Miracle-dz/UniFAR-FTUs huggingface dataset:
The Interaction Force between Scheelite and Scheelite/Fluorite/Calcite Measured Using Atomic Force Microscopy

The mechanism of the formation of the hydrophobic agglomerate in fine scheelite flotation was studied using zeta potential measurement, contact angle measurement, optical microscope measurement, and atomic force microscopy (AFM) colloid probe technology. Zeta potential measurement results confirmed the adsorption of sodium oleate on scheelite, fluorite, and calcite surface and surface potential difference at different pH values of ultrapure water. Contact angle measurement results confirmed the surface of nature scheelite, fluorite, and calcite was hydrophilic, and the surface after thread by sodium oleate solution was hydrophobic. The optical microscope measurement results confirmed the agglomerates could really form in ultrapure water of pH 8 or 10 and in 1 mM sodium oleate solution of pH 10. The agglomerations were empty and not tight in ultrapure water. On the contrary, the hydrophobic agglomerations were larger and denser after treated with sodium oleate solution than that of in ultrapure water. According to the AFM experiment results, the interaction forces on hydrophilic scheelite-scheelite and scheelite-fluorite were repulsive at pH 5.6 and attractive at pH 8 or 10. However, the interaction forces on hydrophilic scheelite-calcite were attractive at pH 5.6, 8 or 10. The interaction forces on hydrophobic scheelite-scheelite, scheelite-fluorite, and scheelite-calcite were attractive strongly due to the existence of hydrophobic force. The measurement results of the interaction forces were in good agreement with the changes of zeta potential and contact angle at different conditions. The combined results could be beneficial to understand the interaction force in fine scheelite flotation.
How Can Personality Enhance Sustainable Career Management? The Mediation Effects of Future Time Perspective in Career Decisions

This study seeks to explore the mediating effects of future time perspective (FTP) between personality variables and career decision-making self-efficacy and career indecision with respect to managing sustainable careers. We used an online survey to collect data from 250 undergraduates for Study 1, in which we explored the mediating role of FTP which focused on the emotional and cognitive personality traits of emotional intelligence, ego resilience, and self-control; and from 249 undergraduate students for Study 2, in which we investigated the mediating effects of FTP on the personality traits of extraversion, conscientiousness, and neuroticism. The results from the first study indicated that emotional intelligence, ego resilience, and self-control had indirect effects on career decision-making self-efficacy and career indecision via FTP. The results of the second study showed that extraversion, conscientiousness, and neuroticism had indirect effects on career decision self-efficacy and career indecision via FTP. These results contribute to an enhanced understanding of the relationship between personalities and career decisions, and they expand our knowledge about the antecedents and consequences of FTP. At the end of this paper, we discuss the theoretical and practical implications of this study and identify directions for future research.
Does FDI benefit incumbent SMEs?

That global networks provide positive externalities to participating firms is a well‑documented fact. Less is known about how the performance of non-participating firms, especially those that are small or medium-sized, changes with exposure to an increase in the presence of globally integrated firms in their vicinity. With global trade being dominated by large firms, the benefits for SMEs are often indirect, e.g. through input relationships with larger companies or through knowledge spillovers that facilitate the adoption of best practices in firms with access to globally integrated peers. This paper combines industry and regional exposure to global links in form of foreign ownership. It uses firm-level microdata for 13 OECD countries, allowing for local spillovers (or crowding out) within the same industry and across industries. Foreign investment in the firm in the same region is associated with increasing productivity of local firms, especially in form of cross-sector externalities. Horizontal (same sector) externalities are negative, especially if they are coming from foreign firms locating in distanced regions. FDI tends to be associated with employment decline in manufacturing firms, but some growth in small firms.
Generating ${\rm N00N}$-states of surface plasmon-polariton pairs with a nanoparticle

We consider a generation of two-particle quantum states in the process of spontaneous parametric down-conversion of light by a dielectric nanoparticle with $\chi^{(2)}$ response. As a particular example, we study the generation of surface plasmon-polariton pairs with a ${\rm GaAs}$ nanoparticle located at the silver-air interface. We show that for certain excitation geometries, ${\rm N00N}$-states of surface plasmon-polariton pairs could be obtained. The effect can be physically interpreted as a result of quantum interference between pairs of induced sources, each emitting either signal or idler plasmon. We then relate the resulting ${\rm N00N}$-pattern to the general symmetry properties of dyadic Green's function of a dipole emitter exciting surface waves. It renders the considered effect as a general way towards a robust generation of ${\rm N00N}$-states of surface waves using spontaneous parametric down-conversion in $\chi^{(2)}$ nanoparticles.
Fog Computing on Constrained Devices: Paving the Way for the Future IoT

In the long term, the Internet of Things (IoT) is expected to become an integral part of people's daily lives. In light of this technological advancement, an ever-growing number of objects with limited hardware may become connected to the Internet. In this chapter, we explore the importance of these constrained devices as well as how we can use them in conjunction with fog computing to change the future of the IoT. First, we present an overview of the concepts of constrained devices, IoT, and fog and mist computing, and then we present a classification of applications according to the amount of resources they require (e.g., processing power and memory). After that, we tie in these topics with a discussion of what can be expected in a future where constrained devices and fog computing are used to push the IoT to new limits. Lastly, we discuss some challenges and opportunities that these technologies may bring.
The PHOTON Wizard - Towards Educational Machine Learning Code Generators

Despite the tremendous efforts to democratize machine learning, especially in applied-science, the application is still often hampered by the lack of coding skills. As we consider programmatic understanding key to building effective and efficient machine learning solutions, we argue for a novel educational approach that builds upon the accessibility and acceptance of graphical user interfaces to convey programming skills to an applied-science target group. We outline a proof-of-concept, open-source web application, the PHOTON Wizard, which dynamically translates GUI interactions into valid source code for the Python machine learning framework PHOTON. Thereby, users possessing theoretical machine learning knowledge gain key insights into the model development workflow as well as an intuitive understanding of custom implementations. Specifically, the PHOTON Wizard integrates the concept of Educational Machine Learning Code Generators to teach users how to write code for designing, training, optimizing and evaluating custom machine learning pipelines.
Improving Automated Segmentation of Radio Shows with Audio Embeddings

Audio features have been proven useful for increasing the performance of automated topic segmentation systems. This study explores the novel task of using audio embeddings for automated, topically coherent segmentation of radio shows. We created three different audio embedding generators using multi-class classification tasks on three datasets from different domains. We evaluate topic segmentation performance of the audio embeddings and compare it against a text-only baseline. We find that a set-up including audio embeddings generated through a non-speech sound event classification task significantly outperforms our text-only baseline by 32.3% in F1-measure. In addition, we find that different classification tasks yield audio embeddings that vary in segmentation performance.
AI safety: state of the field through quantitative lens

Last decade has seen major improvements in the performance of artificial intelligence which has driven wide-spread applications. Unforeseen effects of such massad-option has put the notion of AI safety into the public eye. AI safety is a relatively new field of research focused on techniques for building AI beneficial for humans. While there exist survey papers for the field of AI safety, there is a lack of a quantitative look at the research being conducted. The quantitative aspect gives a data-driven insight about the emerging trends, knowledge gaps and potential areas for future research. In this paper, bibliometric analysis of the literature finds significant increase in research activity since 2015. Also, the field is so new that most of the technical issues are open, including: explainability and its long-term utility, and value alignment which we have identified as the most important long-term research topic. Equally, there is a severe lack of research into concrete policies regarding AI. As we expect AI to be the one of the main driving forces of changes, AI safety is the field under which we need to decide the direction of humanity’s future.
β-cell and type 2 diabetes

The mechanism of non-insulin-dependent diabetes mellitus (NIDDM) remains unclear. It is believedthat both b-cell dysfunction and insulin resistance (IR) play critical roles in the NIDDM. Obesity might contributeto not only IR but also b-cell dysfunction during the compensate stage. High-risk population has higher risk fordeveloping NIDDM than the other population. It has been demonstrated that NIDDM are influenced by envi-ronmental and genetic factors.
Weak localization corrections to the thermal conductivity in 
s
-wave superconductors

We study the thermal conductivity in disordered $s$-wave superconductors. Expanding on previous works for normal metals, we develop a formalism that tackles particle diffusion as well as the weak localization (WL) and weak anti-localization (WAL) effects. Using a Green's functions diagrammatic technique, which takes into account the superconducting nature of the system by working in Nambu space, we identify the system's low-energy modes, the diffuson and the Cooperon. The time scales that characterize the diffusive regime are energy dependent; this is in contrast with the the normal state, where the relevant time scale is the mean free time $\tau_e$, independent of energy. The energy dependence introduces a novel energy scale $\varepsilon_*$, which in disordered superconductors ($\tau_e \Delta\ll 1$, with $\Delta$ the gap) is given by $\varepsilon_* = \sqrt{\Delta/\tau_e}$. From the diffusive behavior of the low-energy modes, we obtain the WL correction to the thermal conductivity. We give explicitly expressions in two dimensions. We determine the regimes in which the correction depends explicitly on $\varepsilon_*$ and propose an optimal regime to verify our results in an experiment.
Protecting Consumers Against Personalized Pricing: A Stopping Time Approach

The widespread availability of behavioral data has led to the development of data-driven personalized pricing algorithms: sellers attempt to maximize their revenue by estimating the consumer's willingness-to-pay and pricing accordingly. Our objective is to develop algorithms that protect consumer interests against personalized pricing schemes. In this paper, we consider a consumer who learns more and more about a potential purchase across time, while simultaneously revealing more and more information about herself to a potential seller. We formalize a strategic consumer's purchasing decision when interacting with a seller who uses personalized pricing algorithms, and contextualize this problem among the existing literature in optimal stopping time theory and computational finance. We provide an algorithm that consumers can use to protect their own interests against personalized pricing algorithms. This algorithmic stopping method uses sample paths to train estimates of the optimal stopping time. To the best of our knowledge, this is one of the first works that provides computational methods for the consumer to maximize her utility when decision making under surveillance. We demonstrate the efficacy of the algorithmic stopping method using a numerical simulation, where the seller uses a Kalman filter to approximate the consumer's valuation and sets prices based on myopic expected revenue maximization. Compared to a myopic purchasing strategy, we demonstrate increased payoffs for the consumer in expectation.
Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions. Recently, Vector-Quantised Variational Autoencoders (VQ-VAE) have been proposed as an efficient generative unsupervised learning approach that can encode images to a small percentage of their initial size, while preserving their decoded fidelity. Here, we show a VQ-VAE inspired network can efficiently encode a full-resolution 3D brain volume, compressing the data to $0.825\%$ of the original size while maintaining image fidelity, and significantly outperforming the previous state-of-the-art. We then demonstrate that VQ-VAE decoded images preserve the morphological characteristics of the original data through voxel-based morphology and segmentation experiments. Lastly, we show that such models can be pre-trained and then fine-tuned on different datasets without the introduction of bias.
Learning Halfspaces with Massart Noise Under Structured Distributions

We study the problem of learning halfspaces with Massart noise in the distribution-specific PAC model. We give the first computationally efficient algorithm for this problem with respect to a broad family of distributions, including log-concave distributions. This resolves an open question posed in a number of prior works. Our approach is extremely simple: We identify a smooth {\em non-convex} surrogate loss with the property that any approximate stationary point of this loss defines a halfspace that is close to the target halfspace. Given this structural result, we can use SGD to solve the underlying learning problem.
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Tensor latent factor models can describe higher-order correlations for spatial data. However, they are computationally expensive to train and are sensitive to initialization, leading to spatially incoherent, uninterpretable results. We develop a novel Multiresolution Tensor Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns. MRTL initializes the latent factors from an approximate full-rank tensor model for improved interpretability and progressively learns from a coarse resolution to the fine resolution to reduce computation. We also prove the theoretical convergence and computational complexity of MRTL. When applied to two real-world datasets, MRTL demonstrates 4~5x speedup compared to a fixed resolution approach while yielding accurate and interpretable latent factors.
Measuring the eccentricity of binary black holes in GWTC-1 by using the inspiral-only waveform

In this paper, we estimate the eccentricity of the 10 BBHs in GWTC-1 by using the inspiral-only BBH waveform template EccentricFD. Firstly, we test our method with simulated eccentric BBHs. Afterwards we apply the method to the real BBH gravitational wave data. We find that the BBHs in GWTC-1 except GW151226, GW170608 and GW170729 admit almost zero eccentricity. Their upper limits on eccentricity rang from 0.033 to 0.084 with 90% credible interval at the reference frequency 10Hz. And their median eccentricities are 0.000. For GW151226, GW170608 and GW170729, the upper limits are higher than 0.1. But the median eccentricities are respectively 0.000, 0.064 and 0.000. We also point out the limitations of the inspiral-only waveform template in eccentricity measurement. The current eccentricity measurement results of GWTC-1 is not enough to constrain the formation mechanisms of BBHs, and more observational data are needed in the future.
Feature-level Malware Obfuscation in Deep Learning

We consider the problem of detecting malware with deep learning models, where the malware may be combined with significant amounts of benign code. Examples of this include piggybacking and trojan horse attacks on a system, where malicious behavior is hidden within a useful application. Such added flexibility in augmenting the malware enables significantly more code obfuscation. Hence we focus on the use of static features, particularly Intents, Permissions, and API calls, which we presume cannot be ultimately hidden from the Android system, but only augmented with yet more such features. We first train a deep neural network classifier for malware classification using features of benign and malware samples. Then we demonstrate a steep increase in false negative rate (i.e., attacks succeed), simply by randomly adding features of a benign app to malware. Finally we test the use of data augmentation to harden the classifier against such attacks. We find that for API calls, it is possible to reject the vast majority of attacks, where using Intents or Permissions is less successful.
Metric-Free Individual Fairness in Online Learning

We study an online learning problem subject to the constraint of individual fairness, which requires that similar individuals are treated similarly. Unlike prior work on individual fairness, we do not assume the similarity measure among individuals is known, nor do we assume that such measure takes a certain parametric form. Instead, we leverage the existence of an auditor who detects fairness violations without enunciating the quantitative measure. In each round, the auditor examines the learner's decisions and attempts to identify a pair of individuals that are treated unfairly by the learner. We provide a general reduction framework that reduces online classification in our model to standard online classification, which allows us to leverage existing online learning algorithms to achieve sub-linear regret and number of fairness violations. Surprisingly, in the stochastic setting where the data are drawn independently from a distribution, we are also able to establish PAC-style fairness and accuracy generalization guarantees (Yona and Rothblum [2018]), despite only having access to a very restricted form of fairness feedback. Our fairness generalization bound qualitatively matches the uniform convergence bound of Yona and Rothblum [2018], while also providing a meaningful accuracy generalization guarantee. Our results resolve an open question by Gillen et al. [2018] by showing that online learning under an unknown individual fairness constraint is possible even without assuming a strong parametric form of the underlying similarity measure.
On the derivative martingale in a branching random walk

We work under the A\"{\i}d\'{e}kon-Chen conditions which ensure that the derivative martingale in a supercritical branching random walk on the line converges almost surely to a nondegenerate nonnegative random variable that we denote by $Z$. It is shown that $\mathbb{E} Z\mathbf{1}_{\{Z\le x\}}=\log x+o(\log x)$ as $x\to\infty$. Also, we provide necessary and sufficient conditions under which $\mathbb{E} Z\mathbf{1}_{\{Z\le x\}}=\log x+{\rm const}+o(1)$ as $x\to\infty$. This more precise asymptotics is a key tool for proving distributional limit theorems which quantify the rate of convergence of the derivative martingale to its limit $Z$. The methodological novelty of the present paper is a three terms representation of a subharmonic function of at most linear growth for a killed centered random walk of finite variance. This yields the aforementioned asymptotics and should also be applicable to other models.
A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation

Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss due to a variety of many-to-one mappings. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence, undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost–benefit ratio of visualization processes can explain such usefulness experienced in practice and postulate that the viewers’ knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers’ knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. However, the existing cost–benefit measure consists of an unbounded divergence term, making the numerical measurements difficult to interpret. This is the second part of a two-part paper, which aims to improve the existing cost–benefit measure. Part I of the paper provided a theoretical discourse about the problem of unboundedness, reported a conceptual analysis of nine candidate divergence measures for resolving the problem, and eliminated three from further consideration. In this Part II, we describe two groups of case studies for evaluating the remaining six candidate measures empirically. In particular, we obtained instance data for (i) supporting the evaluation of the remaining candidate measures and (ii) demonstrating their applicability in practical scenarios for estimating the cost–benefit of visualization processes as well as the impact of human knowledge in the processes. The real world data about visualization provides practical evidence for evaluating the usability and intuitiveness of the candidate measures. The combination of the conceptual analysis in Part I and the empirical evaluation in this part allows us to select the most appropriate bounded divergence measure for improving the existing cost–benefit measure.
Real or Not Real, that is the Question

While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles. In this generalized framework, referred to as RealnessGAN, the discriminator outputs a distribution as the measure of realness. While RealnessGAN shares similar theoretical guarantees with the standard GAN, it provides more insights on adversarial learning. Compared to multiple baselines, RealnessGAN provides stronger guidance for the generator, achieving improvements on both synthetic and real-world datasets. Moreover, it enables the basic DCGAN architecture to generate realistic images at 1024*1024 resolution when trained from scratch.