OpenRaiser/Intern-Atlas huggingface dataset:
Neural Network Surgery with Sets
Venue: arXiv cs.CL 2020
Year: 2020

The cost to train machine learning models has been increasing exponentially, making exploration and research into the correct features and architecture a costly or intractable endeavor at scale. However, using a technique named "surgery" OpenAI Five was continuously trained to play the game DotA 2 over the course of 10 months through 20 major changes in features and architecture. Surgery transfers trained weights from one network to another after a selection process to determine which sections of the model are unchanged and which must be re-initialized. In the past, the selection process relied on heuristics, manual labor, or pre-existing boundaries in the structure of the model, limiting the ability to salvage experiments after modifications of the feature set or input reorderings. We propose a solution to automatically determine which components of a neural network model should be salvaged and which require retraining. We achieve this by allowing the model to operate over discrete sets of features and use set-based operations to determine the exact relationship between inputs and outputs, and how they change across tweaks in model architecture. In this paper, we introduce the methodology for enabling neural networks to operate on sets, derive two methods for detecting feature-parameter interaction maps, and show their equivalence. We empirically validate that we can surgery weights across feature and architecture changes to the OpenAI Five model.
RGait-NET: An Effective Network for Recovering Missing Information from Occluded Gait Cycles
Venue: arXiv cs.CV 2020
Year: 2020

Gait of a person refers to his/her walking pattern, and according to medical studies gait of every individual is unique. Over the past decade, several computer vision-based gait recognition approaches have been proposed in which walking information corresponding to a complete gait cycle has been used to construct gait features for person identification. These methods compute gait features with the inherent assumption that a complete gait cycle is always available. However, in most public places occlusion is an inevitable occurrence, and due to this, only a fraction of a gait cycle gets captured by the monitoring camera. Unavailability of complete gait cycle information drastically affects the accuracy of the extracted features, and till date, only a few occlusion handling strategies to gait recognition have been proposed. But none of these performs reliably and robustly in the presence of a single cycle with incomplete information, and because of this practical application of gait recognition is quite limited. In this work, we develop deep learning-based algorithm to accurately identify the affected frames as well as predict the missing frames to reconstruct a complete gait cycle. While occlusion detection has been carried out by employing a VGG-16 model, the model for frame reconstruction is based on Long-Short Term Memory network that has been trained to optimize a multi-objective function based on dice coefficient and cross-entropy loss. The effectiveness of the proposed occlusion reconstruction algorithm is evaluated by computing the accuracy of the popular Gait Energy Feature on the reconstructed sequence. Experimental evaluation on public data sets and comparative analysis with other occlusion handling methods verify the effectiveness of our approach.
Progressive Neural Index Search for Database System
Venue: arXiv cs.DB 2020
Year: 2020

As a key ingredient of the DBMS, index plays an important role in the query optimization and processing. However, it is a non-trivial task to apply existing indexes or design new indexes for new applications, where both data distribution and query distribution are unknown. To address the issue, we propose a new indexing approach, NIS (Neural Index Search), which searches for the optimal index parameters and structures using a neural network. In particular, NIS is capable for building a tree-like index automatically for an arbitrary column that can be sorted/partitioned using a customized function. The contributions of NIS are twofold. First, NIS constructs a tree-like index in a layer-by-layer way via formalizing the index structure as abstract ordered and unordered blocks. Ordered blocks are implemented using B+-tree nodes or skip lists, while unordered blocks adopt hash functions with different configurations. Second, all parameters of the building blocks (e.g., fanout of B+-tree node, bucket number of hash function and etc.) are tuned by NIS automatically. We achieve the two goals for a given workload and dataset with one RNN-powered reinforcement learning model. Experiments show that the auto-tuned index built by NIS can achieve a better performance than the state-of-the-art index.
Joint Beamforming, User Association, and Height Control for Cellular-Enabled UAV Communications
Venue: arXiv cs.IT 2020
Year: 2020

Supporting reliable and seamless mobility for aerial users, such as unmanned aerial vehicles (UAVs), is a key challenge for the next-generation cellular systems. To tackle this challenge, we propose a joint beamforming, user association, and UAV-height control framework for cellular-connected multi-UAV networks with multiple antenna base stations (BSs). With the aim of maximizing the minimum achievable rate for UAVs subject to co-existed terrestrial user equipment's rate constraints, we devise a hierarchical bi-layer iterative algorithm to optimize BSs' beamforming vectors, UAV association matrix, and the height of UAVs jointly. With the aid of projection gradient method in inner layer iteration and geometric program modelling plus convex-concave procedure in outer layer iteration, our proposed algorithm is proved to converge to a local optimum. Taking mobility characteristics of UAVs into account, we also exploit our proposed algorithm for imperfect channel estimation scenario. Numerical results show that our proposed algorithm can achieve improved UAVs' minimum achievable rate compared with that of the conventional nearest association of UAVs for both perfect and imperfect channel estimation scenarios. Moreover, we also examine the trade-off between the UAVs' minimum achievable rate and the frequency for updating optimization variables with single moving UAV.
Seq2seq Translation Model for Sequential Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

The context information such as product category plays a critical role in sequential recommendation. Recent years have witnessed a growing interest in context-aware sequential recommender systems. Existing studies often treat the contexts as auxiliary feature vectors without considering the sequential dependency in contexts. However, such a dependency provides valuable clues to predict the user's future behavior. For example, a user might buy electronic accessories after he/she buy an electronic product. In this paper, we propose a novel seq2seq translation architecture to highlight the importance of sequential dependency in contexts for sequential recommendation. Specifically, we first construct a collateral context sequence in addition to the main interaction sequence. We then generalize recent advancements in translation model from sequences of words in two languages to sequences of items and contexts in recommender systems. Taking the category information as an item's context, we develop a basic coupled and an extended tripled seq2seq translation models to encode the category-item and item-category-item relations between the item and context sequences. We conduct extensive experiments on three real world datasets. The results demonstrate the superior performance of the proposed model compared with the state-of-the-art baselines.
Modeling and Analysis of Reconfigurable Intelligent Surfaces for Indoor and Outdoor Applications in Future Wireless Networks
Venue: arXiv cs.IT 2020
Year: 2020

Reconfigurable intelligent surface (RIS)-empowered communication is one of the promising 6G technologies that allows the conversion of the wireless channel into an intelligent transmit entity by manipulating the impinging waves using man-made surfaces. In this paper, the potential benefits of using RISs are investigated for indoor/outdoor setups and various frequency bands (from sub 6 GHz to millimeter-waves). First, a general system model with a single RIS is considered and the effect of the total number of reflecting elements on the probabilistic distribution of the received signal-to-noise ratio and error performance is investigated under Rician fading. Also for this case, the path loss exponent is analyzed by considering empirical path loss models. Furthermore, transmission models with multiple RISs are developed and analyzed for indoor and outdoor non line-of-sight (NLOS) scenarios. The conventional RIS selection strategies are also integrated for systems equipped with multiple RISs for the first time. Through extensive simulations, it is demonstrated that the RIS-assisted systems provide promising solutions for indoor/outdoor scenarios at various operating frequencies and exhibit significant results in error performance and achievable data rates even in the presence of system imperfections such as limited range phase adjustment and imperfect channel phase estimation at RISs.
A new Frequency Estimation Sketch for Data Streams
Venue: arXiv cs.DS 2020
Year: 2020

In data stream applications, one of the critical issues is to estimate the frequency of each item in the specific multiset. The multiset means that each item in this set can appear multiple times. The data streams in many applications are high-speed streams which contain massive data, such as real-time IP traffic, graph streams, web clicks and crawls, sensor database, and natural language processing (NLP) [2][6], etc. In these applications, the stream information needs to be recorded by the servers in real time. However, since the data streams in these applications are high-speed, the accurate recording and estimation of item frequencies is always impractical. An alternative approach for addressing this problem is to estimate the item frequencies based on probabilistic data structures, and this approach has been widely used in the high-speed data streams estimation [7][9]. Sketches is one of the typical probabilistic data structures, which are initially designed for the estimation of item frequencies in data streams [10][15]. At present, the sketches have been used in many different scenarios, such as sparse approximation in compressed sensing [16], natural language processing [17, 18], data graph [19, 20], and more [21]. In this paper, we mainly focus on the sketches used for frequency estimation.
LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity information so that the recognition issue is more difficult than conventional VIS face recognition. Recently, NIR-VIS heterogeneous face recognition has attracted considerable attention in the computer vision community because of its convenience and adaptability in practical applications. Various deep learning-based methods have been proposed and substantially increased the recognition performance, but the lack of NIR-VIS training samples leads to the difficulty of the model training process. In this paper, we propose a new Large-Scale Multi-Pose High-Quality NIR-VIS database LAMP-HQ containing 56,788 NIR and 16,828 VIS images of 573 subjects with large diversities in pose, illumination, attribute, scene and accessory. We furnish a benchmark along with the protocol for NIR-VIS face recognition via generation on LAMP-HQ, including Pixel2Pixel, CycleGAN, and ADFL. Furthermore, we propose a novel exemplar-based variational spectral attention network to produce high-fidelity VIS images from NIR data. A spectral conditional attention module is introduced to reduce the domain gap between NIR and VIS data and then improve the performance of NIR-VIS heterogeneous face recognition on various databases including the LAMP-HQ.
ORCA: a Benchmark for Data Web Crawlers
Venue: arXiv cs.DB 2020
Year: 2020

The number of RDF knowledge graphs available on the Web grows constantly. Gathering these graphs at large scale for downstream applications hence requires the use of crawlers. Although Data Web crawlers exist, and general Web crawlers could be adapted to focus on the Data Web, there is currently no benchmark to fairly evaluate their performance. Our work closes this gap by presenting the Orca benchmark. Orca generates a synthetic Data Web, which is decoupled from the original Web and enables a fair and repeatable comparison of Data Web crawlers. Our evaluations show that Orca can be used to reveal the different advantages and disadvantages of existing crawlers. The benchmark is open-source and available at https://github.com/dice-group/orca.
On the I/O complexity of hybrid algorithms for Integer Multiplication
Venue: arXiv cs.CC 2020
Year: 2020

Almost asymptotically tight lower bounds are derived for the Input/Output (I/O) complexity $IO_\mathcal{A}\left(n,M\right)$ of a general class of hybrid algorithms computing the product of two integers, each represented with $n$ digits in a given base $s$, in a two-level storage hierarchy with $M$ words of fast memory, with different digits stored in different memory words. The considered hybrid algorithms combine the Toom-Cook-$k$ (or Toom-$k$) fast integer multiplication approach with computational complexity $Θ\left(c_kn^{\log_k \left(2k-1\right)}\right)$, and "standard" integer multiplication algorithms which compute $Ω\left(n^2\right)$ digit multiplications. We present an $Ω\left(\left(n/\max\{M,n_0\}\right)^{\log_k \left(2k-1\right)}\left(\max\{1,n_0/M\}\right)^2M\right)$ lower bound for the I/O complexity of a class of "uniform, non-stationary" hybrid algorithms, where $n_0$ denotes the threshold size of sub-problems which are computed using standard algorithms with algebraic complexity $Ω\left(n^2\right)$. As a special case, our result yields an asymptotically tight $Θ\left(n^2/M\right)$ lower bound for the I/O complexity of any standard integer multiplication algorithm. As some sequential hybrid algorithms from this class exhibit I/O cost within a $\mathcal{O}\left(k^2\right)$ multiplicative term of the corresponding lower bounds, the proposed lower bounds are almost asymptotically tight and indeed tight for constant values of $k$. By extending these results to a distributed memory model with $n_0$ processors, we obtain both memory-dependent and memory-independent I/O lower bounds for parallel versions of hybrid integer multiplication algorithms. All the lower bounds are derived for the more general class of "non-uniform, non-stationary" hybrid algorithms that allow recursive calls to have a different structure.
Three dimensional waveguide-interconnects for scalable integration of photonic neural networks
Venue: arXiv cs.ET 2020
Year: 2020

Photonic waveguides are prime candidates for integrated and parallel photonic interconnects. Such interconnects correspond to large-scale vector matrix products, which are at the heart of neural network computation. However, parallel interconnect circuits realized in two dimensions, for example by lithography, are strongly limited in size due to disadvantageous scaling. We use three dimensional (3D) printed photonic waveguides to overcome this limitation. 3D optical-couplers with fractal topology efficiently connect large numbers of input and output channels, and we show that the substrate's footprint area scales linearly. Going beyond simple couplers, we introduce functional circuits for discrete spatial filters identical to those used in deep convolutional neural networks.
ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization
Venue: arXiv cs.CV 2020
Year: 2020

Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly from (a series of) images or by merging depth maps with optical flow (OF), research that combines absolute pose regression with OF is limited. We propose ViPR, a novel modular architecture for long-term 6DoF VO that leverages temporal information and synergies between absolute pose estimates (from PoseNet-like modules) and relative pose estimates (from FlowNet-based modules) by combining both through recurrent layers. Experiments on known datasets and on our own Industry dataset show that our modular design outperforms state of the art in long-term navigation tasks.
A Data-driven, Falsification-based Model of Human Driver Behavior
Venue: arXiv cs.RO 2020
Year: 2020

We propose a novel framework to differentiate between vehicle trajectories originating from human and non-human drivers by constructing a data-driven boundary using parametric signal temporal logic (STL). Such construction allows us to evaluate the trajectories, detect rare-events, and reduce the uncertainty of driver behaviors when it assumes the form of a disturbance in control synthesis and evaluation problems. We train a classifier that separates admissible (i.e. human) examples - which arise from real-world demonstrations - and inadmissible (i.e. non-human) examples that are generated by falsifying specifications synthesized from the same real-world driving data. Proceeding in this fashion allows for finding a reasonable boundary of human behaviors exhibited in real-world driving records. The framework is demonstrated using a case study involving a human-driven vehicle approaching a signalized intersection.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
Venue: arXiv cs.CV 2020
Year: 2020

Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the meta-learning process where episodic tasks are formed to train a model that can adapt to class change. However, these task sare independent to each other and existing works mainly rely on limited samples of individual support set in a single meta task. This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set. To avoid this problem, we propose a concise Semantic RegularizationNetwork to learn a common semantic space under the framework of meta-learning. In this space, all class descriptors can be regularized by the learned semantic basis, which can effectively solve the meta shift problem. The key is to train a class encoder and decoder structure that can encode the sample embedding features into the semantic domain with trained semantic basis, and generate a more stable and general class descriptor from the decoder. We evaluate our work by extensive comparisons with previous methods on three benchmark datasets (MiniImageNet, TieredImageNet, and CUB). The results show that the semantic regularization module improves performance by 4%-7% over the baseline method, and achieves competitive results over the current state-of-the-art models.
A New Method for Verifying d-MC Candidates
Venue: arXiv cs.DM 2020
Year: 2020

Network reliability modeling and calculation is a very important study domain in reliability engineering. It is also a popular index for validating and measuring the performance of real-world multi-state flow networks (MFNs), e.g., the applications in internet of things, social networks, clouding computing, and 5G. The d-MC is a vector, the maximum flow of whose related network is d, and any vector less than the d-MC is not a d-MC in MFNs. The MFN reliability can be calculated in terms of d-MCs. Hence, the d-MC is one of the most popular tools for evaluating the MFN reliability. The method to find all d-MCs is through the mathematical programming whose solutions are called d-MC candidates, and all d-MCs are selected from these candidates. In this study, a novel and simple algorithm is proposed to filter out d-MCs from these d-MC candidates after removing duplicates. The time complexity of the proposed algorithm is analyzed along with the demonstration using an example. An experiment with 200 random networks is outlined to compare the proposed, traditional, and best-known algorithms used for verifying d-MC candidates.
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations
Venue: arXiv cs.LG 2020
Year: 2020

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans to correctly perceive the aptitude of agents with different characteristics, including their capabilities and limitations, given visual summaries automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent's strengths and limitations in performing a task, and determine when it might need adjustments to improve its performance.
FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences
Venue: arXiv cs.LG 2020
Year: 2020

We study the problem of classifying interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that classifiers can be applied. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on eight real-world datasets demonstrates the effectiveness of our methods in practice. The results provide evidence that FIBS effectively represents IBTSs for classification algorithms, which contributes to similar or significantly better accuracy compared to state-of-the-art competitors. It also suggests that the feature selection strategy is beneficial to FIBS's performance.