Miracle-dz/UniFAR-FTUs huggingface dataset:
Prominent polarized flares of the blazars AO 0235+164 and PKS 1510–089

We report on multi-band photopolarimetric observations of the blazars AO 0235+164 and PKS 1510–089. These two blazars became in active states in 2008 and 2009, respectively. In these active states, prominent short-term flares were detected in both objects, having amplitudes of > 1 mag within 10 days. The V – J color became bluer when the objects were brighter in these flares. On the other hand, the color of PKS 1510–089 exhibited a trend that it became redder when it was brighter, except for its prominent flare. This feature can be explained by the strong contribution of thermal emission from an accretion disk. The degree of polarization increased at the flares, and reached > 25 % at the maxima in AO 0235+164 and PKS 1510–089. We compare these flares with other short-term flares which were detected by our monitoring of 44 blazars. Those two flares had one of the largest variation amplitudes in both flux and degree of polarization. Furthermore, we found a significant positive correlation between the amplitudes of the flux and degree of polarization in the flares. It indicates that the short-term flares originate from the region where the magnetic field is aligned.
Temporal Network Representation Learning

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a sequence of timestamped edges and are seemingly ubiquitous. Despite the importance of accurately modeling the temporal information, most embedding methods ignore it entirely or approximate the temporal network using a sequence of static snapshot graphs. In this work, we introduce the notion of \emph{temporal walks} for learning dynamic embeddings from temporal networks. Temporal walks capture the temporally valid interactions (\eg, flow of information, spread of disease) in the dynamic network in a lossless fashion. Based on the notion of temporal walks, we describe a general class of embeddings called continuous-time dynamic network embeddings (CTDNEs) that completely avoid the issues and problems that arise when approximating the temporal network as a sequence of static snapshot graphs. Unlike previous work, CTDNEs learn dynamic node embeddings directly from the temporal network at the finest temporal granularity and thus use only temporally valid information. As such CTDNEs naturally support online learning of the node embeddings in a streaming real-time fashion. The experiments demonstrate the effectiveness of this class of embedding methods for prediction in temporal networks.
Is the Mind Inherently Predicting? Exploring Forward and Backward Looking in Language Processing

Abstract Prediction is one characteristic of the human mind. But what does it mean to say the mind is a “prediction machine” and inherently forward looking as is frequently claimed? In natural languages, many contexts are not easily predictable in a forward fashion. In English, for example, many frequent verbs do not carry unique meaning on their own but instead, rely on another word or words that follow them to become meaningful. Upon reading take a the processor often cannot easily predict walk as the next word. But the system can “look back” and integrate walk more easily when it follows take a (e.g., as opposed to *make|get|have a walk). In the present paper, we provide further evidence for the importance of both forward and backward‐looking in language processing. In two self‐paced reading tasks and an eye‐tracking reading task, we found evidence that adult English native speakers’ sensitivity to word forward and backward conditional probability significantly predicted reading times over and above psycholinguistic predictors of reading latencies. We conclude that both forward and backward‐looking (prediction and integration) appear to be important characteristics of language processing. Our results thus suggest that it makes just as much sense to call the mind an “integration machine” which is inherently backward ‘looking. ’
Classification -based method in optimal image interpolation

Atkins, C. Brian. Ph.D., Purdue University, December 1998. Classification-Based Methods in Optimal Image Interpolation. Major Professors: Charles A. Bouman and Jan P. Allebach. In this thesis, we introduce two new approaches to optimal image interpolation which are based on the idea that image data falls into different categories or classes, such as edges of different orientation and smoother gradients. Both these methods work by first classifying the image data in a window around the pixel being interpolated, and then using an interpolation filter designed for the selected class. The first method, which we call Resolution Synthesis (RS), performs the classification by computing probabilities of class membership in a Gaussian mixture model. The second method, which we call Tree-based Resolution Synthesis (TRS), uses a regression tree. Both of these methods are based on stochastic models for image data whose parameters must have been estimated beforehand, by training on sample images. We demonstrate that under some assumptions, both of these methods are actually optimal in the sense that they yield minimum mean-squared error (MMSE) estimates of the target-resolution image, given the source image. We also introduce Enhanced Tree-based RS, which consists of TRS interpolation followed by an enhancement stage. During the enhancement stage, we recursively add adjustments to the pixels in the interpolated image. This has the dual effect of reducing interpolation artifacts while imparting additional sharpening. We present results of the above methods for interpolating images which are free of artifacts. In addition, we present results which demonstrate that RS can be trained for high-quality interpolation of images which
Pinning down the ram-pressure-induced halt of star formation in the Virgo cluster spiral galaxy NGC 4388. A joint inversion of spectroscopic and photometric data

Context. In a galaxy cluster, the evolution of spiral galaxies depends on their cluster environment. Ram pressure due to the rapid motion of a spiral galaxy within the hot intracluster medium removes the galaxy’s interstellar medium from the outer disk. Once the gas has left the disk, star formation stops. The passive evolution of the stellar populations should be detectable in optical spectroscopy and multi-wavelength photometry. Aims. The goal of our study is to recover the stripping age of the Virgo spiral galaxy NGC 4388, i.e. the time elapsed since the halt of star formation in the outer galactic disk using a combined analysis of optical spectra and photometry. Methods. We performed VLT FORS2 long-slit spectroscopy of the inner star-forming and outer gas-free disk of NGC 4388. We developed a non-parametric inversion tool that allows us to reconstruct the star formation history of a galaxy from spectroscopy and photometry. The tool was tested on a series of mock data using Monte Carlo simulations. The results from the non-parametric inversion were refined by applying a parametric inversion method. Results. The star formation history of the unperturbed galactic disk is flat. The non-parametric method yields a rapid decline of star formation <200 Myr ago in the outer disk. The parametric method is not able to distinguish between an instantaneous and a longlasting star formation truncation. We find that 190 ± 30 Myr ago the star formation reached half of its pre-stripping value. Conclusions. We are able to give a precise stripping age that is consistent with revised dynamical models.
Physics-Based Control Methods

"Advances in Spacecraft Systems and Orbit Determinations", discusses the development of new technologies and the limitations of the present technology, used for interplanetary missions. Various experts have contributed to develop the bridge between present limitations and technology growth to overcome the limitations. Key features of this book inform us about the orbit determination techniques based on a smooth research based on astrophysics. The book also provides a detailed overview on Spacecraft Systems including reliability of low-cost AOCS, sliding mode controlling and a new view on attitude controller design based on sliding mode, with thrusters. It also provides a technological roadmap for HVAC optimization. The book also gives an excellent overview of resolving the difficulties for interplanetary missions with the comparison of present technologies and new advancements. Overall, this will be very much interesting book to explore the roadmap of technological growth in spacecraft systems.
Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction

The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relation graphs are usually constructed by handcraft human labeling or nature language processing, which are suffering from heavy resource requirement and low accuracy. Besides, they cannot effectively response to the dynamic changes in relation graphs. Therefore, in this paper, we propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. In particular, we first generate the company relation graph for each trading day according to their historic price. Then we leverage a transformer encoder to encode the price movement information into temporal representations. Afterward, we propose a heterogeneous graph attention network to jointly optimize the embeddings of the financial time series data by transformer encoder and infer the probability of target movements. Finally, we conduct extensive experiments on the stock market in the United States and China. The results demonstrate the effectiveness and superior performance of our proposed methods compared with state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a real-world quantitative algorithm trading system, the accumulated portfolio return obtained by our method significantly outperforms other baselines.
Hierarchical Representation for Multi-view Clustering: From Intra-sample to Intra-view to Inter-view

Multi-view clustering (MVC) aims at exploiting the consistent features within different views to divide samples into different clusters. Existing subspace-based MVC algorithms usually assume linear subspace structures and two-stage similarity matrix construction strategies, thereby posing challenges in imprecise low-dimensional subspace representation and inadequacy of exploring consistency. This paper presents a novel hierarchical representation for MVC method via the integration of intra-sample, intra-view, and inter-view representation learning models. In particular, we first adopt the deep autoencoder to adaptively map the original high-dimensional data into the latent low-dimensional representation of each sample. Second, we use the self-expression of the latent representation to explore the global similarity between samples of each view and obtain the subspace representation coefficients. Third, we construct the third-order tensor by arranging multiple subspace representation matrices and impose the tensor low-rank constraint to sufficiently explore the consistency among views. Being incorporated into a unified framework, these three models boost each other to achieve a satisfactory clustering result. Moreover, an alternating direction method of multipliers algorithm is developed to solve the challenging optimization problem. Extensive experiments on both simulated and real-world multi-view datasets show the superiority of the proposed method over eight state-of-the-art baselines.
Temporal Cycle-Consistency Learning

We introduce a self-supervised representation learning method based on the task of temporal alignment between videos. The method trains a network using temporal cycle-consistency (TCC), a differentiable cycle-consistency loss that can be used to find correspondences across time in multiple videos. The resulting per-frame embeddings can be used to align videos by simply matching frames using nearest-neighbors in the learned embedding space. To evaluate the power of the embeddings, we densely label the Pouring and Penn Action video datasets for action phases. We show that (i) the learned embeddings enable few-shot classification of these action phases, significantly reducing the supervised training requirements; and (ii) TCC is complementary to other methods of self-supervised learning in videos, such as Shuffle and Learn and Time-Contrastive Networks. The embeddings are also used for a number of applications based on alignment (dense temporal correspondence) between video pairs, including transfer of metadata of synchronized modalities between videos (sounds, temporal semantic labels), synchronized playback of multiple videos, and anomaly detection. Project webpage: https://sites.google. com/view/temporal-cycle-consistency .
Risk-Aware Bid Optimization for Online Display Advertisement

This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget. We propose a risk-aware data-driven bid optimization model that maximizes the expected profit for the advertiser by exploiting historical data to design upfront a bidding policy, mapping the type of advertisement opportunity to a bid price, and accounting for the risk of violating the budget constraint during a given period of time. After employing a Lagrangian relaxation, we derive a parametrized closed-form expression for the optimal bidding strategy. Using a real-world dataset, we demonstrate that our risk-averse method can effectively control the risk of overspending the budget while achieving a competitive level of profit compared with the risk-neutral model and a state-of-the-art data-driven risk-aware bidding approach.
DuTraffic: Live Traffic Condition Prediction with Trajectory Data and Street Views at Baidu Maps

The task of live traffic condition prediction, which aims at predicting live traffic conditions (i.e., fast, slow, and congested) based on traffic information on roads, plays a vital role in intelligent transportation systems, such as navigation, route planning, and ride-hailing services. Existing solutions have adopted aggregated trajectory data to generate traffic estimates, which inevitably suffer from GPS drift caused by cluttered urban road scenarios. In addition, the trajectory information alone is insufficient to provide evidence for sudden traffic situations and perception of street-wise elements. To alleviate these problems, in this paper, we present DuTraffic, which is a robust and production-ready solution for live traffic condition prediction by taking both trajectory data and street views into account. Specifically, the vision-based detection and segmentation modules are developed to forecast traffic flow by using street views. Then, we propose a spatial-temporal-based module, TRST-Net, to learn the latent trajectory representation. Finally, a bilinear model is introduced to mix these two representations and then predicts live traffic conditions with trajectory data and street views in a mutually complementary manner. The task is recast as a multi-task learning problem, which could benefit from the strong representation of latent space manifold modeling. Extensive experiments conducted on large-scale, real-world datasets from Baidu Maps demonstrate the superiority and effectiveness of DuTraffic. In addition, DuTraffic has already been deployed in production at Baidu Maps since December 2020, handling tens of millions of requests every day. This demonstrates that DuTraffic is a practical and robust industrial solution for live traffic condition prediction.
Contextualized Formula Search Using Math Abstract Meaning Representation

In math formula search, relevance is determined not only by the similarity of formulas in isolation, but also by their surrounding context. We introduce MathAMR, a new unified representation for sentences containing math. MathAMR generalizes Abstract Meaning Representation (AMR) graphs to include math formula operations and arguments. We then use Sentence-BERT to embed linearized MathAMR graphs for use in formula retrieval. In our first experiment, we compare MathAMR against raw text using the same formula representation (Operator Trees), and find that MathAMR produces more effective rankings. We then apply our MathAMR embeddings to reranking runs from the ARQMath-2 formula retrieval task, where in most cases effectiveness measures are improved. The strongest reranked run matches the best P$'$@10 for an original run, and exceeds the original runs in nDCG$'$@10.
A fast radio burst with frequency-dependent polarization detected during Breakthrough Listen observations

Here, we report on the detection and verification of fast radio burst FRB 180301, which occurred on utc 2018 March 1 during the Breakthrough Listen observations with the Parkes telescope. Full-polarization voltage data of the detection were captured – a first for non-repeating FRBs – allowing for coherent de-dispersion and additional verification tests. The coherently de-dispersed dynamic spectrum of FRB 180301 shows complex, polarized frequency structure over a small fractional bandwidth. As FRB 180301 was detected close to the geosynchronous satellite band during a time of known 1–2 GHz satellite transmissions, we consider whether the burst was due to radio interference emitted or reflected from an orbiting object. Based on the pre-ponderance of our verification tests, we cannot conclusively determine FRB 180301 to be either astrophysical or anthropogenic in origin.
Transfer operators and atomic decomposition

We use the method of atomic decomposition and a new family of Banach spaces to study the action of transfer operators associated to piecewise-defined maps. It turns out that these transfer operators are quasi-compact even when the associated potential, the dynamics and the underlying phase space have very low regularity. In particular it is often possible to obtain exponential decay of correlations, the Central Limit Theorem and almost sure invariance principle for fairly general observables, including unbounded ones.
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Forecasting

Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately, or within a sliding temporal window, and fail to model the direct spatial-temporal correlations. Inspired by the recent success of transformers in the graph domain, in this paper, we propose to directly model the cross-spatial-temporal correlations on the adaptive spatial-temporal graph using local multi-head self-attentions. We then propose a novel Adaptive Graph Spatial-Temporal Transformer Network (ASTTN), which stacks multiple spatial-temporal attention layers to apply self-attention on the input graph, followed by linear layers for predictions. Experimental results on public traffic network datasets, METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of our model.
DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning

Predicting future events in dynamic knowledge graphs has attracted significant attention. Existing work models the historical information in a holistic way, which achieves satisfactory performance. However, in real-world scenarios, the influence of historical information on future events is changing over time. Therefore, it is difficult to distinguish the historical information of different roles by invariably embedding historical entities with simple vector stacking. Furthermore, it is laborious to explicitly learn a distributed representation of each historical repetitive fact at different timestamps. This poses a challenge to the widely adopted codec-based architectures. In this paper, we propose a novel model for predicting future events, namely Distributed Attention Network (DA-Net). Rather than obtaining the fixed representations of historical events, DA-Net attempts to learn the distributed attention of future events on repetitive facts at different historical timestamps inspired by human cognitive theory. In human cognitive theory, when humans make a decision, similar historical events are replayed during memory recall. Based on memory, the original intention is adjusted according to their recent knowledge developments, making the action more reasonable to the context. Experiments on four benchmark datasets demonstrate a substantial improvement of DA-Net on multiple evaluation metrics.
Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models

Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking. As a critical bridge between matching and ranking, existing pre-ranking approaches mainly endure sample selection bias (SSB) problem owing to ignoring the entire-chain data dependence, resulting in sub-optimal performances. In this paper, we rethink pre-ranking system from the perspective of the entire sample space, and propose Entire-chain Cross-domain Models (ECM), which leverage samples from the whole cascaded stages to effectively alleviate SSB problem. Besides, we design a fine-grained neural structure named ECMM to further improve the pre-ranking accuracy. Specifically, we propose a cross-domain multi-tower neural network to comprehensively predict for each stage result, and introduce the sub-networking routing strategy with L0 regularization to reduce computational costs. Evaluations on real-world large-scale traffic logs demonstrate that our pre-ranking models outperform SOTA methods while time consumption is maintained within an acceptable level, which achieves better trade-off between efficiency and effectiveness.
Bandit Learning in Many-to-One Matching Markets

The problem of two-sided matching markets is well-studied in social science and economics. Some recent works study how to match while learning the unknown preferences of agents in one-to-one matching markets. However, in many cases like the online recruitment platform for short-term workers, a company can select more than one agent while an agent can only select one company at a time. These short-term workers try many times in different companies to find the most suitable jobs for them. Thus we consider a more general bandit learning problem in many-to-one matching markets where each arm has a fixed capacity and agents make choices with multiple rounds of iterations. We develop algorithms in both centralized and decentralized settings and prove regret bounds of order O(log T) and Olog2 T) respectively. Extensive experiments show the convergence and effectiveness of our algorithms.