nils-herrmann/clean_scirepeval huggingface dataset:
Personalizing ASR for Dysarthric and Accented Speech with Limited Data

Automatic speech recognition (ASR) systems have dramatically improved over the last few years. ASR systems are most often trained from 'typical' speech, which means that underrepresented groups don't experience the same level of improvement. In this paper, we present and evaluate finetuning techniques to improve ASR for users with non-standard speech. We focus on two types of non-standard speech: speech from people with amyotrophic lateral sclerosis (ALS) and accented speech. We train personalized models that achieve 62% and 35% relative WER improvement on these two groups, bringing the absolute WER for ALS speakers, on a test set of message bank phrases, down to 10% for mild dysarthria and 20% for more serious dysarthria. We show that 71% of the improvement comes from only 5 minutes of training data. Finetuning a particular subset of layers (with many fewer parameters) often gives better results than finetuning the entire model. This is the first step towards building state of the art ASR models for dysarthric speech.
Optimal Attacks on Reinforcement Learning Policies

Control policies, trained using the Deep Reinforcement Learning, have been recently shown to be vulnerable to adversarial attacks introducing even very small perturbations to the policy input. The attacks proposed so far have been designed using heuristics, and build on existing adversarial example crafting techniques used to dupe classifiers in supervised learning. In contrast, this paper investigates the problem of devising optimal attacks, depending on a well-defined attacker's objective, e.g., to minimize the main agent average reward. When the policy and the system dynamics, as well as rewards, are known to the attacker, a scenario referred to as a white-box attack, designing optimal attacks amounts to solving a Markov Decision Process. For what we call black-box attacks, where neither the policy nor the system is known, optimal attacks can be trained using Reinforcement Learning techniques. Through numerical experiments, we demonstrate the efficiency of our attacks compared to existing attacks (usually based on Gradient methods). We further quantify the potential impact of attacks and establish its connection to the smoothness of the policy under attack. Smooth policies are naturally less prone to attacks (this explains why Lipschitz policies, with respect to the state, are more resilient). Finally, we show that from the main agent perspective, the system uncertainties and the attacker can be modeled as a Partially Observable Markov Decision Process. We actually demonstrate that using Reinforcement Learning techniques tailored to POMDP (e.g. using Recurrent Neural Networks) leads to more resilient policies.
The geometry of filtrations

We display a symmetric monoidal equivalence between the stable ∞ ‐category of filtered spectra, and quasi‐coherent sheaves on A1/Gm , the quotient in the setting of spectral algebraic geometry of the flat affine line by the canonical action of the flat multiplicative group scheme. Via a Tannaka duality argument, we identify the underlying spectrum and associated graded functors with pull‐backs of quasi‐coherent sheaves along certain morphisms of stacks.
Multi-Point Bandit Algorithms for Nonstationary Online Nonconvex Optimization

Bandit algorithms have been predominantly analyzed in the convex setting with function-value based stationary regret as the performance measure. In this paper, motivated by online reinforcement learning problems, we propose and analyze bandit algorithms for both general and structured nonconvex problems with nonstationary (or dynamic) regret as the performance measure, in both stochastic and non-stochastic settings. First, for general nonconvex functions, we consider nonstationary versions of first-order and second-order stationary solutions as a regret measure, motivated by similar performance measures for offline nonconvex optimization. In the case of second-order stationary solution based regret, we propose and analyze online and bandit versions of the cubic regularized Newton's method. The bandit version is based on estimating the Hessian matrices in the bandit setting, based on second-order Gaussian Stein's identity. Our nonstationary regret bounds in terms of second-order stationary solutions have interesting consequences for avoiding saddle points in the bandit setting. Next, for weakly quasi convex functions and monotone weakly submodular functions we consider nonstationary regret measures in terms of function-values; such structured classes of nonconvex functions enable one to consider regret measure defined in terms of function values, similar to convex functions. For this case of function-value, and first-order stationary solution based regret measures, we provide regret bounds in both the low- and high-dimensional settings, for some scenarios.
Augmenting Music Sheets with Harmonic Fingerprints

Common Music Notation (CMN) is the well-established foundation for the written communication of musical information, such as rhythm or harmony. CMN suffers from the complexity of its visual encoding and the need for extensive training to acquire proficiency and legibility. While alternative notations using additional visual variables (e.g., color to improve pitch identification) have been proposed, the community does not readily accept notation systems that vary widely from the CMN. Therefore, to support student musicians in understanding harmonic relationships, instead of replacing the CMN, we present a visualization technique that augments digital sheet music with a harmonic fingerprint glyph. Our design exploits the circle of fifths, a fundamental concept in music theory, as visual metaphor. By attaching such glyphs to each bar of a composition we provide additional information about the salient harmonic features available in a musical piece. We conducted a user study to analyze the performance of experts and non-experts in an identification and comparison task of recurring patterns. The evaluation shows that the harmonic fingerprint supports these tasks without the need for close-reading, as when compared to a not-annotated music sheet.
Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton-proton collision data

This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb−1 of proton-proton collision data collected at √ s = 13TeV between 2015 and 2017.
An Exo–Kuiper Belt with an Extended Halo around HD 191089 in Scattered Light

We have obtained Hubble Space Telescope STIS and NICMOS and Gemini/GPI scattered-light images of the HD 191089 debris disk. We identify two spatial components: a ring resembling the Kuiper Belt in radial extent (FWHM ∼ 25 au, centered at ∼46 au) and a halo extending to ∼640 au. We find that the halo is significantly bluer than the ring, consistent with the scenario that the ring serves as the “birth ring” for the smaller dust in the halo. We measure the scattering phase functions in the 30°–150° scattering-angle range and find that the halo dust is more forward- and backward-scattering than the ring dust. We measure a surface density power-law index of −0.68 ± 0.04 for the halo, which indicates the slowdown of the radial outward motion of the dust. Using radiative transfer modeling, we attempt to simultaneously reproduce the (visible) total and (near-infrared) polarized intensity images of the birth ring. Our modeling leads to mutually inconsistent results, indicating that more complex models, such as the inclusion of more realistic aggregate particles, are needed.
Implications of BBN bounds for cosmic ray upscattered dark matter

We consider the big bang nucleosynthesis (BBN) bounds on light dark matter whose cross section off nucleons is sufficiently large to enable acceleration by scattering off of cosmic rays in the local galaxy. Such accelerated DM could then deposit energy in terrestrial detectors. Since this signal involves DM of mass $\ensuremath{\sim}\mathrm{keV}\ensuremath{-}\mathrm{GeV}$ and requires large cross sections $\ensuremath{\gtrsim}{10}^{\ensuremath{-}31}\text{ }\text{ }{\mathrm{cm}}^{2}$ in a relativistic kinematic regime, we find that the DM population in this scenario is generically equilibrated with Standard Model particles in the early universe. For sufficiently low DM masses $\ensuremath{\lesssim}10\text{ }\text{ }\mathrm{MeV}$, corresponding to much of the favored region of many cosmic-ray upscattering studies, this equilibrated DM population adds an additional component to the relativistic energy density around $T\ensuremath{\sim}\mathrm{few}$ MeV and thereby spoils the successful predictions of BBN. In the remaining $\ensuremath{\sim}10\text{ }\text{ }\mathrm{MeV}--\mathrm{GeV}$ mass range, the large couplings required in this scenario are either currently excluded or within reach of current or future accelerator-based searches.
Axion-like particles, lepton-flavor violation and a new explanation of $a_\mu$ and $a_e$

Axion-like particles (ALPs) with lepton-flavor violating couplings can be probed in exotic muon and tau decays. The sensitivity of different experiments depends strongly on the ALP mass and its couplings to leptons and photons. For ALPs that can be resonantly produced, the sensitivity of three-body decays such as $\mu\to 3e$ and $\tau\to 3\mu$ exceeds by many orders of magnitude that of radiative decays like $\mu\to e\gamma$ and $\tau\to\mu\gamma$. Searches for these two types of processes are therefore highly complementary. We discuss experimental constraints on ALPs with a single dominant lepton-flavor violating coupling. Allowing for one or more such couplings offers qualitatively new ways to explain the anomalies related to the magnetic moments of the muon and electron.
Using Gaia for studying Milky Way star clusters

We review the implications of the Gaia Data Release 2 catalogue for studying the dynamics of Milky Way globular clusters, focusing on two separate topics. The first one is the analysis of the full 6-dimensional phase-space distribution of the entire population of Milky Way globular clusters: their mean proper motions (PM) can be measured with an exquisite precision (down to 0.05 mas yr−1, including systematic errors). Using these data, and a suitable ansatz for the steady-state distribution function (DF) of the cluster population, we then determine simultaneously the best-fit parameters of this DF and the total Milky Way potential. We also discuss possible correlated structures in the space of integrals of motion. The second topic addresses the internal dynamics of a few dozen of the closest and richest globular clusters, again using the Gaia PM to measure the velocity dispersion and internal rotation, with a proper treatment of spatially correlated systematic errors. Clear rotation signatures are detected in 10 clusters, and a few more show weaker signatures at a level ∼0.05 mas yr−1. PM dispersion profiles can be reliably measured down to 0.1 mas yr−1, and agree well with the line-of-sight velocity dispersion profiles from the literature.
Electron ionization via dark matter-electron scattering and the Migdal effect

As dark matter searches push into the sub-GeV mass range, they look for electrons ionized by direct scattering with dark particles. However, dark matter scattering off nuclei can also produce electrons through the Migdal effect. In this paper the authors show that the Migdal effect can contribute significantly to signal rates and has to be included when interpreting experimental results.
The Black Hole–Bulge Mass Relation Including Dwarf Galaxies Hosting Active Galactic Nuclei

We present a new relationship between central black hole (BH) mass and host galaxy stellar bulge mass extending to the lowest BH masses known in dwarf galaxies (MBH ≲ 105 M⊙; M⋆ ∼ 109 M⊙). We have obtained visible and near-infrared Hubble Space Telescope imaging of seven dwarf galaxies with optically selected broad-line active galactic nuclei (AGNs) and BH mass estimates from single-epoch spectroscopy. We perform 2D photometric modeling with GALFIT to decompose the structure of these galaxies and find that the majority have an inner bulge/pseudo-bulge component with an exponential disk that dominates the total stellar mass. Using the modeling results and color-dependent mass-to-light ratios, we determine the stellar mass of each photometric component in each galaxy. We determine the MBH–Mbulge relation using a total of 12 dwarf galaxies hosting broad-line AGNs, along with a comparison sample of 88 galaxies with dynamical BH masses and 37 reverberation-mapped AGNs. We find a strong correlation between BH mass and bulge mass with . The near-linear slope and normalization are in good agreement with correlations found previously when only considering higher-mass systems. This work has quadrupled the number of dwarf galaxies on the BH–bulge mass relation, with implications for BH seeding and predictions for gravitational wave detections of merging BHs at higher redshifts with LISA.
Properties of the simulated circumgalactic medium

The circumgalactic medium (CGM) is closely linked to galaxy formation and evolution, but difficult to characterize observationally and typically poorly resolved in cosmological simulations. We use spherically symmetric, idealized, high-resolution simulations of the CGM in $10^{12}$ and $10^{11}\, \mathrm{M}_\odot$ dark matter haloes to characterize the gas pressure, turbulent and radial velocities, and degree of thermal and effective dynamic pressure support in the overall CGM as well as in its high- and low-temperature phases. We find that the $10^{12}\, \mathrm{M}_\odot$ halo contains a CGM mostly formed of a hot gas halo in hydrostatic equilibrium out of which cold gas condenses and falls on to the central galaxy, while the $10^{11}\, \mathrm{M}_\odot$ halo’s CGM is not in hydrostatic equilibrium, has a wider spread of properties at a given galactocentric radius, does not have a clear separation of hot and cold phases, and is dominated by bulk motions. We also find that the degree of pressure support in the $10^{11}\, \mathrm{M}_\odot$ halo is strongly dependent on the parameters of the galactic winds of the central galaxy. These results promote the idea that there is no ‘average’ CGM and care must be taken when setting the initial conditions for a small-box simulation of a patch of the CGM.
The properties of nuclear star clusters and their host galaxies

Nuclear star clusters are found at the centers of most galaxies. They are the densest stellar systems in the Universe, and thus have unique and interesting stellar dynamics. We review how common nuclear star clusters are in galaxies of different masses and types, and then discuss the typical properties of NSCs. We close by discussing the formation of NSCs, and how a picture is emerging of different formation mechanisms being dominant in lower and higher mass galaxies.
Detection of a slow H i bar in the dwarf irregular galaxy DDO 168

We examine the H i total intensity maps of the VLA LITTLE-THINGS galaxies and identify an H i bar in the dwarf irregular galaxy DDO 168 which has a dense and compact dark matter halo that dominates at all radii. This is only the third galaxy found to host an H i bar. Using the H i kinematic data, we apply the Tremaine–Weinberg method to estimate the pattern speed of the bar. The H i bar is found to have an average pattern speed of 23.3 ± 5.9 $\rm km \, s^{-1} \, kpc^{-1}$. Interestingly, for the first time, we find that the observed pattern speeds of the bar in the two kinematic halves are different. We identify the origin of this difference to be the kinematic asymmetry. This observed offset in the pattern speed serves to put a stringent constraint on the lifetime of the bar set by the winding time-scale. The lifetime of the bar is found to be 5.3 × 108 yr, which is two times the dynamical time-scale of the disc. We also find the H i bar in DDO 168 to be a weak bar with a strength of 0.2. If H i bar being weak can be easily disturbed, this could possibly explain why it is extremely rare to observe H i bars in galaxies. We estimate the bar radius to be 1 kpc and the dimensionless ratio, RL/Rb to be ≥2.1 indicating a ‘slow’ bar in DDO 168. Our results confirm the proposition that the dynamical friction with the halo slows down a rotating bar in a galaxy dominated by dark matter halo from inner radii.
DROGON: A Causal Reasoning Framework for Future Trajectory Forecast

We propose DROGON (Deep RObust Goal-Oriented trajectory prediction Network) for accurate vehicle trajectory forecast by considering behavioral intention of vehicles in traffic scenes. Our main insight is that a causal relationship between intention and behavior of drivers can be reasoned from the observation of their relational interactions toward an environment. To succeed in causal reasoning, we build a conditional prediction model to forecast goal-oriented trajectories, which is trained with the following stages: (i) relational inference where we encode relational interactions of vehicles using the perceptual context; (ii) intention estimation to compute the probability distribution of intentional goals based on the inferred relations; and (iii) causal reasoning where we reason about the behavior of vehicles as future locations conditioned on the intention. To properly evaluate the performance of our approach, we present a new large-scale dataset collected at road intersections with diverse interactions of vehicles. The experiments demonstrate the efficacy of DROGON as it consistently outperforms state-of-the-art techniques.
The Sloan Digital Sky Survey Reverberation Mapping Project: Improving Lag Detection with an Extended Multiyear Baseline

We investigate the effects of extended multiyear light curves (9 yr photometry and 5 yr spectroscopy) on the detection of time lags between the continuum variability and broad-line response of quasars at z ≳ 1.5, and compare with the results using 4 yr photometry+spectroscopy presented in a companion paper. We demonstrate the benefits of the extended light curves in three cases: (1) lags that are too long to be detected by the shorter-duration data but can be detected with the extended data; (2) lags that are recovered by the extended light curves but are missed in the shorter-duration data due to insufficient light-curve quality; and (3) lags for different broad-line species in the same object. These examples demonstrate the importance of long-term monitoring for reverberation mapping to detect lags for luminous quasars at high redshift, and the expected performance of the final data set from the Sloan Digital Sky Survey Reverberation Mapping project that will have 11 yr photometric and 7 yr spectroscopic baselines.
How Good is SGD with Random Shuffling?

We study the performance of stochastic gradient descent (SGD) on smooth and strongly-convex finite-sum optimization problems. In contrast to the majority of existing theoretical works, which assume that individual functions are sampled with replacement, we focus here on popular but poorly-understood heuristics, which involve going over random permutations of the individual functions. This setting has been investigated in several recent works, but the optimal error rates remain unclear. In this paper, we provide lower bounds on the expected optimization error with these heuristics (using SGD with any constant step size), which elucidate their advantages and disadvantages. In particular, we prove that after $k$ passes over $n$ individual functions, if the functions are re-shuffled after every pass, the best possible optimization error for SGD is at least $\Omega\left(1/(nk)^2+1/nk^3\right)$, which partially corresponds to recently derived upper bounds. Moreover, if the functions are only shuffled once, then the lower bound increases to $\Omega(1/nk^2)$. Since there are strictly smaller upper bounds for repeated reshuffling, this proves an inherent performance gap between SGD with single shuffling and repeated shuffling. As a more minor contribution, we also provide a non-asymptotic $\Omega(1/k^2)$ lower bound (independent of $n$) for the incremental gradient method, when no random shuffling takes place. Finally, we provide an indication that our lower bounds are tight, by proving matching upper bounds for univariate quadratic functions.
Hedging Non-Tradable Risks with Transaction Costs and Price Impact

A risk-averse agent hedges her exposure to a non-tradable risk factor U using a correlated traded asset S and accounts for the impact of her trades on both factors. The effect of the agent's trades on U is referred to as cross-impact. By solving the agent's stochastic control problem, we obtain a closed-form expression for the optimal strategy when the agent holds a linear position in U. When the exposure to the non-tradable risk factor is non-linear, we provide an approximation to the optimal strategy in closed-form, and prove that the value function is correctly approximated by this strategy when cross-impact and risk-aversion are small. We further prove that when exposure to U is non-linear, the approximate optimal strategy can be written in terms of the optimal strategy for a linear exposure with the size of the position changing dynamically according to the exposure's "Delta" under a particular probability measure.
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension

Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or ellipsis. Moreover, when reasoning over passage text, most of them simply treat it as a word sequence without exploring rich semantic relationships among words. In this paper, we first propose a simple yet effective graph structure learning technique to dynamically construct a question and conversation history aware context graph at each conversation turn. Then we propose a novel Recurrent Graph Neural Network, and based on that, we introduce a flow mechanism to model the temporal dependencies in a sequence of context graphs. The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks. In addition, visualization experiments show that our proposed model can offer good interpretability for the reasoning process.
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation

Batch normalization has been widely used to improve optimization in deep neural networks. While the uncertainty in batch statistics can act as a regularizer, using these dataset statistics specific to the training set impairs generalization in certain tasks. Recently, alternative methods for normalizing feature activations in neural networks have been proposed. Among them, group normalization has been shown to yield similar, in some domains even superior performance to batch normalization. All these methods utilize a learned affine transformation after the normalization operation to increase representational power. Methods used in conditional computation define the parameters of these transformations as learnable functions of conditioning information. In this work, we study whether and where the conditional formulation of group normalization can improve generalization compared to conditional batch normalization. We evaluate performances on the tasks of visual question answering, few-shot learning, and conditional image generation.