Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-based Model Predictive Control for Safe Reinforcement Learning

Reinforcement learning has been successfully used to solve difficult tasks in complex unknown environments. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, realworld applications. In this paper, we attempt to bridge the gap between learning-based techniques that are scalable and highly autonomous but often unsafe and robust control techniques, which have a solid theoretical foundation that guarantees safety but often require extensive expert knowledge to identify the system and estimate disturbance sets. We combine a provably safe learning-based MPC scheme that allows for input-dependent uncertainties with techniques from model-based RL to solve tasks with only limited prior knowledge. We evaluate the resulting algorithm to solve a reinforcement learning task in a simulated cart-pole dynamical system with safety constraints.
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata

This paper studies the control synthesis of motion planning subject to uncertainties. The uncertainties are considered in robot motions and environment properties, giving rise to the probabilistic labeled Markov decision process (PL-MDP). A Model-Free Reinforcement The learning (RL) method is developed to generate a finite-memory control policy to satisfy high-level tasks expressed in linear temporal logic (LTL) formulas. Due to uncertainties and potentially conflicting tasks, this work focuses on infeasible LTL specifications, where a relaxed LTL constraint is developed to allow the agent to revise its motion plan and take violations of original tasks into account for partial satisfaction. And a novel automaton is developed to improve the density of accepting rewards and enable deterministic policies. We proposed an RL framework with rigorous analysis that is guaranteed to achieve multiple objectives in decreasing order: 1) satisfying the acceptance condition of relaxed product MDP and 2) reducing the violation cost over long-term behaviors. We provide simulation and experimental results to validate the performance.
On Adams Completion and Cocompletion

The minimal model of a 1-connected differential graded Lie algebra is obtained as the Adams cocompletion of the differential graded Lie algebra with respect to a chosen set of morphisms in the category of 1-connected differential graded Lie algebras (d.g.l.a.’s)over the field of rationals and d.g. l.a. -homomorphisms. The Postnikov-like approximation of a module is obtained as the Adams completions of the space with the help of a suitable set of morphisms in the category of some specific modules and module homomorphisms. The Cartan-Whitehead decomposition of topological G-module is obtained as the Adams cocompletion of the space with respect to suitable sets of morphisms. Postnikov-like approximation is obtained for a topological G-module, in terms of Adams completion with respect to a suitable sets of morphisms, using cohomology theory of topological G-modules.The ring of fractions of the algebra of all bounded linear operators on a separable infinite dimensional Banach space is isomorphic to the Adams completion of the algebra with respect to a carefully chosen set of morphisms in the category of separable infinite dimensional Banach spaces and bounded linear norm preserving operators of norms at most 1. The nth tensor algebra and symmetric algebra are each isomorphic to the Adams completions of the algebras. The exterior algebra and Clifford algebra are each isomorphic to the Adams completions of the algebra with respect to a chosen set of morphisms in the category of modules and module homomorphisms.
UnOVOST : Unsupervised Offline Video Object Segmentation and Tracking for the 2019 Unsupervised DAVIS Challenge

We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixel masks for salient objects in a video sequence and of tracking these objects consistently through time, without any information about which objects should be tracked. Towards solving this task, we present UnOVOST (Unsupervised Offline Video Object Segmentation and Tracking) as a simple and generic algorithm which is able to track a large variety of objects. This algorithm hierarchically builds up tracks in five stages. First, object proposal masks are generated using Mask R-CNN. Second, masks are sub-selected and clipped so that they do not overlap in the image domain. Third, tracklets are generated by grouping object proposals that are strongly temporally consistent with each other under optical flow warping. Fourth, tracklets are merged into long-term consistent object tracks using their temporal consistency and an appearance similarity metric calculated using an object re-identification network. Finally, the most salient object tracks are selected based on temporal track length and detection confidence scores. We evaluate our approach on the DAVIS 2017 Unsupervised dataset and obtain state-of-the-art performance with a mean J&F score of 58% on the test-dev benchmark. Our approach further achieves first place in the DAVIS 2019 Unsupervised Video Object Segmentation Challenge with a mean of J&F score of 56.4% on the test-challenge benchmark.
Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method

Electronic structure calculations have become an indispensable tool in many areas of materials science and quantum chemistry. Even though the Kohn–Sham formulation of the density-functional theory (DFT) simplifies the many-body problem significantly, one is still confronted with several numerical challenges. In this article we present the projector augmented-wave (PAW) method as implemented in the GPAW program package (https://wiki.fysik.dtu.dk/gpaw) using a uniform real-space grid representation of the electronic wavefunctions. Compared to more traditional plane wave or localized basis set approaches, real-space grids offer several advantages, most notably good computational scalability and systematic convergence properties. However, as a unique feature GPAW also facilitates a localized atomic-orbital basis set in addition to the grid. The efficient atomic basis set is complementary to the more accurate grid, and the possibility to seamlessly switch between the two representations provides great flexibility. While DFT allows one to study ground state properties, time-dependent density-functional theory (TDDFT) provides access to the excited states. We have implemented the two common formulations of TDDFT, namely the linear-response and the time propagation schemes. Electron transport calculations under finite-bias conditions can be performed with GPAW using non-equilibrium Green functions and the localized basis set. In addition to the basic features of the real-space PAW method, we also describe the implementation of selected exchange–correlation functionals, parallelization schemes, ΔSCF-method, x-ray absorption spectra, and maximally localized Wannier orbitals.
Quantum Information Science and Quantum Metrology: Novel Systems and Applications

The current frontier of our understanding of the physical universe is dominated by quantum phenomena. Uncovering the prospects and limitations of acquiring and processing information using quantum effects is an outstanding challenge in physical science. This thesis presents an analysis of several new model systems and applications for quantum information processing and metrology. First, we analyze quantum optomechanical systems exhibiting quantum phenomena in both optical and mechanical degrees of freedom. We investigate the strength of non-classical correlations in a model system of two optical and one mechanical mode. We propose and analyze experimental protocols that exploit these correlations for quantum computation. We then turn our attention to atom-cavity systems involving strong coupling of atoms with optical photons, and investigate the possibility of using them to store information robustly and as relay nodes. We present a scheme for a robust two-qubit quantum gate with inherent error-detection capabilities. We consider several remote entanglement protocols employing this robust gate, and we use these systems to study the performance of the gate in practical applications.
Modelling and simulation of a commercially available dielectric elastomer actuator

To fully harness the potential of dielectric elastomer actuators (DEAs) in soft robots, advanced control methods are needed. An important groundwork for this is the development of a control-oriented model that can adequately describe the underlying dynamics of a DEA. Existing models commonly focus on custom-made DEAs, simplifying the modeling process due to well-known specifications and actuator structures. However, for commercial actuators, only information from the manufacturer is available, necessitating verification or completion during the modeling process. The aim of this paper is to explore how a commercial stacked silicone-based DEA can be modelled and how complex the model should be to properly replicate the features of the actuator. The static description has demonstrated the suitability of Hooke's law. In the case of dynamic description, it is shown that no viscoelastic model is needed for control-oriented modelling. However, if all features of the DEA are considered, the generalised Kelvin-Maxwell model with three Maxwell elements shows good results, stability and computational efficiency.
Bag of Policies for Distributional Deep Exploration

Efficient exploration in complex environments remains a major challenge for reinforcement learning (RL). Compared to previous Thompson sampling-inspired mechanisms that enable temporally extended exploration, i.e., deep exploration, we focus on deep exploration in distributional RL. We develop here a general purpose approach, Bag of Policies (BoP), that can be built on top of any return distribution estimator by maintaining a population of its copies. BoP consists of an ensemble of multiple heads that are updated independently. During training, each episode is controlled by only one of the heads and the collected state-action pairs are used to update all heads off-policy, leading to distinct learning signals for each head which diversify learning and behaviour. To test whether optimistic ensemble method can improve on distributional RL as did on scalar RL, by e.g. Bootstrapped DQN, we implement the BoP approach with a population of distributional actor-critics using Bayesian Distributional Policy Gradients (BDPG). The population thus approximates a posterior distribution of return distributions along with a posterior distribution of policies. Another benefit of building upon BDPG is that it allows to analyze global posterior uncertainty along with local curiosity bonus simultaneously for exploration. As BDPG is already an optimistic method, this pairing helps to investigate if optimism is accumulatable in distributional RL. Overall BoP results in greater robustness and speed during learning as demonstrated by our experimental results on ALE Atari games.
Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems

This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classify single pixels in patches of a whole image, a lot of redundant computations are carried out when using sliding window networks. This set of new architectures solve this issue by either removing redundant computations or using fully convolutional architectures that inherently predict many pixels at once. The implementations of the three models are accessible through a new utility on top of the Caffe library. The utility provides support for a wide range of image input and output formats, pre-processing parameters and methods to equalize the label histogram during training. The Caffe library has been extended by new layers and a new backend for availability on a wider range of hardware such as CPUs and GPUs through OpenCL. On AMD GPUs, speedups of $54\times$ (SK-Net), $437\times$ (U-Net) and $320\times$ (USK-Net) have been observed, taking the SK equivalent SW (sliding window) network as the baseline. The label throughput is up to one megapixel per second. The analyzed neural networks have distinctive characteristics that apply during training or processing, and not every data set is suitable to every architecture. The quality of the predictions is assessed on two neural tissue data sets, of which one is the ISBI 2012 challenge data set. Two different loss functions, Malis loss and Softmax loss, were used during training. The whole pipeline, consisting of models, interface and modified Caffe library, is available as Open Source software under the working title Project Greentea.
Many-to-Many Spoken Language Translation via Unified Speech and Text Representation Learning with Unit-to-Unit Translation

In this paper, we propose a method to learn unified representations of multilingual speech and text with a single model, especially focusing on the purpose of speech synthesis. We represent multilingual speech audio with speech units, the quantized representations of speech features encoded from a self-supervised speech model. Therefore, we can focus on their linguistic content by treating the audio as pseudo text and can build a unified representation of speech and text. Then, we propose to train an encoder-decoder structured model with a Unit-to-Unit Translation (UTUT) objective on multilingual data. Specifically, by conditioning the encoder with the source language token and the decoder with the target language token, the model is optimized to translate the spoken language into that of the target language, in a many-to-many language translation setting. Therefore, the model can build the knowledge of how spoken languages are comprehended and how to relate them to different languages. A single pre-trained model with UTUT can be employed for diverse multilingual speech- and text-related tasks, such as Speech-to-Speech Translation (STS), multilingual Text-to-Speech Synthesis (TTS), and Text-to-Speech Translation (TTST). By conducting comprehensive experiments encompassing various languages, we validate the efficacy of the proposed method across diverse multilingual tasks. Moreover, we show UTUT can perform many-to-many language STS, which has not been previously explored in the literature. Samples are available on https://choijeongsoo.github. io/utut.
Simultaneously Approximating All $\ell_p$-norms in Correlation Clustering

This paper considers correlation clustering on unweighted complete graphs. We give a combinatorial algorithm that returns a single clustering solution that is simultaneously $O(1)$-approximate for all $\ell_p$-norms of the disagreement vector; in other words, a combinatorial $O(1)$-approximation of the all-norms objective for correlation clustering. This is the first proof that minimal sacrifice is needed in order to optimize different norms of the disagreement vector. In addition, our algorithm is the first combinatorial approximation algorithm for the $\ell_2$-norm objective, and more generally the first combinatorial algorithm for the $\ell_p$-norm objective when $1<p<\infty$. It is also faster than all previous algorithms that minimize the $\ell_p$-norm of the disagreement vector, with run-time $O(n^\omega)$, where $O(n^\omega)$ is the time for matrix multiplication on $n \times n$ matrices. When the maximum positive degree in the graph is at most $\Delta$, this can be improved to a run-time of $O(n\Delta^2 \log n)$.
Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data

ABSTRACTRecorded seismic signals are often corrupted by noise. We have developed an automatic noise-attenuation method for single-channel seismic data, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross-validation approach that does not rely on any prior knowledge about the noise level. The efficiency of the thresholding has been improved by adding a preprocessing step based on kurtosis measurement and a postprocessing step based on adaptive hard thresholding. The proposed algorithm can either attenuate the noise (either white or colored) and keep the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or preprocessing in ambient noise studies. We tested the performance of the proposed method on s.
Limit Cycles in High-Resolution Quantized Feedback Systems

In this paper, the existence of limit cycles in high-resolution quantized feedback systems is examined. It is well known that the relay and the quantized feedback systems exhibit self-oscillations, due to their switching nature. However, the quantizer is a more general nonlinearity as compared to the relay, due to its switching at multiple discrete levels. An extension of periodic switching conditions uncovers the existence of self-oscillations in some systems under high quantization resolution. Multiple limit cycle solutions of switching instants and periods have been found, depending on the initial states of the system. Further analysis on the stability of the limit cycle via the Jacobian of the Poincare map reveals numerical bounds on the quantization step size for a stable limit cycle. Analytical results on the existence of limit cycles in first and second order systems are also presented.
Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS

The diffusion model is capable of generating high-quality data through a probabilistic approach. However, it suffers from the drawback of slow generation speed due to the requirement of a large number of time steps. To address this limitation, recent models such as denoising diffusion implicit models (DDIM) focus on generating samples without directly modeling the probability distribution, while models like denoising diffusion generative adversarial networks (GAN) combine diffusion processes with GANs. In the field of speech synthesis, a recent diffusion speech synthesis model called DiffGAN-TTS, utilizing the structure of GANs, has been introduced and demonstrates superior performance in both speech quality and generation speed. In this paper, to further enhance the performance of DiffGAN-TTS, we propose a speech synthesis model with two discriminators: a diffusion discriminator for learning the distribution of the reverse process and a spectrogram discriminator for learning the distribution of the generated data. Objective metrics such as structural similarity index measure (SSIM), mel-cepstral distortion (MCD), F0 root mean squared error (F0 RMSE), short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), as well as subjective metrics like mean opinion score (MOS), are used to evaluate the performance of the proposed model. The evaluation results show that the proposed model outperforms recent state-of-the-art models such as FastSpeech2 and DiffGAN-TTS in various metrics. Our implementation and audio samples are located on GitHub.
Evolutionary Optimized, Monocrystalline Gold Double Wire Gratings as a SERS Sensing Platform

Achieving reliable and quantifiable performance in large-area surface-enhanced Raman spectroscopy (SERS) substrates has long been a formidable challenge. It requires substantial signal enhancement while maintaining a reproducible and uniform response. Conventional SERS substrates are typically made of inhomogeneous materials with random resonator geometries and distributions. As a result, they exhibit several or broadened plasmonic resonances, undesired absorptive losses, and inhomogeneous field enhancement. These limitations diminish the signal strength and hamper reproducibility, making it difficult to conduct comparative studies with high sensitivity. In this study, we propose an approach that utilizes monocrystalline gold flakes to fabricate well-defined gratings composed of plasmonic double-wire resonators, which are fabricated through focused ion-beam lithography. The geometry of the double wire grating substrate (DWGS) was evolutionary optimized to achieve efficient enhancement for both excitation and emission processes. The use of monocrystalline material minimizes absorption losses while enhancing the shape fidelity during the nanofabrication process. The DWGS shows notable reproducibility (RSD=6.6%), repeatability (RSD=5.6%), and large-area homogeneity over areas $>10^4\,\mu$m$^2$. Moreover, it provides a SERS enhancement factor of $\approx 10^6$ for 4-Aminothiophenol (4-ATP) analyte and detection capability for sub-monolayer coverage. The DWGS demonstrates reusability, as well as long-term stability on the shelf. Experimental validation with various analytes, in different states of matter, including biological macromolecules, confirms the sensitive and reproducible nature of DWGSs, thereby establishing them as a promising SERS substrate design for future sensing applications.
End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control

(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic models that are sufficiently accurate and computationally tractable. Data-driven surrogate models for mechanistic models can reduce the computational burden of (e)NMPC; however, such models are typically trained by system identification for maximum prediction accuracy on simulation samples and perform suboptimally in (e)NMPC. We present a method for end-to-end reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We apply our method to two applications derived from an established nonlinear continuous stirred-tank reactor model. The controller performance is compared to that of (e)NMPCs utilizing models trained using system identification, and model-free neural network controllers trained using reinforcement learning. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC, and that, in contrast to the neural network controllers, the (e)NMPC controllers can react to changes in the control setting without retraining.
A Compliant Robotic Leg Based on Fibre Jamming

Humans possess a remarkable ability to react to unpredictable perturbations through immediate mechanical responses, which harness the visco-elastic properties of muscles to maintain balance. Inspired by this behaviour, we propose a novel design of a robotic leg utilising fibre jammed structures as passive compliant mechanisms to achieve variable joint stiffness and damping. We developed multi-material fibre jammed tendons with tunable mechanical properties, which can be 3D printed in one-go without need for assembly. Through extensive numerical simulations and experimentation, we demonstrate the usefulness of these tendons for shock absorbance and maintaining joint stability. We investigate how they could be used effectively in a multi-joint robotic leg by evaluating the relative contribution of each tendon to the overall stiffness of the leg. Further, we showcase the potential of these jammed structures for legged locomotion, highlighting how morphological properties of the tendons can be used to enhance stability in robotic legs.