Miracle-dz/UniFAR-FTUs huggingface dataset:
Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift

The main challenges of using electroencephalogram (EEG) signals to make eye-tracking (ET) predictions are the differences in distributional patterns between benchmark data and real-world data and the noise resulting from the unintended interference of brain signals from multiple sources. Increasing the robustness of machine learning models in predicting eye-tracking position from EEG data is therefore integral for both research and consumer use. In medical research, the usage of more complicated data collection methods to test for simpler tasks has been explored to address this very issue. In this study, we propose a fine-grain data approach for EEG-ET data collection in order to create more robust benchmarking. We train machine learning models utilizing both coarse-grain and fine-grain data and compare their accuracies when tested on data of similar/different distributional patterns in order to determine how susceptible EEG-ET benchmarks are to differences in distributional data. We apply a covariate distributional shift to test for this susceptibility. Results showed that models trained on fine-grain, vector-based data were less susceptible to distributional shifts than models trained on coarse-grain, binary-classified data.
Uniform error bounds for numerical schemes applied to multiscale SDEs in a Wong-Zakai diffusion approximation regime

We study a family of numerical schemes applied to a class of multiscale systems of stochastic differential equations. When the time scale separation parameter vanishes, a well-known homogenization or Wong--Zakai diffusion approximation result states that the slow component of the considered system converges to the solution of a stochastic differential equation driven by a real-valued Wiener process, with Stratonovich interpretation of the noise. We propose and analyse schemes for effective approximation of the slow component. Such schemes satisfy an asymptotic preserving property and generalize the methods proposed in a recent article. We fill a gap in the analysis of these schemes and prove strong error estimates, which are uniform with respect to the time scale separation parameter.
Sensors and actuators for the advanced LIGO A+ upgrade.

Advanced Laser Interferometer Gravitational-wave Observatory (LIGO A+) is a major upgrade to LIGO-the Laser Interferometer Gravitational-wave Observatory. For the A+ project, we have developed, produced, and characterized sensors and electronics to interrogate new optical suspensions designed to isolate optics from vibrations. The central element is a displacement sensor with an integrated electromagnetic actuator known as a BOSEM (Birmingham Optical Sensor and ElectroMagnetic actuator) and its readout and drive electronics required to integrate them into LIGO's control and data system. In this paper, we report on the improvements to the sensors and the testing procedures undertaken to meet the enhanced performance requirements set out by the A+ upgrade to the detectors. The best devices reach a noise level of 4.5 ×10-11m/Hz at a measurement frequency of 1 Hz, an improvement of 6.7 times over standard devices.
Planar Modeling and Sim-to-Real of a Tethered Multimaterial Soft Swimmer Driven by Peano-HASELs

Soft robotics has the potential to revolutionize robotic locomotion, in particular, soft robotic swimmers offer a minimally invasive and adaptive solution to explore and preserve our oceans. Unfortunately, current soft robotic swimmers are vastly inferior to evolved biological swimmers, especially in terms of controllability, efficiency, maneuverability, and longevity. Additionally, the tedious iterative fabrication and empirical testing required to design soft robots has hindered their optimization. In this work, we tackle this challenge by providing an efficient and straightforward pipeline for designing and fabricating soft robotic swimmers equipped with electrostatic actuation. We streamline the process to allow for rapid additive manufacturing, and show how a differentiable simulation can be used to match a simplified model to the real deformation of a robotic swimmer. We perform several experiments with the fabricated swimmer by varying the voltage and actuation frequency of the swimmer's antagonistic muscles. We show how the voltage and frequency vary the locomotion speed of the swimmer while moving in liquid oil and observe a clear optimum in forward swimming speed. The differentiable simulation model we propose has various downstream applications, such as control and shape optimization of the swimmer; optimization results can be directly mapped back to the real robot through our sim-to-real matching.
Safer Traffic Recovery from the Pandemic in London - Spatiotemporal Data Mining of Car Crashes

In the aim to provide evidence for deployment policies towards post-pandemic safer recovery from COVID-19, this study investigated the spatiotemporal patterns of age-involved car crashes and affecting factors, upon answering two main research questions: (1) “What are spatiotemporal patterns of car crashes and any observed changes in two years, 2019 and 2020, in London, and waht were the influential factors for these crashes? ”; (2) “What are spatiotemporal patterns of casualty by age, and how do people’s daily activities affect the patterns pre- and during the pandemic”? Three approaches, spatial analysis (network Kernel Density Estimation, NetKDE), factor analysis, and spatiotemporal data mining (tensor decomposition), had been implemented to identify the temporal patterns of car crashes, detect hot spots, and to understand the effect on citizens’ daily activity on crash patterns pre- and during the pandemic. It had been found from the study that car crashes mainly clustered in the central part of London, especially busier areas around denser hubs of point-of-interest (POIs); the POIs, as an indicator for citizens’ daily activities and travel behaviours, can be of help to analyze their relationships with crash patterns, upon further assessment on interactions through the geographical detector; the casualty patterns varied by age group, with distinctive relationships between POIs and crash pattern for corresponding age group categorised. In all, the paper introduced new approaches for an in-depth analysis of car crashes and their casualty patterns in London to support London’s safer recovery from the pandemic by improving road safety.
DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognition

The performances of automatic speech recognition (ASR) systems degrade drastically under noisy conditions. Explicit distortion modelling (EDM), as a feature compensation step, is able to enhance ASR systems under such conditions by simulating the in-domain noisy speeches from the clean counterparts. Yet, existing distortion models are either non-trainable or unexplainable and often lack controllability and generalization ability. In this paper, we propose a fully explainable and controllable model: DENT-DDSP to achieve EDM. DENT-DDSP utilizes novel differentiable digital signal processing (DDSP) components and requires only 10 seconds of training data to achieve high fidelity. The experiment shows that the simulated noisy data from DENT-DDSP achieves the highest simulation fidelity compared to other baseline models in terms of multi-scale spectral loss (MSSL). Moreover, to validate whether the data simulated by DENT-DDSP are able to replace the scarce in-domain noisy data in the noise-robust ASR tasks, several downstream ASR models with the same architecture are trained using the simulated data and the real data. The experiment shows that the model trained with the simulated noisy data from DENT-DDSP achieves similar performances to the benchmark with a 2.7\% difference in terms of word error rate (WER). The code of the model is released online.
Pseudoconvex submanifolds in Kahler 4-manifolds

On Kahler 4-manifolds, not necessarily compact or of finite topological type, we obtain relationships between the fundamental group of compact embedded Levi-flat or pseudoconvex submanifold and the fundamental group of the ambient manifold $M^4$. When a Levi-flat submanifold $V^3$ has finite fundamental group then $\pi_1(M^4)=\iota_*\pi_1(V^3)$; when a non-separating pseudoconvex submanifold $V^3$ has finite fundamental group, then $\pi_1(M^4)=\iota_*\pi_1(V^3)\rtimes\mathbb{Z}$. As applications, if a Kahler manifold (compact or not) has an embedded holomorphic $\mathbb{P}^1$ of positive self-intersection, it must intersect all other holomorphic $P^1$ of non-negative self-intersection, the fundamental group of $M^4$ is trivial, and no ALE or ALF ends exist. If a Levi-flat submanifold and an embedded holomorphic $\mathbb{P}^1$ of positive self-intersection both exist, they intersect. The total number of ALE plus ALF ends is zero or one regardless of what other kinds of ends exist. We provide examples, such as a 2-ended scalar-flat Kahler metric conformal to the Taub-NUT.
Global Self‐Similar Scaling of Terrestrial Carbon With Aridity

While it is well known that water availability controls vegetation growth and soil microbial activity, how aridity affects ecosystem carbon patterns is not completely understood. Toward a more quantitative assessment of terrestrial carbon stocks, here we apply dimensional analysis and scaling to the global joint distribution of terrestrial carbon stock, obtained from international survey data and harmonized global maps. The results show a remarkable self‐similar behavior of the global carbon stock with dryness index, whereby the key statistics (e.g., mean, quantiles, and standard deviation) of carbon stock tend to scale with the hydroclimatic regime (i.e., aridity) via a universal exponent. Such a scaling reflects the strong coupling between the hydrological cycle and biogeochemical process and enables robust predictions of carbon stocks as a function of aridity only. When normalized by its averages in the corresponding hydroclimatic regime, the carbon stock distributions collapse onto a single double Pareto lognormal distribution, often used in economics to describe income. The presence of this distribution in completely different physical contexts, such as biogeochemistry and economics, hints at generating mechanisms that transcend the details of the specific stock being considered.
Age of View: A New Metric for Evaluating Heterogeneous Information Fusion in Vehicular Cyber-Physical Systems

Heterogeneous information fusion is one of the most critical issues for realizing vehicular cyber-physical systems (VCPSs). This work makes the first attempt at quantitatively measuring the quality of heterogeneous information fusion in VCPS by designing a new metric called Age of View (AoV). Specifically, we derive a sensing model based on a multi-class M/G/1 priority queue and a transmission model based on Shannon theory. On this basis, we formally define AoV by modeling the timeliness, completeness, and consistency of the heterogeneous information fusion in VCPS and formulate the problem aiming to minimize the system's average AoV. Further, we propose a new solution called Multi-agent Difference-Reward-based deep reinforcement learning with a Greedy Bandwidth Allocation (MDR-GBA) to solve the problem. In particular, each vehicle acts as an independent agent and decides the sensing frequencies and uploading priorities of heterogeneous information. Meanwhile, the roadside unit (RSU) decides the Vehicle-to-Infrastructure (V2I) bandwidth allocation for each vehicle based on a greedy scheme. Finally, we build the simulation model and compare the performance of the proposed solution with state-of-the-art algorithms. The experimental results conclusively demonstrate the significance of the new metric and the superiority of the proposed solution.
Constraining Axions with ZTF J1901+1458

The axion-nucleon coupling enables the production of axions through the decay of excited ${}^{57}\textrm{Fe}$ isotopes, and axions produced in the Sun through this process are often a target of helioscope searches. We show for the first time that hot, highly magnetic white dwarfs such as ZTF J1901+1458 are a viable target to search for the X-ray signature of axions that were produced by the ${}^{57}\textrm{Fe}$ transition in the core and then converted to photons in the magnetosphere. We calculate that a 100 ks observation of ZTF J1901+1458 with NuSTAR would constrain the coupling of axions to nucleons and photons at a level below the bounds of both current and future planned helioscopes.
Similarity-based Web Element Localization for Robust Test Automation

Non-robust (fragile) test execution is a commonly reported challenge in GUI-based test automation, despite much research and several proposed solutions. A test script needs to be resilient to (minor) changes in the tested application but, at the same time, fail when detecting potential issues that require investigation. Test script fragility is a multi-faceted problem. However, one crucial challenge is how to reliably identify and locate the correct target web elements when the website evolves between releases or otherwise fail and report an issue. This article proposes and evaluates a novel approach called similarity-based web element localization (Similo), which leverages information from multiple web element locator parameters to identify a target element using a weighted similarity score. This experimental study compares Similo to a baseline approach for web element localization. To get an extensive empirical basis, we target 48 of the most popular websites on the Internet in our evaluation. Robustness is considered by counting the number of web elements found in a recent website version compared to how many of these existed in an older version. Results of the experiment show that Similo outperforms the baseline; it failed to locate the correct target web element in 91 out of 801 considered cases (i.e., 11%) compared to 214 failed cases (i.e., 27%) for the baseline approach. The time efficiency of Similo was also considered, where the average time to locate a web element was determined to be 4 milliseconds. However, since the cost of web interactions (e.g., a click) is typically on the order of hundreds of milliseconds, the additional computational demands of Similo can be considered negligible. This study presents evidence that quantifying the similarity between multiple attributes of web elements when trying to locate them, as in our proposed Similo approach, is beneficial. With acceptable efficiency, Similo gives significantly higher effectiveness (i.e., robustness) than the baseline web element localization approach.
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild

Dynamic facial expression recognition (FER) databases provide important data support for affective computing and applications. However, most FER databases are annotated with several basic mutually exclusive emotional categories and contain only one modality, e.g., videos. The monotonous labels and modality cannot accurately imitate human emotions and fulfill applications in the real world. In this paper, we propose MAFW, a large-scale multi-modal compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment. To ensure high quality of the labels, we filter out the unreliable annotations by an Expectation Maximization (EM) algorithm, and then obtain 11 single-label emotion categories and 32 multi-label emotion categories. To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions. Additionally, we also propose a novel Transformer-based expression snippet feature learning method to recognize the compound emotions leveraging the expression-change relations among different emotions and modalities. Extensive experiments on MAFW database show the advantages of the proposed method over other state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database is publicly available from https://mafw-database.github. io/MAFW.
Convex duality for stochastic shortest path problems in known and unknown environments

This paper gives an introduction to Stochastic Shortest Path (SSP) problems in known and unknown environments from the perspective of convex optimisation. It first recalls results in the known parameter case, and develops understanding through different proofs. It then focuses on the unknown parameter case, where it studies extended value iteration (EVI) operators. This includes the existing operators used in Rosenberg et al. [25] and Tarbouriech et al. [30] based on the `1 norm and supremum norm, as well as defining EVI operators corresponding to other norms and divergences, such as the KL-divergence. This paper shows in general how the EVI operators relate to convex programs, and the form of their dual, where strong duality is exhibited. This paper then focuses on whether the bounds from finite horizon research of Neu and Pike-Burke [20] can be applied to these extended value iteration operators in the SSP setting. It shows that similar bounds to [20] for these operators exist, however they lead to operators that are not in general monotone and have more complex convergence properties. In a special case we observe oscillating behaviour. This paper generates open questions on how research may progress, with several examples that require further examination.
Electron beam polarimeter and energy spectrometer

The backscattering of laser radiation by a relativistic electron beam is a widely used method for measuring the spin polarization of electrons. We consider again the properties of the scattering process paying special attention to recoil electrons. Based on this consideration we propose the concept of the Compton polarimeter in which, in addition to all the polarization components, it becomes possible to accurately measure the energy and other parameters of the electron beam. To demonstrate the capabilities of the method we conduct a Monte Carlo simulations of the polarimeter developed for the FCC-ee project.
Submillimeter wave/THZ technology and rotational spectroscopy of several molecules of astrophysical interest

ii ACKNOWLEDGMENTS . v VITA . vii LIST OF TABLES . xiii LIST OF FIGURES . xv INTRODUCTION . 1 CHAPTER 1. THE FASSST SPECTROMETER . 6 1.1 BACKWARD WAVE OSCILLATORS . 7 1.2 RING CAVITY AND INTERFERENCE FRINGES . 10 1.3 RING CAVITY ALIGNMENT PROCEDURE . 13 1.4 ABSORPTION CELL . 16 1.5 DETECTORS AND PREAMPLIFIERS . 16 1.6 DATA ACQUISITION SYSTEM . 17 1.7 CALIBRATION PROCEDURE . 18 1.8 ANALYSIS OF THE EXPERIMENTAL SPECTRAL LINE SHAPE OF THE FASSST . 20 1.9 FREQUENCY MEASUREMENT ACCURACY OF THE FASSST SPECTROMETER . 23 1.10 MODIFICATIONS AND ENHANCMENTS OF THE CALIBRATION SOFTWARE . 33 CHAPTER 2. THE USE OF CAAARS (COMPUTER AIDED ASSIGNMENT OF ASYMMETRIC ROTOR SPECTRA) IN THE ANALYSIS OF ROTATIONAL SPECTRA . 38 2.
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism

The challenge of fine-grained visual recognition often lies in discovering the key discriminative regions. While such regions can be automatically identified from a large-scale labeled dataset, a similar method might become less effective when only a few annotations are available. In low data regimes, a network often struggles to choose the correct regions for recognition and tends to overfit spurious correlated patterns from the training data. To tackle this issue, this paper proposes the self-boosting attention mechanism, a novel method for regularizing the network to focus on the key regions shared across samples and classes. Specifically, the proposed method first generates an attention map for each training image, highlighting the discriminative part for identifying the ground-truth object category. Then the generated attention maps are used as pseudo-annotations. The network is enforced to fit them as an auxiliary task. We call this approach the self-boosting attention mechanism (SAM). We also develop a variant by using SAM to create multiple attention maps to pool convolutional maps in a style of bilinear pooling, dubbed SAM-Bilinear. Through extensive experimental studies, we show that both methods can significantly improve fine-grained visual recognition performance on low data regimes and can be incorporated into existing network architectures. The source code is publicly available at: https://github.com/GANPerf/SAM
Early Growth of the Star Formation Rate Function in the Epoch of Reionization: An Approach with Rest-frame Optical Emissions

We present a star formation rate function (SFRF) at z ∼ 6 based on star formation rates (SFRs) derived by spectral energy distribution fitting on data from rest-frame UV to optical wavelengths of galaxies in the CANDELS GOODS-South and North fields. The resulting SFRF shows an excess compared to the previous estimations by using rest-frame UV luminosity functions (LFs) corrected for the dust attenuation and is comparable to that estimated from a far-infrared LF. This suggests that the number density of dust-obscured intensively star-forming galaxies at z ∼ 6 has been underestimated in the previous approach based only on rest-frame UV observations. We parameterize the SFRF using the Schechter function and obtain the best-fit parameter of the characteristic SFR (SFR*) when the faint-end slope and characteristic number density are fixed. The best-fit SFR* at z ∼ 6 is comparable to that at z ∼ 2, when the cosmic star formation activity reaches its peak. Together with SFRF estimations with a similar approach using rest-frame UV to optical data, the SFR* is roughly constant from z ∼ 2 to ∼6 and may decrease above z ∼ 6. Since the SFR* is sensitive to the high-SFR end of the SFRF, this evolution of SFR* suggests that the high-SFR end of the SFRF grows rapidly during the epoch of reionization and reaches a similar level observed at z ∼ 2.
Orbital Decay of Double White Dwarfs: Beyond Gravitational-wave Radiation Effects

The traditional description of the orbital evolution of compact-object binaries, like double white dwarfs (DWDs), assumes that the system is driven only by gravitational-wave (GW) radiation. However, the high magnetic fields with intensities of up to gigagausses measured in WDs alert a potential role of the electromagnetic (EM) emission in the evolution of DWDs. We evaluate the orbital dynamics of DWDs under the effects of GW radiation, tidal synchronization, and EM emission by a unipolar inductor generated by the magnetic primary and the relative motion of the nonmagnetic secondary. We show that the EM emission can affect the orbital dynamics for magnetic fields larger than megagausses. We applied the model to two known DWDs, SDSS J0651+2844 and ZTF J1539+5027, for which the GW radiation alone does not fully account for the measured orbital decay rate. We obtain upper limits to the primary’s magnetic field strength, over which the EM emission causes an orbital decay faster than observed. The contribution of tidal locking and the EM emission is comparable, and together they can contribute up to 20% to the measured orbital decay rate. We show that the gravitational waveform for a DWD modeled as purely driven by GWs and including tidal interactions and EM emission can have large relative dephasing detectable in the mHz regime of frequencies relevant for space-based detectors like LISA. Therefore, including physics besides GW radiation in the waveform templates is essential to calibrate the GW detectors using known sources, e.g., ZTF J1539+5027, and to infer binary parameters.