of natural relatives. Evolution experiments combined with inference of residue interactions from sequence information opens the door to a new experimental method for the determination of protein structures.