Miracle-dz/UniFAR-FTUs huggingface dataset:
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

Sparse shrunk additive models and sparse random feature models have been developed separately as methods to learn low-order functions, where there are few interactions between variables, but neither offers computational efficiency. On the other hand, $\ell_2$-based shrunk additive models are efficient but do not offer feature selection as the resulting coefficient vectors are dense. Inspired by the success of the iterative magnitude pruning technique in finding lottery tickets of neural networks, we propose a new method -- Sparser Random Feature Models via IMP (ShRIMP) -- to efficiently fit high-dimensional data with inherent low-dimensional structure in the form of sparse variable dependencies. Our method can be viewed as a combined process to construct and find sparse lottery tickets for two-layer dense networks. We explain the observed benefit of SHRIMP through a refined analysis on the generalization error for thresholded Basis Pursuit and resulting bounds on eigenvalues. From function approximation experiments on both synthetic data and real-world benchmark datasets, we show that SHRIMP obtains better than or competitive test accuracy compared to state-of-art sparse feature and additive methods such as SRFE-S, SSAM, and SALSA. Meanwhile, SHRIMP performs feature selection with low computational complexity and is robust to the pruning rate, indicating a robustness in the structure of the obtained subnetworks. We gain insight into the lottery ticket hypothesis through SHRIMP by noting a correspondence between our model and weight/neuron subnetworks.
Data-driven stochastic model predictive control

We propose a novel data-driven stochastic model predictive control (MPC) algorithm to control linear time-invariant systems with additive stochastic disturbances in the dynamics. The scheme centers around repeated predictions and computations of optimal control inputs based on a non-parametric representation of the space of all possible trajectories, using the fundamental lemma from behavioral systems theory. This representation is based on a single measured input-state-disturbance trajectory generated by persistently exciting inputs and does not require any further identiﬁcation step. Based on stochastic MPC ideas, we enforce the satisfaction of state constraints with a pre-speciﬁed probability level, allowing for a systematic trade-off between control performance and constraint satisfaction. The proposed data-driven stochastic MPC algorithm enables efﬁcient control where robust methods are too conservative, which we demonstrate in a simulation example.
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

We present ShinRL, an open-source library specialized for the evaluation of reinforcement learning (RL) algorithms from both theoretical and practical perspectives. Existing RL libraries typically allow users to evaluate practical performances of deep RL algorithms through returns. Nevertheless, these libraries are not necessarily useful for analyzing if the algorithms perform as theoretically expected, such as if Q learning really achieves the optimal Q function. In contrast, ShinRL provides an RL environment interface that can compute metrics for delving into the behaviors of RL algorithms, such as the gap between learned and the optimal Q values and state visitation frequencies. In addition, we introduce a flexible solver interface for evaluating both theoretically justified algorithms (e.g., dynamic programming and tabular RL) and practically effective ones (i.e., deep RL, typically with some additional extensions and regularizations) in a consistent fashion. As a case study, we show that how combining these two features of ShinRL makes it easier to analyze the behavior of deep Q learning. Furthermore, we demonstrate that ShinRL can be used to empirically validate recent theoretical findings such as the effect of KL regularization for value iteration and for deep Q learning, and the robustness of entropy-regularized policies to adversarial rewards. The source code for ShinRL is available on GitHub: https://github.com/omron-sinicx/ShinRL.
Luminosities and Masses of Single Galactic Post-asymptotic Giant Branch Stars with Distances from Gaia EDR3: The Revelation of an s-process Diversity

Post-asymptotic giant branch (AGB) stars are exquisite probes of AGB nucleosynthesis. However, the previous lack of accurate distances jeopardized comparison with theoretical AGB models. The Gaia Early Data Release 3 (Gaia EDR3) has now allowed for a breakthrough in this research landscape. In this study, we focus on a sample of single Galactic post-AGBs for which chemical abundance studies were completed. We combined photometry with geometric distances to carry out a spectral energy distribution (SED) analysis and derive accurate luminosities. We subsequently determined their positions on the Hertzsprung-Russell (HR) diagram and compared this with theoretical post-AGB evolutionary tracks. While most objects are in the post-AGB phase of evolution, we found a subset of low-luminosity objects that are likely to be in the post-horizontal branch phase of evolution, similar to AGB-manqué objects found in globular clusters. Additionally, we also investigated the observed bimodality in the s-process enrichment of Galactic post-AGB single stars of similar T eff and metallicities. This bimodality was expected to be a direct consequence of luminosity with the s-process rich objects having evolved further on the AGB. However, we find that the two populations, the s-process enriched and non-enriched, have similar luminosities (and hence initial masses), revealing an intriguing chemical diversity. For a given initial mass and metallicity, AGB nucleosynthesis appears inhomogeneous and sensitive to other factors, which could be mass loss, along with convective and non-convective mixing mechanisms. Modeling individual objects in detail will be needed to investigate which parameters and processes dominate the photospheric chemical enrichment in these stars.
Learning to Select the Next Reasonable Mention for Entity Linking

Entity linking aims to establish a link between entity mentions in a document and the corresponding entities in knowledge graphs (KGs). Previous work has shown the effectiveness of global coherence for entity linking. However, most of the existing global linking methods based on sequential decisions focus on how to utilize previously linked entities to enhance the later decisions. In those methods, the order of mention is fixed, making the model unable to adjust the subsequent linking targets according to the previously linked results, which will cause the previous information to be unreasonably utilized. To address the problem, we propose a novel model, called DyMen, to dynamically adjust the subsequent linking target based on the previously linked entities via reinforcement learning, enabling the model to select a link target that can fully use previously linked information. We sample mention by sliding window to reduce the action sampling space of reinforcement learning and maintain the semantic coherence of mention. Experiments conducted on several benchmark datasets have shown the effectiveness of the proposed model.
Replay For Safety

Experience replay \citep{lin1993reinforcement, mnih2015human} is a widely used technique to achieve efficient use of data and improved performance in RL algorithms. In experience replay, past transitions are stored in a memory buffer and re-used during learning. Various suggestions for sampling schemes from the replay buffer have been suggested in previous works, attempting to optimally choose those experiences which will most contribute to the convergence to an optimal policy. Here, we give some conditions on the replay sampling scheme that will ensure convergence, focusing on the well-known Q-learning algorithm in the tabular setting. After establishing sufficient conditions for convergence, we turn to suggest a slightly different usage for experience replay - replaying memories in a biased manner as a means to change the properties of the resulting policy. We initiate a rigorous study of experience replay as a tool to control and modify the properties of the resulting policy. In particular, we show that using an appropriate biased sampling scheme can allow us to achieve a \emph{safe} policy. We believe that using experience replay as a biasing mechanism that allows controlling the resulting policy in desirable ways is an idea with promising potential for many applications.
COSMIC: fast closed-form identification from large-scale data for LTV systems

We introduce a closed-form method for identification of discrete-time linear time-variant systems from data, formulating the learning problem as a regularized least squares problem where the regularizer favors smooth solutions within a trajectory. We develop a closed-form algorithm with guarantees of optimality and with a complexity that increases linearly with the number of instants considered per trajectory. The COSMIC algorithm achieves the desired result even in the presence of large volumes of data. Our method solved the problem using two orders of magnitude less computational power than a general purpose convex solver and was about 3 times faster than a Stochastic Block Coordinate Descent especially designed method. Computational times of our method remained in the order of magnitude of the second even for 10k and 100k time instants, where the general purpose solver crashed. To prove its applicability to real world systems, we test with spring-mass-damper system and use the estimated model to find the optimal control path. Our algorithm was applied to both a Low Fidelity and Functional Engineering Simulators for the Comet Interceptor mission, that requires precise pointing of the on-board cameras in a fast dynamics environment. Thus, this paper provides a fast alternative to classical system identification techniques for linear time-variant systems, while proving to be a solid base for applications in the Space industry and a step forward to the incorporation of algorithms that leverage data in such a safety-critical environment.
Interrelationships among Critical Success Factors of Construction Projects Based on the Structural Equation Model

AbstractClearly identifying the critical success factors (CSFs) of construction projects and a good understanding of the interrelationships among CSFs will help project managers focus on the control of key factors and allow them to make reasonable resource allocations. To explore the interrelationships among CSFs, this research established a CSFs system by first identifying 62 CSFs of construction projects through a literature review, and then refining them to produce 46 CSFs by expert discussions. On the basis of the CSFs system, which consisted of three categories and ten subcategories, this study applied the structural equation model (SEM) to explore the interrelationships among the CSFs. One hundred twenty-four project data that were collected from a questionnaire survey were used to determine the hypothetical model. A detailed evaluation and modification was then performed to revise the model. On the basis of the revised model, which was verified through evaluating the goodness-of-fit (GOF) indexes, .
Uncovering the Local Hidden Community Structure in Social Networks

Hidden community is a useful concept proposed recently for social network analysis. Hidden communities indicate some weak communities whose most members also belong to other stronger dominant communities. Dominant communities could form a layer that partitions all the individuals of a network, and hidden communities could form other layer(s) underneath. These layers could be natural structures in the real-world networks like students grouped by major, minor, hometown, and so on. To handle the rapid growth of network scale, in this work, we explore the detection of hidden communities from the local perspective, and propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the original network. We first expand the seed set from a single seed node based on our modified local spectral method and detect an initial dominant local community. Then we temporarily remove the members of this community as well as their connections to other nodes, and detect all the neighborhood communities in the remaining subgraph, including some “broken communities” that only contain a fraction of members in the original network. The local community and neighborhood communities form a dominant layer, and by reducing the edge weights inside these communities, we weaken this layer’s structure to reveal the hidden layers. Eventually, we repeat the whole process, and all communities containing the seed node can be detected and boosted iteratively. We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy of detection which can be caused by global hidden community detection methods. Extensive experiments show that our method could significantly outperform the state-of-the-art baselines designed for either global hidden community detection or multiple local community detection.
Three-dimensional vision inspection based on structured light projection and neurocalibration

Structured light three dimensional (3D) vision inspection is an increasing common means for acquiring three dimensional geometry of objects due to its short acquisition time, low cost and its robustness in the presence of ambient light. In this paper, we proposed a method of structure light 3D vision inspection based on neurocalibration. First, the principle of linear structured light 3D vision inspection and Back Propagation (BP) neural network are described. Second, a calibration method based on BP neural network without any knowledge of 3D object and computer vision is proposed, which has the advantage of resolving the system nonlinearity. The feasibility of the approach is based on the fact that multilayer feed-forward networks are capable of approximating an arbitrary continuous nonlinear function and solving least squares problem. The network consists of an input layer and an output layer, with a hidden layer in between. The input neurons denote the coordinates of 2D image pixels and the output neurons denote the corresponding coordinates of 3D world points. At the end, the accuracy and the robustness of the proposed method are tested by experiments. It has been shown that the proposed method is simple, robust and effective.
Melt dynamics in surface processing with laser radiation: calculations and applications

The purpose of this paper is more to stimulate a discussion than to set out well proven and undisputed facts. A great many papers are being published on the subject of diffracting optics and these usually fall into one of two classes: those concerned with bulk or with surface relief components. In most cases the authors have experience with one or other technology and often tend to describe the benefits of their own advances without fully putting them into the context of what is offered by alternative technologies. When comparing different forms of diffracting component it is also necessary to compare them with the equivalent conventional ones and to compare all relevant aspects of performance and cost. This critique is intended to be as impartial as possible but it must be admitted that the author's main experience is with surface relief technology.
Now and Then : Analyzing the Republic of Letters as a Communication Network

Huge advances in understanding patterns of human communication, and the underlying social networks where it takes place, have been made recently using massive automatically recorded data sets from digital communication, such as emails and phone calls. However, it is not clear to what extent these results on human behaviour are artefacts of contemporary communication technology and culture and if the fundamental patterns in communication have changed over history. This paper presents an analysis of historical epistolary metadata with the aim of comparing the underlying historical communication patterns with those of contemporary communication. Our work uses a new epistolary dataset containing metadata on over 150000 letters sent between the 16th and 19th centuries. The analyses indicate striking resemblances between contemporary and epistolary communication network patterns, including dyadic interactions and ego-level behaviour. Despite these positive findings, certain aspects of the letter datasets are insufficient to corroborate other similarities or differences for these communication networks.
Efficient labeling algorithms for adjacent quadratic shortest paths

In this article, we study the Adjacent Quadratic Shortest Path Problem (AQSPP), which consists in finding the shortest path on a directed graph when its total weight component also includes the impact of consecutive arcs. We provide a formal description of the AQSPP and propose an extension of Dijkstra's algorithm (that we denote aqD) for solving AQSPPs in polynomial-time and provide a proof for its correctness under some mild assumptions. Furthermore, we introduce an adjacent quadratic A* algorithm (that we denote aqA*) with a backward search for cost-to-go estimation to speed up the search. We assess the performance of both algorithms by comparing their relative performance with benchmark algorithms from the scientific literature and carry out a thorough collection of sensitivity analysis of the methods on a set of problem characteristics using randomly generated graphs. Numerical results suggest that: (i) aqA* outperforms all other algorithms, with a performance being about 75 times faster than aqD and the fastest alternative; (ii) the proposed solution procedures do not lose efficiency when the magnitude of quadratic costs vary; (iii) aqA* and aqD are fastest on random graph instances, compared with benchmark algorithms from scientific literature. We conclude the numerical experiments by presenting a stress test of the AQSPP in the context of real grid graph instances, with sizes up to $16 \times 10^6$ nodes, $64 \times 10^6$ arcs, and $10^9$ quadratic arcs.
Adverse Weather Image Translation with Asymmetric and Uncertainty-aware GAN

Adverse weather image translation belongs to the unsupervised image-to-image (I2I) translation task which aims to transfer adverse condition domain (eg, rainy night) to standard domain (eg, day). It is a challenging task because images from adverse domains have some artifacts and insufficient information. Recently, many studies employing Generative Adversarial Networks (GANs) have achieved notable success in I2I translation but there are still limitations in applying them to adverse weather enhancement. Symmetric architecture based on bidirectional cycle-consistency loss is adopted as a standard framework for unsupervised domain transfer methods. However, it can lead to inferior translation result if the two domains have imbalanced information. To address this issue, we propose a novel GAN model, i.e., AU-GAN, which has an asymmetric architecture for adverse domain translation. We insert a proposed feature transfer network (${T}$-net) in only a normal domain generator (i.e., rainy night->day) to enhance encoded features of the adverse domain image. In addition, we introduce asymmetric feature matching for disentanglement of encoded features. Finally, we propose uncertainty-aware cycle-consistency loss to address the regional uncertainty of a cyclic reconstructed image. We demonstrate the effectiveness of our method by qualitative and quantitative comparisons with state-of-the-art models. Codes are available at https://github.com/jgkwak95/AU-GAN.
Steady-state thermodynamics for population dynamics in fluctuating environments with side information

Steady-state thermodynamics (SST) is a relatively newly emerging subfield of physics, which deals with transitions between steady states. In this paper, we find an SST-like structure in population dynamics of organisms that can sense their fluctuating environments. As heat is divided into two parts in SST, we decompose population growth into two parts: housekeeping growth and excess growth. Then, we derive the Clausius equality and inequality for excess growth. Using numerical simulations, we demonstrate how the Clausius inequality behaves depending on the magnitude of noise and strategies that organisms employ. Finally, we discuss the novelty of our findings and compare them with a previous study.
Local quantum overlapping tomography

Reconstructing the full quantum state of a many-body system requires the estimation of a number of parameters that grows exponentially with system size. Nevertheless, there are situations in which one is only interested in a subset of these parameters and a full reconstruction is not needed. A paradigmatic example is a scenario where one aims at determining all the reduced states only up to a given size. Overlapping tomography provides constructions to address this problem with a number of product measurements much smaller than what is obtained when performing independent tomography of each reduced state. There are however many relevant physical systems with a natural notion of locality where one is mostly interested in the reduced states of neighboring particles. In this work, we study this form of local overlapping tomography. First of all, we show that, contrary to its full version, the number of product-measurement settings needed for local overlapping tomography does not grow with system size. Then, we present strategies for qubit and fermionic systems in selected lattice geometries. The developed methods find a natural application in the estimation of many-body systems prepared in current quantum simulators or quantum computing devices, where interactions are often local.
Traction Control of Electric Vehicles Using Sliding-Mode Controller with Tractive Force Observer

Traction control is an important element in modern vehicles to enhance drive efficiency, safety, and stability. Traction is produced by friction between tire and road, which is a nonlinear function of wheel slip. In this paper, a sliding-mode control approach is used to design a robust traction controller. The control objective is to operate vehicles such that a desired wheel slip ratio is achieved. A nonlinearity observer is employed to estimate tire tractive forces, which are used in the control law. Simulation and experimental results have illustrated the success of the proposed observer-based controller.