Miracle-dz/UniFAR-FTUs huggingface dataset:
An experimental investigation for error-cube PID control

This experimental study investigates the practical benefits and drawbacks of error-cube control for closed-loop PID control structures. The error-cube control approach employs the cube power of the error signal for controllers and this causes variability in control characteristics due to the non-linearity of the cube power operation. The error-cube signal introduces attenuated and magnified error regions. These two characteristic error regions result in a tight control regime and a slack control regime, depending on magnitude of the error signal. The study presents a discussion on non-linear error signals in a practical aspect and demonstrates the effects of non-linear error signals on the step response of closed-loop PID control systems via simulation results and experimental measurements. An enhanced error-cube controller was proposed to improve the control performance of the error-cube control and results are discussed.
On the Optimality of Procrastination Policy for EV Charging Under Net Energy Metering

We address the problem of behind-the-meter EV charging by a prosumer, co-optimized with rooftop solar, electric battery, and flexible consumption devices such as water heaters and HVAC systems. We consider a scenario involving a time-of-use net energy metering tariff, alongside stochastic solar production and random EV charging demand, a finite-horizon surplus-maximization problem is formulated. We show that a procrastination threshold policy that delays EV charging to the last possible moment is optimal when EV charging is cooptimized with flexible demand, and the policy thresholds can be easily computed offline. When battery storage is part of the co-optimization, we show that the prosumer's net consumption is a two-threshold piecewise linear function of the behind-the-meter renewable generation under the optimal policy, and the procrastination threshold policy remains optimal, although the thresholds cannot be computed easily. We propose a straightfor-ward myopic solution which becomes optimal strategy when the storage state of charge doesn't reach boundary, and demonstrate in a numerical simulation that involves real-world data.
The n-vehicle exploration problem is NP-complete

The $n$-vehicle exploration problem (NVEP) is a combinatorial search problem, which tries to find a permutation of $n$ vehicles to make one of the vehicles travel the maximal distance. Assuming the $n$ vehicles travel together, with some vehicles being used to refuel others, and at last all the vehicles return to the start point. NVEP has a fractional form of objective function, and its computational complexity of general case remains open. Given a directed graph $G$, if it has a Hamiltonian path, then the Hamiltonian path can be reduced in polynomial time to a single instance of NVEP, in which the traveling distance is at least $n$. The total length consists of $n$ parts, each of which is required to equal to $1$. We prove that $G$ has a hamiltonian path of total weight of $n$ if and only if the reduced NVEP instance has a sequence of length at least $n$ with each term equals to $1$. Therefore we show that Hamiltonian path $\leq_P$ NVEP, and consequently prove that NVEP is NP-complete.
Bayesian optimization for sparse neural networks with trainable activation functions

In the literature on deep neural networks, there is considerable interest in developing activation functions that can enhance neural network performance. In recent years, there has been renewed scientific interest in proposing activation functions that can be trained throughout the learning process, as they appear to improve network performance, especially by reducing overfitting. In this paper, we propose a trainable activation function whose parameters need to be estimated. A fully Bayesian model is developed to automatically estimate from the learning data both the model weights and activation function parameters. An MCMC-based optimization scheme is developed to build the inference. The proposed method aims to solve the aforementioned problems and improve convergence time by using an efficient sampling scheme that guarantees convergence to the global maximum. The proposed scheme is tested on three datasets with three different CNNs. Promising results demonstrate the usefulness of our proposed approach in improving model accuracy due to the proposed activation function and Bayesian estimation of the parameters.
Attack is Good Augmentation: Towards Skeleton-Contrastive Representation Learning

Contrastive learning, relying on effective positive and negative sample pairs, is beneficial to learn informative skeleton representations in unsupervised skeleton-based action recognition. To achieve these positive and negative pairs, existing weak/strong data augmentation methods have to randomly change the appearance of skeletons for indirectly pursuing semantic perturbations. However, such approaches have two limitations: 1) solely perturbing appearance cannot well capture the intrinsic semantic information of skeletons, and 2) randomly perturbation may change the original positive/negative pairs to soft positive/negative ones. To address the above dilemma, we start the first attempt to explore an attack-based augmentation scheme that additionally brings in direct semantic perturbation, for constructing hard positive pairs and further assisting in constructing hard negative pairs. In particular, we propose a novel Attack-Augmentation Mixing-Contrastive learning (A$^2$MC) to contrast hard positive features and hard negative features for learning more robust skeleton representations. In A$^2$MC, Attack-Augmentation (Att-Aug) is designed to collaboratively perform targeted and untargeted perturbations of skeletons via attack and augmentation respectively, for generating high-quality hard positive features. Meanwhile, Positive-Negative Mixer (PNM) is presented to mix hard positive features and negative features for generating hard negative features, which are adopted for updating the mixed memory banks. Extensive experiments on three public datasets demonstrate that A$^2$MC is competitive with the state-of-the-art methods.
A Risk-Sensitive Global Maximum Principle for Controlled Fully Coupled FBSDEs with Applications

This paper is concerned with a kind of risk-sensitive optimal control problem for fully coupled forward-backward stochastic systems. The control variable enters the diffusion term of the state equation and the control domain is not necessarily convex. A new global maximum principle is obtained without assuming that the value function is smooth. The maximum condition, the first- and second-order adjoint equations heavily depend on the risk-sensitive parameter. An optimal control problem with a fully coupled linear forward-backward stochastic system and an exponential-quadratic cost functional is discussed. The optimal feedback control and optimal cost are obtained by using Girsanov's theorem and completion-of-squares approach via risk-sensitive Riccati equations. A local solvability result of coupled risk-sensitive Riccati equations is given by Picard-Lindel$\ddot{o}$f's Theorem.
A multifidelity approach to continual learning for physical systems

We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting catastrophic forgetting. On its own the multifidelity continual learning method shows robust results that limit forgetting across several datasets. Additionally, we show that the multifidelity method can be combined with existing continual learning methods, including replay and memory aware synapses, to further limit catastrophic forgetting. The proposed continual learning method is especially suited for physical problems where the data satisfy the same physical laws on each domain, or for physics-informed neural networks, because in these cases we expect there to be a strong correlation between the output of the previous model and the model on the current training domain.
Multiple steady states and symmetry breaking in a stably stratified, valley-shaped enclosure heated from below

We delineate the complete structure of steady laminar flows within a stably stratified, valley-shaped triangular cavity heated from below through linear stability analysis and Navier-Stokes simulations. We derive an exact solution to the quiescent conduction state, which bifurcates to two pairs of flow patterns with symmetric and asymmetric circulations. We characterize the flow via the stratification perturbation parameter, $\Pi_s$, which is a measure of the strength of the surface heat flux relative to the background stable stratification. Beyond a threshold value of $\Pi_s$, a unique bifurcation event transpires, giving rise to two discernible types of eigenmodes. One type is marked by a single dominant circulation positioned at the valley's center, while the other one exhibits dual circulations of equal strength within the same central valley region. Three-dimensional Navier-Stokes simulations confirm that the central-circulation eigenmode generates a pair of asymmetric steady states, whereas the dual-circulation eigenmode leads to distinct upslope and downslope symmetric convection patterns. The dissimilarity between the two symmetric patterns originates from the absence of a horizontal symmetry axis within the geometry. Linear stability analysis and Navier-Stokes simulations jointly confirm the linear instability of the two symmetric states, indicating that the slightest asymmetric perturbation will induce a transition towards the asymmetric steady state. Overall, our investigation reveals that, for a given set of dimensionless parameters, the Navier-Stokes equations admit at least five possible steady-state solutions. Two of these solutions, namely the quiescent, pure conduction state as well as the counter-intuitive symmetric downslope state, have previously been overlooked in stably stratified valley-shaped cavities heated from below.
Digraph Colouring and Arc-Connectivity

The dichromatic number $\vec\chi(D)$ of a digraph $D$ is the minimum size of a partition of its vertices into acyclic induced subgraphs. We denote by $\lambda(D)$ the maximum local edge connectivity of a digraph $D$. Neumann-Lara proved that for every digraph $D$, $\vec\chi(D) \leq \lambda(D) + 1$. In this paper, we characterize the digraphs $D$ for which $\vec\chi(D) = \lambda(D) + 1$. This generalizes an analogue result for undirected graph proved by Stiebitz and Toft as well as the directed version of Brooks' Theorem proved by Mohar. Along the way, we introduce a generalization of Haj\'os join that gives a new way to construct families of dicritical digraphs that is of independent interest.
Boundary control of an axially moving accelerated/decelerated belt system

In this paper, a boundary controller with disturbance observer is proposed for the vibration suppression of an axially moving belt system. The model of the belt system is described by a nonhomogeneous partial differential equation and a set of ordinary differential equations with consideration of the high‐acceleration/deceleration and unknown spatiotemporally varying distributed disturbance. Applying the finite‐dimensional backstepping control and Lyapunov's direct method, a boundary controller is developed to stabilize the belt system at the small neighborhood of its equilibrium position and a disturbance observer is introduced to attenuate the effect of unknown external disturbance. The S‐curve acceleration/deceleration method is adopted to plan the speed of the belt. With the proposed control scheme, the spillover instability problems are avoided, the uniform boundedness and the stability of the closed‐loop belt system can be achieved. Simulations are provided to illustrate the effectiveness of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.
The Interstellar Medium Scaling Relations Using Inner H i and an Application of Estimating Dust Mass

We select a disk-like galaxy sample with observations of the H I, H2, and dust from the Herschel Reference Survey, and derive inner H I masses within the optical radius. We find that the inner gas-to-dust ratio is almost independent of gas-phase metallicity, and confirm that the inner gas mass (H I+H2) shows a tighter relation with dust mass and monochromatic 500 μm luminosity than the integral gas mass. It supports that dust is more closely associated with cospatial cold gas than the overall cold gas. Based on the newly calibrated relationship between inner gas mass and dust mass, we predict dust masses for disk-dominated galaxies from the xCOLD GASS sample. The predicted dust masses show scaling relations consistent with fiducial ones in the literature, supporting their robustness. Additionally, we find that at a given dust mass and star formation rate (SFR), the galactic Wide-field Infrared Explorer W3 luminosities show a significant dependence on the [N ii] luminosity and the stellar mass surface density. Such dependence highlights the caveat of using the W3 luminosity as an integral SFR indicator, and is consistent with findings of studies that target star-forming regions in more nearby galaxies and accurately derive dust masses based on mapping-mode spectroscopy.
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training

The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking. While effective and efficient, dual-encoders are brittle to variations in query distributions and noisy queries. Data augmentation can make models more robust but introduces overhead to training set generation and requires retraining and index regeneration. We present Contrastive Alignment POst Training (CAPOT), a highly efficient finetuning method that improves model robustness without requiring index regeneration, the training set optimization, or alteration. CAPOT enables robust retrieval by freezing the document encoder while the query encoder learns to align noisy queries with their unaltered root. We evaluate CAPOT noisy variants of MSMARCO, Natural Questions, and Trivia QA passage retrieval, finding CAPOT has a similar impact as data augmentation with none of its overhead.
Photometric Correction for Infrared Sensors

Infrared thermography has been widely used in several domains to capture and measure temperature distributions across surfaces and objects. This methodology can be further expanded to 3D applications if the spatial distribution of the temperature distribution is available. Structure from Motion (SfM) is a photometric range imaging technique that makes it possible to obtain 3D renderings from a cloud of 2D images. To explore the possibility of 3D reconstruction via SfM from infrared images, this article proposes a photometric correction model for infrared sensors based on temperature constancy. Photometric correction is accomplished by estimating the scene irradiance as the values from the solution to a differential equation for microbolometer pixel excitation with unknown coefficients and initial conditions. The model was integrated into an SfM framework and experimental evaluations demonstrate the contribution of the photometric correction for improving the estimates of both the camera motion and the scene structure. Further, experiments show that the reconstruction quality from the corrected infrared imagery achieves performance on par with state-of-the-art reconstruction using RGB sensors.
Fredholm inversion around a singularity: application to cointegration in Banach space

This paper consider inverting a holomorphic Fredholm operator pencil. Speciﬁcally, we provide necessary and suﬃcient conditions for the inverse of a holomorphic Fredholm operator pencil to have a simple pole and a second order pole. Based on those results, a closed-form expression of the Laurent expansion of the inverse around an isolated singularity is obtained in each case. As an application of the results, we obtain a suitable extension of the Granger-Johansen representation theory for random sequences taking values in a separable Banach space. Due to our closed-form expression of the inverse, we may fully characterize I(1) and I(2) solutions except a term that depends on initial values.
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition

Named Entity Recognition (NER) is a fundamental NLP tasks with a wide range of practical applications. The performance of state-of-the-art NER methods depends on high quality manually anotated datasets which still do not exist for some languages. In this work we aim to remedy this situation in Slovak by introducing {texttt{WikiGoldSK}, the first sizable human labelled Slovak NER dataset. We benchmark it by evaluating state-of-the-art multilingual Pretrained Language Models and comparing it to the existing silver-standard Slovak NER dataset.We also conduct few-shot experiments and show that training on a sliver-standard dataset yields better results.To enable future work that can be based on Slovak NER, we release the dataset, code, as well as the trained models publicly under permissible licensing terms at https://github.com/NaiveNeuron/WikiGoldSK
A Novel Point-Based Algorithm for Multi-Agent Control Using the Common Information Approach

The Common Information (CI) approach provides a systematic way to transform a multi-agent stochastic control problem to a single-agent partially observed Markov decision problem (POMDP) called the coordinator's POMDP. However, such a POMDP can be hard to solve due to its extraordinarily large action space. We propose a new algorithm for multi-agent stochastic control problems, called coordinator's heuristic search value iteration (CHSVI), that combines the CI approach and point-based POMDP algorithms for large action spaces. We demonstrate the algorithm through optimally solving several benchmark problems.
A Multilevel Method for Many-Electron Schrödinger Equations Based on the Atomic Cluster Expansion

The atomic cluster expansion (ACE) (Drautz, 2019) yields a highly efficient and intepretable parameterisation of symmetric polynomials that has achieved great success in modelling properties of many-particle systems. In the present work we extend the practical applicability of the ACE framework to the computation of many-electron wave functions. To that end, we develop a customized variational Monte-Carlo algorithm that exploits the sparsity and hierarchical properties of ACE wave functions. We demonstrate the feasibility on a range of proof-of-concept applications to one-dimensional systems.
Epidemic Control on a Large-Scale-Agent-Based Epidemiology Model using Deep Deterministic Policy Gradient

To mitigate the impact of the pandemic, several measures include lockdowns, rapid vaccination programs, school closures, and economic stimulus. These interventions can have positive or unintended negative consequences. Current research to model and determine an optimal intervention automatically through round-tripping is limited by the simulation objectives, scale (a few thousand individuals), model types that are not suited for intervention studies, and the number of intervention strategies they can explore (discrete vs continuous). We address these challenges using a Deep Deterministic Policy Gradient (DDPG) based policy optimization framework on a large-scale (100,000 individual) epidemiological agent-based simulation where we perform multi-objective optimization. We determine the optimal policy for lockdown and vaccination in a minimalist age-stratified multi-vaccine scenario with a basic simulation for economic activity. With no lockdown and vaccination (mid-age and elderly), results show optimal economy (individuals below the poverty line) with balanced health objectives (infection, and hospitalization). An in-depth simulation is needed to further validate our results and open-source our framework.
Achieving Long-term Fairness in Submodular Maximization through Randomization

Submodular function optimization has numerous applications in machine learning and data analysis, including data summarization which aims to identify a concise and diverse set of data points from a large dataset. It is important to implement fairness-aware algorithms when dealing with data items that may contain sensitive attributes like race or gender, to prevent biases that could lead to unequal representation of different groups. With this in mind, we investigate the problem of maximizing a monotone submodular function while meeting group fairness constraints. Unlike previous studies in this area, we allow for randomized solutions, with the objective being to calculate a distribution over feasible sets such that the expected number of items selected from each group is subject to constraints in the form of upper and lower thresholds, ensuring that the representation of each group remains balanced in the long term. Here a set is considered feasible if its size does not exceed a constant value of $b$. Our research includes the development of a series of approximation algorithms for this problem.
Head-tail Loss: A simple function for Oriented Object Detection and Anchor-free models

This paper presents a new loss function for the prediction of oriented bounding boxes, named head-tail-loss. The loss function consists in minimizing the distance between the prediction and the annotation of two key points that are representing the annotation of the object. The first point is the center point and the second is the head of the object. However, for the second point, the minimum distance between the prediction and either the head or tail of the groundtruth is used. On this way, either prediction is valid (with the head pointing to the tail or the tail pointing to the head). At the end the importance is to detect the direction of the object but not its heading. The new loss function has been evaluated on the DOTA and HRSC2016 datasets and has shown potential for elongated objects such as ships and also for other types of objects with different shapes.
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling

Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generators can serve as drop-in replacements for the original priors during MCMC inference. While this approach enables efficient inference, it loses information about the hyperparameters of the original models, and consequently makes inference over hyperparameters impossible and the learned priors indistinct. To overcome this limitation, we condition the VAE on stochastic process hyperparameters. This allows the joint encoding of hyperparameters with GP realizations and their subsequent estimation during inference. Further, we demonstrate that our proposed method, PriorCVAE, is agnostic to the nature of the models which it approximates, and can be used, for instance, to encode solutions of ODEs. It provides a practical tool for approximate inference and shows potential in real-life spatial and spatiotemporal applications.