Miracle-dz/UniFAR-FTUs huggingface dataset:
Bracket Bond Strength Effects of Incorporation of NovaMin into an Orthodontic Bonding Resin

BRACKET BOND STRENGTH EFFECTS OF INCORPORATION OF NOVAMIN INTO AN ORTHODONTIC BONDING RESIN Jeffrey D. Waterhouse, D.D.S. Marquette University Despite advances in caries research and the development of protect ive mechanisms and materials, orthodontic white spot decalcification remains a significa t problem. Increased plaque buildup around orthodontic appliances, combined with inadequate hygiene and a susceptib le patient, can lead to unesthetic demineralized lesions in enamel. Relatively new to the arena of caries prevention, NovaMin, a bioactive g lass, has shown some promise in areas of remineralization of early lesions, protection ag ainst acidic challenge, and has demonstrated antiplaque properties. This study explored a new applic ation of the material, incorporation into an orthodontic bonding composite resin. Objectives of the study were: to determine if the experimental resin could release ionic pr ecursors to enamel formation in solution, and the effects on bracket shear bond strength. For ion release, NovaMin concentrations of 7.5, 15 and 22.5 wt% were incorporate d into a commercially available resin, TransBond LV. Resin discs were immer sed in 5 mL of both deionized water and lactic acid (pH~5). Calcium ion concentrations wer e m asured using a calcium-selective electrode. Solutions were replaced every 24 hours f or the first week, then remained unchanged for an additional 5 weeks. Controls were the resin wi thout the modification and solutions without a resin disc. Concentrations declined rapidly over t he fi st two days, reaching levels below that of the control solutions. Without solution repl ac ment, a significant increase in calcium ion concentration was observed for NovaMin levels of 15 and 22.5%. For bond strength, 39 extracted human premolar teeth were tested in three groups of 13. Standard twin brackets were bonded with either TransBond LV or the same res in with 15 or 22.5% NovaMin incorporated. Shear bond strength was tested using a universal testi ng machine. ARI scores were also obtained. There was no significant difference in mean s hear bond strength between the three groups (TransBond LV (15.13 ± 4.18MPa), TransBond LV + 15% Nov aMin (13.55 ± 2.84MPa), and TransBond LV + 22.5% NovaMin (13.27 ± 4.34MPa)). There was, however, a significant difference in ARI scores between the group without the modification and both NovaMin groups, with the as-provided TransBond LV showing less residual adhesi ve on the enamel after debond.
All-Weather Deep Outdoor Lighting Estimation

We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LDR panorama, and regress the parameters of the Lalonde-Mathews outdoor illumination model. This model is trained such that it a) reconstructs the appearance of the sky, and b) renders the appearance of objects lit by this illumination. We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network. We demonstrate, via extensive experiments, that both our panorama and singe image networks outperform the state of the art, and unlike prior work, are able to handle weather conditions ranging from fully sunny to overcast skies.
Transition to the Maternal Role in Women with Addiction Problems to Psychoactive Substances-Conceptual Map

The transition to motherhood is seen as a phenomenon where are interwoven multiple variables, where the experience of being a mother can be so significant andfull of pleasure, as associated with suffering and unhappiness processes.If we associate this phenomenon, itself multidimensional, the problem of adding the psychoactive substances, the spectrum of its complexity increases, whereas the mother experiences a set of changes associated with pregnancy, childbirth and postpartum, at the same time with their behaviourto addition to psychoactive substances. Looking for the integration of the concepts involved in the problems of transition to the maternal role in women with addition problems to psychoactive substances, we used the conceptual map as a basic tool for the organization of scientific knowledge in the field of study. We conclude from its analysis, that women with addition problems to psychoactive substances, in the process of transition to the maternal role, experience adjustment processes of adaptation and transition, where their past contexts, present and future perspectives converge maternal identity, as the individual characteristics of each woman (self-esteem, personal integrity and self-confidence).
Crown-structured optical harmonics

At levels of laser intensity below threshold for multiphoton ionization, the parametric generation of optical harmonics in gases and other isotropic media is subject to selection rules with origins in angular momentum conservation. The recently developed optics of vector polarization modes provides an unprecedented opportunity to exploit these principles in the production of high harmonic beams with distinctive forms of transverse intensity profile, comprising discrete sub-wavelength filaments in crown-like arrays. A detailed analysis of the fundamental electrodynamics elicits the mechanism, and delivers results illustrating the transverse structures and spatial dimensions of harmonic output that can be achieved.
A Progress Report on Bitext Parsing

Recent work has shown that a reranking approach can be used to improve the syntactic parsing of a sentence given a translation of that sentence, an automatically generated parse of that translation, and a word alignment between them. Such approaches rely on reducing syntactic divergence as measured using overlapping feature functions capturing different types of divergence. These feature functions are combined in a log-linear model which is trained to maximize parsing accuracy. We conduct our research in the framework of N-best parse reranking. However, we apply reranking to bitext and add only features based on syntactic projection from German to English. The system takes as input (i) English sentences with a list of automatically generated syntactic parses, (ii) a translation of the English sentences into German, (iii) an automatically generated parse of the German translation, and (iv) an automatically generated word alignment between the original sentences and the translations. The system is trained using the gold standard trees of 3718 sentences from the Penn English treebank that have been translated into German. We achieve an improvement in F1 on held out test data and this improvement is statistically significant.
Continuity results for degenerate diffusion equations with $L^{p}_t L^{q}_{x}$ drifts

In this paper, we study local uniform continuity of nonnegative weak solutions to degenerate diffusion-drift equations in the form \[ u_{t} = \Delta u^{m} + \nabla\cdot \left( B (x,t) \, u\right), \quad \text{for } m \geq 1 \] assuming a vector field $B \in L^{p}_t L^{q}_{x}$. Regarding local Holder continuity, we provide a sharp condition on $p$ and $q$, which is referred to as the subcritical region. In the critical region, the divergence-free condition is essential to providing uniform continuity which depends on the modulus continuity of $B$.
Compressive Hyperspherical Energy Minimization

Recent work on minimum hyperspherical energy (MHE) has demonstrated its potential in regularizing neural networks and improving their generalization. MHE was inspired by the Thomson problem in physics, where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy. Despite the practical effectiveness, MHE suffers from local minima as their number increases dramatically in high dimensions, limiting MHE from unleashing its full potential in improving network generalization. To address this issue, we propose compressive minimum hyperspherical energy (CoMHE) as an alternative regularization for neural networks. Specifically, CoMHE utilizes a projection mapping to reduce the dimensionality of neurons and minimizes their hyperspherical energy. According to different constructions for the projection matrix, we propose two major variants: random projection CoMHE and angle-preserving CoMHE. Furthermore, we provide theoretical insights to justify its effectiveness. We show that CoMHE consistently outperforms MHE by a significant margin in comprehensive experiments, and demonstrate its diverse applications to a variety of tasks such as image recognition and point cloud recognition.
In silico evidence that protein unfolding is as a precursor of the protein aggregation.

We present a computational study on the folding and aggregation of proteins in an aqueous environment, as a function of its concentration. We show how the increase of the concentration of individual protein species can induce a partial unfolding of the native conformation without the occurrence of aggregates. A further increment of the protein concentration results in the complete loss of the folded structures and induces the formation of protein aggregates. We discuss the effect of the protein interface on the water fluctuations in the protein hydration shell and their relevance in the protein-protein interaction.
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function

We present an algorithm based on the \emph{Optimism in the Face of Uncertainty} (OFU) principle which is able to learn Reinforcement Learning (RL) modeled by Markov decision process (MDP) with finite state-action space efficiently. By evaluating the state-pair difference of the optimal bias function $h^{*}$, the proposed algorithm achieves a regret bound of $\tilde{O}(\sqrt{SAHT})$\footnote{The symbol $\tilde{O}$ means $O$ with log factors ignored. } for MDP with $S$ states and $A$ actions, in the case that an upper bound $H$ on the span of $h^{*}$, i.e., $sp(h^{*})$ is known. This result outperforms the best previous regret bounds $\tilde{O}(S\sqrt{AHT}) $\citep{fruit2019improved} by a factor of $\sqrt{S}$. Furthermore, this regret bound matches the lower bound of $\Omega(\sqrt{SAHT}) $\citep{jaksch2010near} up to a logarithmic factor. As a consequence, we show that there is a near optimal regret bound of $\tilde{O}(\sqrt{SADT})$ for MDPs with a finite diameter $D$ compared to the lower bound of $\Omega(\sqrt{SADT}) $\citep{jaksch2010near}.
Real-time Attention Based Look-alike Model for Recommender System

Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Matthew effect" becomes increasingly evident. While the head contents get more and more popular, many competitive long-tail contents are difficult to achieve timely exposure because of lacking behavior features. This issue has badly impacted the quality and diversity of recommendations. To solve this problem, look-alike algorithm is a good choice to extend audience for high quality long-tail contents. But the traditional look-alike models which widely used in online advertising are not suitable for recommender systems because of the strict requirement of both real-time and effectiveness. This paper introduces a real-time attention based look-alike model (RALM) for recommender systems, which tackles the challenge of conflict between real-time and effectiveness. RALM realizes real-time look-alike audience extension benefiting from seeds-to-user similarity prediction and improves the effectiveness through optimizing user representation learning and look-alike learning modeling. For user representation learning, we propose a novel neural network structure named attention merge layer to replace the concatenation layer, which significantly improves the expressive ability of multi-fields feature learning. On the other hand, considering the various members of seeds, we design global attention unit and local attention unit to learn robust and adaptive seeds representation with respect to a certain target user. At last, we introduce seeds clustering mechanism which not only reduces the time complexity of attention units prediction but also minimizes the loss of seeds information at the same time. According to our experiments, RALM shows superior effectiveness and performance than popular look-alike models. RALM has been successfully deployed in "Top Stories" Recommender System of WeChat, leading to great improvement on diversity and quality of recommendations. As far as we know, this is the first real-time look-alike model applied in recommender systems.
The BETHY/JSBACH Carbon Cycle Data Assimilation System: experiences and challenges

We present the concept of the Carbon Cycle Data Assimilation System and describe its evolution over the last two decades from an assimilation system around a simple diagnostic model of the terrestrial biosphere to a system for the calibration and initialization of the land component of a comprehensive Earth system model. We critically review the capability of this modeling framework to integrate multiple data streams, to assess their mutual consistency and with the model, to reduce uncertainties in the simulation of the terrestrial carbon cycle, to provide, in a traceable manner, reanalysis products with documented uncertainty, and to assist the design of the observational network. We highlight some of the challenges we met and experience we gained, give recommendations for operating the system, and suggest directions for future development.
Discovery of a 2.8 s Pulsar in a 2 Day Orbit High-mass X-Ray Binary Powering the Ultraluminous X-Ray Source ULX-7 in M51

We discovered 2.8 s pulsations in the X-ray emission of the ultraluminous X-ray source (ULX) M51 ULX-7 within the UNSEeN project, which was designed to hunt for new pulsating ULXs (PULXs) with XMM-Newton. The pulse shape is sinusoidal, and large variations of its amplitude were observed even within single exposures (pulsed fraction from less than 5% to 20%). Source M51 ULX-7 is variable, generally observed at an X-ray luminosity between 1039 and 1040 erg s−1, located in the outskirts of the spiral galaxy M51a at a distance of 8.6 Mpc. According to our analysis, the X-ray pulsar orbits in a 2 day binary with a projected semimajor axis 28 lt-s. For a neutron star (NS) of 1.4 M⊙, this implies a lower limit on the companion mass of 8 M⊙, placing the system hosting M51 ULX-7 in the high-mass X-ray binary class. The barycentric pulse period decreased by ≃0.4 ms in the 31 days spanned by our 2018 May–June observations, corresponding to a spin-up rate . In an archival 2005 XMM-Newton exposure, we measured a spin period of ∼3.3 s, indicating a secular spin-up of , a value in the range of other known PULXs. Our findings suggest that the system consists of a massive donor, possibly an OB giant or supergiant, and a moderately magnetic (dipole field component in the range 1012 G G) accreting NS with weakly beamed emission ( ).
Approximation of Search Times for On-street Parking Based on Supply and Demand.

We propose a method for approximating the probability p({\tau}, n) of searching for on-street parking longer than time {\tau} from the start of a parking search near a given destination n, based on high-resolution maps of parking demand and supply in a city. We verify the method by comparing its outcomes to the estimates obtained with an agent-based model of on-street parking search. As a practical example, we construct maps of cruising time for the Israeli city of Bat Yam, and demonstrate that despite the low overall demand-to-supply ratio of 0.65, excessive demand in the city center results in parking searches of longer than 10 minutes. We discuss the application of the proposed approach for urban planning.
High-Resolution Markov State Models for the Dynamics of Trp-Cage Miniprotein Constructed over Slow Folding Modes Identified by State-Free Reversible VAMPnets.

State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of learning the leading eigenfunctions of the transfer operator of a dynamical system from trajectory data. In molecular dynamics simulations, these data-driven collective variables (CVs) capture the slowest modes of the dynamics and are useful for enhanced sampling and free energy estimation. In this work, we employ SRV coordinates as a feature set for Markov state model (MSM) construction. Compared to the current state of the art, MSMs constructed from SRV coordinates are more robust to the choice of input features, exhibit faster implied timescale convergence, and permit the use of shorter lagtimes to construct higher kinetic resolution models. We apply this methodology to study the folding kinetics and conformational landscape of the Trp-cage miniprotein. Folding and unfolding mean first passage times are in good agreement with prior literature, and a nine macrostate model is presented. The unfolded ensemble comprises a central kinetic hub with interconversions to several metastable unfolded conformations and which serves as the gateway to the folded ensemble. The folded ensemble comprises the native state, a partially unfolded intermediate "loop" state, and a previously unreported short-lived intermediate that we were able to resolve due to the high time-resolution of the SRV-MSM. We propose SRVs as an excellent candidate for integration into modern MSM construction pipelines.
Computation of Bioelectric Signals for Medical Diagnostics : A Review

Bioelectrical signals are the electric stimulation of living tissue generated by biological processes. It includes the Electrocardiogram (ECG), Electroencephalogram (EEG), Magneto encephalogram (MEG), Galvanic Skin Response (GSR) and Electromyogram (ECG). These bioelectric signals are generated from heart, brain, skin and muscle, respectively and are used for diagnosing person’s state of health. The advancement in computing technology provides us an opportunity to analyze the bioelectrical signal automatically and detect any arrhythmia found in a particular organ or tissue. This paper presents a review of computational procedures employed for automatic analysis of bioelectrical signals for medical diagnosis. The ECG signals are mainly used for arrhythmia detection. Automatic analysis of the ECG and its interpretation can detect different heart arrhythmias like normal sinus rhythm, bundle branch block, sinus syndrome, ventricular and atrial fibrillations. The EEG is found to be an important attribute for autism, epilepsy and alzheimer detections. The MEG signals are also used as a diagnostic tool for Alzheimer detection. The GSR is primarily used to examine the function of nervous system. Now a days, wireless GSR healthcare system is used for the stress management. The EMG is proved as a tool for rehabilitation and sports biomechanics. It is used to detect myopathy which is a kind of muscle disease. We critically evaluate different computation methods that are developed in the past for automatic analysis of bioelectrical signals and validate their effectiveness to use as an expert for diagnosing the arrhythmias.
Impact of electron capture rates for nuclei far from stability on core-collapse supernovae

The impact of electron-capture (EC) cross sections on neutron-rich nuclei on the dynamics of core-collapse during infall and early post-bounce is studied performing spherically symmetric simulations in general relativity using a multigroup scheme for neutrino transport and full nuclear distributions in extended nuclear statistical equilibrium models. We thereby vary the prescription for EC rates on individual nuclei, the nuclear interaction for the EoS, the mass model for the nuclear statistical equilibrium distribution and the progenitor model. In agreement with previous works, we show that the individual EC rates are the most important source of uncertainty in the simulations, while the other inputs only marginally influence the results. A recently proposed analytic formula to extrapolate microscopic results on stable nuclei for EC rates to the neutron rich region, with a functional form motivated by nuclear-structure data and parameters fitted from large scale shell model calculations, is shown to lead to a sizable (16%) reduction of the electron fraction at bounce compared to more primitive prescriptions for the rates, leading to smaller inner core masses and slower shock propagation. We show that the EC process involves ≈ 130 different nuclear species around 86 Kr mainly in the N = 50 shell closure region, and establish a list of the most important nuclei to be studied in order to constrain the global rates.
A new identity for the sum of products of the generalized hypergeometric functions

Reduction formulas for sums of products of hypergeometric functions can be traced back to Euler. This topic has an intimate connection to summation and transformation formulas, contiguous relations and algebraic properties of the (generalized) hypergeometric differential equation. Over recent several years, important discoveries have been made in this subject by Gorelov, Ebisu, Beukers and Jouhet and Feng, Kuznetsov and Yang. In this paper, we give a generalization of Feng, Kuznetsov and Yang identity covering also Ebisu's and Gorelov's formulas as particular cases.
Markovian Model for Broadcast in Wireless Body Area Networks

Wireless body area networks became recently a vast field of investigation. A large amount of research in this field is dedicated to the evaluation of various communication protocols, e.g., broadcast or convergecast, against human body mobility. Most of the time this evaluation is done via simulations and in many situations only synthetic data is used for the human body mobility. In this paper we propose for the first time in Wireless Body Area Networks a Markovian analytical model specifically designed for WBAN networks. The main objective of the model is to evaluate the efficiency of a multi-hop transmission in the case of a diffusion-based broadcast protocol, with respect to various performance parameters (e.g., cover probability, average cover number, hitting probability or average cover time). We validate our model by comparing its results to simulation and show its accuracy. Finally, but not least, we show how our model can be used to analytically evaluate the trade-off between transmission power and redundancy, when the same message is broadcasted several times in order to increase the broadcast reliability while maintaining a low transmission power.
Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites

We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.