Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of German Twitter

This thesis explores the ways by how people express their opinions on German Twitter, examines current approaches to automatic mining of these feelings, and proposes novel methods, which outperform state-of-the-art techniques. For this purpose, I introduce a new corpus of German tweets that have been manually annotated with sentiments, their targets and holders, as well as polar terms and their contextual modifiers. Using these data, I explore four major areas of sentiment research: (i) generation of sentiment lexicons, (ii) fine-grained opinion mining, (iii) message-level polarity classification, and (iv) discourse-aware sentiment analysis. In the first task, I compare three popular groups of lexicon generation methods: dictionary-, corpus-, and word-embedding-based ones, finding that dictionary-based systems generally yield better lexicons than the last two groups. Apart from this, I propose a linear projection algorithm, whose results surpass many existing automatic lexicons. Afterwords, in the second task, I examine two common approaches to automatic prediction of sentiments, sources, and targets: conditional random fields and recurrent neural networks, obtaining higher scores with the former model and improving these results even further by redefining the structure of CRF graphs. When dealing with message-level polarity classification, I juxtapose three major sentiment paradigms: lexicon-, machine-learning-, and deep-learning-based systems, and try to unite the first and last of these groups by introducing a bidirectional neural network with lexicon-based attention. Finally, in order to make the new classifier aware of discourse structure, I let it separately analyze the elementary discourse units of each microblog and infer the overall polarity of a message from the scores of its EDUs with the help of two new approaches: latent-marginalized CRFs and Recursive Dirichlet Process.
Learning Modular Representations for Long-Term Multi-Agent Motion Predictions

Context plays a significant role in the generation of motion for dynamic agents in interactive environments. This work proposes a modular method that utilises a model of the environment to aid motion prediction of tracked agents. This paper shows that modelling the spatial and dynamic aspects of a given environment alongside the local per agent behaviour results in more accurate and informed long-term motion prediction. Further, we observe that this decoupling of dynamics and environment models allows for better generalisation to unseen environments, requiring that only a spatial representation of a new environment be learned. We highlight the model's prediction capability using a benchmark pedestrian tracking problem and by tracking a robot arm performing a tabletop manipulation task. The proposed approach allows for robust and data efficient forward modelling, and relaxes the need for full model re-training in new environments. We evaluate this through an ablation study which shows better performance gain when decoupling representation modules in addition to improved generalisation on tasks with dynamics unseen at training time.
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. Yet, very little has been done to date to provide system-level support to data scientists who wish to develop and deploy responsible machine learning methods. We aim to fill this gap and present FairPrep, a design and evaluation framework for fairness-enhancing interventions. FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning. As part of our contribution, we identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions. We then show how FairPrep can be used to measure the impact of sound best practices, such as hyperparameter tuning and feature scaling. In particular, our results suggest that the high variability of the outcomes of fairness-enhancing interventions observed in previous studies is often an artifact of a lack of hyperparameter tuning. Further, we show that the choice of a data cleaning method can impact the effectiveness of fairness-enhancing interventions.
Analysis of Asymptotic Escape of Strict Saddle Sets in Manifold Optimization

In this paper, we provide some analysis on the asymptotic escape of strict saddles in manifold optimization using the projected gradient descent (PGD) algorithm. One of our main contributions is that we extend the current analysis to include non-isolated and possibly continuous saddle sets with complicated geometry. We prove that the PGD is able to escape strict critical submanifolds under certain conditions on the geometry and the distribution of the saddle point sets. We also show that the PGD may fail to escape strict saddles under weaker assumptions even if the saddle point set has zero measure and there is a uniform escape direction. We provide a counterexample to illustrate this important point. We apply this saddle analysis to the phase retrieval problem on the low-rank matrix manifold, prove that there are only a finite number of saddles, and they are strict saddles with high probability. We also show the potential application of our analysis for a broader range of manifold optimization problems.
Fluid cultures and identifications : the intercultural communication experiences of Saudi international students in the UK

This thesis is an investigation into the intercultural communication experiences of Saudi international students in the UK. It focuses on the friendship experiences that Saudi international students form with home and international students in order to engage in intercultural interactions. It further investigates how Saudi international students experience interactions with individuals from the opposite gender with whom they have limited interactions with in Saudi Arabia. Furthermore, the study focuses on the incidents that may motivate or demotivate Saudi international students from engaging in intercultural communication. Finally, this thesis considers the changes that the participants had introduced to their worldviews, identifications and practices as a result of their overall intercultural experiences in the UK. The findings show that 1) the participants’ identifications of in-group and out-group members were inconsistent and the boundaries between both categories were difficult to identify. 2) In contrast to studies, which suggest that Saudi international students are homogeneous Muslims who come from a collectivistic culture, the findings show that the participants had different worldviews and identifications and cannot be categorized in a fixed category. 3) The findings also illustrate that despite the segregation between men and women in Saudi Arabia, Saudi international students do not necessarily find interaction with individuals from the opposite gender challenging, as some literature claims. 4) The experiences that demotivated the participants from engaging in intercultural communication are not necessarily negative ones, since universities religious organisations, such as the Islamic Society, which was perceived positively by the participants, may also contribute to demotivating their attendees from experiencing intercultural communication. 5) The overall intercultural experiences in the UK had influenced the participants’ worldviews, identifications and practices on various levels. Thus, some participants introduced significant changes to their worldviews and identifications, others accepted changes that do not contradict with their understandings of religion, while others resisted any changes. Overall, the study argues for moving beyond essentialist descriptions for Saudi international students that describe them as a homogeneous Muslim group, coming from a collectivistic culture in a country where men are dominant over women. The study gives valuable insights and practical implications for UK universities and Saudi Cultural Bureaux abroad where Saudi international students form a noticeable number amongst other international students.
Modeling the Thermal Bulge of a Hot Jupiter with the Two-stream Approximation

We revisit the problem of the thermal bulge of asynchronous hot Jupiters using HD 209458 b as a fiducial study. We improve upon previous works by using a double-gray atmosphere model and interior structure from MESA as the background state and then solve for the thermal bulge in response to the semidiurnal component of stellar insolation. The atmosphere model is based on the radiative transfer with Eddington’s two-stream approximation. Two opacity cases are considered: the first introduces a greenhouse effect, and the second exhibits a strong temperature inversion. We find that for the predominant thermal bulges excited by g-modes of lower orders, our results are qualitatively similar to the adiabatic results from Arras & Socrates. It arises because the perturbed heating due to self-absorption of thermal emissions can be significant (i.e., greenhouse effect) against Newtonian damping, thereby leading to almost undamped thermal bulges. We also find that the contribution to the thermal bulge from the evanescent waves in the convective zone is not negligible, implying that the thermal bulge is not merely confined in the atmosphere and radiative envelope. Assuming the torque balance between the thermal and gravitational bulges, we estimate the tidal quality factor of the planet for gravitational tides to match the observed radius. The limitations of our model are also briefly discussed.
Using Fully Convolutional Neural Networks to detect manipulated images in videos

We propose a compact architecture based on fully convolutional neural networks (FCN) to detect manipulated images of human faces. In contrast to existing FCN architectures for classification, here the final layer feature map exhibits large spatial dimensions with non-global receptive field. The final layer features are spatially averaged using global average pooling (GAP) to provide more robust features. We leverage the structure of the FCN to derive a straightforward way for joint classification and forgery localization training and show that the network’s classification performance improves significantly by the addition of a pixelwise classification loss. The trained networks achieve state of the art results in binary classification in the FaceForensics++ dataset and competitive performance in other tasks using a significantly reduced number of parameters and small resolution input images. Additionally, we examine how well the proposed architecture can detect fully generated images using faces from the recently proposed PGAN and StyleGAN methods. We show that this task is easier to learn than detecting manipulated images and that for both cases there is only a small drop of performance when the network is trained using more than one manipulation technique in the training data.
The microstructure of stochastic volatility models with self-exciting jump dynamics

We provide a general probabilistic framework within which we establish scaling limits for a class of continuous-time stochastic volatility models with self-exciting jump dynamics. In the scaling limit, the joint dynamics of asset returns and volatility is driven by independent Gaussian white noises and two independent Poisson random measures that capture the arrival of exogenous shocks and the arrival of self-excited shocks, respectively. Various well-studied stochastic volatility models with and without self-exciting price/volatility co-jumps are obtained as special cases under different scaling regimes. We analyze the impact of external shocks on the market dynamics, especially their impact on jump cascades and show in a mathematically rigorous manner that many small external shocks may tigger endogenous jump cascades in asset returns and stock price volatility.
Neutrino-deuteron scattering: Uncertainty quantification and new 
L1,A
 constraints

We study neutral- and charged-current (anti)neutrino-induced dissociation of the deuteron at energies from threshold up to 150 MeV by employing potentials, as well as one- and two-body currents, derived in chiral effective field theory ($\ensuremath{\chi}\mathrm{EFT}$). We provide uncertainty estimates from $\ensuremath{\chi}\mathrm{EFT}$ truncations of the electroweak current, dependences on the $\ensuremath{\chi}\mathrm{EFT}$ cutoff, and variations in the pool of fit data used to fix the low-energy constants of $\ensuremath{\chi}\mathrm{EFT}$. At 100 MeV of incident (anti)neutrino energy, these uncertainties amount to about 2--3% and are smaller than the sensitivity of the cross sections to the single-nucleon axial form factor, which amounts to $5%$ if one varies the range of the nucleon axial radius within the bands determined by recent lattice quantum chromodynamics evaluations and phenomenological extractions. We conclude that a precise determination of the nucleon axial form factor is required for a high-precision calculation of the neutrino-deuteron cross sections at energies higher than 100 MeV. By matching our low-energy $\ensuremath{\chi}\mathrm{EFT}$ results to those of pionless effective field theory ($\overline{)\ensuremath{\pi}}\phantom{\rule{0.28em}{0ex}}\mathrm{EFT}$), we provide new constraints for the counterterm ${L}_{1,A}$ that parametrizes the strength of the axial two-body current in $\overline{)\ensuremath{\pi}}\phantom{\rule{0.28em}{0ex}}\mathrm{EFT}$. We obtain a value of $4. {9}_{\ensuremath{-}1.5}^{+1.9}\phantom{\rule{4pt}{0ex}}{\mathrm{fm}}^{3}$ at renormalization scale set to pion mass, which is compatible with, albeit narrower than, previous experimental determinations, and comparable to a recent lattice quantum chromodynamics calculation.
Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation

Neural Architecture Search (NAS), aiming at automatically designing network architectures by machines, is expected to bring about a new revolution in machine learning. Despite these high expectation, the effectiveness and efficiency of existing NAS solutions are unclear, with some recent works going so far as to suggest that many existing NAS solutions are no better than random architecture selection. The ineffectiveness of NAS solutions may be attributed to inaccurate architecture evaluation. Specifically, to speed up NAS, recent works have proposed under-training different candidate architectures in a large search space concurrently by using shared network parameters; however, this has resulted in incorrect architecture ratings and furthered the ineffectiveness of NAS. In this work, we propose to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained; this reduces the representation shift caused by the shared parameters and leads to the correct rating of the candidates. Thanks to the block-wise search, we can also evaluate all of the candidate architectures within each block. Moreover, we find that the knowledge of a network model lies not only in the network parameters but also in the network architecture. Therefore, we propose to distill the neural architecture (DNA) knowledge from a teacher model to supervise our block-wise architecture search, which significantly improves the effectiveness of NAS. Remarkably, the performance of our searched architectures has exceeded the teacher model, demonstrating the practicability of our method. Finally, our method achieves a state-of-the-art 78.4% top-1 accuracy on ImageNet in a mobile setting. All of our searched models along with the evaluation code are available at https://github.com/changlin31/DNA.
Interior of a unitarily evaporating black hole

Author(s): Nomura, Y | Abstract: © 2020 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3. We study microscopic operators describing the experience of an observer falling into the horizon of a unitarily evaporating black hole. For a young black hole, these operators can be taken to act only on the degrees of freedom in the black hole region: the soft - or stretched horizon - modes as well as the semiclassical modes in the zone region. On the other hand, for an old black hole, the operators must also involve radiation emitted earlier; the difference between the two cases comes from statistics associated with the coarse graining performed to obtain the effective theory of the interior. We find that the operators relevant for the interior theory can be defined globally as standard linear operators throughout the microstates, which obey the correct algebra up to corrections exponentially suppressed in the ratio of excitation energy to the Hawking temperature. We conjecture that the existence of such global operators is required for the emergence of the semiclassical picture. We also elucidate the relation between the present construction and entanglement wedge reconstruction of the interior.
Clinical and Serological Findings in Juvenile Patients with Idiopathic Arthritis in South West of Iran

Results Sixty children (32 boys and 28 girls) with idiopathic arthritis ranged in age from 1.5 to 16 years. The mean age at the first presentation was 4.92+3.68 years. Oligoarthritis was the most common subtype in 27 (45%), followed by systemic-onset in 17 (28.3%) and polyarthritis in 16 (26.7%) of patients. The most commonly involved joints were knee 53(88.3%), ankle 28(46.6%) and wrist 27(45%). Uveitis was detected in two patients, and positivity for Antinuclear antibody (ANA) titer was revealed in one patient. Conclusions In this study, the pattern of most clinical features in different subtypes of juvenile idiopathic arthritis resembles to other studies. Positive ANA was less; however, the low numbers of Iranian patients with uveitis was noteworthy.
Deep Object Co-segmentation via Spatial-Semantic Network Modulation

Object co-segmentation is to segment the shared objects in multiple relevant images, which has numerous applications in computer vision. This paper presents a spatial and semantic modulated deep network framework for object co-segmentation. A backbone network is adopted to extract multi-resolution image features. With the multi-resolution features of the relevant images as input, we design a spatial modulator to learn a mask for each image. The spatial modulator captures the correlations of image feature descriptors via unsupervised learning. The learned mask can roughly localize the shared foreground object while suppressing the background. For the semantic modulator, we model it as a supervised image classification task. We propose a hierarchical second-order pooling module to transform the image features for classification use. The outputs of the two modulators manipulate the multi-resolution features by a shift-and-scale operation so that the features focus on segmenting co-object regions. The proposed model is trained end-to-end without any intricate post-processing. Extensive experiments on four image co-segmentation benchmark datasets demonstrate the superior accuracy of the proposed method compared to state-of-the-art methods.
Spectropolarimetric Insight into Plasma Sheet Dynamics of a Solar Flare

We examine spectropolarimetric data from the Coronal Multi-channel Polarimeter (CoMP) instrument, acquired during the evolution of the 2017 September 10 X8.2 solar flare on the western solar limb. CoMP captured linearly polarized light from two emission lines of Fe xiii at 1074.7 and 1079.8 nm, from 1.03 to 1.5 solar radii. We focus here on the hot plasma sheet lying above the bright flare loops and beneath the ejected coronal mass ejection. The polarization has a striking and coherent spatial structure, with unexpectedly small polarization aligned with the plasma sheet. By elimination, we find that small-scale magnetic field structure is needed to cause such significant depolarization, and suggest that plasmoid formation during reconnection (associated with the tearing-mode instability) creates magnetic structure on scales below instrument resolution of 6 Mm. We conclude that polarization measurements with new coronagraphs, such as the upcoming Daniel K. Inouye Solar Telescope, will further enhance our understanding of magnetic reconnection and development of turbulence in the solar corona.
A Fine-grained Sentiment Dataset for Norwegian

We here introduce NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. The underlying texts are taken from a corpus of professionally authored reviews from multiple news-sources and across a wide variety of domains, including literature, games, music, products, movies and more. We here present a detailed description of this annotation effort. We provide an overview of the developed annotation guidelines, illustrated with examples and present an analysis of inter-annotator agreement. We also report the first experimental results on the dataset, intended as a preliminary benchmark for further experiments.
Sunspot observations by Hisako Koyama: 1945–1996

Sunspot records are the only observational tracer of solar activity that provides a fundamental, multicentury reference. Its homogeneity has been largely maintained with a succession of long-duration visual observers. In this article, we examine observations of one of the primary reference sunspot observers, Hisako Koyama. By consulting original archives of the National Museum of Nature and Science of Japan (hereafter, NMNS), we retrace the main steps of her solar-observing career, from 1945 to 1996. We also present the reconstruction of a full digital data base of her sunspot observations at the NMNS, with her original drawings and logbooks. Here, we extend the availability of her observational data from 1947–1984 to 1945–1996. Comparisons with the international sunspot number (Version 2) and with the group sunspot number series show a good global stability of Koyama's observations, with only temporary fluctuations over the main interval 1947–1982. Identifying drawings made by alternate observers throughout the series, we find that a single downward baseline shift in the record coincides with the partial contribution of replacement observers mostly after 1983. We determine the correction factor to bring the second part (1983–1996) to the same scale with Koyama's main interval (1947–1982). We find a downward jump by 9 per cent after 1983, which then remains stable until 1995. Overall, the high quality of Koyama's observations with her life-long dedication leaves a lasting legacy of this exceptional personal achievement. With this comprehensive recovery, we now make the totality of this legacy directly accessible and exploitable for future research.