Miracle-dz/UniFAR-FTUs huggingface dataset:
G-induced loss of consciousness: definition, history, current status.

: G-induced loss of consciousness (G-LOC) is defined as "a state of altered perception wherein (one's) awareness of reality is absent as a result of sudden, critical reduction of cerebral blood circulation caused by increased G force. " This phenomenon was first identified in Great Britain in World War I (circa 1918-1919) as "fainting in the air. " In the United States G-LOC was first encountered in 1922 during the Pulitzer Trophy Air Race. Although recognized during World War II as an operational hazard for fighter aircraft, the invention of the pneumatic anti-G suit reduced concern about G-LOC at that time. A 1984 survey of pilots of high performance aircraft has shown G-LOC to be an operational problem--probably one that has caused aircraft mishaps for several years. The concern of this panel was to focus on various approaches in reducing the G-LOC hazard.
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm

Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks. This paper proposes a new unsupervised embedding approach, called Super-AND, which extends the current state-of-the-art model. Super-AND has its unique set of losses that can gather similar samples nearby within a low-density space while keeping invariant features intact against data augmentation. Super-AND outperforms all existing approaches and achieves an accuracy of 89.2% on the image classification task for CIFAR-10. We discuss the practical implications of this method in assisting semi-supervised tasks.
A Proto-Object Based Dynamic Visual Saliency Model with an FPGA Implementation

The ability to attend to salient regions of a visual scene is an innate and necessary preprocessing step for both biological and engineered systems performing high-level visual tasks (e.g. object detection, tracking, and classification). Computational efficiency, in regard to processing bandwidth and speed, is improved by only devoting computational resources to salient regions of the visual stimuli. In this paper, we first present a biologically-plausible, bottom-up, dynamic visual saliency model based on the notion of proto-objects. This is achieved by incorporating the temporal characteristics of the visual stimulus into the model, similarly to the manner in which early stages of the human visual system extracts temporal information. This model outperforms state-of-the-art dynamic visual saliency models in predicting human eye fixations on a commonly-used video dataset with associated eye tracking data. Secondly, for this model to have practical applications, it must be capable of performing its computations in real-time under lowpower, small-size, and lightweight constraints. To address this, we introduce a Field-Programmable Gate Array implementation of the model on an Opal Kelly 7350 Kintex-7 board. This novel hardware implementation allows for processing of up to 23.35 frames per second running on a 100 MHz clock -- better than 26x speedup from the software implementation.
Theoretical polarization of zero phonon lines in point defects

In quantum technologies, point defects in semiconductors are becoming more significant. Understanding the frequency, intensity, and polarization of the zero phonon line is important. The last two properties are the subject of this paper. I present a method for calculating these properties and show the importance of using wave functions from both the ground and excited state. The validity of this method is demonstrated on the divacancy in 4H-SiC. Here, the calculated polarization and radiative lifetimes are in excellent agreement with experimental measurements. In general, this method can help to identify point defects and estimate suitable applications.
How Much Do Underestimated Field Strengths from Zeeman–Doppler Imaging Affect Spin-down Torque Estimates?

Numerous attempts to estimate the rate at which low-mass stars lose angular momentum over their lifetimes fill the literature. One approach is to use magnetic maps derived from Zeeman–Doppler imaging (ZDI) in conjunction with so-called “braking laws. ” The use of ZDI maps has advantages over other methods because it allows information about the magnetic field geometry to be incorporated into the estimate. However, ZDI is known to underestimate photospheric field strengths due to flux cancellation effects. Recently, Lehmann et al. conducted synthetic ZDI reconstructions on a set of flux transport simulations to help quantify the amount by which ZDI underestimates the field strengths of relatively slowly rotating and weak activity solar-like stars. In this paper, we evaluate how underestimated angular momentum loss rate estimates based on ZDI maps may be. We find that they are relatively accurate for stars with strong magnetic fields but may be underestimated by a factor of up to ∼10 for stars with weak magnetic fields. Additionally, we re-evaluate our previous work that used ZDI maps to study the relative contributions of different magnetic field modes to angular momentum loss. We previously found that the dipole component dominates spin-down for most low-mass stars. This conclusion still holds true even in light of the work of Lehmann et al.
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

Since hardware resources are limited, the objective of training deep learning models is typically to maximize accuracy subject to the time and memory constraints of training and inference. We study the impact of model size in this setting, focusing on Transformer models for NLP tasks that are limited by compute: self-supervised pretraining and high-resource machine translation. We first show that even though smaller Transformer models execute faster per iteration, wider and deeper models converge in significantly fewer steps. Moreover, this acceleration in convergence typically outpaces the additional computational overhead of using larger models. Therefore, the most compute-efficient training strategy is to counterintuitively train extremely large models but stop after a small number of iterations. This leads to an apparent trade-off between the training efficiency of large Transformer models and the inference efficiency of small Transformer models. However, we show that large models are more robust to compression techniques such as quantization and pruning than small models. Consequently, one can get the best of both worlds: heavily compressed, large models achieve higher accuracy than lightly compressed, small models.
Visual Camera Re-Localization From RGB and RGB-D Images Using DSAC

We describe a learning-based system that estimates the camera position and orientation from a single input image relative to a known environment. The system is flexible w.r. t. the amount of information available at test and at training time, catering to different applications. Input images can be RGB-D or RGB, and a 3D model of the environment can be utilized for training but is not necessary. In the minimal case, our system requires only RGB images and ground truth poses at training time, and it requires only a single RGB image at test time. The framework consists of a deep neural network and fully differentiable pose optimization. The neural network predicts so called scene coordinates, i.e., dense correspondences between the input image and 3D scene space of the environment. The pose optimization implements robust fitting of pose parameters using differentiable RANSAC (DSAC) to facilitate end-to-end training. The system, an extension of DSAC++ and referred to as DSAC*, achieves state-of-the-art accuracy on various public datasets for RGB-based re-localization, and competitive accuracy for RGB-D based re-localization.
On combining the zero bias transform and the empirical
characteristic function to test normality

We propose a new powerful family of tests of univariate normality. These tests are based on an initial value problem in the space of characteristic functions originating from the fixed point property of the normal distribution in the zero bias transform. Limit distributions of the test statistics are provided under the null hypothesis, as well as under contiguous and fixed alternatives. Using the covariance structure of the limiting Gaussian process from the null distribution, we derive explicit formulas for the first four cumulants of the limiting random element and apply the results by fitting a distribution from the Pearson system. A comparative Monte Carlo power study shows that the new tests are serious competitors to the strongest well established tests.
On transience of frogs on Galton–Watson trees

We consider a random interacting particle system, known as the frog model, on infinite Galton-Watson trees allowing offspring zero and one. The system starts with one awake particle (frog) at the root of the tree and a random number of sleeping particles at the other vertices. Awake frogs move according to simple random walk on the tree and as soon as they encounter sleeping frogs, those will wake up and move independently according to simple random walk. The frog model is called transient, if there are almost surely only finitely many particles returning to the root. In this paper we prove a zero-one law for transience of the frog model and show the existence of a transient phase for certain classes of Galton-Watson trees.
A canonical embedding of $\textbf{Aut}_{\textbf{hol}}({\bf \mathbb C^n})$

The group $\text{Aut}_{\text{hol}}(\mathbb C^n)$ of self-biholomorphisms of $\mathbb C^n$ consists of affine maps if $n=1$, but in higher dimensions it is a large object that has not been described explicitly. Despite the intricacies involved when $n>1$, surprisingly every $F\in \text{Aut}_{\text{hol}}(\mathbb C^n)$ is uniquely determined inside the group by only two data, of infinitesimal and global nature: the $1$-jet of $F$ at $0$, and the complex Hessian of a certain plurisubharmonic function associated to $F$. If $n=1$ this global datum is zero for all $F$, which is then determined solely by its $1$-jet at $0$, and one recovers $\text{Aut}_{\text{hol}}(\mathbb C)= \text{Aff}(\mathbb C)\cong \mathbb C \times \mathbb C^{*}$. Our main result, formulated as the existence of a canonical embedding of $ \text{Aut}_{\text{hol}} ( \mathbb C^n)$, also singles out a natural candidate for moduli space of $ \text{Aut}_{\text{hol}} ( \mathbb C^n)$, for all $n>1$.
Group-scale intrinsic galaxy alignments in the Illustris-TNG and MassiveBlack-II simulations

We study the alignments of satellite galaxies, and their anisotropic distribution, with respect to location and orientation of their host central galaxy in MassiveBlack-II (MB-II) and IllustrisTNG simulations. We find that: the shape of the satellite system in haloes of mass ($\gt 10^{13}\, h^{-1}\, \mathrm{M}_{\odot }$) is well aligned with the shape of the central galaxy at z = 0.06 with the mean alignment between the major axes being ∼Δθ = 12° when compared to a uniform random distribution; that satellite galaxies tend to be anisotropically distributed along the major axis of the central galaxy with a stronger alignment in haloes of higher mass or luminosity; and that the satellite distribution is more anisotropic for central galaxies with lower star formation rate, which are spheroidal, and for red central galaxies. Radially, we find that satellites tend to be distributed along the major axis of the shape of the stellar component of central galaxies at smaller scales and the dark matter component on larger scales. We find that the dependence of satellite anisotropy on central galaxy properties and the radial distance is similar in both the simulations with a larger amplitude in MB-II. The orientation of satellite galaxies tends to point toward the location of the central galaxy at small scales and this correlation decreases with increasing distance, and the amplitude of satellite alignment is higher in high-mass haloes. However, the projected ellipticities do not exhibit a scale-dependent radial alignment, as has been seen in some observational measurements.
Analysis of diversity-accuracy tradeoff in image captioning

We investigate the effect of different model architectures, training objectives, hyperparameter settings and decoding procedures on the diversity of automatically generated image captions. Our results show that 1) simple decoding by naive sampling, coupled with low temperature is a competitive and fast method to produce diverse and accurate caption sets; 2) training with CIDEr-based reward using Reinforcement learning harms the diversity properties of the resulting generator, which cannot be mitigated by manipulating decoding parameters. In addition, we propose a new metric AllSPICE for evaluating both accuracy and diversity of a set of captions by a single value.
Approximating the Quantum Approximate Optimisation Algorithm

The Quantum Approximate Optimisation Algorithm was proposed as a heuristic method for solving combinatorial optimisation problems on near-term quantum computers and may be among the first algorithms to perform useful computations in the post-supremacy, noisy, intermediate scale era of quantum computing. In this work, we exploit the recently proposed digital-analog quantum computation paradigm, in which the versatility of programmable universal quantum computers and the error resilience of quantum simulators are combined to improve platforms for quantum computation. We show that the digital-analog paradigm is suited to the variational quantum approximate optimisation algorithm, due to its inherent resilience against coherent errors, by performing large-scale simulations and providing analytical bounds for its performance in devices with finite single-qubit operation times. We observe regimes of single-qubit operation speed in which the considered variational algorithm provides a significant improvement over non-variational counterparts.
A Primer in BERTology: What We Know About How BERT Works

Abstract Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue, and approaches to compression. We then outline directions for future research.
The Onset of Hadronic Supercriticality in ExpandingSources

An overlooked property of hadronic models is that they can become supercritical by abruptly transforming the energy stored in the relativistic protons into radiation. Supercriticality manifests itself when the proton density exceeds a critical value. We seek to map the complete parameter space of this behaviour in cases where a source, consisting of relativistic protons, is expanding so adiabatic losses become important. We search those critical values that lead the system to the supercritical regime and are closely related to Gamma Ray Bursts. For this reason we adopt the Relativistic Blast Wave (RBW) model which is thought to describe the production of a GRB after the initial explosion.
Visual Commonsense R-CNN

We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). This is also the core reason why VC R-CNN can learn “sense-making” knowledge like chair can be sat — while not just “common” co-occurrences such as chair is likely to exist if table is observed. We extensively apply VC R-CNN features in prevailing models of three popular tasks: Image Captioning, VQA, and VCR, and observe consistent performance boosts across them, achieving many new state-of-the-arts.
Threshold Graphs Maximize Homomorphism Densities

Given a fixed graph $H$ and a constant $c \in [0,1]$, we can ask what graphs $G$ with edge density $c$ asymptotically maximize the homomorphism density of $H$ in $G$. For all $H$ for which this problem has been solved, the maximum is always asymptotically attained on one of two kinds of graphs: the quasi-star or the quasi-clique. We show that for any $H$ the maximizing $G$ is asymptotically a threshold graph, while the quasi-clique and the quasi-star are the simplest threshold graphs having only two parts. This result gives us a unified framework to derive a number of results on graph homomorphism maximization, some of which were also found quite recently and independently using several different approaches. We show that there exist graphs $H$ and densities $c$ such that the optimizing graph $G$ is neither the quasi-star nor the quasi-clique, reproving a result of Day and Sarkar. We rederive a result of Janson et al. on maximizing homomorphism numbers, which was originally found using entropy methods. We also show that for $c$ large enough all graphs $H$ maximize on the quasi-clique, which was also recently proven by Gerbner et al., and in analogy with Kopparty and Rossman we define the homomorphism density domination exponent of two graphs, and find it for any $H$ and an edge.
Supervised Dimensionality Reduction and Visualization using Centroid-encoder

Visualizing high-dimensional data is an essential task in Data Science and Machine Learning. The Centroid-Encoder (CE) method is similar to the autoencoder but incorporates label information to keep objects of a class close together in the reduced visualization space. CE exploits nonlinearity and labels to encode high variance in low dimensions while capturing the global structure of the data. We present a detailed analysis of the method using a wide variety of data sets and compare it with other supervised dimension reduction techniques, including NCA, nonlinear NCA, t-distributed NCA, t-distributed MCML, supervised UMAP, supervised PCA, Colored Maximum Variance Unfolding, supervised Isomap, Parametric Embedding, supervised Neighbor Retrieval Visualizer, and Multiple Relational Embedding. We empirically show that centroid-encoder outperforms most of these techniques. We also show that when the data variance is spread across multiple modalities, centroid-encoder extracts a significant amount of information from the data in low dimensional space. This key feature establishes its value to use it as a tool for data visualization.
The Persistent Radio Jet Coupled to Hard X-Rays in the Soft State of Cyg X-1

We study long-term radio/X-ray correlations in Cyg X-1. We find the persistent existence of a compact radio jet in its soft state. This represents a new phenomenon in black hole binaries, in addition to compact jets in the hard state and episodic ejections of ballistic blobs in the intermediate state. While the radio emission in the hard state is strongly correlated with both the soft and hard X-rays, the radio flux in the soft state is not directly correlated with the flux of the dominant disk blackbody in soft X-rays, but instead it is lagged by about 100 days. We interpret the lag as occurring in the process of advection of the magnetic flux from the donor through the accretion disk. On the other hand, the soft-state radio flux is very tightly correlated with the hard X-ray, 15–50 keV, flux without a measurable lag and at the same rms. This implies that the X-ray-emitting disk corona and the soft-state jet are powered by the same process, probably magnetically.
A tale of two populations: surviving and destroyed dwarf galaxies and the build-up of the Milky Way’s stellar halo

We use magnetohydrodynamical simulations of Milky Way-mass haloes from the Auriga project to investigate the properties of surviving and destroyed dwarf galaxies that are accreted by these haloes over cosmic time. We show that the combined luminosity function of surviving and destroyed dwarfs at infall is similar in the various Auriga haloes, and is dominated by the destroyed dwarfs. There is, however, a strong dependence on infall time: destroyed dwarfs typically have early infall times of less than 6 Gyr (since the big bang), whereas the majority of dwarfs accreted after 10 Gyr have survived to the present day. Because of their late infall, the surviving satellites have higher metallicities at infall than their destroyed counterparts of similar mass at infall; the difference is even more pronounced for the present-day metallicities of satellites, many of which continue to form stars after infall, in particular for $M_{\rm star}\gt 10^7 \, {\rm M}_\odot$. In agreement with previous work, we find that a small number of relatively massive destroyed dwarf galaxies dominate the mass of stellar haloes. However, there is a significant radial dependence: while 90 per cent of the mass in the inner regions (${\lt}20\,$ kpc) is contributed, on average, by only three massive progenitors, the outer regions (${\gt}100\,$ kpc) typically have ∼8 main progenitors of relatively lower mass. Finally, we show that a few massive progenitors dominate the metallicity distribution of accreted stars, even at the metal-poor end. Contrary to common assumptions in the literature, stars from dwarf galaxies of mass $M_{\rm star}\lt 10^7 \, {\rm M}_\odot$ make up less than 10 per cent of the accreted, metal poor stars ([Fe/H] ${\lt}-3$) in the inner $50\,$ kpc.
de Sitter future-past extremal surfaces and the entanglement wedge

We develop further the codim-2 future-past extremal surfaces stretching between the future and past boundaries in de Sitter space, discussed in previous work. We first make more elaborate the construction of such surfaces anchored at more general subregions of the future boundary, and stretching to equivalent subregions at the past boundary. These top-bottom symmetric future-past extremal surfaces cannot penetrate beyond a certain limiting surface in the northern/southern diamond regions: the boundary subregions become the whole boundary for this limiting surface. For multiple disjoint subregions, this construction leads to mutual information vanishing and strong subadditivity being saturated. We then discuss an effective codim-1 envelope surface arising from these codim-2 surfaces. This leads to analogs of the entanglement wedge and subregion duality for these future-past extremal surfaces in de Sitter space.
Using a thousand optimization tasks to learn hyperparameter search strategies

We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks, to variational autoencoders, to non-volume preserving flows on a variety of datasets. As an example application of such a dataset we explore meta-learning an ordered list of hyperparameters to try sequentially. By learning this hyperparameter list from data generated using TaskSet we achieve large speedups in sample efficiency over random search. Next we use the diversity of the TaskSet and our method for learning hyperparameter lists to empirically explore the generalization of these lists to new optimization tasks in a variety of settings including ImageNet classification with Resnet50 and LM1B language modeling with transformers. As part of this work we have opensourced code for all tasks, as well as ~29 million training curves for these problems and the corresponding hyperparameters.