Miracle-dz/UniFAR-FTUs huggingface dataset:
On the computational and statistical complexity of over-parameterized matrix sensing

We consider solving the low rank matrix sensing problem with Factorized Gradient Descend (FGD) method when the true rank is unknown and over-specified, which we refer to as over-parameterized matrix sensing. If the ground truth signal $\mathbf{X}^* \in \mathbb{R}^{d*d}$ is of rank $r$, but we try to recover it using $\mathbf{F} \mathbf{F}^\top$ where $\mathbf{F} \in \mathbb{R}^{d*k}$ and $k>r$, the existing statistical analysis falls short, due to a flat local curvature of the loss function around the global maxima. By decomposing the factorized matrix $\mathbf{F}$ into separate column spaces to capture the effect of extra ranks, we show that $\|\mathbf{F}_t \mathbf{F}_t - \mathbf{X}^*\|_{F}^2$ converges to a statistical error of $\tilde{\mathcal{O}} ({k d \sigma^2/n})$ after $\tilde{\mathcal{O}}(\frac{\sigma_{r}}{\sigma}\sqrt{\frac{n}{d}})$ number of iterations where $\mathbf{F}_t$ is the output of FGD after $t$ iterations, $\sigma^2$ is the variance of the observation noise, $\sigma_{r}$ is the $r$-th largest eigenvalue of $\mathbf{X}^*$, and $n$ is the number of sample. Our results, therefore, offer a comprehensive picture of the statistical and computational complexity of FGD for the over-parameterized matrix sensing problem.
Nonlinear diffusion in transparent media

We consider a prototypical nonlinear parabolic equation whose flux has three distinguished features: it is nonlinear with respect to both the unknown and its gradient, it is homogeneous, and it depends only on the direction of the gradient. For such equation, we obtain existence and uniqueness of entropy solutions to the Dirichlet problem, the homogeneous Neumann problem, and the Cauchy problem. Qualitative properties of solutions, such as finite speed of propagation and the occurrence of waiting-time phenomena, with sharp bounds, are shown. We also discuss the formation of jump discontinuities both at the boundary of the solutions' support and in the bulk.
The Fornax3D project: Assembly histories of lenticular galaxies from a combined dynamical and population orbital analysis

In order to assess the impact of the environment on the formation and evolution of galaxies, accurate assembly histories of such galaxies are needed. However, these measurements are observationally difficult owing to the diversity of formation paths that lead to the same present-day state of a galaxy. In this work, we apply a powerful new technique in order to observationally derive accurate assembly histories through a self-consistent combined stellar dynamical and population galaxy model. We present this approach for three edge-on lenticular galaxies from the Fornax3D project – FCC 153, FCC 170, and FCC 177 – in order to infer their mass assembly histories individually and in the context of the Fornax cluster. The method was tested on mock data from simulations to quantify its reliability. We find that the galaxies studied here have all been able to form dynamically-cold (intrinsic vertical velocity dispersion σz ≲ 50 km s−1) stellar disks after cluster infall. Moreover, the pre-existing (old) high angular momentum components have retained their angular momentum (orbital circularity λz > 0.8) through to the present day. Comparing the derived assembly histories with a comparable galaxy in a low-density environment – NGC 3115 – we find evidence for cluster-driven suppression of stellar accretion and merging. We measured the intrinsic stellar age–velocity-dispersion relation and find that the shape of the relation is consistent with galaxies in the literature across redshift. There is tentative evidence for enhancement in the luminosity-weighted intrinsic vertical velocity dispersion due to the cluster environment. But importantly, there is an indication that metallicity may be a key driver of this relation. We finally speculate that the cluster environment is responsible for the S0 morphology of these galaxies via the gradual external perturbations, or ‘harassment’, generated within the cluster.
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied, in depth, several kinds of attacks, they have done so in isolation. As a result, we lack a comprehensive picture of the risks caused by the attacks, e.g., the different scenarios they can be applied to, the common factors that influence their performance, the relationship among them, or the effectiveness of possible defenses. In this paper, we fill this gap by presenting a first-of-its-kind holistic risk assessment of different inference attacks against machine learning models. We concentrate on four attacks -- namely, membership inference, model inversion, attribute inference, and model stealing -- and establish a threat model taxonomy. Our extensive experimental evaluation, run on five model architectures and four image datasets, shows that the complexity of the training dataset plays an important role with respect to the attack's performance, while the effectiveness of model stealing and membership inference attacks are negatively correlated. We also show that defenses like DP-SGD and Knowledge Distillation can only mitigate some of the inference attacks. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models, and equally serves as a benchmark tool for researchers and practitioners.
On the role of radiation and dimensionality in predicting flow opposed flame spread over thin fuels

In this work a flame-spread model is formulated in three dimensions to simulate opposed flow flame spread over thin solid fuels. The flame-spread model is coupled to a three-dimensional gas radiation model. The experiments [1] on downward spread and zero gravity quiescent spread over finite width thin fuel are simulated by flame-spread models in both two and three dimensions to assess the role of radiation and effect of dimensionality on the prediction of the flame-spread phenomena. It is observed that while radiation plays only a minor role in normal gravity downward spread, in zero gravity quiescent spread surface radiation loss holds the key to correct prediction of low oxygen flame spread rate and quenching limit. The present three-dimensional simulations show that even in zero gravity gas radiation affects flame spread rate only moderately (as much as 20% at 100% oxygen) as the heat feedback effect exceeds the radiation loss effect only moderately. However, the two-dimensional model with the gas radiation model badly over-predicts the zero gravity flame spread rate due to under estimation of gas radiation loss to the ambient surrounding. The two-dimensional model was also found to be inadequate for predicting the zero gravity flame attributes, like the flame length and the flame width, correctly. The need for a three-dimensional model was found to be indispensable for consistently describing the zero gravity flame-spread experiments [1] (including flame spread rate and flame size) especially at high oxygen levels (>30%). On the other hand it was observed that for the normal gravity downward flame spread for oxygen levels up to 60%, the two-dimensional model was sufficient to predict flame spread rate and flame size reasonably well. Gas radiation is seen to increase the three-dimensional effect especially at elevated oxygen levels (>30% for zero gravity and >60% for normal gravity flames).
Predicting the probability distribution of bus travel time to move towards reliable planning of public transport services

An important aspect of the quality of a public transport service is its reliability, which is defined as the invariability of the service attributes. Preventive measures taken during planning can reduce risks of unreliability throughout operations. In order to tackle reliability during the service planning phase, a key piece of information is the long-term prediction of the density of the travel time, which conveys the uncertainty of travel times. We introduce a reliable approach to one of the problems of service planning in public transport, namely the Multiple Depot Vehicle Scheduling Problem (MDVSP), which takes as input a set of trips and the probability density function (p.d.f.) of the travel time of each trip in order to output delay-tolerant vehicle schedules. This work empirically compares probabilistic models for the prediction of the conditional p.d. f. of the travel time, as a first step towards reliable MDVSP solutions. Two types of probabilistic models, namely similarity-based density estimation models and a smoothed Logistic Regression for probabilistic classification model, are compared on a dataset of more than 41,000 trips and 50 bus routes of the city of Montr\'eal. The result of a vast majority of probabilistic models outperforms that of a Random Forests model, which is not inherently probabilistic, thus highlighting the added value of modeling the conditional p.d. f. of the travel time with probabilistic models. A similarity-based density estimation model using a $k$ Nearest Neighbors method and a Kernel Density Estimation predicted the best estimate of the true conditional p.d. f. on this dataset.
Semi-Supervised Action Recognition with Temporal Contrastive Learning

Learning to recognize actions from only a handful of labeled videos is a challenging problem due to the scarcity of tediously collected activity labels. We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds lever-aging the fact that changing video speed does not change an action. Specifically, we propose to maximize the similarity between encoded representations of the same video at two different speeds as well as minimize the similarity between different videos played at different speeds. This way we use the rich supervisory information in terms of ‘time’ that is present in otherwise unsupervised pool of videos. With this simple yet effective strategy of manipulating video playback rates, we considerably outperform video extensions of sophisticated state-of-the-art semi-supervised image recognition methods across multiple diverse bench-mark datasets and network architectures. Interestingly, our proposed approach benefits from out-of-domain unlabeled videos showing generalization and robustness. We also per-form rigorous ablations and analysis to validate our approach. Project page: https://cvir.github. io/TCL/.
Microscopic patterns in the 2D phase-field-crystal model

Using the recently developed theory of rigorously validated numerics, we address the phase-field-crystal model at the microscopic (atomistic) level. We show the existence of critical points and local minimizers associated with ‘classical’ candidates, grain boundaries, and localized patterns. We further address the dynamical relationships between the observed patterns for fixed parameters and across parameter space, then formulate several conjectures on the dynamical connections (or orbits) between steady states.
Microscopic calculation of fission product yields with particle-number projection

Fission fragments’ charge and mass distribution is an important input to applications ranging from basic science to energy production or nuclear non-proliferation. In simulations of nucleosynthesis or calculations of superheavy elements, these quantities must be computed from models, as they are needed in nuclei where no experimental information is available. Until now, standard techniques to estimate these distributions were not capable of accounting for fine-structure effects, such as the oddeven staggering of the charge distributions. In this work, we combine a fully-microscopic collective model of fission dynamics with a recent extension of the particle number projection formalism to provide the highest-fidelity prediction of the primary fission fragment distributions for the neutroninduced fission of U and Pu. We show that particle number projection is an essential ingredient to reproduce odd-even staggering in the charge yields and benchmark the performance of various empirical probability laws that could simulate its effect. This new approach also enables for the first time the realistic determination of two-dimensional isotopic yields within nuclear density functional theory.
An Efficient Linear Programming Rounding-and-Refinement Algorithm for Large-Scale Network Slicing Problem

In this paper, we consider the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements, and propose an efficient two-stage algorithm for solving this NP-hard problem. In the first stage, the proposed algorithm uses an iterative linear programming (LP) rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration; in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between solution quality and computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.
Numerical study of boiling of Liquid Nitrogen on a liquid-liquid contact plane

In this paper, a numerical study is conducted to investigate boiling of a cryogen on a solid surface as well as on a liquid surface. Both single mode and multi-mode boiling is reported for boiling on to a solid surface. In case of boiling on a liquid surface, liquid nitrogen (LN2) is selected as the cryogen (boiling fluid) and water is chosen as the base fluid (heating fluid). Different flow instabilities and their underlying consequences during boiling of a cryogen are also discussed. For the boiling on a solid surface, in the single mode, bubble growth, its departure, and area weighted average heat flux are reported, where they increase linearly with increase in the wall superheat. Asymmetry in the bubble growth and departure of 2 batch of the vapor bubbles have been observed due to local fluctuations and turbulence created just after the pinch off of the 1 batch of vapor bubbles in case of multi-mode boiling on the solid surface. Boiling of LN2 on a liquid surface is reported for a base fluid (Water) temperature of 300 K. Vapor film thickness decreases with time and the minimum film thickness just before rupture is 7.62 μm, dominance of thermocapillary over vapor thrust causes breaking of the vapor film at t = 0.0325 s. The difference in evaporation rate and vapor generation, before and after vapor film collapse is significant.
Unifying Vision-and-Language Tasks via Text Generation

Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on questions that have rare answers. Also, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, achieving similar performance to separately optimized single-task models. Our code is publicly available at: https://github.com/j-min/VL-T5
Accurate Identification of Galaxy Mergers with Stellar Kinematics

To determine the importance of merging galaxies to galaxy evolution, it is necessary to design classification tools that can identify the different types and stages of merging galaxies. Previously, using GADGET-3/SUNRISE simulations of merging galaxies and linear discriminant analysis (LDA), we created an accurate merging galaxy classifier based on imaging predictors. Here, we develop a complementary tool, based on stellar kinematic predictors, derived from the same simulation suite. We design mock stellar velocity and velocity dispersion maps to mimic the specifications of the Mapping Nearby Galaxies at Apache Point (MaNGA) integral field spectroscopy (IFS) survey, and utilize an LDA to create a classification, based on a linear combination of 11 kinematic predictors. The classification varies significantly with mass ratio; the major (minor) merger classifications have a mean statistical accuracy of 80% (70%), a precision of 90% (85%), and a recall of 75% (60%). The major mergers are best identified by predictors that trace global kinematic features, while the minor mergers rely on local features that trace a secondary stellar component. While the kinematic classification is less accurate than the imaging classification, the kinematic predictors are better at identifying post-coalescence mergers. A combined imaging + kinematic classification has the potential to reveal more complete merger samples from imaging and IFS surveys such as MaNGA. We note that since the suite of simulations used to train the classifier covers a limited range of galaxy properties (i.e., the galaxies are of intermediate mass, and disk-dominated), the results may not be applicable to all MaNGA galaxies.
The Infrared Emission and Vigorous Star Formation of Low-redshift Quasars

The star formation activity of the host galaxies of active galactic nuclei provides valuable insights into the complex interconnections between black hole growth and galaxy evolution. A major obstacle arises from the difficulty of estimating accurate star formation rates (SFRs) in the presence of a strong active galactic nucleus. Analyzing the 1–500 μm spectral energy distributions and high-resolution mid-infrared spectra of low-redshift (z < 0.5) Palomar–Green quasars with bolometric luminosity of ∼1044.5–1047.5 erg s−1, we find, from comparison with an independent SFR indicator based on [Ne II] 12.81 μm and [Ne III] 15.56 μm, that the torus-subtracted, total infrared (8–1000 μm) emission yields robust SFRs in the range of ∼1–250 M ⊙ yr−1. Combined with available stellar mass estimates, the vast majority (∼75%–90%) of the quasars lie on or above the main sequence of local star-forming galaxies, including a significant fraction (∼50%–70%) that would qualify as starburst systems. This is further supported by the high star formation efficiencies derived from the gas content inferred from the dust masses. Inspection of high-resolution Hubble Space Telescope images reveals a wide diversity of morphological types, including a number of starbursting hosts that have not experienced significant recent dynamical perturbations. The origin of the high star formation efficiency is unknown.
Nanodomain Structure of Carbon‐Rich Silicon Carbonitride Polymer‐Derived Ceramics

The presence of nanodomains in polymer-derived ceramics constitutes one of the most intriguing features of this class of materials. In the present work, the nanostructure of novel carbon-rich silicon carbonitride (SiCN) ceramics synthesized via thermolysis of poly(methylphenylsilylcarbodiimide), –[Ph(CH3) Si-NCN]n–, at 1300°, 1500°, 1700°, and 2000°C is investigated by micro-Raman spectroscopy, X-ray powder diffractometry, and small-angle X-ray scattering (SAXS). The structural information obtained from these experimental methods is combined together with theoretical modeling of the SAXS data to obtain a detailed model of the temperature-dependent evolution of nanodomains comprised of free carbon, SiC, and Si3N4 in SiCN-based ceramics.
Superconducting qubit gates via analytically-derived accelerated adiabatic pulses

Shortcuts to adiabaticity (STA) is a general methodology for speeding up adiabatic quantum protocols, and has many potential applications in quantum information processing. Unfortunately, analytically constructing STAs for systems having complex interactions and more than a few levels is a challenging task. This is usually overcome by assuming an idealized Hamiltonian (e.g., only a limited subset of energy levels are retained, and the rotating-wave approximation (RWA) is made). Here, we develop an analytic approach that allows one to go beyond these limitations. Our method is general and results in analytically-derived pulse shapes that correct both non-adiabatic errors as well as non-RWA errors. We also show that our approach can yield pulses requiring a smaller driving power than conventional non-adiabatic protocols. We show in detail how our ideas can be used to analytically design high-fidelity single-qubit “tripod” gates in a realistic superconducting fluxonium qubit.
On the Global Optimality of Whittle’s Index Policy for Minimizing the Age of Information

This paper examines the average age minimization problem where only a fraction of the network users can transmit simultaneously over unreliable channels. Finding the optimal scheduling scheme, in this case, is known to be challenging. Accordingly, the Whittle’s index policy was proposed in the literature as a low-complexity heuristic to the problem. Although simple to implement, characterizing this policy’s performance is recognized to be a notoriously tricky task. In the sequel, we provide a new mathematical approach to establish its optimality in the many-users regime for specific network settings. Contrary to previous works in the literature that use restrictive mathematical assumptions, which can be challenging to verify, our novel approach is based on intricate techniques and it is free of any strong mathematical assumptions. These findings showcase that the Whittle’s index policy has analytically provable asymptotic optimality for the AoI minimization problem. Finally, we lay out numerical results that corroborate our theoretical findings and demonstrate the policy’s notable performance in the many-users regime.
Low Bit-Rate Wideband Speech Coding: A Deep Generative Model based Approach

Traditional low bit-rate speech coding approach only handles narrowband speech at 8kHz, which limits further improvements in speech quality. Motivated by recent successful exploration of deep learning methods for image and speech compression, this paper presents a new approach through vector quantization (VQ) of mel-frequency cepstral coefficients (MFCCs) and using a deep generative model called WaveGlow to provide efficient and high-quality speech coding. The coding feature is sorely an 80-dimension MFCCs vector for 16kHz wideband speech, then speech coding at the bit-rate throughout 1000-2000 bit/s could be scalably implemented by applying different VQ schemes for MFCCs vector. This new deep generative network based codec works fast as the WaveGlow model abandons the sample-by-sample autoregressive mechanism. We evaluated this new approach over the multi-speaker TIMIT corpus, and experimental results demonstrate that it provides better speech quality compared with the state-of-the-art classic MELPe codec at lower bit-rate.