Miracle-dz/UniFAR-FTUs huggingface dataset:
Streaming instability of multiple particle species

The streaming instability provides an efficient way of overcoming the growth barriers in the initial stages of the planet formation process. Considering the realistic case of a particle size distribution, the dynamics of the system is altered compared to the outcome of single size models. In order to understand the outcome of the multispecies streaming instability in detail, we perform a large parameter study in terms of particle number, particle size distribution, particle size range, initial metallicity, and initial particle scale height. We study vertically stratified systems and determine the metallicity threshold for filament formation. We compare these with a system where the initial particle distribution is unstratified and find that its evolution follows that of its stratified counterpart. We find that a change in the particle number does not result in significant variation in the efficiency and timing of filament formation. We also see that there is no clear trend for how varying the size distribution in combination with the particle size range changes the outcome of the multispecies streaming instability. Finally, we find that an initial metallicity of Zinit = 0.005 and Zinit = 0.01 both result in similar critical metallicity values for the start of filament formation. Our results show that the inclusion of a particle size distribution into streaming instability simulations, while changing the dynamics as compared to mono-disperse systems, does not result in overall unfavorable conditions for solid growth. We attribute the subdominant role of multiple species to the high-density conditions in the midplane, conditions under which linear stability analysis also predict little difference between single and multiple species.
The Impact of Cosmic Rays on the Kinematics of the Circumgalactic Medium

We use hydrodynamical simulations of two Milky Way–mass galaxies to demonstrate the impact of cosmic-ray pressure on the kinematics of cool and warm circumgalactic gas. Consistent with previous studies, we find that cosmic-ray pressure can dominate over thermal pressure in the inner 50 kpc of the circumgalactic medium (CGM), creating an overall cooler CGM than that of similar galaxy simulations run without cosmic rays. We generate synthetic sight lines of the simulated galaxies’ CGM and use Voigt profile-fitting methods to extract ion column densities, Doppler-b parameters, and velocity centroids of individual absorbers. We directly compare these synthetic spectral line fits with HST/COS CGM absorption-line data analyses, which tend to show that metallic species with a wide range of ionization potential energies are often kinematically aligned. Compared to the Milky Way simulation run without cosmic rays, the presence of cosmic-ray pressure in the inner CGM creates narrower O vi absorption features and broader Si iii absorption features, a quality that is more consistent with observational data. Additionally, because the cool gas is buoyant due to nonthermal cosmic-ray pressure support, the velocity centroids of both cool and warm gas tend to align in the simulated Milky Way with feedback from cosmic rays. Our study demonstrates that detailed, direct comparisons between simulations and observations, focused on gas kinematics, have the potential to reveal the dominant physical mechanisms that shape the CGM.
Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue

Building an interactive artificial intelligence that can ask questions about the real world is one of the biggest challenges for vision and language problems. In particular, goal-oriented visual dialogue, where the aim of the agent is to seek information by asking questions during a turn-taking dialogue, has been gaining scholarly attention recently. While several existing models based on the GuessWhat? dataset [10] have been proposed, the Questioner typically asks simple category-based questions or absolute spatial questions. This might be problematic for complex scenes where the objects share attributes, or in cases where descriptive questions are required to distinguish objects. In this paper, we propose a novel Questioner architecture, called Unified Questioner Transformer (UniQer), for descriptive question generation with referring expressions. In addition, we build a goal-oriented visual dialogue task called CLEVR Ask. It synthesizes complex scenes that require the Questioner to generate descriptive questions. We train our model with two variants of CLEVR Ask datasets. The results of the quantitative and qualitative evaluations show that UniQer outperforms the baseline.
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model

TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions. In this challenge, we use generative model T5 for TextVQA task. Based on pre-trained checkpoint T5-3B from HuggingFace repository, two other pre-training tasks including masked language modeling(MLM) and relative position prediction(RPP) are designed to better align object feature and scene text. In the stage of pre-training, encoder is dedicate to handle the fusion among multiple modalities: question text, object text labels, scene text labels, object visual features, scene visual features. After that decoder generates the text sequence step-by-step, cross entropy loss is required by default. We use a large-scale scene text dataset in pre-training and then fine-tune the T5-3B with the TextVQA dataset only.
Concentration gradients in evaporating binary droplets probed by spatially resolved Raman and NMR spectroscopy

Significance Imagine you spill your drink and miss some spots when cleaning up. The next morning you notice that the stains look quite different on different surfaces. What has happened? In droplets of liquid mixtures, the components evaporate at different rates, which leads to gradients in concentration and surface tension. These gradients can cause, for example, so-called Marangoni flows, which in turn affect the evaporation process. To better understand evaporation-induced liquid flows, the concentration gradients have to be measured without disturbing the liquid. Marker molecules might be surface-active or even may affect the evaporation process. We report here on marker-free and contactless measurements of concentrations by spatially resolved Raman and NMR spectroscopy in evaporating binary droplets.
A Study of Ultrawideband (UWB) Antenna Design for Cognitive Radio Applications

Cognitive radio is rapidly shaping the future of wireless communications. Research on antenna design is very critical for the implementation of cognitive radio. A special antenna is required in cognitive radio for sensing and communicating. For the purpose of spectrum sensing, an Ultrawideband (UWB) antenna is being considered as a potential candidate by many experts. This paper provides a detailed discussion of the existing UWB spectrum sensing antenna designs for cognitive radio system. Simulation results for a promising cognitive radio antenna which provides a reconfigurable function in the range of 5-6 GHz have also been presented and shown to match closely with the measured results.
Detecting deformed commutators with exceptional points in optomechanical sensors

Models of quantum gravity imply a modification of the canonical position-momentum commutation relations. In this paper, working with a binary mechanical system, we examine the effect of quantum gravity on the exceptional points of the system. On the one side, we find that the exceedingly weak effect of quantum gravity can be sensed via pushing the system towards a second-order exceptional point, where the spectra of the non-Hermitian system exhibits non-analytic and even discontinuous behavior. On the other side, the gravity perturbation will affect the sensitivity of the system to deposition mass. In order to further enhance the sensitivity of the system to quantum gravity, we extend the system to the other one which has a third-order exceptional point. Our work provides a feasible way to use exceptional points as a new tool to explore the effect of quantum gravity.
Low power threshold, ultrathin optical limiter based on a nonlinear zone plate.

Ultrathin optical limiters are needed to protect light sensitive components in miniaturized optical systems. However, it has proven challenging to achieve a sufficiently low optical limiting threshold. In this work, we theoretically show that an ultrathin optical limiter with low threshold intensity can be realized using a nonlinear zone plate. The zone plate is embedded with nonlinear saturable absorbing materials that allow the device to focus low intensity light, while high intensity light is transmitted as a plane wave without a focal spot. Based on this proposed mechanism, we use the finite-difference time-domain method to computationally design a zone plate embedded with InAs quantum dots as the saturable absorbing material. The device has a thickness of just 0.5 μm and exhibits good optical limiting behavior with a threshold intensity as low as 0.45 kW/cm2, which is several orders of magnitude lower than bulk limiter counterparts based on a similar mechanism, and also performs favorably compared to current ultrathin flat-optics-based optical limiters. This design can be optimized for different operating wavelengths and threshold intensities by using different saturable absorbing materials. Additionally, the diameter and focal length of the nonlinear zone plate can be easily adjusted to fit different systems and applications. Due to its flexible design, low power threshold, and ultrathin thickness, this optical limiting concept may be promising for application in miniaturized optical systems.
Anomaly Detection and Automated Labeling for Voter Registration File Changes

Voter eligibility in United States elections is determined by a patchwork of state databases containing information about which citizens are eligible to vote. Administrators at the state and local level are faced with the exceedingly difficult task of ensuring that each of their jurisdictions is properly managed, while also monitoring for improper modifications to the database. Monitoring changes to Voter Registration Files (VRFs) is crucial, given that a malicious actor wishing to disrupt the democratic process in the US would be well-advised to manipulate the contents of these files in order to achieve their goals. In 2020, we saw election officials perform admirably when faced with administering one of the most contentious elections in US history, but much work remains to secure and monitor the election systems Americans rely on. Using data created by comparing snapshots taken of VRFs over time, we present a set of methods that make use of machine learning to ease the burden on analysts and administrators in protecting voter rolls. We first evaluate the effectiveness of multiple unsupervised anomaly detection methods in detecting VRF modifications by modeling anomalous changes as sparse additive noise. In this setting we determine that statistical models comparing administrative districts within a short time span and non-negative matrix factorization are most effective for surfacing anomalous events for review. These methods were deployed during 2019-2020 in our organization's monitoring system and were used in collaboration with the office of the Iowa Secretary of State. Additionally, we propose a newly deployed model which uses historical and demographic metadata to label the likely root cause of database modifications. We hope to use this model to predict which modifications have known causes and therefore better identify potentially anomalous modifications.
Text Prior Guided Scene Text Image Super-Resolution

Scene text image super-resolution (STISR) aims to improve the resolution and visual quality of low-resolution (LR) scene text images, while simultaneously boost the performance of text recognition. However, most of the existing STISR methods regard text images as natural scene images, ignoring the categorical information of text. In this paper, we make an inspiring attempt to embed text recognition prior into STISR model. Specifically, we adopt the predicted character recognition probability sequence as the text prior, which can be obtained conveniently from a text recognition model. The text prior provides categorical guidance to recover high-resolution (HR) text images. On the other hand, the reconstructed HR image can refine the text prior in return. Finally, we present a multi-stage text prior guided super-resolution (TPGSR) framework for STISR. Our experiments on the benchmark TextZoom dataset show that TPGSR can not only effectively improve the visual quality of scene text images, but also significantly improve the text recognition accuracy over existing STISR methods. Our model trained on TextZoom also demonstrates certain generalization capability to the LR images in other datasets. The source code of our work is available at: https://github.com/mjq11302010044/TPGSR.
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers

With the development of the Internet of Things(IoT) and Artificial Intelligence(AI) technologies, human activity recognition has enabled various applications, such as smart homes and assisted living. In this paper, we target a new healthcare application of human activity recognition, early mobility recognition for Intensive Care Unit(ICU) patients. Early mobility is essential for ICU patients who suffer from long-time immobilization. Our system includes accelerometer-based data collection from ICU patients and an AI model to recognize patients' early mobility. To improve the model accuracy and stability, we identify features that are insensitive to sensor orientations and propose a segment voting process that leverages a majority voting strategy to recognize each segment's activity. Our results show that our system improves model accuracy from 77.78\% to 81.86\% and reduces the model instability (standard deviation) from 16.69\% to 6.92\%, compared to the same AI model without our feature engineering and segment voting process.
Quantifying urban streetscapes with deep learning: focus on aesthetic evaluation

The disorder of urban streetscapes would negatively affect people's perception of their aesthetic quality. The presence of billboards on building facades has been regarded as an important factor of the disorder, but its quantification methodology has not yet been developed in a scalable manner. To fill the gap, this paper reports the performance of our deep learning model on a unique data set prepared in Tokyo to recognize the areas covered by facades and billboards in streetscapes, respectively. The model achieved 63.17 % of accuracy, measured by Intersection-over-Union (IoU), thus enabling researchers and practitioners to obtain insights on urban streetscape design by combining data of people's preferences.
Probing the Physics of the Solar Atmosphere with the Multi-slit Solar Explorer (MUSE). II. Flares and Eruptions

Current state-of-the-art spectrographs cannot resolve the fundamental spatial (subarcseconds) and temporal (less than a few tens of seconds) scales of the coronal dynamics of solar flares and eruptive phenomena. The highest-resolution coronal data to date are based on imaging, which is blind to many of the processes that drive coronal energetics and dynamics. As shown by the Interface Region Imaging Spectrograph for the low solar atmosphere, we need high-resolution spectroscopic measurements with simultaneous imaging to understand the dominant processes. In this paper: (1) we introduce the Multi-slit Solar Explorer (MUSE), a spaceborne observatory to fill this observational gap by providing high-cadence (<20 s), subarcsecond-resolution spectroscopic rasters over an active region size of the solar transition region and corona; (2) using advanced numerical models, we demonstrate the unique diagnostic capabilities of MUSE for exploring solar coronal dynamics and for constraining and discriminating models of solar flares and eruptions; (3) we discuss the key contributions MUSE would make in addressing the science objectives of the Next Generation Solar Physics Mission (NGSPM), and how MUSE, the high-throughput Extreme Ultraviolet Solar Telescope, and the Daniel K Inouye Solar Telescope (and other ground-based observatories) can operate as a distributed implementation of the NGSPM. This is a companion paper to De Pontieu et al., which focuses on investigating coronal heating with MUSE.
On component interactions in two-stage recommender systems

Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest. These systems produce recommendations in two steps: (i) multiple nominators, tuned for low prediction latency, preselect a small subset of candidates from the whole item pool; (ii) a slower but more accurate ranker further narrows down the nominated items, and serves to the user. Despite their popularity, the literature on two-stage recommenders is relatively scarce, and the algorithms are often treated as mere sums of their parts. Such treatment presupposes that the two-stage performance is explained by the behavior of the individual components in isolation. This is not the case: using synthetic and real-world data, we demonstrate that interactions between the ranker and the nominators substantially affect the overall performance. Motivated by these findings, we derive a generalization lower bound which shows that independent nominator training can lead to performance on par with uniformly random recommendations. We find that careful design of item pools, each assigned to a different nominator, alleviates these issues. As manual search for a good pool allocation is difficult, we propose to learn one instead using a Mixture-of-Experts based approach. This significantly improves both precision and recall at K.
Massey products for algebras over operads

Abstract We define a generalization of Massey products for algebras over a Koszul operad in characteristic zero, extending Massey’s and Allday’s and Retah’s in the associative and Lie cases, respectively. We establish connections with minimal models and with Dimitrova’s universal operadic cohomology class. We compute a Gerstenhaber algebra example and a hypercommutative algebra example related to the Chevalley–Eilenberg complex of the Heisenberg Lie algebra.
Time-Domain Doppler Biomotion Detections Immune to Unavoidable DC Offsets

In the past decades, continuous Doppler radar sensor-based biosignal detections have attracted many research interests. A typical example is the Doppler heartbeat detection. While significant progresses have been achieved, reliable, time-domain accurate demodulation of biosignals in the presence of unavoidable direct current (dc) offsets remains a technical challenge. Aiming to overcome this difficulty, we propose in this article a novel demodulation algorithm that does not need to trace and eliminate dynamic dc offsets based on approximating segmented arcs in a quadrature constellation of sampling data to directional chords. Assisted by the principal component analysis, such chords and their directions can be deterministically determined. Simulations and experimental validations showed full recovery of micrometer-level pendulum movements and strongly noised human heartbeats, verifying the effectiveness and accuracy of the proposed approach.
Evolving-Graph Gaussian Processes

Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data represent a dynamic structure, limiting the applications of GGPs. To overcome this we propose evolving-Graph Gaussian Processes (e-GGPs). The proposed method is capable of learning the transition function of graph vertices over time with a neighbourhood kernel to model the connectivity and interaction changes between vertices. We assess the performance of our method on time-series regression problems where graphs evolve over time. We demonstrate the benefits of e-GGPs over static graph Gaussian Process approaches.
GraphPiece: Efficiently Generating High-Quality Molecular Graph with Substructures

Molecule generation, which requires generating valid molecules with desired properties, is a fundamental but challenging task. Recent years have witnessed the rapid development of atom-level auto-regressive models, which usually construct graphs following sequential actions of adding atom-level nodes and edges. However, these atom-level models ignore high-frequency substructures, which not only capture the regularities of atomic combination in molecules but are also often related to desired chemical properties, and therefore may be sub-optimal for generating high-quality molecules. In this paper, we propose a method to automatically discover such common substructures, which we call graph pieces , from given molecular graphs. We also present a graph piece variational autoencoder (GP-VAE) for generating molecular graphs based on graph pieces. Experiments show that our GP-VAE models not only achieve better performance than the state-of-the-art baseline for distribution-learning, property optimization, and constrained property optimization tasks but are also computationally efﬁcient.
Studies on Absorption of Eutectic Mixture. I. A Comparison of the Behavior of Eutectic Mixture of Sulfathiazole and that of Ordinary Sulfathiazole in Man.

1) Sulfathiazole forms eutectic mixtures with urea, l-ascorbic acid, acetamide, nicotinic acid, nicotinamide, or succinimide. It was observed that a eutectic mixture of sulfathiazole and urea produces a microcrystalline suspension of sulfathiazole in water. 2) Sulfathiazole in a eutectic mixture with urea shows higher absorption and excretion after oral administration than ordinary sulfathiazole. 3) Since urea does not possess solubilizing action on sulfathiazole and also it does not enhance absorption of the drug physiologically, the accelerated absorption or excretion must be attributed to the physical state of sulfathiazole in its eutectic mixture with easily soluble compound, such as urea. 4) It is assumed that this new form of preparation will give a means of adjusting therapeutic effect of medical compounds.
Classification of Consumer Belief Statements From Social Media

Social media offer plenty of information to perform market research in order to meet the requirements of customers. One way how this research is conducted is that a domain expert gathers and categorizes user-generated content into a complex and fine-grained class structure. In many of such cases, little data meets complex annotations. It is not yet fully understood how this can be leveraged successfully for classification. We examine the classification accuracy of expert labels when used with a) many fine-grained classes and b) few abstract classes. For scenario b) we compare abstract class labels given by the domain expert as baseline and by automatic hierarchical clustering. We compare this to another baseline where the entire class structure is given by a completely unsupervised clustering approach. By doing so, this work can serve as an example of how complex expert annotations are potentially beneficial and can be utilized in the most optimal way for opinion mining in highly specific domains. By exploring across a range of techniques and experiments, we find that automated class abstraction approaches in particular the unsupervised approach performs remarkably well against domain expert baseline on text classification tasks. This has the potential to inspire opinion mining applications in order to support market researchers in practice and to inspire fine-grained automated content analysis on a large scale.