Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk of Bitcoin Market: Volatility, Jumps, and Forecasts

Among all the emerging markets, the cryptocurrency market is considered the most controversial and simultaneously the most interesting one. The visibly significant market capitalization of cryptos motivates modern financial instruments such as futures and options. Those will depend on the dynamics, volatility, or even the jumps of cryptos. In this paper, the risk characteristics for Bitcoin are analyzed from a realized volatility dynamics view. The realized variance is estimated with the corrected threshold jump components, realized semi-variance, and signed jumps. Our empirical results show that the BTC is far riskier than any of the other developed financial markets. Up to 68% of the days are identified to be entangled with jumps. However, the discontinuities do not contribute to the variance significantly. The full-sample fitting suggests that future realized variance has a positive relationship with downside risk and a negative relationship with the positive jump. The rolling-window out-of-sample forecasting results reveal that the forecasting horizon plays an important role in choosing forecasting models. For the long horizon risk forecast, explicitly modeling jumps and signed estimators improve forecasting accuracy and give extra utility up to 19 bps annually, while the HAR model without accounting jumps or signed estimators suits the short horizon case best. Lastly, a simple equal-weighted portfolio of BTC not only significantly reduces the size and quantity of jumps but also gives investors higher utility in short horizon case.
An Improving Framework of regularization for Network Compression

Deep Neural Networks have achieved remarkable success relying on the developing high computation capability of GPUs and large-scale datasets with increasing network depth and width in image recognition, object detection and many other applications. However, due to the expensive computation and intensive memory, researchers have concentrated on designing compression methods in recent years. In this paper, we briefly summarize the existing advanced techniques that are useful in model compression at first. After that, we give a detailed description on group lasso regularization and its variants. More importantly, we propose an improving framework of partial regularization based on the relationship between neurons and connections of adjacent layers. It is reasonable and feasible with the help of permutation property of neural network . Experiment results show that partial regularization methods brings improvements such as higher classification accuracy in both training and testing stages on multiple datasets. Since our regularizers contain the computation of less parameters, it shows competitive performances in terms of the total running time of experiments. Finally, we analysed the results and draw a conclusion that the optimal network structure must exist and depend on the input data.
Adaptive Procedures for Discrimination Between Arbitrary Tensor-Product Quantum States

Discrimination between quantum states is a fundamental task in quantum information theory. Given two arbitrary tensor-product quantum states (TPQS) $\rho_{\pm} = \rho_{\pm}^{(1)} \otimes \cdots \otimes \rho_{\pm}^{(N)}$, determining the joint $N$-system measurement to optimally distinguish between the two states is a hard problem. Thus, there is great interest in identifying local measurement schemes that are optimal or close-to-optimal. In this work, we focus on distinguishing between two general TPQS. We begin by generalizing previous work by Acin et al. (Phys. Rev. A 71, 032338) to show that a locally greedy (LG) scheme using Bayesian updating can optimally distinguish between two states that can be written as tensor products of arbitrary pure states. Then, we show that even in the limit of large $N$ the same algorithm cannot distinguish tensor products of mixed states with vanishing error probability. This poor asymptotic behavior occurs because the Helstrom measurement becomes trivial for sufficiently biased priors. Based on this, we introduce a modified locally greedy (MLG) scheme with strictly better performance. In the second part of this work, we compare these simple local schemes with a general dynamic programming (DP) approach that finds the optimal series of local measurements to distinguish the two states. When the subsystems are non-identical, we demonstrate that the ordering of the systems affects performance and we extend the DP technique to determine the optimal ordering adaptively. Finally, in contrast to the binary optimal collective measurement, we show that adaptive protocols on sufficiently large (e.g., qutrit) subsystems must contain non-binary measurements to be optimal. (The code that produced the simulation results in this paper can be found at: this https URL)
K-Theoretic Generalized Donaldson–Thomas Invariants

We introduce the notion of almost perfect obstruction theory on a Deligne–Mumford stack and show that stacks with almost perfect obstruction theories have virtual structure sheaves, which are deformation invariant. The main components in the construction are an induced embedding of the coarse moduli sheaf of the intrinsic normal cone into the associated obstruction sheaf stack and the construction of a $K$-theoretic Gysin map for sheaf stacks. We show that many stacks of interest admit almost perfect obstruction theories. As a result, we are able to define virtual structure sheaves and $K$-theoretic classical and generalized Donaldson–Thomas invariants of sheaves and complexes on Calabi–Yau three-folds.
Modeling Emotion in Complex Stories: The Stanford Emotional Narratives Dataset

Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models. We then introduce the first version of the Stanford Emotional Narratives Dataset (SENDv1): a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. We demonstrate several baseline and state-of-the-art modeling approaches on the SEND, including a Long Short-Term Memory model and a multimodal Variational Recurrent Neural Network, which perform comparably to the human-benchmark. We end by discussing the implications for future research in time-series affective computing.
Video Person Re-ID: Fantastic Techniques and Where to Find Them

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github1.
Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.
A Hidden Friend for the Galactic Center Black Hole, Sgr A*

The hierarchical nature of galaxy formation suggests that a supermassive black hole binary could exist in our galactic center. We propose a new approach to constraining the possible orbital configuration of such a binary companion to the galactic center black hole Sagittarius A* (Sgr A*) through the measurement of stellar orbits. Focusing on the star S0–2, we show that requiring its orbital stability in the presence of a companion to Sgr A* yields stringent constraints on the possible configurations of such a companion. Furthermore, we show that precise measurements of time variations in the orbital parameters of S0–2 could yield stronger constraints. Using existing data on S0–2 we derive upper limits on the binary black hole separation as a function of the companion mass. For the case of a circular orbit, we can rule out a 105 M⊙ companion with a semimajor axis greater than 170 au or 0.8 mpc. This is already more stringent than bounds obtained from studies of the proper motion of Sgr A*. Including other stars orbiting the galactic center should yield stronger constraints that could help uncover the presence of a companion to Sgr A*. We show that a companion can also affect the accretion process, resulting in a variability that may be consistent with the measured infrared flaring timescales and amplitudes. Finally, if such a companion exists, it will emit gravitational wave radiation, potentially detectable with the Laser Interferometer Space Antenna (LISA).
More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains non-trivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible. In this paper, we introduce a Bayesian nonparametric methodology to predict the number of new variants in a follow-up study based on a pilot study. When experimental conditions are kept constant between the pilot and follow-up, we find that our prediction is competitive with the best existing methods. Unlike current methods, though, our new method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for more realistic predictions and for optimal allocation of a fixed budget between quality and quantity.
Relativistic ocean r-modes during type-I X-ray bursts

Accreting neutron stars (NS) can exhibit high frequency modulations in their lightcurves during thermonuclear X-ray bursts, known as burst oscillations. These frequencies can be offset from the NS spin frequency by several Hz (where known independently) and can drift by 1–3 Hz. One plausible explanation is that a wave is present in the bursting ocean, the rotating frame frequency of which is the offset. The frequency of the wave should decrease (in the rotating frame) as the burst cools hence explaining the drift. A strong candidate is a buoyant r-mode. To date, models that calculated the frequency of this mode taking into account the radial structure neglected relativistic effects and predicted rotating frame frequencies of ∼4 Hz and frequency drifts of >5 Hz; too large to be consistent with observations. We present a calculation that includes frame-dragging and gravitational redshift that reduces the rotating frame frequency by up to $30 \, {\rm per\, cent}$ and frequency drift by up to $20 \, {\rm per\, cent}$. Updating previous models for the ocean cooling in the aftermath of the burst to a model more representative of detailed calculations of thermonuclear X-ray bursts reduces the frequency of the mode still further. This model, combined with relativistic effects, can reduce the rotating frequency of the mode to ∼2 Hz and frequency drift to ∼2 Hz, which is closer to the observed values.
FlauBERT: Unsupervised Language Model Pre-training for French

Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.
Spinning and excited black holes in Einstein-scalar-Gauss–Bonnet theory

We construct rotating black holes in Einstein-scalar-Gauss–Bonnet theory with a quadratic coupling function. We map the domain of existence of the rotating fundamental solutions, we construct radially excited rotating black holes (including their existence lines), and we show that there are angularly excited rotating black holes. The bifurcation points of the radially and angularly excited solutions branching out of the Schwarzschild solution follow a regular pattern.
TextSLAM: Visual SLAM with Planar Text Features

We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline. The key idea of our novel text-based visual SLAM is to treat each detected text as a planar feature which is rich of textures and semantic meanings. The text feature is compactly represented by three parameters and integrated into visual SLAM by adopting the illumination-invariant photometric error. We also describe important details involved in implementing a full pipeline of text-based visual SLAM. To our best knowledge, this is the first visual SLAM method tightly coupled with the text features. We tested our method in both indoor and outdoor environments. The results show that with text features, the visual SLAM system becomes more robust and produces much more accurate 3D text maps that could be useful for navigation and scene understanding in robotic or augmented reality applications.
G3AN: Disentangling Appearance and Motion for Video Generation

Creating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion. To tackle this challenge, we introduce G3AN, a novel spatio-temporal generative model, which seeks to capture the distribution of high dimensional video data and to model appearance and motion in disentangled manner. The latter is achieved by decomposing appearance and motion in a three-stream Generator, where the main stream aims to model spatio-temporal consistency, whereas the two auxiliary streams augment the main stream with multi-scale appearance and motion features, respectively. An extensive quantitative and qualitative analysis shows that our model systematically and significantly outperforms state-of-the-art methods on the facial expression datasets MUG and UvA-NEMO, as well as the Weizmann and UCF101 datasets on human action. Additional analysis on the learned latent representations confirms the successful decomposition of appearance and motion.
Mercury, Moon, Mars

The evolution of the interior of stagnant-lid bodies is comparatively easier to model and predict with respect to the Earth's due to the absence of the large uncertainties associated with the physics of plate tectonics, its onset time and efficiency over the planet's history. Yet, the observational record for these bodies is both scarcer and sparser with respect to the Earth's. It is restricted to a limited number of samples and a variety of remote-sensing measurements of billions-of-years-old surfaces whose actual age is difficult to determine precisely. Combining these observations into a coherent picture of the thermal and convective evolution of the planetary interior represents thus a major challenge. In this chapter, we review key processes and (mostly geophysical) observational constraints that can be used to infer the global characteristics of mantle convection and thermal evolution of the interior of Mercury, the Moon and Mars, the three major terrestrial bodies where a stagnant lid has likely been present throughout most of their history.
On Neural Learnability of Chaotic Dynamics

Neural networks are of interest for prediction and uncertainty quantification of nonlinear dynamics. The learnability of chaotic dynamics by neural models, however, remains poorly understood. In this paper, we show that a parsimonious feed-forward network trained on a few data points suffices for accurate prediction of local divergence rates on the whole attractor of the Lorenz 63 system. We show that the neural mappings consist of a series of geometric stretching and compressing operations that indicate topological mixing and, therefore, chaos. Thus, chaotic dynamics is learnable. The emergence of topological mixing within the neural system demands a parsimonious neural structure. We synthesize parsimonious structure using an approach that matches the spectrum of learning dynamics with that of a polynomial regression machine derived from the polynomial Lorenz equations.
Reservoir Computing Using a Spin-Wave Delay-Line Active-Ring Resonator Based on Yttrium-Iron-Garnet Film

The authors demonstrate the use of a propagating spin waves for implementing a reservoir computing architecture. The proposed concept utilises an active ring resonator comprising a magnetic thin film delay line integrated into a feedback loop. These systems exhibit strong nonlinearity and delayed response behaviour, two important properties required for an effective reservoir computing implementation. In a simple design, we exploit the nonlinear damping of spin waves at different feedback gains to inject input data into the active ring resonator and use a microwave diode to read out the amplitude of the spin waves circulating in the ring. We employ two baseline tasks, namely the short term memory and parity check tasks, to evaluate the suitability of this architecture for processing time series data.
Continual egocentric object recognition

We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn. This setting has a set of unique characteristics:it assumes an egocentric point-of-view bound to the needs of a singleperson, which implies a relatively low diversity of data and a coldstart with no data; it requires to operate in an open world, where newobjects can be encounteredat any time; supervision is scarce and hasto be solicited to the user, and completelyunsupervised recognitionof new objects should be possible. Note that this setting differs fromthe one addressed in the open world recognition literature, where supervised feedback is always requested to be able to incorporate newobjects. We propose a first solution to this problem in the form ofa memory-based incremental framework that is capable of storinginformation of each and any object it encounters, while using the supervision of the user to learn to discriminate between known and unknown objects. Our approach is based on four main features: the useof time and space persistence (i.e., the appearance of objects changesrelatively slowly), the use of similarity as the main driving principlefor object recognition and novelty detection, the progressive introduction of new objects in a developmental fashion and the selectiveelicitation of user feedback in an online active learning fashion. Experimental results show the feasibility of open world, generic objectrecognition, the ability to recognize, memorize and re-identify newobjects even in complete absence of user supervision, and the utilityof persistence and incrementality in boosting performance.
The Strength of the 2175 Å Feature in the Attenuation Curves of Galaxies at 0.1 < z ≲ 3

We update the spectral modeling code MAGPHYS to include a 2175 Å absorption feature in its UV to near-IR dust attenuation prescription. This allows us to determine the strength of this feature and the shape of the dust attenuation curve in ∼5000 star-forming galaxies at in the COSMOS field. We find that a 2175 Å absorption feature of ∼1/3 the strength of that in the Milky Way is required for models to minimize residuals. We characterize the total effective dust attenuation curves as a function of several galaxy properties and find that the UV slopes of the attenuation curve for COSMOS galaxies show a strong dependence on star formation rate (SFR) and total dust attenuation (AV), such that galaxies with higher SFR and AV have shallower curves and vice versa. These results are consistent with expectations from radiative transfer that attenuation curves become shallower as the effective dust optical depth increases. We do not find significant trends in the strength of the 2175 Å absorption feature as a function of galaxy properties, but this may result from the high uncertainties associated with this measurement. The updated code is publicly available online (following acceptance).
Isomorphism types of Rogers semilattices in the analytical hierarchy.

A numbering of a countable family $S$ is a surjective map from the set of natural numbers $\omega$ onto $S$. A numbering $\nu$ is reducible to a numbering $\mu$ if there is an effective procedure which given a $\nu$-index of an object from $S$, computes a $\mu$-index for the same object. The reducibility between numberings gives rise to a class of upper semilattices, which are usually called Rogers semilattices. The paper studies Rogers semilattices for families $S \subset P(\omega)$ belonging to various levels of the analytical hierarchy. We prove that for any non-zero natural numbers $m\neq n$, any non-trivial Rogers semilattice of a $\Pi^1_m$-computable family cannot be isomorphic to a Rogers semilattice of a $\Pi^1_n$-computable family. One of the key ingredients of the proof is an application of the result by Downey and Knight on degree spectra of linear orders.