Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite energy traveling waves for the Gross-Pitaevskii equation in the subsonic regime

Abstract:In this paper we study the existence of finite energy traveling waves for the Gross-Pitaevskii equation. This problem hasdeserved a lot of attention in the literature, but the existence of solutions in the whole subsonic range was a standing open problem till the work of Mari\\c\{s\} in 2013. However, such result is valid only in dimension 3 and higher. In this paperwe first prove the existence of finite energy traveling waves for almost every value of the speed in the subsonic range. Our argument works identically well in dimensions 2 and 3.With this result in hand, a compactness argument could fill the range of admissible speeds. We are able to do so in dimension 3,recovering the aforementioned result by Mari\\c\{s\}. The planar case turns out to be more intricate and the compactness argumentworks only under an additional assumption on the vortex set of the approximating solutions.
GALNT6 Knockdown Inhibits the Proliferation and Migration of Colorectal Cancer Cells and Increases the Sensitivity of Cancer Cells to 5-FU

The incidence of colorectal cancer (CRC) is increasing annually worldwide, highlighting the need for novel therapeutics to be developed. GALNT6 is a member of the N-acetylgalactosyltransferase enzyme family, which exhibits oncogenic functions in several types of cancers, such as breast cancer and ovarian cancer. However, the function of GALNT6 in CRC has not received much attention in recent years and is therefore poorly understood. In this study, the GALNT6 gene was screened using RNA-seq data obtained from The Cancer Genome Atlas (TCGA). RNA-seq data from 50 pairs of matched CRC patients in TCGA were obtained and analyzed, and the protein expression levels of GALNT6 were verified in 10 pairs of clinical samples. These samples showed that GALNT6 was highly expressed in CRC tissues. Functional analysis also showed that GALNT6 knockdown inhibited the proliferation and migration of CRC cells and increased the number of apoptotic cells. Furthermore, GALNT6 knockdown suppressed tumor growth in vivo. GALNT6 also regulated the AKT pathway based on ingenuity pathway analysis and western blotting assay. Finally, GALNT6 knockdown was observed to increase the sensitivity of CRC cells to 5-Fluorouracil (5-FU). These results, taken together, show that GALNT6 knockdown inhibits proliferation and migration of CRC cells and increases cellular sensitivity to 5-FU. It is therefore possible that targeting GALNT6 might prove to be an effective avenue for exploration in any attempt to develop new therapies for the treatment of CRC.
Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Deep transfer learning using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown strong predictive power in characterization of breast lesions. However, pretrained convolutional neural networks (CNNs) require 2D inputs, limiting the ability to exploit the rich 4D (volumetric and temporal) image information inherent in DCE-MRI that is clinically valuable for lesion assessment. Training 3D CNNs from scratch, a common method to utilize high-dimensional information in medical images, is computationally expensive and is not best suited for moderately sized healthcare datasets. Therefore, we propose a novel approach using transfer learning that incorporates the 4D information from DCE-MRI, where volumetric information is collapsed at feature level by max pooling along the projection perpendicular to the transverse slices and the temporal information is contained either in second-post contrast subtraction images. Our methodology yielded an area under the receiver operating characteristic curve of 0.89+/-0.01 on a dataset of 1161 breast lesions, significantly outperforming a previous approach that incorporates the 4D information in DCE-MRI by the use of maximum intensity projection (MIP) images.
Distributed MST: A Smoothed Analysis

We study smoothed analysis of distributed graph algorithms, focusing on the fundamental minimum spanning tree (MST) problem. With the goal of studying the time complexity of distributed MST as a function of the "perturbation" of the input graph, we posit a smoothing model that is parameterized by a smoothing parameter 0 ≤ ϵ(n) ≤ 1 which controls the amount of random edges that can be added to an input graph G per round. Informally, ϵ(n) is the probability (typically a small function of n, e.g., n--¼) that a random edge can be added to a node per round. The added random edges, once they are added, can be used (only) for communication. We show upper and lower bounds on the time complexity of distributed MST in the above smoothing model. We present a distributed algorithm that, with high probability, 1 computes an MST and runs in Õ(min{1/√ϵ(n)2O(√log n), D+ √n}) rounds2 where ϵ is the smoothing parameter, D is the network diameter and n is the network size. To complement our upper bound, we also show a lower bound of Ω(min{1/√ϵ(n), D + √n}). We note that the upper and lower bounds essentially match except for a multiplicative 2O(√log n) polylog(n) factor. Our work can be considered as a first step in understanding the smoothed complexity of distributed graph algorithms.
Probing the robustness of nested multi-layer networks

We consider a multi-layer network with two layers, $\mathcal{L}_{1}$, $\mathcal{L}_{2}$. Their intra-layer topology shows a scale-free degree distribution and a core-periphery structure. A nested structure describes the inter-layer topology, i.e., some nodes from $\mathcal{L}_{1}$, the generalists, have many links to nodes in $\mathcal{L}_{2}$, specialists only have a few. This structure is verified by analyzing two empirical networks from ecology and economics. To probe the robustness of the multi-layer network, we remove nodes from $\mathcal{L}_{1}$ with their inter- and intra-layer links and measure the impact on the size of the largest connected component, $F_{2}$, in $\mathcal{L}_{2}$, which we take as a robustness measure. We test different attack scenarios by preferably removing peripheral or core nodes. We also vary the intra-layer coupling between generalists and specialists, to study their impact on the robustness of the multi-layer network. We find that some combinations of attack scenario and intra-layer coupling lead to very low robustness values, whereas others demonstrate high robustness of the multi-layer network because of the intra-layer links. Our results shed new light on the robustness of bipartite networks, which consider only inter-layer, but no intra-layer links.
Renewable protein-based monomer for thermosets: a case study on phthalonitrile resin

Further enriching the source of bio-based raw materials for thermosets is of significant importance in the current development context. In this paper, we synthesized a novel bio-based phthalonitrile monomer derived from L-tyrosine to verify the feasibility of a bio-based L-tyrosine molecular design platform for thermosets, and to prove that the L-tyrosine building-block can confer high performance. The biomass content of L-tyrosine-based phthalonitrile monomer reaches 56%, higher than other bio-based phthalonitrile resins. The structure was confirmed by 1H NMR, 13C NMR, Fourier-Transform Infrared Spectroscopy (FTIR) and elemental analyses. The curing behavior was investigated by FTIR spectra, differential scanning calorimetry (DSC) and in situ FTIR spectra. The processability was studied by rheological analysis. And a study of the curing kinetics of the TPN monomer was carried out by non-isothermal DSC. Finally, the physical properties of the cured polymer were evaluated by dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA). The results indicate that the phthalonitrile resin exhibited a high glass transition temperature (Tg) and excellent thermal properties compared with petroleum-based bisphenol A phthalonitrile resin. The results also demonstrate that introducing L-tyrosine into thermosets is feasible and can simultaneously impart excellent performances to the materials with relatively high level of biomass.
Biospectroscopy diagnosis of bacterial interaction with environmental molecules

Aims: Bacteria are universal micro-organisms that can be found in almost all aquatic and terrestrial environments and strongly affect ecological systems at different spatial scales. The activities of bacteria are profound on the physicochemical features of natural environments, while natural environments shape bacterial behaviours through physical and chemical alterations. Different molecules present in the environment produce significant effects on bacteria. Therefore, it is required to study the rich and complementary interactions between bacteria and molecules. In this thesis, four research studies were conducted to investigate the interactions between bacteria and nanoparticles or carbonaceous substrates, deploying state-of-the-art techniques which can yield new insights. Methods: Raman micro-spectroscopy was employed in this thesis as a diagnostic tool to detect the biochemical alterations of bacteria post-exposure to different chemical molecules. Unlike conventional methods, such as light/electron microscopy, molecular analysis techniques and bacterial behaviour assays, Raman spectroscopy provides detailed information of biological constituents of bacteria that interact with diverse molecules. In addition, computational analysis including principal component analysis and linear discriminant analysis (PCA and LDA) was used to process the Raman spectral data. Results and Discussion: Raman spectra characterize the interaction between bacteria with different molecules. Spectral characterization showed the specific binding of nanoparticles with nucleic acids and amino acids in bacteria, and the different chemotactic behaviours of bacteria towards carbohydrates, organic acids and alkanes. Distinct spectral alterations allowed the evaluation of the alkane affinity in bacteria, and enabled quantification of the concentrations of glucose or organic acids in the aquatic phase. Furthermore, computational analysis of spectral alterations illustrated the effects of nutrient cations on alkane affinity in bacteria, and indicated the selective affinity of bacteria towards different organic carbonaceous molecules in the mixture of carbonaceous substances. Findings from this thesis showed that Raman spectroscopy is a rapid, reliable and non-destructive approach to investigate the interaction of bacterial cells with diverse molecules, which implies techniques involved in Raman spectroscopy can diagnose subcellular changes both in situ and in vivo post-exposure to different natural conditions or chemical molecules.
Parameter Analysis and Optimization of Friction Pendulum Bearings in Underground Stations Based on Genetic Algorithm

ABSTRACT This paper analyses the influence of friction coefficient and equivalent radius on the seismic reduction effect of friction pendulum bearings in the underground station under far-field and near-field earthquakes with different strength. It is found that the parameters of the friction pendulum bearings will obviously influence seismic reduction effectiveness. By combining genetic algorithm with finite element method, the parameters of friction pendulum bearings in the station can be optimized. By using this method, the optimal parameters range can be obtained, which can provide reference for parameter design of friction pendulum bearings in underground stations.
Evidence of Dust Grain Evolution from Extinction Mapping in the IC 63 Photodissociation Region

Photodissociation regions (PDRs) are parts of the ISM consisting of predominantly neutral gas, located at the interface between H ii regions and molecular clouds. The physical conditions within these regions show variations on very short spatial scales, and therefore PDRs constitute ideal laboratories for investigating the properties and evolution of dust grains. We have mapped IC 63 at high resolution from the UV to the NIR (275 nm to 1.6 μm), using the Hubble Space Telescope WFC3. Using a Bayesian SED fitting tool, we simultaneously derive a set of stellar (Teff, , distance) and extinction (AV, RV) parameters for 520 background stars. We present maps of AV and RV with a resolution of 25 arcsec based on these results. The extinction properties vary across the PDR, with values for AV between 0.5 and 1.4 mag, and a decreasing trend in RV, going from 3.7 at the front of the nebula to values as low as 2.5 further in. This provides evidence for evolution of the dust optical properties. We fit two modified blackbodies to the MIR and FIR SED, obtained by combining the AV map with data from Spitzer and Herschel. We derive effective temperatures (30 and 227 K) and the ratio of opacities at 160 μm to V band κ160/κV (7.0 × 10−4 and 2.9 × 10−9) for the two dust populations. Similar fits to individual pixels show spatial variations of κ160/κV. The analysis of our HST data, combined with these Spitzer and Herschel data, provides the first panchromatic view of dust within a PDR.
Counting extensions revisited

We consider rooted subgraphs in random graphs, that is, extension counts such as (i) the number of triangles containing a given vertex or (ii) the number of paths of length three connecting two given vertices. In 1989, Spencer gave sufficient conditions for the event that, with high probability, these extension counts are asymptotically equal for all choices of the root vertices. For the important strictly balanced case, Spencer also raised the fundamental question as to whether these conditions are necessary. We answer this question by a careful second moment argument, and discuss some intriguing problems that remain open.
Selective hybridization between the main band and the superstructure band in the 
Bi2Sr2CaCu2O8+δ
 superconductor

High-resolution laser-based angle-resolved photoemission measurements have been carried out on Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (Bi2212) and Bi$_2$Sr$_{2-x}$La$_x$CuO$_{6+\delta}$ (Bi2201) superconductors. Unexpected hybridization between the main band and the superstructure band in Bi2212 is clearly revealed. In the momentum space where one main Fermi surface intersects with one superstructure Fermi surface, four bands are observed instead of two. The hybridization exists in both superconducting state and normal state, and in Bi2212 samples with different doping levels. Such a hybridization is not observed in Bi2201. This phenomenon can be understood by considering the bilayer splitting in Bi2212, the selective hybridization of two bands with peculiar combinations, and the altered matrix element effects of the hybridized bands. These observations provide strong evidence on the origin of the superstructure band which is intrinsic to the CuO$_2$ planes. Therefore, understanding physical properties and superconductivity mechanism in Bi2212 should consider the complete Fermi surface topology which involves the main bands, the superstructure bands and their interactions.
Statistical evaluation of models for sorption and desorption isotherms for barleys

This study presents a novel approach to evaluate water sorption and desorption isotherm modeling consisting in statistical evaluation of the fit followed by the ranking of the models. Water sorption and desorption isotherms for BRS Elis and BRS Caue barley cultivars were evaluated at 40, 50, and 60°C. Data were analyzed by the GAB, Freundlich, Halsey, Henderson, Langmuir, Oswin, and Smith models. The BET model was also fitted to determine the moisture content. All models were submitted to five tests to determine whether the model was statistically significant. Then, the models were ranked using corrected Akaike information criterion. At all temperatures, the equilibrium moisture content increases as water activity increases and temperature decreases. Data showed no hysteresis in both cultivars. The statistical parameters evaluated indicate the goodness of the fit for all models except for the GAB model for BRS Elis cultivar at 60°C. The analysis with the corrected Akaike information criterion revealed that the Oswin and Henderson models showed best results at 40 and 50°C for both cultivars studied. At 60°C, the Freundlich model was the best for both cultivars. For both cultivars, the value of isosteric heat decreases with an increase in moisture content.
Dark Energy Survey Year 1 results: the lensing imprint of cosmic voids on the cosmic microwave background

Cosmic voids gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint on degree scales. We use the simulated CMB lensing convergence map from the Marenostrum Institut de Ciencias de l’Espai (MICE) N-body simulation to calibrate our detection strategy for a given void definition and galaxy tracer density. We then identify cosmic voids in Dark Energy Survey (DES) Year 1 data and stack the Planck 2015 lensing convergence map on their locations, probing the consistency of simulated and observed void lensing signals. When fixing the shape of the stacked convergence profile to that calibrated from simulations, we find imprints at the 3σ significance level for various analysis choices. The best measurement strategies based on the MICE calibration process yield S/N ≈ 4 for DES Y1, and the best-fitting amplitude recovered from the data is consistent with expectations from MICE (A ≈ 1). Given these results as well as the agreement between them and N-body simulations, we conclude that the previously reported excess integrated Sachs–Wolfe (ISW) signal associated with cosmic voids in DES Y1 has no counterpart in the Planck CMB lensing map.
Wind Turbines Partial Load Power Regulation Using a Fast MPC Approach

In this paper, the highly acknowledged advantages of the Model Predictive Control (MPC) approach are utilized to regulate the wind turbines' output power in the partial load region. In this region, the purpose of the designed controller is to capture maximum power from the wind. When the wind speed is above rated wind speed (full load region), control strategy is to keep output power and generator speed at their rated value. Because of the wind turbine's nonlinearities and constraints of the variables this is a challenging problem and two MPC-based methods are proposed, i.e. online and offline MPC schemes, that guarantee all constraints satisfaction and handling the systems nonlinearities. At the end, effectiveness of these two methods are compared.
The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey

Machine learning models have made many decision support systems to be faster, more accurate and more efficient. However, applications of machine learning in network security face more disproportionate threat of active adversarial attacks compared to other domains. This is because machine learning applications in network security such as malware detection, intrusion detection, and spam filtering are by themselves adversarial in nature. In what could be considered an arms race between attackers and defenders, adversaries constantly probe machine learning systems with inputs which are explicitly designed to bypass the system and induce a wrong prediction. In this survey, we first provide a taxonomy of machine learning techniques, styles, and algorithms. We then introduce a classification of machine learning in network security applications. Next, we examine various adversarial attacks against machine learning in network security and introduce two classification approaches for adversarial attacks in network security. First, we classify adversarial attacks in network security based on a taxonomy of network security applications. Secondly, we categorize adversarial attacks in network security into a problem space vs. feature space dimensional classification model. We then analyze the various defenses against adversarial attacks on machine learning-based network security applications. We conclude by introducing an adversarial risk model and evaluate several existing adversarial attacks against machine learning in network security using the risk model. We also identify where each attack classification resides within the adversarial risk model
Layer contraction of smectic liquid crystals and the compensation model

ABSTRACT A distinct feature of the layer spacing has been observed on entering the SmC phase: the layer thickness initially decreases down about few degrees below the SmA to SmC transition temperature and then starts to increase again despite to the molecular tilt angle increases further with a reduction in temperature. This observation cannot be explained by the tilting of the molecule as an entity. Our results of the IR spectroscopy show that the orientational behaviours of terminal chains are distinctly different from that of the rigid core and clearly show that any model considering orientation of the molecule as an entity cannot be correct. A compensation model is used to explain it, where a contraction of the layer caused by the tilt of the mesogen is re-compensated by a simultaneous expansion of the layer of the fluorinated chain.
Enhanced Molecular Infrared Spectroscopy Employing Bilayer Graphene Acoustic Plasmon Resonator

Graphene plasmon resonators with the ability to support plasmonic resonances in the infrared region make them a promising platform for plasmon-enhanced spectroscopy techniques. Here we propose a resonant graphene plasmonic system for infrared spectroscopy sensing that consists of continuous graphene and graphene ribbons separated by a nanometric gap. Such a bilayer graphene resonator can support acoustic graphene plasmons (AGPs) that provide ultraconfined electromagnetic fields and strong field enhancement inside the nano-gap. This allows us to selectively enhance the infrared absorption of protein molecules and precisely resolve the molecular structural information by sweeping graphene Fermi energy. Compared to the conventional graphene plasmonic sensors, the proposed bilayer AGP sensor provides better sensitivity and improvement of molecular vibrational fingerprints of nanoscale analyte samples. Our work provides a novel avenue for enhanced infrared spectroscopy sensing with ultrasmall volumes of molecules.
Pulsed all-fiber gas Raman laser oscillator based on hollow-core photonic crystal fibers

Fiber gas Raman laser based on hollow-core photonic crystal fibers has recently attracted enormous interest due to the giant gas Raman shift and relatively low Raman threshold. However, almost all fiber gas Raman lasers are single-pass structure, and have been demonstrated by free-space coupling of the pump light through bulky gas cells. Here, we have reported a pulsed all-fiber gas Raman laser oscillator. The resonant cavity is formed by splicing solid-core fibers and a 50- meter-long hydrogen-filled hollow-core photonic crystal fiber and further introducing homemade fiber Bragg gratings at the Stokes wavelength. Pumping by a homemade pulsed 1540 nm fiber amplifier with the pulse width of 30 ns, efficient Stokes wave at 1693 nm is obtained by pure rotational stimulated Raman scattering of hydrogen. When the repetition frequency is 3 MHz, the maximum Stokes power of 1.2 W is achieved, corresponding the optical-optical efficiency inside hollow-core photonic crystal fiber of 42.7%. Moreover, it is observed that the Raman threshold obviously decreases when the repetition frequency is consistent with the resonant frequency of the resonant cavity. This work provides good guidance for achieving all-fiber structure and low-threshold pulsed fiber gas Raman lasers, which is significant for the development and application of fiber gas Raman lasers.