Miracle-dz/UniFAR-FTUs huggingface dataset:
RECENTLY REDISCOVERED BARYONYCHINE TEETH (DINOSAURIA: THEROPODA): NEW MORPHOLOGIC DATA, RANGE EXTENSION & SIMILARITY TO CERATOSAURUS

Newly identified baryonychine teeth from the UK Wealden illustrate the morphologic variability and stratigraphic range of this taxon. Whilst common in the formations in which they occur, their distinctive fluted and finely serrated teeth are often misidentified as crocodilian, which are superficially similar in appearance. Fourteen teeth from mainland Weald collections of the British Museum of Natural History (some collected over 150 years ago, all stored with goniopholid specimens) demonstrate the presence of baryonychines in the Hauterivian and possibly Valanginian stages of the Early Cretaceous: the oldest record of this group. An unusual laterally compressed, posteriorly recurved, and fluted tooth from the Purbeck Limestone (Late Jurassic – earliest Cretaceous) may extend the range of baryonychines even further. Some specimens from the Barremian Wessex Fm (Isle of Wight, UK) and older mainland deposits show both lingual and labial fluting, previously seen only in teeth from Spain. Spanish specimens can no longer be considered phylogenetically distinct based on this character. The fluted premaxillary and anterior dentary teeth of Ceratosaurus (and to a lesser extent, some other ceratosaurs e.g. Masiakasaurus) bear remarkable similarity to the fluted teeth of baryonychines. Whether this represents convergence or a real phylogenetic signal requires further analysis. However, isolated teeth identified as Ceratosaurus sp. from the Late Jurassic of Tendaguru, Tanzania, can no longer be assigned with confidence to this taxon and should be reassessed, especially as ceratosaur lateral teeth are unknown from these strata. The observed phylogenetic trend towards reduced denticle size in baryonychines through to spinosaurines suggests that the hypothetical ancestral condition possessed larger denticles, similar to typical theropods and what is seen in the Tendaguru teeth. A ceratosaur origin for baryonychines has been previously suggested and subsequently rejected by recent workers. However, this relationship should be critically reassessed considering the similarity between these taxa, and recent phylogenetic analyses placing Ceratosaurus closer to the Tetanurae.
Performance and Effective Method of Experimenting Micromixer Using Finished smooth glasses and Cohesive Tape

Micromixer has been drawing upon various branches of engineering science and allied areas within biology and biomedicine. In the present study, an easy and fast fabrication method on hydrophilic micromixer using Optically Clear Adhesive (OCA) double-sided tape together with glass is proposed. Different experiments on types of planar hydrophilic micormixers with baffle structures are designed for hydrophillic microchannel. Flow is driven by capillary action using surface tension and flow tests are carried out to show that the double-sided OCA tape-glass micromixer can achieve the mixing result of 71% in gray level image analysis. © 2017 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved
Thalamus Modulates Consciousness via Layer-Specific Control of Cortex

Functional MRI and electrophysiology studies suggest that consciousness depends on large-scale thalamocortical and corticocortical interactions. However, it is unclear how neurons in different cortical layers and circuits contribute. We simultaneously recorded from central lateral thalamus (CL) and across layers of the frontoparietal cortex in awake, sleeping, and anesthetized macaques. We found that neurons in thalamus and deep cortical layers are most sensitive to changes in consciousness level, consistent across different anesthetic agents and sleep. Deep-layer activity is sustained by interactions with CL. Consciousness also depends on deep-layer neurons providing feedback to superficial layers (not to deep layers), suggesting that long-range feedback and intracolumnar signaling are important. To show causality, we stimulated CL in anesthetized macaques and effectively restored arousal and wake-like neural processing. This effect was location and frequency specific. Our findings suggest layer-specific thalamocortical correlates of consciousness and inform how targeted deep brain stimulation can alleviate disorders of consciousness.
EMPLOYEE DIVERSITY AND ORGANIZATIONAL INNOVATION IN FOOD AND BEVERAGE FIRMS IN PORT HARCOURT

This empirical study examined the relationship between employee diversity and organizational innovation in Food and Beverage firms in Port Harcourt, Rivers State. The quasi-experimental research design was adopted because it is a cross sectional research survey. The population of this study consists of 280 employees in selected five Food and Beverage firms out of the 14 registered Food and Beverage firms operating in Port Harcourt. Sample size of 125 respondents was determined using the Taro Yamane‟s formula (1964). Data were collected through copies of questionnaire and were analyzed using Spearman‟s Rank Order Correlation Coefficient Statistical technique via the use of Statistical Package for Social Sciences (SPSS) version 21.0. The findings in this study established the fact that there is significant relationship between Employee diversity-dimensions and Organizational innovation measures. It is therefore recommended that, the management of the targeted food and beverage companies under study should control and streamline the manifestations of diversity in such a way that promotes uniqueness but yet retains some level of standardization and workforce cohesion. Management should emphasize on control mechanisms within the organization which coordinate behaviours and manage deviance as a result of diversity or differences within the workforce in such a way that workers values would be more aligned to achieving organizational goal.
Immunohistochemistry-based molecular subtypes of breast cancer in Bosnian women: single-institution pilot study

Introduction: to evaluate the frequency and significance of immunohistochemistry-based molecular subtypes of breast cancer and investigate their association with traditional pathological features for breast cancer among Bosnian women. Materials and methods: this study included 100 female patients with primary invasive breast cancer. Immunohistochemical analyses for estrogen receptor (ER), progesterone receptor (PR), HER-2 and Ki-67 were performed to define four biological subtypes: luminal A, luminal B, HER-2-positive and triple-negative. Results: the frequency of luminal A, luminal B, HER2-positive and triple-negative subtypes of breast cancer was 44%, 39%, 11% and 6%, respectively. Molecular subtypes of breast cancer among Bosnian women showed to be independent of traditional pathological features (p>0.05). Ki-67 showed significant di erence regarding luminal B tumor type, where high (≥14%) Ki-67 score was predominantly represented in 36 (92.3%) cases (p<0.001). Conclusion: immunohistochemistry-based molecular subtypes of breast cancer in Bosnian women somehow vary in pathological features, i.e. luminal A subtype in this sample comprised mostly ductal histological type, moderate di erentiation with the involvement of lymph nodes, known as worse prognostic factors, although with no statistical significance.
Lookahead: a Far-Sighted Alternative of Magnitude-based Pruning

Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures. Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization. Our experimental results demonstrate that the proposed method consistently outperforms the magnitude pruning on various networks including VGG and ResNet, particularly in the high-sparsity regime.
Online Learning of the Kalman Filter With Logarithmic Regret

In this article, we consider the problem of predicting observations generated online by an unknown, partially observable linear system, which is driven by Gaussian noise. In the linear Gaussian setting, the optimal predictor in the mean square error sense is the celebrated Kalman filter, which can be explicitly computed when the system model is known. When the system model is unknown, we have to learn how to predict observations online based on finite data, suffering possibly a nonzero regret with respect to the Kalman filter's prediction. We show that it is possible to achieve a regret of the order of $\text{poly}\log (N)$ with high probability, where $N$ is the number of observations collected. This is achieved using an online least-squares algorithm, which exploits the approximately linear relation between future observations and past observations. The regret analysis is based on the stability properties of the Kalman filter, recent statistical tools for finite sample analysis of system identification, and classical results for the analysis of least-squares algorithms for time series. Our regret analysis can also be applied to other predictors, e.g., multiple step-ahead prediction, or prediction under exogenous inputs including closed-loop prediction. A fundamental technical contribution is that our bounds hold even for the class of nonexplosive systems (including marginally stable systems), which was not addressed before in the case of online prediction.
Isometries from gauge transformations

In four dimensions one can use the chiral part of the spin connection as the main object that encodes geometry. The metric is then recovered algebraically from the curvature of this connection. We address the question of how isometries can be identified in this ‘pure connection’ formalism. We show that isometries are recovered from gauge transformation parameters satisfying the requirement that the Lie derivative of the connection along a vector field generating an isometry is a gauge transformation. This requirement can be rewritten as a first order differential equation involving the gauge transformation parameter only. Once a gauge transformation satisfying this equation is found, the isometry generating vector field is recovered algebraically. We work out examples of the new formalism being used to determine isometries, and also prove a general statement: a negative definite connection on a compact manifold does not have symmetries. This is the precise ‘pure connection’ analog of the well-known Riemannian geometry statement that there are no Killing vector fields on compact manifolds with negative Ricci curvature.
Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning

We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems where the goal is to find a policy (using data from several tasks represented by Markov Decision Processes (MDPs)) that can be updated by one step of stochastic policy gradient for the realized MDP. In particular, using stochastic gradients in MAML update step is crucial for RL problems since computation of exact gradients requires access to a large number of possible trajectories. For this formulation, we propose a variant of the MAML method, named Stochastic Gradient Meta-Reinforcement Learning (SG-MRL), and study its convergence properties. We derive the iteration and sample complexity of SG-MRL to find an $\epsilon$-first-order stationary point, which, to the best of our knowledge, provides the first convergence guarantee for model-agnostic meta-reinforcement learning algorithms. We further show how our results extend to the case where more than one step of stochastic policy gradient method is used in the update during the test time.
Breaking Reciprocity in Integrated Photonic Devices Through Dynamic Modulation

Nonreciprocal components, such as isolators and circulators, are crucial components for photonic systems. In this article we review theoretical and experimental progress towards developing nonreciprocal photonic devices based on dynamic modulation. Particularly, we focus on approaches that operate at optical wavelengths and device architectures that have the potential for chip-scale integration. We first discuss the requirements for constructing an isolator or circulator using dynamic modulation. We review a number of different isolator and circulator architectures, including waveguide and resonant devices, and describe their underlying operating principles. We then compare these device architectures from a system-level performance perspective, considering how their figures of merit, such as footprint, bandwidth, isolation, and insertion loss, scale with respect to device degrees of freedom.
Einstein’s fluctuation relation and Gibbs states far from equilibrium

We examine a class of one-dimensional lattice-gases characterised by a gradient condition which is a necessary and sufficient condition for the existence of Gibbs-type homogeneous stationary states. We show how, defining appropriate boundary conditions, this condition leads to a novel fluctuation relation under reservoir exchange, unrelated to the Gallavotti–Cohen symmetry. We show that it can be interpreted as a nonequilibrium and nonlinear generalisation of Einstein’s relation, leading to Onsager reciprocity relations in the limit of a small reservoir imbalance. We illustrate these results with two examples.
Keeping It Cool: Much Orbit Migration, yet Little Heating, in the Galactic Disk

A star in the Milky Way’s disk can now be at a Galactocentric radius quite distant from its birth radius for two reasons: either its orbit has become eccentric through radial heating, which increases its radial action JR (“blurring”), or merely its angular momentum Lz has changed and thereby its guiding radius (“churning”). We know that radial orbit migration is strong in the Galactic low-α disk and set out to quantify the relative importance of these two effects, by devising and applying a parameterized model ( ) for the distribution in the stellar disk. This model describes the orbit evolution for stars of age τ and metallicity , presuming that coeval stars were initially born on (near-)circular orbits, and with a unique at a given birth angular momentum and age. We fit this model to APOGEE red clump stars, accounting for the complex selection function of the survey. The best-fit model implies changes of angular momentum of and changes of radial action as at 8 kpc. This suggests that the secular orbit evolution of the disk is dominated by diffusion in angular momentum, with radial heating being an order of magnitude lower.
Sparse Recovery with Non-Linear Fourier Features

Random non-linear Fourier features have recently shown remarkable performance in a wide-range of regression and classification applications. Motivated by this success, this article focuses on a sparse non-linear Fourier feature (NFF) model. We provide a characterization of the sufficient number of data points that guarantee perfect recovery of the unknown parameters with high-probability. In particular, we show how the sufficient number of data points depends on the kernel matrix associated with the probability distribution function of the input data. We compare our results with the recoverability bounds for the bounded orthonormal systems and provide examples that illustrate sparse recovery under the NFF model.
Analysis of Membership Probability in Nearby Young Moving Groups with Gaia DR2

We analyze the membership probability of young stars belonging to nearby moving groups with Gaia DR2 data. The sample of 1429 stars was identified from “The Catalog of Suspected Nearby Young Moving Group Stars. ” Good-quality parallax and proper motion values were retrieved for 890 stars from the Gaia DR2 database. The analysis for membership probability is performed in the framework of the LACEwING algorithm. From the analysis it is confirmed that 279 stars do not belong to any of the known moving groups. We estimated the U, V, W space velocity values for 250 moving group members, which were found to be more accurate than previous values listed in the literature. The velocity ellipses of all the moving groups are well constrained within the “good box,” a widely used criterion to identify moving group members. The age of moving group members are uniformly estimated from the analysis of the Gaia color–magnitude diagram with MIST isochrones. We found a spread in the age distribution of stars belonging to some moving groups, which needs to be understood from further studies.
Regret Bounds for Discounted MDPs

Recently, it has been shown that carefully designed reinforcement learning (RL) algorithms can achieve near-optimal regret in the episodic or the average-reward setting. However, in practice, RL algorithms are applied mostly to the infinite-horizon discounted-reward setting, so it is natural to ask what the lowest regret an algorithm can achieve is in this case, and how close to the optimal the regrets of existing RL algorithms are. In this paper, we prove a regret lower bound of $\Omega\left(\frac{\sqrt{SAT}}{1 - \gamma} - \frac{1}{(1 - \gamma)^2}\right)$ when $T\geq SA$ on any learning algorithm for infinite-horizon discounted Markov decision processes (MDP), where $S$ and $A$ are the numbers of states and actions, $T$ is the number of actions taken, and $\gamma$ is the discounting factor. We also show that a modified version of the double Q-learning algorithm gives a regret upper bound of $\tilde{O}\left(\frac{\sqrt{SAT}}{(1 - \gamma)^{2.5}}\right)$ when $T\geq SA$. Compared to our bounds, previous best lower and upper bounds both have worse dependencies on $T$ and $\gamma$, while our dependencies on $S, A, T$ are optimal. The proof of our upper bound is inspired by recent advances in the analysis of Q-learning in the episodic setting, but the cyclic nature of infinite-horizon MDPs poses many new challenges.
Time-dependent relaxed magnetohydrodynamics: Inclusion of cross helicity constraint using phase-space action

A new formulation of time-dependent Relaxed Magnetohydrodynamics (RxMHD) is derived variationally from Hamilton's Action Principle using microscopic conservation of mass, and macroscopic conservation of total magnetic helicity, cross helicity and entropy, as the only constraints on variations of density, pressure, fluid velocity, and magnetic vector potential over a relaxation domain. A novel phase-space version of the MHD Lagrangian is derived, which gives Euler--Lagrange equations consistent with previous work on exact ideal and relaxed MHD equilibria with flow, but generalizes the relaxation concept from statics to dynamics. The application of the new dynamical formalism is illustrated for short-wavelength linear waves, and the interface connection conditions for Multiregion Relaxed MHD (MRxMHD) are derived. The issue of whether $\vec{E} + \vec{u}\times\vec{B} = 0$ should be a constraint is discussed.
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies

Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs). Learning branching policies for MILP has become an active research area, with most works proposing to imitate the strong branching rule and specialize it to distinct classes of problems. We aim instead at learning a policy that generalizes across heterogeneous MILPs: our main hypothesis is that parameterizing the state of the B&B search tree can aid this type of generalization. We propose a novel imitation learning framework, and introduce new input features and architectures to represent branching. Experiments on MILP benchmark instances clearly show the advantages of incorporating an explicit parameterization of the state of the search tree to modulate the branching decisions, in terms of both higher accuracy and smaller B&B trees. The resulting policies significantly outperform the current state-of-the-art method for "learning to branch" by effectively allowing generalization to generic unseen instances.
Probing the seesaw mechanism with cosmological data

We investigate cosmological consequences of an inflationary model which incorporates a generic seesaw extension (types I and II) of the Standard Model of Particle Physics. A non-minimal coupling between the inflaton field and the Ricci scalar is considered as well as radiative corrections at one loop order. This connection between the inflationary dynamics with neutrino physics results in a predictive model whose observational viability is investigated in light of the current cosmic microwave background data, baryon acoustic oscillation observations and type Ia supernovae measurements. Our results show that the non-minimal coupled seesaw potential provides a good description of the observational data when radiative corrections are positive. Such result favours the type II seesaw mechanism over type I and may be an indication for physics beyond the Standard Model.
Selecting time-series hyperparameters with the artificial jackknife

This article proposes a generalisation of the delete-$d$ jackknife to solve hyperparameter selection problems for time series. This novel technique is compatible with dependent data since it substitutes the jackknife removal step with a fictitious deletion, wherein observed datapoints are replaced with artificial missing values. In order to emphasise this point, I called this methodology artificial delete-$d$ jackknife. As an illustration, it is used to regulate vector autoregressions with an elastic-net penalty on the coefficients. A software implementation, ElasticNetVAR.jl, is available on GitHub.
EFX Exists for Three Agents

We study the problem of distributing a set of indivisible items among agents with additive valuations in a fairmanner. The fairness notion under consideration is Envy-freeness up to anyitem (EFX). Despite significant efforts by many researchers for several years, the existence of EFX allocations has not been settled beyond the simple case of two agents. In this paper, we show constructively that an EFX allocation always exists for three agents. Furthermore, we falsify the conjecture by Caragiannis et al.[9] by showing an instance with three agents for which there is a partial EFX allocation (some items are not allocated) with higher Nash welfare than that of any complete EFX allocation.
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.