Miracle-dz/UniFAR-FTUs huggingface dataset:
Superhydrophobicity through Coatings Prepared by Chemical Methods

Superhydrophobic surfaces were first observed in nature like on a lotus leaf. The surfaces need to have hierarchical micro- and nanoscale roughness and low surface energy to achieve superhydrophobicity. Their unique behavior against water leads to various appli- cations like corrosion resistance, oil-water separation, self-cleaning properties, anti-icing properties, drag reduction, and antibacterial properties. To investigate the wetting behav- ior of the coating, water contact angle, contact angle hysteresis and sliding angle must be measured. If WCA is higher than 150 (cid:1) and sliding angle and contact angle hysteresis are below 10 (cid:1) , then it can be concluded that the surface is superhydrophobic. Various fabrication methods including lithography, templating, chemical vapor deposition, layer-by-layer deposition, colloidal aggregation, and electrospinning and electrospraying especially wet chemical method are thoroughly studied. Among all fabrication methods, the wet chemi- cal technique is one of the promising methods due to its low cost and capability of large-scale production and also the substrate shape and dimensions having a minimal effect on the process. Superhydrophobic coatings still lack sufficient mechanical endurance. Also, in all traditional superhydrophobic coatings, it is necessary to lower the surface energy by a low-energy polymeric material that does not have suitable bonding and stability in harsh environments.
Nephroprotective potential of Cynara scolymus L. floral extract in cisplatin induced nephrotoxicity in rats

The study was aimed to determine the alterations in antioxidant, biochemical and histopathological parameters in cisplatin (cDDP) induced nephrotoxicity and its protection by treatment with hydroalcoholic extract of Cynara scolymus. Acute nephrotoxicity was induced by cDDP (12 mg/kg) in wistar rats. Nephrotoxic rats were treated with quercetin (50 mg/kg) and hydro-alcoholic floral extract (150 and 300 mg/kg body weight) by oral gavage. Cisplatin treatment elevated (P<0.05) the levels of blood urea nitrogen, creatinine, uric acid, malondialdehyde but lowered (P<0.05) total plasma proteins, total thiols, blood glutathione levels and antioxidant enzymes as compared to the control. Pre and post treatment with plant extract at 150 and 300 mg/kg attenuated the altered levels of various enzymatic and oxidative parameters in blood and renal tissue in a dose dependent manner. The extract attenuated the degenerative and necrotic changes of proximal convoluted tubules induced by cDDP which initiates the good nephroprotective potential of C. scolymus extract.
Individualized Teacher-Child Relationship (ITCR) Strategy to Enhance Academic Performance of Children with Attention Deficit Hyperactivity Disorder (ADHD)

Attention Deficit Hyperactivity Disorder (ADHD) is a common psychological disorder of childhood. This disorder has negative impact on the students’ learning. Teachers tackle and handle this problem in a variety of ways based on their knowledge and experience. Similarly I also had ADHD children in my class understudy. Teaching to ADHD children was a challenging job for me because they had extreme energy and restlessness in their behaviour and were unable to focus while learning something. This Action Research had been designed to solve this immediate problem with a major focus on the adjustment of their behaviour and improve academic learning outcomes. In order to achieve the objectives I used Williford’s strategy of “individualized teacher-child relationship (ITCR). This strategy provides important contexts for the promotion of children’s emotional health and learning. The data were collected through multiple means; class assessments, observational notes and weekly discussions with the subjects. It was found that individualized teacher-child relationship (ITCR) strategy is effective for teaching ADHD children and it has a positive impact on students’ behaviour and learning outcomes.
The Journal of Physiology https://jp.msubmit.net JP-RP-2018-277396R1 Title: You are as fast as your motor neurons: Speed of recruitment and maximal discharge of motor neurons determine the maximal rate of force development

Author Contribution: Alessandro Del Vecchio: Conception or design of the work; Acquisition or analysis or interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work Francesco Negro: Acquisition or analysis or interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work Ales Holobar: Acquisition or analysis or interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work Andrea Casolo: Acquisition or analysis or interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be
Modulation of cholinergic contraction by cathinone: beneficial effects in airway diseases

Infusion of khat leaves is an African traditional remedy used to treat airway diseases. The beneficial effects of Khat are thought to be due to the activity of its main active component cathinone. Cathinone inhibited EFS-induced ACh release and contractions of smooth muscle and this activity could be responsible for the beneficial effects seen in airway disease. The mechanism of action of this natural product appears to be via the activation of both pre-junctional α2 adrenergic and 5-HT7 receptors. This is a novel study describing how cathinone modulates airway tone, and may go someway to explaining the traditional use of khat as a remedy to alleviate respiratory disease symptoms. In conclusion, cathinone may have beneficial effects in airway diseases with heightened cholinergic tone. There is some rationale in following up on these observations given previous experience of other traditional remedies being developed for therapeutic use.
Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation

Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training algorithms have not achieved the same performance as CNNs. In this letter, we aim to understand the intrinsic limitations of SNN training to design better algorithms. First, the pros and cons of typical SNN training algorithms are analyzed. Then it is found that the spatiotemporal backpropagation algorithm (STBP) has potential in training deep SNNs due to its simplicity and fast convergence. Later, the main bottlenecks of the STBP algorithm are analyzed, and three conditions for training deep SNNs with the STBP algorithm are derived. By analyzing the connection between CNNs and SNNs, we propose a weight initialization algorithm to satisfy the three conditions. Moreover, we propose an error minimization method and a modified loss function to further improve the training performance. Experimental results show that the proposed method achieves 91.53% accuracy on the CIFAR10 data set with 1% accuracy increase over the STBP algorithm and decreases the training epochs on the MNIST data set to 15 epochs (over 13 times speed-up compared to the STBP algorithm). The proposed method also decreases classification latency by over 25 times compared to the CNN-SNN conversion algorithms. In addition, the proposed method works robustly for very deep SNNs, while the STBP algorithm fails in a 19-layer SNN.
THE EXPRESSION OF P53 AND MDM4 IN ORAL, LARYNGEAL AND CUTANEOUS SQUAMOUS CELL CARCINOMA; A COMPARATIVE STUDY BY TISSUE MICROARRAY

Background: P53 and MDM4 had been known to have dual mechanism depending on their localization. Nuclear p53 can bind to DNA and activate proapoptotic gene expression; cytoplasmic p53 can trigger transcription-independent apoptosis by directly interacting with Bcl-2 family members. Objectives: The aim of current study to evaluate and compare the expression of P53, MDM4 in oral , laryngeal and cutaneous SCC by microarray(TMA) and to investigate the correlation of expression of these markers with histopathological grading of tumor. Methods: One-hundred twenty paraffin embedded SCC sample of Iraqi patients collected during period 2009-2012, retrieved for TMA construction and the expression of P53 and MDM4 was examined by immunohistochemistry. Results: Data showed males with SCC were more than females ( 63.3%/76 , 36.7%/44). Most cancer types expressed both nuclear and cytoplasmic staining of P53 and MDM4 in different percentage, cutaneous Scc Nuclear/cytoplasm expression of P53 were ( 38.8%/94.6%), laryngeal (55%/90%), oral (36.8%/86.8%). Cutaneous SCC N/Cyt expression of MDM4 were (91.8%/72.9%), laryngeal (91.3%/76.3%), oral (97.5%/62.5%). There is no significant difference in expression of both protein markers and tumor types. Obvious significant correlation showed between tumor grading P53 nuclear expression.
Numerical investigation of direct IQ modulation of spin-VCSELs for coherent communications

We propose and numerically investigate a method for direct in-phase quadrature (IQ) modulation of spin-controlled vertical-cavity surface-emitting lasers (spin-VCSELs) for coherent communications. In particular, an injection-locked spin-VCSEL is investigated by using spin-flip rate equations for generating quadrature amplitude modulation (QAM) signals. A polarizer 45° tilted to the polarization axis of the injected external light is used to extract the in-phase and quadrature-phase components of the output light from the injection-locked spin-VCSEL. It is confirmed that QAM signals can be generated with the proposed method successfully, and long electron spin relaxation time and small linear birefringence in the spin-VCSEL are suitable for efficiently generating clear QAM constellations with a low error vector magnitude. A detuning condition for the injection locking of the spin-VCSEL is found to be important for obtaining wide modulation bandwidth of the direct IQ modulation.
A newly designed culture medium for Flavobacterium psychrophilum

A strain of Flavobacterium psychrophilum was isolated on KDM-2 agar, the medium for BKD (bacterial kidney disease) agent, from ayu Plecoglossus altivelis infected with bacterial coldwater disease (BCWD) in Kyoto Prefecture, but it could not grow on modified Cytophaga agar, which has been recommended for the isolation of BCWD agent from ayu. Therefore, a new culture medium for this strain (AK-0206) was designed by modifying KDM-2. As a result, tryptone yeast extract fetal bovine serum (TYFBS) medium consisting of 0.05% tryptone, 0.02% yeast extract and 5% FBS was developed. The TYFBS agar (agar 2%) proved to provide a higher detection rate of F. psychrophilum from non-diseased and diseased ayu than modified Cytophaga and another commonly used medium, tryptone yeast extract salts (TYES) medium.
Efficient in-plane nanomagnetic non-majority logic gate based arithmetic computation

Nanomagnetic Logic (NML) based computation started emerging as a key alternative towards the Beyond CMOS based Rebooting Computing paradigm. Arithmetic computation using this NML can be performed using majority (based on the principles of Magnetic Quantum-dot Cellular Automata) and non-majority logic gates. However there have been extensive studies on the majority logic gates based NML arithmetic architectures, its counterpart needs extensive analysis. Kurtz et al., and Varga et al., have performed the pioneering work in designing non- majority based `AND', `OR' operations. Here in this study, we introduce the non-majority voting gate based half-subtractor and adder architecture design methodology. To the best of author's knowledge this is the first time demonstration of arithmetic nanomagnetic implementation using non-majority gate. Subsequently, it can be inferred from the micromagnetic simulations and modeling that an asymmetrically shaped nanomagnet can be used as a compute nanomagnet in performing computation. The results show that only 4 nanomagnets are required to perform half subtractor/adder leading to area efficient design. This reduction is achieved by exploiting the principles of SP hybrid anisotropy and Boolean optimizations. Thus it can be observed that the non-majority voting based computation can lead to area efficient nanomagnetic arithmetic architecture design. Consequently, the thermal bit-stability analysis has also been carried out for the proposed sub-5o nm design.
Effect of Colored Noise on Circadian Oscillation and Internal Signal Stochastic Resonance in Biological Clock System

The effect of colored noise on circadian oscillation and internal signal stochastic resonance(ISSR) has been studied in a Neurospora circadian clock system. The result shows that the correlation time of colored noise can affect strongly the strength of ISSR. For the case of no external signal(ES), the correlation time of colored noise plays a suppressive role for ISSR, and the suppressive role is increased with the increment of correlation time of colored noise. When the ES is injected to the system, with the increasing of the correlation time of colored noise, not only its suppressive role for ISSR is increased, but also the single peak ISSR can be transformed as internal single stochastic bi-resonance (ISSBR). There exists an optimal frequency of ES for the ISSR information amplification, while the ISSR is suppressed for another frequency of ES. In contrast to external colored noise, internal colored noise is more efficient to sustain and amplify the ISSR information. Furthermore, there exists a critical noise intensity for destroying the difference between white and colored noises.
Personality Bias of Music Recommendation Algorithms

Recommender systems, like other tools that make use of machine learning, are known to create or increase certain biases. Earlier work has already unveiled different performance of recommender systems for different user groups, depending on gender, age, country, and consumption behavior. In this work, we study user bias in terms of another aspect, i.e., users’ personality. We investigate to which extent state-of-the-art recommendation algorithms yield different accuracy scores depending on the users’ personality traits. We focus on the music domain and create a dataset of Twitter users’ music consumption behavior and personality traits, measuring the latter in terms of the OCEAN model. Investigating recall@K and NDCG@K of the recommendation algorithms SLIM, embarrassingly shallow autoencoders for sparse data (EASE), and variational autoencoders for collaborative filtering (Mult-VAE) on this dataset, we find several significant differences in performance between user groups scoring high vs. groups scoring low on several personality traits.
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems

Reinforcement learning for recommendation (RL4Rec) methods are increasingly receiving attention as an effective way to improve long-term user engagement. However, applying RL4Rec online comes with risks: exploration may lead to periods of detrimental user experience. Moreover, few researchers have access to real-world recommender systems. Simulations have been put forward as a solution where user feedback is simulated based on logged historical user data, thus enabling optimization and evaluation without being run online. While simulators do not risk the user experience and are widely accessible, we identify an important limitation of existing simulation methods. They ignore the interaction biases present in logged user data, and consequently, these biases affect the resulting simulation. As a solution to this issue, we introduce a debiasing step in the simulation pipeline, which corrects for the biases present in the logged data before it is used to simulate user behavior. To evaluate the effects of bias on RL4Rec simulations, we propose a novel evaluation approach for simulators that considers the performance of policies optimized with the simulator. Our results reveal that the biases from logged data negatively impact the resulting policies, unless corrected for with our debiasing method. While our debiasing methods can be applied to any simulator, we make our complete pipeline publicly available as the Simulator for OFfline leArning and evaluation (SOFA): the first simulator that accounts for interaction biases prior to optimization and evaluation.
Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions

Post-click conversion, a pre-defined action on a web service after a click, is an essential form of feedback, as it directly contributes to the final revenue and accurately captures user preferences for items, compared with the ambiguous click. However, naively using post-click conversions can lead to severe bias when learning or evaluating recommenders because of the selection bias between clicked and unclicked data. In this study, we address the offline evaluation problem of algorithmic recommendations with biased post-click conversions. A possible solution to address this bias is to use the inverse propensity score estimator, as it can provide an unbiased evaluation even with the selection bias. However, this estimator is known to be subject to variance and instability problems, which can be severe in the recommendation setting, as feedback is often highly sparse. To address these limitations with the previous unbiased estimator, we propose a doubly robust estimator for the ground-truth ranking performance of a given recommender. The proposed estimator is unbiased against the ground-truth ranking metric and improves the variance and estimation error tail bound of the existing unbiased estimator. Finally, to evaluate the empirical efficacy of the proposed estimator, we conduct empirical evaluations using semi-synthetic and two public real-world datasets. The results show that the proposed metric reveals a better model evaluation performance compared with existing baseline metrics, particularly in a situation with severe selection bias.
The implementation of stigmergy in network-assisted multi-agent system

Multi-agent system (MAS) needs to mobilize multiple simple agents to complete complex tasks. However, it is difficult to coherently coordinate distributed agents by means of limited local information. In this demo, we propose a decentralized collaboration method named as "stigmergy" in network-assisted MAS, by exploiting digital pheromones (DP) as an indirect medium of communication and utilizing deep reinforcement learning (DRL) on top. Correspondingly, we implement an experimental platform, where KHEPERA IV robots form targeted specific shapes in a decentralized manner. Experimental results demonstrate the effectiveness and efficiency of the proposed method. Our platform could be conveniently extended to investigate the impact of network factors (e.g., latency, data rate, etc).
Automated Bug Reproduction from User Reviews for Android Applications

Bug-related user reviews of mobile applications have negative influence on their reputation and competence, and thus these reviews are highly regarded by developers. Before bug fixing, developers need to manually reproduce the bugs reported in user reviews, which is an extremely time-consuming and tedious task. Hence, it is highly expected to automate this process. However, it is challenging to do so since user reviews are hard to understand and poorly informative for bug reproduction (especially lack of reproduction steps). In this paper, we propose RepRev to automatically Reproduce Android application bugs from user Reviews. Specifically, RepRev leverages natural language processing techniques to extract valuable information for bug reproduction. Then, it ranks GUI components by semantic similarity with the user review and dynamically searches on apps with a novel one-step exploration technique. To evaluate RepRev, we construct a benchmark including 63 crash-related user reviews from Google Play, which have been reproduced successfully by three graduate students. On this benchmark, RepRev presents comparable performance with humans, which successfully reproduces 44 user reviews in our benchmark (about 70%) with 432.2 seconds average time. We make the implementation of our approach publicly available, along with the artifacts and experimental data we used [4]. CCS CONCEPTS• Software and its engineering → Software maintenance tools.
GROOT: a real-time streaming system of high-fidelity volumetric videos

We present GROOT, a mobile volumetric video streaming system that delivers three-dimensional data to mobile devices for a fully immersive virtual and augmented reality experience. The system design for streaming volumetric videos should be fundamentally different from conventional 2D video streaming systems. First, the amount of data required to deliver the 3D volume is considerably larger than conventional videos with frames of 2D images, even compared to high-resolution 2D or 360° videos. Second, the 3D data representation, which encodes the surface of objects within the volume, is a sparse and unorganized data structure with varying scales, whereas a conventional video is composed of a sequence of images with the fixed-size 2D grid structure. GROOT is a streaming framework with a novel data structure that enables not only real-time transmission and decoding on mobile devices but also continuous on-demand user view adaptation. Specifically, we modify the conventional octree to introduce the independence of leaf nodes with minimal memory overhead, which enables parallel decoding of highly irregular 3D data. We also developed a suite of techniques to compress color information and filter out 3D points outside of a user's view, which efficiently minimizes the data size and decoding cost. Our extensive evaluation shows that GROOT achieves more stable but faster frame rates compared to any previous method to stream and visualize volumetric videos on mobile devices.
Exploring Clustering of Bandits for Online Recommendation System

Cluster-of-bandit policy leverages contextual bandits in a collaborative filtering manner and aids personalized services in the online recommendation system (RecSys). When facing insufficient observations, the cluster-of-bandit policy could achieve more outstanding performance because of knowledge sharing. Cluster-of-bandit policy aims to maximize the cumulative feedback, e.g., clicks, from users. Nevertheless, in the way of their goal exist two kinds of uncertainties. First, cluster-of-bandit algorithms make recommendations according to their uncertain estimation of user interests. Second, cluster-of-bandit algorithms transfer relevant knowledge upon uncertain and noisy user clusters. Existing algorithms only consider the first one, while leaving the latter one untouched. To address the two challenges together, in this paper, we propose the ClexB policy for online RecSys. On the one hand, ClexB estimates user clustering more accurately and with less uncertainty via explorable-clustering. On the other hand, ClexB also exploits and explores user interests by sharing information within and among user clusters. In summary, ClexB explores knowledge transfer and further aids the inferences about user interests. Besides, we provide extensive empirical experiments on both the synthetic and real-world datasets and regret analysis, further consolidating the superiority of ClexB.