Miracle-dz/UniFAR-FTUs huggingface dataset:
A Method of Bivariate Trimming for Robust Estimation of the Correlation Coefficient

SUMMARY It is proposed that bivariate data should be trimmed of those points which define the convex hull, for robust estimation of the product-moment correlation coefficient. Properties of this method are examined by a Monte Carlo investigation. Other applications are mentioned. THE product-moment correlation coefficient, like many other parametric estimators, is sensitive to outliers and disturbances in the tails of the bivariate distribution of quantitative variables. For this reason there is much to recommend the routine application of a trimming procedure before this statistic is calculated. Previous authors, e.g. Nath (1971), have investigated what may be termed a rectangular trimming procedure: that is, each distribution independently is truncated in each tail. While this method has the virtue of simplicity, there may be certain objections to its use. Firstly, it takes no account of the multivariate structure of the data. This may well be important particularly if the truncated sample will be used in more complex multivariate procedures. Secondly, the rectangular trimmed product-moment correlation coefficient is almost certain to be biased toward zero as an estimate of the population correlation. Nath (1971) gives an example of a correlation of 079 which reduced to 065 on single truncation of either distribution resulting in 23 per cent of the sample being eliminated. A bias correction may be calculated assuming a bivariate normal distribution, but is computationally tedious, even though Dyer (1973) has proposed a method avoiding complex iteration.
Hierarchical Codebook Design with Dynamic Metasurface Antennas for Energy-Efficient Arrays

Dynamic metasurface antennas (DMA) provide a solution to form compact, cost-effective, energy-efficient multiple-input-multiple output (MIMO) arrays. In this paper, we implement a practical hierarchical codebook with a realistic DMA design through electromagnetic simulations. We leverage existing DMA models to derive a novel method for enhancing the beamforming gain. We find that the proposed method provides better coverage and spectral efficiency results than prior methods. We also present and verify a new technique for creating wide beamwidths through the DMA and hierarchical codebook. Additionally, we use a detailed transmitter architecture model to determine the power consumption savings of the DMA compared to a typical phased array. The DMA largely outperforms a passive phased array in terms of spectral and energy efficiency due to high component loss from a high-resolution passive phase shifter. While the DMA provides lower spectral efficiency results than the active phased array, the DMA achieves a higher energy efficiency because of the significant power consumption for the active phase shifters. Therefore, we find that DMAs in a realistic wireless environment provide sufficient coverage and spectral efficiency compared to typical phased arrays while maintaining a substantially lower power consumption.
Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs

Amidst decline in public trust in technology, computing ethics have taken center stage, and critics have raised questions about corporate “ethics washing. ” Yet few studies examine the actual implementation of AI ethics values in technology companies. Based on a qualitative analysis of technology workers tasked with integrating AI ethics into product development, we find that workers experience an environment where policies, practices, and outcomes are decoupled. We analyze AI ethics workers as ethics entrepreneurs who work to institutionalize new ethics-related practices within organizations. We show that ethics entrepreneurs face three major barriers to their work. First, they struggle to have ethics prioritized in an environment centered around software product launches. Second, ethics are difficult to quantify in a context where company goals are incentivized by metrics. Third, the frequent reorganization of teams makes it difficult to access knowledge and maintain relationships central to their work. Consequently, individuals take on great personal risk when raising ethics issues, especially when they come from marginalized backgrounds. These findings shed light on complex dynamics of institutional change at technology companies.
Fraction of Clumpy Star-forming Galaxies at 0.5 ≤ z ≤ 3 in UVCANDELS: Dependence on Stellar Mass and Environment

High-resolution imaging of galaxies in rest-frame UV has revealed the existence of giant star-forming clumps prevalent in high-redshift galaxies. Studying these substructures provides important information about their formation and evolution and informs theoretical galaxy evolution models. We present a new method to identify clumps in galaxies’ high-resolution rest-frame UV images. Using imaging data from CANDELS and UVCANDELS, we identify star-forming clumps in an HST/F160W ≤ 25 AB mag sample of 6767 galaxies at 0.5 ≤ z ≤ 3 in four fields, GOODS-N, GOODS-S, EGS, and COSMOS. We use a low-passband filter in Fourier space to reconstruct the background image of a galaxy and detect small-scale features (clumps) on the background-subtracted image. Clumpy galaxies are defined as those having at least one off-center clump that contributes a minimum of 10% of the galaxy’s total rest-frame UV flux. We measure the fraction of clumpy galaxies (f clumpy) as a function of stellar mass, redshift, and galaxy environment. Our results indicate that f clumpy increases with redshift, reaching ∼65% at z ∼ 1.5. We also find that f clumpy in low-mass galaxies ( 9.5≤log(M*/M⊙)≤10 ) is 10% higher compared to that of their high-mass counterparts ( log(M*/M⊙)>10.5 ). Moreover, we find no evidence of significant environmental dependence of f clumpy for galaxies at the redshift range of this study. Our results suggest that the fragmentation of gas clouds under violent disk instability remains the primary driving mechanism for clump formation, and incidents common in dense environments, such as mergers, are not the dominant processes.
Semiparametrically Optimal Cointegration Test

This paper aims to address the issue of semiparametric efficiency for cointegration rank testing in finite-order vector autoregressive models, where the innovation distribution is considered an infinite-dimensional nuisance parameter. Our asymptotic analysis relies on Le Cam's theory of limit experiment, which in this context takes the form of Locally Asymptotically Brownian Functional (LABF). By leveraging the structural version of LABF, an Ornstein-Uhlenbeck experiment, we develop the asymptotic power envelopes of asymptotically invariant tests for both cases with and without a time trend. We propose feasible tests based on a nonparametrically estimated density and demonstrate that their power can achieve the semiparametric power envelopes, making them semiparametrically optimal. We validate the theoretical results through large-sample simulations and illustrate satisfactory size control and excellent power performance of our tests under small samples. In both cases with and without time trend, we show that a remarkable amount of additional power can be obtained from non-Gaussian distributions.
Convex optimization over a probability simplex

We propose a new iteration scheme, the Cauchy-Simplex, to optimize convex problems over the probability simplex $\{w\in\mathbb{R}^n\ |\ \sum_i w_i=1\ \textrm{and}\ w_i\geq0\}$. Other works have taken steps to enforce positivity or unit normalization automatically but never simultaneously within a unified setting. This paper presents a natural framework for manifestly requiring the probability condition. Specifically, we map the simplex to the positive quadrant of a unit sphere, envisage gradient descent in latent variables, and map the result back in a way that only depends on the simplex variable. Moreover, proving rigorous convergence results in this formulation leads inherently to tools from information theory (e.g. cross entropy and KL divergence). Each iteration of the Cauchy-Simplex consists of simple operations, making it well-suited for high-dimensional problems. We prove that it has a convergence rate of ${O}(1/T)$ for convex functions, and numerical experiments of projection onto convex hulls show faster convergence than similar algorithms. Finally, we apply our algorithm to online learning problems and prove the convergence of the average regret for (1) Prediction with expert advice and (2) Universal Portfolios.
HyHTM: Hyperbolic Geometry based Hierarchical Topic Models

Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lowerlevel topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We present HyHTM - a Hyperbolic geometry based Hierarchical Topic Models - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models. Experimental results with four baselines show that HyHTM can better attend to parent-child relationships among topics. HyHTM produces coherent topic hierarchies that specialise in granularity from generic higher-level topics to specific lowerlevel topics. Further, our model is significantly faster and leaves a much smaller memory footprint than our best-performing baseline.We have made the source code for our algorithm publicly accessible.
Learning Higher-order Object Interactions for Keypoint-based Video Understanding

Action recognition is an important problem that requires identifying actions in video by learning complex interactions across scene actors and objects. However, modern deep-learning based networks often require significant computation, and may capture scene context using various modalities that further increases compute costs. Efficient methods such as those used for AR/VR often only use human-keypoint information but suffer from a loss of scene context that hurts accuracy. In this paper, we describe an action-localization method, KeyNet, that uses only the keypoint data for tracking and action recognition. Specifically, KeyNet introduces the use of object based keypoint information to capture context in the scene. Our method illustrates how to build a structured intermediate representation that allows modeling higher-order interactions in the scene from object and human keypoints without using any RGB information. We find that KeyNet is able to track and classify human actions at just 5 FPS. More importantly, we demonstrate that object keypoints can be modeled to recover any loss in context from using keypoint information over AVA action and Kinetics datasets.
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition

Audio-visual speech recognition (AVSR) research has gained a great success recently by improving the noise-robustness of audio-only automatic speech recognition (ASR) with noise-invariant visual information. However, most existing AVSR approaches simply fuse the audio and visual features by concatenation, without explicit interactions to capture the deep correlations between them, which results in sub-optimal multimodal representations for downstream speech recognition task. In this paper, we propose a cross-modal global interaction and local alignment (GILA) approach for AVSR, which captures the deep audio-visual (A-V) correlations from both global and local perspectives. Specifically, we design a global interaction model to capture the A-V complementary relationship on modality level, as well as a local alignment approach to model the A-V temporal consistency on frame level. Such a holistic view of cross-modal correlations enable better multimodal representations for AVSR. Experiments on public benchmarks LRS3 and LRS2 show that our GILA outperforms the supervised learning state-of-the-art. Code is at https://github.com/YUCHEN005/GILA.
Perceptive Locomotion through Whole-Body MPC and Optimal Region Selection

Real-time synthesis of legged locomotion maneuvers in challenging industrial settings is still an open problem, requiring simultaneous determination of footsteps locations several steps ahead while generating whole-body motions close to the robot's limits. State estimation and perception errors impose the practical constraint of fast re-planning motions in a model predictive control (MPC) framework. We first observe that the computational limitation of perceptive locomotion pipelines lies in the combinatorics of contact surface selection. Re-planning contact locations on selected surfaces can be accomplished at MPC frequencies (50-100 Hz). Then, whole-body motion generation typically follows a reference trajectory for the robot base to facilitate convergence. We propose removing this constraint to robustly address unforeseen events such as contact slipping, by leveraging a state-of-the-art whole-body MPC (Croccodyl). Our contributions are integrated into a complete framework for perceptive locomotion, validated under diverse terrain conditions, and demonstrated in challenging trials that push the robot's actuation limits, as well as in the ICRA 2023 quadruped challenge simulation.
Convex Geometric Trajectory Tracking Using Lie Algebraic MPC for Autonomous Marine Vehicles

Controlling marine vehicles in challenging environments is a complex task due to the presence of nonlinear hydrodynamics and uncertain external disturbances. Despite nonlinear model predictive control (MPC) showing potential in addressing these issues, its practical implementation is often constrained by computational limitations. In this letter, we propose an efficient controller for trajectory tracking of marine vehicles by employing a convex error-state MPC on the Lie group. By leveraging the inherent geometric properties of the Lie group, we can construct globally valid error dynamics and formulate a quadratic programming-based optimization problem. Our proposed MPC demonstrates effectiveness in trajectory tracking through extensive-numerical simulations, including scenarios involving ocean currents. Notably, our method substantially reduces computation time compared to nonlinear MPC, making it well-suited for real-time control applications with long prediction horizons or involving small marine vehicles.
Numerical analysis of the advection-diffusion of a solute in random media

We consider the problem of numerically approximating the solution of the coupling of the flow equation in a random porous medium, with the advection-diffusion equation. More precisely, we present and analyse a numerical method to compute the mean value of the spread of a solute introduced at the initial time, and the mean value of the macro-dispersion, defined at the temporal derivative of the spread. We propose a Monte-Carlo method to deal with the uncertainty, i.e. with the randomness of the permeability field. The flow equation is solved using finite element. The advection-diffusion equation is seen as a Fokker-Planck equation, and its solution is approximated thanks to a probabilistic particular method. The spread is indeed the expected value of a function of the solution of the corresponding stochastic differential equation, and is computed using an Euler scheme for the stochastic differential equation and a Monte-Carlo method. Error estimates on the mean spread and on the mean dispersion are established, under various assumptions, in particular on the permeability random field.
Empirical Analysis of the Inductive Bias of Recurrent Neural Networks by Discrete Fourier Transform of Output Sequences

A unique feature of Recurrent Neural Networks (RNNs) is that it incrementally processes input sequences. In this research, we aim to uncover the inherent generalization properties, i.e., inductive bias, of RNNs with respect to how frequently RNNs switch the outputs through time steps in the sequence classification task, which we call output sequence frequency. Previous work analyzed inductive bias by training models with a few synthetic data and comparing the model's generalization with candidate generalization patterns. However, when examining the output sequence frequency, previous methods cannot be directly applied since enumerating candidate patterns is computationally difficult for longer sequences. To this end, we propose to directly calculate the output sequence frequency for each model by regarding the outputs of the model as discrete-time signals and applying frequency domain analysis. Experimental results showed that Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have an inductive bias towards lower-frequency patterns, while Elman RNN tends to learn patterns in which the output changes at high frequencies. We also found that the inductive bias of LSTM and GRU varies with the number of layers and the size of hidden layers.
Public perception of generative AI on Twitter: an empirical study based on occupation and usage

The emergence of generative AI has sparked substantial discussions, with the potential to have profound impacts on society in all aspects. As emerging technologies continue to advance, it is imperative to facilitate their proper integration into society, managing expectations and fear. This paper investigates users’ perceptions of generative AI using 3M posts on Twitter from January 2019 to March 2023, especially focusing on their occupation and usage. We find that people across various occupations, not just IT-related ones, show a strong interest in generative AI. The sentiment toward generative AI is generally positive, and remarkably, their sentiments are positively correlated with their exposure to AI. Among occupations, illustrators show exceptionally negative sentiment mainly due to concerns about the unethical usage of artworks in constructing AI. People use ChatGPT in diverse ways, and notably the casual usage in which they “play with” ChatGPT tends to be associated with positive sentiments. These findings would offer valuable lessons for policymaking on the emergence of new technology and also empirical insights for the considerations of future human-AI symbiosis.
A BSDE approach to the asymmetric risk-sensitive optimization and its applications

This paper is devoted to proposing a new asymmetric risk-sensitive criterion involving different risk attitudes toward varying risk sources. The criterion can only be defined through the initial value of the minimal solutions of quadratic backward stochastic differential equations (BSDEs). Before uncovering the mean-variance representation for the introduced criterion by the variational approach, some axioms are given for the first time to characterize a variance decomposition of square integrable random variables. The stochastic control problems under this criterion are described as a kind of stochastic recursive control problems that includes controlled quadratic BSDEs. An asymmetric risk-sensitive global stochastic maximum principle is derived when the quadratic BSDEs are equipped with bounded data. A closed-form solution of a stochastic linear-quadratic risk-sensitive control problem is obtained by introducing a novel completion-of-squares technique for controlled quadratic BSDEs. In addition, a dynamic portfolio optimization problem featuring a stochastic return rate is provided as an application of the asymmetric risk-sensitive control.
A Geometric Calibration of the Tip of the Red Giant Branch in the Milky Way using Gaia DR3

We use the latest parallaxes measurements from Gaia DR3 to obtain a geometric calibration of the tip of the red giant branch (TRGB) in Cousins I magnitudes as a standard candle for cosmology. We utilise the following surveys: SkyMapper DR3, APASS DR9, ATLAS Refcat2, and Gaia DR3 synthetic photometry to obtain multiple zero-point calibrations of the TRGB magnitude, $M_{I}^{TRGB}$. Our sample contains Milky Way halo stars at high galactic latitudes (|b| > 36) where the impact of metallicity, dust, and crowding are minimised. The magnitude of the TRGB is identified using Sobel edge detection, but this approach introduced a systematic offset. To address this issue, we utilised simulations with PARSEC isochrones and showed how to calibrate and remove this bias. Applying our method within the colour range where the slope of the TRGB is relatively flat for metal-poor halo stars (1.55 < (BP − RP) < 2.25), we find a weighted average $M_{I}^{TRGB} = -4.042 \pm 0.041$ (stat) ±0.031 (sys) mag. A geometric calibration of the Milky Way TRGB has the benefit of being independent of other distance indicators and will help probe systematics in the local distance ladder, leading to improved measurements of the Hubble constant.
Revisiting Proprioceptive Sensing for Articulated Object Manipulation

Robots that assist humans will need to interact with articulated objects such as cabinets or microwaves. Early work on creating systems for doing so used proprioceptive sensing to estimate joint mechanisms during contact. However, nowadays, almost all systems use only vision and no longer consider proprioceptive information during contact. We believe that proprioceptive information during contact is a valuable source of information and did not find clear motivation for not using it in the literature. Therefore, in this paper, we create a system that, starting from a given grasp, uses proprioceptive sensing to open cabinets with a position-controlled robot and a parallel gripper. We perform a qualitative evaluation of this system, where we find that slip between the gripper and handle limits the performance. Nonetheless, we find that the system already performs quite well. This poses the question: should we make more use of proprioceptive information during contact in articulated object manipulation systems, or is it not worth the added complexity, and can we manage with vision alone? We do not have an answer to this question, but we hope to spark some discussion on the matter. The codebase and videos of the system are available at https://tlpss.github. io/revisiting-proprioception-for-articulated-manipulation/.
A Dictionary-based approach to Time Series Ordinal Classification

Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels associated with the time series. This characteristic is referred to as ordinality, and can be exploited to improve the methods performance. The area dealing with ordinal time series is the Time Series Ordinal Classification (TSOC) field, which is yet unexplored. In this work, we present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC problems is performed. Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.