Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Elevated Temperatures on the States of Water and Their Correlation with the Proton Conductivity of Nafion

For the first time, we report the effects of elevated temperatures, from 80 to 100 °C, on the changes in the states of water and ion–water channels and their correlation with the proton conductivity of Nafion NR212, which was investigated using a Fourier transform infrared spectroscopy study. Experimentally, three types of water aggregates, protonated water (H+(H2O)n), nonprotonated hydrogen (H)-bonded water (H2O···H2O), and non-H-bonded water, were found in Nafion, and the existence of those three types of water was confirmed through ab initio molecular dynamics simulation. We found that the proton conductivity of Nafion increased for up to 80 °C, but from 80 to 100 °C, the conductivity did not increase; rather, all of those elevated temperatures showed identical conductivity values. The proton conductivities at lower relative humidities (RHs) (up to 50%) remained nearly identical for all elevated temperatures (80, 90, and 100 °C); however, from 60% RH (over λ = 4), the conductivity remarkably jumped for all elevated temperatures. The results indicated that the amount of randomly arranged water gradually increased and created more H-bonded water networks in Nafion at above 60% RH. From the deconvolution of the O–H bending band, it was found that the volume fraction fi (i=each deconvoluted band) of H-bonded water for elevated temperatures (>80–100 °C) increased remarkably higher than for 60 °C.
Growth of mango (Mangifera indica L.) rootstocks as influenced by pre-sowing treatments

An experiment was carried out at Navsari Agricultural University, Navsari during 2014 to evaluate the effect of pre-sowing treatments on survival percentage and growth of mango rootstocks. Mango stones were soaked in aqueous solutions of GA3 (100 and 200 ppm), Beejamruth (2 % and 3 %) and Thiourea (1 % and 2 %) for 12 and 24 hours. The trial was evaluated in Completely Randomized Design based on factorial concept and the treatments were replicated thrice. Imposition of treatments led to significant differences at 5 % level of significance for all parameters chosen in this study. Mango stones when treated with Thiourea at 1 % had the maximum shoot length (49.93 cm), root length (34.38 cm), shoot dry weight (21.08 g) and total dry weight (26.36 g). The highest number of lateral roots (10.90) and survival percentage (64.17) was observed in mango stones dipped in 100 ppm GA3. Between the two soaking duration, soaking mango stones for 24 hours recorded higher values for shoot length (45.03 cm), root length (32.79 cm), number of lateral roots (9.83), survival percentage (62.72), shoot root fresh weight ratio (4.30), shoot dry weight (21.33 g), total dry weight (26.28 g) and shoot root dry weight ratio (4.32). Thus, survival percentage and growth of mango rootstocks can be substantially improved by soaking mango stones in aqueous solutions of 100 ppm GA3 or Thiourea at 1 % for 24 hours before sowing.
FRIEDEL-CRAFTS ALKYLATION OF ANISOLE AND BENZYL BROMIDE USING ZIF-8 AS AN EFFICIENT CATALYST

: A highly porous zeolite imidazolate frameworks (ZIF-8) was synthesized from the reaction of zinc nitrate hexahydrate and 2-methylimidazole by a solvothermal method in DMF. The ZIF-8 was characterized using several techniques including X-ray powder diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), thermogravimetric analysis (TGA), Fourier transform infrared (FT-IR), atomic absorption spectrophotometry (AAS), and nitrogen physisorption measurements. Highly crystalline porous ZIF-8 was achieved with Langmuir surface areas of more than 1700 m 2 /g being observed. The ZIF-8 could be effectively used as a solid acid catalyst for the Friedel-Crafts alkylation reaction between anisole and benzyl bromide with no contribution from homogeneous catalysis of active acid species leaching into reaction solution.
Testing River2D and Flow-3D for sudden dam-break flow simulations

Instantaneous dam-break flows were generated using a 2-D flow model and a 3-D computational fluid dynamics (CFD) model. The dam breaks generated unsteady and varying flows that moved over dry beds, large obstacles, and non-flat bed topographies. Flows were characterized by strong hydraulic jumps and waves that traversed across the computational domain. Three test cases were conducted using high quality experimental data of sudden dam-break flows to compare the performance of the River2D and Flow3D models. Cases included a relatively straight channel configuration with a change in bed levels at the dam location; the presence of an isolated obstacle downstream of the dam; and a raised triangular sill in the channel floor downstream of the dam. The study showed that the River2D model performed well when the flood moved over a flat bed. However, River2D's groundwater model caused an excessive amount of surface water to flow underground. The Flow3D model performed well in all 3 test cases without requiring excessively long computational times. It was concluded that Flow3D is suited for practical flood applications when near-dam flow data are required. 19 refs., 2 tabs. , 8 figs.
Random polynomials: Central limit theorems for the real roots

The number of real roots has been a central subject in the theory of random polynomials and random functions since the fundamental papers of Littlewood-Offord and Kac in the 1940s. In this paper, we establish the Central Limit Theorem for the number of real roots of random polynomials with coefficients having moderate growth, generalizing and strengthening a classical result of Maslova.
Intra-Ensemble in Neural Networks

Improving model performance is always the key problem in machine learning including deep learning. However, stand-alone neural networks always suffer from marginal effect when stacking more layers. At the same time, ensemble is an useful technique to further enhance model performance. Nevertheless, training several independent deep neural networks for ensemble costs multiple resources. If so, is it possible to utilize ensemble in only one neural network? In this work, we propose Intra-Ensemble, an end-to-end ensemble strategy with stochastic channel recombination operations to train several sub-networks simultaneously within one neural network. Additional parameter size is marginal since the majority of parameters are mutually shared. Meanwhile, stochastic channel recombination significantly increases the diversity of sub-networks, which finally enhances ensemble performance. Extensive experiments and ablation studies prove the applicability of intra-ensemble on various kinds of datasets and network architectures.
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.
Convergence of odd-angulations via symmetrization of labeled trees

Fix $p\geq 5$ an odd integer integer. Let $M_n$ be a uniform $p$-angulation with $n$ vertices and endowed with the uniform probability measure on its vertices. We prove that, there exists $C_p\in \mathbb{R}_+$ such that, after rescaling distances by $C_p/n^{1/4}$, $M_n$ converges in distribution for the Gromov-Hausdorff-Prokhorov topology towards the Brownian map. To prove the preceding fact, we introduce a `bootstrapping' principle for distributional convergence of random labelled plane trees. In particular, the latter allows to obtain an invariance principle for labeled multitype Galton-Watson trees, with only a weak assumption on the centering of label displacements
Multi-Target Embodied Question Answering

Embodied Question Answering (EQA) is a relatively new task where an agent is asked to answer questions about its environment from egocentric perception. EQA as introduced in [8] makes the fundamental assumption that every question, e.g., ``what color is the car? ", has exactly one target (``car") being inquired about. This assumption puts a direct limitation on the abilities of the agent. We present a generalization of EQA -- Multi-Target EQA (MT-EQA). Specifically, we study questions that have multiple targets in them, such as ``Is the dresser in the bedroom bigger than the oven in the kitchen? ", where the agent has to navigate to multiple locations (``dresser in bedroom", ``oven in kitchen") and perform comparative reasoning (``dresser" bigger than ``oven") before it can answer a question. Such questions require the development of entirely new modules or components in the agent. To address this, we propose a modular architecture composed of a program generator, a controller, a navigator, and a VQA module. The program generator converts the given question into sequential executable sub-programs; the navigator guides the agent to multiple locations pertinent to the navigation-related sub-programs; and the controller learns to select relevant observations along its path. These observations are then fed to the VQA module to predict the answer. We perform detailed analysis for each of the model components and show that our joint model can outperform previous methods and strong baselines by a significant margin.
Domain-Symmetric Networks for Adversarial Domain Adaptation

Unsupervised domain adaptation aims to learn a model of classifier for unlabeled samples on the target domain, given training data of labeled samples on the source domain. Impressive progress is made recently by learning invariant features via domain-adversarial training of deep networks. In spite of the recent progress, domain adaptation is still limited in achieving the invariance of feature distributions at a finer category level. To this end, we propose in this paper a new domain adaptation method called Domain-Symmetric Networks (SymNets). The proposed SymNet is based on a symmetric design of source and target task classifiers, based on which we also construct an additional classifier that shares with them its layer neurons. To train the SymNet, we propose a novel adversarial learning objective whose key design is based on a two-level domain confusion scheme, where the category-level confusion loss improves over the domain-level one by driving the learning of intermediate network features to be invariant at the corresponding categories of the two domains. Both domain discrimination and domain confusion are implemented based on the constructed additional classifier. Since target samples are unlabeled, we also propose a scheme of cross-domain training to help learn the target classifier. Careful ablation studies show the efficacy of our proposed method. In particular, based on commonly used base networks, our SymNets achieve the new state of the art on three benchmark domain adaptation datasets.
Optimized auxiliary oscillators for the simulation of general open quantum systems

A method for the systematic construction of few-body damped harmonic oscillator networks accurately reproducing the effect of general bosonic environments in open quantum systems is presented. Under the sole assumptions of a Gaussian environment and regardless of the system coupled to it, an algorithm to determine the parameters of an equivalent set of interacting damped oscillators obeying a Markovian quantum master equation is introduced. By choosing a suitable coupling to the system and minimizing an appropriate distance between the two-time correlation function of this effective bath and that of the target environment, the error induced in the reduced dynamics of the system is brought under rigorous control. The interactions among the effective modes provide remarkable flexibility in replicating non-Markovian effects on the system even with a small number of oscillators, and the resulting Lindblad equation for the system and the modes may therefore be integrated at a very reasonable computational cost using standard methods for Markovian problems, even in strongly nonperturbative coupling regimes and at arbitrary temperatures including zero. We apply the method to an exactly solvable problem in order to demonstrate its accuracy, and present two studies based on current research in the context of coherent transport in biological aggregates and organic photovoltaics as more realistic examples of its use and potential; performance and versatility are highlighted, and theoretical and numerical advantages over existing methods, as well as possible future improvements, are discussed.
Extraction of Extracellular Matrix in Static and Dynamic Candida Biofilms Using Cation Exchange Resin and Untargeted Analysis of Matrix Metabolites by Ultra-High-Performance Liquid Chromatography-Tandem Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-TOF-MS)

Fungal infections caused by Candida albicans poses a great threat to human health. The ability of biofilm formation is believed to be associated with resistance-related Candida infections. Currently, knowledge on extracellular matrix (EM) of C. albicans biofilm is limited. In this study, we introduced ion exchange resin, i.e., cation exchange resin (CER) and anion exchange resin (AER), in EM extraction of C. albicans biofilm as well as several non-albicans Candida (NAC) biofilms under static and dynamic states in combination with vortexing and ultrasonication (VU). The metabolites extracted from the dynamic C. albicans biofilm matrix using the CER-VU and VU were identified with ultra-high-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) via untargeted filtration. Compared with other physical and chemical extraction methods, CER-VU was demonstrated to be an ideal approach with high-yield acquisitions of EM constituents including proteins, triglycerides and carbohydrates and low-level damages on fungal cell viability and integrity. The untargeted MS analysis further showed the high efficacy of CER-VU, as a large quantity of metabolites (217 versus 198) was matched comprising a great number of lipids, carbohydrates, amino acids, nucleic acids and their derivatives together with a high involvement of signaling pathways compared with the VU alone. However, combining the results from both the CER-VU and VU methods could generate more metabolites. In summary, the EM analysis of the dynamic C. albicans biofilm expands our understanding upon a comprehensive depiction of matrix components and provides another effective approach for EM extraction.
Frobenius Coin-Exchange Generating Functions

Abstract We study variants of the Frobenius coin-exchange problem: Given n positive relatively prime parameters, what is the largest integer that cannot be represented as a nonnegative integral linear combination of the given integers? This problem and its siblings can be understood through generating functions with 0/1 coefficients according to whether or not an integer is representable. In the 2-parameter case, this generating function has an elegant closed form, from which many corollaries follow, including a formula for the Frobenius problem. We establish a similar closed form for the generating function indicating all integers with exactly k representations, with similar wide-ranging corollaries.
Affordance Analysis of Virtual and Augmented Reality Mediated Communication

Virtual and augmented reality communication platforms are seen as promising modalities for next-generation remote face-to-face interactions. Our study attempts to explore non-verbal communication features in relation to their conversation context for virtual and augmented reality mediated communication settings. We perform a series of user experiments, triggering nine conversation tasks in 4 settings, each containing corresponding non-verbal communication features. Our results indicate that conversation types which involve less emotional engagement are more likely to be acceptable in virtual reality and augmented reality settings with low-fidelity avatar representation, compared to scenarios that involve high emotional engagement or intellectually difficult discussions. We further systematically analyze and rank the impact of low-fidelity representation of micro-expressions, body scale, head pose, and hand gesture in affecting the user experience in one-on-one conversations, and validate that preserving micro-expression cues plays the most effective role in improving bi-directional conversations in future virtual and augmented reality settings.
G5.89: An Explosive Outflow Powered by a Proto-Stellar Merger?

The explosive outflows are a newly-discovered family of molecular outflows associated with high-mass star forming regions. Such energetic events are possibly powered by the release of gravitational energy related with the formation of a (proto)stellar merger or a close stellar binary. Here, we present sensitive and high angular resolution observations (0.85$''$) archival CO(J=3-2) observations carried out with the Submillimeter Array (SMA) of the high-mass star forming region G5.89$-$0.39 that reveal the possible presence of an explosive outflow. We find six well-defined and narrow straight filament-like ejections pointing back approximately to the center of an expanding molecular and ionized shell located at the center of this region. These high velocity ($-$120 to $+$100 km s$^{-1}$) filaments follow a Hubble-like velocity law with the radial velocities increasing with the projected distance. The estimated kinematical age of the filaments is about of 1000 yrs, a value similar to the dynamical age found for the expanding ionized shell. G5.89 is the thus the third explosive outflow reported in the galaxy (together with Orion BN-KL and DR21) and argues in favor of the idea that this is a frequent phenomenon. In particular, explosive outflows, in conjunction with runaway stars, demonstrate that dynamical interactions in such groups are a very important ingredient in star formation.
ALMA 200 pc Resolution Imaging of Smooth Cold Dusty Disks in Typical z ∼ 3 Star-forming Galaxies

We present high-fidelity, 30 mas (200 pc) resolution ALMA rest-frame 240 μm observations of cold dust emission in three typical main-sequence star-forming galaxies (SFGs) at z ∼ 3 in the Hubble Ultra-Deep Field (HUDF). The cold dust is distributed within the smooth disklike central regions of star formation 1–3 kpc in diameter, despite their complex and disturbed rest-frame UV and optical morphologies. No dust substructures or clumps are seen down to ≃1–3 yr−1 (1σ) per 200 pc beam. No dust emission is observed at the locations of UV-emitting clumps, which lie ≃2–10 kpc from the bulk of star formation. Clumpy substructures can contribute no more than 1%–7% of the total star formation in these galaxies (3σ upper limits). The lack of star-forming substructures in our HUDF galaxies is to be contrasted with the multiple substructures characteristic of submillimeter-selected galaxies (SMGs) at the same cosmic epoch, particularly the far-IR-bright SMGs with similarly high-fidelity ALMA observations of Hodge et al. Individual star-forming substructures in these SMGs contain ∼10%–30% of their total star formation. A substructure in these SMGs is often comparably bright in the far-infrared to (or in some cases brighter than) our typical SFGs, suggesting that these SMGs originate from a class of disruptive events involving multiple objects at the scale of our HUDF galaxies. The scale of the disruptive event found in our main-sequence SFGs, characterized by the lack of star-forming substructures at our resolution and sensitivity, could be less violent, e.g., gas-rich disk instability or minor mergers.
SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking

The greatest challenge facing visual object tracking is the simultaneous requirements on robustness and discrimination power. In this paper, we propose a SiamFC-based tracker, named SPM-Tracker, to tackle this challenge. The basic idea is to address the two requirements in two separate matching stages. Robustness is strengthened in the coarse matching (CM) stage through generalized training while discrimination power is enhanced in the fine matching (FM) stage through a distance learning network. The two stages are connected in series as the input proposals of the FM stage are generated by the CM stage. They are also connected in parallel as the matching scores and box location refinements are fused to generate the final results. This innovative series-parallel structure takes advantage of both stages and results in superior performance. The proposed SPM-Tracker, running at 120fps on GPU, achieves an AUC of 0.687 on OTB-100 and an EAO of 0.434 on VOT-16, exceeding other real-time trackers by a notable margin.
Forbush Decreases and <2 Day GCR Flux Non-recurrent Variations Studied with LISA Pathfinder

Non-recurrent short-term variations of the galactic cosmic-ray (GCR) flux above 70 MeV n−1 were observed between 2016 February 18 and 2017 July 3 on board the European Space Agency LISA Pathfinder (LPF) mission orbiting around the Lagrange point L1 at 1.5 × 106 km from Earth. The energy dependence of three Forbush decreases is studied and reported here. A comparison of these observations with others carried out in space down to the energy of a few tens of MeV n−1 shows that the same GCR flux parameterization applies to events of different intensity during the main phase. FD observations in L1 with LPF and geomagnetic storm occurrence are also presented. Finally, the characteristics of GCR flux non-recurrent variations (peaks and depressions) of duration <2 days and their association with interplanetary structures are investigated. It is found that, most likely, plasma compression regions between subsequent corotating high-speed streams cause peaks, while heliospheric current sheet crossing causes the majority of the depressions.
High-Resolution Representations for Labeling Pixels and Regions

High-resolution representation learning plays an essential role in many vision problems, e.g., pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite{SunXLW19}, recently developed for human pose estimation, maintains high-resolution representations through the whole process by connecting high-to-low resolution convolutions in \emph{parallel} and produces strong high-resolution representations by repeatedly conducting fusions across parallel convolutions. In this paper, we conduct a further study on high-resolution representations by introducing a simple yet effective modification and apply it to a wide range of vision tasks. We augment the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions rather than only the representation from the high-resolution convolution as done in~\cite{SunXLW19}. This simple modification leads to stronger representations, evidenced by superior results. We show top results in semantic segmentation on Cityscapes, LIP, and PASCAL Context, and facial landmark detection on AFLW, COFW, $300$W, and WFLW. In addition, we build a multi-level representation from the high-resolution representation and apply it to the Faster R-CNN object detection framework and the extended frameworks. The proposed approach achieves superior results to existing single-model networks on COCO object detection. The code and models have been publicly available at \url{this https URL}.