Miracle-dz/UniFAR-FTUs huggingface dataset:
Primordial feature constraints from BOSS + eBOSS

Understanding the universe in its pristine epoch is crucial in order to obtain a concise comprehension of the late-time universe. Although current data in cosmology are compatible with Gaussian primordial perturbations whose power spectrum follows a nearly scale-invariant power law, this need not be the case when a fundamental theoretical construction is assumed. These extended models lead to sharp features in the primordial power spectrum, breaking its scale invariance. In this work, we obtain combined constraints on four primordial feature models by using the final data release of the BOSS galaxies and eBOSS quasars. By pushing towards the fundamental mode of these surveys and using the larger eBOSS volume, we were able to extend the feature parameter space (i.e. the feature frequency ω) by a factor of four compared to previous analyses using BOSS. While we did not detect any significant features, previous work showed that next-generation galaxy surveys such as DESI will improve the sensitivity to features by a factor of 7, and will also extend the parameter space by a factor of 2.5.
Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis

In speech synthesis, a generative adversarial network (GAN), training a generator (speech synthesizer) and a discriminator in a min-max game, is widely used to improve speech quality. An ensemble of discriminators is commonly used in recent neural vocoders (e.g., HiFi-GAN) and end-to-end text-to-speech (TTS) systems (e.g., VITS) to scrutinize waveforms from multiple perspectives. Such discriminators allow synthesized speech to adequately approach real speech; however, they require an increase in the model size and computation time according to the increase in the number of discriminators. Alternatively, this study proposes a Wave-U-Net discriminator, which is a single but expressive discriminator with Wave-U-Net architecture. This discriminator is unique; it can assess a waveform in a sample-wise manner with the same resolution as the input signal, while extracting multilevel features via an encoder and decoder with skip connections. This architecture provides a generator with sufficiently rich information for the synthesized speech to be closely matched to the real speech. During the experiments, the proposed ideas were applied to a representative neural vocoder (HiFi-GAN) and an end-to-end TTS system (VITS). The results demonstrate that the proposed models can achieve comparable speech quality with a 2.31 times faster and 14.5 times more lightweight discriminator when used in HiFi-GAN and a 1.90 times faster and 9.62 times more lightweight discriminator when used in VITS.1
Correlation study of temporal and emission properties of quiescent magnetars

We measured temporal and emission properties of quiescent magnetars using archival Chandra and XMM-Newton data, produced a list of the properties for 17 magnetars, and revisited previously suggested correlations between the properties. Our studies carried out with a larger sample, better spectral characterizations, and more thorough analyses not only confirmed previously-suggested correlations but also found new ones. The observed correlations differ from those seen in other neutron-star populations but generally accord with magnetar models. Specifically, the trends of the intriguing correlations of blackbody luminosity ($L_{\rm BB}$) with the spin-inferred dipole magnetic field strength ($B_{\rm S}$) and characteristic age ($\tau_{\rm c}$) were measured to be $L_{\rm BB}\propto B_{\rm S}^{1.5}$ and $L_{\rm BB}\propto \tau_{\rm c}^{-0.6}$, supporting the twisted magnetosphere and magnetothermal evolution models for magnetars. We report the analysis results and discuss our findings in the context of magnetar models.
PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models

Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-learning models. In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices. More specifically, we seek to design techniques that could provide high-accuracy HR estimation with low-frequency PPG sampling, small model size, and fast inference time. First, we show that by combining signal processing and ML, it is possible to reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing higher HR estimation accuracy. This combination also helps to reduce the ML model feature size, leading to smaller models. Additionally, we present a comprehensive analysis on different ML models and feature sizes to compare their accuracy, model size, and inference time. The models explored include Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were conducted using both a widely-utilized dataset and our self-collected dataset. The experimental results show that our method by combining signal processing and ML had only 5 % error for HR estimation using low-frequency PPG data. Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time.
Sharp metastability transition for two-dimensional bootstrap percolation with symmetric isotropic threshold rules

We study two-dimensional critical bootstrap percolation models. We establish that a class of these models including all isotropic threshold rules with a convex symmetric neighbourhood, undergoes a sharp metastability transition. This extends previous instances proved for several specific rules. The paper supersedes a draft by Alexander Holroyd and the first author from 2012. While it served a role in the subsequent development of bootstrap percolation universality, we have chosen to adopt a more contemporary viewpoint in its present form.
GM-NeRF: Learning Generalizable Model-Based Neural Radiance Fields from Multi-View Images

In this work, we focus on synthesizing high-fidelity novel view images for arbitrary human performers, given a set of sparse multi-view images. It is a challenging task due to the large variation among articulated body poses and heavy self-occlusions. To alleviate this, we introduce an effective generalizable framework Generalizable Model-based Neural Radiance Fields (GM-NeRF) to synthesize free-viewpoint images. Specifically, we propose a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy which can alleviate the misalignment between inaccurate geometry prior and pixel space. On top of that, we further conduct neural rendering and partial gradient back propagation for efficient perceptual supervision and improvement of the perceptual quality of synthesis. To evaluate our method, we conduct experiments on synthesized datasets THuman2.0 and Multi-garment, and real-world datasets Genebody and ZJUMocap. The results demonstrate that our approach outperforms state-of-the-art methods in terms of novel view synthesis and geometric reconstruction.
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates

Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple 2D input views. However, a well-known drawback of NeRFs is the less-than-ideal performance under a small number of views, due to insufficient constraints enforced by volumetric rendering. To address this issue, we introduce SCADE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes. To constrain NeRF reconstruction, we leverage geometric priors in the form of per-view depth estimates produced with state-of-the-art monocular depth estimation models, which can generalize across scenes. A key challenge is that monocular depth estimation is an illposed problem, with inherent ambiguities. To handle this issue, we propose a new method that learns to predict, for each view, a continuous, multimodal distribution of depth estimates using conditional Implicit Maximum Likelihood Estimation (cIMLE). In order to disambiguate exploiting multiple views, we introduce an original space carving loss that guides the NeRF representation to fuse multiple hypothesized depth maps from each view and distill from them a common geometry that is consistent with all views. Experiments show that our approach enables higher fidelity novel view synthesis from sparse views. Our project page can be found at scade-spacecarving-nerfs.github. io.
Orbital Decay in an Accreting and Eclipsing 13.7 Minute Orbital Period Binary with a Luminous Donor

We report the discovery of ZTF J0127+5258, a compact mass-transferring binary with an orbital period of 13.7 minutes. The system contains a white dwarf accretor, which likely originated as a post–common envelope carbon–oxygen (CO) white dwarf, and a warm donor (T eff,donor = 16,400 ± 1000 K). The donor probably formed during a common envelope phase between the CO white dwarf and an evolving giant that left behind a helium star or white dwarf in a close orbit with the CO white dwarf. We measure gravitational wave–driven orbital inspiral with ∼51σ significance, which yields a joint constraint on the component masses and mass transfer rate. While the accretion disk in the system is dominated by ionized helium emission, the donor exhibits a mixture of hydrogen and helium absorption lines. Phase-resolved spectroscopy yields a donor radial velocity semiamplitude of 771 ± 27 km s−1, and high-speed photometry reveals that the system is eclipsing. We detect a Chandra X-ray counterpart with L X ∼ 3 × 1031 erg s−1. Depending on the mass transfer rate, the system will likely either evolve into a stably mass-transferring helium cataclysmic variable, merge to become an R CrB star, or explode as a Type Ia supernova in the next million years. We predict that the Laser Space Interferometer Antenna (LISA) will detect the source with a signal-to-noise ratio of 24 ± 6 after 4 yr of observations. The system is the first LISA-loud mass-transferring binary with an intrinsically luminous donor, a class of sources that provide the opportunity to leverage the synergy between optical and infrared time domain surveys, X-ray facilities, and gravitational-wave observatories to probe general relativity, accretion physics, and binary evolution.
Multiple Access Design for Symbiotic Radios: Facilitating Massive IoT Connections With Cellular Networks

Symbiotic radio (SR) has emerged as a spectrum- and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information. In both of the schemes, the receiver jointly decodes the information from the BS and IoT devices. To evaluate the above two schemes, the closed-form expressions of the ergodic rates in high signal-to-noise ratio (SNR) regimes and outage probabilities for cellular and IoT transmissions are derived by using extreme value theory, generalized-K distribution approximation and Gaussian-Chebyshev quadrature methods. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.
An explicit upper bound for $L(1, \chi)$ when $\chi$ is quadratic

We consider Dirichlet $L$-functions $L(s, \chi)$ where $\chi$ is a non-principal quadratic character to the modulus $q$. We make explicit a result due to Pintz and Stephens by showing that $|L(1, \chi)|\leq \frac{1}{2}\log q$ for all $q\geq 2\cdot 10^{23}$ and $|L(1, \chi)|\leq \frac{9}{20}\log q$ for all $q\geq 5\cdot 10^{50}$.
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video super-resolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https://vlis2022.github. io/cvpr23/egvsr.
Regularity and asymptotics of densities of inverse subordinators

In this article densities (and their derivatives) of subordinators and inverse subordinators are considered. Under minor restrictions, generally milder than the existing in the literature, using a useful modification of the saddle point method, we obtain the large asymptotic behaviour of these densities (and their derivatives) for a specific region of space and time and quantify how the ratio between time and space affects the explicit speed of convergence. The asymptotics is governed by an exponential term depending on the Laplace exponent of the subordinator and the region represents the behaviour of the subordinator when it is atypically small (the inverse one is larger than usual). As a result a route to the derivation of novel general or particular fine estimates for densities with explicit constants in the speed of convergence in the region of the lower envelope/the law of iterated logarithm is available. Furthermore, under mild conditions, we provide a power series representation for densities (and their derivatives) of subordinators and inverse subordinators. This representation is explicit and based on the derivatives of the convolution of the tails of the corresponding L\'evy measure, whose smoothness is also investigated. In this context the methods adopted are based on Laplace inversion and strongly rely on the theory of Bernstein functions extended to the cut complex plane. As a result, smoothness properties of densities (and their derivatives) and their behaviour near zero immediately follow.
Estimating Maximal Symmetries of Regression Functions via Subgroup Lattices

We present a method for estimating the maximal symmetry of a continuous regression function. Knowledge of such a symmetry can be used to significantly improve modelling by removing the modes of variation resulting from the symmetries. Symmetry estimation is carried out using hypothesis testing for invariance strategically over the subgroup lattice of a search group G acting on the feature space. We show that the estimation of the unique maximal invariant subgroup of G generalises useful tools from linear dimension reduction to a non linear context. We show that the estimation is consistent when the subgroup lattice chosen is finite, even when some of the subgroups themselves are infinite. We demonstrate the performance of this estimator in synthetic settings and apply the methods to two data sets: satellite measurements of the earth's magnetic field intensity; and the distribution of sunspots.
Consistent Quantum Causes

Developing a quantum analog of the modern classical theory of causation, as formulated by Pearl and others using directed acyclic graphs, requires a theory of random or stochastic time development at the microscopic level, where the noncommutation of Hilbert-space projectors cannot be ignored. The Consistent Histories approach provides such a theory. How it works is shown by applying it to simple examples involving beam splitters and a Mach-Zehnder interferometer. It justifies the usual laboratory intuition that properly tested apparatus can reveal the earlier microscopic cause (e.g., radioactive decay) of a later macroscopic outcome. The general approach is further illustrated by how it resolves the Bell inequalities paradox. The difficulties encountered in an approach known as Quantum Causal Models can be traced to its lack of a satisfactory theory of quantum random processes.
Bounds on Species Scale and the Distance Conjecture

The species scale Λs≤Mpl$\Lambda _s\le M_{\rm pl}$ serves as a UV cutoff in the gravitational sector of an EFT and can depend on the moduli of the theory as the spectrum of the theory varies. It is argued that the dependence of the species scale Λs(ϕ)$\Lambda _s (\phi )$ on massless (or light) modes ϕi$\phi ^i$ satisfies Mpld−2|Λs′/Λs|2
MagicEye: An Intelligent Wearable Towards Independent Living of Visually Impaired

Individuals with visual impairments often face a multitude of challenging obstacles in their daily lives. Vision impairment can severely impair a person's ability to work, navigate, and retain independence. This can result in educational limits, a higher risk of accidents, and a plethora of other issues. To address these challenges, we present MagicEye, a state-of-the-art intelligent wearable device designed to assist visually impaired individuals. MagicEye employs a custom-trained CNN-based object detection model, capable of recognizing a wide range of indoor and outdoor objects frequently encountered in daily life. With a total of 35 classes, the neural network employed by MagicEye has been specifically designed to achieve high levels of efficiency and precision in object detection. The device is also equipped with facial recognition and currency identification modules, providing invaluable assistance to the visually impaired. In addition, MagicEye features a GPS sensor for navigation, allowing users to move about with ease, as well as a proximity sensor for detecting nearby objects without physical contact. In summary, MagicEye is an innovative and highly advanced wearable device that has been designed to address the many challenges faced by individuals with visual impairments. It is equipped with state-of-the-art object detection and navigation capabilities that are tailored to the needs of the visually impaired, making it one of the most promising solutions to assist those who are struggling with visual impairments.
Pitman's Theorem, Black-Scholes Equation, and Derivative Pricing for Fundraisers

We propose a financial market model that comprises a savings account and a stock, where the stock price process is modeled as a one-dimensional diffusion, wherein two types of agents exist: an ordinary investor and a fundraiser who buys or sells stocks as funding activities. Although the investor information is the natural filtration of the diffusion, the fundraiser possesses extra information regarding the funding, as well as additional cash flows as a result of the funding. This concept is modeled using Pitman's theorem for the three-dimensional Bessel process. Two contributions are presented: First, the prices of European options for the fundraiser are derived. Second, a numerical scheme is proposed for call option prices in a market with a bubble, where multiple solutions exist for the Black-Scholes equation and the derivative prices are characterized as the smallest nonnegative supersolution. More precisely, the call option price in such a market is approximated from below by the prices for the fundraiser. This scheme overcomes the difficulty that stems from the discrepancy that the payoff shows linear growth, whereas the price function shows strictly sublinear growth.
Differentially Private Synthetic Control

Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurements from other similar observations (i.e., donor pool ) to predict a counterfactual time series of interest (i.e., target unit) by analyzing the relationship between the target and the donor pool before the intervention. As synthetic control tools are increasingly applied to sensitive or proprietary data, formal privacy protections are often required. In this work, we provide the first algorithms for differentially private synthetic control with explicit error bounds. Our approach builds upon tools from non-private synthetic control and differentially private empirical risk minimization. We provide upper and lower bounds on the sensitivity of the synthetic control query and provide explicit error bounds on the accuracy of our private synthetic control algorithms. We show that our algorithms produce accurate predictions for the target unit, and that the cost of privacy is small. Finally, we empirically evaluate the performance of our algorithm, and show favorable performance in a variety of parameter regimes, as well as providing guidance to practitioners for hyperparameter tuning.
The Gallium Neutrino Absorption Cross Section and its Uncertainty

In the recent Baksan Experiment on Sterile Transitions (BEST), a suppressed rate of neutrino absorption on a gallium target was observed, consistent with earlier results from neutrino source calibrations of the SAGE and GALLEX/GNO solar neutrino experiments. The BEST collaboration, utilizing a 3.4 MCi 51Cr neutrino source, found observed-to-expected counting rates at two very short baselines of R=0.791 plus/minus 0.05 and 0.766 plus/minus 0.05, respectively. Among recent neutrino experiments, BEST is notable for the simplicity of both its neutrino spectrum, line neutrinos from an electron-capture source whose intensity can be measured to a estimated precision of 0.23%, and its absorption cross section, where the precisely known rate of electron capture to the gallium ground state, 71Ge(e,nue)71Ga(g.s.), establishes a minimum value. However, the absorption cross section uncertainty is a common systematic in the BEST, SAGE, and GALLEX/GNO neutrino source experiments. Here we update that cross section, considering a variety of electroweak corrections and the role of transitions to excited states, to establish both a central value and reasonable uncertainty, thereby enabling a more accurate assessment of the statistical significance of the gallium anomalies. Results are given for 51Cr and 37Ar sources. The revised neutrino capture rates are used in a re-evaluation of the BEST and gallium anomalies.