Miracle-dz/UniFAR-FTUs huggingface dataset:
Translation interface modulation in NC-pyrrhotites: Direct imaging by TEM and a model toward understanding partially disordered structural states

Abstract The crystallographic complexity of “hexagonal” or “intermediate” pyrrhotites (Fe1-xS with 0.125 > x > 0.080) is a long-standing and challenging problem. Integral (e.g., 5C) and non-integral NC type structures found within this group at ambient temperatures are characterized by sharp but complicated electron diffraction patterns, which were found to be interpretable in terms of a translation interface modulation (TIM) superstructure. Transmission electron microscopy (TEM) dark-field images obtained using superstructure reflections show dense arrangements of stripes, which can be interpreted as arrays of closely spaced anti-phase domain boundaries (APB). The displacement vector at the interface is R = 1/8[001] of a metrically hexagonal 4C superstructure cell and the involved translations are solely confined to the Fe sublattice. The vacancy arrangement of the APB-free monoclinic 4C-pyrrhotite serves as a base of the TIM superstructure and therefore NC structures can be regarded as two super-imposed ordering phenomena relating to the arrangements of individual vacancies and APBs, respectively. APBs are chemically non-conservative and govern the higher Fe/S ratios of intermediate NC-pyrrhotites. If oriented strictly parallel to (001) the APBs can be regarded as completely filled Fe double layers within the 4C stacking sequence. However, direct imaging of APBs shows waviness and variable disorder on mesoscopic scales, yielding essentially aperiodic structures. A high degree of self-organization among APBs has been observed within apparent diffusion profiles around exsolved troilite lamellae and along interfaces with 4C-pyrrhotite, where complicated eightfold node arrangements occur. Our TEM observations indicate that all NC-type pyrrhotites can be treated by the TIM approach and that the concepts of polytypism and polysomatism in pyrrhotite are not fully capable in representing the observed structural complexities.
Polydopamine-functionalized polymer particles as templates for mineralization of hydroxyapatite: biomimetic and in vitro bioactivity

The use of polymer particles as organic templates for producing inorganic materials is an intriguing approach, because it offers the synthetic feasibility of organic–inorganic hybrid materials which may promise a variety of interesting applications. Here, inspired by mussel-adhesion phenomena in nature, we combined the use of the active surface of polydopamine (PDA) with biomimetic mineralization of hydroxyapatite (HAP) to synthesize a novel HAP-based, organic–inorganic hybrid material. Monodisperse micro-sized polystyrene (PS) particles were first prepared as inert polymer templates via dispersion polymerization, which, subsequently, were coated with PDA through the self-polymerization of dopamine in a weakly alkaline aqueous environment (pH = 8.5). This chemical modification activated the inert surface of PS particles to become bioactive. Thus, these synthesized PS/PDA composite particles were ideal templates for the biomimetic mineralization of HAP when they were incubated in a simulated body fluid (SBF) solution in aid of the biocompatible PDA layer. During the PDA-assisted biomimetic mineralization, the PDA layer interacted with the mineral ions introduced, and anchored the mineralization of HAP exclusively onto the surfaces of the composite particles. The detailed structures and morphologies of HAP-based hybrid materials (PS/PDA/HAP hybrid materials) were characterized. These HAP-based hybrid materials did not show a significant in vitro cytotoxicity against HeLa cells, indicating their potential biocapacity for drug delivery, protein and gene transfer, and as coating materials for bioimplants or as the injectable scaffold materials in bone tissue engineering for defect filling and regeneration.
Preparation and study of spectra characteristics of expandable graphite

In this work,the expandable graphite(EG) with high expanding volume has been successfully prepared by chemical oxidation method using graphite powders(≤30μm) as raw materials,H2SO4 and KMnO4 as oxidant,and H3PO4 as assisted intercalation agent.The micro-structure,morphology,spectral properties and thermal stability of the obtained products are characterized by XRD,Raman,SEM,EDS,FT-IR and TG-DTA.The experimental results show that the optimum volume ratio of H2SO4 and H3PO4 is 2∶1,while the swelling volume at 400℃ gets to 102ml/g.The elements such as P,S and Mn have been found in the layers of EG,therefore,there could be H3PO4,H2SO4,and dihydric phosphate sulphate containing Mn in the layers of EG.It is also demonstrated that the initial expanding temperature of EG is about 160℃ through TG-DTA curves.The ordered structure of graphite hasn't been absolutely destroyed after it is oxidized and intercalated,and part of EG sill has a good thermal stability under air atmosphere.In addition,the possible mechanism of EG formation and expansion has been discussed.
Detection of Carbamate Pesticides based on MWNTs-chitosan Modified Carbon Nanotube Electrode Biosensor with Immobilized Acetylcholinesterase

A stable and highly sensitive enzyme biosensor based on MWNTs-chitosan modified glassy carbon electrode (GCE) with efficient immobilization of acetylcholinesterase (AChE) has been devised for rapid determination of carbamate pesticides especially methomyl, aldicarb, carbaryl and fenobucarb. AChE was immobilized via layer-by-layer self-assembly modification technique. The study indicated that the inhibition of carbamate pesticides on the AChE activity was proportional to concentration of carbamate pesticides in the 10 -10 g·L -1 to 10 -3 g·L -1 concentration range and the limitation of detecting methomyl is up to 10 -12 g·L -1 and others is 10 -11 g∙L -1 . So the enzyme biosensor may be an alternative new tool for analyzing pesticide remains in
Unusual Metallic Microporous Boron Nitride Networks

Two metallic zeolite-like microporous BN crystals with all-sp2 bonding networks are predicted from an unbiased structure search based on the particle-swarm optimization (PSO) algorithm in combination with first-principles density functional theory (DFT) calculations. The stabilities of both microporous structures are confirmed via the phonon spectrum analysis and Born–Oppenheimer molecular dynamics simulations with temperature control at 1000 K. The unusual metallicity for the microporous BN allotropes stems from the delocalized p electrons along the axial direction of the micropores. Both microporous BN structures entail large surface areas, ranging from 3200 to 3400 m2/g. Moreover, the microporous BN structures show a preference toward organic molecule adsorption (e.g., the computed adsorption energy for CH3CH2OH is much more negative than that of H2O). This preferential adsorption can be exploited for water cleaning, as demonstrated recently using porous boron BN nanosheets (Nat. Commun. 2013, 4, 1777).
Influence of Dissolved Organic Matter on Sorption and Desorption of 1,2,4-trichlorobenzene and 1,2,4,5-tetrachlorobenzene onto Wood Char

ABSTRACT Sorption and desorption of 1, 2, 3-trichlorobenzene (TCB) and 1,2,4,5-tetrachlorobenzene (TeCB) onto wood char prepared from maple wood shavings heated at 500°C were studied in the presence of dissolved organic matter (DOM), including humic acid (HA), L-malic acid (L-MA), and peptone. Compared to TCB, TeCB exhibited more nonlinear and stronger sorption onto wood char. Nonlinearity of the sorption isotherms increased in the presence of DOM. The presence of HA enhanced the sorption capacity and desorption hysteresis of TCB and TeCB on wood char mainly due to the strong sorption of HA on wood char surface. Moreover, there were positive relations between Kd values of TCB and TeCB and the HA concentration (p < 0.01). In contrast, peptone reduced the sorption capacity and increased the sorption reversibility because of the partition of TCB and TeCB in peptone solution. L-MA at 50-200 mg·L−1 also leads to a decrease in sorption capacity and irreversibility attributed to solubilization, although the sorbed L-MA on the wood char surface can slightly increase TCB and TeCB sorption. At the same concentration, peptone leads to a higher decrease in TCB sorption than L-MA. Also, negative correlations were found between Kd values of TCB and TeCB and the L-MA and peptone concentration (p < 0.01). Our results may help to understand the different impacts of DOM on the transport and fate of halogenated aromatic hydrocarbons in aquatic environments polluted with chars.
Imaginary couplings in non-Hermitian coupled-mode theory: Effects on exceptional points of optical resonators

Exceptional point (EP) degeneracies in coupled cavities with gain and loss provide on-chip photonic devices with unconventional features and performance. However, such systems with realistic structures often miss the exact EPs even in simulation, and the mechanism of this EP disruption has yet to be thoroughly identified. Here, we extend the coupled-mode theory of one-dimensional non-Hermitian resonator arrays to study the effects of the imaginary part of the inter-cavity coupling, which is a second-order term and attributed to material amplification, absorption, and radiation. By taking an appropriate gauge for the model, we clarify that the imaginary coupling components have a symmetric form in the effective Hamiltonian and hence represent non-Hermiticity. These additional factors can lift the gain- and loss-based EP degeneracies. However, they are proportional to the sum of the imaginary permittivities for involved cavity pairs. Thus, when the amplification and absorption of adjacent cavities are balanced, their contribution to the imaginary coupling is canceled, and the EP singularity can be restored. Radiation-induced imaginary couplings measure the change in net radiation loss by the interference between cavity modes. Their impact on the EP can also be counteracted by small cavity resonance detuning even in loss-biased cases. We show and analyze eligible simulation examples based on photonic crystal nanocavities, and highlight design of an ideal EP degeneracy that is protected by generalized PT symmetry and induced by radiation.
INDIVIDUAL MEASUREMENT OCCASIONS

Multiple existing studies have developed multivariate growth models with nonlinear functional forms to explore joint development where two longitudinal records are associated over time. However, multiple repeated outcomes are not necessarily synchronous. Accordingly, it is of interest to investigate an association between two repeated variables on different occasions, for example, how a short-term change of one variable affects a long-term change of the other(s). One statistical tool for such analyses is longitudinal mediation models. In this study, we extend latent growth mediation models with linear trajectories (Cheong et al., 2003) and develop two models to evaluate mediational processes where the bilinear spline (i.e., the linear-linear piecewise) growth model is utilized to capture the change patterns. We define the mediational process as either the baseline covariate or the change of covariate influencing the change of the mediator, which, in turn, affects the change of the outcome. We present the proposed models by simulation studies. Our simulation studies demonstrate that the proposed mediational models can provide unbiased and accurate point estimates with target coverage probabilities with a 95% confidence interval. To illustrate modeling procedures, we analyze empirical longitudinal records of multiple disciplinary subjects, including reading, mathematics, and science test scores, from Grade K to Grade 5. The empirical analyses demonstrate that the proposed model can estimate covariates’ direct and indirect effects on the change of the outcome. Through the real-world data analyses, we also provide a set of feasible recommendations for empirical researchers. We also provide the corresponding code for the proposed models.
Self Training with Ensemble of Teacher Models

In order to train robust deep learning models, large amounts of labelled data is required. However, in the absence of such large repositories of labelled data, unlabeled data can be exploited for the same. Semi-Supervised learning aims to utilize such unlabeled data for training classification models. Recent progress of self-training based approaches have shown promise in this area, which leads to this study where we utilize an ensemble approach for the same. A by-product of any semi-supervised approach may be loss of calibration of the trained model especially in scenarios where unlabeled data may contain out-of-distribution samples, which leads to this investigation on how to adapt to such effects. Our proposed algorithm carefully avoids common pitfalls in utilizing unlabeled data and leads to a more accurate and calibrated supervised model compared to vanilla self-training based student-teacher algorithms. We perform several experiments on the popular STL-10 database followed by an extensive analysis of our approach and study its effects on model accuracy and calibration.
A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO$_2$ Monitoring Using Smartphone Cameras

Blood oxygen saturation (SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula>) is an important indicator forpulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula>. Most of these works are contact-based, requiring users to cover a phone’s camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
A proof theoretic basis for relational semantics

Logic has proved essential for formally modeling software based systems. Such formal descriptions, frequently called specifications, have served not only as requirements documentation and formalisation, but also for providing the mathematical foundations for their analysis and the development of automated reasoning tools. Logic is usually studied in terms of its two inherent aspects: syntax and semantics. The relevance of the latter resides in the fact that producing logical descriptions of real-world phenomena, requires people to agree on how such descriptions are to be interpreted and understood by human beings, so that systems can be built with confidence in accordance with their specification. On the more practical side, the metalogical relation between syntax and semantics, determines important aspects of the conclusions one can draw from the application of certain analysis techniques, like model checking. Abstract model theory (i.e., the mathematical perspective on semantics of logical languages) is of little practical value to software engineering endeavours. From our point of view, values (those that can be assigned to constants and variables) should not be just points in a platonic domain of interpretation, but elements that can be named by means of terms over the signature of the specification. In a nutshell, we are not interested in properties that require any semantic information not representable using the available syntax. In this paper we present a framework supporting the proof theoretical formalisation of classes of relational models for behavioural logical languages, whose domains of discourse are guaranteed to be formed exclusively by nameable values.
Complexity-Optimized Sparse Bayesian Learning for Scalable Classification Tasks

Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic model with very competitive generalization. However, SBL needs to invert a big covariance matrix with complexity $O(M^3)$ (M: feature size) for updating the regularization priors, making it difficult for problems with high dimensional feature space or large data size. As it may easily suffer from the memory overflow issue in such problems. This paper addresses this issue with a newly proposed diagonal Quasi-Newton (DQN) method for SBL called DQN-SBL where the inversion of big covariance matrix is ignored so that the complexity is reduced to $O(M)$. The DQN-SBL is thoroughly evaluated for non linear and linear classifications with various benchmarks of different sizes. Experimental results verify that DQN-SBL receives competitive generalization with a very sparse model and scales well to large-scale problems.
Controlled invariant sets: implicit closed-form representations and applications

We revisit the problem of computing (robust) controlled invariant sets for discrete-time linear systems. Departing from previous approaches, we consider implicit, rather than explicit, representations for controlled invariant sets. Moreover, by considering such representations in the space of states and finite input sequences we obtain closed-form expressions for controlled invariant sets. An immediate advantage is the ability to handle high-dimensional systems since the closed-form expression is computed in a single step rather than iteratively. To validate the proposed method, we present thorough case studies illustrating that in safety-critical scenarios the implicit representation suffices in place of the explicit invariant set. The proposed method is complete in the absence of disturbances, and we provide a weak completeness result when disturbances are present.
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision

Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained language models (PLMs). The pre-training tasks for PLMs are not question-answering or MRC-based tasks, making existing PLMs unable to be directly used for unsupervised MRC. Specifically, MRC aims to spot an accurate answer span from the given document, but PLMs focus on token filling in sentences. In this paper, we propose a new framework for unsupervised MRC. Firstly, we propose to learn to spot answer spans in documents via self-supervised learning, by designing a self-supervision pretext task for MRC - Spotting-MLM. Solving this task requires capturing deep interactions between sentences in documents. Secondly, we apply a simple sentence rewriting strategy in the inference stage to alleviate the expression mismatch between questions and documents. Experiments show that our method achieves a new state-of-the-art performance for unsupervised MRC.
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images

In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation. Training such dialogue systems generally requires a large-scale dataset consisting of multi-turn dialogues that involve images, but such datasets rarely exist. In response, this paper proposes a 45k multi-modal dialogue dataset created with minimal human intervention. Our method to create such a dataset consists of (1) preparing and pre-processing text dialogue datasets, (2) creating image-mixed dialogues by using a text-to-image replacement technique, and (3) employing a contextual-similarity-based filtering step to ensure the contextual coherence of the dataset. To evaluate the validity of our dataset, we devise a simple retrieval model for dialogue sentence prediction tasks. Automatic metrics and human evaluation results on such tasks show that our dataset can be effectively used as training data for multi-modal dialogue systems which require an understanding of images and text in a context-aware manner. Our dataset and generation code is available at https://github.com/shh1574/multi-modal-dialogue-dataset.
Topology-Guided Path Planning for Reliable Visual Navigation of MAVs

Visual navigation has been widely used for state estimation of micro aerial vehicles (MAVs). For stable visual navigation, MAVs should generate perception-aware paths which guarantee enough visible landmarks. Many previous works on perception-aware path planning focused on sampling-based planners. However, they may suffer from sample inefficiency, which leads to computational burden for finding a global optimal path. To address this issue, we suggest a perception-aware path planner which utilizes topological information of environments. Since the topological class of a path and visible landmarks during traveling the path are closely related, the proposed algorithm checks distinctive topological classes to choose the class with abundant visual information. Topological graph is extracted from the generalized Voronoi diagram of the environment and initial paths with different topological classes are found. To evaluate the perception quality of the classes, we divide the initial path into discrete segments where the points in each segment share similar visual information. The optimal class with high perception quality is selected, and a graph-based planner is utilized to generate path within the class. With simulations and real-world experiments, we confirmed that the proposed method could guarantee accurate visual navigation compared with the perception-agnostic method while showing improved computational efficiency than the sampling-based perception-aware planner.
Topologically protected two-fluid edge states

Edge states reveal the nontrivial topology of energy band in the bulk. As localized states at boundaries, many-body edge states may obey a special symmetry that is broken in the bulk. When local particle-particle interaction is induced, they may support a particular property. We consider an extended two-dimensional Su-Schrieffer-Heeger Hubbard model and examine the appearance of $\eta$-pairing states, which are excited eigenstates related to superconductivity. In the absence of Hubbard interaction, the energy band is characterized by topologically invariant polarization in association with edge states. In the presence of on-site Hubbard interaction, $\eta$-pairing edge states appear in the topologically nontrivial phase, resulting in the condensation of pairs at the boundary. In addition, as Hamiltonian eigenstates, the edge states contain paired fermions and unpaired fermions. Neither affects the other; they act as two-fluid states. From numerical simulations of many-body scattering processes, a clear manifestation and experimental detection scheme of topologically protected two-fluid edge states are provided.