Miracle-dz/UniFAR-FTUs huggingface dataset:
0 N ov 2 01 8 A BAYESIAN NONPARAMETRICS BASED ROBUST PARTICLE FILTER ALGORITHM

This paper is concerned with the online estimation of a nonlinear dynamic system from a series of noisy measurements. The focus is on cases wherein outliers are present in-between normal noises. We assume that the outliers follow an unknown generating mechanism which deviates from that of normal noises, and then model the outliers using a Bayesian nonparametric model called Dirichlet process mixture (DPM). A sequential particle-based algorithm is derived for posterior inference for the outlier model as well as the state of the system to be estimated. The resulting algorithm is termed DPM based robust PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to “speak for itself” to determine the complexity and structure of the outlier model. Simulation results show that it performs remarkably better than two state-of-the-art methods especially when outliers appear frequently along time.
My title

Efficient generation of spin polarization is very important for spintronics and quantum computation. We propose a new mechanism of Chiral Phonon activated Spin Seebeck (CPASS) effect in nonmagnetic materials in the absence of magnetic field and spin-orbital coupling. Owing to nonequilibrium distribution of chiral phonons under temperature gradient, we investigate the resulted chiral phonon activated spin polarization by solving the Boltzmann transport equation. The CPASS coefficients, with both band and phonon-drag contributions, exhibit linear dependence on the temperature gradient. The above two contributions are opposite for negative charge carriers and their relative magnitude is tunable by the chemical potential modulation. The CPASS effect where the spin accumulation is induced by chiral phonons, provides opportunities for the exploration of advanced spintronic devices based on chiral materials even in the absence of magnetic order and spin-orbit coupling.
The antithrombotic management of atrial fibrillation

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
CERN-PH-TH / 2011-206 SHEP-11-22 The decay B s → μ + μ − : updated SUSY constraints and prospects

We perform a study of the impact of the recently released limits on BR(Bs → μ+μ−) by LHCb and CMS on several SUSY models. We show that the obtained constraints can be superior to those which are derived from direct searches for SUSY particles in some scenarios, and the use of a double ratio of purely leptonic decays involving Bs → μ+μ− can further strengthen such constraints. We also discuss the experimental sensitivity and prospects for observation of Bs → μ+μ− during the √ s = 7 TeV run of the LHC, and its potential implications. ∗Electronic address: a.g. akeroyd@soton.ac. uk †Electronic address: mahmoudi@in2p3.fr ‡Electronic address: Diego.Martinez. Santos@cern.ch 1 ar X iv :1 10 8. 30 18 v2 [ he pph ] 1 2 Ja n 20 12
N ov 2 01 5 Investigation of the magnetic structure and crystal field states of pyrochlore antiferromagnet Nd 2 Zr

J. Xu, 2, ∗ V. K. Anand, † A. K. Bera, 3 M. Frontzek, D. L. Abernathy, N. Casati, K. Siemensmeyer, and B. Lake 2, ‡ Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Hahn-Meitner Platz 1, D-14109 Berlin, Germany Institut für Festkörperphysik, Technische Universität Berlin, Hardenbergstrae 36, D-10623 Berlin, Germany Solid State Physics Division, Bhabha Atomic Research Centre, Mumbai 400085, India Paul Scherrer Institute, 5232 Villigen PSI, Switzerland Instrument and Source Division, Neutron Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA (Dated: November 10, 2015)
Habits of fluid and electrolytes intake in elite athletes

Introduction: Dehydration develops when the body fluid losses exceed fluid intake. It may occur during exercise, heat stress, restricted fluid intake, or any combination of these. Marginal dehydration (loss of > 2% body weight) can compromise aerobic exercise performance, particularly in hot weather conditions, and may disturb fluid and electrolyte balance. The aim of the study was to determine the quantity, type and dynamic of fluid intake during athletic performance in endurance sports (football and basketball) in two age categories: juniors (under the age of 18) and seniors (over the age of 18). Methods: Research included 100 athletes playing in Premier League in Bosnia-Herzegovina. We formed groups by sport type (football and basketball) and age (<18 and ≥18 years). Questionnaire with questions about the fluid intake habits was used for data collection. Results: There were 53 football players and 47 basketball players. All the participants were male. Average age of the participants was 19.3 ± 4.58. Habit of weighing before and after training was present in less than 44% of players among all the groups. Seniors were more frequently measuring their weight compared to junior players (p=0.01). Basketball players and players younger than 18 years were most frequently taking more than 2L of water per day. Most of the players, regardless of sport type or age group were not taking at least ½ L of isotonic fluid before the training. Signs of dehydration were more frequently observed in players under 18 years old, with most frequent sign being dry throat and sudden fatigue.Conclusion: Water and electrolytes intake before, during and after training of the athletes were inadequate regardless of type of sports and the age of athletes.
Recent developments in observation and simulation

A number of observational and theoretical aspects of solar magnetoconvection are considered in this review. We discuss recent developments in our understanding of the smallscale structure of the magnetic field on the solar surface and its interaction with convective flows, which is at the centre of current research. Topics range from plage areas in active regions over the magnetic network shaped by supergranulation to the ubiquituous ‘turbulent’ internetwork fields. On the theoretical side, we focus upon magnetic field generation by small-scale dynamo action.
Fe b 20 09 IMPERIAL-TP-2009-CH-01 QMUL-PH-09-02 Non-geometric backgrounds , doubled geometry and generalised T-duality C

String backgrounds with a local torus fibration such as T-folds are naturally formulated in a doubled formalism in which the torus fibres are doubled to include dual coordinates conjugate to winding number. Here we formulate and explore a generalisation of this construction in which all coordinates are doubled, so that the doubled space is a twisted torus, i.e. a compact space constructed from identifying a group manifold under a discrete subgroup. This incorporates reductions with duality twists, T-folds and a class of flux compactifications, together with the non-geometric backgrounds expected to arise from these through T-duality. It also incorporates backgrounds that are not even locally geometric, and suggests a generalisation of T-duality to a more general context. We discuss the effective field theory arising from such an internal sector, give a world-sheet sigma model formulation of string theory on such backgrounds and illustrate our discussion with detailed examples. c.hull@imperial.ac. uk rreidedwards@gmail.com (Visiting Researcher)
qu an tph ] 1 5 M ar 2 00 9 Diffusive spin transport ∗ Cord

Information to be stored and transported requires physical carriers. The quantum bit of information (qubit) can for instance be realised as the spin 1 2 degree of freedom of a massive particle like an electron or as the spin 1 polarisation of a massless photon. In this lecture, I first use irreducible representations of the rotation group to characterise the spin dynamics in a least redundant manner. Specifically, I describe the decoherence dynamics of an arbitrary spin S coupled to a randomly fluctuating magnetic field in the Liouville space formalism. Secondly, I discuss the diffusive dynamics of the particle’s position in space due to the presence of randomly placed impurities. Combining these two dynamics yields a coherent, unified picture of diffusive spin transport, as applicable to mesoscopic electronic devices or photons propagating in cold atomic clouds.
Integral equations , quasi-Monte Carlo methods and risk modelling Dedicated to the 80 th anniversary of Ian

We survey a QMC approach to integral equations and develop some new applications to risk modeling. In particular, a rigorous error bound derived from KoksmaHlawka type inequalities is achieved for certain expectations related to the probability of ruin in Markovian models. The method is based on a new concept of isotropic discrepancy and its applications to numerical integration. The theoretical results are complemented by numerical examples and computations.
Can Ω Λ remain constant to late times ?

Motivated by the cosmological constant and the coincidence problems, we consider a cosmological model where the dark sectors are interacting together through a phenomenological decay law 9 ρΛ “ QρnΛ in a FRW spacetime with spatial curvature. We show that the only value of n for which the late-time matter energy density to dark energy density ratio (rm “ ρm{ρΛ) is constant (which could provide an explanation to the coincidence problem) is n “ 3{2. For each value of Q, there are two distinct solutions. One of them involves a spatial curvature approaching zero at late times (ρk « 0) and is stable when the interaction is weaker than a critical value Q0 “ ́ a 32πG{c2. The other one allows for a non-negligible spatial curvature (ρk ff 0) at late times and is stable when the interaction is stronger than Q0. We constrain the model parameters using various observational data (SNeIa, GRB, CMB, BAO, OHD). The limits obtained on the parameters exclude the regions where the cosmological constant problem is significantly ameliorated and do not allow for a completely satisfying explanation for the coincidence problem. PACS numbers: 95.35. +d 95.36. +x 98.80. Es Submitted to: Class. Quantum Grav. Can ΩΛ remain constant to late times? 2
Tell Me Something New: A New Framework for Asynchronous Parallel Learning

We present a novel approach for parallel computation in the context of machine learning that we call “Tell Me Something New” (TMSN). This approach involves a set of independent workers that use broadcast to update each other when they observe “something new”. TMSN does not require synchronization or a head node and is highly resilient against failing machines or laggards. We demonstrate the utility TMSN by applying it to learning boosted trees. We show that our implementation is 10 times faster than XGBoost [1] and LightGBM [2] on the splice-site prediction problem [3, 4].
Emotion Recognition from Human Speech: Emphasizing on Relevant Feature Selection and Majority Voting Technique

This paper proposes an approach to detect emotion from human speech employing majority voting technique over several machine learning techniques. The contribution of this work is in two folds: firstly it selects those features of speech which is most promising for classification and secondly it uses the majority voting technique that selects the exact class of emotion. Here, majority voting technique has been applied over Neural Network (NN), Decision Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input vector of NN, DT, SVM and KNN consists of various acoustic and prosodic features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech signal many feature have been extracted and only promising features have been selected. To consider a feature as promising, Fast Correlation based feature selection (FCBF) and Fisher score algorithms have been used and only those features are selected which are highly ranked by both of them. The proposed approach has been tested on Berlin dataset of emotional speech [3] and Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows that majority voting technique attains better accuracy over individual machine learning techniques. The employment of the proposed approach can effectively recognize the emotion of human beings in case of social robot, intelligent chat client, call-center of a company etc.
Lower bounds on the Deterministic and Quantum Communication Complexity of Hamming Distance

Alice and Bob want to know if two strings of length n are almost equal. That is, do they differ on \textit{at most} a bits? Let 0\leq a\leq n-1. We show that any deterministic protocol, as well as any error-free quantum protocol (C* version), for this problem requires at least n-2 bits of communication. We show the same bounds for the problem of determining if two strings differ in exactly a bits. We also prove a lower bound of n/2-1 for error-free Q* quantum protocols. Our results are obtained by lower-bounding the ranks of the appropriate matrices.
Trouble with the Curve: Predicting Future MLB Players Using Scouting Reports

In baseball, a scouting report profiles a player's characteristics and traits, usually intended for use in player valuation. This work presents a first-of-its-kind dataset of almost 10,000 scouting reports for minor league, international, and draft prospects. Compiled from articles posted to this http URL and this http URL, each report consists of a written description of the player, numerical grades for several skills, and unique IDs to reference their profiles on popular resources like this http URL, FanGraphs, and Baseball-Reference. With this dataset, we employ several deep neural networks to predict if minor league players will make the MLB given their scouting report. We open-source this data to share with the community, and present a web application demonstrating language variations in the reports of successful and unsuccessful prospects.
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control

Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures. We propose a novel approach to automatically find strong topologies for continuous control tasks while only adding a minor overhead in terms of interactions in the environment. To achieve this, we combine Neuroevolution techniques with off-policy training and propose a novel architecture mutation operator. Experiments on five continuous control benchmarks show that the proposed Actor-Critic Neuroevolution algorithm often outperforms the strong Actor-Critic baseline and is capable of automatically finding topologies in a sample-efficient manner which would otherwise have to be found by expensive architecture search.
Magnetic resonance imaging of a solid, multilobular ameloblastoma in the mandible of a pony

Summary This report describes a multilobular ameloblastoma that invaded the left mandible of a 31-year-old pony. The pony presented initially with a palpable mass that protruded from the left mandibular region. Ante mortem radiographs of the mass revealed an invasive, multilobular, bony mass with significant loss of dentition. One month following initial diagnostic work-up, the pony was subjected to euthanasia due to unrelated clinical signs of colic, secondary to a strangulating lipoma. Magnetic resonance imaging (MRI) of the head was performed post mortem to further characterise the mass and determine the extent of its invasion. The macroscopic and histopathological morphology and immunohistochemical properties of the neoplastic cells strongly supported a diagnosis of a multi-cystic ameloblastoma. To the authors' knowledge, this is the first report of an MRI of an ameloblastoma in the horse.
Detecting Multiple Speech Disfluencies Using a Deep Residual Network with Bidirectional Long Short-Term Memory

Stuttering is a speech impediment affecting tens of millions of people on an everyday basis. Even with its commonality, there is minimal data and research on the identification and classification of stuttered speech. This paper tackles the problem of detection and classification of different forms of stutters. As opposed to most existing works that identify stutters with language models, our work proposes a model that relies solely on acoustic features, allowing for identification of several variations of stutter disfluencies without the need for speech recognition. Our model uses a deep residual network and bidirectional long short-term memory layers to classify different types of stutters and achieves an average miss rate of 10.03%, outperforming the state-of-the-art by almost 27%.
Knowledge-Aided Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than the number of antennas. To address this challenge, we propose a beamspace channel estimation scheme based on deep learning (DL) in this paper. Specifically, the beamspace channel estimation problem can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). Then, by exploiting the Gaussian mixture prior distribution of the beamspace channel elements, we derive a new shrinkage function to refine the classical AMP algorithm. Finally, by replacing the activation function in the conventional DNN with the derived Gaussian mixture shrinkage function, we propose a complex-valued Gaussian mixture LAMP (GM-LAMP) network specialized for estimating the beamspace channel. The simulation results show that, compared with the existing LAMP network and other conventional channel estimation schemes, the proposed GM-LAMP network considering the channel knowledge can improve the channel estimation accuracy with a low pilot overhead.
Equations of Motion Formulation of a Pendulum Containing N-point Masses

This paper presents a general formulation of equations of motion of a pendulum with n point mass by use of two different methods. The first one is obtained by using Lagrange Mechanics and mathematical induction(inspection), and the second one is derived by defining a vector. Today, these equations can be obtained by employing numerous programs; however, this study gives a very compact form of these equations that is more efficient than solving Euler-Lagrange Equations for every pendulum with more complex structures than simple or double pendulum. Additionally, we investigate what will happen to our n-point mass system when we take limit as number of point masses goes infinity under well-defined assumptions. We find out that it converges to hanging rope system.
Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series

Abstract This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk toward zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography. Supplementary materials for this article are available online.
A finite sequence of Hahn-type discrete orthogonal polynomials

ABSTRACT By considering a specific Sturm–Liouville problem, we introduce a finite sequence of Hahn-type discrete polynomials and prove that they are finitely orthogonal on the real line. We then compute their norm square value by using Dougall's bilateral sum and obtain all moments corresponding to the introduced polynomials.
Evidence for higher Earth-system sensitivity from long-term carbon-cycle observations

Projections of future temperature are critical for developing sound strategies to address climate risks, but depend on deeply uncertain Earth system properties, including the Earth-system sensitivity (ESS). The ESS is the long-term (e.g., millennial-scale and longer) global warming resulting from a doubling of atmospheric carbon dioxide (CO$_2$) levels, relative to pre-industrial conditions. Long-term carbon cycle models provide a common approach to estimate ESS, but previous efforts either lack a formal data assimilation framework, or focus on paleo periods with the most available data. Here, we improve on ESS estimates by using a Bayesian approach to fuse deep-time paleoclimate data with a long-term carbon cycle model. Our updated ESS estimate of 5.1 $^{\circ}$C (3.8-6.6 $^{\circ}$C; 5-95% range) shows a higher and narrower range than previous assessments, implying increased long-term future temperatures and risks. Our sensitivity analysis reveals that chemical and plant-assisted weathering parameters interact strongly with ESS in affecting the simulated atmospheric CO$_2$. Research into improving the understanding about these weathering processes hence provides potentially powerful avenues for further constraining this fundamental and policy-relevant Earth-system property.
On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge Modeling Systems

User knowledge modeling systems are used as the most effective technology for grabbing new user's attention. Moreover, the quality of service (QOS) is increased by these intelligent services. This paper proposes two user knowledge classifiers based on artificial neural networks used as one of the influential parts of knowledge modeling systems. We employed multi-layer perceptron (MLP) and adaptive neural fuzzy inference system (ANFIS) as the classifiers. Moreover, we used real data contains the user's degree of study time, repetition number, their performance in exam, as well as the learning percentage, as our classifier's inputs. Compared with well-known methods like KNN and Bayesian classifiers used in other research with the same data sets, our experiments present better performance. Although, the number of samples in the train set is not large enough, the performance of the neuro-fuzzy classifier in the test set is 98.6% which is the best result in comparison with others. However, the comparison of MLP toward the ANFIS results presents performance reduction, although the MLP performance is more efficient than other methods like Bayesian and KNN. As our goal is evaluating and reporting the efficiency of a neuro-fuzzy classifier for user knowledge modeling systems, we utilized many different evaluation metrics such as Receiver Operating Characteristic and the Area Under its Curve, Total Accuracy, and Kappa statistics.