nils-herrmann/clean_scirepeval huggingface dataset:
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks

Deep-learning based classification algorithms have been shown to be susceptible to adversarial attacks: minor changes to the input of classifiers can dramatically change their outputs, while being imperceptible to humans. In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations. Drawing on ideas from information and coding theory, we propose a general class of defenses for detecting classifier errors caused by abnormally small input perturbations. We further show theoretical guarantees for the performance of this detection method. We present experimental results with (a) a voice recognition system, and (b) a digit recognition system using the MNIST database, to demonstrate the effectiveness of the proposed defense methods. The ideas in this paper are motivated by a simple analogy between AI classifiers and the standard Shannon model of a communication system.
Automatic Delineation of Kidney Region in DCE-MRI

Delineation of the kidney region in dynamic contrast-enhanced magnetic resonance Imaging (DCE-MRI) is required during post-acquisition analysis in order to quantify various aspects of renal function, such as filtration and perfusion or blood flow. However, this can be obfuscated by the Partial Volume Effect (PVE), caused due to the mixing of any single voxel with two or more signal intensities from adjacent regions such as liver region and other tissues. To avoid this problem, firstly, a kidney region of interest (ROI) needs to be defined for the analysis. A clinician may choose to select a region avoiding edges where PV mixing is likely to be significant. However, this approach is time-consuming and labour intensive. To address this issue, we present Dynamic Mode Decomposition (DMD) coupled with thresholding and blob analysis as a framework for automatic delineation of the kidney region. This method is first validated on synthetically generated data with ground-truth available and then applied to ten healthy volunteers' kidney DCE-MRI datasets. We found that the result obtained from our proposed framework is comparable to that of a human expert. For example, while our result gives an average Root Mean Square Error (RMSE) of 0.0097, the baseline achieves an average RMSE of 0.1196 across the 10 datasets. As a result, we conclude automatic modelling via DMD framework is a promising approach.
A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications

Spoken language understanding (SLU) acts as a critical component in goal-oriented dialog systems. It typically involves identifying the speakers intent and extracting semantic slots from user utterances, which are known as intent detection (ID) and slot filling (SF). SLU problem has been intensively investigated in recent years. However, these methods just constrain SF results grammatically, solve ID and SF independently, or do not fully utilize the mutual impact of the two tasks. This paper proposes a multi-head self-attention joint model with a conditional random field (CRF) layer and a prior mask. The experiments show the effectiveness of our model, as compared with state-of-the-art models. Meanwhile, online education in China has made great progress in the last few years. But there are few intelligent educational dialog applications for students to learn foreign languages. Hence, we design an intelligent dialog robot equipped with different scenario settings to help students learn communication skills.
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting

Graph convolution network based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, like region-wise distance or functional similarity. To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks. Leveraging the advantage of multi-modal machine learning, we propose to develop modality interaction mechanisms for this problem, in order to reduce generalization error by reinforcing the learning of multimodal coordinated representations. In this work, we propose two interaction techniques for handling features in lower layers and higher layers respectively. In lower layers, we propose grouped GCN to combine the graph connectivity from different modalities for more complete spatial feature extraction. In higher layers, we adapt multi-linear relationship networks to GCN by exploring the dimension transformation and freezing part of the covariance structure. The adapted approach, called multi-linear relationship GCN, learns more generalized features to overcome the train-test divergence induced by time shifting. We evaluated our model on ridehailing demand forecasting problem using two real-world datasets. The proposed technique outperforms state-of-the art baselines in terms of prediction accuracy, training efficiency, interpretability and model robustness.
Are sample means in multi-armed bandits positively or negatively biased?

It is well known that in stochastic multi-armed bandits (MAB), the sample mean of an arm is typically not an unbiased estimator of its true mean. In this paper, we decouple three different sources of this selection bias: adaptive \emph{sampling} of arms, adaptive \emph{stopping} of the experiment, and adaptively \emph{choosing} which arm to study. Through a new notion called ``optimism'' that captures certain natural monotonic behaviors of algorithms, we provide a clean and unified analysis of how optimistic rules affect the sign of the bias. The main takeaway message is that optimistic sampling induces a negative bias, but optimistic stopping and optimistic choosing both induce a positive bias. These results are derived in a general stochastic MAB setup that is entirely agnostic to the final aim of the experiment (regret minimization or best-arm identification or anything else). We provide examples of optimistic rules of each type, demonstrate that simulations confirm our theoretical predictions, and pose some natural but hard open problems.
Flexible and inflexible $CR$ submanifolds

In this paper we prove new embedding results for compactly supported deformations of $CR$ submanifolds of $\mathbb{C}^{n+d}$: We show that if $M$ is a $2$-pseudoconcave $CR$ submanifold of type $(n,d)$ in $\mathbb{C}^{n+d}$, then any compactly supported $CR$ deformation stays in the space of globally $CR$ embeddable in $\mathbb{C}^{n+d}$ manifolds. This improves an earlier result, where $M$ was assumed to be a quadratic $2$-pseudoconcave $CR$ submanifold of $\mathbb{C}^{n+d}$. We also give examples of weakly $2$-pseudoconcave $CR$ manifolds admitting compactly supported $CR$ deformations that are not even locally $CR$ embeddable.
Life in the fast lane: a direct view of the dynamics, formation, and evolution of the Milky Way’s bar

Studies of the ages, abundances, and motions of individual stars in the Milky Way provide one of the best ways to study the evolution of disc galaxies over cosmic time. The formation of the Milky Way’s barred inner region in particular is a crucial piece of the puzzle of disc galaxy evolution. Using data from APOGEE and Gaia, we present maps of the kinematics, elemental abundances, and age of the Milky Way bulge and disc that show the barred structure of the inner Milky Way in unprecedented detail. The kinematic maps allow a direct, purely kinematic determination of the bar’s pattern speed of $41\pm 3\, \mathrm{km\, s}^{-1}\, \mathrm{kpc}^{-1}$ and of its shape and radial profile. We find the bar’s age, metallicity, and abundance ratios to be the same as those of the oldest stars in the disc that are formed in its turbulent beginnings, while stars in the bulge outside of the bar are younger and more metal-rich. This implies that the bar likely formed ${\approx}8\, \mathrm{Gyr}$ ago, when the decrease in turbulence in the gas disc allowed a thin disc to form that quickly became bar-unstable. The bar’s formation therefore stands as a crucial epoch in the evolution of the Milky Way, a picture that is in line with the evolutionary path that emerges from observations of the gas kinematics in external disc galaxies over the last ${\approx}10\,
Particle motion near high-spin black holes

General relativity predicts that the Kerr black hole develops qualitatively new and surprising features in the limit of maximal spin. Most strikingly, the region of spacetime near the event horizon stretches into an infinitely long throat and displays an emergent conformal symmetry. Understanding dynamics in this NHEK (near-horizon extreme Kerr) geometry is necessary for connecting theory to upcoming astronomical observations of high-spin black holes. We review essential properties of NHEK and its relationship to the rapidly rotating Kerr black hole. We then completely solve the geodesic equation in the NHEK region and describe how the resulting trajectories transform under the action of its enhanced symmetries. In the process, we derive explicit expressions for the angular integrals appearing in the Kerr geodesic equation and obtain a useful formula, valid at arbitrary spin, for a particle’s polar angle in terms of its radial motion. These results will aid in the analytic computation of astrophysical observables relevant to ongoing and future experiments.
Dark matter seeping through dynamic gauge kinetic mixing

We show for the first time that the loop-driven kinetic mixing between visible and dark Abelian gauge bosons can facilitate dark matter production in the early Universe by creating a ‘dynamic’ portal, which depends on the energy of the process. The required smallness of the strength of the portal interaction, suited for freeze-in, is justified by a suppression arising from the mass of a heavy vector-like fermion. The strong temperature sensitivity associated with the interaction is responsible for most of the dark matter production during the early stages of reheating.
How to quench a dwarf galaxy: The impact of inhomogeneous reionization on dwarf galaxies and cosmic filaments

We use the SPHINX suite of high-resolution cosmological radiation hydrodynamics simulations to study how spatially and temporally inhomogeneous reionization impacts the baryonic content of dwarf galaxies and cosmic filaments. We compare simulations with and without stellar radiation to isolate the effects of radiation feedback from that of supernova, cosmic expansion, and numerical resolution. We find that the gas content of cosmic filaments can be reduced by more than 80 per cent following reionization. The gas inflow rates into haloes with ${M_{\rm vir}\lesssim 10^8\, \mathrm{M}_{\odot }}$ are strongly affected and are reduced by more than an order of magnitude compared to the simulation without reionization. A significant increase in gas outflow rates is found for halo masses ${M_{\rm vir}\lesssim 7\times 10^7\, \mathrm{M}_{\odot }}$. Our simulations show that inflow suppression (i.e. starvation), rather than photoevaporation, is the dominant mechanism by which the baryonic content of high-redshift dwarf galaxies is regulated. At fixed redshift and halo mass, there is a large scatter in the halo baryon fractions that is entirely dictated by the timing of reionization in the local region surrounding a halo which can change by Δz ≳ 3 at fixed mass. Finally, although the gas content of high-redshift dwarf galaxies is significantly impacted by reionization, we find that most haloes with ${M_{\rm vir}\lesssim 10^8\, \mathrm{M}_{\odot }}$ can remain self-shielded and form stars long after reionization, until their local gas reservoir is depleted, suggesting that Local Group dwarf galaxies do not necessarily exhibit star formation histories that peak prior to z = 6. Significantly larger simulation boxes will be required to capture the full process of reionization and understand how our results translate to environments not probed by our current work.
The chaotic nature of TRAPPIST-1 planetary spin states

The TRAPPIST-1 system has seven known terrestrial planets arranged compactly in a mean motion resonant chain around an ultracool central star, some within the estimated habitable zone. Given their short orbital periods of just a few days, it is often presumed that the planets are tidally locked such that the spin rate is equal to that of the orbital mean motion. However, the compact, and resonant, nature of the system implies that there can be significant variations in the mean motion of these planets due to their mutual interactions. We show that such fluctuations can then have significant effects on the spin states of these planets. In this paper, we analyse, using detailed numerical simulations, the mean motion histories of the three planets that are thought to lie within or close to the habitable zone of the system: planets d, e, and f. We demonstrate that, depending on the strength of the mutual interactions within the system, these planets can be pushed into spin states which are effectively non-synchronous. We find that it can produce significant libration of the spin state, if not complete circulation in the frame co-rotating with the orbit. We also show that these spin states are likely to be unable to sustain long-term stability, with many of our simulations suggesting that the spin evolves, under the influence of tidal synchronization forces, into quasi-stable attractor states, which last on time-scales of thousands of years.
Nonperturbative test of consistency relations and their violation

In this paper, we verify the large scale structure consistency relations using N-body simulations, including modes in the highly non-linear regime. These relations (pointed out by Kehagias & Riotto and Peloso & Pietroni) follow from the symmetry of the dynamics under a shift of the Newtonian potential by a constant and a linear gradient, and predict the absence of certain poles in the ratio between the (equal time) squeezed bispectrum and power spectrum. The consistency relations, as symmetry statements, are exact, but have not been previously checked beyond the perturbative regime. Our test using N-body simulations not only offers a non-perturbative check, but also serves as a warm-up exercise for applications to observational data. A number of subtleties arise when taking the squeezed limit of the bispectrum--we show how to circumvent or address them. An interesting by-product of our investigation is an explicit demonstration that the linear-gradient symmetry is unaffected by the periodic boundary condition of the simulations. Lastly, we verify using simulations that the consistency relations are violated when the initial conditions are non-gaussian (of the local fNL type). The methodology developed here paves the way for constraining primordial non-gaussianity using large scale structure data, including (numerous) highly non-linear modes that are otherwise hard to interpret and utilize.
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning

Motivated by applications in Reinforcement Learning (RL), in this paper, we study a nonlinear Stochastic Approximation (SA) algorithm under Markovian noise, and derive its finite-sample convergence bounds. Our proof is based on the Lyapunov drift arguments, and to handle the Markovian noise, we exploit the fast mixing of the underlying Markov chain. 
Our result is used to show the finite-sample bounds of the popular Q-learning with linear function approximation algorithm for solving the RL problem. Since Q-learning with linear function approximation may diverge in general, we study it under a condition on the behavior policy that ensures the stability of the algorithm. Due to the generality of our SA results, we do not need to make the unnatural assumption that the samples are i.i.d. (since they are Markovian), and do not require an additional projection step in the algorithm to maintain the boundedness of the iterates.
Heavy dark matter particle annihilation in dwarf spheroidal galaxies: Radio signals at the SKA telescope

A weakly interacting dark matter candidate is difficult to detect at high-energy colliders like the LHC, if its mass is close to, or higher than a TeV. On the other hand, pair-annihilation of such particles may give rise to $e^+ e^-$ pairs in dwarf spheroidal galaxies (dSph), which in turn can lead to radio synchrotron signals that are detectable at the upcoming Square Kilometre Array (SKA) telescope within a moderate observation time. We investigate the circumstances under which this complementarity between collider and radio signals of dark matter can be useful in probing physics beyond the standard model of elementary particles. Both particle physics issues and the roles of diffusion and electromagnetic energy loss of the $e^\pm$ are taken into account. First, the criteria for detectability of trans-TeV dark matter are analysed independently of the particle physics model(s) involved. We thereafter use some benchmarks based on a popular scenario, namely, the minimal supersymmetric standard model. It is thus shown that the radio flux from a dSph like Draco should be observable in about 100 hours at the SKA, for dark matter masses upto 4-8 TeV. In addition, the regions in the space spanned by astrophysical parameters, for which such signals should be detectable at the SKA, are marked out.
Equivalent and Approximate Transformations of Deep Neural Networks

Two networks are equivalent if they produce the same output for any given input. In this paper, we study the possibility of transforming a deep neural network to another network with a different number of units or layers, which can be either equivalent, a local exact approximation, or a global linear approximation of the original network. On the practical side, we show that certain rectified linear units (ReLUs) can be safely removed from a network if they are always active or inactive for any valid input. If we only need an equivalent network for a smaller domain, then more units can be removed and some layers collapsed. On the theoretical side, we constructively show that for any feed-forward ReLU network, there exists a global linear approximation to a 2-hidden-layer shallow network with a fixed number of units. This result is a balance between the increasing number of units for arbitrary approximation with a single layer and the known upper bound of $\lceil log(n_0+1)\rceil +1$ layers for exact representation, where $n_0$ is the input dimension. While the transformed network may require an exponential number of units to capture the activation patterns of the original network, we show that it can be made substantially smaller by only accounting for the patterns that define linear regions. Based on experiments with ReLU networks on the MNIST dataset, we found that $l_1$-regularization and adversarial training reduces the number of linear regions significantly as the number of stable units increases due to weight sparsity. Therefore, we can also intentionally train ReLU networks to allow for effective loss-less compression and approximation.
COSET: A Benchmark for Evaluating Neural Program Embeddings

Neural program embedding can be helpful in analyzing large software, a task that is challenging for traditional logic-based program analyses due to their limited scalability. A key focus of recent machine-learning advances in this area is on modeling program semantics instead of just syntax. Unfortunately evaluating such advances is not obvious, as program semantics does not lend itself to straightforward metrics. In this paper, we introduce a benchmarking framework called COSET for standardizing the evaluation of neural program embeddings. COSET consists of a diverse dataset of programs in source-code format, labeled by human experts according to a number of program properties of interest. A point of novelty is a suite of program transformations included in COSET. These transformations when applied to the base dataset can simulate natural changes to program code due to optimization and refactoring and can serve as a "debugging" tool for classification mistakes. We conducted a pilot study on four prominent models: TreeLSTM, gated graph neural network (GGNN), AST-Path neural network (APNN), and DYPRO. We found that COSET is useful in identifying the strengths and limitations of each model and in pinpointing specific syntactic and semantic characteristics of programs that pose challenges.
A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion

In computer vision and image processing tasks, image fusion has evolved into an attractive research field. However, recent existing image fusion methods are mostly built on pixel-level operations, which may produce unacceptable artifacts and are time-consuming. In this paper, a symmetric encoder-decoder with a residual block (SEDR) for infrared and visible image fusion is proposed. For the training stage, the SEDR network is trained with a new dataset to obtain a fixed feature extractor. For the fusion stage, first, the trained model is utilized to extract the intermediate features and compensation features of two source images. Then, extracted intermediate features are used to generate two attention maps, which are multiplied to the input features for refinement. In addition, the compensation features generated by the first two convolutional layers are merged and passed to the corresponding deconvolutional layers. At last, the refined features are fused for decoding to reconstruct the final fused image. Experimental results demonstrate that the proposed fusion method (named as SEDRFuse) outperforms the state-of-the-art fusion methods in terms of both subjective and objective evaluations.
VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019

We describe our submitted system for the ZeroSpeech Challenge 2019. The current challenge theme addresses the difficulty of constructing a speech synthesizer without any text or phonetic labels and requires a system that can (1) discover subword units in an unsupervised way, and (2) synthesize the speech with a target speaker's voice. Moreover, the system should also balance the discrimination score ABX, the bit-rate compression rate, and the naturalness and the intelligibility of the constructed voice. To tackle these problems and achieve the best trade-off, we utilize a vector quantized variational autoencoder (VQ-VAE) and a multi-scale codebook-to-spectrogram (Code2Spec) inverter trained by mean square error and adversarial loss. The VQ-VAE extracts the speech to a latent space, forces itself to map it into the nearest codebook and produces compressed representation. Next, the inverter generates a magnitude spectrogram to the target voice, given the codebook vectors from VQ-VAE. In our experiments, we also investigated several other clustering algorithms, including K-Means and GMM, and compared them with the VQ-VAE result on ABX scores and bit rates. Our proposed approach significantly improved the intelligibility (in CER), the MOS, and discrimination ABX scores compared to the official ZeroSpeech 2019 baseline or even the topline.