Miracle-dz/UniFAR-FTUs huggingface dataset:
Properties of a Stilbene-Containing Gemini Photosurfactant: Light-Triggered Changes in Surface Tension and Aggregation

A new photosurfactant has been synthesized, and its photoreactions in water, interfacial properties, and changes in aggregation have been characterized. The compound is a stilbene-containing gemini photosurfactant (SGP), which is of interest because the headgroup spacer is a stilbene chromophore, and it may be prepared initially in the trans (E-SGP) form. Molecular simulations show that UV-induced reactions cause significant changes in molecular conformation and especially the relative orientation of hydrophobic chains. 1H nuclear magnetic resonance, UV−vis absorption, and liquid chromatography−mass spectroscopy results are consistent with photodimerization in aqueous solutions. Irradiation causes significant changes in surface tension (maximum Δγ = −12 mN m-1) and wettability (Δθ = −15° on hydrophobic glass). Furthermore, small-angle neutron scattering shows the initial E-SGP form is present as large vesicle-like aggregates whereas the UV-induced dimer gives small 20 A spherical charged micelles. These r.
Discrimination of 13C among upland rices having different water use efficiencies

Discrimination of stable carbon isotopes in leaves is physiologically linked to water-use efficiency. Twenty-eight contrasting rice (Oryza sativa L.) cultivars were grown in the Philippines as upland rice under continuous, mild water stress. In situ measurements of leaf level water-use efficiency (WUE), net photosynthesis, conductance, water potential and leaf rolling were reported previously. The present study used leaf samples from the same experiment to determine, by means of a mass spectrometer, the carbon isotope discrimination (-) and total C and N content. Among rice cultivars, - ranged from 19.8 to 21.5960, with japonica types having the lowest mean -, aus types, the greatest, and indica types, intermediate-the opposite pattern to that observed for WUE. Carbon isotope discrimination was negatively correlated with WUE across all cultivars (P < 0.001) and within japonica (P < 0 -01) and aus (P < 0.05) groups, but not among indica rices. No correlation was observed between - and any other measured variable. Varietal differences in - and WUE were not related to leaf N content or stomata1 conductance. High WUE was associated with high leaf C content (P < 0.01), with aus cultivars having the lowest mean WUE and C content among the genetic groups. We conclude that even under variable field conditions, analysis of foliar - is a potential tool to identify water-use efficient rice genotypes.
Faster Re-translation Using Non-Autoregressive Model For Simultaneous Neural Machine Translation

Recently, simultaneous translation has gathered a lot of attention since it enables compelling applications such as subtitle translation for a live event or real-time video-call translation. Some of these translation applications allow editing of partial translation giving rise to re-translation approaches. The current re-translation approaches are based on autoregressive sequence generation models (ReTA), which generate tar-get tokens in the (partial) translation sequentially. The multiple re-translations with sequential generation inReTAmodelslead to an increased inference time gap between the incoming source input and the corresponding target output as the source input grows. Besides, due to the large number of inference operations involved, the ReTA models are not favorable for resource-constrained devices. In this work, we propose a faster re-translation system based on a non-autoregressive sequence generation model (FReTNA) to overcome the aforementioned limitations. We evaluate the proposed model on multiple translation tasks and our model reduces the inference times by several orders and achieves a competitive BLEUscore compared to the ReTA and streaming (Wait-k) models.The proposed model reduces the average computation time by a factor of 20 when compared to the ReTA model by incurring a small drop in the translation quality. It also outperforms the streaming-based Wait-k model both in terms of computation time (1.5 times lower) and translation quality.
Few-Shot Named Entity Recognition: A Comprehensive Study

This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve the model generalization ability for few-shot settings: (1) meta-learning to construct prototypes for different entity types, (2) supervised pre-training on noisy web data to extract entity-related generic representations and (3) self-training to leverage unlabeled in-domain data. Different combinations of these schemes are also considered. We perform extensive empirical comparisons on 10 public NER datasets with various proportions of labeled data, suggesting useful insights for future research. Our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned on domain labels; (ii) We create new state-of-the-art results on both few-shot and training-free settings compared with existing methods. We will release our code and pre-trained models for reproducible research.
Erratum: Potentiation of carbon monoxide-induced relaxation of rat aorta by YC-1

The authors would like to revise a statement in their paper on page 348, first column, lines 12–16, “Thus, Furchgott and Jothianandan (1991) found that CO was much less potent than NO (by a factor of one thousand) in relaxing rabbit aorta, but both gaseous molecules had nearly the same potency in relaxing dog coronary artery. ” The statement should be amended to read, “Thus, Furchgott and Jothianandan (1991) found that CO was much less potent than NO (by a factor of one thousand) in -relaxing rabbit aorta and dog coronary artery. ” 679
Graph-Based Non-Linear Least Squares Optimization for Visual Place Recognition in Changing Environments

Visual place recognition is an important subproblem of mobile robot localization. Since it is a special case of image retrieval, the basic source of information is the pairwise similarity of image descriptors. However, the embedding of the image retrieval problem in this robotic task provides additional structure that can be exploited, e.g., spatio-temporal consistency. Several algorithms exist to exploit this structure, e.g., sequence processing approaches or descriptor standardization approaches for changing environments. In this letter, we propose a graph-based framework to systematically exploit different types of additional structure and information. The graphical model is used to formulate a non-linear least squares problem that can be optimized with standard tools. Beyond sequences and standardization, we propose the usage of intra-set similarities within the database and/or the query image set as additional source of information. If available, our approach also allows to seamlessly integrate additional knowledge about poses of database images. We evaluate the system on a variety of standard place recognition datasets and demonstrate performance improvements for a large number of different configurations including different sources of information, different types of constraints, and online or offline place recognition setups.
Two-step Multiset Regression Analysis (MsRA) Algorithm

In the present work, a multiset regression analysis strategy is developed by designing a two-step feature extraction procedure.Multiplepredictorspaces,inwhichthesamevariablesaremeasuredondifferentsourcesofobjectsorthesamenumberof objectsisobservedondifferentvariables,arecollected,preparingmultipleregressiondatapairsincombinationwithresponsespaces. It focuses on finding the common regression structures across predictor spaces, which can be XY regression correlations or predictor variations closely related with each other, for the interpretation of response. Therefore, two different subspaces are separated from each otherineachpredictorspace.Oneisthecommonsubspacerevealingthegeneralregressionfeaturesshared byallpredictorspacesand the other is the specific subspace driven by the unique regression information that is more predictor space-dependent. This is achieved by solving a mathematical optimization problem, in which theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with both a simple numerical case and the experimental data from the literatures. From the cross-set viewpoint, the proposed approach provides a more meaningful characterization of the inherent predictor information for regression modeling. Further development and application potential are suggested.
AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition

Recognizing human emotion/expressions automatically is quite an expected ability for intelligent robotics, as it can promote better communication and cooperation with humans. Current deep-learning-based algorithms may achieve impressive performance in some lab-controlled environments, but they always fail to recognize the expressions accurately for the uncontrolled in-the-wild situation. Fortunately, facial action units (AU) describe subtle facial behaviors, and they can help distinguish uncertain and ambiguous expressions. In this work, we explore the correlations among the action units and facial expressions, and devise an AU-Expression Knowledge Constrained Representation Learning (AUE-CRL) framework to learn the AU representations without AU annotations and adaptively use representations to facilitate facial expression recognition. Specifically, it leverages AU-expression correlations to guide the learning of the AU classifiers, and thus it can obtain AU representations without incurring any AU annotations. Then, it introduces a knowledge-guided attention mechanism that mines useful AU representations under the constraint of AU-expression correlations. In this way, the framework can capture local discriminative and complementary features to enhance facial representation for facial expression recognition. We conduct experiments on the challenging uncontrolled datasets to demonstrate the superiority of the proposed framework over current state-of-the-art methods. Codes and trained models are available at https://github.com/HCPLab-SYSU/AUE-CRL.
Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents

Goal-oriented conversational agents are becoming prevalent in our daily lives. For these systems to engage users and achieve their goals, they need to exhibit appropriate social behavior as well as provide informative replies that guide users through tasks. The first component of the research in this paper applies statistical modeling techniques to understand conversations between users and human agents for customer service. Analyses show that social language used by human agents is associated with greater users' responsiveness and task completion. The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
Prosocial Norm Emergence in Multi-agent Systems

Multi-agent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multi-agent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multi-agent system can be reflected in the social norms among its members. It is well recognized that the norms that arise in society are not always beneficial to its members. We focus on prosocial norms, which help achieve positive outcomes for society and often provide guidance to agents to act in a manner that takes into account the welfare of others. Specifically, we propose Cha, a framework for the emergence of prosocial norms. Unlike previous norm emergence approaches, Cha supports continual change to a system (agents may enter and leave) and dynamism (norms may change when the environment changes). Importantly, Cha agents incorporate prosocial decision-making based on inequity aversion theory, reflecting an intuition of guilt arising from being antisocial. In this manner, Cha brings together two important themes in prosociality: decision-making by individuals and fairness of system-level outcomes. We demonstrate via simulation that Cha can improve aggregate societal gains and fairness of outcomes.
Promoting Graph Awareness in Linearized Graph-to-Text Generation

Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graph-encoding neural networks. However, recent applications of pretrained transformers to linearizations of graph inputs have yielded state-of-the-art generation results on graph-to-text tasks. Here, we explore the ability of these linearized models to encode local graph structures, in particular their invariance to the graph linearization strategy and their ability to reconstruct corrupted inputs. Our findings motivate solutions to enrich the quality of models' implicit graph encodings via scaffolding. Namely, we use graph-denoising objectives implemented in a multi-task text-to-text framework. We find that these denoising scaffolds lead to substantial improvements in downstream generation in low-resource settings.
CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds

Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D – the first unsupervised and end-to-end deep learning-based framework – to drive the learning of dense correspondence between 3D shapes by means of deformation-like reconstruction to overcome the need for annotated data. Specifically, CorrNet3D consists of a deep feature embedding module and two novel modules called correspondence indicator and symmetric deformer. Feeding a pair of raw point clouds, our model first learns the pointwise features and passes them into the indicator to generate a learnable correspondence matrix used to permute the input pair. The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence. The extensive experiments on both synthetic and real-world datasets of rigid and non-rigid 3D shapes show our CorrNet3D outperforms state-of-the-art methods to a large extent, including those taking meshes as input. CorrNet3D is a flexible framework in that it can be easily adapted to supervised learning if annotated data are available. The source code and pre-trained model will be available at https://github.com/ZENGYIMINGEAMON/CorrNet3D.git.
Nucleation Mechanism of Electrochemical Deposition of Cu on Reduced Graphene Oxide Electrodes

The nucleation mechanism of electrochemical deposition of Cu on reduced graphene oxide (rGO) electrodes has been systematically studied on the basis of the cyclic voltammetry, Tafel plot, and chronoamperometry. Our results show that the experimental parameters including electrolyte concentration, deposition potential, solution pH, and the presence of background electrolyte can determine the nucleation mechanism. Scanning electron microscopy is employed to study the nucleation and growth of Cu on rGO electrodes. This study is significant in development of the electrochemical method in practical applications based on the rGO electrodes.
Multi-Channel Sequential Behavior Networks for User Modeling in Online Advertising

Multiple content providers rely on native advertisement for revenue by placing ads within the organic content of their pages. We refer to this setting as ``queryless'' to differentiate from search advertisement where a user submits a search query and gets back related ads. Understanding user intent is critical because relevant ads improve user experience and increase the likelihood of delivering clicks that have value to our advertisers. This paper presents Multi-Channel Sequential Behavior Network (MC-SBN), a deep learning approach for embedding users and ads in a semantic space in which relevance can be evaluated. Our proposed user encoder architecture summarizes user activities from multiple input channels--such as previous search queries, visited pages, or clicked ads--into a user vector. It uses multiple RNNs to encode sequences of event sessions from the different channels and then applies an attention mechanism to create the user representation. A key property of our approach is that user vectors can be maintained and updated incrementally, which makes it feasible to be deployed for large-scale serving. We conduct extensive experiments on real-world datasets. The results demonstrate that MC-SBN can improve the ranking of relevant ads and boost the performance of both click prediction and conversion prediction in the queryless native advertising setting.
The group structure of dynamical transformations between quantum reference frames

Recently, it was shown that when reference frames are associated to quantum systems, the transformation laws between such quantum reference frames need to be modified to take into account the quantum and dynamical features of the reference frames. This led to a relational description of the phase space variables of the quantum system of which the quantum reference frames are part of. While such transformations were shown to be symmetries of the system's Hamiltonian, the question remained unanswered as to whether they enjoy a group structure, similar to that of the Galilei group relating classical reference frames in quantum mechanics. In this work, we identify the canonical transformations on the phase space of the quantum systems comprising the quantum reference frames, and show that these transformations close a group structure defined by a Lie algebra, which is different from the usual Galilei algebra of quantum mechanics. We further find that the elements of this new algebra are in fact the building blocks of the quantum reference frames transformations previously identified, which we recover. Finally, we show how the transformations between classical reference frames described by the standard Galilei group symmetries can be obtained from the group of transformations between quantum reference frames by taking the zero limit of the parameter that governs the additional noncommutativity introduced by the quantum nature of inertial transformations.
Inference for Low-rank Tensors - No Need to Debias

In this paper, we consider the statistical inference for several low-rank tensor models. Specifically, in the Tucker low-rank tensor PCA or regression model, provided with any estimates achieving some attainable error rate, we develop the data-driven confidence regions for the singular subspace of the parameter tensor based on the asymptotic distribution of an updated estimate by two-iteration alternating minimization. The asymptotic distributions are established under some essential conditions on the signal-to-noise ratio (in PCA model) or sample size (in regression model). If the parameter tensor is further orthogonally decomposable, we develop the methods and theory for inference on each individual singular vector. For the rank-one tensor PCA model, we establish the asymptotic distribution for general linear forms of principal components and confidence interval for each entry of the parameter tensor. Finally, numerical simulations are presented to corroborate our theoretical discoveries. In all these models, we observe that different from many matrix/vector settings in existing work, debiasing is not required to establish the asymptotic distribution of estimates or to make statistical inference on low-rank tensors. In fact, due to the widely observed statisticalcomputational-gap for low-rank tensor estimation, one usually requires stronger conditions than the statistical (or information-theoretic) limit to ensure the computationally feasible estimation is achievable. Surprisingly, such conditions “incidentally” render a feasible low-rank tensor inference without debiasing.
Coherent scattering 2D cooling in levitated cavity optomechanics

The strong light-matter optomechanical coupling offered by Coherent Scattering (CS) set-ups have allowed the experimental realisation of quantum ground state cavity cooling of the axial motion of a levitated nanoparticle [U. Deli\'{c} et al., Science 367, 892 (2020)]. An appealing milestone is now quantum 2D cooling of the full in-plane motion, in any direction in the transverse plane. By a simple adjustment of the trap polarisation, one obtains two nearly equivalent modes, with similar frequencies $\omega_{x}\sim\omega_{y}$ and optomechanical couplings $g_{x}\simeq g_{y}$ -- in this experimental configuration we identify an optimal trap ellipticity, nanosphere size and cavity linewidth which allows for efficient 2D cooling. Moreover, we find that 2D cooling to occupancies $n_{x}+n_{y}\lesssim1$ at moderate vacuum ($10^{-6}$ mbar) is possible in a"Goldilocks"zone bounded by $\sqrt{\kappa\Gamma/4}\lesssim g_{x},g_{y}\lesssim|\omega_{x}-\omega_{y}|\lesssim\kappa$, where one balances the need to suppress dark modes whilst avoiding far-detuning of either mode or low cooperativities, and $\kappa$ ($\Gamma$) is the cavity decay rate (motional heating rate). With strong-coupling regimes $g_{x},g_{y}\gtrsim\kappa$ in view one must consider the genuine three-way hybridisation between $x$, $y$ and the cavity light mode resulting in hybridized bright/dark modes. Finally, we show that bright/dark modes in the levitated set-up have a simple geometrical interpretation, related by rotations in the transverse plane, with implications for directional sensing.
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge

WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc. This challenge mainly pays attention to the webly-supervised fine-grained recognition problem. In the literature, existing deep learning methods highly rely on large-scale and high-quality labeled training data, which poses a limitation to their practicability and scalability in real world applications. In particular, for fine-grained recognition, a visual task that requires professional knowledge for labeling, the cost of acquiring labeled training data is quite high. It causes extreme difficulties to obtain a large amount of high-quality training data. Therefore, utilizing free web data to train fine-grained recognition models has attracted increasing attentions from researchers in the fine-grained community. This challenge expects participants to develop webly-supervised fine-grained recognition methods, which leverages web images in training fine-grained recognition models to ease the extreme dependence of deep learning methods on large-scale manually labeled datasets and to enhance their practicability and scalability. In this technical report, we have pulled together the top WebFG 2020 solutions of total 54 competing teams, and discuss what methods worked best across the set of winning teams, and what surprisingly did not help.