Miracle-dz/UniFAR-FTUs huggingface dataset:
Approaching intonational distance and change

The main aim of this thesis is to begin to extend phonetic distance measurements to the domain of intonation. Existing studies of segmental phonetic distance have strong associations with historical linguistic questions. I begin with this context and demonstrate problems with the use of feature systems in these segmental measures. Then I attempt to draw strands from the disparate fields of quantitative historical linguistics and intonation together. The intonation of Belfast and Glasgow English provides a central case study for this. Previous work suggests that both varieties display nuclear rises on statements, yet they have never been formally compared. This thesis presents two main hypotheses on the source of these statement rises: the Alignment hypothesis and the Transfer hypothesis. The Alignment hypothesis posits that statement rises were originally more typical statement falls but have changed into rises over time through gradual phonetic change to the location of the pitch peak. The Transfer hypothesis considers that statement rises have come about through pragmatic transfer of rises onto a statement context, either from question rises or continuation rises. I evaluate these hypotheses using the primary parameters of alignment and scaling as phonetic distance measurements. The main data set consists of data from 3 Belfast English and 3 Glasgow English speakers in a Sentence reading task and Map task. The results crucially indicate that the origin of the statement rises in Belfast and Glasgow English respectively may be different. The Glasgow statement nuclear tones show support for the Alignment hypothesis, while the Belfast nuclear tones fit best with the Transfer hypothesis. The fundamental differences between Glasgow and Belfast are the earlier alignment of the peak (H) in Glasgow and the presence of a final low (L) tonal target in Glasgow and a final high (H) target in
The comparative effects of visual-only instruction versus modality principle instruction on algebraic problem accuracy and perceived mental effort at varying levels of task complexity for undergraduate nursing students

The purpose of this study was to compare the effects of two instructional formats on math accuracy and perceived mental effort during a series of math problems that varied in levels of complexity. The multimedia instruction results were compared against a traditional form of instruction using visual-only teaching materials. Few studies examine the impact of instructional design on learning outcomes math instruction within nursing with a lack of research describing how math is taught to nursing students other than traditional lecture or textbook. Nursing students demonstrate low performance rates on math problems that involve mixed numbers that also tend to range in complexity levels. One explanation is cognitive load. Research indicates that tasks of high complexity have negative effects on accuracy and perceived mental effort. Measuring perceived mental effort in addition to accuracy provides a stronger indicator of cognitive load because is performance assessed and the mechanics of the cognitive load processes. Understanding the cognitive load processes acts as a conduit to properly designing instruction specifically with the modality principle. The modality principle shows positive effects on accuracy and perceived mental effort. The modality principle has a larger and more positive impact on learning outcomes when the learning material is complex because the instructional format reduces cognitive load because of the visual and audio presentations. Data were analyzed using independent t-tests between the two instructional groups based on three levels of complexity in addition to paired sample t-tests to examine the difference in scores from pre- to post-assessment. Results indicated that while there was better accuracy with the instruction designed using the modality principle, perceived mental effort was rated higher than the control group that received visual-only instruction. Furthermore, ancillary analysis indicated that confidence was rated lower for the experimental group post instruction.
Effective management for aggregated flows in server-based QoS routing

The support of real-time applications on the Internet requires a routing mechanism that provides quality-of-service. Such mechanisms called QoS routing can be realized by server-based routing method. However, in large-scale networks, the processing load imposed on the server would be too high. We focus here on the use of flow aggregation schemes as a way of reducing the processing load. From simulation results, roughly speaking, as a trunk can accommodate a larger number of flows within it, a smaller processing load can be realized on the server at the cost of increased blocking probability.
CMRNet: Camera to LiDAR-Map Registration

In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network (CNN) to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i. e. , CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.27m and 1.07° median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17°. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.
Cosmology in 
f(Q)
 geometry

The universal character of the gravitational interaction provided by the equivalence principle motivates a geometrical description of gravity. The standard formulation of General Relativity a la Einstein attributes gravity to the spacetime curvature, to which we have grown accustomed. However, this perception has masked the fact that two alternative, though equivalent, formulations of General Relativity in flat spacetimes exist, where gravity can be fully ascribed either to torsion or to non-metricity. The latter allows a simpler geometrical formulation of General Relativity that is oblivious to the affine spacetime structure. Generalisations along this line permit to generate teleparallel and symmetric teleparallel theories of gravity with exceptional properties. In this work we explore modified gravity theories based on non-linear extensions of the non-metricity scalar. After presenting some general properties and briefly studying some interesting background cosmologies (including accelerating solutions with relevance for inflation and dark energy), we analyse the behaviour of the cosmological perturbations. Tensor perturbations feature a re-scaling of the corresponding Newton's constant, while vector perturbations do not contribute in the absence of vector sources. In the scalar sector we find two additional propagating modes, hinting that $f(Q)$ theories introduce, at least, two additional degrees of freedom. These scalar modes disappear around maximally symmetric backgrounds because of the appearance of an accidental residual gauge symmetry corresponding to a restricted diffeomorphism. We finally discuss the potential strong coupling problems of these maximally symmetric backgrounds caused by the discontinuity in the number of propagating modes.
Block-coordinate and incremental aggregated nonconvex proximal gradient methods: a unified view

In this paper we study block-coordinate proximal gradient algorithms for minimizing the sum of a separable smooth function and a (nonseparable) nonsmooth function, both of which are allowed to be nonconvex. Two prominent special cases are the regularized finite sum minimization and the sharing problem. The main tool in the analysis of the block-coordinate algorithm is the forward-backward envelope (FBE), which serves as a Lyapunov function. Favorable properties of the FBE under extra assumptions lead to an accelerated variant. Moreover, our analysis covers the popular Finito/MISO algorithm as a special case, allowing to address nonsmooth and nonconvex problems with rather general sampling strategies.
Sequential estimation of quantiles with applications to A/B testing and best-arm identification

Consider the problem of sequentially estimating quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i. d. observations. We propose new, theoretically sound and practically tight confidence sequences for quantiles, that is, sequences of confidence intervals which are valid uniformly over time. We give two methods for tracking a fixed quantile and two methods for tracking all quantiles simultaneously. Specifically, we provide explicit expressions with small constants for intervals whose widths shrink at the fastest possible $\sqrt{t^{-1} \log\log t}$ rate, as determined by the law of the iterated logarithm (LIL). As a byproduct, we give a non-asymptotic concentration inequality for the empirical distribution function which holds uniformly over time with the LIL rate, thus strengthening Smirnov's asymptotic empirical process LIL, and extending the famed Dvoretzky-Kiefer-Wolfowitz (DKW) inequality to hold uniformly over all sample sizes while only being about twice as wide in practice. This inequality directly yields sequential analogues of the one- and two-sample Kolmogorov-Smirnov tests, and a test of stochastic dominance. We apply our results to the problem of selecting an arm with an approximately best quantile in a multi-armed bandit framework, proving a state-of-the-art sample complexity bound for a novel allocation strategy. Simulations demonstrate that our method stops with fewer samples than existing methods by a factor of five to fifty. Finally, we show how to compute confidence sequences for the difference between quantiles of two arms in an A/B test, along with corresponding always-valid $p$-values.
Modeling non-thermal emission from the jet-launching region of M 87 with adaptive mesh refinement

Context. The galaxy M 87 harbors a kiloparsec-scale relativistic jet, whose origin coincides with a compact source thought to be a supermassive black hole. Observational millimeter very long baseline interferometry campaigns are capable of resolving the jet-launching region at the scale of the event horizon. In order to provide a context for interpreting these observations, realistic general-relativistic magnetohydrodynamical (GRMHD) models of the accretion flow are constructed. Aims. Electrons in the jet are responsible for the observed synchrotron radiation, which is emitted in frequencies ranging from radio to near-infrared (NIR) and optical. The characteristics of the emitted radiation depend on the shape of the electrons’ energy-distribution function (eDF). The dependency on the eDF is omitted in the modeling of the first Event Horizon Telescope results. In this work, we aim to model the M 87 spectral-energy distribution from radio up to optical frequencies using a thermal-relativistic Maxwell–Jüttner distribution, as well as a relativistic κ-distribution function. The power-law index of the eDF is modeled based on sub-grid, particle-in-cell parametrizations for sub-relativistic reconnection. Methods. A GRMHD simulation in Cartesian–Kerr–Schild coordinates, using eight levels of adaptive mesh refinement (AMR), forms the basis of our model. To obtain spectra and images, the GRMHD data was post-processed with the ray-tracing code RAPTOR, which is capable of ray tracing through GRMHD simulation data that is stored in multi-level AMR grids. The resulting spectra and images maps are compared with observations. Results. We obtain radio spectra in both the thermal-jet and κ-jet models consistent with radio observations. Additionally, the κ-jet models also recover the NIR and optical emission. The images show a more extended structure at 43 GHz and 86 GHz and more compact emission at 228 GHz. The models recover the observed source sizes and core shifts and obtain a jet power of ≈1043 ergs s−1. In the κ-jet models, both the accretion rates and jet powers are approximately two times lower than the thermal-jet model. The frequency cut-off observed at ν ≈ 1015 Hz is recovered when the accelerator size is 106 − 108 cm, this could potentially point to an upper limit for plasmoid sizes in the jet of M 87.
Itinerancy-dependent noncollinear spin textures in 
SrFeO3, CaFeO3
, and 
CaFeO3/SrFeO3
 heterostructures probed via resonant x-ray scattering

Non-collinear, multi-q spin textures can give rise to exotic, topologically protected spin structures such as skyrmions, but the reason for their formation over simple single-q structures is not well understood. While lattice frustration and the Dzyaloshinskii-Moriya interaction are known to produce non-collinear spin textures, the role of electron itinerancy in multi-q formation is much less studied. Here we investigated the non-collinear, helical spin structures in epitaxial films of the perovskite oxides SrFeO3 and CaFeO3 using magnetotransport and resonant soft x-ray magnetic diffraction. Metallic SrFeO3 exhibits features in its magnetoresistance that are consistent with its recently proposed multi-q structure. Additionally, the magnetic Bragg peak of SrFeO3 measured at the Fe L edge resonance energy asymmetrically broadens with decreasing temperature in its multi-q state. In contrast, insulating CaFeO3 has a symmetric scattering peak with an intensity 10x weaker than SrFeO3. Enhanced magnetic scattering at O K edge prepeak energies demonstrates the role of a negative charge transfer energy and the resulting oxygen ligand holes in the magnetic ordering of these ferrates. By measuring magnetic diffraction of CaFeO3/SrFeO3 superlattices with thick CaFeO3 layers, we find that the CaFeO3 helical ordering is coherent across 1 unit cell-thick SrFeO3 layers but not 6 unit cell-thick layers. We conclude that insulating CaFeO3 supports only a simple single-q helical structure in contrast to metallic SrFeO3 that hosts multi-q structures. Our results provide important insight into the role of electron itinerancy in the formation of multi-q spin structures.
Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory entails brain-wide switching between activity states. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Schizophrenia patients show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations.
Toward Rate Estimation for Transient Surveys. I. Assessing Transient Detectability and Volume Sensitivity for iPTF

The past couple of decades have seen an emergence of transient detection facilities in various avenues of time-domain astronomy that have provided us with a rich data set of transients. The rates of these transients have implications in star formation, progenitor models, evolution channels, and cosmology measurements. The crucial component of any rate calculation is the detectability and spacetime volume sensitivity of a survey to a particular transient type as a function of many intrinsic and extrinsic parameters. Fully sampling that multidimensional parameter space is challenging. Instead, we present a scheme to assess the detectability of transients using supervised machine learning. The data product is a classifier that determines the detection likelihood of sources resulting from an image subtraction pipeline associated with time-domain survey telescopes, taking into consideration the intrinsic properties of the transients and the observing conditions. We apply our method to assess the spacetime volume sensitivity of type Ia supernovae (SNe Ia) in the intermediate Palomar Transient Factory (iPTF) and obtain the result, . With rate estimates in the literature, this volume sensitivity gives a count of 680–1160 SNe Ia detectable by iPTF, which is consistent with the archival data. With a view toward wider applicability of this technique we do a preliminary computation for long-duration type IIp supernovae (SNe IIp) and find . This classifier can be used for computationally fast spacetime volume sensitivity calculation of any generic transient type using their light-curve properties. Hence, it can be used as a tool to facilitate calculation of transient rates in a range of time-domain surveys, given suitable training sets.
Density-based Clustering with Best-scored Random Forest

Single-level density-based approach has long been widely acknowledged to be a conceptually and mathematically convincing clustering method. In this paper, we propose an algorithm called "best-scored clustering forest" that can obtain the optimal level and determine corresponding clusters. The terminology "best-scored" means to select one random tree with the best empirical performance out of a certain number of purely random tree candidates. From the theoretical perspective, we first show that consistency of our proposed algorithm can be guaranteed. Moreover, under certain mild restrictions on the underlying density functions and target clusters, even fast convergence rates can be achieved. Last but not least, comparisons with other state-of-the-art clustering methods in the numerical experiments demonstrate accuracy of our algorithm on both synthetic data and several benchmark real data sets.
Flattening a Hierarchical Clustering through Active Learning

We investigate active learning by pairwise similarity over the leaves of trees originating from hierarchical clustering procedures. In the realizable setting, we provide a full characterization of the number of queries needed to achieve perfect reconstruction of the tree cut. In the non-realizable setting, we rely on known important-sampling procedures to obtain regret and query complexity bounds. Our algorithms come with theoretical guarantees on the statistical error and, more importantly, lend themselves to linear-time implementations in the relevant parameters of the problem. We discuss such implementations, prove running time guarantees for them, and present preliminary experiments on real-world datasets showing the compelling practical performance of our algorithms as compared to both passive learning and simple active learning baselines.
A Search of TESS Full-frame Images for a Simultaneous Optical Counterpart to FRB 181228

FRB 181228 was detected by the Molonglo Synthesis Radio Telescope (MOST) at a position and time coincident with Transiting Exoplanet Survey Satellite (TESS) observations, representing the first simultaneous multiwavelength data collection for a fast radio burst (FRB). The large imaged field of view of TESS allows a search over the uncertainty region produced by MOST. However, the TESS pixel scale of 21″ and the full-frame image (FFI) cadence of 30 minutes is not optimal for the detection of a fast optical burst (FOB) with a possible millisecond duration. We search the TESS FFIs and find no events with a limiting TESS magnitude of 16, assuming a 30 minute event duration, corresponding to an optical flux density upper limit of approximately 2000 Jy for a ∼1 ms signal duration, assuming no signal loss. In addition, the cosmic-ray mitigation method for TESS significantly reduces its sensitivity to short-timescale transients, which we quantify. We compare our results to the predictions of Yang et al. and find that the upper limit is a factor of 2000 higher than the predicted maximum optical flux density. However, we find that if FRB 181228 had occurred in the galaxy thought to host the nearest FRB detection to date (37 Mpc), an FOB may have been detectable by TESS. In the near future, when the Canadian Hydrogen Intensity Mapping Experiment and Australian SKA Pathfinder will detect hundreds to thousands of FRBs, TESS may be able to detect FOBs from those rare bright and nearby FRBs within this large population (if more sophisticated cosmic-ray excision can be implemented).
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection

It is well known that deep learning approaches to face recognition and facial landmark detection suffer from biases in modern training datasets. In this work, we propose to use synthetic face images to reduce the negative effects of dataset biases on these tasks. Using a 3D morphable face model, we generate large amounts of synthetic face images with full control over facial shape and color, pose, illumination, and background. With a series of experiments, we extensively test the effects of priming deep nets by pre-training them with synthetic faces. We observe the following positive effects for face recognition and facial landmark detection tasks: 1) Priming with synthetic face images improves the performance consistently across all benchmarks because it reduces the negative effects of biases in the training data. 2) Traditional approaches for reducing the damage of dataset bias, such as data augmentation and transfer learning, are less effective than training with synthetic faces. 3) Using synthetic data, we can reduce the size of real-world datasets by 75% for face recognition and by 50% for facial landmark detection while maintaining performance. Thus, offering a means to focus the data collection process on less but higher quality data.
Exploring particle escape in supernova remnants through gamma rays

The escape process of particles accelerated at supernova remnant (SNR) shocks is one of the poorly understood aspects of the shock acceleration theory. Here we adopt a phenomenological approach to study the particle escape and its impact on the gamma-ray spectrum resulting from hadronic collisions both inside and outside of a middle-aged SNR. Under the assumption that in the spatial region immediately outside of the remnant the diffusion coefficient is suppressed with respect to the average Galactic one, we show that a significant fraction of particles are still located inside the SNR long time after their nominal release from the acceleration region. This fact results into a gamma-ray spectrum that resembles a broken power law, similar to those observed in several middle-aged SNRs. Above the break, the spectral steepening is determined by the diffusion coefficient outside of the SNR and by the time dependence of maximum energy. Consequently, the comparison between the model prediction and actual data will contribute to determining these two quantities, the former being particularly relevant within the predictions of the gamma-ray emission from the halo of escaping particles around SNRs, which could be detected with future Cherenkov telescope facilities. We also calculate the spectrum of runaway particles injected into the Galaxy by an individual remnant. Assuming that the acceleration stops before the SNR enters the snowplow phase, we show that the released spectrum can be a featureless power law only if the accelerated spectrum is ∝ p−α with α > 4.