Miracle-dz/UniFAR-FTUs huggingface dataset:
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives

Policy robustness in Reinforcement Learning may not be desirable at any cost: the alterations caused by robustness requirements from otherwise optimal policies should be explainable, quantifiable and formally verifiable. In this work we study how policies can be maximally robust to arbitrary observational noise by analysing how they are altered by this noise through a stochastic linear operator interpretation of the disturbances, and establish connections between robustness and properties of the noise kernel and of the underlying MDPs. Then, we construct sufficient conditions for policy robustness, and propose a robustness-inducing scheme, applicable to any policy gradient algorithm, that formally trades off expected policy utility for robustness through lexicographic optimisation, while preserving convergence and sub-optimality in the policy synthesis.
On The Robustness of Self-Supervised Representations for Spoken Language Modeling

Self-supervised representations have been extensively studied for discriminative and generative tasks. However, their robustness capabilities have not been extensively investigated. This work focuses on self-supervised representations for spoken generative language models. First, we empirically demonstrate how current state-of-the-art speech representation models lack robustness to basic signal variations that do not alter the spoken information. To overcome this, we propose an effective and efﬁcient method to learn robust self-supervised speech representation for generative spoken language modeling. The proposed approach is based on applying a set of signal transformations to the speech signal and optimizing the model using an iterative pseudo-labeling scheme. Our method signiﬁcantly improves over the evaluated baselines when considering encoding metrics. We additionally evaluate our method on the speech-to-speech translation task. We consider Spanish-English and French-English conversions and empirically demonstrate the beneﬁts of following the proposed approach.
An improved algorithm for Generalized Čech complex construction

In this paper, we present an algorithm that computes the generalized ˇCech complex for a ﬁnite set of disks where each may have a diﬀerent radius in 2D space. An extension of this algorithm is also proposed for a set of balls in 3D space with diﬀerent radius. To compute a k -simplex, we lever-age the computation performed in the round of ( k − 1)-simplices such that we can reduce the number of potential candidates to verify to improve the eﬃciency. An eﬃcient veriﬁcation method is proposed to conﬁrm if a k -simplex can be constructed on the basis of the ( k − 1)-simplices. We demonstrate the performance with a comparison to some closely related algorithms.
Tuning of Mixture-of-Experts Mixed-Precision Neural Networks

Deep learning has become a useful data analysis method, however mainstream adaption in distributed computer software and embedded devices has been low so far. Often, adding deep learning inference in mainstream applications and devices requires new hardware with signal processors suited for convolutional neural networks. This work adds new data types (quantized 16-bit and 8-bit integer, 16-bit floating point) to Caffe in order to save memory and increase inference speed on existing commodity graphics processors with OpenCL, common in everyday devices. Existing models can be executed effortlessly in mixed-precision mode. Additionally, we propose a variation of mixture-of-experts to increase inference speed on AlexNet for image classification. We managed to decrease memory usage up to 3.29× while increasing inference speed up to 3.01× on certain devices. We demonstrate with five simple examples how the presented techniques can easily be applied to different machine learning problems. The whole pipeline, consisting of models, example python scripts and modified Caffe library, is available as Open Source software.
Vertical Semi-Federated Learning for Efficient Online Advertising

The traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its application to advertising systems. To this end, we advocate a new learning setting Semi-VFL (Vertical Semi-Federated Learning) to tackle these challenge. Semi-VFL is proposed to achieve a practical industry application fashion for VFL, by learning a federation-aware local model which performs better than single-party models and meanwhile maintain the convenience of local-serving. For this purpose, we propose the carefully designed Joint Privileged Learning framework (JPL) to i) alleviate the absence of the passive party's feature and ii) adapt to the whole sample space. Specifically, we build an inference-efficient single-party student model applicable to the whole sample space and meanwhile maintain the advantage of the federated feature extension. New representation distillation methods are designed to extract cross-party feature correlations for both the overlapped and non-overlapped data. We conducted extensive experiments on real-world advertising datasets. The results show that our method achieves the best performance over baseline methods and validate its superiority in the Semi-VFL setting.
Automatic Context-Driven Inference of Engagement in HMI: A Survey

—An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection. Therefore, to develop successful human-centered human-machine interaction applications, automatic engagement inference is one of the tasks required to achieve engaging interactions between humans and machines, and to make machines attuned to their users, hence enhancing user satisfaction and technology acceptance. Several factors contribute to engagement state inference, which include the interaction context and interactants’ behaviours and identity. Indeed, engagement is a multi-faceted and multi-modal construct that requires high accuracy in the analysis and interpretation of contextual, verbal and non-verbal cues. Thus, the development of an automated and intelligent system that accomplishes this task has been proven to be challenging so far. This paper presents a comprehensive survey on previous work in engagement inference for human-machine interaction, entailing interdisciplinary deﬁnition, engagement components and factors, publicly available datasets, ground truth assessment, and most commonly used features and methods, serving as a guide for the development of future human-machine interaction interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement perception modules are integrated sets apart the presented survey from existing surveys.
Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI

We investigate the cross-calibration of the Hinode/Solar Optical Telescope-Spectro-Polarimeter (SOT-SP) and Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) instrument metadata, specifically the correspondence of the scaling and pointing information. Accurate calibration of these data sets gives the correspondence needed by interinstrument studies and learning-based magnetogram systems, and is required for physically meaningful photospheric magnetic field vectors. We approach the problem by robustly fitting geometric models on correspondences between images from each instrument’s pipeline. This technique is common in computer vision, but several critical details are required when using scanning-slit spectrograph data like Hinode/SOT-SP. We apply this technique to data spanning a decade of the Hinode mission. Our results suggest corrections to the published Level 2 Hinode/SOT-SP data. First, an analysis on approximately 2700 scans suggests that the reported pixel size in Hinode/SOT-SP Level 2 data is incorrect by around 1%. Second, analysis of over 12,000 scans shows that the pointing information is often incorrect by dozens of arcseconds with a strong bias. Regression of these corrections indicates that thermal effects have caused secular and cyclic drift in Hinode/SOT-SP pointing data over its mission. We offer two solutions. First, direct coalignment with SDO/HMI data via our procedure can improve alignments for many Hinode/SOT-SP scans. Second, since the pointing errors are predictable, simple post-hoc corrections can substantially improve the pointing. We conclude by illustrating the impact of this updated calibration on derived physical data products needed for research and interpretation. Among other things, our results suggest that the pointing errors induce a hemispheric bias in estimates of radial current density.
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. Then, our system performs a two-step retrieval to return answers. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document. In the absence of labels for question matching or answer relevance, we design 3 novel, self-supervised and semantically-guided losses. We evaluate our model against two strong retrieval-based question answering baselines. Evaluators ask their own questions and rate the answers retrieved by our baselines and own system according to their relevance. They find that our system retrieves more relevant answers, while achieving speeds 20 times faster. Our self-supervised losses also help the summarizer achieve higher scores in ROUGE, as well as in human evaluation metrics.
Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning

Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devices is key to enable post-silicon tuning and debugging. This analysis is typically performed by experienced human experts. However, with the fast development in semiconductor industry, test cases can contain hundreds of variables. The resulting high-dimensionality poses enormous challenges to experts. Thereby, some recent prior works have introduced data-driven variable selection algorithms to tackle these problems and achieved notable success. Nevertheless, for these methods, experts are not involved in training and inference phases, which may lead to bias and inaccuracy due to the lack of prior knowledge. Hence, this work for the first time aims to design a novel conditional variable selection approach while keeping experts in the loop. In this way, we expect that our algorithm can be more efficiently and effectively trained to identify the most critical variables under certain expert knowledge. Extensive experiments on both synthetic and real-world datasets from industry have been conducted and shown the effectiveness of our method.
Physically meaningful uncertainty quantification in probabilistic wind turbine power curve models as a damage-sensitive feature

A wind turbines’ power curve is an easily accessible form of damage-sensitive data, and as such is a key part of structural health monitoring (SHM) in wind turbines. Power curve models can be constructed in a number of ways, but the authors argue that probabilistic methods carry inherent benefits in this use case, such as uncertainty quantification and allowing uncertainty propagation analysis. Many probabilistic power curve models have a key limitation in that they are not physically meaningful – they return mean and uncertainty predictions outside of what is physically possible (the maximum and minimum power outputs of the wind turbine). This paper investigates the use of two bounded Gaussian processes (GPs) in order to produce physically meaningful probabilistic power curve models. The first model investigated was a warped heteroscedastic Gaussian process, and was found to be ineffective due to specific shortcomings of the GP in relation to the warping function. The second model – an approximated GP with a Beta likelihood was highly successful and demonstrated that a working bounded probabilistic model results in better predictive uncertainty than a corresponding unbounded one without meaningful loss in predictive accuracy. Such a bounded model thus offers increased accuracy for performance monitoring and increased operator confidence in the model due to guaranteed physical plausibility.
Sparsity-Constrained Optimal Transport

Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm but it leads to fully-dense transportation plans, meaning that all sources are (fractionally) matched with all targets. To address this issue, several works have investigated quadratic regularization instead. This regularization preserves sparsity and leads to unconstrained and smooth (semi) dual objectives, that can be solved with off-the-shelf gradient methods. Unfortunately, quadratic regularization does not give direct control over the cardinality (number of nonzeros) of the transportation plan. We propose in this paper a new approach for OT with explicit cardinality constraints on the transportation plan. Our work is motivated by an application to sparse mixture of experts, where OT can be used to match input tokens such as image patches with expert models such as neural networks. Cardinality constraints ensure that at most $k$ tokens are matched with an expert, which is crucial for computational performance reasons. Despite the nonconvexity of cardinality constraints, we show that the corresponding (semi) dual problems are tractable and can be solved with first-order gradient methods. Our method can be thought as a middle ground between unregularized OT (recovered in the limit case $k=1$) and quadratically-regularized OT (recovered when $k$ is large enough). The smoothness of the objectives increases as $k$ increases, giving rise to a trade-off between convergence speed and sparsity of the optimal plan.
Transforming an arbitrary ﬁnite group into quantum circuits

We provide two methods to map each element of a given ﬁnite group into a diﬀerent quantum circuit. In the ﬁrst one, we directly sum its unitary representation with an identity matrix to obtain a new unitary matrix suitable for decomposing into a quantum circuit. In the second one, we use a Variational Quantum Algorithm trained after the absolute presentation of the group to construct the quantum circuits. This article provides explicit examples and numerical simulation.
Double Graphs Regularized Multi-view Subspace Clustering

Recent years have witnessed a growing academic interest in multi-view subspace clustering. In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC firstly learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method.
HSD: A hierarchical singing annotation dataset

Commonly music has an obvious hierarchical structure, especially for the singing parts which usually act as the main melody in pop songs. However, most of the current singing annotation datasets only record symbolic information of music notes, ignoring the structure of music. In this paper, we propose a hierarchical singing annotation dataset that consists of 68 pop songs from Youtube. This dataset records the onset/offset time, pitch, duration, and lyric of each musical note in an enhanced LyRiCs (LRC) format to present the hierarchical structure of music. We annotate each song in a two-stage process: first, create initial labels with the corresponding musical notation and lyrics file; second, manually calibrate these labels referring to the raw audio. We mainly validate the labeling accuracy of the proposed dataset by comparing it with an automatic singing transcription (AST) dataset. The result indicates that the proposed dataset reaches the labeling accuracy of AST datasets.
Finding NEEMo: Geometric Fitting using Neural Estimation of the Energy Mover's Distance

A novel neural architecture was recently developed that enforces an exact upper bound on the Lipschitz constant of the model by constraining the norm of its weights in a minimal way, resulting in higher expressiveness compared to other techniques. We present a new and interesting direction for this architecture: estimation of the Wasserstein metric (Earth Mover's Distance) in optimal transport by employing the Kantorovich-Rubinstein duality to enable its use in geometric fitting applications. Specifically, we focus on the field of high-energy particle physics, where it has been shown that a metric for the space of particle-collider events can be defined based on the Wasserstein metric, referred to as the Energy Mover's Distance (EMD). This metrization has the potential to revolutionize data-driven collider phenomenology. The work presented here represents a major step towards realizing this goal by providing a differentiable way of directly calculating the EMD. We show how the flexibility that our approach enables can be used to develop novel clustering algorithms.
KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way

Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we obtain a surprisingly effective system that is simple to realize and can operate under various environmental conditions using different LiDAR sensors. Our odometry estimation approach relies on point-to-point ICP combined with adaptive thresholding for correspondence matching, a robust kernel, a simple but widely applicable motion compensation approach, and a point cloud subsampling strategy. This yields a system with only a few parameters that in most cases do not even have to be tuned to a specific LiDAR sensor. Our system performs on par with state-of-the-art methods under various operating conditions using different platforms using the same parameters: automotive platforms, UAV-based operation, vehicles like segways, or handheld LiDARs. We do not require integrating IMU data and solely rely on 3D point clouds obtained from a wide range of 3D LiDAR sensors, thus, enabling a broad spectrum of different applications and operating conditions. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.
Emergent Communication: Generalization and Overfitting in Lewis Games

Lewis signaling games are a class of simple communication games for simulating the emergence of language. In these games, two agents must agree on a communication protocol in order to solve a cooperative task. Previous work has shown that agents trained to play this game with reinforcement learning tend to develop languages that display undesirable properties from a linguistic point of view (lack of generalization, lack of compositionality, etc). In this paper, we aim to provide better understanding of this phenomenon by analytically studying the learning problem in Lewis games. As a core contribution, we demonstrate that the standard objective in Lewis games can be decomposed in two components: a co-adaptation loss and an information loss. This decomposition enables us to surface two potential sources of overfitting, which we show may undermine the emergence of a structured communication protocol. In particular, when we control for overfitting on the co-adaptation loss, we recover desired properties in the emergent languages: they are more compositional and generalize better.
Capturing rogue waves by multi-point statistics

As an example of a complex system with extreme events, we investigate ocean wave states exhibiting rogue waves. We present a statistical method of data analysis based on multi-point statistics which for the first time allows the grasping of extreme rogue wave events in a highly satisfactory statistical manner. The key to the success of the approach is mapping the complexity of multi-point data onto the statistics of hierarchically ordered height increments for different time scales, for which we can show that a stochastic cascade process with Markov properties is governed by a Fokker–Planck equation. Conditional probabilities as well as the Fokker–Planck equation itself can be estimated directly from the available observational data. With this stochastic description surrogate data sets can in turn be generated, which makes it possible to work out arbitrary statistical features of the complex sea state in general, and extreme rogue wave events in particular. The results also open up new perspectives for forecasting the occurrence probability of extreme rogue wave events, and even for forecasting the occurrence of individual rogue waves based on precursory dynamics.
Geography-aware Optimal UAV 3D Placement for LOS Relaying: A Geometry Approach

Many emerging technologies for the next generation wireless network prefer line-of-sight (LOS) propagation conditions to fully release their performance advantages. This paper studies 3D unmanned aerial vehicle (UAV) placement to establish LOS links for two ground terminals in deep shadow in a dense urban environment. The challenge is that the LOS region for the feasible UAV positions can be arbitrary due to the complicated structure of the environment. While most existing works rely on simplified stochastic LOS models and problem relaxations, this paper focuses on establishing theoretical guarantees for the optimal UAV placement to ensure LOS conditions for two ground users in an actual propagation environment. It is found that it suffices to search a bounded 2D area for the globally optimal 3D UAV position. Thus, this paper develops an exploration-exploitation algorithm with a linear trajectory length and achieves above 99% global optimality over several real city environments being tested in our experiments. To further enhance the search capability in an ultra-dense environment, a dynamic multi-stage algorithm is developed and theoretically shown to find an $\epsilon$-optimal UAV position with a search length $O(1/\epsilon)$. Significant performance advantages are demonstrated in several numerical experiments for wireless communication relaying and wireless power transfer.