OpenRaiser/Intern-Atlas huggingface dataset:
CIAO: An Optimization Framework for Client-Assisted Data Loading
Venue: arXiv cs.DB 2021
Year: 2021

Data loading has been one of the most common performance bottlenecks for many big data applications, especially when they are running on inefficient human-readable formats, such as JSON or CSV. Parsing, validating, integrity checking and data structure maintenance are all computationally expensive steps in loading these formats. Regardless of these costs, many records may be filtered later during query evaluation due to highly selective predicates -- resulting in wasted computation. Meanwhile, the computing power of client ends is typically not exploited. Here, we explore investing limited cycles of clients on prefiltering to accelerate data loading and enable data skipping for query execution. In this paper, we present CIAO, a tunable system to enable client cooperation with the server to enable efficient partial loading and data skipping for a given workload. We proposed an efficient algorithm that would select a near-optimal predicate set to push down within a given budget. Moreover, CIAO will address the trade-off between client cost and server savings by setting different budgets for different clients. We implemented CIAO and evaluated its performance on three real-world datasets. Our experimental results show that the system substantially accelerates data loading by up to 21x and query execution by up to 23x and improves end-to-end performance by up to 19x within a budget of 1.0 microseconds latency per record on clients.
Dynamic Neural Garments
Venue: arXiv cs.CV 2021
Year: 2021

A vital task of the wider digital human effort is the creation of realistic garments on digital avatars, both in the form of characteristic fold patterns and wrinkles in static frames as well as richness of garment dynamics under avatars' motion. Existing workflow of modeling, simulation, and rendering closely replicates the physics behind real garments, but is tedious and requires repeating most of the workflow under changes to characters' motion, camera angle, or garment resizing. Although data-driven solutions exist, they either focus on static scenarios or only handle dynamics of tight garments. We present a solution that, at test time, takes in body joint motion to directly produce realistic dynamic garment image sequences. Specifically, given the target joint motion sequence of an avatar, we propose dynamic neural garments to jointly simulate and render plausible dynamic garment appearance from an unseen viewpoint. Technically, our solution generates a coarse garment proxy sequence, learns deep dynamic features attached to this template, and neurally renders the features to produce appearance changes such as folds, wrinkles, and silhouettes. We demonstrate generalization behavior to both unseen motion and unseen camera views. Further, our network can be fine-tuned to adopt to new body shape and/or background images. We also provide comparisons against existing neural rendering and image sequence translation approaches, and report clear quantitative improvements.
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with biases
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, artificial neural networks have developed into a powerful tool for dealing with a multitude of problems for which classical solution approaches reach their limits. However, it is still unclear why randomly initialized gradient descent optimization algorithms, such as the well-known batch gradient descent, are able to achieve zero training loss in many situations even though the objective function is non-convex and non-smooth. One of the most promising approaches to solving this problem in the field of supervised learning is the analysis of gradient descent optimization in the so-called overparameterized regime. In this article we provide a further contribution to this area of research by considering overparameterized fully-connected rectified artificial neural networks with biases. Specifically, we show that for a fixed number of training data the mean squared error using batch gradient descent optimization applied to such a randomly initialized artificial neural network converges to zero at a linear convergence rate as long as the width of the artificial neural network is large enough, the learning rate is small enough, and the training input data are pairwise linearly independent.
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps
Venue: arXiv cs.CV 2021
Year: 2021

3D microscopy is key in the investigation of diverse biological systems, and the ever increasing availability of large datasets demands automatic cell identification methods that not only are accurate, but also can imply the uncertainty in their predictions to inform about potential errors and hence confidence in conclusions using them. While conventional deep learning methods often yield deterministic results, advances in deep Bayesian learning allow for accurate predictions with a probabilistic interpretation in numerous image classification and segmentation tasks. It is however nontrivial to extend such Bayesian methods to cell detection, which requires specialized learning frameworks. In particular, regression of density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which hinders any meaningful probabilistic output. We herein propose a deep learning-based cell detection framework that can operate on large microscopy images and outputs desired probabilistic predictions by (i) integrating Bayesian techniques for the regression of uncertainty-aware density maps, where peak detection can be applied to generate cell proposals, and (ii) learning a mapping from the numerous proposals to a probabilistic space that is calibrated, i.e. accurately represents the chances of a successful prediction. Utilizing such calibrated predictions, we propose a probabilistic spatial analysis with Monte-Carlo sampling. We demonstrate this in revising an existing description of the distribution of a mesenchymal stromal cell type within the bone marrow, where our proposed methods allow us to reveal spatial patterns that are otherwise undetectable. Introducing such probabilistic analysis in quantitative microscopy pipelines will allow for reporting confidence intervals for testing biological hypotheses of spatial distributions.
Neural ranking models for document retrieval
Venue: arXiv cs.IR 2021
Year: 2021

Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.
Location Trace Privacy Under Conditional Priors
Venue: arXiv cs.AI 2021
Year: 2021

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a Rényi divergence based privacy framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for sensitive locations in a user's trace.
Kronecker Products, Low-Depth Circuits, and Matrix Rigidity
Venue: arXiv cs.DS 2021
Year: 2021

For a matrix $M$ and a positive integer $r$, the rank $r$ rigidity of $M$ is the smallest number of entries of $M$ which one must change to make its rank at most $r$. There are many known applications of rigidity lower bounds to a variety of areas in complexity theory, but fewer known applications of rigidity upper bounds. In this paper, we use rigidity upper bounds to prove new upper bounds in a few different models of computation. Our results include: $\bullet$ For any $d> 1$, and over any field $\mathbb{F}$, the $N \times N$ Walsh-Hadamard transform has a depth-$d$ linear circuit of size $O(d \cdot N^{1 + 0.96/d})$. This circumvents a known lower bound of $Ω(d \cdot N^{1 + 1/d})$ for circuits with bounded coefficients over $\mathbb{C}$ by Pudlák (2000), by using coefficients of magnitude polynomial in $N$. Our construction also generalizes to linear transformations given by a Kronecker power of any fixed $2 \times 2$ matrix. $\bullet$ The $N \times N$ Walsh-Hadamard transform has a linear circuit of size $\leq (1.81 + o(1)) N \log_2 N$, improving on the bound of $\approx 1.88 N \log_2 N$ which one obtains from the standard fast Walsh-Hadamard transform. $\bullet$ A new rigidity upper bound, showing that the following classes of matrices are not rigid enough to prove circuit lower bounds using Valiant's approach: $-$ for any field $\mathbb{F}$ and any function $f : \{0,1\}^n \to \mathbb{F}$, the matrix $V_f \in \mathbb{F}^{2^n \times 2^n}$ given by, for any $x,y \in \{0,1\}^n$, $V_f[x,y] = f(x \wedge y)$, and $-$ for any field $\mathbb{F}$ and any fixed-size matrices $M_1, \ldots, M_n \in \mathbb{F}^{q \times q}$, the Kronecker product $M_1 \otimes M_2 \otimes \cdots \otimes M_n$. This generalizes recent results on non-rigidity, using a simpler approach which avoids needing the polynomial method.
Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models
Venue: arXiv cs.SE 2021
Year: 2021

Software defect prediction models are classifiers that are constructed from historical software data. Such software defect prediction models have been proposed to help developers optimize the limited Software Quality Assurance (SQA) resources and help managers develop SQA plans. Prior studies have different goals for their defect prediction models and use different techniques for generating visual explanations of their models. Yet, it is unclear what are the practitioners' perceptions of (1) these defect prediction model goals, and (2) the model-agnostic techniques used to visualize these models. We conducted a qualitative survey to investigate practitioners' perceptions of the goals of defect prediction models and the model-agnostic techniques used to generate visual explanations of defect prediction models. We found that (1) 82%-84% of the respondents perceived that the three goals of defect prediction models are useful; (2) LIME is the most preferred technique for understanding the most important characteristics that contributed to a prediction of a file, while ANOVA/VarImp is the second most preferred technique for understanding the characteristics that are associated with software defects in the past. Our findings highlight the significance of investigating how to improve the understanding of defect prediction models and their predictions. Hence, model-agnostic techniques from explainable AI domain may help practitioners to understand defect prediction models and their predictions.
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives; motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives' motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space.
DNN2LR: Automatic Feature Crossing for Credit Scoring
Venue: arXiv cs.LG 2021
Year: 2021

Credit scoring is a major application of machine learning for financial institutions to decide whether to approve or reject a credit loan. For sake of reliability, it is necessary for credit scoring models to be both accurate and globally interpretable. Simple classifiers, e.g., Logistic Regression (LR), are white-box models, but not powerful enough to model complex nonlinear interactions among features. Fortunately, automatic feature crossing is a promising way to find cross features to make simple classifiers to be more accurate without heavy handcrafted feature engineering. However, credit scoring is usually based on different aspects of users, and the data usually contains hundreds of feature fields. This makes existing automatic feature crossing methods not efficient for credit scoring. In this work, we find local piece-wise interpretations in Deep Neural Networks (DNNs) of a specific feature are usually inconsistent in different samples, which is caused by feature interactions in the hidden layers. Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR. We give definition of the interpretation inconsistency in DNN, based on which a novel feature crossing method for credit scoring prediction called DNN2LR is proposed. Apparently, the final model, i.e., a LR model empowered with cross features, generated by DNN2LR is a white-box model. Extensive experiments have been conducted on both public and business datasets from real-world credit scoring applications. Experimental shows that, DNN2LR can outperform the DNN model, as well as several feature crossing methods. Moreover, comparing with the state-of-the-art feature crossing methods, i.e., AutoCross, DNN2LR can accelerate the speed for feature crossing by about 10 to 40 times on datasets with large numbers of feature fields.
Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions
Venue: arXiv cs.DB 2021
Year: 2021

A way of finding interesting or exceptional records from instant-stamped temporal data is to consider their "durability," or, intuitively speaking, how well they compare with other records that arrived earlier or later, and how long they retain their supremacy. For example, people are naturally fascinated by claims with long durability, such as: "On January 22, 2006, Kobe Bryant dropped 81 points against Toronto Raptors. Since then, this scoring record has yet to be broken." In general, given a sequence of instant-stamped records, suppose that we can rank them by a user-specified scoring function $f$, which may consider multiple attributes of a record to compute a single score for ranking. This paper studies "durable top-$k$ queries", which find records whose scores were within top-$k$ among those records within a "durability window" of given length, e.g., a 10-year window starting/ending at the timestamp of the record. The parameter $k$, the length of the durability window, and parameters of the scoring function (which capture user preference) can all be given at the query time. We illustrate why this problem formulation yields more meaningful answers in some practical situations than other similar types of queries considered previously. We propose new algorithms for solving this problem, and provide a comprehensive theoretical analysis on the complexities of the problem itself and of our algorithms. Our algorithms vastly outperform various baselines (by up to two orders of magnitude on real and synthetic datasets).
Position Location for Futuristic Cellular Communications -- 5G and Beyond
Venue: arXiv cs.IT 2021
Year: 2021

With vast mmWave spectrum and narrow beam antenna technology, precise position location is now possible in 5G and future mobile communication systems. In this article, we describe how centimeterlevel localization accuracy can be achieved, particularly through the use of map-based techniques. We show how data fusion of parallel information streams, machine learning, and cooperative localization techniques further improve positioning accuracy.
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features
Venue: arXiv cs.CV 2021
Year: 2021

Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Point-wise Rotation Invariant Network, focusing on rotation invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. Spherical Voxel Convolution and Point Re-sampling are proposed to extract rotation invariant features for each point. In addition, we extend PRIN to a sparse version called SPRIN, which directly operates on sparse point clouds. Both PRIN and SPRIN can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. Results show that, on the dataset with randomly rotated point clouds, SPRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide thorough theoretical proof and analysis for point-wise rotation invariance achieved by our methods. Our code is available on https://github.com/qq456cvb/SPRIN.
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

6D pose estimation from a single RGB image is a challenging and vital task in computer vision. The current mainstream deep model methods resort to 2D images annotated with real-world ground-truth 6D object poses, whose collection is fairly cumbersome and expensive, even unavailable in many cases. In this work, to get rid of the burden of 6D annotations, we formulate the 6D pose refinement as a Markov Decision Process and impose on the reinforcement learning approach with only 2D image annotations as weakly-supervised 6D pose information, via a delicate reward definition and a composite reinforced optimization method for efficient and effective policy training. Experiments on LINEMOD and T-LESS datasets demonstrate that our Pose-Free approach is able to achieve state-of-the-art performance compared with the methods without using real-world ground-truth 6D pose labels.
Learning to Shift Attention for Motion Generation
Venue: arXiv cs.RO 2021
Year: 2021

One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query. Previous approaches fail to capture all modes or tend to average modes of the demonstrations and thus generate invalid trajectories. The other difficulty is the small number of demonstrations that cannot cover the entire working space. To overcome this problem, a motion generation model with extrapolation ability is needed. Previous works restrict task queries as local frames and learn representations in local frames. We propose a model to solve both problems. For multiple modes, we suggest to learn local latent representations of motion trajectories with a density estimation method based on real-valued non-volume preserving (RealNVP) transformations that provides a set of powerful, stably invertible, and learnable transformations. To improve the extrapolation ability, we propose to shift the attention of the robot from one local frame to another during the task execution. In experiments, we consider the docking problem used also in previous works where a trajectory has to be generated to connect two dockers without collision. We increase complexity of the task and show that the proposed method outperforms other approaches. In addition, we evaluate the approach in real robot experiments.
Dual-Awareness Attention for Few-Shot Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existing FSOD systems follow FSC approaches, ignoring critical issues such as spatial variability and uncertain representations, and consequently result in low performance. Observing this, we propose a novel \textbf{Dual-Awareness Attention (DAnA)} mechanism that enables networks to adaptively interpret the given support images. DAnA transforms support images into \textbf{query-position-aware} (QPA) features, guiding detection networks precisely by assigning customized support information to each local region of the query. In addition, the proposed DAnA component is flexible and adaptable to multiple existing object detection frameworks. By adopting DAnA, conventional object detection networks, Faster R-CNN and RetinaNet, which are not designed explicitly for few-shot learning, reach state-of-the-art performance in FSOD tasks. In comparison with previous methods, our model significantly increases the performance by 47\% (+6.9 AP), showing remarkable ability under various evaluation settings.
Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions
Venue: arXiv cs.NI 2021
Year: 2021

Internet of Everything (IoE) applications such as haptics, human-computer interaction, and extended reality, using the sixth-generation (6G) of wireless systems have diverse requirements in terms of latency, reliability, data rate, and user-defined performance metrics. Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services. Such a new framework for 6G can be based on digital twins. Digital twins use a virtual representation of the 6G physical system along with the associated algorithms (e.g., machine learning, optimization), communication technologies (e.g., millimeter-wave and terahertz communication), computing systems (e.g., edge computing and cloud computing), as well as privacy and security-related technologists (e.g., blockchain). First, we present the key design requirements for enabling 6G through the use of a digital twin. Next, the architectural components and trends such as edge-based twins, cloud-based-twins, and edge-cloud-based twins are presented. Furthermore, we provide a comparative description of various twins. Finally, we outline and recommend guidelines for several future research directions.