Miracle-dz/UniFAR-FTUs huggingface dataset:
A CONSTRUCTION OF UNIFORM LIGHTNESS SCALES ON VARIOUS BACKGROUNDS IN UNIFIED TERMS

The attribute of intensity in color is called either brightness or lightness according to whether color is perceived to belong to an aperture or self-luminous area or to a surface. The former varies from dim to bright whereas the latter from black to white in the case of achromatic color (Committee on colorimetry, 1953; Judd, 1951). For the brightness the bril scale has been proposed by Hanes (1949 a, 1949 b) on the basis of his own experimental data coupled with the result obtained by Troland (1930), which has stirred up some discussion (Michels, 1954; Stevens & Galanter, 1957). As to the lightness the Munsell value scale is widely known. The series of patches extending from black to white by subjectively equal steps has been established as the result of extraordinarily thorough investigations; indeed, according to Stevens, it is probably the best determined category-scale in existence (Stevens, 1958, p.642). As will be discussed later, however, it might be a matter of controversy to regard the Munsell value function as a category scale. The value scale in the Munsell renotation, which has wide practical utility at present in industry and commerce as well as in science, stems from the results of the experiments by Munsell, Sloan, and Godlove (1933; Godlove, 1933**). Therein, two procedures gave, in complete agreement, a subjectively uniform scale for the lightness as a function of the reflectance of stimulus. The adjective uniform is for the sake of indicating that the same amount of increase in the numerical value assigned is to correspond to the subjectively equal increase in lightness. One of the procedures was cummulating jnd's and another was the method of equisection. As a rule, two scales developed by these methods are found to be nonlinearly related to each other in the attribute of intensity (Stevens, 1957, 1958 a; Stevens & Galanter, 1957), which has provided the basis to cast doubts as to the assumption since Fechner that jnd's are subjectively equal throughout the stimulus range. Hence, the coincidence of both scales in the above mentioned experiments seems to be exceptional and it is especially astonishing when the circumstances are taken into account that only five jnd's were experimentally determined and values interpolated from these were cummulated by graphical integration; the procedure containing some problems in it (Luce & Edward, 1958). It is to be noted that agreement of the same kind was also reported by * The two experiments involved were carried
Knowledge, Networks, and Knowledge Networks: A Review and Research Agenda

A large and growing body of empirical research shows that social relationships and the networks these relationships constitute are influential in explaining the processes of knowledge creation, diffusion, absorption and use. We refer to such networks as 'knowledge networks. ' We advance an understanding of knowledge networks at multiple levels by conducting a systematic review and analysis of empirical research published on this topic in leading management, psychology, sociology and economics journals. We develop a comprehensive framework that organizes the knowledge networks literature, which we use to review extant empirical research within and across multiple disciplines and levels of analysis. We identify points of coherence and conflict in theoretical arguments and empirical results within and across levels and identify emerging themes and promising areas for future research.
Non-Parametric Manifold Learning

We introduce an estimator for distances in a compact Riemannian manifold based on graph Laplacian estimates of the Laplace-Beltrami operator. We upper bound the error in the estimate of manifold distances, or more precisely an estimate of a spectrally truncated variant of manifold distance of interest in non-commutative geometry (cf. [Connes and Suijelekom, 2020]), in terms of spectral errors in the graph Laplacian estimates and, implicitly, several geometric properties of the manifold. A consequence is a proof of consistency for (untruncated) manifold distances. The estimator resembles, and in fact its convergence properties are derived from, a special case of the Kontorovic dual reformulation of Wasserstein distance known as Connes' Distance Formula.
A blowup formula for virtual enumerative invariants on projective surfaces

We apply virtual localization to the problem of finding blowup formulae for virtual sheaf-theoretic invariants on a smooth projective surface. This leads to a general procedure that can be used to express virtual enumerative invariants on the blowup in terms of those on the original surface. We use an enhanced master space construction over the moduli spaces of $m$-stable sheaves introduced by Nakajima and Yoshioka. Our work extends their analogous results for the equivariant moduli spaces of framed sheaves on $\mathbb{P}^2$. In contrast to their work, we make no use of GIT methods and work with an arbitrary smooth complex projective surface, assuming only the absence of strictly semistable sheaves. The main examples to keep in mind are Mochizuki's virtual analogue of the Donaldson invariant and the virtual $\chi_y$-genus of the moduli space of Gieseker semistable sheaves on the surface.
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance. Demonstration videos are available at https://caipeide.github. io/autorace-dirl/.
How balance and sample size impact bias in the estimation of causal treatment effects: A simulation study

Observational studies are often used to understand relationships between exposures and outcomes. They do not, however, allow conclusions about causal relationships to be drawn unless statistical techniques are used to account for the imbalance of confounders across exposure groups. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the imbalances between exposure groups by weighting the groups to look alike on the observed confounders. Despite the plethora of available methods to estimate PSBW, there is little guidance on what one defines as adequate balance, and unbiased and robust estimation of the causal treatment effect is not guaranteed unless several conditions hold. Accurate inference requires that 1) the treatment allocation mechanism is known, 2) the relationship between the baseline covariates and the outcome is known, 3) adequate balance of baseline covariates is achieved post-weighting, 4) a proper set of covariates to control for confounding bias is known, and 5) a large enough sample size is available. In this article we use simulated data of various sizes to investigate the influence of these five factors on statistical inference. Our findings provide evidence that the maximum KolmogorovSmirnov statistic is the proper statistical measure to assess balance on the baseline covariates, in contrast to the mean standardised mean difference used in many applications, and 0.1 is a suitable threshold to consider as acceptable balance. Finally, we recommend that 60 − 80 observations, per confounder per treatment group, are required to obtain a reliable and unbiased estimation of the causal treatment effect.
Cobordism distance on the projective space of the knot concordance group

The cobordism distance on the knot concordance group is used to define a measure of how close two knots are to being linearly dependent. Roughly stated, d(K,J) is defined by minimizing the cobordism distance between pairs of knots in cyclic subgroups containing K and J. When made precise, this leads to the definition of the projective space of the knot concordance group. We explore basic properties of this projective space and its integer-valued metric by considering torus knots. Twist knots are used to show that the associated simplicial complex contains an infinite set of simplices of arbitrarily large dimension.
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering

Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlooked. First, the accumulative error, inflicted by learning from noisy clustering assignments, degrades the effectiveness of the clustering model. This problem is called Feature Randomness. Second, reconstructing the adjacency matrix sets the model to learn irrelevant similarities for the clustering task. This problem is called Feature Drift. Furthermore, the theoretical relation between the aforementioned problems has not yet been investigated. We study these issues from two aspects: (1) there is a trade-off between Feature Randomness and Feature Drift when clustering and reconstruction are performed at the same level, and (2) the problem of Feature Drift is more pronounced for GAE models, compared with vanilla auto-encoder models. Thus, we reformulate the GAE-based clustering methodology. Our solution is two-fold. First, we propose a sampling operator <inline-formula><tex-math notation="LaTeX">$\Xi$</tex-math><alternatives><mml:math><mml:mi>Ξ</mml:mi></mml:math><inline-graphic xlink:href="bouguessa-ieq1-3220948.gif"/></alternatives></inline-formula> that triggers a protection mechanism against Feature Randomness. Second, we propose an operator <inline-formula><tex-math notation="LaTeX">$\Upsilon$</tex-math><alternatives><mml:math><mml:mi>ϒ</mml:mi></mml:math><inline-graphic xlink:href="bouguessa-ieq2-3220948.gif"/></alternatives></inline-formula> that triggers a correction mechanism against Feature Drift by gradually transforming the reconstructed graph into a clustering-oriented one. As principal advantages, our solution grants a considerable improvement in clustering effectiveness and can be easily tailored to GAE models.
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis

Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has become a vibrant and important research topic in natural language processing. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. It is not clear how models utilize multimodal information for sentiment predictions. Despite recent advances in techniques for enhancing the explainability of machine learning models, they often target unimodal scenarios (e.g., images, sentences), and little research has been done on explaining multimodal models. In this paper, we present an interactive visual analytics system, M2Lens, to visualize and explain multimodal models for sentiment analysis. M2Lens provides explanations on intra- and inter-modal interactions at the global, subset, and local levels. Specifically, it summarizes the influence of three typical interaction types (i.e., dominance, complement, and conflict) on the model predictions. Moreover, M2Lens identifies frequent and influential multimodal features and supports the multi-faceted exploration of model behaviors from language, acoustic, and visual modalities. Through two case studies and expert interviews, we demonstrate our system can help users gain deep insights into the multimodal models for sentiment analysis.
Provably Efficient Multi-Task Reinforcement Learning with Model Transfer

We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player information sharing. We design and analyze an algorithm based on the idea of model transfer, and provide gap-dependent and gap-independent upper and lower bounds that characterize the intrinsic complexity of the problem.
Clinical Relation Extraction Using Transformer-based Models

The newly emerged transformer technology has a tremendous impact on NLP research. In the general English domain, transformer-based models have achieved state-of-the-art performances on various NLP benchmarks. In the clinical domain, researchers also have investigated transformer models for clinical applications. The goal of this study is to systematically explore three widely used transformer-based models (i.e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain. We developed a series of clinical RE models based on three transformer architectures, namely BERT, RoBERTa, and XLNet. We evaluated these models using 2 publicly available datasets from 2018 MADE1.0 and 2018 n2c2 challenges. We compared two classification strategies (binary vs. multi-class classification) and investigated two approaches to generate candidate relations in different experimental settings. In this study, we compared three transformer-based (BERT, RoBERTa, and XLNet) models for relation extraction. We demonstrated that the RoBERTa-clinical RE model achieved the best performance on the 2018 MADE1.0 dataset with an F1-score of 0.8958. On the 2018 n2c2 dataset, the XLNet-clinical model achieved the best F1-score of 0.9610. Our results indicated that the binary classification strategy consistently outperformed the multi-class classification strategy for clinical relation extraction. Our methods and models are publicly available at this https URL. We believe this work will improve current practice on clinical relation extraction and other related NLP tasks in the biomedical domain.
Controlled engineering of a vortex-bright soliton dynamics using a constant driving force

A vortex-bright (VB) soliton can precess around a fixed point. Here, we find numerically that the fixed point and the associated precessional orbits can be shifted by applying a constant driving force on the bright component, the displacement is proportional to the force with a minus sign. This robust dynamics is then discussed theoretically by treating the VB soliton as an effective point particle. By appropriately tuning the force, the VB soliton can be guided following an arbitrary trajectory with very weak density modulations, including that it can be repeatedly pinned and released at will. This finding opens a highly flexible and controllable approach of engineering the dynamics of vortical structures in Bose–Einstein condensates.
A Probit Estimation of Urban Bases of Environmental Awareness: Evidence from Sylhet City, Bangladesh

This paper evaluates the significant factors contributing to environmental awareness among individuals living in the urban area of Sylhet, Bangladesh. Ordered Probit(OPM) estimation is applied on the value of ten measures of individual environmental concern. The estimated results of OPM reveal the dominance of higher education, higher income, and full-employment status on environmental concern and environmentally responsible behavior. Younger and more educated respondents tended to be more knowledgeable and concerned than older and less educated respondents. The marginal effect of household size, middle-income level income, and part-time employment status of the survey respondents played a less significant role in the degree of environmental awareness. Findings also validate the "age hypothesis" proposed by Van Liere and Dunlap (1980), and the gender effect reveals an insignificant role in determining the degree of environmental concern. Environmental awareness among urban individuals with higher income increased linearly with environmental awareness programs which may have significant policy importance, such as environmental awareness programs for old-aged and less-educated individuals, and may lead to increased taxation on higher income groups to mitigate city areas' pollution problems.
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning

We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy. Motivated by stochastic optimization and the federated learning (FL) paradigm, we focus on the case where a small fraction of data samples are randomly sub-sampled in each round to participate in the learning process, which also enables privacy amplification. To obtain even stronger local privacy guarantees, we study this in the shuffle privacy model, where each client randomizes its response using a local differentially private (LDP) mechanism and the server only receives a random permutation (shuffle) of the clients' responses without their association to each client. The principal result of this paper is a privacy-optimization performance trade-off for discrete randomization mechanisms in this sub-sampled shuffle privacy model. This is enabled through a new theoretical technique to analyze the Renyi Differential Privacy (RDP) of the sub-sampled shuffle model. We numerically demonstrate that, for important regimes, with composition our bound yields significant improvement in privacy guarantee over the state-of-the-art approximate Differential Privacy (DP) guarantee (with strong composition) for sub-sampled shuffled models. We also demonstrate numerically significant improvement in privacy-learning performance operating point using real data sets.
A Bilevel Programming Framework for Joint Edge Resource Management and Pricing

The emerging edge computing (EC) paradigm promises to provide low latency and ubiquitous computation to numerous mobile and Internet of Things (IoT) devices at the network edge. How to efficiently allocate geographically distributed heterogeneous edge resources to a variety of services is a challenging task. While this problem has been studied extensively in recent years, most of the previous work has largely ignored the preferences of the services when making edge resource allocation decisions. To this end, this article introduces a novel bilevel optimization model, which explicitly takes the service preferences into consideration, to study the interaction between an EC platform and multiple services. The platform manages a set of edge nodes (ENs) and acts as the leader while the services are the followers. Given the service placement and resource pricing decisions of the leader, each service decides how to optimally divide its workload to different ENs. The proposed framework not only maximizes the profit of the platform but also minimizes the cost of every service. When there is a single EN, we derive a simple analytic solution for the underlying problem. For the general case with multiple ENs and multiple services, we present a Karush–Kuhn–Tucker-based solution and a duality-based solution, combining with a series of linearizations, to solve the bilevel problem. Extensive numerical results are shown to illustrate the efficacy of the proposed model.
Joint Power and Gain Allocation in MDM-WDM Optical Communication Networks Based on Enhanced Gaussian Noise Model

Achieving reliable communication over different wavelength channels and modes is one of the main goals of Mode Division Multiplexing-Wavelength Division Multiplexing (MDM-WDM) transmission. The reliability can be described by the minimum Signal to Noise Ratio (SNR) margin which depends on launch power, the gain of Few-Mode Erbium-Doped Fiber Amplifiers (FM-EDFA), and the nonlinear impairments of Few-Mode Fiber (FMF). In this paper, we develop the Enhanced Gaussian Noise (EGN) nonlinear model for FMF, which can be used in both weak and strong coupling regimes. We validate the model by comparing simulation results with those obtained through the Split-Step Fourier Method. Based on our proposed EGN model, we address the problem of joint optimized power and gain allocation based on minimum SNR margin maximization when accounting for practical FM-EDFA constraints such as saturation power and maximum gain. The problem is solved using a convex optimization approach and considering different scenarios such as the best equal power, optimized power, and joint optimized power and gain. Results demonstrate that the minimum SNR margin improvement for the joint optimized power and gain allocation compared to the best equal power allocation is $1.4~dB$ and $1.7~dB$ for MDM-single channel and single-mode fiber-WDM systems, respectively.
An Experimental Comparison of the Sooting Behavior of Synthetic Jet Fuels

An experimental study is conducted for the laminar, atmospheric pressure, sooting, coflow diffusion flames of prevaporized Jet A-1 and four synthetic jet fuels to compare their sooting characteristics and flame structures. Soot volume fraction, species concentration, and temperature profiles are measured, using the laser extinction measurement method, gas chromatography, and fine wire thermocouples, respectively. To evaluate the sooting tendency of the fuels, their smoke point heights are measured using the ASTM D1322 method and compared. The synthetic jet fuels under investigation are (1) Fully Synthetic Jet Fuel (FSJF), which is a coal-to-liquids (CtL) kerosene plus some coal tar derived material from Sasol, (2) Fischer–Tropsch Synthetic Paraffinic Kerosene (FT-SPK), which is a gas-to-liquids (GtL) product from Shell, (3) SPK plus naphthenic cut (50% by volume), and (4) SPK plus hexanol (20% by volume). The threshold sooting index (TSI) of the proposed surrogate mixtures for the fuels are calculated and.
The Posttransit Tail of WASP-107b Observed at 10830 Å

Understanding the effects of high-energy radiation and stellar winds on planetary atmospheres is vital for explaining the observed properties of close-in exoplanets. Observations of transiting exoplanets in the triplet of metastable helium lines at 10830 Å allow extended atmospheres and escape processes to be studied for individual planets. We observed one transit of WASP-107b with NIRSPEC on Keck at 10830 Å. Our observations, for the first time, had significant posttransit phase coverage, and we detected excess absorption for over an hour after fourth contact. The data can be explained by a comet-like tail extending out to ∼7 planet radii, which corresponds to roughly twice the Roche lobe radius of the planet. Planetary tails are expected based on three-dimensional simulations of escaping exoplanet atmospheres, particularly those including the interaction between the escaped material and strong stellar winds, and have been previously observed at 10830 Å in at least one other exoplanet. With both the largest midtransit absorption signal and the most extended tail observed at 10830 Å, WASP-107b remains a keystone exoplanet for atmospheric escape studies.