Miracle-dz/UniFAR-FTUs huggingface dataset:
What Do We Mean by “Accessibility Research”?: A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019

Accessibility research has grown substantially in the past few decades, yet there has been no literature review of the field. To understand current and historical trends, we created and analyzed a dataset of accessibility papers appearing at CHI and ASSETS since ASSETS’ founding in 1994. We qualitatively coded areas of focus and methodological decisions for the past 10 years (2010-2019, N=506 papers), and analyzed paper counts and keywords over the full 26 years (N=836 papers). Our findings highlight areas that have received disproportionate attention and those that are underserved—for example, over 43% of papers in the past 10 years are on accessibility for blind and low vision people. We also capture common study characteristics, such as the roles of disabled and nondisabled participants as well as sample sizes (e.g., a median of 13 for participant groups with disabilities and older adults). We close by critically reflecting on gaps in the literature and offering guidance for future work in the field.
Quantum Mathematics in Artificial Intelligence

In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, decision making, and and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.
UFA-FUSE: A Novel Deep Supervised and Hybrid Model for Multifocus Image Fusion

Traditional and deep learning-based fusion methods generated the intermediate decision map to obtain the fusion image through a series of postprocessing procedures. However, the fusion results generated by these methods are easy to lose some source image details or results in artifacts. Inspired by the image reconstruction techniques based on deep learning, we propose a multifocus image fusion network framework without any postprocessing to solve these problems in the end-to-end and supervised learning ways. To sufficiently train the fusion model, we have generated a large-scale multifocus image data set with ground-truth fusion images. What is more, to obtain a more informative fusion image, we further designed a novel fusion strategy based on unity fusion attention, which is composed of a channel attention module and a spatial attention module. Specifically, the proposed fusion approach mainly comprises three key components: feature extraction, feature fusion, and image reconstruction. We first utilize seven convolutional blocks to extract the image features from source images. Then, the extracted convolutional features are fused by the proposed fusion strategy in the feature fusion layer. Finally, the fused image features are reconstructed by four convolutional blocks. Experimental results demonstrate that the proposed approach for multifocus image fusion achieves remarkable fusion performance and superior time efficiency compared to 19 state-of-the-art fusion methods.
Discovery of the acetyl cation, CH3CO+, in space and in the laboratory.

Using the Yebes 40m and IRAM 30m radiotelescopes, we detected two series of harmonically related lines in space that can be fitted to a symmetric rotor. The lines have been seen towards the cold dense cores TMC-1, L483, L1527, and L1544. High level of theory ab initio calculations indicate that the best possible candidate is the acetyl cation, CH3CO+, which is the most stable product resulting from the protonation of ketene. We have produced this species in the laboratory and observed its rotational transitions Ju = 10 up to Ju = 27. Hence, we report the discovery of CH3CO+ in space based on our observations, theoretical calculations, and laboratory experiments. The derived rotational and distortion constants allow us to predict the spectrum of CH3CO+ with high accuracy up to 500 GHz. We derive an abundance ratio N(H2CCO)/N(CH3CO+)~44. The high abundance of the protonated form of H2CCO is due to the high proton affinity of the neutral species. The other isomer, H2CCOH+, is found to be 178.9 kJ mol-1 above CH3CO+. The observed intensity ratio between the K=0 and K=1 lines, ~2.2, strongly suggests that the A and E symmetry states have suffered interconversion processes due to collisions with H and/or H2, or during their formation through the reaction of H 3 + with H2CCO.
Towards Stratified Space Learning: Linearly Embedded Graphs

In this paper, we consider the simplest class of stratified spaces – linearly embedded graphs. We present an algorithm that learns the abstract structure of an embedded graph and models the specific embedding from a point cloud sampled from it. We use tools and inspiration from computational geometry, algebraic topology, and topological data analysis and prove the correctness of the identified abstract structure under assumptions on the embedding. The algorithm is implemented in the Julia package Skyler, which we used for the numerical simulations in this paper.
Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring the spatial distribution to be decomposed as Fourier series. This approach does not scale well to control problems requiring exploration in search space of more than two dimensions. To address this issue, we propose the use of tensor trains, a recent low-rank tensor decomposition technique from the field of multilinear algebra. The proposed solution is efficient, both computationally and storagewise, hence making it suitable for its online implementation in robotic systems. The approach is applied to a peg-in-hole insertion task requiring full 6-D end-effector poses, implemented with a seven-axis Franka Emika Panda robot. In this experiment, ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors.
Network structure and dynamics of effective models of nonequilibrium quantum transport

Across all scales of the physical world, dynamical systems can often be usefully represented as abstract networks that encode the system's units and inter-unit interactions. Understanding how physical rules shape the topological structure of those networks can clarify a system's function and enhance our ability to design, guide, or control its behavior. In the emerging area of quantum network science, a key challenge lies in distinguishing between the topological properties that reflect a system's underlying physics and those that reflect the assumptions of the employed conceptual model. To elucidate and address this challenge, we study networks that represent non-equilibrium quantum-electronic transport through quantum antidot devices -- an example of an open, mesoscopic quantum system. The network representations correspond to two different models of internal antidot states: a single-particle, non-interacting model and an effective model for collective excitations including Coulomb interactions. In these networks, nodes represent accessible energy states and edges represent allowed transitions. We find that both models reflect spin conservation rules in the network topology through bipartiteness and the presence of only even-length cycles. The models diverge, however, in the minimum length of cycle basis elements, in a manner that depends on whether electrons are considered to be distinguishable. Furthermore, the two models reflect spin-conserving relaxation effects differently, as evident in both the degree distribution and the cycle-basis length distribution. Collectively, these observations serve to elucidate the relationship between network structure and physical constraints in quantum-mechanical models. More generally, our approach underscores the utility of network science in understanding the dynamics and control of quantum systems.
Locality Sensitive Hashing for Efficient Similar Polygon Retrieval

Locality Sensitive Hashing (LSH) is an effective method of indexing a set of items to support efficient nearest neighbors queries in high-dimensional spaces. The basic idea of LSH is that similar items should produce hash collisions with higher probability than dissimilar items. We study LSH for (not necessarily convex) polygons, and use it to give efficient data structures for similar shape retrieval. Arkin et al. represent polygons by their"turning function"- a function which follows the angle between the polygon's tangent and the $ x $-axis while traversing the perimeter of the polygon. They define the distance between polygons to be variations of the $ L_p $ (for $p=1,2$) distance between their turning functions. This metric is invariant under translation, rotation and scaling (and the selection of the initial point on the perimeter) and therefore models well the intuitive notion of shape resemblance. We develop and analyze LSH near neighbor data structures for several variations of the $ L_p $ distance for functions (for $p=1,2$). By applying our schemes to the turning functions of a collection of polygons we obtain efficient near neighbor LSH-based structures for polygons. To tune our structures to turning functions of polygons, we prove some new properties of these turning functions that may be of independent interest. As part of our analysis, we address the following problem which is of independent interest. Find the vertical translation of a function $ f $ that is closest in $ L_1 $ distance to a function $ g $. We prove tight bounds on the approximation guarantee obtained by the translation which is equal to the difference between the averages of $ g $ and $ f $.
The role of atmospheric outflows in the migration of hot Jupiters

Many of observed hot Jupiters are subject to atmospheric outflows. Numerical simulations have shown that the matter escaping from the atmosphere can accumulate outside the orbit of the planet, forming a torus. In a few 108 yr, the mass of the torus can become large enough to exert a significant gravitational effect on the planet. Accumulation of mass, in its own turn, is hindered by the activity of the star, which leads to the photoevaporation of the torus matter. We explore the role of these and other factors in the planet’s migration in the epoch when the protoplanetary disc has already disappeared. Using HD 209458 system as an example, we show that the gravitational interaction with the torus leads to the possibility of migration of the planet to its observable position, starting from an orbit ≳0.3 au.
Measuring Recommender System Effects with Simulated Users

Imagine a food recommender system -- how would we check if it is \emph{causing} and fostering unhealthy eating habits or merely reflecting users' interests? How much of a user's experience over time with a recommender is caused by the recommender system's choices and biases, and how much is based on the user's preferences and biases? Popularity bias and filter bubbles are two of the most well-studied recommender system biases, but most of the prior research has focused on understanding the system behavior in a single recommendation step. How do these biases interplay with user behavior, and what types of user experiences are created from repeated interactions? In this work, we offer a simulation framework for measuring the impact of a recommender system under different types of user behavior. Using this simulation framework, we can (a) isolate the effect of the recommender system from the user preferences, and (b) examine how the system performs not just on average for an"average user"but also the extreme experiences under atypical user behavior. As part of the simulation framework, we propose a set of evaluation metrics over the simulations to understand the recommender system's behavior. Finally, we present two empirical case studies -- one on traditional collaborative filtering in MovieLens and one on a large-scale production recommender system -- to understand how popularity bias manifests over time.
Formation and Photoluminescence of Fluorescent Polymers

1Department of Chemistry, Tsinghua University, Beijing 100084, China 2Department of Materials Science and Engineering, The University of Tennessee, Knoxville, TN 37996, USA 3KCL (Oy Keskuslaboratorio-Centrallaboratorium Ab), Tekniikantie, P.O. Box 70, 02151 Espoo, Finland 4Department of Applied Chemistry, Peking University, Beijing 100871, China 5Department of Chemistry, Zhengzhou University, Zhengzhou 450001, China 6Departament of Chemistry, Zhejiang University, Hangzhou 310027, China
Freezing and high temperature thresholds of photosystem 2 compared to ice nucleation, frost and heat damage in evergreen subalpine plants

Freezing and high temperature thresholds of photosystem 2 (PS2), ice formation and frost and heat damage were measured in leaves of evergreen subalpine plants under conditions of naturally low (winter) to high (summer) PS2 efficiencies (FV/FM). The temperature-dependent change in basic Chl fluorescence (F0) (T-F0) technique that is usually used to assess the high temperature threshold of PS2 in a new approach was applied to test freezing temperature thresholds of PS2. T-F0 curves (+5 °C to −10 °C at 2 K h−1) revealed a significant, sudden increase in F0 on extracellular ice formation (−4.0 or −5.5 °C). The rise in F0 was recorded 0.3–0.6 K below ice nucleation (10–20 min later) and was produced by freeze dehydration of cells. The rise in F0 was not caused by frost damage, as during winter LT50 was lower than −27 °C and not by formation of ice on the leaf surface. Hence, F0 measurements during freezing are a useful tool to distinguish between surface ice and extracellular ice inside the leaf tissue which cannot be differentiated by other ice-detecting methods. PS2 efficiency significantly affected the shape of the high temperature T-F0 curves (20–65 °C at 1 K min−1). Under FV/FM >0.6, two F0 maxima were recorded. The fast rise phase to the first F0 maximum corresponded with tissue heat damage (LT50: 46.9–54.3 °C). The second F0 maximum occurred at leaf temperatures between 55 and 60 °C. Under FV/FM 10 K; hence, under low FV/FM, it cannot be determined by the T-F0 technique.
On the stability of boundary equilibria in Filippov systems

The leading-order approximation to a Filippov system \begin{document}$ f $\end{document} about a generic boundary equilibrium \begin{document}$ x^* $\end{document} is a system \begin{document}$ F $\end{document} that is affine one side of the boundary and constant on the other side. We prove \begin{document}$ x^* $\end{document} is exponentially stable for \begin{document}$ f $\end{document} if and only if it is exponentially stable for \begin{document}$ F $\end{document} when the constant component of \begin{document}$ F $\end{document} is not tangent to the boundary. We then show exponential stability and asymptotic stability are in fact equivalent for \begin{document}$ F $\end{document} . We also show exponential stability is preserved under small perturbations to the pieces of \begin{document}$ F $\end{document} . Such results are well known for homogeneous systems. To prove the results here additional techniques are required because the two components of \begin{document}$ F $\end{document} have different degrees of homogeneity. The primary function of the results is to reduce the problem of the stability of \begin{document}$ x^* $\end{document} from the general Filippov system \begin{document}$ f $\end{document} to the simpler system \begin{document}$ F $\end{document} . Yet in general this problem remains difficult. We provide a four-dimensional example of \begin{document}$ F $\end{document} for which orbits appear to converge to \begin{document}$ x^* $\end{document} in a chaotic fashion. By utilising the presence of both homogeneity and sliding motion the dynamics of \begin{document}$ F $\end{document} can in this case be reduced to the combination of a one-dimensional return map and a scalar function.
Non-Bayesian Parametric Missing-Mass Estimation

We consider the classical problem of missing-mass estimation, which deals with estimating the total probability of unseen elements in a sample. The missing-mass estimation problem has various applications in machine learning, statistics, language processing, ecology, sensor networks, and others. The naive, constrained maximum likelihood (CML) estimator is inappropriate for this problem since it tends to overestimate the probability of the observed elements. Similarly, the constrained Cramér-Rao bound (CCRB), which is a lower bound on the mean-squared-error (MSE) of unbiased estimators of the entire probability mass function (pmf) vector, does not provide a relevant bound for missing-mass estimation. In this paper, we introduce a non-Bayesian parametric model of the problem of missing-mass estimation. We introduce the concept of missing-mass unbiasedness by using the Lehmann unbiasedness definition. We derive a non-Bayesian CCRB-type lower bound on the missing-mass MSE (mmMSE), named the missing-mass CCRB (mmCCRB), based on the missing-mass unbiasedness. The proposed mmCCRB can be used for system design and for the performance evaluation of existing estimators. Moreover, based on the mmCCRB, we propose a new method to improve estimators by an iterative missing-mass Fisher-scoring method. Finally, we demonstrate via numerical simulations that the biased mmCCRB is a valid and informative lower bound on the mmMSE of state-of-the-art estimators for this problem: the CML, asymptotic profile maximum likelihood (aPML), Good-Turing, and Laplace estimators. We also show that the mmMSE and missing-mass bias of the Laplace estimator is reduced by using the new missing-mass Fisher-scoring method.
Best practices for fNIRS publications

Abstract The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers.
Intracage reactions in sodalites

Sodalites containing thiocyanate [Na8(AlSiO4)6(SCN)2·xH2O] and nitrite [Na8(AlSiO4)6(NO2)2·xH2O] have been synthesised by precipitation from aluminosilicate solutions at low temperature. Cation-exchange reactions in aqueous solution were undertaken on the thiocyanate material to afford the fully substituted silver and lithium analogues. The mild and low-temperature conditions under which cation exchange occurs contrast markedly with similar, previously described reactions on this family of compounds involving melts and direct reaction between solids at 300–800 °C. These compounds undergo slow intracage oxidation reactions of the anions on heating in air to yield sodalites containing OCN–,Sx–, and SO42– from SCN– and NO3– from NO2–. The extent and direction of these oxidation reactions depends upon the nature of the cation also present in the cage. The products of these indirect syntheses have been characterised by power X-ray and neutron diffraction, i.r. and u.v. –visible spectroscopy, and thermogravimetric analysis.
Fabrication of an NH2-MIL-88B photonic film for naked-eye sensing of organic vapors

Metal–organic frameworks (MOFs) as thin films have gained considerable attention in recent years because of their potential applications in different fields. However, preparation of integrated photonic MOF films still remains a major challenge. This study reports an approach for the fabrication of NH2-MIL-88B photonic films with potential for general application to other MOF films based on the simple spin-coating technique. A systematic study on the effect of MOF suspension concentration, rotation speed, and rotation cycles on the thickness and optical quality of coated films is presented. Moreover, the sensing performance of the NH2-MIL-88B photonic film deposited on a silicon wafer is evaluated using ultraviolet-visible reflection analysis and an optical digital camera, revealing the selective response to various organic vapors. More importantly, such selective sensing behavior can be easily and rapidly visualized by the naked eye.