Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularizing Model-Based Planning with Energy-Based Models

Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data. Learned dynamics models can be directly used for planning actions but this has been challenging because of inaccuracies in the learned models. In this paper, we focus on planning with learned dynamics models and propose to regularize it using energy estimates of state transitions in the environment. We visually demonstrate the effectiveness of the proposed method and show that off-policy training of an energy estimator can be effectively used to regularize planning with pre-trained dynamics models. Further, we demonstrate that the proposed method enables sample-efficient learning to achieve competitive performance in challenging continuous control tasks such as Half-cheetah and Ant in just a few minutes of experience.
Explicit properties of the simplest inhomogeneous matrix-product-state including the Riemann metric of the MPS manifold

We consider the simplest inhomogeneous matrix-product-state for an open chain of N quantum spins that involves only two angles per site and two angles per bond with the following direct physical meanings. The two angles associated to the site k are the two Bloch angles that parametrize the two orthonormal eigenvectors of the reduced density matrix of the spin k alone. The two angles associated to the bond parametrize the entanglement properties of the Schmidt decomposition across the bond . Explicit results are given for the reduced density matrix of two consecutive sites that is needed to evaluate the energy of two-body Hamiltonians, and for the reduced density matrix of two sites at distance r that is needed to evaluate the spin–spin correlations at distance r. The global structure of the MPS manifold as parametrized by these angles is then characterized by its explicit Riemann metric. Finally, the generalizations to any tree-like structure without loops and to the chain with periodic boundary conditions are discussed.
The Interaction of Frictional Slip and Adhesion for a Stiff Sphere on a Compliant Substrate

How friction affects adhesion is addressed. The problem is considered in the context of a very stiff sphere adhering to a compliant, isotropic, linear elastic substrate and experiencing adhesion and frictional slip relative to each other. The adhesion is considered to be driven by very large attractive tractions between the sphere and the substrate that can act only at very small distances between them. As a consequence, the adhesion behavior can be represented by the Johnson–Kendall–Roberts model, and this is assumed to prevail also when frictional slip is occurring. Frictional slip is considered to be resisted by a uniform, constant shear traction at the slipping interface, a model that is considered to be valid for small asperities and for compliant elastomers in contact with stiff material. A simple model for the interaction of friction and adhesion is utilized, in which some of the work done against frictional resistance is assumed to be stored reversibly. This behavior is considered to arise from surface microstructures associated with frictional slip such as interface dislocations, where these microstructures store some elastic strain energy in a reversible manner. When it is assumed that a fixed fraction of the work done against friction is stored reversibly, we obtain good agreement with data.
DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems

As we approach the exascale era, the size and complexity of HPC systems continues to increase, raising concerns about their manageability and sustainability. For this reason, more and more HPC centers are experimenting with fine-grained monitoring coupled with Operational Data Analytics (ODA) to optimize efficiency and effectiveness of system operations. However, while monitoring is a common reality in HPC, there is no well-stated and comprehensive list of requirements, nor matching frameworks, to support holistic and online ODA. This leads to insular ad-hoc solutions, each addressing only specific aspects of the problem. In this paper we propose Wintermute, a novel generic framework to enable online ODA on large-scale HPC installations. Its design is based on the results of a literature survey of common operational requirements. We implement Wintermute on top of the holistic DCDB monitoring system, offering a large variety of configuration options to accommodate the varying requirements of ODA applications. Moreover, Wintermute is based on a set of logical abstractions to ease the configuration of models at a large scale and maximize code re-use. We highlight Wintermute's flexibility through a series of practical case studies, each targeting a different aspect of the management of HPC systems, and then demonstrate the small resource footprint of our implementation.
Kinematical gravitational charge algebra

When formulated in terms of connection and coframes, and in the time gauge, the phase space of general relativity consists of a pair of conjugate fields: the flux 2-form and the Ashtekar connection. On this phase-space, one has to impose the Gauss constraints, the vector, and scalar Hamiltonian constraints. These are respectively generating local SU(2) gauge transformations, spatial diffeomorphisms, and time diffeomorphisms. We write the Gauss and space diffeomorphism constraints as conservation laws for a set of boundary charges, representing spin and momenta, respectively. We prove that these kinematical charges generate a local Poincar\'e ISU(2) symmetry algebra. This gives strong support to the recent proposal of Poincar\'e charge networks as a new realm for discretized general relativity [Classical Quantum Gravity 36, 195014 (2019)].
The Theory behind Controllable Expressive Speech Synthesis: a Cross-disciplinary Approach

As part of the Human-Computer Interaction field, Expressive speech synthesis is a very rich domain as it requires knowledge in areas such as machine learning, signal processing, sociology, and psychology. In this chapter, we will focus mostly on the technical side. From the recording of expressive speech to its modeling, the reader will have an overview of the main paradigms used in this field, through some of the most prominent systems and methods. We explain how speech can be represented and encoded with audio features. We present a history of the main methods of Text-to-Speech synthesis: concatenative, parametric and statistical parametric speech synthesis. Finally, we focus on the last one, with the last techniques modeling Text-to-Speech synthesis as a sequence-to-sequence problem. This enables the use of Deep Learning blocks such as Convolutional and Recurrent Neural Networks as well as Attention Mechanism. The last part of the chapter intends to assemble the different aspects of the theory and summarize the concepts.
Prediction Focused Topic Models via Vocab Selection

Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, interpretable topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models are able to learn much more coherent topics while maintaining competitive predictions.
Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials

We investigate the use of invariant polynomials in the construction of data-driven interatomic potentials for material systems. The ‘atomic body-ordered permutation-invariant polynomials’ comprise a systematic basis and are constructed to preserve the symmetry of the potential energy function with respect to rotations and permutations. In contrast to kernel based and artificial neural network models, the explicit decomposition of the total energy as a sum of atomic body-ordered terms allows to keep the dimensionality of the fit reasonably low, up to just 10 for the 5-body terms. The explainability of the potential is aided by this decomposition, as the low body-order components can be studied and interpreted independently. Moreover, although polynomial basis functions are thought to extrapolate poorly, we show that the low dimensionality combined with careful regularisation actually leads to better transferability than the high dimensional, kernel based Gaussian Approximation Potential.
Unsupervised Multi-stream Highlight detection for the Game "Honor of Kings"

With the increasing popularity of E-sport live, Highlight Flashback has been a critical functionality of live platforms, which aggregates the overall exciting fighting scenes in a few seconds. In this paper, we introduce a novel training strategy without any additional annotation to automatically generate highlights for game video live. Considering that the existing manual edited clips contain more highlights than long game live videos, we perform pair-wise ranking constraints across clips from edited and long live videos. A multi-stream framework is also proposed to fuse spatial, temporal as well as audio features extracted from videos. To evaluate our method, we test on long game live videos with an average length of about 15 minutes. Extensive experimental results on videos demonstrate its satisfying performance on highlights generation and effectiveness by the fusion of three streams.
A study into the influence of visual prototyping methods and immersive technologies on the perception of abstract product properties

Virtual Reality (VR) and Augmented Reality (AR) software are becoming increasingly sophisticated, and they are finding new uses in a wide range of applications. From a product design perspective, the ability to visualise computer generated3Dmodels,in natural and more intuitive ways may offer many benefits. However, as yet there have been limited studies that have systematically evaluated the benefits of these technologies for product design related applications. This study is focused on furniture design, and it looks at both technologies, to assess how their immersive qualities can affect human perception of what is being viewed. The purpose of the study is twofold; first it aims to find out how increasingly immersive technologies affect human perception of comfort, ergonomics and style, and second it investigates the differences in the experiences of design-trained and non-design-trained users. The study comprised each participant viewing three different armchairs in six different media, with each medium providing a different viewing experience. The media used were 3D isometric views of CAD images printed on paper,3D interactive CAD models, AR, VR, viewing real armchairs with no tactile interaction and experiencing real armchairs with tactile interaction. After viewing the armchairs in each medium, a questionnaire rating the comfort, ergonomics and style for three different armchairs was filled out by participants. In addition, the participants were also asked to score their level of confidence in the provided ratings. The study shows that there are differences, between the media considered, in the level of confidence with which participants are able to rate products. In general, it shows that more immersive viewing experiences lead to increased levels of confidence. While no purely visual interaction can compete with real physical interaction, the study also demonstrates that for all participants AR and in particular VR can offer a viewing experience that is comparable with true reality.
Optimal design of a kinesthetic haptic device mechanism for enhancing its impedance characteristics

OPTIMAL DESIGN OF A KINESTHETIC HAPTIC DEVICE MECHANISM FOR ENHANCING ITS IMPEDANCE CHARACTERISTICS In this work, the optimal design of modified version of 3 degrees of freedom RCUBE mechanism has been studied in order to develop a high-performance haptic device mechanism. A high-performance haptic mechanism is achieved by having high transparency and high-frequency range. These two properties are determined by the mechanical impedance of the mechanism. Hence, to increase the quality of a haptic mechanism, its mechanical impedance performance must be enhanced. This refers to have low inertia, low friction, high back-drivability, high force output, high structural stiffness, and high manipulability for the mechanism. All these properties are designated by kinematic, stiffness, and dynamic properties of the mechanism. Hence, as a first step of this thesis, kinematic, stiffness and dynamic models of the mechanism are analytically procured. The analytical model of stiffness is achieved via the virtual joint method. Then, in order to obtain the objective function for the design procedure, performance metrics affecting the above-mentioned properties are reviewed and produced. Since these metrics have common parameters such as link lengths and the cross-section area of the links, there is a highly non-linear and contradictory relationship between the metrics. In order to deal with the non-linearity and to determine the global optimum design, an evolutionary optimization method, genetic algorithm, is preferred. The optimization time is reduced by investigating the most critical poses of the workspace and reducing the performance metrics to simpler forms. The link lengths and the cross-section areas are optimized. Carbon fiber tubes are used as links. The Pareto-front solution set is obtained as a result of the optimization procedure.Finally, an optimal solution is proposed and evaluated for the design of this modified R-CUBE mechanism to be used in haptic applications.
Joint Active and Passive Beamforming Optimization for Intelligent Reflecting Surface Assisted SWIPT Under QoS Constraints

Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in this paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and the energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based optimization method. Moreover, by exploiting the short-range coverage of IRS, we further propose a more computationally efficient algorithm by optimizing the phase shifts at all IRSs in parallel. Simulation results demonstrate the effectiveness of employing multiple IRSs for enhancing the performance of SWIPT systems as well as the significant performance gains achieved by our proposed algorithms over benchmark schemes. The impact of IRS on the transmitter/receiver design for SWIPT is also unveiled.
A Multi-label, Dual-Output Deep Neural Network for Automated Bug Triaging

Bug tracking enables the monitoring and resolution of issues and bugs within organizations. Bug triaging, or assigning bugs to the owner(s) who will resolve them, is a critical component of this process because there are many incorrect assignments that waste developer time and reduce bug resolution throughput. In this work, we explore the use of a novel two-output deep neural network architecture (Dual DNN) for triaging a bug to both an individual team and developer, simultaneously. Dual DNN leverages this simultaneous prediction by exploiting its own guess of the team classes to aid in developer assignment. A multi-label classification approach is used for each of the two outputs to learn from all interim owners, not just the last one who closed the bug. We make use of a heuristic combination of the interim owners (owner-importance-weighted labeling) which is converted into a probability mass function (pmf). We employ a two-stage learning scheme, whereby the team portion of the model is trained first and then held static to train the team-developer and bug-developer relationships. The scheme employed to encode the team-developer relationships is based on an organizational chart (org chart), which renders the model robust to organizational changes as it can adapt to role changes within an organization. There is an observed average lift (with respect to both team and developer assignment) of 13%-points in 11-fold incremental-learning cross-validation (IL-CV) accuracy for Dual DNN utilizing owner-weighted labels compared with the traditional multi-class classification approach. Furthermore, Dual DNN with owner-weighted labels achieves average 11-fold IL-CV accuracies of 76% (team assignment) and 55% (developer assignment), outperforming reference models by 14%- and 25%-points, respectively, on a proprietary dataset with 236,865 entries.
Time representation in neural network models trained to perform interval timing tasks

To predict and maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then take the right action at the right time. However, we still lack a systematic understanding of the neural mechanism of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory and appropriately combine time information with spatial information processing and decision making. Here, we addressed this problem by training neural network models on four timing tasks: interval production, interval comparison, timed spatial reproduction, and timed decision making. We studied time-coding principles of the network after training, and found them consistent with existing experimental observations. We reveal that neural networks perceive time intervals through the evolution of population state along a stereotypical trajectory, maintain time intervals by line attractors along which the activities of most neurons vary monotonically with the duration of the maintained interval, and adjust the evolution speed of the state trajectory for producing or comparing time intervals. Spatial information or decision choice preserves the profiles of neuronal activities as functions of time intervals maintained in working memory or flow of time, and is coded in the amplitudes of these profiles. Decision making is combined with time perception through two firing sequences with mutual inhibition. Our work discloses fundamental principles of the neuronal coding of time that supports the brain for flexible temporal processing. These principles facilitate generalizable decoding of time and non-time information.
Illegible Text to Readable Text: An Image-to-Image Transformation using Conditional Sliced Wasserstein Adversarial Networks

Automatic text recognition from ancient handwritten record images is an important problem in the genealogy domain. However, critical challenges such as varying noise conditions, vanishing texts, and variations in handwriting makes the recognition task difficult. We tackle this problem by developing a handwritten-to-machine-print conditional Generative Adversarial network (HW2MP-GAN) model that formulates handwritten recognition as a text-Image-to-text-Image translation problem where a given image, typically in an illegible form, is converted into another image, close to its machine-print form. The proposed model consists of three-components including a generator, and word-level and character-level discriminators. The model incorporates Sliced Wasserstein distance (SWD) and U-Net architectures in HW2MP-GAN for better quality image-to-image transformation. Our experiments reveal that HW2MP-GAN outperforms state-of-the-art baseline cGAN models by almost 30 in Frechet Handwritten Distance (FHD), 0.6 in average Levenshtein distance and 39% in word accuracy for image-to-image translation on IAM database. Further, HW2MP-GAN improves handwritten recognition word accuracy by 1.3% compared to base-line handwritten recognition models on IAM database.
Neural Memory Plasticity for Anomaly Detection

In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However, we observe that the attention based knowledge retrieval mechanisms used in current NMNs restricts them from achieving their full potential as the attention process retrieves information based on a set of static connection weights. This is suboptimal in a setting where there are vast differences among samples in the data domain; such as anomaly detection where there is no consistent criteria for what constitutes an anomaly. In this paper, we propose a plastic neural memory access mechanism which exploits both static and dynamic connection weights in the memory read, write and output generation procedures. We demonstrate the effectiveness and flexibility of the proposed memory model in three challenging anomaly detection tasks in the medical domain: abnormal EEG identification, MRI tumour type classification and schizophrenia risk detection in children. In all settings, the proposed approach outperforms the current state-of-the-art. Furthermore, we perform an in-depth analysis demonstrating the utility of neural plasticity for the knowledge retrieval process and provide evidence on how the proposed memory model generates sparse yet informative memory outputs.