Miracle-dz/UniFAR-FTUs huggingface dataset:
Query Rewriting in TaoBao Search

In e-commerce search engines, query rewriting (QR) is a crucial technique that improves shopping experience by reducing the vocabulary gap between user queries and product catalog. Recent works have mainly adopted the generative paradigm. However, they hardly ensure high-quality generated rewrites and do not consider personalization, which leads to degraded search relevance. In this work, we present Contrastive Learning Enhanced Query Rewriting (CLE-QR), the solution used in Taobao product search. It uses a novel contrastive learning enhanced architecture based on "query retrieval-semantic relevance ranking-online ranking". It finds the rewrites from hundreds of millions of historical queries while considering relevance and personalization. Specifically, we first alleviate the representation degeneration problem during the query retrieval stage by using an unsupervised contrastive loss, and then further propose an interaction-aware matching method to find the beneficial and incremental candidates, thus improving the quality and relevance of candidate queries. We then present a relevance-oriented contrastive pre-training paradigm on the noisy user feedback data to improve semantic ranking performance. Finally, we rank these candidates online with the user profile to model personalization for the retrieval of more relevant products. We evaluate CLE-QR on Taobao Product Search, one of the largest e-commerce platforms in China. Significant metrics gains are observed in online A/B tests. CLE-QR has been deployed to our large-scale commercial retrieval system and serviced hundreds of millions of users since December 2021. We also introduce its online deployment scheme, and share practical lessons and optimization tricks of our lexical match system.
A Study and Analysis of Colour Space selection for Insignificant Shadow Detection

Though shadows provide realism to the scene, it is found to be problematic in many computer vision and cognitive science applications. Many researches are going on to detect and remove them significantly. There are variety of features that can be used for identifying shadows. This papers experiments the importance of Colour space selection to detect shadow from scenes and videos. Colour features can improve the performance of object tracking and scene understanding. Hence, several well known Colour spaces are experimented. From the outcome, the performance of shadow detection can be improved significantly through the appropriate Colour space selection strategy. The experimental results on real-time scenes show that proposed Colour space is efficient over other Colour spaces.
C-Cast: A Real-Time Forecasting Model for a Controlled Sequence

Predictive control is an advanced control method that is used successfully in industrial control applications. One of the most fundamental demands for predictive control is the accurate forecasting of a ''controlled sequence" using exogenous sequences which consist of multiple attributes (manipulated variables and operation signals). Given a controlled sequence and exogenous sequences, how can we effectively forecast the future behavior of a controlled sequence? In this paper, we present C-Cast, an efficient and effective method for forecasting a time-evolving controlled sequence with exogenous sequences. Our proposed method has the following properties: (a) Adaptive: it captures important time-evolving patterns and operation shift in a time-evolving controlled sequence (b) Effective: it performs accurate forecasting. (c) Practical: it enables real-time controlled sequence forecasting fast enough to satisfy the limitation required for predictive control. Extensive experiments on a real dataset demonstrate that C-Cast consistently outperforms the best existing state-of-the-art methods as regards accuracy, and the execution speed is sufficiently fast.
Embedding Global and Local Influences for Dynamic Graphs

Graph embedding is becoming increasingly popular due to its ability of representing large-scale graph data by mapping nodes to low-dimensional space. Current research usually focuses on transductive learning, which aims to generates fixed node embeddings by training the whole graph. However, dynamic graph changes constantly with new node additions and interactions. Unlike transductive learning, inductive learning attempts to dynamically generate node embeddings over time even for unseen nodes, which is more suitable for real-world applications. Therefore, we propose an inductive dynamic graph embedding method called AGLI by aggregating global and local influences. We propose an aggregator function that integrates global influence with local influence to generate node embeddings at any time. We conduct extensive experiments on several real-world datasets and compare AGLI with several state-of-the-art baseline methods on various tasks. The experimental results show that AGLI achieves better performance than the state-of-the-art baseline methods.
CS-MLGCN: Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks

Community Search (CS) is one of the fundamental tasks in network science and has attracted much attention due to its ability to discover personalized communities with a wide range of applications. Given any query nodes, CS seeks to find a densely connected subgraph containing query nodes. Most existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in many applications such as biological, social, and transportation networks, interactions between objects span multiple aspects, yielding networks with multiple views, called multiplex networks. Existing CS approaches in multiplex networks adopt pre-defined subgraph patterns to model the communities, which cannot find communities that do not have such pre-defined patterns in real-world networks. In this paper, we propose a query-driven graph convolutional network in multiplex networks, CS-MLGCN, that can capture flexible community structures by learning from the ground-truth communities in a data-driven fashion. CS-MLGCN first combines the local query-dependent structure and global graph embedding in each type of proximity and then uses an attention mechanism to incorporate information on different types of relations. Experiments on real-world graphs with ground-truth communities validate the quality of the solutions we obtain and the efficiency of our model.
Efficient Compression Method for Roadside LiDAR Data

Roadside LiDAR (Light Detection and Ranging) sensors are recently being explored for intelligent transportation systems aiming at safer and faster traffic management and vehicular operations. A key challenge in such systems is to efficiently transfer massive point-cloud data from the roadside LiDAR devices to the edge connected through a 5G network for real-time processing. In this paper, we consider the problem of compressing roadside (i.e. static) LiDAR data in real-time that provides a unique condition unexplored by current methods. Existing point-cloud compression methods assume moving LiDARs (that are mounted on vehicles) and do not exploit spatial consistency across frames over time. To this end, we develop a novel grouped wavelet technique for static roadside LiDAR data compression (i.e. SLiC). Our method compresses LiDAR data both spatially and temporally using a kd-tree data structure based on Haar wavelet coefficients. Experimental results show that SLiC can compress up to 1.9× more effectively than the state-of-the-art compression method can do. Moreover, SLiC is computationally more efficient to achieve 2× improvement in bandwidth usage over the best alternative. Even with this impressive gain in communication and storage efficiency, SLiC retains down-the-pipeline application's accuracy.
Carter constant and angular momentum

We investigate the Carter-like constant in the case of a particle moving in a nonrelativistic dipolar potential. This special case is a missing link between the Carter constant in stationary and axially symmetric spacetimes (SASS) such as Kerr solution and its possible Newtonian counterpart. We use this system to carry over the definition of angular momentum from the Newtonian mechanics to the relativistic SASS.
Predicting Multi-level Socioeconomic Indicators from Structural Urban Imagery

Understanding economic development and designing government policies requires accurate and timely measurements of socioeconomic activities. In this paper, we show how to leverage city structural information and urban imagery like satellite images and street view images to accurately predict multi-level socioeconomic indicators. Our framework consists of four steps. First, we extract structural information from cities by transforming real-world street networks into city graphs (GeoStruct). Second, we design a contrastive learning-based model to refine urban image features by looking at geographic similarity between images, with images that are geographically close together having similar features (GeoCLR). Third, we propose using street segments as containers to adaptively fuse the features of multi-view urban images, including satellite images and street view images (GeoFuse). Finally, given the city graph with a street segment as a node and a neighborhood area as a subgraph, we jointly model street- and neighborhood-level socioeconomic indicator predictions as node and subgraph classification tasks. The novelty of our method is that we introduce city structure to organize multi-view urban images and model the relationships between socioeconomic indicators at different levels. We evaluate our framework on the basis of real-world datasets collected in multiple cities. Our proposed framework improves performance by over 10% when compared to state-of-the-art baselines in terms of prediction accuracy and recall.
CLNews: The First Dataset of the Chilean Social Outbreak for Disinformation Analysis

Disinformation is one of the main threats that loom on social networks. Detecting disinformation is not trivial and requires training and maintaining fact-checking teams, which is labor-intensive. Recent studies show that the propagation structure of claims and user messages allows a better understanding of rumor dynamics. Despite these findings, the availability of verified claims and structural propagation data is low. This paper presents a new dataset with Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies. The dataset contains verified claims checked during the Chilean social outbreak, which allows for studying the phenomenon of disinformation during this crisis. We study propagation patterns of verified content in CLNews, showing differences between false rumors and other types of content. Our results show that false rumors are more persistent than the rest of verified contents, reaching more people than truthful news and presenting low barriers of readability to users. The dataset is fully available and helps understand the phenomenon of disinformation during social crises being one of the first of its kind to be released.
JavaScript&Me, A Tool to Support Research into Code Transformation and Browser Security

Doing research into code variations and their applications to browser security is challenging. One of the most important aspects of this research is to choose a relevant dataset on which machine learning algorithms can be applied to yield useful results. Although JavaScript code is widely available on various sources, such as package managers, code hosting platforms, and websites, collecting a large corpus of JavaScript and curating it is not a simple task. We present a novel open-source tool that helps with this task by allowing the automatic and systematic collection, processing, and transformation of JavaScript code. These three steps are performed by independent modules, and each one can be extended to incorporate new features, such as additional code sources, or transformation tools, adding to the flexibility of our tool and expanding its usability. Additionally, we use our tool to create a corpus of around 270k JavaScript files, including regular, minified, and obfuscated code, on which we perform a brief analysis. The conclusions from this analysis show the importance of properly curating a dataset before using it in research tasks, such as machine learning classifiers, reinforcing the relevance of our tool.
YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving

In this paper, we propose a multi-task convolutional neural network (CNN) architecture optimized for a low power automotive grade SoC. We introduce a network based on a unified architecture where the encoder is shared among the two tasks namely detection and segmentation. The pro-posed network runs at 25FPS for 1280x800 resolution. We briefly discuss the methods used to optimize the network architecture such as using native YUV image directly, optimization of layers & feature maps and applying quantization. We also focus on memory bandwidth in our design as convolutions are data intensives and most SOCs are bandwidth bottlenecked. We then demonstrate the efficiency of our proposed network for a dedicated CNN accelerators presenting the key performance indicators (KPI) for the detection and segmentation tasks obtained from the hardware execution and the corresponding run-time.
Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation

In a news recommender system, a user tends to click on a news article if she is interested in its topic understood by looking at its title. Such a behavior is possible since, when viewing the title, humans naturally think of the contextual meaning of each title word by leveraging their own background knowledge. Motivated by this, we propose a novel personalized news recommendation framework CAST (Context-aware Attention network with a Selection module for Title word representation), which is capable of enriching title words by leveraging body text that fully provides the whole content of a given article as the context. Through extensive experiments, we demonstrate (1) the effectiveness of core modules in CAST, (2) the superiority of CAST over 9 state-of-the-art news recommendation methods, and (3) the interpretability with CAST.
Approximation algorithms for distributionally-robust stochastic optimization with black-box distributions

Two-stage stochastic optimization is a widely used framework for modeling uncertainty, where we have a probability distribution over possible realizations of the data, called scenarios, and decisions are taken in two stages: we make first-stage decisions knowing only the underlying distribution and before a scenario is realized, and may take additional second-stage recourse actions after a scenario is realized. The goal is typically to minimize the total expected cost. A common criticism levied at this model is that the underlying probability distribution is itself often imprecise! To address this, an approach that is quite versatile and has gained popularity in the stochastic-optimization literature is the distributionally robust 2-stage model: given a collection D of probability distributions, our goal now is to minimize the maximum expected total cost with respect to a distribution in D. We provide a framework for designing approximation algorithms in such settings when the collection D is a ball around a central distribution and the central distribution is accessed only via a sampling black box. We first show that one can utilize the sample average approximation (SAA) method—solve the distributionally robust problem with an empirical estimate of the central distribution—to reduce the problem to the case where the central distribution has polynomial-size support. Complementing this, we show how to approximately solve a fractional relaxation of the SAA (i.e., polynomial-scenario central-distribution) problem. Unlike in 2-stage stochastic- or robust- optimization, this turns out to be quite challenging. We utilize the ellipsoid method in conjunction with several new ideas to show that this problem can be approximately solved provided that we have an (approximation) algorithm for a certain max-min problem that is akin to, and generalizes, the k-max-min problem—find the worst-case scenario consisting of at most k elements—encountered in 2-stage robust optimization. We obtain such a procedure for various discrete-optimization problems; by complementing this via LP-rounding algorithms that provide local (i.e., per-scenario) approximation guarantees, we obtain the first approximation algorithms for the distributionally robust versions of a variety of discrete-optimization problems including set cover, vertex cover, edge cover, facility location, and Steiner tree, with guarantees that are, except for set cover, within O(1)-factors of the guarantees known for the deterministic version of the problem.
RelpNet: Relation-based Link Prediction Neural Network

Node-based link prediction methods have occupied a dominant position in the graph link prediction task. These methods commonly aggregate node features from the subgraph to generate the potential link representation. However, in constructing subgraphs, these methods extract each node's local neighborhood from the target node pair separately without considering the correlation between them and the whole node pair. As a result, many nodes in the subgraph may have little contribution to predicting the potential edge. Aggregating these node features will reduce the model's accuracy and efficiency. In addition, these methods indirectly represent the potential link by the node embeddings in the subgraph. We argue that this formalism is not the best choice for link prediction. In this paper, we propose a relation-based link prediction neural network named RelpNet, which aggregates edge features along the structural interactions between two target nodes and directly represents their relationship. RelpNet first extracts paths between the target node pair as structural interactions, which have strong correlations with the whole node pair and fewer nodes and edges than node-based methods' subgraph. To aggregate edge embeddings along the links between edges, we propose transforming the paths into a line graph. Then, the Tree-LSTM model is adopted to transfer and aggregate the node embeddings in the line graph as a comprehensive representation of the target node pair. We evaluate RelpNet on 7 benchmark datasets against 15 popular and state-of-the-art approaches, and the results demonstrate its significant superiority and high training efficiency.
Mechanical separation of chiral dipoles by chiral light

We calculate optical forces and torques exerted on a chiral dipole by chiral light fields and reveal genuine chiral forces in combining the chiral contents of both light field and dipolar matter. Here, the optical chirality is characterized in a general way through the definition of optical chirality density and chirality flow. We show, in particular, that both terms have mechanical effects associated, respectively, with reactive and dissipative components of the chiral forces. Remarkably, these chiral force components are directly related to standard observables: optical rotation for the reactive component and circular dichroism for the dissipative one. As a consequence, the resulting forces and torques are dependent on the enantiomeric form of the chiral dipole. This suggests promising strategies for using chiral light forces to mechanically separate chiral objects according to their enantiomeric form.
X-RAY VARIABILITY AND HARDNESS OF ESO 243–49 HLX-1: CLEAR EVIDENCE FOR SPECTRAL STATE TRANSITIONS

The ultraluminous X-ray (ULX) source ESO 243–49 HLX-1, which reaches a maximum luminosity of 1042 erg s−1 (0.2–10 keV), currently provides the strongest evidence for the existence of intermediate-mass black holes (IMBHs). To study the spectral variability of the source, we conduct an ongoing monitoring campaign with the Swift X-ray Telescope (XRT), which now spans more than two years. We found that HLX-1 showed two fast rise and exponential decay type outbursts in the Swift XRT light curve with increases in the count rate of a factor ∼40 separated by 375 ± 13 days. We obtained new XMM-Newton and Chandra dedicated pointings that were triggered at the lowest and highest luminosities, respectively. From spectral fitting, the unabsorbed luminosities ranged from 1.9 × 1040 to 1.25 × 1042 erg s−1. We confirm here the detection of spectral state transitions from HLX-1 reminiscent of Galactic black hole binaries (GBHBs): at high luminosities, the X-ray spectrum showed a thermal state dominated by a disk component with temperatures of 0.26 keV at most, and at low luminosities the spectrum is dominated by a hard power law with a photon index in the range 1.4–2.1, consistent with a hard state. The source was also observed in a state consistent with the steep power-law state, with a photon index of ∼3.5. In the thermal state, the luminosity of the disk component appears to scale with the fourth power of the inner disk temperature, which supports the presence of an optically thick, geometrically thin accretion disk. The low fractional variability (rms of 9% ± 9%) in this state also suggests the presence of a dominant disk. The spectral changes and long-term variability of the source cannot be explained by variations of the beaming angle and are not consistent with the source being in a super-Eddington accretion state as is proposed for most ULX sources with lower luminosities. All this indicates that HLX-1 is an unusual ULX as it is similar to GBHBs, which have non-beamed and sub-Eddington emission, but with luminosities three orders of magnitude higher. In this picture, a lower limit on the mass of the black hole of >9000 M☉ can be derived, and the relatively low disk temperature in the thermal state also suggests the presence of an IMBH of a few 103 M☉.
The Way toward Sustainability: Policy Attention Evolution of Chinese Local Governments to Promote Entrepreneurship of Returnees Based on Grounded Theory and Social Network Analysis

As an important livelihood strategy choice to promote the sustainable development of themselves and regions, returnees’ entrepreneurship has played a vital role in the governance of all-level Chinese local governments since the 21st century. The policy attention reflects the importance that the local governments attach to this issue, and the evolution process also reflects relevant policies’ internal rules and characteristics. Based on this, this paper, taking 242 policy texts related to returnees’ entrepreneurship issued by local governments from 2000 to 2021 as the research objects, uses grounded theory and social network analysis in order to explore the policy attention evolution from the aspects of time, space, specific policy domains and cooperative relationship among policy subjects. Finally, this research finds the main conclusions as follows: (1) The policy attention evolution on time dimension has the characteristic of synchronism, different levels and “reciprocal U”. (2) The policy attention evolution on space dimension is closely linked to “pull” and “push” forces of local population flow and has neighborhood effects. (3) The policy attention evolution on specific policy areas will gradually spread and diversify with the change in local governance environment and stage goals. (4) The evolution trend of collaborative social network among policy objects has the characteristic of “loose-central-loose”.