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Found 111 papers, 38 papers with code
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective
To this end, we propose to solve the FKGC problem with the data augmentation technique.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Self-Improvement Programming for Temporal Knowledge Graph Question Answering
Temporal Knowledge Graph Question Answering (TKGQA) aims to answer questions with temporal intent over Temporal Knowledge Graphs (TKGs).
GT-Rain Single Image Deraining Challenge Report
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA
Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations.
Diving Deep into Regions: Exploiting Regional Information Transformer for Single Image Deraining
Our RTB is used for attention selection of rain-affected and unaffected regions and local modeling of mixed scales.
Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics
Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes.
Enhancing Digital Hologram Reconstruction Using Reverse-Attention Loss for Untrained Physics-Driven Deep Learning Models with Uncertain Distance
Untrained Physics-based Deep Learning (DL) methods for digital holography have gained significant attention due to their benefits, such as not requiring an annotated training dataset, and providing interpretability since utilizing the governing laws of hologram formation.
Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation
Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices.
All in One: RGB, RGB-D, and RGB-T Salient Object Detection
We propose an innovative model framework that provides a unified solution for the salient object detection task of three types of data (RGB, RGB-D, and RGB-T).
Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement
The generalization capability of existing image restoration and enhancement (IRE) methods is constrained by the limited pre-trained datasets, making it difficult to handle agnostic inputs such as different degradation levels and scenarios beyond their design scopes.
Cut-and-Paste: Subject-Driven Video Editing with Attention Control
This paper presents a novel framework termed Cut-and-Paste for real-word semantic video editing under the guidance of text prompt and additional reference image.
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.
TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models
This integration yields the following key enhancements: 1) empowering ChatGPT with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes.
Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning
We find that Hawkeye is able to generate GUI event sequences targeting changed functions more reliably than FastBot2 and ARES for the open source Apps and the large commercial App.
DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences
It is centered on multi-category video games, consisting of two {components}: Balance-driven Implicit Preferences Learning for data pre-processing and Clustering-based Diversified Recommendation {Module} for final prediction.
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation
Specifically, the denoising process considers both social network structure and user interaction interests in a global view.
Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation
Specifically, in hyperbolic space, we set smaller margins in the area near to the origin, which is conducive to distinguishing between highly similar positive items and negative ones.
Link-Context Learning for Multimodal LLMs
The ability to learn from context with novel concepts, and deliver appropriate responses are essential in human conversations.
Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing
Multi-stage architectures have exhibited efficacy in image dehazing, which usually decomposes a challenging task into multiple more tractable sub-tasks and progressively estimates latent hazy-free images.
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Then, we propose a novel Hierarchical U-net TransFormer (HUTFormer) to address the issues of long-term traffic forecasting.
Described Object Detection: Liberating Object Detection with Flexible Expressions
In this paper, we advance them to a more practical setting called Described Object Detection (DOD) by expanding category names to flexible language expressions for OVD and overcoming the limitation of REC only grounding the pre-existing object.
Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic
Referential dialogue is a superset of various vision-language (VL) tasks.
Ranked #10 on
Visual Question Answering
on ViP-Bench
Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers
The coherence loss uses the feature centers generated by the static images to guide the frame attention in the video model.
MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
This work proposes a Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 updates, called MKOR, that improves the training time and convergence properties of deep neural networks (DNNs).
Advancing Referring Expression Segmentation Beyond Single Image
To overcome this limitation, we propose a more realistic and general setting, named Group-wise Referring Expression Segmentation (GRES), which expands RES to a collection of related images, allowing the described objects to be present in a subset of input images.
Attacking Pre-trained Recommendation
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks.
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
Co-Salient Object Detection with Co-Representation Purification
Such irrelevant information in the co-representation interferes with its locating of co-salient objects.
A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel
In this paper, we proposed a novel super resolution (SR) model for cluster characteristics prediction.
MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval
The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts.
Stereo Image Rain Removal via Dual-View Mutual Attention
Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e. g., rain removal and super-resolution.
Long-Range Zero-Shot Generative Deep Network Quantization
We find it is because: 1) a normal generator is hard to obtain high diversity of synthetic data, since it lacks long-range information to allocate attention to global features; 2) the synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness.
NoiSER: Noise is All You Need for Low-Light Image Enhancement
In this paper, we present an embarrassingly simple yet effective solution to a seemingly impossible mission, low-light image enhancement (LLIE) without access to any task-related data.
OSIC: A New One-Stage Image Captioner Coined
To obtain rich features, we use the Swin Transformer to calculate multi-level features, and then feed them into a novel dynamic multi-sight embedding module to exploit both global structure and local texture of input images.
Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark
Specifically, we present a decoupled interaction module (DIM) that aims for sufficient dual-view information interaction.
Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion
In order to represent the facts happening in a specific time, temporal knowledge graph (TKG) embedding approaches are put forward.
Ranked #2 on
Link Prediction
on GDELT
Evolution is Still Good: Theoretical Analysis of Evolutionary Algorithms on General Cover Problems
Theoretical studies on evolutionary algorithms have developed vigorously in recent years.
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management.
PointScatter: Point Set Representation for Tubular Structure Extraction
Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task.
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics
The contribution in this paper could be helpful in channel modeling, network optimization, positioning and other wireless channel characteristics related work by largely reducing workload of simulation or measurement.
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification).
A Survey on Incomplete Multi-view Clustering
However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods.
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.
Customized Conversational Recommender Systems
In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals.
Ranked #3 on
Traffic Prediction
on PEMS-BAY
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
However, the patterns of time series and the dependencies between them (i. e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data.
Ranked #2 on
Traffic Prediction
on PEMS-BAY
(using extra training data)
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization
To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated.
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring
Blind image deblurring (BID) remains a challenging and significant task.
Image Harmonization by Matching Regional References
To achieve visual consistency in composite images, recent image harmonization methods typically summarize the appearance pattern of global background and apply it to the global foreground without location discrepancy.
Interactive Style Transfer: All is Your Palette
Our IST method can serve as a brush, dip style from anywhere, and then paint to any region of the target content image.
A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation
Full-field traffic state information (i. e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways.
Efficient Deep Embedded Subspace Clustering
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
FocusCut: Diving Into a Focus View in Interactive Segmentation
However, the global view makes the model lose focus from later clicks, and is not in line with user intentions.
Ranked #5 on
Interactive Segmentation
on SBD
Deep Color Consistent Network for Low-Light Image Enhancement
Current low-light image enhancement methods can well improve the illumination.
Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement
The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism.
Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing
Based on this dataset, we then propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person.
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning
This will lead to low-quality and unreliable representations of KGs.
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks.
Heterogeneous Graph Neural Network with Multi-view Representation Learning
Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph.
A Simple Approach to Automated Spectral Clustering
First, we propose to find the most reliable affinity matrix via grid search or Bayesian optimization among a set of candidates given by different AMC methods with different hyperparameters, where the reliability is quantified by the \textit{relative-eigen-gap} of graph Laplacian introduced in this paper.
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks
Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K-FAC's larger memory footprint hinders its applicability to large models.
Many-to-English Machine Translation Tools, Data, and Pretrained Models
While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages.
Phase transition gravitational waves from pseudo-Nambu-Goldstone dark matter and two Higgs doublets
We investigate the potential stochastic gravitational waves from first-order electroweak phase transitions in a model with pseudo-Nambu-Goldstone dark matter and two Higgs doublets.
High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology
Insight-HXMT observations of Swift J0243.6+6124: the evolution of RMS pulse fractions at super-Eddington luminosity
The results show a general trend of the pulse fraction increasing with luminosity and energy at super-critical luminosity.
High Energy Astrophysical Phenomena
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
The theorems show that a relatively sharper regularizer leads to a tighter error bound, which is consistent with our numerical results.
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
A Survey on Concept Factorization: From Shallow to Deep Representation Learning
In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods.
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations
This novel approach can encode physics models, i. e., classical traffic flow models, into the Gaussian process architecture and so as to regularize the ML training process.
Convolutional Neural Network Training with Distributed K-FAC
Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales.
Unsupervised Vehicle Re-identification with Progressive Adaptation
For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as ``pseudo target samples''.
Unsupervised Vehicle Re-Identification
Vehicle Re-Identification
The Limit of the Batch Size
For the first time we scale the batch size on ImageNet to at least a magnitude larger than all previous work, and provide detailed studies on the performance of many state-of-the-art optimization schemes under this setting.
Bilateral Attention Network for RGB-D Salient Object Detection
To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task.
Ranked #3 on
RGB-D Salient Object Detection
on RGBD135
Gradient-Induced Co-Saliency Detection
Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images.
Ranked #7 on
Co-Salient Object Detection
on CoSOD3k
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition
Overall, our work unified graph embedding features to promotes systematic research on human action recognition.
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
To address this issue, this study presents a new modeling framework, named physics regularized machine learning (PRML), to encode classical traffic flow models (referred as physical models) into the ML architecture and to regularize the ML training process.
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time.
Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.
Convolutional Dictionary Pair Learning Network for Image Representation Learning
To address this issue, we propose a novel generalized end-to-end representation learning architecture, dubbed Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which integrates the learning schemes of the CNN and dictionary pair learning into a unified framework.
Compressed DenseNet for Lightweight Character Recognition
LDB is a convolutional block similarly as dense block, but it can reduce the computation cost and weight size to (1/L, 2/L), compared with original ones, where L is the number of layers in blocks.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features.
Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery
In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR.
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition
But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition
We intuitively find that M$^2$Net can essentially promote the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy.
Kernelized Multiview Subspace Analysis by Self-weighted Learning
With the popularity of multimedia technology, information is always represented or transmitted from multiple views.
Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification
Our method can jointly re-cover the underlying clean data, clean labels and clean weighting spaces by decomposing the original data, predicted soft labels or weights into a clean part plus an error part by fitting noise.
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning.
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.
A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining
To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i. e., Residual path and Dense path.
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner.
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
The use of RGB-D information for salient object detection has been extensively explored in recent years.
Ranked #4 on
RGB-D Salient Object Detection
on RGBD135
Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL).
Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification
A Kernel-Induced Label Propagation (Kernel-LP) framework by mapping is proposed for high-dimensional data classification using the most informative patterns of data in kernel space.
Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers.
Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning
More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.
Joint Label Prediction based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation
To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data.
Concurrency Protocol Aiming at High Performance of Execution and Replay for Smart Contracts
Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wider range of industrial areas, how to execute smart contracts efficiently becomes a big challenge nowadays.
Databases Distributed, Parallel, and Cluster Computing
Knowledge Graph Embedding with Hierarchical Relation Structure
To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS.
FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning
Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90\% scaling efficiency.
Distributed, Parallel, and Cluster Computing
Human activity recognition based on time series analysis using U-Net
Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method.
Robust Subspace Clustering with Compressed Data
Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e. g., clustering) of compressed data.
ImageNet Training in Minutes
If we can make full use of the supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in one minute.
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
In this work, we present Velox, a new component of the Berkeley Data Analytics Stack.
Databases
