Miracle-dz/UniFAR-FTUs huggingface dataset:
Lévy Adaptive B-spline Regression via Overcomplete Systems

The estimation of functions with varying degrees of smoothness is a challenging problem in the nonparametric function estimation. In this paper, we propose the LABS (L\'{e}vy Adaptive B-Spline regression) model, an extension of the LARK models, for the estimation of functions with varying degrees of smoothness. LABS model is a LARK with B-spline bases as generating kernels. The B-spline basis consists of piecewise k degree polynomials with k-1 continuous derivatives and can express systematically functions with varying degrees of smoothness. By changing the orders of the B-spline basis, LABS can systematically adapt the smoothness of functions, i.e., jump discontinuities, sharp peaks, etc. Results of simulation studies and real data examples support that this model catches not only smooth areas but also jumps and sharp peaks of functions. The proposed model also has the best performance in almost all examples. Finally, we provide theoretical results that the mean function for the LABS model belongs to the certain Besov spaces based on the orders of the B-spline basis and that the prior of the model has the full support on the Besov spaces.
The Evolution of NGC 7465 as Revealed by Its Molecular Gas Properties

We present ALMA observations of CO isotopologues and high-density molecular tracers (HCN, HCO+, CN, etc.) in NGC 7465, an unusually gas-rich early-type galaxy that acquired its cold gas recently. In the inner 300 pc, the molecular gas kinematics are misaligned with respect to all other galaxy components; as the gas works its way inward, it is torqued into polar orbits about the stellar kinematically decoupled core (KDC), indicating that the stellar KDC is not related to the current gas accretion event. The galaxy also exhibits unusually high 12CO/13CO line ratios in its nucleus but typical 13CO/C18O ratios. Our calculations show that this result does not necessarily indicate an unusual [12CO/13CO] abundance ratio but rather that 12CO (1–0) is optically thin due to high temperatures and/or large line widths associated with the inner decoupled, misaligned molecular structure. Line ratios of the higher-density tracers suggest that the densest phase of molecular gas in NGC 7465 has a lower density than is typical for nearby galaxies, possibly as a result of the recent gas accretion. All of the observed molecular properties of NGC 7465 are consistent with it having acquired its molecular (and atomic) gas from a spiral galaxy. Further detailed studies of the CO isotopologues in other early-type galaxies would be valuable for investigating the histories of those that may have acquired their gas from dwarfs. Finally, these ALMA data also show an unidentified line source that is probably a background galaxy similar to those found at z = 1–3 in blind CO surveys.
Not Now, Ask Later: Users Weaken Their Behavior Change Regimen Over Time, But Expect To Re-Strengthen It Imminently

How effectively do we adhere to nudges and interventions that help us control our online browsing habits? If we have a temporary lapse and disable the behavior change system, do we later resume our adherence, or has the dam broken? In this paper, we investigate these questions through log analyses of 8,000+ users on HabitLab, a behavior change platform that helps users reduce their time online. We find that, while users typically begin with high-challenge interventions, over time they allow themselves to slip into easier and easier interventions. Despite this, many still expect to return to the harder interventions imminently: they repeatedly choose to be asked to change difficulty again on the next visit, declining to have the system save their preference for easy interventions.
A Peculiar ICME Event in August 2018 Observed With the Global Muon Detector Network

We demonstrate that global observations of high‐energy cosmic rays contribute to understanding unique characteristics of a large‐scale magnetic flux rope causing a magnetic storm in August 2018. Following a weak interplanetary shock on August 25, 2018, a magnetic flux rope caused an unexpectedly large geomagnetic storm. It is likely that this event became geoeffective because the flux rope was accompanied by a corotating interaction region and compressed by high‐speed solar wind following the flux rope. In fact, a Forbush decrease was observed in cosmic‐ray data inside the flux rope as expected, and a significant cosmic‐ray density increase exceeding the unmodulated level before the shock was also observed near the trailing edge of the flux rope. The cosmic‐ray density increase can be interpreted in terms of the adiabatic heating of cosmic rays near the trailing edge of the flux rope, as the corotating interaction region prevents free expansion of the flux rope and results in the compression near the trailing edge. A northeast‐directed spatial gradient in the cosmic‐ray density was also derived during the cosmic‐ray density increase, suggesting that the center of the heating near the trailing edge is located northeast of Earth. This is one of the best examples demonstrating that the observation of high‐energy cosmic rays provides us with information that can only be derived from the cosmic ray measurements to observationally constrain the three‐dimensional macroscopic picture of the interaction between coronal mass ejections and the ambient solar wind, which is essential for prediction of large magnetic storms.
Testing quantum gravity with interactive information sensing

Abstract We suggest a test of a central prediction of perturbatively quantized general relativity: the coherent communication of quantum information between massive objects through gravity. To do this, we introduce the concept of interactive quantum information sensing, a protocol tailored to the verification of dynamical entanglement generation between a pair of systems. Concretely, we propose to monitor the periodic wavefunction collapse and revival in an atomic interferometer which is gravitationally coupled to a mechanical oscillator. We prove a theorem which shows that, under the assumption of time-translation invariance, this collapse and revival is possible if and only if the gravitational interaction forms an entangling channel. Remarkably, as this approach improves at moderate temperatures and relies primarily upon atomic coherence, our numerical estimates indicate feasibility with current devices.
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data

Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive electroencephalography (EEG) datasets. We consider how to adapt techniques and architectures used for language modeling (LM) that appear capable of ingesting awesome amounts of data toward the development of encephalography modeling with DNNs in the same vein. We specifically adapt an approach effectively used for automatic speech recognition, which similarly (to LMs) uses a self-supervised training objective to learn compressed representations of raw data signals. After adaptation to EEG, we find that a single pre-trained model is capable of modeling completely novel raw EEG sequences recorded with differing hardware, and different subjects performing different tasks. Furthermore, both the internal representations of this model and the entire architecture can be fine-tuned to a variety of downstream BCI and EEG classification tasks, outperforming prior work in more task-specific (sleep stage classification) self-supervision.
The Hidden Tasks of Generative Adversarial Networks: An Alternative Perspective on GAN Training

We present an alternative perspective on the training of generative adversarial networks (GANs), showing that the training step for a GAN generator decomposes into two implicit sub-problems. In the ﬁrst, the discriminator provides new target data to the generator in the form of inverse examples produced by approximately inverting classiﬁer labels. In the second, these examples are used as targets to update the generator via least-squares regression, regardless of the main loss speciﬁed to train the network. We experimentally validate our main theoretical result and discuss implications for alternative training methods that are made possible by making these sub-problems explicit. We also introduce a simple representation of inductive bias in networks, which we apply to describing the generator’s output relative to its regression targets.
Quantitative Resilience of Linear Driftless Systems

This paper introduces the notion of quantitative resilience of a control system. Following prior work, we study linear driftless systems enduring a loss of control authority over some of their actuators. Such a malfunction results in actuators producing possibly undesirable inputs over which the controller has real-time readings but no control. By definition, a system is resilient if it can still reach a target after a partial loss of control authority. However, after a malfunction, a resilient system might be significantly slower to reach a target compared to its initial capabilities. We quantify this loss of performance through the new concept of quantitative resilience. We define such a metric as the maximal ratio of the minimal times required to reach any target for the initial and malfunctioning systems. Naïve computation of quantitative resilience directly from the definition is a complex task as it requires solving four nested, possibly nonlinear, optimization problems. The main technical contribution of this work is to provide an efficient method to compute quantitative resilience. Relying on control theory and on two novel geometric results we reduce the computation of quantitative resilience to a single linear optimization problem. We demonstrate our method on an opinion dynamics scenario.
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content

The conceptualization of a claim lies at the core of argument mining. The segregation of claims is complex, owing to the divergence in textual syntax and context across different distributions. Another pressing issue is the unavailability of labeled unstructured text for experimentation. In this paper, we propose LESA, a framework which aims at advancing headfirst into expunging the former issue by assembling a source-independent generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model. We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset. Experimental results show that LESA improves upon the state-of-the-art performance across six benchmark claim datasets by an average of 3 claim-F1 points for in-domain experiments and by 2 claim-F1 points for general-domain experiments. On our dataset too, LESA outperforms existing baselines by 1 claim-F1 point on the in-domain experiments and 2 claim-F1 points on the general-domain experiments. We also release comprehensive data annotation guidelines compiled during the annotation phase (which was missing in the current literature).
On graph norms for complex‐valued functions

For any given graph H$H$ , one may define a natural corresponding functional ∥. ∥H$\Vert . \Vert _H$ for real‐valued functions by using homomorphism density. One may also extend this to complex‐valued functions, once H$H$ is paired with a 2‐edge‐colouring α$\alpha$ to assign conjugates. We say that H$H$ is real‐norming (respectively complex‐norming) if ∥. ∥H$\Vert . \Vert _H$ (respectively ∥.∥H,α$\Vert .\Vert _{H,\alpha }$ for some α$\alpha$ ) is a norm on the vector space of real‐valued (respectively complex‐valued) functions. These generalise the Gowers octahedral norms, a widely used tool in extremal combinatorics to quantify quasi randomness. We unify these two seemingly different notions of graph norms in real‐ and complex‐valued settings. Namely, we prove that H$H$ is complex‐norming if and only if it is real‐norming and simply call the property norming. Our proof does not explicitly construct a suitable 2‐edge‐colouring α$\alpha$ but obtains its existence and uniqueness, which may be of independent interest. As an application, we give various example graphs that are not norming. In particular, we show that hypercubes are not norming, which resolves the last outstanding problem posed in Hatami's pioneering work on graph norms.
Oceanic Superrotation on Tidally Locked Planets

Is there oceanic superrotation on exoplanets? Atmospheric superrotation, characterized by west-to-east winds over the equator, is a common phenomenon in the atmospheres of Venus, Titan, Saturn, Jupiter, and tidally locked exoplanets. The stratospheric atmosphere of Earth is also superrotating during the westerly phase of the quasi-biennial oscillation. However, whether the same phenomenon can occur in the ocean is poorly known. Through numerical simulations, here we show that oceanic superrotation does occur on tidally locked terrestrial planets around low-mass stars. Its formation (spun up from rest) is associated with surface winds, the equatorward momentum convergence by Rossby waves, and the eastward propagation of Kelvin waves in the ocean. Its maintenance is driven by equatorward momentum transports of coupled Rossby–Kelvin waves in the ocean excited from the uneven stellar radiation distribution. The width of the superrotation is mainly constrained by the Rossby deformation radius in the ocean, while its strength is more complex. Many factors can influence the strength, including planetary rotation rate, stellar flux, greenhouse gas concentration, seawater salinity, and bottom drag, but a scaling theory for the strength of the oceanic superrotation is lacking. This work confirms that oceanic superrotation can occur on tidally locked terrestrial planets with seawater oceans and suggests that it may also occur on tidally locked hot planets with magma oceans that will possibly be observed in the near future.
GPU Optimization for High-Quality Kinetic Fluid Simulation

Fluid simulations are often performed using the incompressible Navier-Stokes equations (INSE), leading to sparse linear systems which are difficult to solve efficiently in parallel. Recently, kinetic methods based on the adaptive-central-moment multiple-relaxation-time (ACM-MRT) model [1], [2] have demonstrated impressive capabilities to simulate both laminar and turbulent flows, with quality matching or surpassing that of state-of-the-art INSE solvers. Furthermore, due to its local formulation, this method presents the opportunity for highly scalable implementations on parallel systems such as GPUs. However, an efficient ACM-MRT-based kinetic solver needs to overcome a number of computational challenges, especially when dealing with complex solids inside the fluid domain. In this article, we present multiple novel GPU optimization techniques to efficiently implement high-quality ACM-MRT-based kinetic fluid simulations in domains containing complex solids. Our techniques include a new communication-efficient data layout, a load-balanced immersed-boundary method, a multi-kernel launch method using a simplified formulation of ACM-MRT calculations to enable greater parallelism, and the integration of these techniques into a parametric cost model to enable automated prameter search to achieve optimal execution performance. We also extended our method to multi-GPU systems to enable large-scale simulations. To demonstrate the state-of-the-art performance and high visual quality of our solver, we present extensive experimental results and comparisons to other solvers.
The galaxy–galaxy strong lensing cross-sections of simulated ΛCDM galaxy clusters

We investigate a recent claim that observed galaxy clusters produce an order of magnitude more galaxy-galaxy strong lensing (GGSL) than simulated clusters in a ΛCDM cosmology. We take galaxy clusters from the c-eagle hydrodynamical simulations and calculate the expected amount of GGSL for sources placed behind the clusters at different redshifts. The probability of a source lensed by one of the most massive c-eagle clusters being multiply imaged by an individual cluster member is in good agreement with that inferred for observed clusters. We show that numerically converged results for the GGSL probability require higher resolution simulations than had been used previously. On top of this, different galaxy formation models predict cluster substructures with different central densities, such that the GGSL probabilities in ΛCDM cannot yet be robustly predicted. Overall, we find that galaxygalaxy strong lensing within clusters is not currently in tension with the ΛCDM cosmological model.
CNN with large memory layers

This work is centred around the recently proposed product key memory structure [46], implemented for a number of computer vision applications. The memory structure can be regarded as a simple computation primitive suitable to be augmented to nearly all neural network architectures. The memory block allows implementing sparse access to memory with square root complexity scaling with respect to the memory capacity. The latter scaling is possible due to the incorporation of Cartesian product space decomposition of the key space for the nearest neighbour search. We have tested the memory layer on the classification, image reconstruction and relocalization problems and found that for some of those, the memory layers can provide significant speed/accuracy improvement with the high utilization of the key-value elements, while others require more careful fine-tuning and suffer from dying keys. To tackle the later problem we have introduced a simple technique of memory re-initialization which helps us to eliminate unused key-value pairs from the memory and engage them in training again. We have conducted various experiments and got improvements in speed and accuracy for classification and PoseNet relocalization models. We showed that the re-initialization has a huge impact on a toy example of randomly labeled data and observed some gains in performance on the image classification task. We have also demonstrated the generalization property perseverance of the large memory layers on the relocalization problem, while observing the spatial correlations between the images and the selected memory cells. Thesis Supervisor: Victor Lempitsky Title: Associate Professor, PhD
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling

Deep neural networks are powerful statistical learners. However, their predictions do not come with an explanation of their process. To analyze these models, explanation methods are being developed. We present a novel explanation method, called OLM, for natural language processing classifiers. This method combines occlusion and language modeling, which are techniques central to explainability and NLP, respectively. OLM gives explanations that are theoretically sound and easy to understand. We make several contributions to the theory of explanation methods. Axioms for explanation methods are an interesting theoretical concept to explore their basics and deduce methods. We introduce a new axiom, give its intuition and show it contradicts another existing axiom. Additionally, we point out theoretical difficulties of existing gradient-based and some occlusion-based explanation methods in natural language processing. We provide an extensive argument why evaluation of explanation methods is difficult. We compare OLM to other explanation methods and underline its uniqueness experimentally. Finally, we investigate corner cases of OLM and discuss its validity and possible improvements.
Structures of bulk hexagonal post transition metal chalcogenides from dispersion-corrected density functional theory

We use dispersion-corrected density functional theory to determine the relative energies of competing polytypes of bulk layered hexagonal post transition metal chalcogenides to search for the most stable structures of these potentially technologically important semiconductors. We show that there is some degree of consensus among dispersion-corrected exchange-correlation functionals regarding the energetic orderings of polytypes, but we find that for each material there are multiple stacking orders with relative energies of less than 1 meV per monolayer unit cell, implying that stacking faults are expected to be abundant in all post transition metal chalcogenides. By fitting a simple model to all our energy data, we predict that the most stable hexagonal structure has the P63/mmc space group in each case but that the stacking order differs between GaS, GaSe, GaTe, and InS, on the one hand, and InSe and InTe, on the other. At zero pressure, the relative energies obtained with different functionals disagree by around 1–5 meV per monolayer unit cell, which is not sufficient to identify the most stable structure unambiguously; however, multigigapascal pressures reduce the number of competing phases significantly. At higher pressures, an AB′-stacked structure of the most stable monolayer polytype is found to be the most stable bulk structure.
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild

Predicting future frames of video sequences is challenging due to the complex and stochastic nature of the problem. Video prediction methods based on variational auto-encoders (VAEs) have been a great success, but they require the training data to contain multiple possible futures for an observed video sequence. This is hard to be fulfilled when videos are captured in the wild where any given observation only has a determinate future. As a result, training a vanilla VAE model with these videos inevitably causes posterior collapse. To alleviate this problem, we propose a novel VAE structure, dabbed VAE-in-VAE or VAE$^2$. The key idea is to explicitly introduce stochasticity into the VAE. We treat part of the observed video sequence as a random transition state that bridges its past and future, and maximize the likelihood of a Markov Chain over the video sequence under all possible transition states. A tractable lower bound is proposed for this intractable objective function and an end-to-end optimization algorithm is designed accordingly. VAE$^2$ can mitigate the posterior collapse problem to a large extent, as it breaks the direct dependence between future and observation and does not directly regress the determinate future provided by the training data. We carry out experiments on a large-scale dataset called Cityscapes, which contains videos collected from a number of urban cities. Results show that VAE$^2$ is capable of predicting diverse futures and is more resistant to posterior collapse than the other state-of-the-art VAE-based approaches. We believe that VAE$^2$ is also applicable to other stochastic sequence prediction problems where training data are lack of stochasticity.