Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised Sound Event Detection using Random Augmentation and Consistency Regularization

Sound event detection is a core module for acoustic environmental analysis. Semi-supervised learning technique allows to largely scale up the dataset without increasing the annotation budget, and recently attracts lots of research attention. In this work, we study on two advanced semi-supervised learning techniques for sound event detection. Data augmentation is important for the success of recent deep learning systems. This work studies the audio-signal random augmentation method, which provides an augmentation strategy that can handle a large number of different audio transformations. In addition, consistency regularization is widely adopted in recent state-of-the-art semi-supervised learning methods, which exploits the unlabelled data by constraining the prediction of different transformations of one sample to be identical to the prediction of this sample. This work finds that, for semi-supervised sound event detection, consistency regularization is an effective strategy, especially the best performance is achieved when it is combined with the MeanTeacher model.
Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms

Numerical weather forecasting using high-resolution physical models often requires extensive computational resources on supercomputers, which diminishes their wide usage in most real-life applications. As a remedy, applying deep learning methods has revealed innovative solutions within this field. To this end, we introduce a novel deep learning architecture for forecasting high-resolution spatio-temporal weather data. Our approach extends the conventional encoder-decoder structure by integrating Convolutional Long-short Term Memory and Convolutional Neural Networks. In addition, we incorporate attention and context matcher mechanisms into the model architecture. Our Weather Model achieves significant performance improvements compared to baseline deep learning models, including ConvLSTM, TrajGRU, and U-Net. Our experimental evaluation involves high-scale, real-world benchmark numerical weather datasets, namely the ERA5 hourly dataset on pressure levels and WeatherBench. Our results demonstrate substantial improvements in identifying spatial and temporal correlations with attention matrices focusing on distinct parts of the input series to model atmospheric circulations. We also compare our model with high-resolution physical models using the benchmark metrics and show that our Weather Model is accurate and easy to interpret.
Learning to Combat Noisy Labels via Classification Margins

A deep neural network trained on noisy labels is known to quickly lose its power to discriminate clean instances from noisy ones. After the early learning phase has ended, the network memorizes the noisy instances, which leads to a significant degradation in its generalization performance. To resolve this issue, we propose MARVEL (MARgins Via Early Learning), a new robust learning method where the memorization of the noisy instances is curbed. We propose a new test statistic that tracks the goodness of"fit"of every instance based on the epoch-history of its classification margins. If its classification margin is small in a sequence of consecutive learning epochs, that instance is declared noisy and the network abandons learning on it. Consequently, the network first flags a possibly noisy instance, and then waits to see if learning on that instance can be improved and if not, the network learns with confidence that this instance can be safely abandoned. We also propose MARVEL+, where arduous instances can be upweighted, enabling the network to focus and improve its learning on them and consequently its generalization. Experimental results on benchmark datasets with synthetic label noise and real-world datasets show that MARVEL outperforms other baselines consistently across different noise levels, with a significantly larger margin under asymmetric noise.
Extreme-ultraviolet bursts and nanoflares in the quiet-Sun transition region and corona

The quiet solar corona consists of myriads of loop-like features, with magnetic fields originating from network and internetwork regions on the solar surface. The continuous interaction between these different magnetic patches leads to transient brightenings or bursts that might contribute to the heating of the solar atmosphere. However, it remains unclear whether such transients, which are mostly observed in the EUV, play a significant role in atmospheric heating. We revisit the open question of these bursts as a prelude to the new high-resolution EUV imagery expected from the recently launched Solar Orbiter. We use EUV images recorded by the SDO/AIA to investigate statistical properties of the bursts. We detect the bursts in the 171 {\AA} filter images of AIA in an automated way through a pixel-wise analysis by imposing different intensity thresholds. By exploiting the high cadence (12 s) of the AIA observations, we find that the distribution of lifetimes of these events peaks at about 120 s. The sizes of the detected bursts are limited by the spatial resolution, which indicates that a larger number of events might be hidden in the AIA data. We estimate that about 100 new bursts appear per second on the whole Sun. The detected bursts have nanoflare-like energies of $10^{24}$\,erg per event. Based on this, we estimate that at least 100 times more events of a similar nature would be required to account for the energy that is required to heat the corona. When AIA observations are considered alone, the EUV bursts discussed here therefore play no significant role in the coronal heating of the quiet Sun. If the coronal heating of the quiet Sun is mainly bursty, then the high-resolution EUV observations from Solar Orbiter may be able to reduce the deficit in the number of EUV bursts seen with SDO/AIA at least partly by detecting more such events.
STRUCTURE IN LIQUID METHANOL FROM SPATIAL DISTRIBUTION FUNCTIONS

A structural approach that employs the spatial distribution functions of atoms has been shown recently [J. Chem. Phys. 99, 3049 (1993)] to greatly improve our understanding of the local structure in liquid water. In the present study we obtain the oxygen–oxygen and oxygen–carbon spatial distribution functions, gOO(r,Ω) and gOC(r,Ω), respectively, for liquid methanol and use them to characterize its equilibrium structure. For this purpose molecular dynamics simulations with the three‐site model of Haughney, Ferrario, and McDonald are carried out at a temperature of 25 °C. Using the spatial distribution functions we demonstrate that the dominant H‐bonded structure in this liquid is an open, nonlinear (‘‘zig–zag’’) chain of monomers packed spatially in a tetrahedral manner. gOO(r,Ω) yields an average coordination number of 1.92 which agrees well with results from chain length analysis. There is no evidence in our structural data to support a local planar assembly of oxygen sites. We also observe features in .
Finite Codimensionality Method in Infinite-dimensional Optimization Problems

This paper is devoted to establishing an enhanced Fritz John type first-order necessary condition for a general constrained nonlinear infinite-dimensional optimization problem. Unlike traditional constraint qualifications in optimization theory, a condition of finite codimensionality is employed to ensure the existence of nontrivial Lagrange multipliers. As applications, first-order necessary conditions for optimal control problems of some deterministic/stochastic control systems are derived in a unified manner. Compared with the existing constraint qualifications, the finite codimensionality condition, which is equivalent to some suitable {\it a priori} estimates, can offer a more straightforward verification process in these applications.
Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE

In recent years, extending variational autoencoder's framework to learn disentangled representations has received much attention. We address this problem by proposing a framework capable of disentangling class-related and class-independent factors of variation in data. Our framework employs an attention mechanism in its latent space in order to improve the process of extracting class-related factors from data. We also deal with the multimodality of data distribution by utilizing mixture models as learnable prior distributions, as well as incorporating the Bhattacharyya coefficient in the objective function to prevent highly overlapping mixtures. Our model's encoder is further trained in a semi-supervised manner, with a small fraction of labeled data, to improve representations' interpretability. Experiments show that our framework disentangles class-related and class-independent factors of variation and learns interpretable features. Moreover, we demonstrate our model's performance with quantitative and qualitative results on various datasets.
Settling the Sharp Reconstruction Thresholds of Random Graph Matching

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are complete graphs with correlated Gaussian weights and the Erdős-Rényi model where the two graphs are subsampled from a common parent Erdős-Rényi graph $\mathcal{G}(n, p)$. For dense graphs with $p=n^{-o(1)}$, we prove that there exists a sharp threshold, above which one can correctly match all but a vanishing fraction of the vertices and below which correctly matching any positive fraction is impossible, a phenomenon known as the “all-or-nothing” phase transition. Even more strikingly, in the Gaussian setting, above the threshold all vertices can be exactly matched with high probability. In contrast, for sparse Erdős-Rényi graphs with $p=n^{-\Theta(1)}$, we show that the all-or-nothing phenomenon no longer holds and we determine the thresholds up to a constant factor. Along the way, we also derive the sharp threshold for exact recovery, sharpening the existing results in Erdős-Rényi graphs [1], [2]. The proof of the negative results builds upon a tight characterization of the mutual information based on the truncated second-moment computation in [3] and an “area theorem” that relates the mutual information to the integral of the reconstruction error. The positive results follows from a tight analysis of the maximum likelihood estimator that takes into account the cycle structure of the induced permutation on the edges.
On Secure Distributed Linearly Separable Computation

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. The data center assigns a subset of input datasets to each server in an uncoded manner, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such that the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost (number of symbols the user should receive). Then we focus on the case where the computation cost of each server (number of datasets assigned to each server) is minimum and aim to minimize the size of the randomness variable (i.e., randomness size) introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely fractional repetition assignment and cyclic assignment. These schemes are optimal subject to using these assignments. Motivated by the observation of the general limitation of these two schemes on the randomness size, we propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained.
Proton interactions with soil organic matter: the importance of aggregation and the weak acids of humin

Samples of three organic‐rich soils (ombrotrophic peat, podzol H‐horizon, humic ranker) were extensively washed with dilute nitric acid, dialysed against deionised water, and then subjected to acid‐base titrations over the pH range 3–10, in 0.3–300 mm NaNO3, and with soil concentrations in the range 2–150 g l−1. The results for the three soils were quantitatively similar. Comparison of the titration data with previously published results for humic acids isolated from the same soils showed the soil organic matter to have a greater ionic strength dependency of proton binding and to possess relatively greater buffering capacity at high pH, attributable to weak acid groups (c. 2–5 mmol g–1) in the humin fraction of the soils. To describe the soil titration data quantitatively, we modified Humic Ion‐Binding Model VI‐FD, which utilizes a fixed Donnan volume to describe counterion accumulation, by increasing the content of weak acid groups. When artefacts in pH measurement caused by the suspension effect were taken into account, the resulting Model VI‐FD2 provided good or fair simulations of all the titration data. The results suggest that soil structure, specifically aggregation, plays a significant role in cation binding by organic soils in situ. The lack of dependence of the titration results on soil suspension concentration suggests that the findings can be applied to soils in situ.
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation

Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data. The amplification of biases in language technology has mainly been examined with respect to specific phenomena, such as gender bias. In this work, we go beyond the study of gender in MT and investigate how bias amplification might affect language in a broader sense. We hypothesize that the ‘algorithmic bias’, i.e. an exacerbation of frequently observed patterns in combination with a loss of less frequent ones, not only exacerbates societal biases present in current datasets but could also lead to an artificially impoverished language: ‘machine translationese’. We assess the linguistic richness (on a lexical and morphological level) of translations created by different data-driven MT paradigms – phrase-based statistical (PB-SMT) and neural MT (NMT). Our experiments show that there is a loss of lexical and syntactic richness in the translations produced by all investigated MT paradigms for two language pairs (EN-FR and EN-ES).
Many-Body Quantum Teleportation via Operator Spreading in the Traversable Wormhole Protocol

Thomas Schuster,1, ∗ Bryce Kobrin,1, 2, ∗ Ping Gao,3 Iris Cong,4 Emil T. Khabiboulline,4 Norbert M. Linke,5 Mikhail D. Lukin,4 Christopher Monroe,5 Beni Yoshida,6 and Norman Y. Yao1, 2 Department of Physics, University of California, Berkeley, California 94720 USA Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA Joint Quantum Institute and Department of Physics, University of Maryland, College Park, MD 20742 USA Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada (Dated: August 9, 2022)
Graphs of Joint Types, Noninteractive Simulation, and Stronger Hypercontractivity

In this paper, we study the type graph, namely, a bipartite graph induced by a joint type. We investigate the maximum edge density of induced bipartite subgraphs of this graph having a number of vertices on each side on an exponential scale in the length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> of the type. This can be seen as an isoperimetric problem. We provide asymptotically sharp bounds for the exponent of the maximum edge density as the length of the type goes to infinity. We also study the biclique rate region of the type graph, which is defined as the set of <inline-formula> <tex-math notation="LaTeX">$(R_{1},R_{2})$ </tex-math></inline-formula> such that there exists a biclique of the type graph which has respectively <inline-formula> <tex-math notation="LaTeX">$2^{nR_{1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2^{nR_{2}}$ </tex-math></inline-formula> vertices on the two sides. We provide asymptotically sharp bounds for the biclique rate region as well. We then discuss the connections of these results to noninteractive simulation and hypercontractivity inequalities. Furthermore, as an application of our results, a new outer bound for the zero-error capacity region of the binary adder channel is provided, which improves the previously best known bound, due to Austrin, Kaski, Koivisto, and Nederlof. Our proofs in this paper are based on the method of types and linear algebra.
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring

Modern software systems rely on Deep Neural Networks (DNN) when processing complex, unstructured inputs, such as images, videos, natural language texts or audio signals. Provided the intractably large size of such input spaces, the intrinsic limitations of learning algorithms and the ambiguity about the expected predictions for some of the inputs, not only there is no guarantee that DNN’s predictions are always correct, but rather developers must safely assume a low, though not negligible, error probability. A fail-safe Deep Learning based System (DLS) is one equipped to handle DNN faults by means of a supervisor, capable of recognizing predictions that should not be trusted and that should activate a healing procedure bringing the DLS to a safe state.In this paper, we propose an approach to use DNN uncertainty estimators to implement such supervisor. We first discuss advantages and disadvantages of existing approaches to measure uncertainty for DNNs and propose novel metrics for the empirical assessment of the supervisor that rely on such approaches. We then describe our publicly available tool UNCERTAINTY-WIZARD, which allows transparent estimation of uncertainty for regular tf.keras DNNs. Lastly, we discuss a large-scale study conducted on four different subjects to empirically validate the approach, reporting the lessons-learned as guidance for software engineers who intend to monitor uncertainty for fail-safe execution of DLS.
The Friedland–Hayman inequality and Caffarelli’s contraction theorem

The Friedland-Hayman inequality is a sharp inequality concerning the growth rates of homogeneous, harmonic functions with Dirichlet boundary conditions on complementary cones dividing Euclidean space into two parts. In this paper, we prove an analogous inequality in which one divides a convex cone into two parts, placing Neumann conditions on the boundary of the convex cone, and Dirichlet conditions on the interface. This analogous inequality was already proved by us jointly with Sarah Raynor. Here we present a new proof that permits us to characterize the case of equality. In keeping with the two-phase free boundary theory introduced by Alt, Caffarelli, and Friedman, such an improvement can be expected to yield further regularity in free boundary problems.
Deep Evolutionary Learning for Molecular Design

In this paper, we propose a deep evolutionary learning (DEL) process that integrates fragment-based deep generative model and multi-objective evolutionary computation for molecular design. Our approach enables (1) evolutionary operations in the latent space of the generative model, rather than the structural space, to generate novel promising molecular structures for the next evolutionary generation, and (2) generative model fine-tuning using newly generated high-quality samples. Thus, DEL implements a data-model co-evolution concept which improves both sample population and generative model learning. Experiments on two public datasets indicate that sample population obtained by DEL exhibits improved property distributions, and dominates samples generated by multi-objective Bayesian optimization algorithms.
Single Image Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition

Characterizing and removing motion blur caused by camera shake or object motion remains an important task for image restoration. In recent years, removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. Characterization of motion blur, on the other hand, has received less attention and progress in model-based methods for restoration lags behind that of data-driven end-to-end approaches. In this paper, we propose a general, non-parametric model for dense non-uniform motion blur estimation. Given a blurry image, we estimate a set of adaptive basis kernels as well as the mixing coefficients at pixel level, producing a per-pixel map of motion blur. This rich but efficient forward model of the degradation process allows the utilization of existing tools for solving inverse problems. We show that our method overcomes the limitations of existing non-uniform motion blur estimation and that it contributes to bridging the gap between model-based and data-driven approaches for deblurring real photographs.
Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making

How to attribute responsibility for autonomous artificial intelligence (AI) systems’ actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people’s perceptions of eight different notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful difference in how people perceived these agents’ moral responsibility; human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these findings, such as the need for explainable AI in high-stakes scenarios.