Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-chain cyanoacrylates and long-chain cyanoacrylates (Dermabond) have different antimicrobial effects

Objective To compare the antimicrobial effect in vitro of a short-chain cyanoacrylate with a long-chain cyanoacrylate (Dermabond, Ethicon, Johnson and Johnson, USA) against bacterial strains. Methods and analysis The following bacterial strains were analysed: Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia and Pseudomonas aeruginosa. For each microorganism, standardised sterile discs (6 mm) containing 10 µL of ethyl-cyanoacrylate and 2-octyl cyanoacrylate were applied to the plate. All plates received a blank filter-paper disc with no adhesive (control). All plates were incubated for 24 hours, after which the bacterial inhibitory halos, if present, were measured in millimetres in its greater length. Results Inhibitory halos were observed for both adhesives for S. aureus. Inhibition halos were observed only for ethyl-cyanoacrylate for K. pneumoniae and E. coli. No inhibition halo was observed for P. aeruginosa in any sample. The relationship between the total size of the inhibition halos and the diameter of the paper filter for S. aureus was statistically significant compared with 2-octyl cyanoacrylate. Conclusion Data shown conclude that ethyl-cyanoacrylate showed in vitro bacteriostatic activity for S. aureus, E. coli and K. pneumoniae. 2-Octyl cyanoacrylate showed in vitro lower bacteriostatic activity only against S. aureus when compared with ethyl-cyanoacrylate. No in vitro bactericidal activity of ethyl-cyanoacrylate or 2-octyl cyanoacrylate was observed.
The role of health shocks, parental education and personality traits

In the last decade, the unadjusted gender pay gap, defined as the raw percentage difference between male and female average gross hourly earnings, decreased in most European Union (EU) countries. Despite this progressive reduction, the average gender wage gap in 2014 in the EU (weighted by the number of employees) still stood at 16.6 per cent, indicating that women earned, on average, 84 per cent of that of men. This figure masks substantial variations across countries: although the gap is relatively narrow in Italy (6.1), Belgium (6.6) and Slovenia (7.0), it becomes significantly larger in countries such as Austria (22.2), Germany (22.3) and Estonia (28.1) (Source: Eurostat). Starting from the seminal works by Blinder (1973) and Oaxaca (1973), a large body of literature has investigated the gender wage gap to quantify the part that can be explained by differences in individual characteristics, such as education or job experience, and the so-called unexplained part. The latter component can be attributable to gender discrimination, justified by, for instance, a supposedly lower attachment to work by women. Much of this body of literature has treated the distribution of men and women into different working sectors and occupations as exogenously given. This approach does not acknowledge two potentially relevant factors. First, differences in individual characteristics may lead women to prefer jobs that pay lower wages on average. Second, to some extent, women might be prevented
ACCURATE QUANTITATION OF 17 POLYPHENOLS FROM PROPOLIS EXTRACTS BY REVERSED-PHASE LIQUID CHROMATOGRAPHY WITH DIODE ARRAY DETECTION

A method based on reversed-phase liquid chromatography (RPLC) with diode array detection (DAD) has been developed and validated in order to quantitate several polyphenols from different extracts. Samples were separated on a Fortis C18 reversed-phase column (250 x 4.6 mm, 5 µm) with a gradient of acetonitrile and aqueous phosphoric acid, pH=2.5. The complete separation of seventeen polyphenols was achieved within 60 min. All calibration curves expressed good linearity (r 2 > 0.997) within the test range. The recovery of this method was in the range 88.12–107.87% and for the intra-day and inter-day assays, the values of deviation coefficient were less than 5%. The assay was successfully applied to the quantitation of polyphenols from 9 samples of propolis ethanolic extracts purchased from Roumanian market. The results indicated that this developed RP-LC assay could be readily utilized as a quality control method of various natural extracts.
Nonlinear Pricing Under Regulation: Comparing Cap Rules and Taxes in the Laboratory

Se reporta un experimento que contrasta los impactos de un impuesto y un límite de cantidad en un mercado con un solo producto y dos consumidores con preferencias privadas. Se discuten los efectos sobre la canasta de elección y el excedente del consumidor. La política regulatoria varía según el tratamiento experimental. Con regulación, el objetivo es reducir a la mitad el tamaño de la opción grande original. En comparación al grupo sin intervención, los vendedores que enfrentan un límite de cantidad intentan servir a los compradores por separado con una frecuencia similar. Con un impuesto, es menos probable que los vendedores ofrezcan dos alternativas. El excedente del consumidor no es afectado cuando hay un límite de cantidad, mientras que los compradores con alta preferencia por el producto ven su excedente disminuido por el impuesto. Estos resultados tienen implicaciones para la formulación de políticas en la industria minorista de alimentos y otras en las que las autoridades pretenden regular el consumo y al mismo tiempo proteger el excedente del consumidor.
How may COVID-19 viral membrane resist when exposed radiation to treatment : effects on Phospholipids and cholesterol

In this report, we have investigated the ability of the essential phospholipids and cholesterol found viral membrane of SARS-COV-2 to protect it from the radiation exposure. The applied mehod was Geometric Progression fitting procedure to obtained numerically the atomic number equivalenace (Zeq), for any photon energy absorption exposure (EBF) and absorption (EABF) buildup factors. The used energy range E∈ [0.015,15] MeV up to an ultimate penetration length of 40 mpf for viral membrane essential components phospholipids and cholesterol. Most importantly, the calculated Zeq values were found to vary with incident photon energy and phospholipids compositions. Also, both EBF and EABF were depend strongly on the photon energy and penetration depths. For phospholipids, these two factors almost coincide in the energy range studied while it slightly different for cholesterol. At energies < 150 keV, both buildup factors were found to to inversely proportional to Zeq values of the phospholipids and cholesterol. The buildup factors EABF and EBF were found to be larger for cholesterol than other phospholipids at nearly 60 keV for COVID-19 viral membrane. The above results have shown that the viral membrane can resist the radiation effects and prevent the coronavirus form being inactivated when exposed to γ- or X-rays radiotherapy treatment in medical trails except for a specific radiation energy depending on the mfp (i.e., 60 keV at 5 mfp).
The effect of target salience and size in visual search within naturalistic scenes under degraded vision

We address two questions concerning eye guidance during visual search in naturalistic scenes. First, search has been described as a task in which visual salience is unimportant. Here, we revisit this question by using a letter-in-scene search task that minimizes any confounding effects that may arise from scene guidance. Second, we investigate how important the different regions of the visual field are for different subprocesses of search (target localization, verification). In Experiment 1, we manipulated both the salience (low vs. high) and the size (small vs. large) of the target letter (a “T”), and we implemented a foveal scotoma (radius: 1°) in half of the trials. In Experiment 2, observers searched for high- and low-salience targets either with full vision or with a central or peripheral scotoma (radius: 2.5°). In both experiments, we found main effects of salience with better performance for high-salience targets. In Experiment 1, search was faster for large than for small targets, and high-salience helped more for small targets. When searching with a foveal scotoma, performance was relatively unimpaired regardless of the target's salience and size. In Experiment 2, both visual-field manipulations led to search time costs, but the peripheral scotoma was much more detrimental than the central scotoma. Peripheral vision proved to be important for target localization, and central vision for target verification. Salience affected eye movement guidance to the target in both central and peripheral vision. Collectively, the results lend support for search models that incorporate salience for predicting eye-movement behavior.
The Change P82L in the Rift Valley Fever Virus NSs Protein Confers Attenuation in Mice

Rift Valley fever virus (RVFV) is a mosquito-borne bunyavirus that causes an important disease in ruminants, with great economic losses. The infection can be also transmitted to humans; therefore, it is considered a major threat to both human and animal health. In a previous work, we described a novel RVFV variant selected in cell culture in the presence of the antiviral agent favipiravir that was highly attenuated in vivo. This variant displayed 24 amino acid substitutions in different viral proteins when compared to its parental viral strain, two of them located in the NSs protein that is known to be the major virulence factor of RVFV. By means of a reverse genetics system, in this work we have analyzed the effect that one of these substitutions, P82L, has in viral attenuation in vivo. Rescued viruses carrying this single amino acid change were clearly attenuated in BALB/c mice while their growth in an interferon (IFN)-competent cell line as well as the production of interferon beta (IFN-β) did not seem to be affected. However, the pattern of nuclear NSs accumulation was modified in cells infected with the mutant viruses. These results highlight the key role of the NSs protein in the modulation of viral infectivity.
What Can HCI Learn from Sexual Consent?: A Feminist Process of Embodied Consent for Interactions with Emerging Technologies

Sexual consent has undergone a transformation toward an “enthusiastic” feminist model that emphasizes consent as an ongoing and voluntary process of negotiation and affirmation. This paper considers how such a model can advance understandings of consent in HCI research and design in relation to embodied interactions with emerging technologies that also occur outside of sexual interactions. We apply the popular “FRIES” model of sexual consent (Freely given, Reversible, Informed, Enthusiastic and Specific) to three areas of embodied interaction: 1) bodily-play interactions, 2) persuasive interactions with smart technologies, and 3) intimate interactions with anthropomorphized devices. Based on erotic play practices, we contribute a “TEASE” process guideline (Traffic lights, Establish ongoing dialogue, Aftercare, Safewords, and Explicate soft/hard limits) to advance consensual practice in HCI and develop implementation scenarios.
The right touch: design of artificial antigen-presenting cells to stimulate the immune system

With the ever expanding possibilities to build supramolecular structures, chemists are challenged to mimic nature including the construction of artificial cells or functions thereof. Within the field of immunology, effective immunotherapy critically depends on efficient production of antigen-specific cytotoxic T-cells. Herein lies an opportunity for chemists to design and synthesize so-called artificial antigen presenting cells (aAPCs) that can promote T-cell activation and their subsequent expansion. In this review we discuss the current status of aAPC development, also focusing on developments in nanoscience which might improve future designs. As synthetic mimics of natural antigen-presenting cells, aAPCs encompass three basic signals required for T-cell activation: MHC–antigen complexes, costimulatory molecules and soluble immune modulating compounds. Both spatial and temporal organization of these signals during aAPC/T-cell contact is important for efficient T-cell activation. We discuss how signals have been incorporated in several aAPC designs, but also how physical properties such as size and shape are essential for targeting the aAPCs to T-cell rich areas in vivo.
Recognizing a spatial extreme dependence structure: A deep learning approach

Understanding the behavior of extreme environmental events is crucial for evaluating economic losses, assessing risks, and providing health care, among many other related aspects. In a spatial context, relevant for environmental events, the dependence structure is extremely important, influencing joint extreme events and extrapolating on them. Thus, recognizing or at least having preliminary information on the patterns of these dependence structures is a valuable knowledge for understanding extreme events. In this study, we address the question of automatic recognition of spatial asymptotic dependence versus asymptotic independence, using a convolutional neural network (CNN). We designed a CNN architecture as an efficient classifier of a dependence structure. Extremal dependence measures are used to train the CNN. We tested our methodology on simulated and real datasets: air temperature data at 2 m over Iraq and rainfall data along the east coast of Australia.
HeterSkinNet

Character rigging is universally needed in computer graphics but notoriously laborious. We present a new method, HeterSkinNet, aiming to fully automate such processes and significantly boost productivity. Given a character mesh and skeleton as input, our method builds a heterogeneous graph that treats the mesh vertices and the skeletal bones as nodes of different types and uses graph convolutions to learn their relationships. To tackle the graph heterogeneity, we propose a new graph network convolution operator that transfers information between heterogeneous nodes. The convolution is based on a new distance HollowDist that quantifies the relations between mesh vertices and bones. We show that HeterSkinNet is robust for production characters by providing the ability to incorporate meshes and skeletons with arbitrary topologies and morphologies (e.g., out-of-body bones, disconnected mesh components, etc.). Through exhaustive comparisons, we show that HeterSkinNet outperforms state-of-the-art methods by large margins in terms of rigging accuracy and naturalness. HeterSkinNet provides a solution for effective and robust character rigging.
Sleep quality, chronotype and social jet lag of adolescents from a population with a very late chronotype

Sleep disorders can be a negative factor both for learning as for the mental and physical development of adolescents. It has been shown that, in many populations, adolescents tend to have a “poor” sleep quality, and a very “late” chronotype. Furthermore, these features peak at adolescence, in the sense that adults tend to sleep better and have an earlier chronotype. But what happens when we consider adolescents in a population where already adults have poor sleep quality and a very late chronotype? We have conducted two non-clinical studies in the city of Bariloche, Argentina aimed at measuring sleep quality, chronotype, and social jet lag, using the Pittsburgh and Munich questionnaires. These were administered individually to groups of high school students, as well as to smaller samples of adults and preadolescents, in order to study differences between adolescents and these groups. The results show that in this population sleep quality is much poorer than in most other healthy populations recorded elsewhere. Furthermore, sleep quality is consistently worse for adolescents than for the other groups. The difference with adults seems to be due mainly to increased daytime sleepiness and sleep latency, whereas the difference with preadolescents seems to be due mainly to shorter sleep duration. We also found that the chronotypes of all the groups are very late, with a peak at an age between 18 and 24 ys. Social jet lag and sleep onset latency are also large, and they peak at adolescence, which suggests that they might be closely related to the large prevalence of poor sleep quality that we find in adolescents.
Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists

The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report the results of a workshop conducted in a consulting company to understand how this transition is perceived by practitioners. Starting from the need for making AI experiments reproducible, the main themes that emerged are related to the use of the Jupyter Notebook as the primary prototyping tool, and the lack of support for software engineering best practices as well as data science specific functionalities.
Boosting Adversarial Transferability through Enhanced Momentum

Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack methods have achieved great white-box attack performance, but exhibit low transferability when attacking other models. Various momentum iterative gradient-based methods are shown to be effective to improve the adversarial transferability. In what follows, we propose an enhanced momentum iterative gradient-based method to further enhance the adversarial transferability. Specifically, instead of only accumulating the gradient during the iterative process, we additionally accumulate the average gradient of the data points sampled in the gradient direction of the previous iteration so as to stabilize the update direction and escape from poor local maxima. Extensive experiments on the standard ImageNet dataset demonstrate that our method could improve the adversarial transferability of momentum-based methods by a large margin of 11.1% on average. Moreover, by incorporating with various input transformation methods, the adversarial transferability could be further improved significantly. We also attack several extra advanced defense models under the ensemble-model setting, and the enhancements are remarkable with at least 7.8% on average.
Training image classifiers using Semi-Weak Label Data

In Multiple Instance learning (MIL), weak labels are provided at the bag level with only presence/absence information known. However, there is a considerable gap in performance in comparison to a fully supervised model, limiting the practical applicability of MIL approaches. Thus, this paper introduces a novel semi-weak label learning paradigm as a middle ground to mitigate the problem. We define semi-weak label data as data where we know the presence or absence of a given class and the exact count of each class as opposed to knowing the label proportions. We then propose a two-stage framework to address the problem of learning from semi-weak labels. It leverages the fact that counting information is non-negative and discrete. Experiments are conducted on generated samples from CIFAR-10. We compare our model with a fully-supervised setting baseline, a weakly-supervised setting baseline and learning from pro-portion (LLP) baseline. Our framework not only outperforms both baseline models for MIL-based weakly super-vised setting and learning from proportion setting, but also gives comparable results compared to the fully supervised model. Further, we conduct thorough ablation studies to analyze across datasets and variation with batch size, losses architectural changes, bag size and regularization
Magnetization textures in twisted bilayer 
CrX3
 (
X
=Br, I)

Motivated by the recent experiment demonstration of stacking dependent interlayer magnetic interaction [T. Song et al., Nat. Mater. 18, 1298 (2019); T. Li et al., Nat. Mater. 18, 1303 (2019); W. Chen et al., Science 366, 983 (2019)], we investigate the magnetization textures and the control possibilities in the moir\'{e} pattern formed of twisted bilayer two-dimensional (2D) magnets CrX$_3$ (X=Br, I). We find that the stacking dependent interlayer magnetic interaction results in the formation of periodic magnetization domains in a long-period moir\'{e} pattern. Magnetization textures with various topological numbers can be constructed, depending on the winding of the textures around the domain walls. A uniform external magnetic field competes with the lateral modulated interlayer magnetic interaction and can be utilized to tune the magnetization textures.
Enhancing Robustness of On-Line Learning Models on Highly Noisy Data

Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this article, we extend a two-layer on-line data selection framework: Robust Anomaly Detector (RAD) with a newly designed ensemble prediction where both layers contribute to the final anomaly detection decision. To adapt to the on-line nature of anomaly detection, we consider additional features of conflicting opinions of classifiers, repetitive cleaning, and oracle knowledge. We on-line learn from incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. We specifically focus on three use cases, (i) detecting 10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big data jobs, and (iii) recognising 100 celebrities faces. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98.95 percent for IoT device attacks (i.e., +7%), up to 85.03 percent for cloud task failures (i.e., +14%) under 40 percent label noise, and for its extension, it can reach up to 77.51 percent for face recognition (i.e., +39%) under 30 percent label noise. The proposed RAD and its extensions are general and can be applied to different anomaly detection algorithms.
ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation

Text-based video segmentation is a challenging task that segments out the natural language referred objects in videos. It essentially requires semantic comprehension and fine-grained video understanding. Existing methods introduce language representation into segmentation models in a bottom-up manner, which merely conducts vision-language interaction within local receptive fields of ConvNets. We argue that such interaction is not fulfilled since the model can barely construct region-level relationships given partial observations, which is contrary to the description logic of natural language/referring expressions. In fact, people usually describe a target object using relations with other objects, which may not be easily understood without seeing the whole video. To address the issue, we introduce a novel top-down approach by imitating how we human segment an object with the language guidance. We first figure out all candidate objects in videos and then choose the refereed one by parsing relations among those high-level objects. Three kinds of object-level relations are investigated for precise relationship understanding, i.e., positional relation, text-guided semantic relation, and temporal relation. Extensive experiments on A2D Sentences and J-HMDB Sentences show our method outperforms state-of-the-art methods by a large margin. Qualitative results also show our results are more explainable.