Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanochain-structured mesoporous tungsten carbide and its superior electrocatalysis

A unique nanochain-structured mesoporous tungsten carbide (m-NCTC) was synthesized through a simple combined hydrothermal reaction–post heat-treatment approach. When loaded with Pt, the nanostructure (Pt/m-NCTC), as a catalyst, demonstrates high unit mass electroactivity (323 A (g Pt)−1) and high resistance to CO poisoning for methanol oxidation, and is much superior to Pt/C, one of the known excellent electrocatalysts. Its high reaction activity and strong poison-resistivity is very likely due to the unique mesoporous nanochain structure and high specific surface area (113 m2 g−1). This work provides a universal and economic method to synthesize novel mesoporous structured materials and provides scientific insight of mesoporous structured electrocatalysis, thus leading to various important applications as a catalyst in fuel cells, solar cells, sensors and in organic synthesis reactions.
Photomodification of a poly(acrylonitrile‐co‐butadiene‐co‐styrene) containing diaryltetrazolyl groups

A new type of membrane polymer, containing a photosensitive moiety, was synthesized. The photochemically induced functionalization and crosslinking of poly [acrylonitrile-co-butadiene-co-styrene-co-2-(4-ethenyl) phenyl-5-phenyl-2H-tetrazole], ABSV, has been studied. A variety of dipolarophiles and phenolic compounds were applied to alter the polarity of the membrane surface. The functionalization and crosslinking were initiated by photoirradiation. DSC and contact angle measurements were used to characterize the modified polymer. Pervaporation experiments with a cyclohexane/benzene mixture (5 wt% benzene) were carried out, with the results showing, that the photochemical treatment significantly effects both permselectivity and flux. Solubility parameters were found to be useful to interpret the pervaporation data. In general, the selectivity towards benzene increases with the polarity of the membrane surface, i.e., with increasing polarity of the modifier. A mechanism to explain the influence of both functionalization and crosslinking on the membrane performance, is proposed. © 1994 John Wiley & Sons, Inc.
Metalorganic chemical vapor deposition of N-polar GaN films on vicinal SiC substrates using indium surfactants

The effect of indium surfactants on the growth of N-polar GaN films on vicinal C-face SiC substrates by metalorganic chemical vapor deposition was investigated. Triangular hillocks formed on the surface of N-polar GaN without indium, resulting in a rough surface. When indium surfactants were introduced during GaN growth, the surface roughness was reduced from 18.1 to 3.5 nm over a 20 × 20 μm2 area. The photoluminescence characteristics of N-polar GaN film were also improved because of a reduction of carbon caused by the presence of indium, demonstrating that indium is a useful surfactant in the growth of N-polar GaN.
Effects of Particle Size and Breadmaking on Physiological Responses of Meal‐Fed Rats to AACC Wheat Bran

Adult rats were meal-fed diets containing 20% AACC certified hard red spring wheat bran (as received, ground, incorporated into bread) for 12 days to determine the short-term processing-induced effects on gastrointestinal function and serum lipids. Fecal nitrogen, fecal fat, transit time and serum triglyceride levels were influenced by the consumption of wheat bran, but not by the grinding or breadmaking procedures. Reduction of bran particle size decreased dietary hemicellulose and reduced fecal bulk as well as defecation frequency. Incorporating bran into bread reduced dietary neutral detergent fiber (NDF), reduced intestinal passage rate of the ground wheat bran and decreased degradation of dietary fiber components in the unground wheat bran.
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification

We consider the problem of multi-label classification, where the labels lie on a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is known. Encouraged by the recent success of hyperbolic embeddings in capturing hierarchical relations, we propose to jointly learn the classifier parameters as well as the label embeddings. Such a joint learning is expected to provide a twofold advantage: i) the classifier generalises better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy. We propose a novel formulation for the joint learning and empirically evaluate its efficacy. The results show that the joint learning improves over the baseline that employs label co-occurrence based pre-trained hyperbolic embeddings. Moreover, the proposed classifiers achieve state-of-the-art generalization on standard benchmarks. We also present evaluation of the hyperbolic embeddings obtained by joint learning and show that they represent the hierarchy more accurately than the other alternatives.
Asymmetric self-play for automatic goal discovery in robotic manipulation

We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play for goal discovery, where two agents, Alice and Bob, play a game. Alice is asked to propose challenging goals and Bob aims to solve them. We show that this method can discover highly diverse and complex goals without any human priors. Bob can be trained with only sparse rewards, because the interaction between Alice and Bob results in a natural curriculum and Bob can learn from Alice's trajectory when relabeled as a goal-conditioned demonstration. Finally, our method scales, resulting in a single policy that can generalize to many unseen tasks such as setting a table, stacking blocks, and solving simple puzzles. Videos of a learned policy is available at https://robotics-self-play.github. io.
Mapping the Landscape of COVID-19 Crisis Visualizations

In response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact people’s attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research.
Distribution System Voltage Prediction from Smart Inverters using Decentralized Regression

As photovoltaic (PV) penetration continues to rise and smart inverter functionality continues to expand, smart inverters and other distributed energy resources (DERs) will play increasingly important roles in distribution system power management and security. In this paper, it is demonstrated that a constellation of smart inverters in a simulated distribution circuit can enable precise voltage predictions using an asynchronous and decentralized prediction algorithm. Using simulated data and a constellation of 15 inverters in a ring communication topology, the Cola algorithm is shown to accomplish the learning task required for voltage magnitude prediction with far less communication overhead than fully connected P2P learning protocols. Additionally, a dynamic stopping criterion is proposed that does not require a regularizer like the original Cola stopping criterion.
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search

Result relevance prediction is an essential task of e-commerce search engines to boost the utility of search engines and ensure smooth user experience. The last few years eyewitnessed a flurry of research on the use of Transformer-style models and deep text-match models to improve relevance. However, these two types of models ignored the inherent bipartite network structures that are ubiquitous in e-commerce search logs, making these models ineffective. We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction. We design, for the first time, an end-to-end First-and-Second-order Relevance prediction model for e-commerce item relevance. The model is augmented by the neighborhood structures of bipartite networks that are built using the information of user behavioral feedback, including clicks and purchases. To ensure that edges accurately encode relevance information, we introduce external knowledge generated from BERT to refine the network of user behaviors. This allows the new model to integrate information from neighboring items and queries, which are highly relevant to the focus query-item pair under consideration. Results of offline experiments showed that the new model significantly improved the prediction accuracy in terms of human relevance judgment. An ablation study showed that the First-and-Second-order model gained a 4.3% average gain over the First-order model. Results of an online A/B test revealed that the new model derived more commercial benefits compared to the base model.
Bootstrapping Motor Skill Learning with Motion Planning

Learning a robot motor skill from scratch is impractically slow; so much so that in practice, learning must typically be bootstrapped using human demonstration. However, relying on human demonstration necessarily degrades the autonomy of robots that must learn a wide variety of skills over their operational lifetimes. We propose using kinematic motion planning as a completely autonomous, sample efficient way to bootstrap motor skill learning for object manipulation. We demonstrate the use of motion planners to bootstrap motor skills in two complex object manipulation scenarios with different policy representations: opening a drawer with a dynamic movement primitive representation, and closing a microwave door with a deep neural network policy. We also show how our method can bootstrap a motor skill for the challenging dynamic task of learning to hit a ball off a tee, where a kinematic plan based on treating the scene as static is insufficient to solve the task, but sufficient to bootstrap a more dynamic policy. In all three cases, our method is competitive with human-demonstrated initialization, and significantly out-performs starting with a random policy. This approach enables robots to to efficiently and autonomously learn motor policies for dynamic tasks without human demonstration.
Towards Interpretable Ensemble Learning for Image-based Malware Detection

Deep learning (DL) models for image-based malware detection have exhibited their capability in producing high prediction accuracy. But model interpretability is posing challenges to their widespread application in security and safety-critical application domains. This paper aims for designing an Interpretable Ensemble learning approach for image-based Malware Detection (IEMD). We first propose a Selective Deep Ensemble Learning-based (SDEL) detector and then design an Ensemble Deep Taylor Decomposition (EDTD) approach, which can give the pixel-level explanation to SDEL detector outputs. Furthermore, we develop formulas for calculating fidelity, robustness and expressiveness on pixel-level heatmaps in order to assess the quality of EDTD explanation. With EDTD explanation, we develop a novel Interpretable Dropout approach (IDrop), which establishes IEMD by training SDEL detector. Experiment results exhibit the better explanation of our EDTD than the previous explanation methods for image-based malware detection. Besides, experiment results indicate that IEMD achieves a higher detection accuracy up to 99.87% while exhibiting interpretability with high quality of prediction results. Moreover, experiment results indicate that IEMD interpretability increases with the increasing detection accuracy during the construction of IEMD. This consistency suggests that IDrop can mitigate the tradeoff between model interpretability and detection accuracy.
Tunable wettability of polymer films by partial engulfment of nanoparticles

A series of poly(methyl methacrylate) (PMMA) surfaces decorated by Cu nanoparticles (NP) with gradually varied morphology were prepared by high-pressure ${\mathrm{CO}}_{2}$ treatment at various time spans. Combining the characterizations of transmission electron microscopy (TEM) and atomic force microscopy (AFM), an accurate three-dimensional view of the morphology of the surfaces was presented. Subsequently, the wettability of the surfaces decreases near linearly with the increase of the apparent height of the decorating NPs in both static (static contact angle) and dynamic (contact angle hysteresis) aspects. The observed tendency contradicts to the Wenzel or Cassie-Baxter model and is explained by the contribution of nanomeniscus formed between the decorating NP and the flat substrate. The capillary pressure from this meniscus is negative and results in the increase of the contact angle with the apparent height $({H}_{N})$ of the Cu NPs decorating the PMMA surface. In addition, the effect of the coverage $({C}_{N})$ by NPs on the wettability can be explained on the same basis. Our experiment demonstrates the important influence of the nanomeniscus on the wettability, which is usually not taken into account. The results in this work provide a comprehensive understanding of how nanostructure affects the wettability of the decorated surfaces and shed light on how to obtain certain wettability through nanostructuring of the surface morphology.
Intensity correlation OCT -- a true classical equivalent of quantum OCT able to achieve up to 2-fold resolution improvement in standard OCT images

Quantum Optical Coherence Tomography (Q-OCT) uses quantum properties of light to provide several advantages over its classical counterpart, OCT: it achieves a twice better axial resolution with the same spectral bandwidth and it is immune to even orders of dispersion. Since these features are very sought-after in OCT imaging, many hardware and software techniques have been created to mimic the quantum behaviour of light and achieve these features using traditional OCT systems. The most recent, purely algorithmic scheme – an improved version of Intensity Correlation Spectral Domain OCT named ICA-SD-OCT showed even-order dispersion cancellation and reduction of artefacts. The true capabilities of this method were unfortunately severely undermined, both in terms of its relation to Q-OCT and in terms of its main performance parameters. In this work, we provide experimental demonstrations as well as numerical and analytical arguments to show that ICA-SD-OCT is a true classical equivalent of Q-OCT, more specifically its Fourier domain version, and therefore it enables a true two-fold axial resolution improvement. We believe that clarification of all the misconceptions about this very promising algorithm will highlight the great value of this method for OCT and consequently lead to its practical applications for resolutionand quality-enhanced OCT imaging. Introduction Optical Coherence Tomography (OCT) has become an indispensable tool in medicine due to its ability to visualise internal structures of biomedical objects on a micrometre scale and in a non-contact and non-invasive way. OCT is based on an interferometric measurement of the time of flight of light and can be performed by axially translating a mirror in a reference arm as in Time-domain OCT or by keeping the mirror fixed and measuring the spectrum of light as in Fourier-domain OCT. OCT’s axial resolution – so the resolution in the direction of light propagation in an object – is inversely proportional to the spectral bandwidth of the light source. Assuming a Gaussian profile, axial resolution, δz, is given by δz = 2ln2
Scalable Anytime Planning for Multi-Agent MDPs

We present a scalable tree search planning algorithm for large multi-agent sequential decision problems that require dynamic collaboration. Teams of agents need to coordinate decisions in many domains, but naive approaches fail due to the exponential growth of the joint action space with the number of agents. We circumvent this complexity through an anytime approach that allows us to trade computation for approximation quality and also dynamically coordinate actions. Our algorithm comprises three elements: online planning with Monte Carlo Tree Search (MCTS), factored representations of local agent interactions with coordination graphs, and the iterative Max-Plus method for joint action selection. We evaluate our approach on the benchmark SysAdmin domain with static coordination graphs and achieve comparable performance with much lower computation cost than our MCTS baselines. We also introduce a multi-drone delivery domain with dynamic, i.e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods. We provide an open-source implementation of our algorithm at https://github.com/JuliaPOMDP/FactoredValueMCTS.jl.
Space-Efficient Algorithms for Reachability in Directed Geometric Graphs

The problem of graph Reachability is to decide whether there is a path from one vertex to another in a given graph. In this paper, we study the Reachability problem on three distinct graph families - intersection graphs of Jordan regions, unit contact disk graphs (penny graphs), and chordal graphs. For each of these graph families, we present space-efficient algorithms for the Reachability problem. For intersection graphs of Jordan regions, we show how to obtain a"good"vertex separator in a space-efficient manner and use it to solve the Reachability in polynomial time and $O(m^{1/2}\log n)$ space, where $n$ is the number of Jordan regions, and $m$ is the total number of crossings among the regions. We use a similar approach for chordal graphs and obtain a polynomial-time and $O(m^{1/2}\log n)$ space algorithm, where $n$ and $m$ are the number of vertices and edges, respectively. However, we use a more involved technique for unit contact disk graphs (penny graphs) and obtain a better algorithm. We show that for every $\epsilon>0$, there exists a polynomial-time algorithm that can solve Reachability in an $n$ vertex directed penny graph, using $O(n^{1/4+\epsilon})$ space. We note that the method used to solve penny graphs does not extend naturally to the class of geometric intersection graphs that include arbitrary size cliques.
Publishing patterns reflect political polarization in news media

Digital news outlets rely on a variety of outside contributors, from freelance journalists, to political commentators, to executives and politicians. These external dependencies create a network among news outlets, traced along the contributors they share. Using connections between outlets, we demonstrate how contributors' publishing trajectories tend to align with outlet political leanings. We also show how polarized clustering of outlets translates to differences in the topics of news covered and the style and tone of articles published. In addition, we demonstrate how contributors who cross partisan divides tend to focus on less explicitly political topics. This work addresses an important gap in the media polarization literature, by highlighting how structural factors on the production side of news media create an ecosystem shaped by political leanings, independent of the priorities of any one person or organization.
MRI Images, Brain Lesions and Deep Learning

Medical brain image analysis is a necessary step in Computer Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.
Multiscale regression on unknown manifolds

We consider the regression problem of estimating functions on $ \mathbb{R}^D $ but supported on a $ d $-dimensional manifold $ \mathcal{M} ~~\subset \mathbb{R}^D $ with $ d \ll D $. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on $ \mathcal{M} $ at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension $ d $, instead of an unknown manifold embedded in $ \mathbb{R}^D $. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in $ D $ and exponential in $ d $. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees.
OpenHPS: An Open Source Hybrid Positioning System

Positioning systems and frameworks use various techniques to determine the position of an object. Some of the existing solutions combine different sensory data at the time of positioning in order to compute more accurate positions by reducing the error introduced by the used individual positioning techniques. We present OpenHPS, a generic hybrid positioning system implemented in TypeScript, that can not only reduce the error during tracking by fusing different sensory data based on different algorithms, but also also make use of combined tracking techniques when calibrating or training the system. In addition to a detailed discussion of the architecture, features and implementation of the extensible open source OpenHPS framework, we illustrate the use of our solution in a demonstrator application fusing different positioning techniques. While OpenHPS offers a number of positioning techniques, future extensions might integrate new positioning methods or algorithms and support additional levels of abstraction including symbolic locations.