Miracle-dz/UniFAR-FTUs huggingface dataset:
The interplay of fast waves and slow convection in geodynamo simulations nearing Earth’s core conditions

Ground observatory and satellite-based determinations of temporal variations in the geomagnetic field probe a decadal to annual timescale range where Earth’s core slow, inertialess convective motions and rapidly propagating, inertia-bearing hydromagnetic waves are in interplay. Here we numerically model and jointly investigate these two important features with the help of a geodynamo simulation that (to date) is the closest to the dynamical regime of Earth’s core. This model also considerably enlarges the scope of a previous asymptotic scaling analysis, which in turn strengthens the relevance of the approach to describe Earth’s core dynamics. Three classes of hydrodynamic and hydromagnetic waves are identified in the model output, all with propagation velocity largely exceeding that of convective advection: axisymmetric, geostrophic Alfvén torsional waves, and non-axisymmetric, quasi-geostrophic Alfvén and Rossby waves. The contribution of these waves to the geomagnetic acceleration amounts to an enrichment and flattening of its energy density spectral profile at decadal timescales, thereby providing a constraint on the extent of the $f^{-4}$ range observed in the geomagnetic frequency power spectrum. As the model approaches Earth’s core conditions, this spectral broadening arises because the decreasing inertia allows for waves at increasing frequencies. Through non-linear energy transfers with convection underlain by Lorentz stresses, these waves also extract an increasing amount of energy from the underlying convection as their key timescale decreases towards a realistic value. The flow and magnetic acceleration energies carried by waves both linearly increase with the ratio of the magnetic diffusion timescale to the Alfvén timescale, highlighting the dominance of Alfvén waves in the signal and the stabilizing control of magnetic dissipation at non-axisymmetric scales. Extrapolation of the results to Earth’s core conditions supports the detectability of Alfvén waves in geomagnetic observations, either as axisymmetric torsional oscillations or through the geomagnetic jerks caused by non-axisymmetric waves. In contrast, Rossby waves appear to be too fast and carry too little magnetic energy to be detectable in geomagnetic acceleration signals of limited spatio-temporal resolution.
Projected Wasserstein gradient descent for high-dimensional Bayesian inference

We propose a projected Wasserstein gradient descent method (pWGD) for high-dimensional Bayesian inference problems. The underlying density function of a particle system of WGD is approximated by kernel density estimation (KDE), which faces the long-standing curse of dimensionality. We overcome this challenge by exploiting the intrinsic low-rank structure in the difference between the posterior and prior distributions. The parameters are projected into a low-dimensional subspace to alleviate the approximation error of KDE in high dimensions. We formulate a projected Wasserstein gradient flow and analyze its convergence property under mild assumptions. Several numerical experiments illustrate the accuracy, convergence, and complexity scalability of pWGD with respect to parameter dimension, sample size, and processor cores.
A reproduction of Apple’s bi-directional LSTM models for language identification in short strings

Language Identification is the task of identifying a document’s language. For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments. In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog post. We confirm the bi-LSTM model’s performance and find that it outperforms current open-source language identifiers. We further find that its language identification mistakes are due to confusion between related languages.
VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention

This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech (TTS) model using a very deep Variational Autoencoder (VDVAE) with Residual Attention mechanism, which refines the textual-to-acoustic alignment layer-wisely. Hierarchical latent variables with different temporal resolutions from the VDVAE are used as queries for residual attention module. By leveraging the coarse global alignment from previous attention layer as an extra input, the following attention layer can produce a refined version of alignment. This amortizes the burden of learning the textual-to-acoustic alignment among multiple attention layers and outperforms the use of only a single attention layer in robustness. An utterance-level speaking speed factor is computed by a jointly-trained speaking speed predictor, which takes the mean-pooled latent variables of the coarsest layer as input, to determine number of acoustic frames at inference. Experimental results show that VARA-TTS achieves slightly inferior speech quality to an AR counterpart Tacotron 2 but an order-of-magnitude speed-up at inference; and outperforms an analogous non-AR model, BVAE-TTS, in terms of speech quality.
Efficient Algorithms for Federated Saddle Point Optimization

We consider strongly convex-concave minimax problems in the federated setting, where the communication constraint is the main bottleneck. When clients are arbitrarily heterogeneous, a simple Minibatch Mirror-prox achieves the best performance. As the clients become more homogeneous, using multiple local gradient updates at the clients significantly improves upon Minibatch Mirror-prox by communicating less frequently. Our goal is to design an algorithm that can harness the benefit of similarity in the clients while recovering the Minibatch Mirror-prox performance under arbitrary heterogeneity (up to log factors). We give the first federated minimax optimization algorithm that achieves this goal. The main idea is to combine (i) SCAFFOLD (an algorithm that performs variance reduction across clients for convex optimization) to erase the worst-case dependency on heterogeneity and (ii) Catalyst (a framework for acceleration based on modifying the objective) to accelerate convergence without amplifying client drift. We prove that this algorithm achieves our goal, and include experiments to validate the theory.
A model for traffic incident prediction using emergency braking data

This article presents a model for traffic incident prediction. Specifically, we address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. Based on relevant risk factors for traffic accidents and corresponding data categories, we evaluate different options for preprocessing sparse data and different Machine Learning models. Furthermore, we present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles as well as weather, traffic and road data, respectively. After model evaluation and optimisation, we found that a Random Forest model trained on artificially balanced (under-sampled) data provided the highest classification accuracy of 85% on the original imbalanced data. Finally, we present our conclusions and discuss further work; from gathering more data over a longer period of time to build stronger classification systems, to addition of internal factors such as the driver's visual and cognitive attention.
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers

With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the popular parameter server framework. In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updates it by waiting and averaging all estimates obtained from its neighbors, and then corrects it on the basis of its local dataset. However, the synchronization phase can be time consuming due to the need to wait for \textit{stragglers}, i.e., slower workers. An efficient way to mitigate this effect is to let each worker wait only for updates from the fastest neighbors before updating its local parameter. The remaining neighbors are called \textit{backup workers. } To minimize the globally training time over the network, we propose a fully distributed algorithm to dynamically determine the number of backup workers for each worker. We show that our algorithm achieves a linear speedup for convergence (i.e., convergence performance increases linearly with respect to the number of workers). We conduct extensive experiments on MNIST and CIFAR-10 to verify our theoretical results.
Same File, Different Changes: The Potential of Meta-Maintenance on GitHub

Online collaboration platforms such as GitHub have provided software developers with the ability to easily reuse and share code between repositories. With clone-and-own and forking becoming prevalent, maintaining these shared files is important, especially for keeping the most up-to-date version of reused code. Different to related work, we propose the concept of meta-maintenance—i.e., tracking how the same files evolve in different repositories with the aim to provide useful maintenance opportunities to those files. We conduct an exploratory study by analyzing repositories from seven different programming languages to explore the potential of meta-maintenance. Our results indicate that a majority of active repositories on GitHub contains at least one file which is also present in another repository, and that a significant minority of these files are maintained differently in the different repositories which contain them. We manually analyzed a representative sample of shared files and their variants to understand which changes might be useful for meta-maintenance. Our findings support the potential of meta-maintenance and open up avenues for future work to capitalize on this potential.
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning

We provide a new adaptive method for online convex optimization, MetaGrad, that is robust to general convex losses but achieves faster rates for a broad class of special functions, including exp-concave and strongly convex functions, but also various types of stochastic and non-stochastic functions without any curvature. We prove this by drawing a connection to the Bernstein condition, which is known to imply fast rates in offline statistical learning. MetaGrad further adapts automatically to the size of the gradients. Its main feature is that it simultaneously considers multiple learning rates, which are weighted directly proportional to their empirical performance on the data using a new meta-algorithm. We provide three versions of MetaGrad. The full matrix version maintains a full covariance matrix and is applicable to learning tasks for which we can afford update time quadratic in the dimension. The other two versions provide speed-ups for high-dimensional learning tasks with an update time that is linear in the dimension: one is based on sketching, the other on running a separate copy of the basic algorithm per coordinate. We evaluate all versions of MetaGrad on benchmark online classification and regression tasks, on which they consistently outperform both online gradient descent and AdaGrad. *An earlier conference version of this paper appeared at NeurIPS 2016 (Van Erven, Koolen, 2016). 1 ar X iv :2 10 2. 06 62 2v 2 [ cs . L G ] 3 0 A ug 2 02 1
The role of extracellular matrix proteins in gastric cancer development via epithelial-mesenchymal transition

Aim: To acquire a deeper perception of EMT, we evaluated the expression of some candidate extra cellular matrix (ECM) proteins including THBS2, OSMR and CHI3L1 which were collected from RNA-seq bioinformatic analyses. Background: Gastric cancer (GC) is a major incident gastrointestinal cancer with a high rate of mortality. Metastasis is a challenging issue in gastric cancer treatment. Epithelial mesenchymal transition (EMT) of cancer cells is a complicated process controlled by different cells and molecular pathways regarded as an important step at the onset of metastasis. Methods: AGS gastric cancer cell line was cultured and treated by TGF-β. EMT induction was verified by measuring the expression of E-cadherin, Snail, β-catenin and Vimentin genes by real time PCR. Then, following our previous study, we evaluated the expression of THBS2, OSMR and CHI3L1 genes in EMT induced cells by real time PCR. Results: Downregulation of E-cadherin and upregulation of Snail, β-catenin and Vimentin genes were verified in AGS treated cells in comparison with none-treated cells (P-value = 0.0355, P-value = 0.007, P-value = 0.0059, P-value = 0.0206 respectively). Also, upregulation of THBS2, OSMR and CHI3L1 were validated in these cells after EMT induction (P-value = 0.0147, P-value = 0.05, P-value = 0.05 respectively). Conclusion: Our morphological and molecular results validated EMT induction by TGF- β cytokine in AGS gastric cancer cell line. Furthermore, significant upregulation of candidate genes including THBS2, OSMR and CHI3L1 verified the role of these proteins in gastric cancer invasiveness. However, further studies are needed for the validation of prognostic value of these markers.
The synergistic impact of NSAIDs and aggressive hydration therapy on the rate of post-ERCP pancreatitis in high -risk and low -risk patients

Aim: The main complication of Endoscopic retrograde cholangiopancreatography (ERCP) is post-ERCP pancreatitis (PEP). Background: Based on demographic characteristics and underlying issues and ERCP indication, patients are categorized as high risk or low risk. There have been no studies on the synergistic effects of NSAIDS and hydration therapy, separately sorted by the risk assessment of PEP in different groups of patients. Methods: This study included 281 eligible participants after exclusion. According to demographic characteristics and co-morbidities, the patients were divided to high risk and low risk. The high-risk group was divided randomly into two subgroups and both of them received NSAIDs (100 mg rectal Diclofenac). One group received standard hydration (1.5mg/kg/hr), another the other received aggressive hydration (3mg/kg/h). The low-risk group received standard hydration. One of its subgroups received NSAIDs, while others did not. The efficacy of these preventions was compared across 4 subgroups. Results: The mean age was 59.85±17.17. Eight hours after ERCP, the amylase and lipase were significantly higher in the high-risk group with standard hydration (P=0.00). Amylase, lipase 8 hours, between two low risk subgroups, NSAIDs had no significant effect (P=0.38, P=0.95, respectively). After adjustment based on cannulation, manipulation and duration of time, the results had no change (P=0.64, P=0.19, P=0.61). Conclusion: The aggressive hydration could significantly decrease the risk of PEP. However, the low-risk group was exposed to the lowest risk of PEP. NSAIDs could not help to decrease the rate PEP in the low-risk groups alone. Overall, it seems hydration and NSAIDs therapy had synergistic outcome in high-risk patients.
inDepth: Force-based Interaction with Objects beyond A Physical Barrier

We propose inDepth, a novel system that enables force-based interaction with objects beyond a physical barrier by using scalable force sensor modules. inDepth transforms a physical barrier (eg. glass showcase or 3D display) to a tangible input interface that enables users to interact with objects out of reach, by applying finger pressure on the barrier’s surface. To achieve this interaction, our system tracks the applied force as a directional vector by using three force sensors installed underneath the barrier. Meanwhile, our force-to-depth conversion algorithm translates force intensity into a spatial position along its direction beyond the barrier. Finally, the system executes various operations on objects in that position based on the type of application. In this paper, we introduce inDepth concept and its design space. We also demonstrate example applications, including selecting items in showcases and manipulating 3D rendered models.
Megathrust Slip Behavior for Great Earthquakes Along the Sumatra-Andaman Subduction Zone Mapped From Satellite GOCE Gravity Field Derivatives

During the last two decades, space geodesy allowed mapping accurately rupture areas, slip distribution, and seismic coupling by obtaining refined inversion models and greatly improving the study of great megathrust earthquakes. A better understanding of these phenomena involving large areas of hundreds of square kilometers came from the last gravity satellite mission that allowed detecting mass transfer through the Earth interior. In this work, we performed direct modeling of satellite GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) derived gravity gradients up to degree/order N = 200 of the harmonic expansion and then corrected this by the effect of topography. Cutting off the model up to this degree/order allows inferring mass heterogeneities located at an approximate depth of 31 km, just along the plate interface where most (but not all) significant slip occurs. Then, we compared the vertical gravity gradient to well-constrained coseismic slip models for three of the last major earthquakes along the Sunda interface. We analyzed seismic rupture behavior for recent and for historical earthquakes along this subduction margin and the relationship of the degree of interseismic coupling using the gravity signal. From this, we found that strong slip patches occurred along minima gravity gradient lobes and that the maximum vertical displacements were related quantitatively to the gravity-derived signal. The degree of interseismic coupling also presents a good correspondence to the vertical gravity gradient, showing an inverse relationship, with low degrees of coupling over regions of relatively higher density. This along-strike segmentation of the gravity signal agrees with the along-strike seismic segmentation observed from recent and historical earthquakes. The thermally controlled down-dip ending of the locked fault zone along central Sumatra also presented an inverse relationship with the density structure along the forearc inferred using our modeling. From this work, we inferred different mass heterogeneities related to persistent tectonic features along the megathrust and along the marine forearc, which may control strain accumulation and release along the megathrust. Combining these data with geodetical and seismological data could possibly delimit and monitor areas with a higher potential seismic hazard around the world.
Touching the Past: Developing and Evaluating A Heritage kit for Visualizing and Analyzing Historical Artefacts Using Tangible Augmented Reality

My research explores the use of tangible user interfaces and augmented reality to interact with virtual representations of historical artefacts within the cultural heritage domain. As a practice-led research, this research project aims to design, develop and evaluate tangible user interfaces combined with augmented reality to provide cultural heritage professionals and archaeology experts with an accessible and intuitive tangible augmented reality interface for the visualization, analysis and interpretations of historical artefacts. This research is interdisciplinary in nature combining the fields of design, HCI and heritage; and the newly explored interaction methods will suggest new possibilities for the future of Tangible User Interfaces and augmented reality research in general and cultural heritage in particular. In the first year of my PhD, I developed a prototype that showcased a tangible interface to interact with augmented historical artefacts, and preliminary testing with experts yielded very positive results. In the second year, the research will investigate how different tangible interaction methods in augmented reality can promote realism and immersion and enhance the user experience.
Insights into human evolution from 60 years of research on chimpanzees at Gombe

Abstract Abstract Sixty years of research on chimpanzees (Pan troglodytes) at Gombe National Park, Tanzania have revealed many similarities with human behaviour, including hunting, tool use and coalitionary killing. The close phylogenetic relationship between chimpanzees and humans suggests that these traits were present in the last common ancestor of Pan and Homo (LCAPH). However, findings emerging from studies of our other closest living relative, the bonobo (Pan paniscus), indicate that either bonobos are derived in these respects, or the many similarities between chimpanzees and humans evolved convergently. In either case, field studies provide opportunities to test hypotheses for how and why our lineage has followed its peculiar path through the adaptive landscape. Evidence from primate field studies suggests that the hominin path depends on our heritage as apes: inefficient quadrupeds with grasping hands, orthograde posture and digestive systems that require high-quality foods. Key steps along this path include: (a) changes in diet; (b) increased use of tools; (c) bipedal gait; (d) multilevel societies; (e) collective foraging, including a sexual division of labour and extensive food transfers; and (f) language. Here I consider some possible explanations for these transitions, with an emphasis on contributions from Gombe.
Effects of Pelargonium sidoides extract vs roxithromycin on chemokine levels in nasal secretions of patients with uncomplicated acute rhinosinusitis

Previous investigations suggest the use of extract from the roots of Pelargonium sidoides (EPs 7630) for the therapy of uncomplicated rhinosinusitis. The aim of this prospective study was to compare the effects of herbal drug EPs 7630 and antibiotic roxithromycin on chemokine production in nasal mucosa and clinical parameters in patients with uncomplicated acute bacterial rhinosinusitis (ABRS).
Wearable Crazy Eights: Wearable Ideation Methods for Encouraging Divergent Design Concepts

Participatory design with wearable users entails engaging people in the design process from the early ideation phases. However, user-generated wearable concepts are often limited by the narrow design space of commercially available wearables. This paper presents an ideation scaffolding method we developed for eliciting wearable concepts, called Wearable Crazy Eights, where participants used an ideation deck and sketched up to 8 concepts in 8 minutes. Herein, we discuss the artifacts produced from our ideation method in a study with 46 participants comparing 3 groups. By comparing the 3 groups we were able to parse the effects of each activity on the resulting ideas. Our contribution is a replicable and customizable ideation method for encouraging outside-the-box thinking in wearable studies.
Organ-Specific Surveillance and Long-Term Residency Strategies Adapted by Tissue-Resident Memory CD8+ T Cells

Tissue-resident CD8+ T cells (CD8+ TRM) populate lymphoid and non-lymphoid tissues after infections as first line of defense against re-emerging pathogens. To achieve host protection, CD8+ TRM have developed surveillance strategies that combine dynamic interrogation of pMHC complexes on local stromal and hematopoietic cells with long-term residency. Factors mediating CD8+ TRM residency include CD69, a surface receptor opposing the egress-promoting S1P1, CD49a, a collagen-binding integrin, and CD103, which binds E-cadherin on epithelial cells. Moreover, the topography of the tissues of residency may influence TRM retention and surveillance strategies. Here, we provide a brief summary of these factors to examine how CD8+ TRM reconcile constant migratory behavior with their long-term commitment to local microenvironments, with a focus on epithelial barrier organs and exocrine glands with mixed connective—epithelial tissue composition.