Miracle-dz/UniFAR-FTUs huggingface dataset:
Subword Segmental Language Modelling for Nguni Languages

Subwords have become the standard units of text in NLP, enabling efficient open-vocabulary models. With algorithms like byte-pair encoding (BPE), subword segmentation is viewed as a preprocessing step applied to the corpus before training. This can lead to sub-optimal segmentations for low-resource languages with complex morphologies. We propose a subword segmental language model (SSLM) that learns how to segment words while being trained for autoregressive language modelling. By unifying subword segmentation and language modelling, our model learns subwords that optimise LM performance. We train our model on the 4 Nguni languages of South Africa. These are low-resource agglutinative languages, so subword information is critical. As an LM, SSLM outperforms existing approaches such as BPE-based models on average across the 4 languages. Furthermore, it outperforms standard subword segmenters on unsupervised morphological segmentation. We also train our model as a word-level sequence model, resulting in an unsupervised morphological segmenter that outperforms existing methods by a large margin for all 4 languages. Our results show that learning subword segmentation is an effective alternative to existing subword segmenters, enabling the model to discover morpheme-like subwords that improve its LM capabilities.
Dimensionality of datasets in object detection networks

In recent years, convolutional neural networks (CNNs) are used in a large number of tasks in computer vision. One of them is object detection for autonomous driving. Although CNNs are used widely in many areas, what happens inside the network is still unexplained on many levels. Our goal is to determine the effect of Intrinsic dimension (i.e. minimum number of parameters required to represent data) in different layers on the accuracy of object detection network for augmented data sets. Our investigation determines that there is difference between the representation of normal and augmented data during feature extraction.
PoliGraph: Automated Privacy Policy Analysis using Knowledge Graphs

Privacy policies disclose how an organization collects and handles personal information. Recent work has made progress in leveraging natural language processing (NLP) to automate privacy policy analysis and extract data collection statements from different sentences, considered in isolation from each other. In this paper, we view and analyze, for the first time, the entire text of a privacy policy in an integrated way. In terms of methodology: (1) we define PoliGraph, a type of knowledge graph that captures statements in a privacy policy as relations between different parts of the text; and (2) we develop an NLP-based tool, PoliGraph-er, to automatically extract PoliGraph from the text. In addition, (3) we revisit the notion of ontologies, previously defined in heuristic ways, to capture subsumption relations between terms. We make a clear distinction between local and global ontologies to capture the context of individual privacy policies, application domains, and privacy laws. Using a public dataset for evaluation, we show that PoliGraph-er identifies 40% more collection statements than prior state-of-the-art, with 97% precision. In terms of applications, PoliGraph enables automated analysis of a corpus of privacy policies and allows us to: (1) reveal common patterns in the texts across different privacy policies, and (2) assess the correctness of the terms as defined within a privacy policy. We also apply PoliGraph to: (3) detect contradictions in a privacy policy, where we show false alarms by prior work, and (4) analyze the consistency of privacy policies and network traffic, where we identify significantly more clear disclosures than prior work.
Observed Adversaries in Deep Reinforcement Learning

In this work, we point out the problem of observed adver- saries for deep policies. Speciﬁcally, recent work has shown that deep reinforcement learning is susceptible to adversarial attacks where an observed adversary acts under environmental constraints to invoke natural but adversarial observations. This setting is particularly relevant for HRI since HRI-related robots are expected to perform their tasks around and with other agents. In this work, we demonstrate that this effect persists even with low-dimensional observations. We further show that these adversarial attacks transfer across victims, which potentially allows malicious attackers to train an ad- versary without access to the target victim.
Evidence for gravitational quadrupole moment variations in the companion of PSR J2051−0827

We have conducted radio timing observations of the eclipsing millisecond binary pulsar J2051-0827 with the European Pulsar Timing Array network of telescopes and the Parkes radio telescope, spanning over 13 years. The increased data span allows significant measurements of the orbital eccentricity, e = (6.2 {\pm} 1.3) {\times} 10^{-5} and composite proper motion, {\mu}_t = 7.3 {\pm} 0.4 mas/yr. Our timing observations have revealed secular variations of the projected semi-major axis of the pulsar orbit which are much more extreme than those previously published; and of the orbital period of the system. Investigation of the physical mechanisms producing such variations confirm that the variations of the semi-major axis are most probably caused by classical spin-orbit coupling in the binary system, while the variations in orbital period are most likely caused by tidal dissipation leading to changes in the gravitational quadrupole moment of the companion.
ASR data augmentation using cross-lingual multi-speaker TTS and cross-lingual voice conversion

We explore cross-lingual multi-speaker speech synthesis and cross-lingual voice conversion applied to data augmentation for automatic speech recognition (ASR) systems. Through extensive experiments, we show that our approach permits the application of speech synthesis and voice conversion to improve ASR systems on a target language using only one target-language speaker during model training. We managed to close the gap between ASR models trained with synthesized versus human speech compared to other works that use many speakers. Finally, we show that it is possible to obtain promising ASR training results with our data augmentation method using only a single real speaker in a target language.
Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts

This study presents a new approach to metaphorical paraphrase generation by masking literal tokens of literal sentences and unmasking them with metaphorical language models. Unlike similar studies, the proposed algorithm does not only focus on verbs but also on nouns and adjectives. Despite the fact that the transfer rate for the former is the highest (56%), the transfer of the latter is feasible (24% and 31%). Human evaluation showed that our system-generated metaphors are considered more creative and metaphorical than human-generated ones while when using our transferred metaphors for data augmentation improves the state of the art in metaphorical sentence classification by 3% in F1.
Testing Stationarity and Change Point Detection in Reinforcement Learning

We consider offline reinforcement learning (RL) methods in possibly nonstationary environments. Many existing RL algorithms in the literature rely on the stationarity assumption that requires the system transition and the reward function to be constant over time. However, the stationarity assumption is restrictive in practice and is likely to be violated in a number of applications, including traffic signal control, robotics and mobile health. In this paper, we develop a consistent procedure to test the nonstationarity of the optimal Q-function based on pre-collected historical data, without additional online data collection. Based on the proposed test, we further develop a sequential change point detection method that can be naturally coupled with existing state-of-the-art RL methods for policy optimization in nonstationary environments. The usefulness of our method is illustrated by theoretical results, simulation studies, and a real data example from the 2018 Intern Health Study. A Python implementation of the proposed procedure is available at https://github.com/limengbinggz/CUSUM-RL.
Frame-dragging effects on magnetic fields near a rotating black hole

We discuss the role of general relativity frame dragging acting on magnetic field lines near a rotating (Kerr) black hole. Near ergosphere the magnetic structure becomes strongly influenced and magnetic null points can develop. We consider aligned magnetic fields as well as fields inclined with respect to the rotation axis, and the two cases are shown to behave in profoundly different ways. Further, we construct surfaces of equal values of local electric and magnetic intensities, which have not yet been discussed in the full generality of a boosted rotating black hole.
The brown dwarf atmosphere monitoring (BAM) project - I. The largest near-IR monitoring survey of L and T dwarfs

Using SofI on the 3.5m New Technology Telescope, we have conducted an extensive near-infrared monitoring survey of an unbiased sample of 69 brown dwarfs spanning the L0 and T8 spectral range, with at least one example of each spectral type. Each target was observed for a 2-4 hour period in the Js-band, and the median photometric precision of the data is ~0.7%. A total of 14 brown dwarfs were identified as variables with min-to-max amplitudes ranging from 1.7% to 10.8% over the observed duration. All variables satisfy a statistical significance threshold with a p-value <5% based on comparison with the median reference star light curve. Approximately half of the variables show sinusoidal amplitude variations similar to 2M2139, and the remainder shows short timescale evolving light curves similar to SIMP0136. The L/T transition has been suggested to be a region of a higher degree of variability if patchy clouds are present and this survey was designed to test the patchy cloud model with photometric monitoring of both the L/T transition and non-transition brown dwarfs. Considering the targets identified as variable with the same statistical threshold, the measured variability frequency of 13^{+10}_{-4}% for the L7 -- T4 transition region is indistinguishable from that of the earlier spectral types (32^{+11}_{-8}%), the later spectral types (13^{+10}_{-4}%), or the combination of all non-transition region brown dwarfs (21^{+7}_{-5}%). The variables are not concentrated at the transition, in a specific colour, or with binaries. We note that of the systems previously monitored for variability only ~60% maintained the state of variability (variable or constant), with the remaining switching states. The 14 variables include nine newly identified variables that will provide important systems for follow-up multi-wavelength monitoring to further investigate brown dwarf atmosphere physics.
Magnetic phases of spin-(3/2) fermions on a spatially anisotropic square lattice

We study the magnetic phase diagram of spin-(3/2) fermions in a spatially anisotropic square optical lattice at quarter filling (corresponding to one particle per lattice site). In the limit of the large on-site repulsion the system can be mapped to the so-called Sp(N) Heisenberg spin model with N=4. We analyze the Sp(N) spin model with the help of the large-N field-theoretical approach and show that the effective theory corresponds to the Sp(N) extension of the CP{sup N-1} model, with the Lorentz invariance generically broken. We obtain the renormalization flow of the model couplings and show that although the Sp(N) terms are seemingly irrelevant, their presence leads to a renormalization of the CP{sup N-1} part of the action, driving a phase transition. We further consider the influence of the external magnetic field (the quadratic Zeeman effect) and present the qualitative analysis of the ground-state phase diagram.
Quasi-periodical components in the radial distributions of cosmologically remote objects

ABSTRACT A statisticalanalysisofradial(line-of-sight)1D-distributionsofbrightestclustergalax-ies (BCGs) within the redshift interval 0.044 6z 60.78 and Mg II absorption-linesystems (0.37 6 z 6 2.28) is carried out. Power spectra and two-point radial cor-relation functions are calculated. It is found that both radial distributions of spec-troscopic redshifts of 52,683 BCGs and 32,840 Mg II absorption systems incorporatesimilar quasi-periodical components relatively to the comoving distance. Signiﬁcanceof the components exceeds 4σ-level and admits an increase ( >∼5σ) for some broadsubsamples. For the ΛCDM cosmological model the periodicities correspond to spatialcomoving scales (98±3) and (101±2) h −1 Mpc, respectively. These quasi-periods turnout to be close to the characteristicscale(101±6)h −1 Mpc of the quasi-periodicalcom-ponent obtained earlier for the radial distribution of luminous red galaxies (LRGs). Onthe other hand, the scales are close to the spatial scale corresponding to the baryonacoustic oscillations (BAOs) revealed by many authors at the last decade. Fouriertransform phases obtained for the BCGs and LRGs are found to be close, while thephases calculated for the Mg II absorption systems and LRGs are opposite. Discus-sions of the results in a context of the BAO and large-scale structure characteristicscales are outlined.Key words: galaxies: distances and redshifts; quasars: absorption lines; cosmology:observations – distance scale – large-scale structure of Universe
Accommodating population differences when validating risk prediction models

Validation of risk prediction models in independent data provides a more rigorous assessment of model performance than internal assessment, for example, done by cross‐validation in the data used for model development. However, several differences between the populations that gave rise to the training and the validation data can lead to seemingly poor performance of a risk model. In this paper we formalize the notions of “similarity” or “relatedness” of the training and validation data, and define reproducibility and transportability. We address the impact of different distributions of model predictors and differences in verifying the disease status or outcome on measures of calibration, accuracy and discrimination of a model. When individual level information from both the training and validation data sets is available, we propose and study weighted versions of the validation metrics that adjust for differences in the risk factor distributions and in outcome verification between the training and validation data to provide a more comprehensive assessment of model performance. We provide conditions on the risk model and the populations that gave rise to the training and validation data that ensure a model's reproducibility or transportability, and show how to check these conditions using weighted and unweighted performance measures. We illustrate the method by developing and validating a model that predicts the risk of developing prostate cancer using data from two large prostate cancer screening trials.
Electric conductivity from the solution of the relativistic Boltzmann equation

We present numerical results of electric conductivity ${\ensuremath{\sigma}}_{\text{el}}$ of a fluid obtained solving the relativistic transport Boltzmann equation in a box with periodic boundary conditions. We compute ${\ensuremath{\sigma}}_{\text{el}}$ using two methods: the definition itself, i.e., applying an external electric field, and the evaluation of the Green-Kubo relation based on the time evolution of the current-current correlator. We find a very good agreement between the two methods. We also compare numerical results with analytic formulas in relaxation time approximation (RTA) where the relaxation time for ${\ensuremath{\sigma}}_{\text{el}}$ is determined by the transport cross section ${\ensuremath{\sigma}}_{\text{tr}}$, i.e., the differential cross section weighted with the collisional momentum transfer. We investigate the electric conductivity dependence on the microscopic details of the two-body scatterings: isotropic and anisotropic cross section as well as massless and massive particles. We find that the RTA underestimates considerably ${\ensuremath{\sigma}}_{\text{el}}$; for example, at screening masses ${m}_{D}\ensuremath{\sim}T$, such underestimation can be as large as a factor of 2. Furthermore, we study a more realistic case for a quark-gluon system (QGP) considering both a quasiparticle model tuned to lattice QCD (lQCD) thermodynamics, as well as the case of a perturbative QCD (pQCD) gas with running coupling. Also, for these cases more directly related to the description of the QGP system, we find that the RTA significantly underestimates the ${\ensuremath{\sigma}}_{\text{el}}$ by about 60%\char21{}80%.
Effect of magnetic resonance imaging pre-processing on the performance of model-based prostate tumor probability mapping

Objective. Multi-parametric magnetic resonance imaging (mpMRI) has become an important tool for the detection of prostate cancer in the past two decades. Despite the high sensitivity of MRI for tissue characterization, it often suffers from a lack of specificity. Several well-established pre-processing tools are publicly available for improving image quality and removing both intra- and inter-patient variability in order to increase the diagnostic accuracy of MRI. To date, most of these pre-processing tools have largely been assessed individually. In this study we present a systematic evaluation of a multi-step mpMRI pre-processing pipeline to automate tumor localization within the prostate using a previously trained model. Approach. The study was conducted on 31 treatment-naïve prostate cancer patients with a PI-RADS-v2 compliant mpMRI examination. Multiple methods were compared for each pre-processing step: (1) bias field correction, (2) normalization, and (3) deformable multi-modal registration. Optimal parameter values were estimated for each step on the basis of relevant individual metrics. Tumor localization was then carried out via a model-based approach that takes both mpMRI and prior clinical knowledge features as input. A sequential optimization approach was adopted for determining the optimal parameters and techniques in each step of the pipeline. Main results. The application of bias field correction alone increased the accuracy of tumor localization (area under the curve (AUC) = 0.77; p-value = 0.004) over unprocessed data (AUC = 0.74). Adding normalization to the pre-processing pipeline further improved diagnostic accuracy of the model to an AUC of 0.85 (p-value = 0.000 12). Multi-modal registration of apparent diffusion coefficient images to T2-weighted images improved the alignment of tumor locations in all but one patient, resulting in a slight decrease in accuracy (AUC = 0.84; p-value = 0.30). Significance. Overall, our findings suggest that the combined effect of multiple pre-processing steps with optimal values has the ability to improve the quantitative classification of prostate cancer using mpMRI. Clinical trials: NCT03378856 and NCT03367702.
PLASMA COMPRESSION IN MAGNETIC RECONNECTION REGIONS IN THE SOLAR CORONA

It has been proposed that particles bouncing between magnetized flows converging in a reconnection region can be accelerated by the first-order Fermi mechanism. Analytical considerations of this mechanism have shown that the spectral index of accelerated particles is related to the total plasma compression within the reconnection region, similarly to the case of the diffusive shock acceleration mechanism. As a first step to investigate the efficiency of Fermi acceleration in reconnection regions in producing hard energy spectra of particles in the solar corona, we explore the degree of plasma compression that can be achieved at reconnection sites. In particular, we aim to determine the conditions for the strong compressions to form. Using a two-dimensional resistive MHD numerical model, we consider a set of magnetic field configurations where magnetic reconnection can occur, including a Harris current sheet, a force-free current sheet, and two merging flux ropes. Plasma parameters are taken to be characteristic of the solar corona. Numerical simulations show that strong plasma compressions (≥4) in the reconnection regions can form when the plasma heating due to reconnection is efficiently removed by fast thermal conduction or the radiative cooling process. The radiative cooling process that is negligible in the typical 1 MK corona can play an important role in the low corona/transition region. It is found that plasma compression is expected to be strongest in low-beta plasma β ∼ 0.01–0.07 at reconnection magnetic nulls.
Curcumin-loaded hydroxypropyl-β-cyclodextrin inclusion complex with enhanced dissolution and oral bioavailability for epilepsy treatment

Abstract Curcumin, the main bioactive component of turmeric, has a wild range of beneficial effects on central nervous diseases, including anti-Alzheimer’s disease, antioxidant stress, and anti-inflammation. Currently, it has been demonstrated the anti-epileptic potential. However, curcumin has poor water solubility, high sensitivity to light and heat, and low absorption, which results in low bioavailability and greatly limits the clinical application of curcumin, as well as the elusive effects in anti-epileptic treatment. This study aimed to develop a curcumin hydroxypropyl-β-cyclodextrin inclusion complex (CUR-HP-β-CD) to improve its bioavailability and facilitate its potential development as an anti-epileptic drug. The CUR-HP-β-CD was generated by the solvent evaporation method, which has efficient entrapment, high solubility, and facilitated bioavailability and brain distribution. The solubility of the CUR-HP-β-CD was 63.5, 60.1, and 52.9 times that of the unformulated curcumin in H2O, HCl (pH 1.2), and PBS (pH 6.8), respectively. The bioavailability of CUR-HP-β-CD is improved 2.8 times and 38.7 folds higher brain concentrations. Moreover, the therapeutic anti-epileptic effects of CUR-HP-β-CD were much more effective in pentylenetetrazol (PTZ)-induced zebrafish and mouse models. This study showed a simple and reproducible strategy to effectively improve the bioavailability and therapeutic effects of curcumin, which could be potentially used in epilepsy treatment.
Collapse times for attractive Bose–Einstein condensates

We argue that the main mechanism for condensate collapse in an attractive Bose–Einstein condensate is the loss of coherence between atoms a finite distance apart, rather than the growth of the occupation number in non-condensate modes. Since the former mechanism is faster than the latter by a factor of approximately 3/2, this helps to dispel the apparent failure of field theoretical models in predicting the collapse time of the condensate.