Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial Effect of Bamboo (Phyllosrachys Bambusoides) Essential Oil on Trichophyton and Pityrosporum

1. 무좀균에 대한 각 시료들이 항균력으로 terbinafine > 100%-bamboo oil> 50%-bamboo oil순으로 나타내었다. 무좀균에 대한 Bamboo oil에 대한 영향으로는 T.M > E.F > T. R의 순서대로 항균력을 나타내었으며, 특히 T.M에 대하여 가장 우수하게 나타내었다. 2. 비듬균에 대한 각 시료들이 항균력으로는 terbinafine > 100%-bamboo oil > 50%-bamboo oil순으로 나타내었다. 비듬균에 대한 Bamboo oil에 대한 영향으로는 P.O-75 > P.O-77의 순서대로 항균력을 나타내었으며, 특히 P.O-75에 대하여 가장 우수 하게 나타내었다. 3. 50%-bamboo oil은 무좀균과 비듬균에 대하여 항균력이 있는 겋으로 나타내었으au, 100%-Bmboo oilㅇ의 항균력과 비슷하게 나타내었으며, 경비 절감 측면에서 기대를 걸 수 있을 것으로 사료된다. 【In order to develop a antimicrobial agent, bamboo oil was extracted by high temperature suction from dired bamboo truk and then antimictobial activities against Trichophyton and Pityrosporum are investigated. 1. Bamboo oil showed the strongest antimicrobial effect on Trichophyton mentagrophytes > Epidermophyton floccusum > Trichophyton rubrun. Bamboo oil showed the strongest antimicrobial activity on Trichophyton mentagrophytes. 2. Bamboo oil showed the strongest antimicrobial effect on Pityroporum ovale 75 > Pityrosporum ovale 77. Bamboo oil showe the strongest antimicrobial activity on Pityroporum ovale 75.3 Antimicrobial effect in 50%-ethanol extracts of bamboo oil are similar to 100%-bamboo oil. 】
BUZz: BUffer Zones for defending adversarial examples in image classification

We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination of deep neural networks and simple image transformations. While straight forward in implementation, this defense yields a unique security property which we term buffer zones. In this paper, we formalize the concept of buffer zones. We argue that our defense based on buffer zones is secure against state-of-the-art black box attacks. We are able to achieve this security even when the adversary has access to the {\em entire} original training data set and unlimited query access to the defense. We verify our security claims through experimentation using FashionMNIST, CIFAR-10 and CIFAR-100. We demonstrate $<10\%$ attack success rate -- significantly lower than what other well-known defenses offer -- at only a price of a 15-20\% drop in clean accuracy. By using a new intuitive metric we explain why this trade-off offers a significant improvement over prior work.
Electromagnetic Energy Balance Equations and Poynting Theorem

Poynting theorem plays a very important role in analyzing electromagnetic phenomena. The electromagnetic power flux density is usually expressed with the Poynting vector. However, since Poynting theorem basically focuses on the power balance in a system, it is not so convenient in some situations to use it for evaluating the electromagnetic energies. The energy balance issue for time varying fields is revisited in this paper, and a set of energy balance equations are introduced, and a modified method for evaluating power flux is proposed.
Improving One-Shot NAS by Suppressing the Posterior Fading

Neural architecture search (NAS) has demonstrated much success in automatically designing effective neural network architectures. To improve the efficiency of NAS, previous approaches adopt weight sharing method to force all models share the same set of weights. However, it has been observed that a model performing better with shared weights does not necessarily perform better when trained alone. In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the Posterior Fading problem, which compromises the effectiveness of shared weights. To alleviate this problem, we present a novel approach to guide the parameter posterior towards its true distribution. Moreover, a hard latency constraint is introduced during the search so that the desired latency can be achieved. The resulted method, namely Posterior Convergent NAS (PC-NAS), achieves state-of-the-art performance under standard GPU latency constraint on ImageNet.
Lyman continuum escape fraction and mean free path of hydrogen ionizing photons for bright z ∼ 4 QSOs from SDSS DR14

Context. One of the main challenges in observational cosmology is related to the redshift evolution of the average hydrogen (HI) ionization in the Universe, as evidenced by the changing in ionization level of the intergalactic medium (IGM) through cosmic time. Starting from the first cosmic reionization, the rapid evolution of the IGM physical properties in particular poses severe constraints for the identification of the sources responsible for maintaining its high level of ionization up to lower redshifts. Aims. In order to probe the ionization level of the IGM and the ionization capabilities of bright quasi-stellar objects (QSOs) at z = 4, we selected a sample of 2508 QSOs drawn from the Sloan Digital Sky Survey (SDSS, DR14) in the redshift interval 3.6 ≤ z ≤ 4.6 and absolute magnitude range −29.0 ≲ M1450 ≲ −26.0. Particularly, we focus on the estimate of the escape fraction of HI-ionizing photons and their mean free path (MFP), which are fundamental for characterizing the surrounding IGM. Methods. Starting from UV/optical rest-frame spectra of the whole QSO sample from the SDSS survey, we estimated the escape fraction and free path individually for each of the QSOs. We calculated the Lyman continuum (LyC) escape fraction as the flux ratio blueward (∼900 Å rest frame) and redward (∼930 Å rest frame) of the Lyman limit. We then obtained the probability distribution function (PDF) of the individual free paths of the QSOs in the sample and studied its evolution in luminosity and redshift, comparing our results with those in literature. Results. We find a lower limit to the mean LyC escape fraction of 0.49, in agreement with the values obtained for both brighter and fainter sources at the same redshift. We show that the free paths of ionizing photons are characterized by a skewed distribution function that peaks at low values, with an average of ∼49 − 59 proper Mpc at z ∼ 4, after possible associated absorbers (AAs) were excluded. This value is higher than the one obtained at the same redshift by many authors in the literature using different techniques. Moreover, the PDF of free path gives information that is complementary to the MFP derived through the stacking technique. Finally, we also find that the redshift evolution of this parameter might be milder than previously thought. Conclusions. Our new determination of the MFP at z ∼ 4 implies that previous estimates of the HI photoionization rate ΓHI available in the literature should be corrected by a factor of 1.2−1.7. These results have important implications when they are extrapolated at the epoch of reionization.
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning

Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go). In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration. These algorithms iteratively solve short-horizon decision problems and converge to the optimal solution of the original one. By using model-free algorithms as solvers of the short-horizon problems we derive fully model-free algorithms which are instances of the multi-step DP framework. As model-free algorithms are prone to instabilities w.r. t. the decision problem horizon, this simple approach can help in mitigating these instabilities and results in an improved model-free algorithms. We test this approach and show results on both discrete and continuous control problems.
The asymptotic spectrum of the Hessian of DNN throughout training

The dynamics of DNNs during gradient descent is described by the so-called Neural Tangent Kernel (NTK). In this article, we show that the NTK allows one to gain precise insight into the Hessian of the cost of DNNs. When the NTK is fixed during training, we obtain a full characterization of the asymptotics of the spectrum of the Hessian, at initialization and during training. In the so-called mean-field limit, where the NTK is not fixed during training, we describe the first two moments of the Hessian at initialization.
Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach

We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate sub-networks for the ligand bonded topology and the ligand-protein contact map. Recent work has indicated that dataset bias drives many past promising results derived from combining deep learning and docking. Our dual-graph network allows contributions from ligand identity that give rise to such biases to be distinguished from effects of protein-ligand interactions on classification. We show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased dataset is constructed. We next develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms a baseline docking program (AutoDock Vina) in a variety of tests, including on cross-docking datasets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence.
Chiral transition and meson melting with finite chemical potential in an improved soft-wall AdS/QCD Model

We give a further study on the improved soft-wall AdS/QCD model with two flavors. The chiral transition behaviors are studied in the case of finite chemical potential, with the chiral phase diagram obtained at zero quark mass. The thermal spectral functions of the vector and axial-vector mesons are calculated, and the in-medium melting properties of the mesons are investigated. We find that the chiral transition behaviors and the meson melting properties at finite chemical potential can be qualitatively described by the improved soft-wall AdS/QCD model, except in the region of large chemical potential. The reason for these inadequate descriptions may be that the background geometry adopted in the model is not a dynamical one which is able to produce the QCD equation of state. To give a quantitative description for these low-energy phenomenologies, we shall consider a more consistent AdS/QCD model which treats the background fields and the chiral fields on the same footing.
Predicting phenotypes from brain connection structure

This article focuses on the problem of predicting a response variable based on a network‐valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro‐psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high‐dimensional brain network into low‐dimensional pre‐specified features prior to applying standard predictive algorithms. These methods are sensitive to feature choice and inevitably discard important information. Instead, we propose a nonparametric Bayes class of models that utilize the entire adjacency matrix defining brain region connections to adaptively detect predictive algorithms, while maintaining interpretability. The Bayesian Connectomics (BaCon) model class utilizes Poisson–Dirichlet processes to find a lower dimensional, bidirectional (covariate, subject) pattern in the adjacency matrix. The small n, large p problem is transformed into a ‘small n, small q’ problem, facilitating an effective stochastic search of the predictors. A spike‐and‐slab prior for the cluster predictors strikes a balance between regression model parsimony and flexibility, resulting in improved inferences and test case predictions. We describe basic properties of the BaCon model and develop efficient algorithms for posterior computation. The resulting methods are found to outperform existing approaches and applied to a creative reasoning dataset.
Biologically-Inspired Spatial Neural Networks

We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions. To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space. Our experiments show that in the case where the network performs two different tasks, the neurons naturally split into clusters, where each cluster is responsible for processing a different task. This behavior not only corresponds to the biological systems, but also allows for further insight into interpretability or continual learning.
Title Assessing the fecal microbiota : an optimized ion torrent 16 S rRNA gene-based analysis protocol

Assessing the distribution of 16S rRNA gene sequences within a biological sample represents the current state-of-the-art for determination of human gut microbiota composition. Advances in dissecting the microbial biodiversity of this ecosystem have very much been dependent on the development of novel high-throughput DNA sequencing technologies, like the Ion Torrent. However, the precise representation of this bacterial community may be affected by the protocols used for DNA extraction as well as by the PCR primers employed in the amplification reaction. Here, we describe an optimized protocol for 16S rRNA gene-based profiling of the fecal microbiota. Citation: Milani C, Hevia A, Foroni E, Duranti S, Turroni F, et al. (2013) Assessing the Fecal Microbiota: An Optimized Ion Torrent 16S rRNA Gene-Based Analysis Protocol. PLoS ONE 8(7): e68739. doi:10.1371/journal.pone. 0068739 Editor: Christopher Quince, University of Glasgow, United Kingdom Received January 2, 2013; Accepted June 3, 2013; Published July 15, 2013 Copyright: 2013 Milani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors thank GenProbio srl for the financial support of the Laboratory of Probiogenomics. This work was financially supported by Fondazione Cariparma to MV and by a FEMS Advanced Fellowship 2011 and an IRCSET Embark postdoctoral fellowship to FT. This work was also financially supported by a Ph.D. fellowship (Spinner 2013, Regione Emilia Romagna) to S.D. DvS is a member of The Alimentary Pharmabiotic Centre, which is a Centre for Science and Technology (CSET) funded by the Science Foundation Ireland (SFI), through the Irish Government’s National Development Plan (Grant no. 02/CE/B124 and 07/CE/ B1368). The authors also acknowledge the support of the Spanish Plan Nacional de I+D (grant AGL2010-14952). Arancha Hevia was the recipient of an FPI grant, and Borja Sánchez was the recipient of a Juan de la Cierva postdoctoral contract. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: GenProbio srl provided the financial support of the Laboratory of Probiogenomics for this study. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors. * E-mail: amargolles@ipla.csic. es (AM); marco.ventura@unipr.it (MV) . These authors contributed equally to this work.
Introduction to Non-Commutative Geometry

A review is made of some recent results in noncommutative geometry, especially those aspects which might in some way be of use in the study of strings and membranes. Efforts to add a gravitational field to noncommutative models of space-time are also reviewed. Special emphasis is placed on the case which could be considered as the noncommutative analog of a parallelizable space-time. It is argued that, at least in this case, there is a rigid relation between the noncommutative structure of the space-time on the one hand and the nature of the gravitational field which remains as a ‘shadow’ in the commutative limit on the other.
New EEG parameters correlated with sevoflurane concentration in dogs: tau and burst.

Background: Electroencephalographic (EEG) indicators for anaesthesia depth are being developed. Here, we propose novel EEG parameters created by using two kinds of waveforms discriminated by voltage thresholds from a new perspective. Methods: Six young-adult beagles and 6 senior beagles were anaesthetised with end-tidal sevoflurane (SEV) of 2.0%-4.0% in 0.5% intervals, and 2-channel EEG (256 Hz) was recorded. Two events were discriminated: a consecutive part ({\tau}) with a peak-to-peak potential difference (Vpp) within 1-6, 8, 12, or 20 mcrV, and another part (burst) with Vpp outside the threshold. Number of {\tau} (N{\tau}), mean {\tau} duration (M{\tau}), total percentage of {\tau} (SR{\tau}), mean burst duration (Mbst), and amplitude of burst (Abst) were evaluated as anaesthesia depth indicators using Pearsons correlation coefficients (r). Results: When Vpp was near the suppression wave threshold, N{\tau} had the highest correlation with SEV in both groups. As SEV was increased until onset of burst suppression, N{\tau} decreased, M{\tau} remained unchanged, and Mbst and Abst increased. In the young-adult group, mean |r| exceeded 0.95 for N{\tau} with Vpp of 4-6 mcrV, for Mbst with Vpp of 8-12 mcrV, and for Abst with Vpp of 4-12 mcrV. In the senior group, r exceeded 0.90 for N{\tau} with Vpp of 4 mcrV and for Abst with Vpp of 5-12 mcrV. Conclusion: We developed new EEG parameters that were almost perfectly correlated with volatile anaesthetic concentrations using traditional bipolar recording. This study suggests that the effect of anaesthetics on EEG can be investigated from two directions: pacemaker ({\tau}) and generator (burst).
Mis-orientation controlled cross-plane thermoelectricity in twisted bilayer graphene

The introduction of 'twist' or relative rotation between two atomically thin van der Waals (vdW) membranes gives rise to periodic Moire potential, leading to a substantial altercation of the band structure of the planar assembly. While most of the recent experiments primarily focus on the electronic-band hybridization by probing in-plane transport properties, here we report out-of-plane thermoelectric measurements across the van der Waals gap in twisted bilayer graphene (tBLG), which exhibits an interplay of twist-dependent inter-layer electronic and phononic hybridization. We show that at a large twist angle, the thermopower is entirely driven by a novel phonon drag effect at the sub-nanometer scale, while the electronic component of the thermopower is recovered only when the misorientation between the layers is reduced to less than two degrees. Our experiment shows that cross-plane thermoelectricity at a low angle is exceptionally sensitive to the nature of band dispersion and may provide fundamental insights into the coherence of electronic states in twisted bilayer graphene.
Implications from Simulated Strong Gravitational Lensing Systems: Constraining Cosmological Parameters Using Gaussian Processes

Strong gravitational lensing systems (SGL) encode cosmology information in source/lens distance ratios as , which can be used to precisely constrain cosmological parameters. In this paper, based on future measurements of 390 strong-lensing systems from the forthcoming Large Synoptic Survey Telescope (LSST) survey, we have successfully reconstructed the distance ratio (with the source redshift zs ∼ 4.0) directly from the data without assuming any parametric form. A recently developed method based on a model-independent reconstruction approach, Gaussian Processes, is used in our study of these strong-lensing systems. Our results show that independent measurement of the matter density parameter (Ωm) can be expected from such strong-lensing statistics. More specifically, one can expect Ωm to be estimated at the precision of ΔΩm ∼ 0.015 in the concordance ΛCDM model, which provides comparable constraints on Ωm with Planck 2015 results. In the framework of modified gravity theory (Dvali–Gabadadze–Porrati), 390 detectable galactic lenses from the future LSST survey can lead to stringent fits of ΔΩm ∼ 0.030. Finally, we have discussed three possible sources of systematic errors (sample incompleteness, the determination of length of lens redshift bin, and the choice of lens redshift shells), and quantified their effects on the final cosmological constraints. Our results strongly indicate that future strong-lensing surveys, with the accumulation of a larger and more accurate sample of detectable galactic lenses, will considerably benefit from the methodology described in this analysis.
On the Probabilistic Degrees of Symmetric Boolean functions

The probabilistic degree of a Boolean function $f:\{0,1\}^n\rightarrow \{0,1\}$ is defined to be the smallest $d$ such that there is a random polynomial $\mathbf{P}$ of degree at most $d$ that agrees with $f$ at each point with high probability. Introduced by Razborov (1987), upper and lower bounds on probabilistic degrees of Boolean functions --- specifically symmetric Boolean functions --- have been used to prove explicit lower bounds, design pseudorandom generators, and devise algorithms for combinatorial problems. In this paper, we characterize the probabilistic degrees of all symmetric Boolean functions up to polylogarithmic factors over all fields of fixed characteristic (positive or zero).
Gamma-Ray and Neutrino Emissions due to Cosmic-Ray Protons Accelerated at Intracluster Shocks in Galaxy Clusters

We examine the cosmic-ray protons (CRp) accelerated at collisionless shocks in galaxy clusters using cosmological structure formation simulations. We find that in the intracluster medium (ICM) within the virial radius of simulated clusters, only ∼7% of shock kinetic energy flux is dissipated by the shocks that are expected to accelerate CRp—that is, supercritical, quasi-parallel (Q∥) shocks with sonic Mach number Ms ≥ 2.25. The rest is dissipated at subcritical shocks and quasi-perpendicular shocks, both of which may not accelerate CRp. Adopting the diffusive shock acceleration (DSA) model recently presented in Ryu et al., we quantify the DSA of CRp in simulated clusters. The average fraction of the shock kinetic energy transferred to CRp via DSA is assessed at ∼(1–2) × 10−4. We also examine the energization of CRp through reacceleration using a model based on the test-particle solution. Assuming that the ICM plasma passes through shocks three times on average through the history of the universe and that CRp are reaccelerated only at supercritical Q∥-shocks, the CRp spectrum flattens by ∼0.05–0.1 in slope and the total amount of CRp energy increases by ∼40%–80% from reacceleration. We then estimate diffuse γ-ray and neutrino emissions, resulting from inelastic collisions between CRp and thermal protons. The predicted γ-ray emissions from simulated clusters lie mostly below the upper limits set by Fermi-LAT for observed clusters. The neutrino fluxes toward nearby clusters would be ≲10−4 of the IceCube flux at Eν = 1 PeV and ≲10−6 of the atmospheric neutrino flux in the energy range of Eν ≤ 1 TeV.