Miracle-dz/UniFAR-FTUs huggingface dataset:
Extreme throat initial data set and horizon area-angular momentum inequality for axisymmetric black holes

We present a formula that relates the variations of the area of extreme throat initial data with the variation of an appropriate defined mass functional. From this expression we deduce that the first variation, with fixed angular momentum, of the area is zero and the second variation is positive definite evaluated at the extreme Kerr throat initial data. This indicates that the area of the extreme Kerr throat initial data is a minimum among this class of data. And hence the area of generic throat initial data is bounded from below by the angular momentum. Also, this result strongly suggests that the inequality between area and angular momentum holds for generic asymptotically flat axially symmetric black holes. As an application, we prove this inequality in the nontrivial family of spinning Bowen-York initial data.
Small Gauge Transformations and Universal Geometry in Heterotic Theories

We explore the observation that small gauge transformations in heterotic supergravity are related to a choice of connection on the moduli space. We start by describing the action of small gauge transformations in heterotic supergravity. We show a convenient gauge fixing is holomorphic gauge together with residual gauge fixing involving the holomorphic top form. This is related to a choice of holomorphic structure and Lee form on the universal bundle. The field strength for this connection is related to second order deformation theory and we point out it is generically the case that higher order deformations do not commute. At the level of supergravity, this gauge fixing allows us to determine the Hodge decomposition, and so a map between parameters and field deformations. At large radius, we give a prescription for how to compute the alpha' corrections to the Hodge decomposition and apply this to the moduli space metric.
A Different VIM: Visualizing Incremental Machine Learning

Incremental learning is used to continuously update and tune an existing model, as more data points come in. However, there is a tradeoff between speed and accuracy as the model becomes stable. From a big data angle, computing machine learning models is challenging when data sets cannot fit in main memory or when they exceed CPU capacity. On the other hand, data summarization is a fundamental technique that has promise to accelerate data science computations and compress a data set. Keeping these motivations in mind, we present an innovative system, VIM, that computes machine learning models in an incremental manner, visualizing continuous learning of model parameters as the data set is scanned. Our system is fast, it works for a wide spectrum of machine learning models and it can handle data sets larger than main memory. We developed an intuitive GUI which: (1) guides the user to upload data sets and choose a machine learning model, (2) offers interactive visualization of model parameters, and (3) helps getting an approximate model, stopping early, without reading the whole data set.
ExeKG: Executable Knowledge Graph System for User-friendly Data Analytics

Data analytics including machine learning (ML) is essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad-hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable knowledge graph (KG), which represent KGs that rely on semantic technologies to formally encode ML knowledge and solutions. These KGs can be translated to executable scripts in a reusable and modularised fashion. The demo attendees will use our system to modify, integrate and create executable KGs via a graphic user interface, which offer a user-friendly way to understand, configure, reuse, and create data analytics pipelines.
A digital filtering system for decoding in-line holograms

A system of digital filters is presented, which is applied directly to the in-line hologram and maps the hologram into an image of the boundaries (edges) of the objects encoded in it provided that the object distribution function is real. The first stage is an approximate inverse filter in the form of a truncated series expansion in the Fourier transform domain, having basis functions coskb (u2+v2). The first term in the expansion corresponds to conventional optical reconstruction which is contaminated by the twin-image; and the twin-image is suppressed by the successive terms. The non-linear post filtering consists of a covariance filter which replaces each input pixel by the estimate of the local covariance. A thresholding may be added prior to the covariance filtering if the edges of the reconstructed objects are not sharp enough.
Pattern Adaptive Specialist Network for Learning Trading Patterns in Stock Market

Stock prediction is a challenging task due to the uncertainty of stock markets. Despite the success of previous works, most of them rely on the assumption that stock data follow the identically identical distribution while the existence of multiple trading patterns in stock market violates it, ignoring multiple patterns in stock market will inevitably lead to the performance decline, and the lack of pattern prior knowledge further hinders the learning of patterns. In this paper, we propose a novel training process Pattern Adaptive Training based on Optimal Transport (OT) to train a set of predictors specializing in diverse patterns while without any prior pattern knowledge and inconsistent assumption. Based on this process, we further mine the potential fitness rank among specialists and design the Pattern Adaptive Specialist Network (PASN) with proposed ranking based selector to choose appropriate specialist predictor for samples. Extensive experimental results show that our method achieves best IC and other metrics on real-world stock datasets.
Deep Extreme Mixture Model for Time Series Forecasting

Time Series Forecasting (TSF) has been a topic of extensive research, which has many real world applications such as weather prediction, stock market value prediction, traffic control etc. Many machine learning models have been developed to address TSF, yet, predicting extreme values remains a challenge to be effectively addressed. Extreme events occur rarely, but tend to cause a huge impact, which makes extreme event prediction important. Assuming light tailed distributions, such as Gaussian distribution, on time series data does not do justice to the modeling of extreme points. To tackle this issue, we develop a novel approach towards improving attention to extreme event prediction. Within our work, we model time series data distribution, as a mixture of Gaussian distribution and Generalized Pareto distribution (GPD). In particular, we develop a novel Deep eXtreme Mixture Model (DXtreMM) for univariate time series forecasting, which addresses extreme events in time series. The model consists of two modules: 1) Variational Disentangled Auto-encoder (VD-AE) based classifier and 2) Multi Layer Perceptron (MLP) based forecaster units combined with Generalized Pareto Distribution (GPD) estimators for lower and upper extreme values separately. VD-AE Classifier model predicts the possibility of occurrence of an extreme event given a time segment, and forecaster module predicts the exact value. Through extensive set of experiments on real-world datasets we have shown that our model performs well for extreme events and is comparable with the existing baseline methods for normal time step forecasting.
Hierarchical Reinforcement Learning using Gaussian Random Trajectory Generation in Autonomous Furniture Assembly

In this paper, we propose a Gaussian Random Trajectory guided Hierarchical Reinforcement Learning (GRT-HL) method for autonomous furniture assembly. The furniture assembly problem is formulated as a comprehensive human-like long-horizon manipulation task that requires a long-term planning and a sophisticated control. Our proposed model, GRT-HL, draws inspirations from the semi-supervised adversarial autoencoders, and learns latent representations of the position trajectories of the end-effector. The high-level policy generates an optimal trajectory for furniture assembly, considering the structural limitations of the robotic agents. Given the trajectory drawn from the high-level policy, the low-level policy makes a plan and controls the end-effector. We first evaluate the performance of GRT-HL compared to the state-of-the-art reinforcement learning methods in furniture assembly tasks. We demonstrate that GRT-HL successfully solves the long-horizon problem with extremely sparse rewards by generating the trajectory for planning.
Cognize Yourself: Graph Pre-Training via Core Graph Cognizing and Differentiating

While Graph Neural Networks (GNNs) have become de facto criterion in graph representation learning, they still suffer from label scarcity and poor generalization. To alleviate these issues, graph pre-training has been proposed to learn universal patterns from unlabeled data via applying self-supervised tasks. Most existing graph pre-training methods only use a single self-supervised task, which will lead to insufficient knowledge mining. Recently, there are also some works that try to use multiple self-supervised tasks, however, we argue that these methods still suffer from a serious problem, which we call it graph structure impairment. That is, there actually exists structural gaps among several tasks due to the divergence of optimization objectives, which means customized graph structures should be provided for different self-supervised tasks. Graph structure impairment not only significantly hurts the generalizability of pre-trained GNNs, but also leads to suboptimal solution, and there is no study so far to address it well. Motivated by Meta-Cognitive theory, we propose a novel model named Core Graph Cognizing and Differentiating (CORE) to deal with the problem in an effective approach. Specifically, CORE consists of cognizing network and differentiating process, the former cognizes a core graph which stands for the essential structure of the graph, and the latter allows it to differentiate into several task-specific graphs for different tasks. Besides, this is also the first study to combine graph pre-training with cognitive theory to build a cognition-aware model. Several experiments have been conducted to demonstrate the effectiveness of CORE.
Aries: Accurate Metric-based Representation Learning for Fast Top-k Trajectory Similarity Query

With the prevalence of location-based services (LBS), trajectories are being generated rapidly. As is widely used in LBS, top-k trajectory similarity query serves as a key operation, deeply empowering applications such as travel route recommendation and carpooling. Given the rise of deep learning, trajectory representation has been well-proven to speed up this operator. However, existing representation-based computing modes remain two major problems understudied: the low quality of trajectory representation and insufficient support for various trajectory similarity metrics, which make them difficult to apply in practice. Therefore, we propose an Accurate metric-based representation learning approach for fast top-k trajectory similarity query, named Aries. Specifically, Aries has two sophisticated modules: (1) An novel trajectory embedding strategy enhanced by the bidirectional LSTM encoder and spatial attention mechanism, which can extract more precise and comprehensive knowledge. (2) A deep metric learning network aggregating multiple measures for better top-k query. Extensive experiments conducted on real trajectory dataset show that Aries achieves both impressive accuracy and lower training time compared with state-of-the-art solutions. In particular, it achieves 5x-10x speedup and 10%-20% accuracy improvement over Euclidean, Hausdorff, DTW, and EDR measures. Besides, our method can maintain stable performance when handling various scenarios, without repeated training in order to adapt to diverse similarity metrics.
Scalable Graph Sampling on GPUs with Compressed Graph

GPU is a powerful accelerator for parallel computation. Graph sampling is a fundamental technology for large-scale graph analysis and learning. To accelerate graph sampling using GPUs, recently some solutions like NextDoor, C-SAW have been proposed. However, these solutions cannot handle large graphs efficiently because of the massive memory footprint and expensive transfer cost between CPU and GPU. In this work, we introduce a Chunk-wise Graph Compression format (CGC) to effectively reduce the graph size and save the graph transfer cost. Meanwhile, CGC supports fast visiting any single neighbor of a vertex and is friendly to the graph sampling task. Specifically, CGC first balances the graph compression ratio and decompression efficiency by dividing a neighbor vertex list into chunks. Then it applies a new compression strategy called linear estimation to compress each chunk and allows users to visit a single vertex in O(1) time complexity. Finally, based on the CGC, we develop a scalable GPU-based graph sampling framework GraSS, and evaluate the efficiency and scalability of GraSS on both real-world and synthetic graphs. The empirical results demonstrate that GraSS can support various graph sampling methods on large graphs with high efficiency when the state-of-the-art solutions are out-of-memory or exceed the time limit.
Graph Neural Networks Pretraining Through Inherent Supervision for Molecular Property Prediction

Recent global events have emphasized the importance of accelerating the drug discovery process. A way to deal with the issue is to use machine learning to increase the rate at which drugs are made available to the public. However, chemical labeled data for real-world applications is extremely scarce making traditional approaches less effective. A fruitful course of action for this challenge is to pretrain a model using related tasks with large enough datasets, with the next step being finetuning it for the desired task. This is challenging as creating these datasets requires labeled data or expert knowledge. To aid in solving this pressing issue, we introduce MISU - Molecular Inherent SUpervision, a unique method for pretraining graph neural networks for molecular property prediction. Our method leapfrogs past the need for labeled data or any expert knowledge by introducing three innovative components that utilize inherent properties of molecular graphs to induce information extraction at different scales, from the local neighborhood of an atom to substructures in the entire molecule. Our empirical results for six chemical-property-prediction tasks show that our method reaches state-of-the-art results compared to numerous baselines.
CNewsTS - A Large-scale Chinese News Dataset with Hierarchical Topic Category and Summary

In this paper, we present a large Chinese news article dataset with 4.4 million articles. These articles are obtained from different news channels and sources. They are labeled with multi-level topic categories, and some of them also have summaries. This is the first Chinese news dataset that has both hierarchical topic labels and article full texts. And it is also the largest Chinese news topic dataset. We describe the data collection, annotation and quality evaluation process. The basic statistics of the dataset, comparison with other datasets and benchmark experiments are also presented.
SmartQuery: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction

Graph neural networks have achieved significant success in representation learning. However, the performance gains come at a cost; acquiring comprehensive labeled data for training can be prohibitively expensive. Active learning mitigates this issue by searching the unexplored data space and prioritizing the selection of data to maximize model's performance gain. In this paper, we propose a novel method SMARTQUERY, a framework to learn a graph neural network with very few labeled nodes using a hybrid uncertainty reduction function. This is achieved using two key steps: (a) design a multi-stage active graph learning framework by exploiting diverse explicit graph information and (b) introduce label propagation to efficiently exploit known labels to assess the implicit embedding information. Using a comprehensive set of experiments on three network datasets, we demonstrate the competitive performance of our method against state-of-the-arts on very few labeled data (up to 5 labeled nodes per class).
Contrastive Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. Additionally, we point out a serious information leak issue in prior datasets. We correct this issue and release the corrected datasets. Extensive experiments demonstrate the effectiveness of our approach C2DSR.
RaDaR: A Real-Word Dataset for AI powered Run-time Detection of Cyber-Attacks

Artificial Intelligence techniques on malware run-time behavior have emerged as a promising tool in the arms race against sophisticated and stealthy cyber-attacks. While data of malware run-time features are critical for research and benchmark comparisons, unfortunately, there is a dearth of real-world datasets due to multiple challenges to their collection. The evasive nature of malware, its dependence on connected real-world conditions to execute, and its potential repercussions pose significant challenges for executing malware in laboratory settings. Consequently, prior open datasets rely on isolated virtual sandboxes to run malware, resulting in data that is not representative of malware behavior in the wild. This paper presents RaDaR, an open real-world dataset for run-time behavioral analysis of Windows malware. RaDaR is collected by executing malware on a real-world testbed with Internet connectivity and in a timely manner, thus providing a close-to-real-world representation of malware behavior. To enable an unbiased comparison of different solutions and foster multiple verticals in malware research, RaDaR provides a multi-perspective data collection and labeling of malware activity. The multi-perspective collection provides a comprehensive view of malware activity across the network, operating system (OS), and hardware. On the other hand, the multi-perspective labeling provides four independent perspectives to analyze the same malware, including its methodology, objective, capabilities, and the information it exfiltrates. To date, RaDaR includes 7 million network packets, 11.3 million OS system call traces, and 3.3 million hardware events of 10,434 malware samples having different methodologies (3 classes) and objectives (9 classes), spread across 30 well-known malware families.
Detecting Significant Differences Between Information Retrieval Systems via Generalized Linear Models

Being able to compare Information Retrieval(IR) systems correctly is pivotal to improving their quality. Among the most popular tools for statistical significance testing, we list t-test and ANOVA that belong to the linear models family. Therefore, given the relevance of linear models for IR evaluation, a great effort has been devoted to studying how to improve them to better compare IR systems. Linear models rely on assumptions that IR experimental observations rarely meet, e.g. about the normality of the data or the linearity itself. Even though linear models are, in general, resilient to violations of their assumptions, departing from them might reduce the effectiveness of the tests. Hence, we investigate the use of the Generalized Linear Models (GLM) framework, a generalization of the traditional linear modelling that relaxes assumptions about the distribution and the shape of the models. To the best of our knowledge, there has been little or no investigation on the use of GLMs for comparing IR system performance. We discuss how GLM work and how they can be applied in the context of IR evaluation. In particular, we focus on the link function used to build GLMs, which allows for the model to have non-linear shapes. We conduct thorough experimentation using two TREC collections and several evaluation measures. Overall, we show how the log and logit links are able to identify more and more consistent significant differences (up to 25% more with 50 topics) than the identity link used today and with a comparable, or slightly better, risk of publication bias.
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies

Due to the success of graph convolutional networks (GCNs) in effectively extracting features in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering. Existing works, while encouraging, typically adopt simple aggregation operation on the user-item bipartite graph to model user and item representations, but neglect to mine the sufficient dependencies between nodes, e.g., the relationships between users/items and their neighbors (or congeners), resulting in inadequate graph representation learning. To address these problems, we propose a novel Multi-Dependency Graph Collaborative Filtering (MDGCF) model, which mines the neighborhood- and homogeneous-level dependencies to enhance the representation power of graph-based CF models. Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features. Besides, by adaptively mining the homogeneous-level dependencies among users and items, we construct two homogeneous graphs, based on which we further aggregate features from homogeneous users and items to supplement their representations, respectively. Extensive experiments on three real-world benchmark datasets demonstrate the effectiveness of the proposed MDGCF. Further experiments reveal that our model can capture rich dependencies between nodes for explaining user behaviors.