Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Dynamic Measurement Intervals for State Estimation in Future Distribution Systems

The ongoing energy transition introduces new load and generation units like photovoltaic generation in the medium-voltage systems network. These generation units increase the variance of the power injections, resulting in increased stochasticity of the power flow values. For confident assessment of the system’s operating condition, a measurement scheme is essential for accurate state estimation at every minute of the day. This paper presents a performance assessment of the Weighted Least Square (WLS) distribution system state estimation algorithm with respect to the measurement intervals, and proposes a measurement scheme considering the measurement locations and intervals that satisfy a required error limit. The scheme is tested on a synthetic test model of a Dutch MV network, which takes into account projected (year 2040) and highly stochastic power flow profiles. Numerical results show that the proposed algorithm for the measurement scheme determination successfully satisfies the chosen error limit.
Generating Lode Runner Levels by Learning Player Paths with LSTMs

Machine learning has been a popular tool in many different fields, including procedural content generation. However, procedural content generation via machine learning (PCGML) approaches can struggle with controllability and coherence. In this paper, we attempt to address these problems by learning to generate human-like paths, and then generating levels based on these paths. We extract player path data from gameplay video, train an LSTM to generate new paths based on this data, and then generate game levels based on this path data. We demonstrate that our approach leads to more coherent levels for the game Lode Runner in comparison to an existing PCGML approach.
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification

We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources. Overall we find weak correlations of non-expert annotations with the diagnostic label, and low agreement between different annotation sources. We then study multi-task learning (MTL) with the annotations as additional labels, and show that non-expert annotations can improve (ensembles of) state-of-the-art convolutional neural networks via MTL. We hope that our dataset can be used in further research into multiple annotations and/or MTL. All data and models are available on Github: https://github.com/raumannsr/ENHANCE.
Multi-modal estimation of the properties of containers and their content: survey and evaluation

—Acoustic and visual sensing can support the contact-less estimation of the weight of a container and the amount of its content when the container is manipulated by a person. However, transparencies (both of the container and of the content) and the variability of materials, shapes and sizes make this problem challenging. In this paper, we present an open benchmarking framework and an in-depth comparative analysis of recent methods that estimate the capacity of a container, as well as the type, mass, and amount of its content. These methods use learned and handcrafted features, such as mel-frequency cepstrum co-efﬁcients, zero-crossing rate, spectrograms, with different types of classiﬁers to estimate the type and amount of the content with acoustic data, and geometric approaches with visual data to determine the capacity of the container. Results on a newly distributed dataset show that audio alone is a strong modality and methods achieves a weighted average F1-score up to 81% and 97% for content type and level classiﬁcation, respectively. Estimating the container capacity with vision-only approaches and ﬁlling mass with multi-modal, multi-stage algorithms reaches up to 65% weighted average capacity and mass scores.
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

We introduce a method that allows to automatically segment images into semantically meaningful regions without human supervision. Derived regions are consistent across different images and coincide with human-defined semantic classes on some datasets. In cases where semantic regions might be hard for human to define and consistently label, our method is still able to find meaningful and consistent semantic classes. In our work, we use pretrained StyleGAN2 generative model: clustering in the feature space of the generative model allows to discover semantic classes. Once classes are discovered, a synthetic dataset with generated images and corresponding segmentation masks can be created. After that a segmentation model is trained on the synthetic dataset and is able to generalize to real images. Additionally, by using CLIP we are able to use prompts defined in a natural language to discover some desired semantic classes. We test our method on publicly available datasets and show state-of-the-art results.
Open-Ended Learning Leads to Generally Capable Agents

In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem. We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. We show that through constructing an open-ended learning process, which dynamically changes the training task distributions and training objectives such that the agent never stops learning, we achieve consistent learning of new behaviours. The resulting agent is able to score reward in every one of our humanly solvable evaluation levels, with behaviour generalising to many held-out points in the universe of tasks. Examples of this zero-shot generalisation include good performance on Hide and Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks we characterise the behaviour of our agent, and find interesting emergent heuristic behaviours such as trial-and-error experimentation, simple tool use, option switching, and cooperation. Finally, we demonstrate that the general capabilities of this agent could unlock larger scale transfer of behaviour through cheap finetuning.
Persistent Reinforcement Learning via Subgoal Curricula

Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents. However, the success of current reinforcement learning algorithms is predicated on an often under-emphasised requirement – each trial needs to start from a ﬁxed initial state distribution. Unfortunately, resetting the environment to its initial state after each trial requires substantial amount of human supervision and extensive instrumentation of the environment which defeats the purpose of autonomous reinforcement learning. In this work, we propose Value-accelerated Persistent Reinforcement Learning (VaPRL), which generates a curriculum of initial states such that the agent can bootstrap on the success of easier tasks to efﬁciently learn harder tasks. The agent also learns to reach the initial states proposed by the curriculum, minimizing the reliance on human interventions into the learning. We observe that VaPRL reduces the interventions required by three orders of magnitude compared to episodic RL while outperforming prior state-of-the art methods for reset-free RL both in terms of sample efﬁciency and asymptotic performance on a variety of simulated robotics problems 1 .
Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification

Text-to-image person re-identification (ReID) aims to search for images containing a person of interest using textual descriptions. However, due to the significant modality gap and the large intra-class variance in textual descriptions, text-to-image ReID remains a challenging problem. Accordingly, in this paper, we propose a Semantically Self-Aligned Network (SSAN) to handle the above problems. First, we propose a novel method that automatically extracts semantically aligned part-level features from the two modalities. Second, we design a multi-view non-local network that captures the relationships between body parts, thereby establishing better correspondences between body parts and noun phrases. Third, we introduce a Compound Ranking (CR) loss that makes use of textual descriptions for other images of the same identity to provide extra supervision, thereby effectively reducing the intra-class variance in textual features. Finally, to expedite future research in text-to-image ReID, we build a new database named ICFG-PEDES. Extensive experiments demonstrate that SSAN outperforms state-of-the-art approaches by significant margins. Both the new ICFG-PEDES database and the SSAN code are available at https://github.com/zifyloo/SSAN.
Combinatorial classification of (±1)-skew projective spaces

The non-commutative projective scheme $\operatorname{\mathsf{Proj_{nc}}} S$ of a $(\pm 1)$-skew polynomial algebra S in n variables is considered to be a $(\pm 1)$-skew projective space of dimension n − 1. In this paper, using combinatorial methods, we give a classification theorem for $(\pm 1)$-skew projective spaces. Specifically, among other equivalences, we prove that $(\pm 1)$-skew projective spaces $\operatorname{\mathsf{Proj_{nc}}} S$ and $\operatorname{\mathsf{Proj_{nc}}} S^{\prime}$ are isomorphic if and only if certain graphs associated with S and Sʹ are switching (or mutation) equivalent. We also discuss invariants of $(\pm 1)$-skew projective spaces from a combinatorial point of view.
Einstein-{\ae}ther Scalar-tensor Cosmology

We propose an Einstein-æther scalar-tensor cosmological model. In particular in the scalar-tensor Action Integral we introduce the æther field with æther coefficients to be functions of the scalar field. This cosmological model extends previous studies on Lorentz-violating theories. For a spatially flat Friedmann–Lemâıtre–Robertson– Walker background space we write the field equations which are of second-order with dynamical variables the scale factor and the scalar field. The physical evolution of the field equations depends upon three unknown functions which are related to the scalar-tensor coupling function, the scalar field potential and the æther coefficient functions. We investigate the existence of analytic solutions for the field equations and the integrability properties according to the existence of linear in the momentum conservation laws. We define a new set of variables in which the dynamical evolution depends only upon the scalar field potential. Furthermore, the asymptotic behaviour and the cosmological history is investigated where we find that the theory provides inflationary eras similar with that of scalar-tensor theory but with Lorentz-violating terms provided by the æther field. Finally, in the new variables we found that the field equations are integrable due to the existence of nonlocal conservation laws for arbitrary functional forms of the three free functions.
Development of a NIC driver in C#

Drivers have a special status among the developer community that sees them as mysterious and inaccessible. We think their extensive communication with the hardware and their need of high performance are the cause of this bad reputation. According to a widely held view, these two requirements cannot be met using high level languages. However high level languages' compilers and runtimes made great progress these past years to enhance the performance of programs. The use of these languages can also reduce by a significant amount the number of bugs and security issues introduced by the programmers by taking care of some error-prone parts like memory allocation and accesses. We also think that using high level languages can help to demystify the drivers' development. With this project, we try to develop a driver for a network card, the Intel 82599, in C\#. Our goal is to find out the feasibility of such a development and the performance of such a driver. We will also be able to tell what could be missing today in C\# to write a driver. We base our driver on the model proposed by Pirelli (2020) and its implementation in C.
Audio-to-Score Alignment Using Deep Automatic Music Transcription

Audio-to-score alignment (A2SA) is a multimodal task consisting in the alignment of audio signals to music scores. Recent literature confirms the benefits of Automatic Music Transcription (AMT) for A2SA at the frame-level. In this work, we aim to elaborate on the exploitation of AMT Deep Learning (DL) models for achieving alignment at the note-level. We propose a method which benefits from HMM-based score-to-score alignment and AMT, showing a remarkable advancement beyond the state-of-the-art. We design a systematic procedure to take advantage of large datasets which do not offer an aligned score. Finally, we perform a thorough comparison and extensive tests on multiple datasets.
Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates

Given a dataset $\mathcal{D}$, we are interested in computing the mean of a subset of $\mathcal{D}$ which matches a predicate. ABae leverages stratified sampling and proxy models to efficiently compute this statistic given a sampling budget $N$. In this document, we theoretically analyze ABae and show that the MSE of the estimate decays at rate $O(N_1^{-1} + N_2^{-1} + N_1^{1/2}N_2^{-3/2})$, where $N=K \cdot N_1+N_2$ for some integer constant $K$ and $K \cdot N_1$ and $N_2$ represent the number of samples used in Stage 1 and Stage 2 of ABae respectively. Hence, if a constant fraction of the total sample budget $N$ is allocated to each stage, we will achieve a mean squared error of $O(N^{-1})$ which matches the rate of mean squared error of the optimal stratified sampling algorithm given a priori knowledge of the predicate positive rate and standard deviation per stratum.
HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks

Data-intensive applications impact many domains, and their steadily increasing size and complexity demands highperformance, highly usable environments. We integrate a set of ideas developed in various data science and data engineering frameworks. They employ a set of operators on specific data abstractions that include vectors, matrices, tensors, graphs, and tables. Our key concepts are inspired from systems like MPI, HPF (High-Performance Fortran), NumPy, Pandas, Spark, Modin, PyTorch, TensorFlow, RAPIDS(NVIDIA), and OneAPI (Intel). Further, it is crucial to support different languages in everyday use in the Big Data arena, including Python, R, C++, and Java. We note the importance of Apache Arrow and Parquet for enabling language agnostic high performance and interoperability. In this paper, we propose High-Performance Tensors, Matrices and Tables (HPTMT), an operator-based architecture for data-intensive applications, and identify the fundamental principles needed for performance and usability success. We illustrate these principles by a discussion of examples using our software environments, Cylon and Twister2 that embody HPTMT.
Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021

This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-Supervised Learning Track. Temporal Action Localization (TAL) requires to not only precisely locate the temporal boundaries of action instances, but also accurately classify the untrimmed videos into specific categories. However, Weakly-Supervised TAL indicates locating the action instances using only video-level class labels. In this paper, to train a supervised temporal action localizer, we adopt Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through ``local and global"temporal context aggregation and complementary as well as progressive boundary refinement. As for the WSTAL, a novel framework is proposed to handle the poor quality of CAS generated by simple classification network, which can only focus on local discriminative parts, rather than locate the entire interval of target actions. Further inspired by the transfer learning method, we also adopt an additional module to transfer the knowledge from trimmed videos (HACS Clips dataset) to untrimmed videos (HACS Segments dataset), aiming at promoting the classification performance on untrimmed videos. Finally, we employ a boundary regression module embedded with Outer-Inner-Contrastive (OIC) loss to automatically predict the boundaries based on the enhanced CAS. Our proposed scheme achieves 39.91 and 29.78 average mAP on the challenge testing set of supervised and weakly-supervised temporal action localization track respectively.
Finite-Time Gradient Descent-Based Adaptive Neural Network Finite-Time Control Design for Attitude Tracking of a 3-DOF Helicopter

This paper investigates a novel finite-time gradient descent-based adaptive neural network finite-time control strategy for the attitude tracking of a 3-DOF lab helicopter platform subject to composite disturbances. First, the radial basis function neural network (RBFNN) is applied to estimate lumped disturbances, where the weights, centers and widths of the RBFNN are trained online via finite-time gradient descent algorithm. Then, a finite-time backstepping control scheme is constructed to fulfill the tracking control of the elevation and pitch angles. In addition, a hybrid finite-time differentiator (HFTD) is introduced for approximating the intermediate control signal and its derivative to avoid the problem of"explosion of complexity"in the traditional backstepping design protocol. Moreover, the errors caused by the HFTD can be attenuated by the combination of compensation signals. With the aid of the stability theorem, it is proved that the closed-loop system is semi-globally uniformly ultimately boundedness in finite time. Finally, a comparison result is provided to illustrate the effectiveness and advantages of the designed control strategy.
A Persistent Homology-Based Topological Loss for CNN-Based Multiclass Segmentation of CMR

Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that characterise anatomy. Therefore, whilst sharing a high spatial overlap with the ground truth, inferred CNN-based segmentations can lack coherence, including spurious connected components, holes and voids. Such results are implausible, violating anticipated anatomical topology. In response, (single-class) persistent homology-based loss functions have been proposed to capture global anatomical features. Our work extends these approaches to the task of multi-class segmentation. Building an enriched topological description of all class labels and class label pairs, our loss functions make predictable and statistically significant improvements in segmentation topology using a CNN-based post-processing framework. We also present (and make available) a highly efficient implementation based on cubical complexes and parallel execution, enabling practical application within high resolution 3D data for the first time. We demonstrate our approach on 2D short axis and 3D whole heart CMR segmentation, advancing a detailed and faithful analysis of performance on two publicly available datasets.
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning

Reinforcement learning (RL) studies how an agent comes to achieve reward in an environment through interactions over time. Recent advances in machine RL have surpassed human expertise at the world’s oldest board games and many classic video games, but they require vast quantities of experience to learn successfully — none of today’s algorithms account for the human ability to learn so many different tasks, so quickly. Here we propose a new approach to this challenge based on a particularly strong form of model-based RL which we call Theory-Based Reinforcement Learning, because it uses human-like intuitive theories — rich, abstract, causal models of physical objects, intentional agents, and their interactions — to explore and model an environment, and plan effectively to achieve task goals. We instantiate the approach in a video game playing agent called EMPA (the Exploring, Modeling, and Planning Agent), which performs Bayesian inference to learn
Adversarial Random Forest Classifier for Automated Game Design

Autonomous game design, generating games algorithmically, has been a longtime goal within the technical games research field. However, existing autonomous game design systems have relied in large part on human-authoring for game design knowledge, such as fitness functions in search-based methods. In this paper, we describe an experiment to attempt to learn a human-like fitness function for autonomous game design in an adversarial manner. While our experimental work did not meet our expectations, we present an analysis of our system and results that we hope will be informative to future autonomous game design research.
Exploring the Interdependencies Between Transmit Waveform Ambiguity Function Shape and Off-Axis Bearing Estimation

The frequency dependent beampatterns of an active sonar projector filters the acoustic signal that is transmitted into the medium, also known as the transmit waveform. This filtering encodes information about the target’s bearing relative to the main response axis. For any given projector and transmit waveform spectrum, there exists an optimal angle of operation which maximizes the Fisher Information (FI) of the target bearing estimate. Previous investigations into this phenomena show that for narrowband (i.e, high Q) Linear Frequency Modulated (LFM) waveforms, the angle of maximum FI is solely determined by its center frequency fc. Steering the region of maximum bearing estimation precision is then achieved by appropriate selection of the LFM waveform’s center frequency fc. This fine bearing estimation is accomplished without steering the projector’s main response axis. In addition to LFM waveforms, a wide variety of other active sonar waveform types exist that possess distinct spectral characteristics. These other waveforms possess different Ambiguity Function (AF) shapes from the LFM and are typically utilized to suite the range-Doppler resolution requirements of the active sonar system. This paper investigates the transmit waveform impact on off-axis bearing estimation performance and the spectral filtering impact on the waveform’s AF shape. High Q waveforms perform similarly to the LFM for off-axis bearing estimation while the transducer’s spectral filtering perturbs the waveform’s AF shape.