Miracle-dz/UniFAR-FTUs huggingface dataset:
On the smallest singular value of symmetric random matrices

We show that for an $n\times n$ random symmetric matrix $A_n$ , whose entries on and above the diagonal are independent copies of a sub-Gaussian random variable $\xi$ with mean 0 and variance 1, \begin{equation*}\mathbb{P}[s_n(A_n) \le \epsilon/\sqrt{n}] \le O_{\xi}(\epsilon^{1/8} + \exp(\! -\Omega_{\xi}(n^{1/2}))) \quad \text{for all } \epsilon \ge 0. \end{equation*} This improves a result of Vershynin, who obtained such a bound with $n^{1/2}$ replaced by $n^{c}$ for a small constant c, and $1/8$ replaced by $(1/8) - \eta$ (with implicit constants also depending on $\eta > 0$ ). Furthermore, when $\xi$ is a Rademacher random variable, we prove that \begin{equation*}\mathbb{P}[s_n(A_n) \le \epsilon/\sqrt{n}] \le O(\epsilon^{1/8} + \exp(\! -\Omega((\! \log{n})^{1/4}n^{1/2}))) \quad \text{for all } \epsilon \ge 0. \end{equation*} The special case $\epsilon = 0$ improves a recent result of Campos, Mattos, Morris, and Morrison, which showed that $\mathbb{P}[s_n(A_n) = 0] \le O(\exp(\! -\Omega(n^{1/2}))). $ Notably, in a departure from the previous two best bounds on the probability of singularity of symmetric matrices, which had relied on somewhat specialized and involved combinatorial techniques, our methods fall squarely within the broad geometric framework pioneered by Rudelson and Vershynin, and suggest the possibility of a principled geometric approach to the study of the singular spectrum of symmetric random matrices. The main innovations in our work are new notions of arithmetic structure – the Median Regularized Least Common Denominator (MRLCD) and the Median Threshold, which are natural refinements of the Regularized Least Common Denominator (RLCD)introduced by Vershynin, and should be more generally useful in contexts where one needs to combine anticoncentration information of different parts of a vector.
Ice stream formation

Ice streams are bands of fast-flowing ice in ice sheets. We investigate their formation as an example of spontaneous pattern formation, based on positive feedbacks between dissipation and basal sliding. Our focus is on temperature-dependent subtemperate sliding, where faster sliding leads to enhanced dissipation and hence warmer temperatures, weakening the bed further, although we also treat a hydromechanical feedback mechanism that operates on fully molten beds. We develop a novel thermomechanical model capturing ice-thickness scale physics in the lateral direction while assuming the the flow is shallow in the main downstream direction. Using that model, we show that formation of a steady-in-time pattern can occur by the amplification in the downstream direction of noisy basal conditions, and often leads to the establishment of a clearly-defined ice stream separated from slowly-flowing, cold-based ice ridges by narrow shear margins, with the ice stream widening in the downstream direction. We are able to show that downward advection of cold ice is the primary stabilizing mechanism, and give an approximate, analytical criterion for pattern formation.
Debiasing Classifiers: Is Reality at Variance With Expectation?

Many methods for debiasing classifiers have been proposed, but their effectiveness in practice remains unclear. We evaluate the performance of pre-processing and post-processing debiasers for improving fairness in random forest classifiers trained on a suite of data sets. Specifically, we study how these debiasers generalize with respect to both out-of-sample test error for computing fairness -- performance and fairness -- fairness trade-offs, and on the change in other fairness metrics that were not explicitly optimised. Our results demonstrate that out-of-sample performance on fairness and performance can vary substantially and unexpectedly. Moreover, the variance in estimation arises from class imbalances with respect to both the outcome and the protected classes. Our results highlight the importance of evaluating out-of-sample performance in practical usage.
Causal Autoregressive Flows

Two apparently unrelated fields -- normalizing flows and causality -- have recently received considerable attention in the machine learning community. In this work, we highlight an intrinsic correspondence between a simple family of flows and identifiable causal models. We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks. First, we show that causal models derived from both affine and additive flows are identifiable. This provides a generalization of the additive noise model well-known in causal discovery. Second, we derive a bivariate measure of causal direction based on likelihood ratios, leveraging the fact that flow models estimate normalized log-densities of data. Such likelihood ratios have well-known optimality properties in finite-sample inference. Third, we demonstrate that the invertibility of flows naturally allows for direct evaluation of both interventional and counterfactual queries. Finally, throughout a series of experiments on synthetic and real data, the proposed method is shown to outperform current approaches for causal discovery as well as making accurate interventional and counterfactual predictions.
Localization in Terahertz-Operating Energy Harvesting Software-Defined Metamaterials

Software-Defined Metamaterials (SDMs) show a strong potential for advancing the engineered control of electromagnetic waves. As such, they are envisioned to enable a variety of exciting applications, among others in the domains of smart textiles, high-resolution structural monitoring, and sensing in challenging environments. Many of the applications envisage deformations of the SDM structure, such as its bending, stretching or rolling, which implies that the locations of metamaterial elements will be changing relative to one another. In this paper, we argue that if the metamaterial elements would be accurately localizable, this location information could potentially be utilized for enabling novel SDM applications, as well as for optimizing the control of the elements themselves. To enable their localization, we assume that these elements are controlled wirelessly through a Terahertz (THz)-operating nanonetwork. We consider the elements to be energy-constrained, with their sole powering option being to harvest environmental energy. By means of simulation, we demonstrate sub-millimeter accuracy of the two-way Time of Flight (ToF)-based localization, as well as high availability of the service (i.e., consistently more than 80% of the time), which is a result of the low energy consumed during localization. Finally, we qualitatively characterize the latency of the proposed localization service, as well as outline several challenges and future research directions.
Atomic data for the Gaia-ESO Survey

We describe the atomic and molecular data that were used for the abundance analyses of FGK-type stars carried out within the Gaia-ESO Survey. We present an unprecedented effort to create a homogeneous line list, which was used by several abundance analysis groups to calculate synthetic spectra and equivalent widths. The atomic data are accompanied by quality indicators and detailed references to the sources. The atomic and molecular data are made publicly available in electronic form. In general experimental transition probabilities were preferred but theoretical values were also used. Astrophysical gf-values were avoided due to the model-dependence of such a procedure. For elements whose lines are significantly affected by hyperfine structure or isotopic splitting a concerted effort has been made to collate the necessary data for the individual line components. We also performed a detailed investigation of available data for line broadening due to collisions with neutral hydrogen atoms. Synthetic spectra calculated for the Sun and Arcturus were used to assess the blending properties of the lines. Among a subset of over 1300 lines of 35 elements in the wavelength ranges from 475 nm to 685 nm and from 850 nm to 895 nm we identified about 200 lines of 24 species which have accurate gf-values and are free of blends in the spectra of the Sun and Arcturus. For the broadening due to collisions with neutral hydrogen we recommend data based on Anstee-Barklem-O'Mara theory, where available, and to avoid lines of neutral species otherwise. Theoretical broadening data by R.L. Kurucz should be used for Sc II, Ti II, and Y II lines. For ionised rare-earth species the Uns\"old approximation with an enhancement factor of 1.5 for the line width can be used. Desirable improvements in atomic data were identified for a number of species, including Al I, S I, Cr II, Na I, Si I, Ca II, and Ni I.
Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps

Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks. Knowledge of the objects' geometry and preconditions of the target task should be incorporated when determining the proper grasp to execute. However, several factors contribute to the challenges of realizing these grasps such as noise when controlling the robot, unknown object properties, and difficulties modeling complex object-object interactions. We propose a method that decomposes this problem and optimizes for grasp robustness, precision, and task performance by learning three cascaded networks. We evaluate our method in simulation on three common assembly tasks: inserting gears onto pegs, aligning brackets into corners, and inserting shapes into slots. Our policies are trained using a curriculum based on large-scale self-supervised grasp simulations with procedurally generated objects. Finally, we evaluate the performance of the first two tasks with a real robot where our method achieves 4.28mm error for bracket insertion and 1.44mm error for gear insertion.
Short-time deep-learning based source separation for speech enhancement in reverberant environments with beamforming

The source separation-based speech enhancement problem with multiple beamforming in reverberant indoor environments is addressed in this paper. We propose that more generic solutions should cope with time-varying dynamic scenarios with moving microphone array or sources such as those found in voice-based human-robot interaction or smart speaker applications. The effectiveness of ordinary source separation methods based on statistical models such as ICA and NMF depends on the analysis window size and cannot handle reverberation environments. To address these limitations, a short-term source separation method based on a temporal convolutional network in combination with compact bilinear pooling is presented. The proposed scheme is virtually independent of the analysis window size and does not lose effectiveness when the analysis window is shortened to 1.6s, which in turn is very interesting to tackle the source separation problem in time-varying scenarios. Also, improvements in WER as high as 80% were obtained when compared to ICA and NMF with multi-condition reverberant training and testing, and with time-varying SNR experiments to simulate a moving target speech source. Finally, the experiment with the estimation of the clean signal employing the proposed scheme and a clean trained ASR provided a WER 13% lower than the one obtained with the corrupted signal and a multi-condition trained ASR. This surprising result contradicts the widely adopted practice of using multi-condition trained ASR systems and reinforce the use of speech enhancement methods for user profiling in HRI environments.
Interpretable Representation Learning for Speech and Audio Signals Based on Relevance Weighting

The learning of interpretable representations from raw data presents significant challenges for time series data like speech. In this work, we propose a relevance weighting scheme that allows the interpretation of the speech representations during the forward propagation of the model itself. The relevance weighting is achieved using a sub-network approach that performs the task of feature selection. A relevance sub-network, applied on the output of first layer of a convolutional neural network model operating on raw speech signals, acts as an acoustic filterbank (FB) layer with relevance weighting. A similar relevance sub-network applied on the second convolutional layer performs modulation filterbank learning with relevance weighting. The full acoustic model consisting of relevance sub-networks, convolutional layers and feed-forward layers is trained for a speech recognition task on noisy and reverberant speech in the Aurora-4, CHiME-3 and VOiCES datasets. The proposed representation learning framework is also applied for the task of sound classification in the UrbanSound8K dataset. A detailed analysis of the relevance weights learned by the model reveals that the relevance weights capture information regarding the underlying speech/audio content. In addition, speech recognition and sound classification experiments reveal that the incorporation of relevance weighting in the neural network architecture improves the performance significantly.
MESA Models with Magnetic Braking

Two magnetic braking models are implemented in MESA for use in the MIST stellar model grids. Stars less than about 1.3 solar masses are observed to spin down over time through interaction with their magnetized stellar winds (i.e., magnetic braking). This is the basis for gyrochronology and is fundamental to the evolution of lower-mass stars. The detailed physics behind magnetic braking are uncertain, as are 1D stellar evolution models. Thus, we calibrate our models and compare to data from open clusters. Each braking model tested here is capable of reproducing aspects of the data, with important distinctions; neither fully accounts for the observations. The Matt et al. prescription matches the slowly rotating stars observed in open clusters but tends to overestimate the presence of rapidly rotating stars. The Garraffo et al. prescription often produces too much angular momentum loss to accurately match the observed slow sequence for lower-mass stars but reproduces the bimodal nature of slowly and rapidly rotating stars observed in open clusters fairly well. Our models additionally do not reproduce the observed solar lithium depletion, corroborating previous findings that effects other than rotation may be important. We find additional evidence that some level of mass dependency may be missing in these braking models to match the rotation periods observed in clusters older than 1 Gyr better.
An Empirical-cum-Statistical Approach to Power-Performance Characterization of Concurrent GPU Kernels

Growing deployment of power and energy efficient throughput accelerators (GPU) in data centers demands enhancement of power-performance co-optimization capabilities of GPUs. Realization of exascale computing using accelerators requires further improvements in power efficiency. With hardwired kernel concurrency enablement in accelerators, inter- and intra-workload simultaneous kernels computation predicts increased throughput at lower energy budget. To improve Performance-per-Watt metric of the architectures, a systematic empirical study of real-world throughput workloads (with concurrent kernel execution) is required. To this end, we propose a multi-kernel throughput workload generation framework that will facilitate aggressive energy and performance management of exascale data centers and will stimulate synergistic power-performance co-optimization of throughput architectures. Also, we demonstrate a multi-kernel throughput benchmark suite based on the framework that encapsulates symmetric, asymmetric and co-existing (often appears together) kernel based workloads. On average, our analysis reveals that spatial and temporal concurrency within kernel execution in throughput architectures saves energy consumption by 32%, 26% and 33% in GTX470, Tesla M2050 and Tesla K20 across 12 benchmarks. Concurrency and enhanced utilization are often correlated but do not imply significant deviation in power dissipation. Diversity analysis of proposed multi-kernels confirms characteristic variation and power-profile diversity within the suite. Besides, we explain several findings regarding power-performance co-optimization of concurrent throughput workloads.
Quasi-equilibrium models of high-redshift disc galaxy evolution

In recent years, simple models of galaxy formation have been shown to provide reasonably good matches to available data on high-redshift luminosity functions. However, these prescriptions are primarily phenomenological, with only crude connections to the physics of galaxy evolution. Here we introduce a set of galaxy models that are based on a simple physical framework but incorporate more sophisticated models of feedback, star formation, and other processes. We apply these models to the high-redshift regime, showing that most of the generic predictions of the simplest models remain valid. In particular, the stellar mass--halo mass relation depends almost entirely on the physics of feedback (and is thus independent of the details of small-scale star formation) and the specific star formation rate is a simple multiple of the cosmological accretion rate. We also show that, in contrast, the galaxy's gas mass is sensitive to the physics of star formation, although the inclusion of feedback-driven star formation laws significantly changes the naive expectations. While these models are far from detailed enough to describe every aspect of galaxy formation, they inform our understanding of galaxy formation by illustrating several generic aspects of that process, and they provide a physically-grounded basis for extrapolating predictions to faint galaxies and high redshifts currently out of reach of observations. If observations show violations from these simple trends, they would indicate new physics occurring inside the earliest generations of galaxies.
On the halo-mass and radial scale dependence of the lensing is low effect

The canonical Lambda cold dark matter (ΛCDM) cosmological model makes precise predictions for the clustering and lensing properties of galaxies. It has been shown that the lensing amplitude of galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS) is lower than expected given their clustering properties. We present new measurements and modelling of galaxies in the BOSS LOWZ sample. We focus on the radial and stellar mass dependence of the lensing amplitude mismatch. We find an amplitude mismatch of around $35{{\ \rm per\ cent}}$ when assuming ΛCDM with Planck Cosmological Microwave Background (CMB) constraints. This offset is independent of halo mass and radial scale in the range Mhalo ∼ 1013.3−1013.9h−1 M⊙ and $r=0.1\! -\! 60 \, h^{-1} \mathrm{Mpc}$ ($k \approx 0.05\! -\! 20 \, h \, {\rm Mpc}^{-1}$). The observation that the offset is both mass and scale independent places important constraints on the degree to which astrophysical processes (baryonic effects, assembly bias) can fully explain the effect. This scale independence also suggests that the ‘lensing is low’ effect on small and large radial scales probably have the same physical origin. Resolutions based on new physics require a nearly uniform suppression, relative to ΛCDM predictions, of the amplitude of matter fluctuations on these scales. The possible causes of this are tightly constrained by measurements of the CMB and of the low-redshift expansion history.
High-resolution imaging follow-up of doubly imaged quasars

We report upon 3 years of follow-up and confirmation of doubly imaged quasar lenses through imaging campaigns from 2016 to 2018 with the Near-Infrared Camera2 (NIRC2) on the W. M. Keck Observatory. A sample of 57 quasar lens candidates are imaged in adaptive-optics-assisted or seeing-limited K′-band observations. Out of these 57 candidates, 15 are confirmed as lenses. We form a sample of 20 lenses adding in a number of previously known lenses that were imaged with NIRC2 in 2013–14 as part of a pilot study. By modelling these 20 lenses, we obtain K′-band relative photometry and astrometry of the quasar images and the lens galaxy. We also provide the lens properties and predicted time delays to aid planning of follow-up observations necessary for various astrophysical applications, e.g. spectroscopic follow-up to obtain the deflector redshifts for the newly confirmed systems. We compare the departure of the observed flux ratios from the smooth-model predictions between doubly and quadruply imaged quasar systems. We find that the departure is consistent between these two types of lenses if the modelling uncertainty is comparable.
dMVX: Secure and Efficient Multi-Variant Execution in a Distributed Setting

Multi-variant execution (MVX) systems amplify the effectiveness of software diversity techniques. The key idea is to run multiple diversified program variants in lockstep while providing them with the same input and monitoring their run-time behavior for divergences. Thus, adversaries have to compromise all program variants simultaneously to mount an attack successfully. Recent work proposed distributed, heterogeneous MVX systems that leverage different ABIs and ISAs to increase the diversity between program variants further. However, existing distributed MVX system designs suffer from high performance overhead due to time-consuming network transactions required for the MVX operations. This paper presents dMVX, a novel hybrid distributed MVX design, which incorporates new techniques that significantly reduce the overhead of MVX systems in a distributed setting. Our key insight is that we can intelligently reduce the MVX operations that use expensive network transfers. First, we can limit the monitoring of system calls that are not security-critical. Second, we observe that, in many circumstances, we can also safely avoid replication operations needed for I/O related system calls. Our evaluation shows that dMVX reduces the performance degradation from over 50% to 3.1% for realistic server benchmarks.
Single Channel Voice Separation for Unknown Number of Speakers Under Reverberant and Noisy Settings

We present a unified network for voice separation of an unknown number of speakers. The proposed approach is composed of several separation heads optimized together with a speaker classification branch. The separation is carried out in the time domain, together with parameter sharing between all separation heads. The classification branch estimates the number of speakers while each head is specialized in separating a different number of speakers. We evaluate the proposed model under both clean and noisy reverberant settings. Results suggest that the proposed approach is superior to the baseline model by a significant margin. Additionally, we present a new noisy and reverberant dataset of up to five different speakers speaking simultaneously.