Miracle-dz/UniFAR-FTUs huggingface dataset:
Event-related data conditioning for acoustic event classification

Models based on diverse attention mechanisms have recently shined in tasks related to acoustic event classification (AEC). Among them, self-attention is often used in audio-only tasks to help the model recognize different acoustic events. Self-attention relies on the similarity between time frames, and uses global information from the whole segment to highlight specific features within a frame. In real life, information related to acoustic events will attenuate over time, which means the information within some frames around the event deserves more attention than distant time global information that may be unrelated to the event. This paper shows that self-attention may over-enhance certain segments of audio representations, and smooth out the boundaries between events representations and background noises. Hence, this paper proposes an event-related data conditioning (EDC) for AEC. EDC directly works on spectrograms. The idea of EDC is to adaptively select the frame-related attention range based on acoustic features, and gather the event-related local information to represent the frame. Experiments show that: 1) compared with spectrogram-based data augmentation methods and trainable feature weighting and self-attention, EDC outperforms them in both the original-size mode and the augmented mode; 2) EDC effectively gathers event-related local information and enhances boundaries between events and backgrounds, improving the performance of AEC.
TransDrift: Modeling Word-Embedding Drift using Transformer

In modern NLP applications, word embeddings are a crucial backbone that can be read-ily shared across a number of tasks. However as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift, a transformer-based prediction model for word embeddings. Leveraging the ﬂexibility of transformer, our model accurately learns the dynamics of the embedding drift and predicts the future embedding. In experiments, we compare with existing methods and show that our model makes signiﬁcantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classiﬁcation tasks, we show that our embeddings lead to superior performance compared to the previous methods. We will release the code and datasets at https://github.com/transdrift/
Generalization Bounds for Data-Driven Numerical Linear Algebra

Data-driven algorithms can adapt their internal structure or parameters to inputs from unknown application-specific distributions, by learning from a training sample of inputs. Several recent works have applied this approach to problems in numerical linear algebra, obtaining significant empirical gains in performance. However, no theoretical explanation for their success was known. In this work we prove generalization bounds for those algorithms, within the PAC-learning framework for data-driven algorithm selection proposed by Gupta and Roughgarden (SICOMP 2017). Our main results are closely matching upper and lower bounds on the fat shattering dimension of the learning-based low rank approximation algorithm of Indyk et al.~(NeurIPS 2019). Our techniques are general, and provide generalization bounds for many other recently proposed data-driven algorithms in numerical linear algebra, covering both sketching-based and multigrid-based methods. This considerably broadens the class of data-driven algorithms for which a PAC-learning analysis is available.
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization

—Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs in many domains, the optimization of GNNs is less well studied, and the performance on node classiﬁcation heavily suffers from the long-tailed node degree distribution. This paper focuses on improving the performance of GNNs via normaliza- tion. In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (Reshaping the long-tailed distribution into a normal-like distribution via normalization). The scale operation of ResNorm reshapes the node-wise standard deviation (NStd) distribution so as to improve the accuracy of tail nodes ( i . e ., low-degree nodes). We provide a theoretical interpretation and empirical evidence for understanding the mechanism of the above scale . In addition to the long-tailed distribution issue, over-smoothing is also a fundamental issue plaguing the community. To this end, we analyze the behavior of the standard shift and prove that the standard shift serves as a preconditioner on the weight matrix, increasing the risk of oversmoothing. With the over-smoothing issue in mind, we design a shift operation for ResNorm that simulates the degree-speciﬁc parameter strategy in a low-cost manner. Extensive experiments have validated the effectiveness of ResNorm on several node classiﬁcation benchmark datasets.
Introducing the Huber mechanism for differentially private low-rank matrix completion

Performing low-rank matrix completion with sensitive user data calls for privacy-preserving approaches. In this work, we propose a novel noise addition mechanism for preserving differential privacy where the noise distribution is inspired by Huber loss, a well-known loss function in robust statistics. The proposed Huber mechanism is evaluated against existing differential privacy mechanisms while solving the matrix completion problem using the Alternating Least Squares approach. We also propose using the Iteratively Re-Weighted Least Squares algorithm to complete low-rank matrices and study the performance of different noise mechanisms in both synthetic and real datasets. We prove that the proposed mechanism achieves ǫ -differential privacy similar to the Laplace mechanism. Further-more, empirical results indicate that the Huber mechanism outperforms Laplacian and Gaussian in some cases and is comparable, otherwise.
Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification

Few-shot classification aims to learn a model that can generalize well to new tasks when only a few labeled samples are available. To make use of unlabeled data that are more abundantly available in real applications, Ren et al. \shortcite{ren2018meta} propose a semi-supervised few-shot classification method that assigns an appropriate label to each unlabeled sample by a manually defined metric. However, the manually defined metric fails to capture the intrinsic property in data. In this paper, we propose a \textbf{S}elf-\textbf{A}daptive \textbf{L}abel \textbf{A}ugmentation approach, called \textbf{SALA}, for semi-supervised few-shot classification. A major novelty of SALA is the task-adaptive metric, which can learn the metric adaptively for different tasks in an end-to-end fashion. Another appealing feature of SALA is a progressive neighbor selection strategy, which selects unlabeled data with high confidence progressively through the training phase. Experiments demonstrate that SALA outperforms several state-of-the-art methods for semi-supervised few-shot classification on benchmark datasets.
Analysis of arbitrary superconducting quantum circuits accompanied by a Python package: SQcircuit

Superconducting quantum circuits are a promising hardware platform for realizing a fault-tolerant quantum computer. Accelerating progress in this field of research demands general approaches and computational tools to analyze and design more complex superconducting circuits. We develop a framework to systematically construct a superconducting quantum circuit's quantized Hamiltonian from its physical description. As is often the case with quantum descriptions of multicoordinate systems, the complexity rises rapidly with the number of variables. Therefore, we introduce a set of coordinate transformations with which we can find bases to diagonalize the Hamiltonian efficiently. Furthermore, we broaden our framework's scope to calculate the circuit's key properties required for optimizing and discovering novel qubits. We implement the methods described in this work in an open-source Python package SQcircuit. In this manuscript, we introduce the reader to the SQcircuit environment and functionality. We show through a series of examples how to analyze a number of interesting quantum circuits and obtain features such as the spectrum, coherence times, transition matrix elements, coupling operators, and the phase coordinate representation of eigenfunctions.
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection

Consider the problem of imputing missing values in a dataset. One the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability of learning conditional distributions directly, but suffer from the practical requirement for appropriate model specification of each and every variable. On the other hand, recent methods using deep generative modeling benefit from the capacity and efficiency of learning with neural network function approximators, but are often difficult to optimize and rely on stronger data assumptions. In this work, we study an approach that marries the advantages of both: We propose *HyperImpute*, a generalized iterative imputation framework for adaptively and automatically configuring column-wise models and their hyperparameters. Practically, we provide a concrete implementation with out-of-the-box learners, optimizers, simulators, and extensible interfaces. Empirically, we investigate this framework via comprehensive experiments and sensitivities on a variety of public datasets, and demonstrate its ability to generate accurate imputations relative to a strong suite of benchmarks. Contrary to recent work, we believe our findings constitute a strong defense of the iterative imputation paradigm.
From Bi-Level to One-Level: A Framework for Structural Attacks to Graph Anomaly Detection

The success of graph neural networks stimulates the prosperity of graph mining and the corresponding downstream tasks including graph anomaly detection (GAD). However, it has been explored that those graph mining methods are vulnerable to structural manipulations on relational data. That is, the attacker can maliciously perturb the graph structures to assist the target nodes to evade anomaly detection. In this paper, we explore the structural vulnerability of two typical GAD systems: unsupervised FeXtra-based GAD and supervised GCN-based GAD. Specifically, structural poisoning attacks against GAD are formulated as complex bi-level optimization problems. Our first major contribution is then to transform the bi-level problem into one-level leveraging different regression methods. Furthermore, we propose a new way of utilizing gradient information to optimize the one-level optimization problem in the discrete domain. Comprehensive experiments demonstrate the effectiveness of our proposed attack algorithm BinarizedAttack.
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

Estimating personalized effects of treatments is a complex, yet pervasive problem. To tackle it, recent developments in the machine learning (ML) literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools: due to their flexibility, modularity and ability to learn constrained representations, neural networks in particular have become central to this literature. Unfortunately, the assets of such black boxes come at a cost: models typically involve countless nontrivial operations, making it difficult to understand what they have learned. Yet, understanding these models can be crucial -- in a medical context, for example, discovered knowledge on treatment effect heterogeneity could inform treatment prescription in clinical practice. In this work, we therefore use post-hoc feature importance methods to identify features that influence the model's predictions. This allows us to evaluate treatment effect estimators along a new and important dimension that has been overlooked in previous work: We construct a benchmarking environment to empirically investigate the ability of personalized treatment effect models to identify predictive covariates -- covariates that determine differential responses to treatment. Our benchmarking environment then enables us to provide new insight into the strengths and weaknesses of different types of treatment effects models as we modulate different challenges specific to treatment effect estimation -- e.g. the ratio of prognostic to predictive information, the possible nonlinearity of potential outcomes and the presence and type of confounding.
Quest for eternal oscillons

Nonlinear field theories produce unstable but long-lived configurations known as oscillons. These structures have been studied with asymmetric and symmetric double-well potentials and extended to other forms of potentials. In the present work, we examine the consequences of considering higher-order field theories, where we have used a generalization of the symmetric double-well potential and a $\phi^6$ potential. Consequently, we have found $3+1$ spherically symmetric oscillons with significantly large lifetimes without parameter fine-tuning.
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces

We introduce a unified framework for group equivariant networks on homogeneous spaces derived from a Fourier perspective. We consider tensor-valued feature fields, before and after a convolutional layer. We present a unified derivation of kernels via the Fourier domain by leveraging the sparsity of Fourier coefficients of the lifted feature fields. The sparsity emerges when the stabilizer subgroup of the homogeneous space is a compact Lie group. We further introduce a nonlinear activation, via an elementwise nonlinearity on the regular representation after lifting and projecting back to the field through an equivariant convolution. We show that other methods treating features as the Fourier coefficients in the stabilizer subgroup are special cases of our activation. Experiments on $SO(3)$ and $SE(3)$ show state-of-the-art performance in spherical vector field regression, point cloud classification, and molecular completion.
Pseudospectrum and binary black hole merger transients

The merger phase of binary black hole coalescences is a transient between an initial oscillating regime (inspiral) and a late exponentially damped phase (ringdown). In spite of the non-linear character of Einstein equations, the merger dynamics presents a surprisingly simple behaviour consistent with effective linearity. On the other hand, energy loss through the event horizon and by scattering to infinity renders the system non-conservative. Hence, the infinitesimal generator of the (effective) linear dynamics is a non-selfadjoint operator. Qualitative features of transients in linear dynamics driven by non-selfadjoint (in general, non-normal) operators are captured by the pseudospectrum of the time generator. We propose the pseudospectrum as a unifying framework to thread together the phases of binary black hole coalescences, from the inspiral-merger transition up to the late quasinormal mode ringdown.
Good Quantum LDPC Codes with Linear Time Decoders

We construct a new explicit family of good quantum low-density parity-check codes which additionally have linear time decoders. Our codes are based on a three-term chain (2m× m)V →δ0 (2m)E →δ1 2F where V (X-checks) are the vertices, E (qubits) are the edges, and F (Z-checks) are the squares of a left-right Cayley complex, and where the maps are defined based on a pair of constant-size random codes CA,CB:2m→2Δ where Δ is the regularity of the underlying Cayley graphs. One of the main ingredients in the analysis is a proof of an essentially-optimal robustness property for the tensor product of two random codes.
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination

The learned policy of model-free offline reinforcement learning (RL) methods is often constrained to stay within the support of datasets to avoid possible dangerous out-of-distribution actions or states, making it challenging to handle out-of-support region. Model-based RL methods offer a richer dataset and benefit generalization by generating imaginary trajectories with either trained forward or reverse dynamics model. However, the imagined transitions may be inaccurate, thus downgrading the performance of the underlying offline RL method. In this paper, we propose to augment the offline dataset by using trained bidirectional dynamics models and rollout policies with double check. We introduce conservatism by trusting samples that the forward model and backward model agree on. Our method, confidence-aware bidirectional offline model-based imagination, generates reliable samples and can be combined with any model-free offline RL method. Experimental results on the D4RL benchmarks demonstrate that our method significantly boosts the performance of existing model-free offline RL algorithms and achieves competitive or better scores against baseline methods.
Creating gesture controlled games for robot-assisted stroke rehabilitation

Regular training exercises are fundamental to regain functional use of arm and hand control after a stroke. With the SCRIPT system, the patient can practice hand excercising independently at home by playing gesture controlled games using a robotic glove (orthosis). The system could offer prolonged rehabilitation out of the clinic, with low cost treatment. In the first version of the system (Script 1), a set of therapeutic games were developed within the project and tested in formative and summative evaluations. The main findings indicate that motivational aspects play a major role. The main issues detected concerns the challenge for the patients to understand and remember the correct gestures. Following a User Centered Design process, these findings helped to improve the new version of the system (Script 2).
A Green Pea Starburst Arising from a Galaxy–Galaxy Merger

Green Pea galaxies are low-redshift starburst dwarf galaxies, with properties similar to those of the high-redshift galaxies that reionized the universe. We report the first mapping of the spatial distribution of atomic hydrogen (Hi) in and around a Green Pea, GP J0213+0056 at z = 0.0399, using the Giant Metrewave Radio Telescope (GMRT). Like many Green Peas, GP J0213+0056 shows strong Hi 21 cm emission in single-dish spectroscopy, strong Lyα emission, and a high [Oiii]λ5007 Å/[Oii]λ3727 Å luminosity ratio, O32 ≈ 8.8, consistent with a high leakage of Lyman-continuum radiation. Our GMRT Hi 21 cm images show that the Hi 21 cm emission in the field of GP J0213+0056 arises from an extended broken-ring structure around the Green Pea, with the strongest emission coming from a region between GP J0213+0056 and a companion galaxy lying ≈4.7 kpc away, and little Hi 21 cm emission coming from the Green Pea itself. We find that the merger between GP J0213+0056 and its companion is likely to have triggered the starburst, and led to a disturbed Hi spatial and velocity distribution, which in turn allowed Lyα (and, possibly, Lyman-continuum) emission to escape the Green Pea. Our results suggest that such mergers, and the resulting holes in the Hi distribution, are a natural way to explain the tension between the requirements of cold gas to fuel the starburst and the observed leakage of Lyα and Lyman-continuum emission in Green Pea galaxies and their high-redshift counterparts.
Pure Exploration of Causal Bandits

Causal bandit problem integrates causal inference with multi-armed bandits. The pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we can choose to either intervene one variable or do no intervention, and observe the random outcomes of all random variables, with the goal that using as few rounds as possible, we can output an intervention that gives the best (or almost best) expected outcome on the reward variable Y with probability at least 1 − δ , where δ is a given conﬁdence level. We provide ﬁrst gap-dependent fully adaptive pure exploration algorithms on three types of causal models including parallel graphs, general graphs with small number of backdoor parents
Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case

In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have gained popularity as they have proven both to be computationally efficient and yield impressive results in multiple fields. Following this breakthrough, extensive research has been done in order to improve the original publication, resulting in a variety of different VAE models in response to different tasks. In this paper we present Pythae, a versatile open-source Python library providing both a unified implementation and a dedicated framework allowing straightforward, reproducible and reliable use of generative autoencoder models. We then propose to use this library to perform a case study benchmark where we present and compare 19 generative autoencoder models representative of some of the main improvements on downstream tasks such as image reconstruction, generation, classification, clustering and interpolation. The open-source library can be found at https://github.com/clementchadebec/benchmark_VAE.
Deep Learning and Handheld Augmented Reality Based System for Optimal Data Collection in Fault Diagnostics Domain

—Compared to current AI or robotic systems, hu- mans navigate their environment with ease, making tasks such as data collection trivial. However, humans ﬁnd it harder to model complex relationships hidden in the data. AI systems, especially deep learning (DL) algorithms, impressively capture those complex relationships. Symbiotically coupling humans and computational machines’ strengths can simultaneously minimize the collected data required and build complex input-to-output mapping models. This paper enables this coupling by presenting a novel human-machine interaction framework to perform fault diagnostics with minimal data. Collecting data for diagnosing faults for complex systems is difﬁcult and time-consuming. Minimizing the required data will increase the practicability of data-driven models in diagnosing faults. The framework provides instructions to a human user to collect data that mitigates the difference between the data used to train and test the fault diagnostics model. The framework is composed of three components: (1) a reinforcement learning algorithm for data collection to develop a training dataset, (2) a deep learning algorithm for diagnosing faults, and (3) a handheld augmented reality application for data collection for testing data. The proposed framework has provided above 100% precision and recall on a novel dataset with only one instance of each fault condition. Additionally, a usability study was conducted to gauge the user experience of the handheld augmented reality application, and all users were able to follow the provided steps.