Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Linearization Control Based Output Tracking Method for Nonlinear Uncertain System Using Linear Extended State Observer

In this paper, a trajectory linearization control based control law using a linear extended state observer (LESO) is proposed for the output tracking problem of a nonlinear system with uncertainties. First, based on the tracking error dynamics derived by Taylor expansion for the original nonlinear system along the desired trajectory, a feedback linearization (FL) based control law is designed to stabilize a linear time‐varying (LTV) system. To reduce the controller performance sensitive to uncertainties, with partial model information, LESO is constructed to estimate the tracking error vector, as well as the uncertainties in an integrated manner. The closed‐loop stability of the system under the proposed compound scheme is established. Also, simulation results on affine and non‐affine nonlinear uncertain systems are provided to demonstrate the effectiveness of the control strategy. Finally, the issues of sensitivity to sensor noise and the guidelines of tuning parameters are discussed.
Estimation of finite population mean using multi-auxiliary variables

Abstract This paper proposes new ratio and product type estimators for estimating the mean of finite population using information on multi-auxiliary variables. The Bias and Mean square error (M. S. E.) of these estimators are calculated. These estimators are compared for their precision with usual mean per unit, ratio and product estimators and are found to be more efficient in many practical situations.
Optimized solution strategy for solving systems of equations

The focus of this paper is the determination of an optimum solution strategy that improves the rate of convergence when solving general systems of equations. Improved convergence is accomplished by using a logic‐based algorithm, which we call logical equation set decomposition (LESD), to decompose large systems into subsets of smaller systems. Our goal is to reduce the computational complexity of solving large equation sets by solving multiple smaller equation subsets. An occurrence matrix is used to optimize the number of subsets as well as the order of solution. Improved convergence rates were verified by integrating LESD into a standard numerical equation solver with a conventional Netwon's method as the numerical engine. Linear and nonlinear equation sets were used to benchmark convergence rate. The results showed orders of magnitude reduction in computational time when using LESD for both linear and nonlinear equation sets.
Phase shifts in multilayer dielectric beam splitters

Some properties of the reflectivity and transmissivity of multilayer dielectric optical coatings are derived from energy conservation applied to a beam splitter acting as a beam combiner. It is shown that dispersion in the phase shifts of the beam splitter coatings influences which quadrature amplitude is measured in a homodyne detector, and, in a Michelson interferometer, how the white-light fringe pattern is disposed about the zero-path-difference condition. The functions of reflectivity and transmissivity that determine these properties are shown to be not completely constrained by considerations of energy conservation and time reversibility, in contrast to commonly employed models for beam splitter reflectivity and transmissivity. Some representative beam splitter coating designs are used to illustrate this point.
Sustaining school improvement initiatives: advice from educational leaders

ABSTRACT There are reports about school improvement initiatives that have been successfully implemented and evaluated within relatively brief time scales. However, many initiatives do not survive over longer terms. Our purpose in this study was to identify successful strategies for achieving long-term school improvement. We interviewed 12 leaders at four educational sites. We adopted complex adaptive systems perspectives to analyse the interview transcripts, provide rich descriptions of contexts, illustrate examples of practices, and synthesise participants’ accounts to highlight key areas for attention and action. Participants described purposeful programme selection with clear goals, strategic staff leadership and continuous professional education, accountable professional networks, data sharing and feedback, time and resource management, distributed multilevel leadership, and a supportive school culture. We recommend adopting a complex adaptive systems perspective to communicate and facilitate processes of change, including planning, enacting, and evaluating school improvement as a continuous long-term process rather than as an end product of any singular initiative.
Measuring attostrains in a slow-light fiber Bragg grating

We report a new generation of slow-light FBG strain sensor with a strain resolution (or minimum detectable strain) as low as 30 fepsilon/√Hz at 30 kHz, which is one order of magnitude lower than the record held by the previous generation. This sensor has an ultra-stable output (no drift in 4 days) and is capable of resolving an absolute strain of ~250 attostrains by integrating its output for ~8 hours, which is also a new record for an FBG fiber sensor. These improvements were accomplished by first maximizing the slope of the slow-light resonances, and hence the strain sensitivity. To this end the apodized FBG was written in a deuterium-loaded fiber with a femtosecond infrared laser, then thermally annealed. The three main sources of noise in the sensor system were also carefully reduced. The dominant source of noise, laser frequency noise, was reduced by interrogating the FBG with an ultra-stable laser (linewidth under 200 Hz) with a low intensity noise. The phase noise was minimized by selecting the proper FBG length (~25 mm). When used as an acoustic sensor, the same grating had a measured average pressure resolution of 50 μPa/√Hz between 3 kHz and 6 kHz, one order of magnitude lower than the previous lowest reported value for an FBG sensor.
BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements

Understanding of human visual perception has historically inspired the design of computer vision architectures. As an example, perception occurs at different scales both spatially and temporally, suggesting that the extraction of salient visual information may be made more effective by paying attention to specific features at varying scales. Visual changes in the body due to physiological processes also occur at different scales and with modality-specific characteristic properties. Inspired by this, we present BigSmall, an efficient architecture for physiological and behavioral measurement. We present the first joint camera-based facial action, cardiac, and pulmonary measurement model. We propose a multi-branch network with wrapping temporal shift modules that yields both accuracy and efficiency gains. We observe that fusing low-level features leads to suboptimal performance, but that fusing high level features enables efficiency gains with negligible loss in accuracy. Experimental results demonstrate that BigSmall significantly reduces the computational costs. Furthermore, compared to existing task-specific models, BigSmall achieves comparable or better results on multiple physiological measurement tasks simultaneously with a unified model.
On the simulation of gravitational lensing

Gravitational lensing refers to the deflection of light by the gravity of celestial bodies, often predominantly composed of dark matter. Seen through a gravitational lens, the images of distant galaxies appear distorted. In this paper we discuss simulation of the image distortion by gravitational lensing. The objective is to enhance our understanding of how gravitational lensing works through a simple tool to visualise hypotheses. The simulator can also generate synthetic data for the purpose of machine learning, which will hopefully allow us to invert the distortion function, something which is not analytically possible at present.
Are uGLAD? Time will tell!

We frequently encounter multiple series that are temporally correlated in our surroundings, such as EEG data to examine alterations in brain activity or sensors to monitor body movements. Segmentation of multivariate time series data is a technique for identifying meaningful patterns or changes in the time series that can signal a shift in the system's behavior. However, most segmentation algorithms have been designed primarily for univariate time series, and their performance on multivariate data remains largely unsatisfactory, making this a challenging problem. In this work, we introduce a novel approach for multivariate time series segmentation using conditional independence (CI) graphs. CI graphs are probabilistic graphical models that represents the partial correlations between the nodes. We propose a domain agnostic multivariate segmentation framework `$\texttt{tGLAD}$' which draws a parallel between the CI graph nodes and the variables of the time series. Consider applying a graph recovery model $\texttt{uGLAD}$ to a short interval of the time series, it will result in a CI graph that shows partial correlations among the variables. We extend this idea to the entire time series by utilizing a sliding window to create a batch of time intervals and then run a single $\texttt{uGLAD}$ model in multitask learning mode to recover all the CI graphs simultaneously. As a result, we obtain a corresponding temporal CI graphs representation. We then designed a first-order and second-order based trajectory tracking algorithms to study the evolution of these graphs across distinct intervals. Finally, an `Allocation' algorithm is used to determine a suitable segmentation of the temporal graph sequence. $\texttt{tGLAD}$ provides a competitive time complexity of $O(N)$ for settings where number of variables $D<<N$. We demonstrate successful empirical results on a Physical Activity Monitoring data.
Protective Self-Adaptive Pruning to Better Compress DNNs

Adaptive network pruning approach has recently drawn significant attention due to its excellent capability to identify the importance and redundancy of layers and filters and customize a suitable pruning solution. However, it remains unsatisfactory since current adaptive pruning methods rely mostly on an additional monitor to score layer and filter importance, and thus faces high complexity and weak interpretability. To tackle these issues, we have deeply researched the weight reconstruction process in iterative prune-train process and propose a Protective Self-Adaptive Pruning (PSAP) method. First of all, PSAP can utilize its own information, weight sparsity ratio, to adaptively adjust pruning ratio of layers before each pruning step. Moreover, we propose a protective reconstruction mechanism to prevent important filters from being pruned through supervising gradients and to avoid unrecoverable information loss as well. Our PSAP is handy and explicit because it merely depends on weights and gradients of model itself, instead of requiring an additional monitor as in early works. Experiments on ImageNet and CIFAR-10 also demonstrate its superiority to current works in both accuracy and compression ratio, especially for compressing with a high ratio or pruning from scratch.
A search for Kuiper Belt occultations using the Weizmann Fast Astronomical Survey Telescope

Measuring the size distribution of small (km-scale) KBOs can help constrain models of Solar System formation and planetary migration. Such small, distant bodies are hard to detect with current or planned telescopes, but can be identified as sub-second occultations of background stars. We present the analysis of data from the Weizmann Fast Astronomical Survey Telescope (W-FAST), consisting of fast photometry of ~10^6 star hours at a frame rate of 10-25 Hz. Our pipeline utilizes a matched-filter approach with a large template bank, including red-noise treatment, and injection of simulated events for estimating the detection efficiency. The KBO radius at which our survey is 10% (50%) efficient is 1.1 (2.0) km. The data from 2020-2021 observing seasons were analyzed and no occultations were identified. We discuss a sample of sub-second false-positive events, both occultation-like and flare-like, which are still not fully understood but could be instructive for future surveys looking for short-duration events. We use our null-detection result to set limits on the km-scale KBO number density. Our individual radius bin limits are consistent with most previous works, with N(r>1km)<=10^6 deg^-2 (95% confidence limit). Our integrated (all size) limits, assuming a power law normalized to large (~45 km) KBOs gives a power law index q<3.93 (95% confidence limit). Finally, our results are in tension with a recently reported KBO detection from the ground, at the p=4x10^-4 level.
360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View

Seeing only a tiny part of the whole is not knowing the full circumstance. Bird's-eye-view (BEV) perception, a process of obtaining allocentric maps from egocentric views, is restricted when using a narrow Field of View (FoV) alone. In this work, mapping from 360{\deg} panoramas to BEV semantics, the 360BEV task, is established for the first time to achieve holistic representations of indoor scenes in a top-down view. Instead of relying on narrow-FoV image sequences, a panoramic image with depth information is sufficient to generate a holistic BEV semantic map. To benchmark 360BEV, we present two indoor datasets, 360BEV-Matterport and 360BEV-Stanford, both of which include egocentric panoramic images and semantic segmentation labels, as well as allocentric semantic maps. Besides delving deep into different mapping paradigms, we propose a dedicated solution for panoramic semantic mapping, namely 360Mapper. Through extensive experiments, our methods achieve 44.32% and 45.78% in mIoU on both datasets respectively, surpassing previous counterparts with gains of +7.60% and +9.70% in mIoU. Code and datasets are available at the project page: https://jamycheung.github. io/360BEV.html.
Beam Management Driven by Radio Environment Maps in O-RAN Architecture

The Massive Multiple-Input Multiple-Output (M-MIMO) is considered as one of the key technologies in 5G, and future 6G networks. From the perspective of, e.g., channel estimation, especially for high-speed users it is easier to implement an M-MIMO network exploiting a static set of beams, i.e., Grid of Beams (GoB). While considering GoB it is important to properly assign users to the beams, i.e., to perform Beam Management (BM). BM can be enhanced by taking into account historical knowledge about the radio environment, e.g., to avoid radio link failures. The aim of this paper is to propose such a BM algorithm, that utilizes location-dependent data stored in a Radio Environment Map (REM). It utilizes received power maps, and user mobility patterns to optimize the BM process in terms of Reinforcement Learning (RL) by using the Policy Iteration method under different goal functions, e.g., maximization of received power or minimization of beam reselections while avoiding radio link failures. The proposed solution is compliant with the Open Radio Access Network (O-RAN) architecture, enabling its practical implementation. Simulation studies have shown that the proposed BM algorithm can significantly reduce the number of beam reselections or radio link failures compared to the baseline algorithm.
Dynamic Vertex Replacement Grammars

Context-free graph grammars have shown a remarkable ability to model structures in real-world relational data. However, graph grammars lack the ability to capture time-changing phenomena since the left-to-right transitions of a production rule do not represent temporal change. In the present work, we describe dynamic vertex-replacement grammars (DyVeRG), which generalize vertex replacement grammars in the time domain by providing a formal framework for updating a learned graph grammar in accordance with modifications to its underlying data. We show that DyVeRG grammars can be learned from, and used to generate, real-world dynamic graphs faithfully while remaining human-interpretable. We also demonstrate their ability to forecast by computing dyvergence scores, a novel graph similarity measurement exposed by this framework.
Light quark and antiquark constraints from new electroweak data

We present a new parton distribution function analysis which includes new data for W boson production in proton-proton collisions and lepton pair production in proton-proton and proton-deuteron collisions. The new data provide strong constraints on the light antiquark parton distribution functions in the proton. We identify an interesting correlation between the $d/u$ ratio and the $\bar{d}/\bar{u}$ ratio which leads to a modification of our previous results for the $d/u$ ratio as the parton momentum fraction $x \rightarrow 1. $
A Constraint on the Amount of Hydrogen from the CO Chemistry in Debris Disks

The faint CO gases in debris disks are easily dissolved into C by UV irradiation, while CO can be reformed via reactions with hydrogen. The abundance ratio of C/CO could thus be a probe of the amount of hydrogen in the debris disks. We conduct radiative transfer calculations with chemical reactions for debris disks. For a typical dust-to-gas mass ratio of debris disks, CO formation proceeds without the involvement of H2 because a small amount of dust grains makes H2 formation inefficient. We find that the CO to C number density ratio depends on a combination of n H Z 0.4 χ −1.1, where n H is the hydrogen nucleus number density, Z is the metallicity, and χ is the far-UV flux normalized by the Habing flux. Using an analytic formula for the CO number density, we give constraints on the amount of hydrogen and metallicity for debris disks. CO formation is accelerated by excited H2 when either the dust-to-gas mass ratio is increased or the energy barrier of chemisorption of hydrogen on the dust surface is decreased. This acceleration of CO formation occurs only when the shielding effects of CO are insignificant. In shielded regions, the CO fractions are almost independent of the parameters of dust grains.
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data

In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable. The identification of sources from observations of system state is vital for attribution and prediction, which inform critical policy decisions. The difficulty of these types of inverse problems lies in the inability to isolate sources and the cost of simulating computational models. Surrogate models may enable the many-query algorithms required for source identification, but data challenges arise from high dimensionality of the state and source, limited ensembles of costly model simulations to train a surrogate model, and few and potentially noisy state observations for inversion due to measurement limitations. The influence of auxiliary processes adds an additional layer of uncertainty that further confounds source identification. We introduce a framework based on (1) calibrating deep neural network surrogates to the flow maps provided by an ensemble of simulations obtained by varying sources, and (2) using these surrogates in a Bayesian framework to identify sources from observations via optimization. Focusing on an atmospheric dispersion exemplar, we find that the expressive and computationally efficient nature of the deep neural network operator surrogates in appropriately reduced dimension allows for source identification with uncertainty quantification using limited data. Introducing a variable wind field as an auxiliary process, we find that a Bayesian approximation error approach is essential for reliable source inversion when uncertainty due to wind stresses the algorithm.
VAD: Vectorized Scene Representation for Efficient Autonomous Driving

Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure information. In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages. On one hand, VAD exploits the vectorized agent motion and map elements as explicit instance-level planning constraints which effectively improves planning safety. On the other hand, VAD runs much faster than previous end-to-end planning methods by getting rid of computation-intensive rasterized representation and hand-designed post-processing steps. VAD achieves state-of-the-art end-to-end planning performance on the nuScenes dataset, outperforming the previous best method by a large margin. Our base model, VAD-Base, greatly reduces the average collision rate by 29.0% and runs 2.5× faster. Besides, a lightweight variant, VAD-Tiny, greatly improves the inference speed (up to 9.3×) while achieving comparable planning performance. We believe the excellent performance and the high efficiency of VAD are critical for the real-world deployment of an autonomous driving system. Code and models are available at https://github.com/hustvl/VAD for facilitating future research.
Text2Tex: Text-driven Texture Synthesis via Diffusion Models

We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts. Our method incorporates inpainting into a pre-trained depth-aware image diffusion model to progressively synthesize high resolution partial textures from multiple viewpoints. To avoid accumulating inconsistent and stretched artifacts across views, we dynamically segment the rendered view into a generation mask, which represents the generation status of each visible texel. This partitioned view representation guides the depth-aware inpainting model to generate and update partial textures for the corresponding regions. Furthermore, we propose an automatic view sequence generation scheme to determine the next best view for updating the partial texture. Extensive experiments demonstrate that our method significantly outperforms the existing text-driven approaches and GAN-based methods.
The stability of split-preconditioned FGMRES in four precisions

We consider the split-preconditioned FGMRES method in a mixed precision framework, in which four potentially different precisions can be used for computations with the coefficient matrix, application of the left preconditioner, application of the right preconditioner, and the working precision. Our analysis is applicable to general preconditioners. We obtain bounds on the backward and forward errors in split-preconditioned FGMRES. Our analysis further provides insight into how the various precisions should be chosen; under certain assumptions, a suitable selection guarantees a backward error on the order of the working precision.
Representational Tenets for Memory Athletics

We describe the current state of world-class memory competitions, including the methods used to prepare for and compete in memory competitions, based on the subjective report of World Memory Championship Grandmaster and co-author Nelson Dellis. We then explore the reported experiences through the lens of the Simulated, Situated, and Structurally coherent Qualia (S3Q) theory of consciousness, in order to propose a set of experiments to help further understand the boundaries of expert memory performance.