Miracle-dz/UniFAR-FTUs huggingface dataset:
Inside out and upside-down: The roles of gas cooling and dynamical heating in shaping the stellar age–velocity relation

Kinematic studies of disc galaxies, using individual stars in the Milky Way or statistical studies of global disc kinematics over time, provide insight into how discs form and evolve. We use a high-resolution, cosmological zoom-simulation of a Milky Way-mass disc galaxy (h277) to tie together local disc kinematics and the evolution of the disc over time. The present-day stellar age–velocity relationship (AVR) of h277 is nearly identical to that of the analogous solar-neighbourhood measurement in the Milky Way. A crucial element of this success is the simulation’s dynamically cold multiphase ISM, which allows young stars to form with a low velocity dispersion (σbirth$\sim \! 6 - 8 \ \mathrm{km\, s}^{-1}$) at late times. Older stars are born kinematically hotter (i.e. the disc settles over time in an ‘upside-down’ formation scenario), and are subsequently heated after birth. The disc also grows ‘inside-out’, and many of the older stars in the present-day solar neighbourhood are present because of radial mixing. We demonstrate that the evolution of σbirth in h277 can be explained by the same model used to describe the general decrease in velocity dispersion observed in disc galaxies from z ∼ 2–3 to the present-day, in which the disc evolves in quasi-stable equilibrium and the ISM velocity dispersion decreases over time due to a decreasing gas fraction. Thus, our results tie together local observations of the Milky Way’s AVR with observed kinematics of high z disc galaxies.
MOPO: Model-based Offline Policy Optimization

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a batch of previously collected data. This problem setting is compelling, because it offers the promise of utilizing large, diverse, previously collected datasets to acquire policies without any costly or dangerous active exploration, but it is also exceptionally difficult, due to the distributional shift between the offline training data and the learned policy. While there has been significant progress in model-free offline RL, the most successful prior methods constrain the policy to the support of the data, precluding generalization to new states. In this paper, we observe that an existing model-based RL algorithm on its own already produces significant gains in the offline setting, as compared to model-free approaches, despite not being designed for this setting. However, although many standard model-based RL methods already estimate the uncertainty of their model, they do not by themselves provide a mechanism to avoid the issues associated with distributional shift in the offline setting. We therefore propose to modify existing model-based RL methods to address these issues by casting offline model-based RL into a penalized MDP framework. We theoretically show that, by using this penalized MDP, we are maximizing a lower bound of the return in the true MDP. Based on our theoretical results, we propose a new model-based offline RL algorithm that applies the variance of a Lipschitz-regularized model as a penalty to the reward function. We find that this algorithm outperforms both standard model-based RL methods and existing state-of-the-art model-free offline RL approaches on existing offline RL benchmarks, as well as two challenging continuous control tasks that require generalizing from data collected for a different task.
Inertial oscillation modes of an inclined dipolar magnetosphere as a source of band-limited noise in X-ray pulsars

Magnetic fields of strongly magnetized stars can trap conducting matter due to frozen-in condition. In the force-free regime, the motion of the matter along the field lines may be considered in the ‘bead on a wire’ approximation. Such a motion, if gravity and centrifugal forces are taken into account, has equilibrium points, some of which are stable. In most cases, stability is possible in about several per cent of the possible locations. Corresponding oscillation frequencies span the range from zero to $\sqrt{3}$ of the spin frequency. We suggest that this variability mode may be excited in some X-ray pulsars during the outbursts and create the peaked broad-band noise component near the break frequency in the power density spectrum, as well as produce some of the quasi-periodic oscillation features in this frequency range. Existence of this variability does not require any changes in mass accretion rate and involves only a small amount of matter infiltrating from the disc and magnetic flow due to interchange instabilities.
Generative Adversarial Networks Applied to Observational Health Data

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it.Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging.The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above.Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics.We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-start Users

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user’s current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work [54]. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) [54] and Estimation—Action—Reflection model [27] in both metrics of success rate and average number of conversation turns.
High time resolution and polarization properties of ASKAP-localized fast radio bursts

Combining high time and frequency resolution full-polarisation spectra of Fast Radio Bursts (FRBs) with knowledge of their host galaxy properties provides an opportunity to study both the emission mechanism generating them and the impact of their propagation through their local environment, host galaxy, and the intergalactic medium. The Australian Square Kilometre Array Pathfinder (ASKAP) telescope has provided the first ensemble of bursts with this information. In this paper, we present the high time and spectral resolution, full polarisation observations of five localised FRBs to complement the results published for the previously studied ASKAP FRB~181112. We find that every FRB is highly polarised, with polarisation fractions ranging from 80 -- 100\%, and that they are generally dominated by linear polarisation. While some FRBs in our sample exhibit properties associated with an emerging archetype (i.e., repeating or apparently non-repeating), others exhibit characteristic features of both, implying the existence of a continuum of FRB properties. When examined at high time resolution, we find that all FRBs in our sample have evidence for multiple sub-components and for scattering at a level greater than expected from the Milky Way. We find no correlation between the diverse range of FRB properties (e.g., scattering time, intrinsic width, and rotation measure) and any global property of their host galaxy. The most heavily scattered bursts reside in the outskirts of their host galaxies, suggesting that the source-local environment rather than the host interstellar medium is likely the dominant origin of the scattering in our sample.
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on https://github.com/DearCaat/ioplin, and the CQU-BPDD dataset is able to be accessed on https://dearcaat.github. io/CQU-BPDD/.
Devising Malware Characterstics using Transformers

With the increasing number of cybersecurity threats, it becomes more difficult for researchers to skim through the security reports for malware analysis. There is a need to be able to extract highly relevant sentences without having to read through the entire malware reports. In this paper, we are finding relevant malware behavior mentions from Advanced Persistent Threat Reports. This main contribution is an opening attempt to Transformer the approach for malware behavior analysis.
Fatty acid markers as an indicator for temporal changes in food sources of the bivalve Quidnipagus palatum

Changes in the fatty acid composition in the tissues of the bivalve Quidnipagus palatum from the Tomigusuku intertidal flat, Okinawa, Japan, collected in four different seasons (November 2000, the beginning of cold season; January 2001, the cold season; May 2001, the rainy season; and July 2001, the warm season) were examined and compared to the composition of surface sediments and suspended particulate materials. Assessment of fatty acid markers suggested that the food sources of Q. palatum differed between seasons and depended on the sources of organic material present in the sediment and water column. Vascular plants and bacteria were the main dietary components from July to November because of their abundance in the sediment. From November to January, macroalgae and phytoplankton were the major food sources of Q. palatum, corresponding to their predominance in sediments and algal blooms, respectively. During the May rainy season, organic matter in the sediment was dominated by diatoms, whereas the water column contained diatoms and resuspended macroalgal detritus. The transition to the warm season by July significantly increased the contribution of diatoms to the organic matter present in both the sediment and the water column. Consequently, from May to July, diatoms became the main food source for Q. palatum.
Bell nonlocality with a single shot

In order to reject the local hidden variables hypothesis, the usefulness of a Bell inequality can be quantified by how small a p-value it will give for a physical experiment. Here we show that the expected p-value is minimized when we maximize the difference between the local and Tsirelson bounds of the Bell inequality, when it is formulated as a nonlocal game. We develop an algorithm for transforming an arbitrary Bell inequality into such an optimal nonlocal game, and show its results for the CGLMP and $I_{nn22}$ inequalities. We present explicit examples of Bell inequalities such that the gap between their local and Tsirelson bounds is arbitrarily close to one, and show that this makes it possible to reject local hidden variables with arbitrarily small p-value in a single shot, without needing to collect statistics. We also develop a new algorithm for calculating local bounds which is significantly faster than the methods currently available, which may be of independent interest.
An Overview of Signal Processing Techniques for Terahertz Communications

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advances in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress establishes a foundation for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques, emphasizing ultramassive multiple-input–multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, vital for overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.
CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency

In this paper, we present a new open source toolkit for speech recognition, named CAT (CTC-CRF based ASR Toolkit). CAT inherits the data-efficiency of the hybrid approach and the simplicity of the E2E approach, providing a full-fledged implementation of CTC-CRFs and complete training and testing scripts for a number of English and Chinese benchmarks. Experiments show CAT obtains state-of-the-art results, which are comparable to the fine-tuned hybrid models in Kaldi but with a much simpler training pipeline. Compared to existing non-modularized E2E models, CAT performs better on limited-scale datasets, demonstrating its data efficiency. Furthermore, we propose a new method called contextualized soft forgetting, which enables CAT to do streaming ASR without accuracy degradation. We hope CAT, especially the CTC-CRF based framework and software, will be of broad interest to the community, and can be further explored and improved.
Portfolio optimization with mixture vector autoregressive models.

Obtaining reliable estimates of conditional covariance matrices is an important task of heteroskedastic multivariate time series. In portfolio optimization and financial risk management, it is crucial to provide measures of uncertainty and risk as accurately as possible. We propose using mixture vector autoregressive (MVAR) models for portfolio optimization. Combining a mixture of distributions that depend on the recent history of the process, MVAR models can accommodate asymmetry, multimodality, heteroskedasticity and cross-correlation in multivariate time series data. For mixtures of Normal components, we exploit a property of the multivariate Normal distribution to obtain explicit formulas of conditional predictive distributions of returns on a portfolio of assets. After showing how the method works, we perform a comparison with other relevant multivariate time series models on real stock return data.
Ranking-Incentivized Quality Preserving Content Modification

The Web is a canonical example of a competitive retrieval setting where many documents' authors consistently modify their documents to promote them in rankings. We present an automatic method for quality-preserving modification of document content --- i.e., maintaining content quality --- so that the document is ranked higher for a query by a non-disclosed ranking function whose rankings can be observed. The method replaces a passage in the document with some other passage. To select the two passages, we use a learning-to-rank approach with a bi-objective optimization criterion: rank promotion and content-quality maintenance. We used the approach as a bot in content-based ranking competitions. Analysis of the competitions demonstrates the merits of our approach with respect to human content modifications in terms of rank promotion, content-quality maintenance and relevance.
Compaction for two models of logarithmic‐depth trees: Analysis and experiments

We are interested in the quantitative analysis of the compaction ratio for two classical families of trees: recursive trees and plane binary increasing trees. These families are typical representatives of tree models with a small depth. Once a tree of size n is compacted by keeping only one occurrence of all fringe subtrees appearing in the tree the resulting graph contains only O(n/lnn) nodes. This result must be compared to classical results of compaction in the families of simply generated trees, where the analogous result states that the compacted structure is of size of order n/lnn . The result about the plane binary increasing trees has already been proved, but we propose a new and generic approach to get the result. Finally, an experimental study is presented, based on a prototype implementation of compacted binary search trees that are modeled by plane binary increasing trees.
IoT-based Emergency Evacuation Systems

Fires, earthquakes, floods, hurricanes, overcrowding, or and even pandemic viruses endanger human lives. Hence, designing infrastructures to handle possible emergencies has become an ever-increasing need. The safe evacuation of occupants from the building takes precedence when dealing with the necessary mitigation and disaster risk management. This thesis deals with designing an IoT system to provide safe and quick evacuation suggestions. The IoT-based evacuation system provides optimal evacuation paths that can be continuously updated based on run-time sensory data, so evacuation guidelines can be adjusted according to visitors occupants that evolve over time. This thesis makes the following main contributions: i) Addressing an up to date state of the art class for IoT architectural styles and patterns; ii) Proposing a set of self-adaptive IoT patterns and assessing their specific quality attributes (fault-tolerance, energy consumption, and performance); iii) Designing an IoT infrastructure and testing its performance in both real-time and design-time applications; iv) Developing a network flow algorithm that facilitates minimizing the time necessary to evacuate people from a scene of a disaster; v) Modeling various social agents and their interactions during an emergency to improve the IoT system accordingly; vi) Evaluating the system by using empirical and real case studies.
Evidence of AGN feedback and sloshing in the X-ray luminous NGC 1550 galaxy group

We present results from Giant Metrewave Radio Telescope and Chandra observations of the NGC 1550 galaxy group. Although previously thought of as relaxed, we show evidence that gas sloshing and active galactic nucleus (AGN) heating have affected the structure of the system. The 610 and 235 MHz radio images show an asymmetric jet–lobe structure with a total size of ∼33 kpc, with a sharp kink at the base of the more extended western jet, and bending of the shorter eastern jet as it enters the lobe. The 235–610 MHz spectral index map shows that both radio lobes have steep spectral indices ($\alpha _{235}^{610}\ge -1.5$), indicating the presence of an old electron population. The X-ray images reveal an asymmetric structure in the hot gas correlated with the radio structure, as well as potential cavities coincident with the radio lobes, with rims and arms of gas that may have been uplifted by the cavity expansion. The X-ray residual map reveals an arc-shaped structure to the east that resembles a sloshing cold front. Radio spectral analysis suggests a radiative age of about 33 Myr for the source, comparable to the sloshing time-scale and dynamical estimates of the age of the lobes. An estimate of the mechanical energy required to inflate the cavities suggests that the AGN of NGC 1550 is capable of balancing radiative losses from the intragroup medium (IGM) and preventing excessive cooling, providing that the AGN jets are efficiently coupled to the IGM gas. In conclusion, we find evidence of sloshing motions from both radio and X-ray structures, suggesting that NGC 1550 was perturbed by a minor merger or infalling galaxy about 33 Myr ago.
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter

Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately compared to a number of competitive baselines.