Miracle-dz/UniFAR-FTUs huggingface dataset:
Green Synthesized Silver and Gold Nanoparticles for Colorimetric Detection of Hg2+, Pb2+, and Mn2+ in Aqueous Medium

In this study, we report a simple and green method for the synthesis of l-tyrosine-stabilized silver (AgNPs) and gold nanoparticles (AuNPs) in aqueous medium under ambient sunlight irradiation. The nanoparticles (NPs) are characterized by UV–visible spectroscopy, high-resolution transmission electron microscopy (HR-TEM), Fourier transform infrared spectroscopy (FT-IR), cyclic voltammetry (CV), and dynamic light scattering (DLS) techniques. The size and shape of the metal NPs could be controlled by changing the concentration of the substrate, metal precursors, and pH of the medium. The synthesized AgNPs are found to be highly sensitive to Hg2+ and Mn2+ ions with the detection limit for both ions as low as 16 nM under optimized conditions. However AuNPs are found to be sensitive to Hg2+ and Pb2+ ions with a detection limit as low as 53 and 16 nM, respectively. The proposed method was found to be useful for colorimetric detection of heavy metal ions in aqueous medium.
The Strategy Behind Anti-Vaxxers' Reply Behavior on Social Media

Although the online campaigns of anti-vaccine advocates, or anti-vaxxers, severely threaten efforts for herd immunity, their propaganda strategies remain poorly understood, as does their reply behavior, which constitutes the most direct form of outreach on social media. Therefore, we empirically ana- lyzed the strategy of anti-vaxxers’ reply behavior on Twitter in terms of interaction frequency, content, and targets. Among the results, anti-vaxxers more frequently conducted reply behavior to other clusters, especially to neutral accounts, and the content of their replies was signiﬁcantly toxic and emotional. Furthermore, the most-targeted users were so-called “decent” accounts with large numbers of followers, including accounts related to health care or representing scientists, policymakers, or media ﬁgures or outlets. We discussed and evaluated the effectiveness of these reply strategies, as well as the possible countermeasures to them. Those ﬁndings should prove useful for developing guidelines for pro-vaxxers and fact-checkers in online communities.
Compositional Fine-Grained Low-Shot Learning

We develop a novel compositional generative model for zero- and few-shot learning to recognize fine-grained classes with a few or no training samples. Our key observation is that generating holistic features for fine-grained classes fails to capture small attribute differences between classes. Therefore, we propose a feature composition framework that learns to extract attribute features from training samples and combines them to construct fine-grained features for rare and unseen classes. Feature composition allows us to not only selectively compose features of every class from only relevant training samples, but also obtain diversity among composed features via changing samples used for the composition. In addition, instead of building holistic features for classes, we use our attribute features to form dense representations capable of capturing fine-grained attribute details of classes. We propose a training scheme that uses a discriminative model to construct features that are subsequently used to train the model itself. Therefore, we directly train the discriminative model on the composed features without learning a separate generative model. We conduct experiments on four popular datasets of DeepFashion, AWA2, CUB, and SUN, showing the effectiveness of our method.
A Need-finding Study for Understanding Text Entry in SmartphoneApp Usage

Text entry makes up about one-fourth of the smartphone interaction events, and is known to be challenging and difficult. However, there has been little study about the characteristics of text entry in the context of smartphone app usage. In this paper, we present a mixed-method in-situ study conducted in 2016 with 17 active smartphone users to better understand text entry in smartphone app usage. Our results show 80% of text was entered into communication apps, with different apps exhibiting distinct usage patterns. We found that structured data such as URLs and email addresses are rarely typed but instead are auto-completed or replaced with search, copy-and-paste is rarely used, and sessions of smartphone usage with text entry involve more apps and last longer. We conclude with a discussion about the implications on the development of systems to better support mobile interaction.
Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks

The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a highly probable scenario behind the leaves exploiting learned patterns from images with non-occluded berries. Our experiments show that the estimate of the number of berries after applying our method is closer to the manually counted reference. In contrast to applying a factor to the berry count, our approach better adapts to local conditions by directly involving the appearance of the visible berries. Furthermore, we show that our approach can identify which areas in the image should be changed by adding new berries without explicitly requiring information about hidden areas.
On the Quantum Chemical Nature of Lead(II) “Lone Pair”

We study the quantum chemical nature of the Lead(II) valence basins, sometimes called the lead “lone pair”. Using various chemical interpretation tools, such as molecular orbital analysis, natural bond orbitals (NBO), natural population analysis (NPA) and electron localization function (ELF) topological analysis, we study a variety of Lead(II) complexes. A careful analysis of the results shows that the optimal structures of the lead complexes are only governed by the 6s and 6p subshells, whereas no involvement of the 5d orbitals is found. Similarly, we do not find any significant contribution of the 6d. Therefore, the Pb(II) complexation with its ligand can be explained through the interaction of the 6s2 electrons and the accepting 6p orbitals. We detail the potential structural and dynamical consequences of such electronic structure organization of the Pb (II) valence domain.
Crystal structure and powder X-ray diffraction data for new Tb3CuAl3Ge2 compound

The crystal structure of new Tb3CuAl3Ge2 quaternary compound was studied by the Rietveld method from powder X-ray diffraction (XRD) data. The Tb3CuAl3Ge2 compound crystallized in the hexagonal Y3NiAl3Ge2-type structure with space group P-62m (no. 189) and lattice parameters a = 7.0041(2) Å, c = 4.1775(1) Å, V = 177.48 Å3. There is only one formula in each unit cell, Z = 1, and the density of Tb3CuAl3Ge2 is ρ x = 7.1696 g cm−3. The reliability factors characterizing the Rietveld refinement results are R p = 6.43%, R wp = 8.65%, R B = 4.81%, and R F = 4.09%, respectively. The powder XRD data of Tb3CuAl3Ge2 were presented and the reliability of indexation is F 30 = 120.9(0.0073, 34).
Measure Concentration on the OFDM-Based Random Access Channel

It is well known that CS can boost massive random access protocols. Usually, the protocols operate in some overloaded regime where the sparsity can be exploited. In this paper, we consider a different approach by taking an orthogonal FFT base, subdivide its image into appropriate sub-channels and let each subchannel take only a fraction of the load. To show that this approach can actually achieve the full capacity we i) provide new concentration inequalities, and ii) devise a sparsity capture effect, i.e where the sub-division can be driven such that the activity in each each sub-channel is sparse by design. We show by simulations that the system is scalable resulting in a coarsely 30-fold capacity increase.
De-Biased Modeling of Search Click Behavior with Reinforcement Learning

Users' clicks on Web search results are one of the key signals for evaluating and improving web search quality and have been widely used as part of current state-of-the-art Learning-To-Rank(LTR) models. With a large volume of search logs available for major search engines, effective models of searcher click behavior have emerged to evaluate and train LTR models. However, when modeling the users' click behavior, considering the bias of the behavior is imperative. In particular, when a search result is not clicked, it is not necessarily chosen as not relevant by the user, but instead could have been simply missed, especially for lower-ranked results. These kinds of biases in the click log data can be incorporated into the click models, propagating the errors to the resulting LTR ranking models or evaluation metrics. In this paper, we propose the De-biased Reinforcement Learning Click model (DRLC). The DRLC model relaxes previously made assumptions about the users' examination behavior and resulting latent states. To implement the DRLC model, convolutional neural networks are used as the value networks for reinforcement learning, trained to learn a policy to reduce bias in the click logs. To demonstrate the effectiveness of the DRLC model, we first compare performance with the previous state-of-art approaches using established click prediction metrics, including log-likelihood and perplexity. We further show that DRLC also leads to improvements in ranking performance. Our experiments demonstrate the effectiveness of the DRLC model in learning to reduce bias in click logs, leading to improved modeling performance and showing the potential for using DRLC for improving Web search quality.
Positive semigroups in lattices and totally real number fields

Abstract Let L be a full-rank lattice in ℝd and write L+ for the semigroup of all vectors with nonnegative coordinates in L. We call a basis X for L positive if it is contained in L+. There are infinitely many such bases, and each of them spans a conical semigroup S(X) consisting of all nonnegative integer linear combinations of the vectors of X. Such S(X) is a sub-semigroup of L+, and we investigate the distribution of the gaps of S(X) in L+, i.e. the points in L+ ∖ S(X). We describe some basic properties and counting estimates for these gaps. Our main focus is on the restrictive successive minima of L+ and of L+ ∖ S(X), for which we produce bounds in the spirit of Minkowski’s successive minima theorem and its recent generalizations. We apply these results to obtain analogous bounds for the successive minima with respect to Weil heights of totally positive sub-semigroups of ideals in totally real number fields.
High Accuracy and Low Complexity Frequency Offset Estimation Based on All-Phase FFT for M-QAM Coherent Optical Systems

A low complexity frequency offset estimation algorithm based on all-phase FFT for MQAM is proposed. Compared with two-stage algorithms such as FFT+CZT and FFT+ZoomFFT, our algorithm can lower computational complexity by 73% and 30% respectively, without loss of the estimation accuracy. © 2021 The Author(s)
Predictive Control Barrier Functions: Enhanced Safety Mechanisms for Learning-Based Control

While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through so-called predictive safety filters, which assess if a proposed learning-based control input can lead to constraint violations and modifies it if necessary to ensure safety for all future time steps. The theoretical guarantees of such predictive safety filters rely on the model assumptions and minor deviations can lead to failure of the filter putting the system at risk. This article introduces an auxiliary soft-constrained predictive control problem that is always feasible at each time step and asymptotically stabilizes the feasible set of the original predictive safety filter problem, thereby providing a recovery mechanism in safety–critical situations. This is achieved by a simple constraint tightening in combination with a terminal control barrier function. By extending discrete-time control barrier function theory, we establish that the proposed auxiliary problem provides a “predictive” control barrier function. The resulting algorithm is demonstrated using numerical examples.
Designing truncated priors for direct and inverse Bayesian problems

Abstract: The Bayesian approach to inverse problems with functional unknowns, has received significant attention in recent years. An important component of the developing theory is the study of the asymptotic performance of the posterior distribution in the frequentist setting. The present paper contributes to the area of Bayesian inverse problems by formulating a posterior contraction theory for linear inverse problems, with truncated Gaussian series priors, and under general smoothness assumptions. Emphasis is on the intrinsic role of the truncation point both for the direct as well as for the inverse problem, which are related through the modulus of continuity as this was recently highlighted by Knapik and Salomond (2018).
On Instrumental Variable Regression for Deep Offline Policy Evaluation

We show that the popular reinforcement learning (RL) strategy of estimating the state-action value (Q-function) by minimizing the mean squared Bellman error leads to a regression problem with confounding, the inputs and output noise being correlated. Hence, direct minimization of the Bellman error can result in significantly biased Q-function estimates. We explain why fixing the target Q-network in Deep Q-Networks and Fitted Q Evaluation provides a way of overcoming this confounding, thus shedding new light on this popular but not well understood trick in the deep RL literature. An alternative approach to address confounding is to leverage techniques developed in the causality literature, notably instrumental variables (IV). We bring together here the literature on IV and RL by investigating whether IV approaches can lead to improved Q-function estimates. This paper analyzes and compares a wide range of recent IV methods in the context of offline policy evaluation (OPE), where the goal is to estimate the value of a policy using logged data only. By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques. We find empirically that state-of-the-art OPE methods are closely matched in performance by some IV methods such as AGMM, which were not developed for OPE. We open-source all our code and datasets at https://github.com/liyuan9988/IVOPEwithACME.
Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Federated Learning (FL) is a decentralized machine-learning paradigm in which a global server iteratively aggregates the model parameters of local users without accessing their data. User heterogeneity has imposed significant challenges to FL, which can incur drifted global models that are slow to converge. Knowledge Distillation has recently emerged to tackle this issue, by refining the server model using aggregated knowledge from heterogeneous users, other than directly aggregating their model parameters. This approach, however, depends on a proxy dataset, making it impractical unless such prerequisite is satisfied. Moreover, the ensemble knowledge is not fully utilized to guide local model learning, which may in turn affect the quality of the aggregated model. In this work, we propose a data-free knowledge distillation approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner, which is then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies powered by theoretical implications show that, our approach facilitates FL with better generalization performance using fewer communication rounds, compared with the state-of-the-art.
The Truth about Power Laws: Theory and Reality

Xiaojun Zhang, Zheng He, Liwei Zhang, Lez Rayman-Bacchus, Yue Xiao, Shuhui Shen School of Mathematical Sciences, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, P. R. China 3 School of Mathematical Sciences, Dalian University of Technology, Dalian, P. R. China 4 Business School, University of Winchester, Winchester, UK Corresponding author. E-mail: sczhxj@uestc.edu. cn; hezh@uestc.edu. cn
Generalisable and distinctive 3D local deep descriptors for point cloud registration

—An effective 3D descriptor should be invariant to different geometric transformations, such as scale and rotation, repeatable in the case of occlusions and clutter, and generalisable in different contexts when data is captured with different sensors. We present a simple but yet effective method to learn generalisable and distinctive 3D local descriptors that can be used to register point clouds captured in different contexts with different sensors. Point cloud patches are extracted, canonicalised with respect to their local reference frame, and encoded into scale and rotation-invariant compact descriptors by a point permutation-invariant deep neural network. Our descriptors can effectively generalise across sensor modalities from locally and randomly sampled points. We evaluate and compare our descriptors with alternative handcrafted and deep learning-based descriptors on several indoor and outdoor datasets reconstructed using both RGBD sensors and laser scanners. Our descriptors outperform most recent descriptors by a large margin in terms of generalisation, and become the state of the art also in benchmarks where training and testing are performed in the same scenarios.
Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees

This article is motivated by the objective of providing a new analytically tractable and fully frequentist framework to characterize and implement regression trees while also allowing a multivariate (potentially high dimensional) response. The connection to regression trees is made by a high dimensional model with dynamic mean vectors over multi-dimensional change axes. Our theoretical analysis is carried out under a single two dimensional change point setting. An optimal rate of convergence of the proposed estimator is obtained, which in turn allows existence of limiting distributions. Distributional behavior of change point estimates are split into two distinct regimes, the limiting distributions under each regime is then characterized, in turn allowing construction of asymptotically valid confidence intervals for 2d-location of change. All results are obtained under a high dimensional scaling s log2 p = o(TwTh), where p is the response dimension, s is a sparsity parameter, and Tw, Th are sampling periods along change axes. We characterize full regression trees by defining a multiple multi-dimensional change point model. Natural extensions of the single 2d-change point estimation methodology are provided. Two applications, first on segmentation of Infrared astronomy satellite (IRAS) data and second to segmentation of digital images are provided. Methodology and theoretical results are supported with monte-carlo simulations.
IDEAL: Independent Domain Embedding Augmentation Learning

Many efforts have been devoted to designing sampling, mining, and weighting strategies in high-level deep metric learning (DML) loss objectives. However, little attention has been paid to low-level but essential data transformation. In this paper, we develop a novel mechanism, the independent domain embedding augmentation learning ({IDEAL}) method. It can simultaneously learn multiple independent embedding spaces for multiple domains generated by predefined data transformations. Our IDEAL is orthogonal to existing DML techniques and can be seamlessly combined with prior DML approaches for enhanced performance. Empirical results on visual retrieval tasks demonstrate the superiority of the proposed method. For example, the IDEAL improves the performance of MS loss by a large margin, 84.5\% $\rightarrow$ 87.1\% on Cars-196, and 65.8\% $\rightarrow$ 69.5\% on CUB-200 at Recall$@1$. Our IDEAL with MS loss also achieves the new state-of-the-art performance on three image retrieval benchmarks, \ie, \emph{Cars-196}, \emph{CUB-200}, and \emph{SOP}. It outperforms the most recent DML approaches, such as Circle loss and XBM, significantly. The source code and pre-trained models of our method will be available at\emph{\url{https://github.com/emdata-ailab/IDEAL}}.
Dynamic Filters in Graph Convolutional Neural Networks

Over the last few years, we have seen increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relationships, and Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data. In particular, there is a strong interest in exploring the possibilities in performing convolution on graphs using an extension of the GNN architecture, generally referred to as Graph Convolutional Neural Networks (GCNN). Convolution on graphs has been achieved mainly in two forms: spectral and spatial convolutions. Due to the higher ﬂexibility in exploring and exploiting the graph structure of data, recently, there is an increasing interest in investigating the possibilities that the spatial approach can oﬀer. The idea of ﬁnding a way to adapt the network behaviour to the inputs they process to maximize the total performances has aroused much interest in the neural networks literature over the years. This paper presents a novel method to adapt the behaviour of a GCNN to the input proposing two ways to perform spatial convolution on graphs using input-based ﬁlters which are dynamically generated. Our model also investigates the problem of discovering and reﬁning relations among nodes. The experimental assessment conﬁrms the capabilities of the proposed approach, which achieves satisfying results using simple architectures with a low number of ﬁlters.
Einstein's special relativity from a space-time duality principle

Einstein based his special theory of relativity on two postulates: (a) physical laws appear the same in all inertial frames, and (b) the speed of light in vacuum is an observer-independent constant. However, it is already known that the principle of the constancy of the speed of light is redundant in constructing the special theory of relativity. Adhering to this idea, we show here that the form of the Lagrangian for a free relativistic particle can be derived if we consider a class of theories that remain invariant under a duality transformation between space and time intervals. Therefore, the special theory of relativity is interpreted as a theory that exhibits a duality symmetry with space intervals and time intervals being dual to each other. The Lorentz transformations are then simply deduced as the linear transformations between inertial observers that leave the (infinitesimal) action of the relativistic particles unchanged.