Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-based OoD-Detector Principles on Graph-based Input Data in Human Action Recognition

Living in a complex world like ours makes it unacceptable that a practical implementation of a machine learning system assumes a closed world. Therefore, it is necessary for such a learning-based system in a real world environment, to be aware of its own capabilities and limits and to be able to distinguish between confident and unconfident results of the inference, especially if the sample cannot be explained by the underlying distribution. This knowledge is particularly essential in safety-critical environments and tasks e.g. self-driving cars or medical applications. Towards this end, we transfer image-based Out-of-Distribution (OoD)-methods to graph-based data and show the applicability in action recognition. The contribution of this work is (i) the examination of the portability of recent image-based OoD-detectors for graph-based input data, (ii) a Metric Learning-based approach to detect OoD-samples, and (iii) the introduction of a novel semi-synthetic action recognition dataset. The evaluation shows that image-based OoD-methods can be applied to graph-based data. Additionally, there is a gap between the performance on intraclass and intradataset results. First methods as the examined baseline or ODIN provide reasonable results. More sophisticated network architectures - in contrast to their image-based application - were surpassed in the intradataset comparison and even lead to less classification accuracy.
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network

We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme. By enforcing detectability of changes in the input using a gradient penalty, we are able to reliably detect out of distribution data. Our uncertainty quantification scales well to large datasets, and using a single model, we improve upon or match Deep Ensembles on notable difficult dataset pairs such as FashionMNIST vs. MNIST, and CIFAR-10 vs. SVHN, while maintaining competitive accuracy.
Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation

Abstract The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation.
Three-dimensional simulations of accretion flow in the progenitor of Tycho’s supernova

We run three-dimensional numerical simulations for the accretion flow around the white dwarf (WD) in the progenitor system of Tycho's supernova (SN). The mass of the WD, mass of the companion star, and the orbital period are set to be 1$M_{\odot}$, 1.6$M_{\odot}$, and 0.794 day, respectively, based on theoretical and observational researches of Tycho's SN remnant (SNR). We find that when the magnetic field in the accreted material is negligible, outflowing wind is concentrated near the equatorial plane. When the magnetic field has energy equipartition with internal energy, polar wind is comparable with the equatorial wind. A carefully chosen magnetic field between the above two cases ($B=5.44\times10^3 \rm{G}$) can roughly reproduce the latitude-dependent wind required to form the peculiar periphery of Tycho's SNR.
Sporadic Ultra-Time-Critical Crowd Messaging in V2X

Life-critical warning message, abbreviated as warning message, is a special event-driven message that carries emergency information in Vehicle-to-Everything (V2X). Three important characteristics that distinguish warning messages from ordinary vehicular messages are sporadicity, crowding, and ultra-time-criticality. Specifically, warning messages come only once in a while in a sporadic manner; however, when they come, they tend to come as a crowd and they need to be delivered in short order. This paper puts forth a medium-access control (MAC) protocol for warning messages. The overall MAC protocol operates by means of interrupt-and-access. To circumvent potential inefficiency arising from message sporadicity, we adopt an override network architecture whereby warning messages are delivered on the spectrum of the ordinary vehicular messages. A vehicle with a warning message first sends an interrupt signal to pre-empt the transmission of ordinary messages, so that the warning message can use the wireless spectrum originally allocated to ordinary messages. In this way, no exclusive spectrum resources need to be pre-allocated to the sporadic warning messages. Following the interrupt, for transmissions of ultra-time-critical crowd messages, we employ advanced channel access techniques to ensure reliable message delivery within an ultra-short time in the order of 10 ms.
Asynchronous effects

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls into signalling that an operation’s implementation needs to be executed, and interrupting a running computation with the operation’s result, to which the computation can react by installing interrupt handlers. We formalise these ideas in a small core calculus, called λæ. We demonstrate the flexibility of λæ using examples ranging from a multi-party web application, to preemptive multi-threading, to remote function calls, to a parallel variant of runners of algebraic effects. In addition, the paper is accompanied by a formalisation of λæ’s type safety proofs in Agda, and a prototype implementation of λæ in OCaml.
On Mochizuki's idea of Anabelomorphy and its applications

Shinichi Mochizuki has introduced many fundamental ideas in his work, amongst one of them is the foundational notion, which I have dubbed anabelomorphy (pronounced as anabel-o-morphy). I coined the term anabelomorphy as a concise way of expressing "Mochizuki's anabelian way of changing ground field, rings etc." The notion of anabelomorphy is firmly grounded in a well-known theorem of Mochizuki which asserts that a $p$-adic field is determined by its absolute Galois group equipped with its (upper numbering) ramification filtration. However as there exist many non-isomorphic $p$-adic fields with topologically isomorphic Galois groups, the ramification filtration itself is fluid i.e. a variable and this variable controls amongst many other things, the additive structure of a $p$-adic local field, and Mochizuki's remarkable suggestion is that this fluidity can and should be exploited in proving theorems in number theory. In this paper I provide a number of results which illustrate the usefulness of Mochizuki's idea. I refer the reader to the Introduction of the paper and the table of contents for the list of all the results proved in this paper.
Quantum Hall effect and Landau levels in the three-dimensional topological insulator HgTe

We review low and high field magnetotransport in 80 nm-thick strained HgTe, a material that belongs to the class of strong three-dimensional topological insulators. Utilizing a top gate, the Fermi level can be tuned from the valence band via the Dirac surface states into the conduction band and allows studying Landau quantization in situations where different species of charge carriers contribute to magnetotransport. Landau fan charts, mapping the conductivity $ \sigma_{xx}(V_g, B) $ in the whole magnetic field - gate voltage range, can be divided into six areas, depending on the state of the participating carrier species. Key findings are: (i) the interplay of bulk holes (spin-degenerate) and Dirac surface electrons (non-degenerate), coexisting for $ E_F $ in the valence band, leads to a periodic switching between odd and even filling factors and thus odd and even quantized Hall voltage values. (ii) A similar though less pronounced behavior we found for coexisting Dirac surface and conduction band electrons. (iii) In the bulk gap, quantized Dirac electrons on the top-surface coexist at lower B with non-quantized ones on the bottom side, giving rise to quantum Hall plateau values depending - for a given filling factor - on the magnetic field strength. In stronger $ B $ fields, Landau level separation increases, charge transfer between different carrier species becomes energetically favorable and leads to the formation of a global (i.e. involving top and bottom surface) quantum Hall state. Simulations using the simplest possible theoretical approach are in line with the basic experimental findings, describing correctly the central features of the transitions from classical to quantum transport in the respective areas of our multicomponent charge carrier system.
Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs

Knee osteoarthritis (OA) is one of the highest disability factors in the world. This musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via radiographic assessment. This visual assessment done by a radiologist requires experience, and suffers from moderate to high inter-observer variability. The recent literature has shown that deep learning methods can reliably perform the OA severity assessment according to the gold standard Kellgren-Lawrence (KL) grading system. However, these methods require large amounts of labeled data, which are costly to obtain. In this study, we propose the Semixup algorithm, a semi-supervised learning (SSL) approach to leverage unlabeled data. Semixup relies on consistency regularization using in- and out-of-manifold samples, together with interpolated consistency. On an independent test set, our method significantly outperformed other state-of-the-art SSL methods in most cases. Finally, when compared to a well-tuned fully supervised baseline that yielded a balanced accuracy (BA) of 70.9 ± 0.8% on the test set, Semixup had comparable performance - BA of 71 ± 0.8% ( ${p}=0.368$ ) while requiring 6 times less labeled data. These results show that our proposed SSL method allows building fully automatic OA severity assessment tools with datasets that are available outside research settings.
Predicting the Orbifold Origin of the MSSM

MSSM‐like string models from the compactification of the heterotic string on toroidal orbifolds (of the kind T6/P ) have distinct phenomenological properties, like the spectrum of vector‐like exotics, the scale of supersymmetry breaking, and the existence of non‐Abelian flavor symmetries. We show that these characteristics depend crucially on the choice of the underlying orbifold point group P. In detail, we use boosted decision trees to predict P from phenomenological properties of MSSM‐like orbifold models. As this works astonishingly well, we can utilize machine learning to predict the orbifold origin of the MSSM.
Redesigning SLAM for Arbitrary Multi-Camera Systems

Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tailored for specific camera configurations. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. To this end, we revisit several common building blocks in visual SLAM. In particular, we propose an adaptive initialization scheme, a sensor-agnostic, information- theoretic keyframe selection algorithm, and a scalable voxel- based map. These techniques make little assumption about the actual camera setups and prefer theoretically grounded methods over heuristics. We adapt a state-of-the-art visual- inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e.g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.
Evidence for Disk Truncation at Low Accretion States of the Black Hole Binary MAXI J1820+070 Observed by NuSTAR and XMM-Newton

We present results from NuSTAR and XMM-Newton observations of the new black hole X-ray binary MAXI J1820+070 at low accretion rates (below 1% of the Eddington luminosity). We detect a narrow Fe Kα emission line, in contrast to the broad and asymmetric Fe Kα line profiles commonly present in black hole binaries at high accretion rates. The narrow line, with weak relativistic broadening, indicates that the Fe Kα line is produced at a large disk radius. Fitting with disk reflection models assuming standard disk emissivity finds a large disk truncation radius (a few tens to a few hundreds of gravitational radii, depending on the disk inclination). In addition, we detect a quasi-periodic oscillation (QPO) varying in frequency between 11.6 ± 0.2 mHz and 2.8 ± 0.1 mHz. The very low QPO frequencies suggest a large size for the optically thin Comptonization region according to the Lense–Thirring precession model, supporting that the accretion disk recedes from the innermost stable circular orbit and is replaced by advection-dominated accretion flow at low accretion rates. We also discuss the possibility of an alternative accretion geometry that the narrow Fe Kα line is produced by a lamppost corona with a large height illuminating the disk.
Quasi-periodic Pulsation Detected in Lyα Emission During Solar Flares

We investigated the quasi-periodic pulsation (QPP) in Lyα, X-ray, and extreme-ultraviolet (EUV) emissions during two solar flares, i.e., an X-class (SOL2012-01-27T) and a C-class (SOL2016-02-08T). The full-disk Lyα and X-ray flux during these solar flares were recorded by the EUV Sensor and X-Ray Sensor on board the Geostationary Operational Environmental Satellite. The flare regions were located from the EUV images measured by the Atmospheric Imaging Assembly. The QPP could be identified as a series of regular and periodic peaks in the light curves, and its quasi-periodicity was determined from the global wavelet and Fourier power spectra. A quasi-periodicity at about 3 minutes is detected during the impulsive phase of the X-class flare, which could be explained as the acoustic wave in the chromosphere. Interestingly, a quasi-periodicity at roughly 1 minute is discovered during the entire evolutionary phases of solar flares, including the precursor, impulsive, and gradual phases. This is the first report of 1 minute QPP in the Lyα emission during solar flares, in particular during the flare precursor. It may be interpreted as a self-oscillatory regime of the magnetic reconnection, such as magnetic dripping.
Generate Descriptive Social Networks for Large Populations from Available Observations: A Novel Methodology and a Generator

When modeling a social dynamics with an agent-oriented approach, researchers have to describe the structure of interactions within the population. Given the intractability of extensive network collecting, they rely on random network generators that are supposed to explore the space of plausible networks. We first identify the needs of modelers, including placing heterogeneous agents on the network given their attributes and differentiating the various types of social links that lead to different interactions. We point out the existence of data in the form of scattered statistics and qualitative observations, that should be used to parameter the generator. We propose a new approach peculiar to agent-based modeling, in which we will generate social links from individuals' observed attributes, and return them as a multiplex network. Interdependencies between socioeconomic attributes, and generative rules, are encoded as Bayesian networks. A methodology guides modelers through the formalization of these parameters. This approach is illustrated by describing the structure of interactions that supports diffusion of contraceptive solutions in rural Kenya.
Cannabinoids: Drug or Medication?

This chapter aims at exploring the use and misuse of cannabinoids as it has become a major societal issue. In the first section, we describe the historical use of cannabis as a natural cure in ancient civilizations. We then explore the current use of cannabinoids in medicine, which includes innovative strategies for treating various diseases such as multiple sclerosis or cancer‐induced pain. In the second section, we consider how the discovery and characterization of the endocannabinoid system have increased knowledge of this system's mode of action. Consumption of cannabis for recreational use however is a significant public health issue today. Scientific advances are confront‐ ed with the adverse health effects that are demonstrated in preclinical and clinical studies based on the psychotic and addictive properties of this compound. In the third section, we therefore provide an overview of the recent findings on the endocannabi‐ noid system using animal models with proposed molecular mechanisms and poten‐ tial interactions with other neuromodulatory systems like the opioid system. Finally, through alternative strategies to current treatments with both phyto‐ and synthetic cannabinoids, we try to reconcile the beneficial aspects of the use of cannabinoids for medication and the aspects associated with addictive properties. protein, guanine nucleotide binding c‐aminobutyric GPCR, coupled receptor; intravenous; MGL, monoacylglycerol lipase; multiple sclerosis; NAPE‐PLD, N phosphatidylethanolamine phospholipase ‐THC, delta‐9‐tetrahydro‐ effect between a placebo. A in various types of pain including chronic, postoperative or neuropathic pain, as well as pain in patients suffering of A randomized clinical assay was conducted on patients with neuropathic pain high or low concentrated cannabis or a placebo. This study showed that cannabis is effective at ameliorating neuropathic pain Similar results were ob‐ tained in another randomized study including adults with AIDS‐associated sensory neuropa‐ where the effect of smoking low concentrated cannabis three times per day over 5 days compared with a placebo Other studies have suggested a potential role of
Corruption-Tolerant Gaussian Process Bandit Optimization

We consider the problem of optimizing an unknown (typically non-convex) function with a bounded norm in some Reproducing Kernel Hilbert Space (RKHS), based on noisy bandit feedback. We consider a novel variant of this problem in which the point evaluations are not only corrupted by random noise, but also adversarial corruptions. We introduce an algorithm Fast-Slow GP-UCB based on Gaussian process methods, randomized selection between two instances labeled "fast" (but non-robust) and "slow" (but robust), enlarged confidence bounds, and the principle of optimism under uncertainty. We present a novel theoretical analysis upper bounding the cumulative regret in terms of the corruption level, the time horizon, and the underlying kernel, and we argue that certain dependencies cannot be improved. We observe that distinct algorithmic ideas are required depending on whether one is required to perform well in both the corrupted and non-corrupted settings, and whether the corruption level is known or not.
Online Metric Algorithms with Untrusted Predictions

Machine-learned predictors, although achieving very good results for inputs resembling training data, cannot possibly provide perfect predictions in all situations. Still, decision-making systems that are based on such predictors need not only benefit from good predictions, but should also achieve a decent performance when the predictions are inadequate. In this article, we propose a prediction setup for arbitrary metrical task systems (MTS) (e.g., caching, k-server, and convex body chasing) and online matching on the line. We utilize results from the theory of online algorithms to show how to make the setup robust. Specifically, for caching, we present an algorithm whose performance, as a function of the prediction error, is exponentially better than what is achievable for general MTS. Finally, we present an empirical evaluation of our methods on real-world datasets, which suggests practicality.
S2COSMOS: Evolution of gas mass with redshift using dust emission

We investigate the evolution of the gas mass fraction for galaxies in the COSMOS field using submillimetre emission from dust at 850 μm. We use stacking methodologies on the 850 μm S2COSMOS map to derive the gas mass fraction of galaxies out to high redshifts, 0 ≤ z ≤ 5, for galaxies with stellar masses of $10^{9.5} \lt M_* (\rm M_{\odot }) \lt 10^{11.75}$. In comparison to previous literature studies we extend to higher redshifts, include more normal star-forming galaxies (on the main sequence), and also investigate the evolution of the gas mass fraction split by star-forming and passive galaxy populations. We find our stacking results broadly agree with scaling relations in the literature. We find tentative evidence for a peak in the gas mass fraction of galaxies at around z ∼ 2.5–3, just before the peak of the star formation history of the Universe. We find that passive galaxies are particularly devoid of gas, compared to the star-forming population. We find that even at high redshifts, high stellar mass galaxies still contain significant amounts of gas.
Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error

The ability to learn new concepts with small amounts of data is a crucial aspect of intelligence that has proven challenging for deep learning methods. Meta-learning for few-shot learning offers a potential solution to this problem: by learning to learn across data from many previous tasks, few-shot learning algorithms can discover the structure among tasks to enable fast learning of new tasks. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be very ambiguous to acquire a single model for that task. The Bayesian meta-learning models can naturally resolve this problem by putting a sophisticated prior distribution and let the posterior well regularized through Bayesian decision theory. However, currently known Bayesian meta-learning procedures such as VERSA suffer from the so-called {\it information preference problem}, that is, the posterior distribution is degenerated to one point and is far from the exact one. To address this challenge, we design a novel meta-regularization objective using {\it cyclical annealing schedule} and {\it maximum mean discrepancy} (MMD) criterion. The cyclical annealing schedule is quite effective at avoiding such degenerate solutions. This procedure includes a difficult KL-divergence estimation, but we resolve the issue by employing MMD instead of KL-divergence. The experimental results show that our approach substantially outperforms standard meta-learning algorithms.