Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster Binary Embeddings for Preserving Euclidean Distances

We propose a fast, distance-preserving, binary embedding algorithm to transform a high-dimensional dataset $\mathcal{T}\subseteq\mathbb{R}^n$ into binary sequences in the cube $\{\pm 1\}^m$. When $\mathcal{T}$ consists of well-spread (i.e., non-sparse) vectors, our embedding method applies a stable noise-shaping quantization scheme to $A x$ where $A\in\mathbb{R}^{m\times n}$ is a sparse Gaussian random matrix. This contrasts with most binary embedding methods, which usually use $x\mapsto \mathrm{sign}(Ax)$ for the embedding. Moreover, we show that Euclidean distances among the elements of $\mathcal{T}$ are approximated by the $\ell_1$ norm on the images of $\{\pm 1\}^m$ under a fast linear transformation. This again contrasts with standard methods, where the Hamming distance is used instead. Our method is both fast and memory efficient, with time complexity $O(m)$ and space complexity $O(m)$. Further, we prove that the method is accurate and its associated error is comparable to that of a continuous valued Johnson-Lindenstrauss embedding plus a quantization error that admits a polynomial decay as the embedding dimension $m$ increases. Thus the length of the binary codes required to achieve a desired accuracy is quite small, and we show it can even be compressed further without compromising the accuracy. To illustrate our results, we test the proposed method on natural images and show that it achieves strong performance.
Query complexity of adversarial attacks

Modern machine learning models are typically highly accurate but have been shown to be vulnerable to small, adversarially-chosen perturbations of the input. There are two main models of attacks considered in the literature: black-box and white-box. We consider these threat models as two ends of a fine-grained spectrum, indexed by the number of queries the adversary can ask. Using this point of view we investigate how many queries the adversary needs to make to design an attack that is comparable to the best possible attack in the white-box model. We analyze two classical learning algorithms on two synthetic tasks for which we prove meaningful security guarantees. The obtained bounds suggest that some learning algorithms are inherently more robust against query-bounded adversaries than others.
The 2019 eruption of recurrent nova V3890 Sgr: observations by Swift, NICER, and SMARTS

V3890 Sgr is a recurrent nova that has been seen in outburst three times so far, with the most recent eruption occurring on 2019 August 27 ut. This latest outburst was followed in detail by the Neil Gehrels Swift Observatory, from less than a day after the eruption until the nova entered the Sun observing constraint, with a small number of additional observations after the constraint ended. The X-ray light curve shows initial hard shock emission, followed by an early start of the supersoft source phase around day 8.5, with the soft emission ceasing by day 26. Together with the peak blackbody temperature of the supersoft spectrum being ∼100 eV, these timings suggest the white dwarf mass to be high, $\sim 1.3\, {\rm M_{\odot }}$. The UV photometric light curve decays monotonically, with the decay rate changing a number of times, approximately simultaneously with variations in the X-ray emission. The UV grism spectra show both line and continuum emission, with emission lines of N, C, Mg, and O being notable. These UV spectra are best dereddened using a Small Magellanic Cloud extinction law. Optical spectra from SMARTS show evidence of interaction between the nova ejecta and wind from the donor star, as well as the extended atmosphere of the red giant being flash-ionized by the supersoft X-ray photons. Data from NICER reveal a transient 83 s quasi-periodic oscillation, with a modulation amplitude of 5 per cent, adding to the sample of novae that show such short variabilities during their supersoft phase.
Homography Estimation with Convolutional Neural Networks Under Conditions of Variance

Planar homography estimation is foundational to many computer vision problems, such as Simultaneous Localization and Mapping (SLAM) and Augmented Reality (AR). However, conditions of high variance confound even the state-of-the-art algorithms. In this report, we analyze the performance of two recently published methods using Convolutional Neural Networks (CNNs) that are meant to replace the more traditional feature-matching based approaches to the estimation of homography. Our evaluation of the CNN based methods focuses particularly on measuring the performance under conditions of significant noise, illumination shift, and occlusion. We also measure the benefits of training CNNs to varying degrees of noise. Additionally, we compare the effect of using color images instead of grayscale images for inputs to CNNs. Finally, we compare the results against baseline feature-matching based homography estimation methods using SIFT, SURF, and ORB. We find that CNNs can be trained to be more robust against noise, but at a small cost to accuracy in the noiseless case. Additionally, CNNs perform significantly better in conditions of extreme variance than their feature-matching based counterparts. With regard to color inputs, we conclude that with no change in the CNN architecture to take advantage of the additional information in the color planes, the difference in performance using color inputs or grayscale inputs is negligible. About the CNNs trained with noise-corrupted inputs, we show that training a CNN to a specific magnitude of noise leads to a "Goldilocks Zone" with regard to the noise levels where that CNN performs best.
Unsupervised Text Style Transfer with Masked Language Models

We propose Masker, an unsupervised text-editing method for style transfer. To tackle cases when no parallel source-target pairs are available, we train masked language models (MLMs) for both the source and the target domain. Then we find the text spans where the two models disagree the most in terms of likelihood. This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain. The deleted tokens are replaced with the target MLM, and by using a padded MLM variant, we avoid having to predetermine the number of inserted tokens. Our experiments on sentence fusion and sentiment transfer demonstrate that Masker performs competitively in a fully unsupervised setting. Moreover, in low-resource settings, it improves supervised methods' accuracy by over 10 percentage points when pre-training them on silver training data generated by Masker.
Knowledge Discovery in Cryptocurrency Transactions: A Survey

Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.
Controllable Story Generation with External Knowledge Using Large-Scale Language Models

Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base. Our framework consists of a keyword predictor, a knowledge retriever, a contextual knowledge ranker, and a conditional text generator. As we do not have access to ground-truth supervision for the knowledge ranker, we make use of weak supervision from sentence embedding. The empirical results show that our model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to prior work on the ROC story dataset. We showcase the controllability of our model by replacing the keywords used to generate stories and re-running the generation process. Human evaluation results show that 77.5% of these stories are successfully controlled by the new keywords. Furthermore, by scaling our model from 124 million to 8.3 billion parameters we demonstrate that larger models improve both the quality of generation (from 74.5% to 93.0% for consistency) and controllability (from 77.5% to 91.5%).
"5G Densification Increases Human Exposure to Radio-Frequency Pollution": True or False?

A very popular theory circulating among non-scientific communities claims that the massive deployment of 5G base stations over the territory, a.k. a. 5G densification, always triggers an uncontrolled and exponential increase of human exposure to Radio Frequency "Pollution" (RFP). To face such concern in a way that can be understood by the layman, in this work we develop a very simple model to compute the RFP, based on a set of worst-case and conservative assumptions. We then provide closed-form expressions to evaluate the RFP variation in a pair of candidate 5G deployments, subject to different densification levels. Results, obtained over a wide set of representative 5G scenarios, dispel the myth: 5G densification triggers an RFP decrease when the radiated power from the 5G base stations is adjusted to ensure a minimum sensitivity at the cell edge. Eventually, we analyze the conditions under which the RFP may increase when the network is densified (e.g., when the radiated power does not scale with the cell size), proving that the amount of RFP is always controlled. Finally, the results obtained by simulation confirm the outcomes of the RFP model.
Variance-Reduced Methods for Machine Learning

Stochastic optimization lies at the heart of machine learning, and its cornerstone is stochastic gradient descent (<monospace>SGD</monospace>), a method introduced over 60 years ago. The last eight years have seen an exciting new development: variance reduction for stochastic optimization methods. These variance-reduced (<monospace>VR</monospace>) methods excel in settings where more than one pass through the training data is allowed, achieving a faster convergence than <monospace>SGD</monospace> in theory and practice. These speedups underline the surge of interest in <monospace>VR</monospace> methods and the fast-growing body of work on this topic. This review covers the key principles and main developments behind <monospace>VR</monospace> methods for optimization with finite data sets and is aimed at nonexpert readers. We focus mainly on the convex setting and leave pointers to readers interested in extensions for minimizing nonconvex functions.
A roadmap for the design of four-terminal spin valves and the extraction of spin di usion length

Graphene is a promising substrate for future spintronics devices owing to its remarkable electronic mobility and low spin-orbit coupling. Hanle precession in spin valve devices is commonly used to evaluate the spin diffusion and spin lifetime properties. In this work, we demonstrate that this method is no longer accurate when the distance between inner and outer electrodes is smaller than six times the spin diffusion length, leading to errors as large as 50% for the calculations of the spin figures of merit of graphene. We suggest simple but efficient approaches to circumvent this limitation by addressing a revised version of the Hanle fit function. Complementarily, we provide clear guidelines for the design of four-terminal spin valves able to yield flawless estimations of the spin lifetime and the spin diffusion coefficient.
On the recursive structure of multigrid cycles

A new non-adaptive recursive scheme for multigrid algorithms is introduced. Governed by a positive parameter $\kappa$ called the cycle counter, this scheme generates a family of multigrid cycles dubbed $\kappa$-cycles. The well-known $V$-cycle, $F$-cycle, and $W$-cycle are shown to be particular members of this rich $\kappa$-cycle family, which satisfies the property that the total number of recursive calls in a single cycle is a polynomial of degree $\kappa$ in the number of levels of the cycle. This broadening of the scope of fixed multigrid cycles is shown to be potentially significant for the solution of some large problems on platforms, such as GPU processors, where the overhead induced by recursive calls may be relatively significant. In cases of problems for which the convergence of standard $V$-cycles or $F$-cycles (corresponding to $\kappa=1$ and $\kappa=2$, respectively) is particularly slow, and yet the cost of $W$-cycles is very high due to the large number of recursive calls (which is in exponential in the number of levels), intermediate values of $\kappa$ may prove to yield significantly faster run-times. This is demonstrated in examples where $\kappa$-cycles are used for the solution of rotated anisotropic diffusion problems, both as a stand-alone solver and as a preconditioner. Moreover, a simple model is presented for predicting the approximate run-time of the $\kappa$-cycle, which is useful in pre-selecting an appropriate cycle counter for a given problem on a given platform. Implementing the $\kappa$-cycle requires making just a small change in the classical multigrid cycle.
On the Rate of Crustal Failures in Young Magnetars

The activity of magnetars is powered by their intense and dynamic magnetic fields and has been proposed as the trigger to extragalactic fast radio bursts. Here we estimate the frequency of crustal failures in young magnetars, by computing the magnetic stresses in detailed magnetothermal simulations including Hall drift and ohmic dissipation. The initial internal topology at birth is poorly known but is likely to be much more complex than a dipole. Thus, we explore a wide range of initial configurations, finding that the expected rate of crustal failures varies by orders of magnitude depending on the initial magnetic configuration. Our results show that this rate scales with the crustal magnetic energy, rather than with the often used surface value of the dipolar component related to the spin-down torque. The estimated frequency of crustal failures for a given dipolar component can vary by orders of magnitude for different initial conditions, depending on how much magnetic energy is distributed in the crustal nondipolar components, likely dominant in newborn magnetars. The quantitative reliability of the expected event rate could be improved by a better treatment of the magnetic evolution in the core and the elastic/plastic crustal response, not included here. Regardless of that, our results are useful inputs in modeling the outburst rate of young Galactic magnetars, and their relation with the fast radio bursts in our and other galaxies.
Quadrature domains and the real quadratic family

We study several classes of holomorphic dynamical systems associated with quadrature domains. Our main result is that real-symmetric polynomials in the principal hyperbolic component of the Mandelbrot set can be conformally mated with a congruence subgroup of P S L ( 2 , Z ) \mathrm {PSL}(2,\mathbb {Z}) , and that this conformal mating is the Schwarz function of a simply connected quadrature domain.
Optimal optical polarization of nitrogen-vacancy center with arbitrary waveform pulse

The current work proposes a method for pulsed-light polarization of nitrogen-vacancy (NV) center electron spin. To evaluate the influence of pulsed spin polarization, we establish a polarization evaluation index based on polarizability and polarization time. Master equation model are utilized to theoretically calculate the spin polarization dynamics under light excitation and the optimal polarization conditions for the conventional methods are obtained. A novel pulsed-light polarization method is proposed by changing the optical pumping rate in the master equation from a fixed value to a time variable and an optimal waveform for proposed method is demonstrated through the variational method, which can simultaneously achieve high polarizability and requires a short polarization time. Hence, the polarization evaluation index is improved by ~10%. Moreover, the proposed method is verified by a pulsed-laser experimental system based on an arbitrary waveform generator. The current report shall expand the application horizon of NV center based quantum sensing.
Real-time Collaboration Between Mixed Reality Users in Geo-referenced Virtual Environment

Collaboration using mixed reality technology is an active area of research, where significant research is done to virtually bridge physical distances. There exist a diverse set of platforms and devices that can be used for a mixed-reality collaboration, and is largely focused for indoor scenarios, where, a stable tracking can be assumed. We focus on supporting collaboration between VR and AR users, where AR user is mobile outdoors, and VR user is immersed in true-sized digital twin. This cross-platform solution requires new user experiences for interaction, accurate modelling of the real-world, and working with noisy outdoor tracking sensor such as GPS. In this paper, we present our results and observations of real-time collaboration between cross-platform users, in the context of a geo-referenced virtual environment. We propose a solution for using GPS measurement in VSLAM to localize the AR user in an outdoor environment. The client applications enable VR and AR user to collaborate across the heterogeneous platforms seamlessly. The user can place or load dynamic contents tagged to a geolocation and share their experience with remote users in real-time.
Current Lightweight Cryptography Protocols in Smart City IoT Networks: A Survey

With the advent of advanced technology, IoT introduces a vast number of devices connecting with each other and collecting a sheer volume of data. Thus, the demands of IoT security is paramount. Cryptography is being used to secure the networks for authentication, confidentiality, data integrity and access control. However, due to the resource constraint nature of IoT devices, the traditional cryptographic protocols may not be suited in all IoT environments. Researchers, as a result, have been proposing various lightweight cryptographic algorithms and protocols. In this paper, we discuss the state of the art lightweight cryptographic protocols for IoT networks and present a comparative analysis of the existing protocols. In doing so, this paper has classified the most current algorithm into two parts, such as symmetric and asymmetric lightweight cryptography. Additionally, we consider several recent developed block cipher and stream cipher algorithms. Furthermore, various research challenges of lightweight cryptography have been addressed.
Tight Approximation Guarantees for Concave Coverage Problems

In the maximum coverage problem, we are given subsets $T_1, \ldots, T_m$ of a universe $[n]$ along with an integer $k$ and the objective is to find a subset $S \subseteq [m]$ of size $k$ that maximizes $C(S) := \Big|\bigcup_{i \in S} T_i\Big|$. It is a classic result that the greedy algorithm for this problem achieves an optimal approximation ratio of $(1-e^{-1})$. In this work we consider a generalization of this problem wherein an element $a$ can contribute by an amount that depends on the number of times it is covered. Given a concave, nondecreasing function $\varphi$, we define $C^{\varphi}(S) := \sum_{a \in [n]}w_a\varphi(|S|_a)$, where $|S|_a = |\{i \in S : a \in T_i\}|$. The standard maximum coverage problem corresponds to taking $\varphi(j) = \min\{j,1\}$. For any such $\varphi$, we provide an efficient algorithm that achieves an approximation ratio equal to the Poisson concavity ratio of $\varphi$, defined by $\alpha_{\varphi} := \inf_{x \in \mathbb{N}} \frac{\mathbb{E}[\varphi(\text{Poi}(x))]}{\varphi(\mathbb{E}[\text{Poi}(x)])}$. Complementing this approximation guarantee, we establish a matching NP-hardness result when $\varphi$ grows in a sublinear way. As special cases, we improve the result of [Barman et al., IPCO, 2020] about maximum multi-coverage, that was based on the unique games conjecture, and we recover the result of [Dudycz et al., IJCAI, 2020] on multi-winner approval-based voting for geometrically dominant rules. Our result goes beyond these special cases and we illustrate it with applications to distributed resource allocation problems, welfare maximization problems and approval-based voting for general rules.
The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. III. Substructures in Protostellar Disks

The prevalence of substructures in ∼1–10 Myr old protoplanetary disks, which are often linked to planet formation, has raised the question of how early such features form and, as a corollary, how early planet formation begins. Here we present observations of seven protostellar disks (aged ∼ 0.1–1 Myr) from the Very Large Array/Atacama Large Millimeter/Submillimeter Array Nascent Disk and Multiplicity Survey of Orion Protostars (VANDAM: Orion) that show clear substructures, thereby demonstrating that these features can form early in the lifetimes of disks. We use simple analytic models as well as detailed radiative transfer modeling to characterize their structure. In particular we show that at least four of the sources have relatively massive envelopes, indicating that they are particularly young, likely the youngest disks with substructures known to date. Several of these disks also have emission from an inner disk that is offset from the center of the ring structure. Given the size of the cleared-out regions of the disk, it is unclear, however, whether these features are related to planet formation, or rather if they are signposts of close-separation binary formation at early times.
Estimands in hematologic oncology trials

The estimand framework included in the addendum to the ICH E9 guideline facilitates discussions to ensure alignment between the key question of interest, the analysis, and interpretation. Therapeutic knowledge and drug mechanism play a crucial role in determining the strategy and defining the estimand for clinical trial designs. Clinical trials in patients with hematological malignancies often present unique challenges for trial design due to complexity of treatment options and existence of potential curative but highly risky procedures, for example, stem cell transplant or treatment sequence across different phases (induction, consolidation, maintenance). Here, we illustrate how to apply the estimand framework in hematological clinical trials and how the estimand framework can address potential difficulties in trial result interpretation. This paper is a result of a cross‐industry collaboration to connect the International Conference on Harmonisation (ICH) E9 addendum concepts to applications. Three randomized phase 3 trials will be used to consider common challenges including intercurrent events in hematologic oncology trials to illustrate different scientific questions and the consequences of the estimand choice for trial design, data collection, analysis, and interpretation. Template language for describing estimand in both study protocols and statistical analysis plans is suggested for statisticians' reference.