Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory tracking control of dynamic non‐holonomic systems with unknown dynamics

Trajectory tracking control of the non-holonomic system has attracted lots of interest recently, but little has been done in the tracking problem of the uncertain dynamic non-holonomic systems. The paper deals with the trajectory tracking problem of the dynamic non-holonomic systems with unknown dynamics. New adaptive robust controllers are proposed for globally tracking problem. The controllers of low dimension and no singular points can make the configuration state asymptotically track the desired trajectory. An application to a wheeled mobile robot is presented, and the effectiveness of the approach is verified by simulation. Copyright © 1999 John Wiley & Sons, Ltd.
Knowledge-Aware Bayesian Co-Attention for Multimodal Emotion Recognition

Multimodal emotion recognition is a challenging research area that aims to fuse different modalities to predict human emotion. However, most existing models that are based on attention mechanisms have difficulty in learning emotionally relevant parts on their own. To solve this problem, we propose to incorporate external emotion-related knowledge in the co-attention based fusion of pre-trained models. To effectively incorporate this knowledge, we enhance the co-attention model with a Bayesian attention module (BAM) where a prior distribution is estimated using the emotion-related knowledge. Experimental results on the IEMOCAP dataset show that the proposed approach can outperform several state-of-the-art approaches by at least 0.7% unweighted accuracy (UA).
A mini-chemical scheme with net reactions for 3D general circulation models. II. 3D thermochemical modelling of WASP-39b and HD 189733b

The chemical inventory of hot Jupiter (HJ) exoplanets atmospheres continue to be observed by various ground and space based instruments in increasing detail and precision. It is expected that some HJs will exhibit strong non-equilibrium chemistry characteristics in their atmospheres, which might be inferred from spectral observations. We aim to model the three dimensional thermochemical non-equilibrium chemistry in the atmospheres of the HJs WASP-39b and HD 189733b. We couple a lightweight, reduced chemical network `mini-chem' that utilises net reaction rate tables to the Exo-FMS General Circulation Model (GCM). We perform GCM models of the exoplanets WASP-39b and HD 189733b as case studies of the coupled mini-chem scheme. The GCM results are then post-processed using the 3D radiative-transfer model gCMCRT to produce transmission and emission spectra to assess the impact of non-equilibrium chemistry on their observable properties. Both simulations show significant departures from chemical equilibrium (CE) due to the dynamical motions of the atmosphere. The spacial distribution of species generally follows closely the dynamical features of the atmosphere rather than the temperature field. Each molecular species exhibits a different quench level in the simulations, also dependent on the latitude of the planet. Major differences are seen in the transmission and emission spectral features between the CE and kinetic models. Our simulations indicate that considering the 3D kinetic chemical structures of HJ atmospheres has an important impact on physical interpretation of observational data. Drawing bulk atmospheric parameters from fitting feature strengths may lead to inaccurate interpretation of chemical conditions in the atmosphere of HJs. Our open source mini-chem module is simple to couple with contemporary HJ GCM models without substantially increasing required computational resources.
On the classification of fibrations by genus two singular curves via fibrations by elliptic curves on surfaces

In 1944 Zariski discovered that Bertini's theorem on variable singular points is no longer true when we pass from a field of characteristic zero to a field of positive characteristic. In other words, he found fibrations by singular curves, which only exist in positive characteristic. Such fibrations are connected with many interesting phenomena. For instance, the extension of Enrique's classification of surfaces to positive characteristic (Bombieri and Mumford in 1976), the counterexamples of Kodaira vanishing theorem (Mukai in 2013 and Zheng in 2016) and the isolated singularities with infinity Milnor number (Hefez, Rodrigues and Salom\~ao in 2019). In this work we are going to show that the smoothing process introduced by Shimada in 1991 can be used to classify the set of fibrations by genus two singular curves, up to isomorphism among their generic fibers, such that their smoothing are elliptic fibrations on rational surfaces. Moreover we will also describe the vector fields that can be used to recover such fibrations by singular curves via quotient of rational elliptic surfaces.
A Finite-volume Approach for Simulation of Liquid-column Separation in Pipelines

A finite-volume approach, based on the MUSCL-Hancock method, is presented and applied to liquid-column separation transients in pipelines. In the mathematical model, sudden closure of a valve on the downstream end of a pipeline initiates the hydraulic transient, while a head tank maintains constant upstream pressure. The two-phase fluid is treated as a homogeneous mixture, and changes in fluid pressure are assumed to occur at constant entropy. Effects of pipe elasticity on wave propagation speed are included in the model by coupling the circumferential stress-strain relation for the pipe wall to the local fluid pressure. In regions of the domain where the solution is smooth, second-order accuracy is achieved by means of data reconstruction based on sloping-difference formulas. Slope limiting prevents the development of spurious oscillations in the neighborhood of steep wave fronts. Data reconstruction leads to a piece-wise linear representation of the solution, which is discontinuous across cell boundaries. In order to advance the solution in time, a Riemann problem is solved on each cell junction to obtain mass and momentum flux contributions. A splitting technique, which separates flux terms from gravity and frictional effects, allows the compatibility equations for the Riemann problem to be expressed as total differentials of fluid velocity and an integral that depends only on the fluid pressure. Predictions for large-amplitude pressure pulses caused by liquid-column separation and rejoining are compared against experimental data available in the literature. Amplitude and timing of the predicted pressure response shows reasonably good agreement with experimental data even when as few as 20-40 computational cells are used to describe axial variations in fluid conditions along a pipeline of approximately 35 m in length. An advantage of the current method is that it does not give rise to spurious oscillations when grid refinement is performed. The presence of nonphysical oscillations has been a drawback of a commonly used method based on discrete vapor cavities and characteristic treatment of wave propagation.
Electromagnetic Scattering by Bianisotropic Spheres

Electromagnetic fields in bulk bianisotropic media contain plane waves whose k-vectors can be found using the method of the index of refraction’s operator and belong to the Fresnel wave surfaces that fall into one of the five hyperbolic classes of the Durach et al. taxonomy of bianisotropic media. Linear combinations of vector spherical harmonics can be used as a set of solutions of vector Helmholtz equations in gyrotropic media to develop Mie’s theory of scattering by anisotropic spheres as accomplished by Lin et al. and Li et al. In this study, we introduced electromagnetic orbitals for bianisotropic media as linear combinations of vector spherical harmonics, which represent solutions of Maxwell’s equations in bianisotropic media. Using these bianisotropic orbitals, we developed a theory of the scattering of electromagnetic radiation by bianisotropic spheres with arbitrary effective material parameters and sizes. As a by-product, we obtained a simple expression for the expansion of a vector plane wave over vector spherical harmonics in a more compact form than the frequently used by Sarkar et al. We obtained the polarizability expressions in the Rayleigh limit in agreement with the results of the electrostatic approximation of Lakhtahia and Sihvola.
SSVMR: Saliency-Based Self-Training for Video-Music Retrieval

With the rise of short videos, the demand for selecting appropriate background music (BGM) for a video has increased significantly, video-music retrieval (VMR) task gradually draws much attention by research community. As other cross-modal learning tasks, existing VMR approaches usually attempt to measure the similarity between the video and music in the feature space. However, they (1) neglect the inevitable label noise; (2) neglect to enhance the ability to capture critical video clips. In this paper, we propose a novel saliency-based self-training framework, which is termed SSVMR. Specifically, we first explore to fully make use of the information containing in the training dataset by applying a semi-supervised method to suppress the adverse impact of label noise problem, where a self-training approach is adopted. In addition, we propose to capture the saliency of the video by mixing two videos at span level and preserving the locality of the two original videos. Inspired by back translation in NLP, we also conduct back retrieval to obtain more training data. Experimental results on MVD dataset show that our SSVMR achieves the state-of-the-art performance by a large margin, obtaining a relative improvement of 34.8% over the previous best model in terms of R@1.
Poisoning Web-Scale Training Datasets is Practical

Deep learning models are often trained on distributed, webscale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionally introduce malicious examples to a model's performance. Our attacks are immediately practical and could, today, poison 10 popular datasets. Our first attack, split-view poisoning, exploits the mutable nature of internet content to ensure a dataset annotator's initial view of the dataset differs from the view downloaded by subsequent clients. By exploiting specific invalid trust assumptions, we show how we could have poisoned 0.01% of the LAION-400M or COYO-700M datasets for just $60 USD. Our second attack, frontrunning poisoning, targets web-scale datasets that periodically snapshot crowd-sourced content -- such as Wikipedia -- where an attacker only needs a time-limited window to inject malicious examples. In light of both attacks, we notify the maintainers of each affected dataset and recommended several low-overhead defenses.
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space

The output distribution of a neural network (NN) over the entire input space captures the complete input-output mapping relationship, offering insights toward a more comprehensive NN understanding. Exhaustive enumeration or traditional Monte Carlo methods for the entire input space can exhibit impractical sampling time, especially for high-dimensional inputs. To make such difficult sampling computationally feasible, in this paper, we propose a novel Gradient-based Wang-Landau (GWL) sampler. We first draw the connection between the output distribution of a NN and the density of states (DOS) of a physical system. Then, we renovate the classic sampler for the DOS problem, the Wang-Landau algorithm, by replacing its random proposals with gradient-based Monte Carlo proposals. This way, our GWL sampler investigates the under-explored subsets of the input space much more efficiently. Extensive experiments have verified the accuracy of the output distribution generated by GWL and also showcased several interesting findings - for example, in a binary image classification task, both CNN and ResNet mapped the majority of human unrecognizable images to very negative logit values.
The Financial Health of a Company and the Risk of its Default: Back to the Future

We theorize the financial health of a company and the risk of its default. A company is financially healthy as long as its equilibrium in the financial system is maintained, which depends on the cost attributable to the probability that equilibrium may decay. The estimate of that probability is based on the credibility and uncertainty of the company's financial forecasts. Accordingly, we develop an equilibrium model establishing ranges of interest rates as a function of predictable corporate performance and of its credit supply conditions. As a result, our model estimates idiosyncratic default risk and provides intrinsically forward-looking PD.
The origin of strain-induced stabilisation of superconductivity in the lanthanum cuprates

Suppression of superconductivity in favour of a striped phase, and its coincidence with a structural transition from a low-temperature orthorhombic (LTO) to a low-temperature tetragonal (LTT) phase, is a ubiquitous feature of hole-doped lanthanum cuprates. We study the effect of anisotropic strain on this transition using density-functional theory on both La$_2$CuO$_4$ and the recently-synthesised surrogate La$_2$MgO$_4$ to decouple electronic and structural effects. Strikingly, we find that compressive strain applied diagonally to the in-plane metal-oxygen bonds dramatically stabilises the LTO phase. Given the mutual exclusivity of 3D superconductivity and long-range static stripe order, we thereby suggest a structural mechanism for understanding experimentally-observed trends in the superconducting $T_{\mathrm{c}}$ under uniaxial pressure, and suggest principles for tuning it.
Intersections of Poisson $ k $-flats in constant curvature spaces

Poisson processes in the space of $k$-dimensional totally geodesic subspaces ($k$-flats) in a $d$-dimensional standard space of constant curvature $\kappa\in\{-1,0,1\}$ are studied, whose distributions are invariant under the isometries of the space. We consider the intersection processes of order $m$ together with their $(d-m(d-k))$-dimensional Hausdorff measure within a geodesic ball of radius $r$. Asymptotic normality for fixed $r$ is shown as the intensity of the underlying Poisson process tends to infinity for all $m$ satisfying $d-m(d-k)\geq 0$. For $\kappa\in\{-1,0\}$ the problem is also approached in the set-up where the intensity is fixed and $r$ tends to infinity. Again, if $2k\le d+1$ a central limit theorem is shown for all possible values of $m$. However, while for $\kappa=0$ asymptotic normality still holds if $2k>d+1$, we prove for $\kappa=-1$ convergence to a non-Gaussian infinitely divisible limit distribution in the special case $m=1$. The proof of asymptotic normality is based on the analysis of variances and general bounds available from the Malliavin--Stein method. We also show for general $\kappa\in\{-1,0,1\}$ that, roughly speaking, the variances within a general observation window $W$ are maximal if and only if $W$ is a geodesic ball having the same volume as $W$. Along the way we derive a new integral-geometric formula of Blaschke--Petkantschin type in a standard space of constant curvature.
How well forecast were the 2004 and 2005 Atlantic and US hurricane seasons?

The 2004 and 2005 hurricane seasons with seven intense hurricane landfalls on the U.S. and an estimated total damage bill approaching US$ 200bn rank as the most active and damaging consecutive hurricane years on record. This high activity was due to a combination of warm sea surface temperatures in the tropical North Atlantic, low vertical wind shear and steering currents that favoured storms being steered towards the U.S. rather than recurving at sea. Currently there are four main organisations that provide seasonal forecasts of Atlantic tropical cyclone activity: TropicalStormRisk (TSR), Prof W Gray, NOAA and the Meteorological Institute, Cuba. How well did the publically available seasonal outlooks anticipate the record-breaking Atlantic and U.S. hurricane activity in 2004 and 2005, and would business have benefited from acting upon the forecasts made? This paper compares the performance of forecasts (deterministic and probabilistic) issued by different centres at different lead times. It also shows why seasonal forecast precision may now be high enough to offer practical benefits.
Graph Generative Model for Benchmarking Graph Neural Networks

As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems. Unfortunately, such graph datasets are often generated from online, highly privacy-restricted ecosystems, which makes research and development on these datasets hard, if not impossible. This greatly reduces the amount of benchmark graphs available to researchers, causing the field to rely only on a handful of publicly-available datasets. To address this problem, we introduce a novel graph generative model, Computation Graph Transformer (CGT) that learns and reproduces the distribution of real-world graphs in a privacy-controlled way. More specifically, CGT (1) generates effective benchmark graphs on which GNNs show similar task performance as on the source graphs, (2) scales to process large-scale graphs, (3) incorporates off-the-shelf privacy modules to guarantee end-user privacy of the generated graph. Extensive experiments across a vast body of graph generative models show that only our model can successfully generate privacy-controlled, synthetic substitutes of large-scale real-world graphs that can be effectively used to benchmark GNN models.
The Messy Nature of Fiber Spectra: Star–Quasar Pairs Masquerading as Dual Type 1 AGNs

Theoretical studies predict that the most significant growth of supermassive black holes (SMBHs) occurs in late-stage mergers, coinciding with the manifestation of dual active galactic nuclei (AGNs), and both major and minor mergers are expected to be important for dual AGN growth. In fact, dual AGNs in minor mergers should be signposts for efficient minor-merger-induced SMBH growth for both the more and less massive progenitor. We identified two candidate dual AGNs residing in apparent minor mergers with mass ratios of ∼1:7 and ∼1:30. Sloan Digital Sky Survey (SDSS) fiber spectra show broad and narrow emission lines in the primary nuclei of each merger while only a narrow [O iii] emission line and a broad and prominent Hα/[N ii] complex is observed in the secondary nuclei. The FWHMs of the broad Hα lines in the primary and secondary nuclei are inconsistent in each merger, suggesting that each nucleus in each merger hosts a Type 1 AGN. However, spatially resolved Large Binocular Telescope optical spectroscopy reveals rest-frame stellar absorption features, indicating the secondary sources are foreground stars and that the previously detected broad lines are likely the result of fiber spillover effects induced by the atmospheric seeing at the time of the SDSS observations. This study demonstrates for the first time that optical spectroscopic searches for Type 1/Type 1 pairs similarly suffer from fiber spillover effects as has been observed previously for Seyfert 2 dual AGN candidates. The presence of foreground stars may not have been clear if an instrument with more limited wavelength range or limited sensitivity had been used.
ImageAssist: Tools for Enhancing Touchscreen-Based Image Exploration Systems for Blind and Low Vision Users

Blind and low vision (BLV) users often rely on alt text to understand what a digital image is showing. However, recent research has investigated how touch-based image exploration on touchscreens can supplement alt text. Touchscreen-based image exploration systems allow BLV users to deeply understand images while granting a strong sense of agency. Yet, prior work has found that these systems require a lot of effort to use, and little work has been done to explore these systems’ bottlenecks on a deeper level and propose solutions to these issues. To address this, we present ImageAssist, a set of three tools that assist BLV users through the process of exploring images by touch — scaffolding the exploration process. We perform a series of studies with BLV users to design and evaluate ImageAssist, and our findings reveal several implications for image exploration tools for BLV users.
Complex saddles of three-dimensional de Sitter gravity via holography

We determine complex saddles of three-dimensional gravity with a positive cosmological constant by applying the recently proposed holography. It is sometimes useful to consider a complexified metric to study quantum gravity as in the case of the no-boundary proposal by Hartle and Hawking. However, there would be too many saddles for complexified gravity, and we should determine which saddles to take. We describe the gravity theory by three-dimensional SL$(2,\mathbb{C})$ Chern-Simons theory. At the leading order in the Newton constant, its holographic dual is given by Liouville theory with a large imaginary central charge. We examine geometry with a conical defect, called a de Sitter black hole, from a Liouville two-point function. We also consider geometry with two conical defects, whose saddles are determined by the monodromy matrix of Liouville four-point function. Utilizing Chern-Simons description, we extend the similar analysis to the case with higher-spin gravity.
Towards Measuring and Scoring Speaker Diarization Fairness

Speaker diarization, or the task of finding"who spoke and when", is now used in almost every speech processing application. Nevertheless, its fairness has not yet been evaluated because there was no protocol to study its biases one by one. In this paper we propose a protocol and a scoring method designed to evaluate speaker diarization fairness. This protocol is applied on a large dataset of spoken utterances and report the performances of speaker diarization depending on the gender, the age, the accent of the speaker and the length of the spoken sentence. Some biases induced by the gender, or the accent of the speaker were identified when we applied a state-of-the-art speaker diarization method.
Leveraging Prior Knowledge in Reinforcement Learning via Double-Sided Bounds on the Value Function

An agent's ability to leverage past experience is critical for efficiently solving new tasks. Approximate solutions for new tasks can be obtained from previously derived value functions, as demonstrated by research on transfer learning, curriculum learning, and compositionality. However, prior work has primarily focused on using value functions to obtain zero-shot approximations for solutions to a new task. In this work, we show how an arbitrary approximation for the value function can be used to derive double-sided bounds on the optimal value function of interest. We further extend the framework with error analysis for continuous state and action spaces. The derived results lead to new approaches for clipping during training which we validate numerically in simple domains.