OpenRaiser/Intern-Atlas huggingface dataset:
Gated Path Selection Network for Semantic Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Semantic segmentation is a challenging task that needs to handle large scale variations, deformations and different viewpoints. In this paper, we develop a novel network named Gated Path Selection Network (GPSNet), which aims to learn adaptive receptive fields. In GPSNet, we first design a two-dimensional multi-scale network - SuperNet, which densely incorporates features from growing receptive fields. To dynamically select desirable semantic context, a gate prediction module is further introduced. In contrast to previous works that focus on optimizing sample positions on the regular grids, GPSNet can adaptively capture free form dense semantic contexts. The derived adaptive receptive fields are data-dependent, and are flexible that can model different object geometric transformations. On two representative semantic segmentation datasets, i.e., Cityscapes, and ADE20K, we show that the proposed approach consistently outperforms previous methods and achieves competitive performance without bells and whistles.
SQLFlow: A Bridge between SQL and Machine Learning
Venue: arXiv cs.DB 2020
Year: 2020

Industrial AI systems are mostly end-to-end machine learning (ML) workflows. A typical recommendation or business intelligence system includes many online micro-services and offline jobs. We describe SQLFlow for developing such workflows efficiently in SQL. SQL enables developers to write short programs focusing on the purpose (what) and ignoring the procedure (how). Previous database systems extended their SQL dialect to support ML. SQLFlow (https://sqlflow.org/sqlflow ) takes another strategy to work as a bridge over various database systems, including MySQL, Apache Hive, and Alibaba MaxCompute, and ML engines like TensorFlow, XGBoost, and scikit-learn. We extended SQL syntax carefully to make the extension working with various SQL dialects. We implement the extension by inventing a collaborative parsing algorithm. SQLFlow is efficient and expressive to a wide variety of ML techniques -- supervised and unsupervised learning; deep networks and tree models; visual model explanation in addition to training and prediction; data processing and feature extraction in addition to ML. SQLFlow compiles a SQL program into a Kubernetes-native workflow for fault-tolerable execution and on-cloud deployment. Current industrial users include Ant Financial, DiDi, and Alibaba Group.
RGB-D Odometry and SLAM
Venue: arXiv cs.CV 2020
Year: 2020

The emergence of modern RGB-D sensors had a significant impact in many application fields, including robotics, augmented reality (AR) and 3D scanning. They are low-cost, low-power and low-size alternatives to traditional range sensors such as LiDAR. Moreover, unlike RGB cameras, RGB-D sensors provide the additional depth information that removes the need of frame-by-frame triangulation for 3D scene reconstruction. These merits have made them very popular in mobile robotics and AR, where it is of great interest to estimate ego-motion and 3D scene structure. Such spatial understanding can enable robots to navigate autonomously without collisions and allow users to insert virtual entities consistent with the image stream. In this chapter, we review common formulations of odometry and Simultaneous Localization and Mapping (known by its acronym SLAM) using RGB-D stream input. The two topics are closely related, as the former aims to track the incremental camera motion with respect to a local map of the scene, and the latter to jointly estimate the camera trajectory and the global map with consistency. In both cases, the standard approaches minimize a cost function using nonlinear optimization techniques. This chapter consists of three main parts: In the first part, we introduce the basic concept of odometry and SLAM and motivate the use of RGB-D sensors. We also give mathematical preliminaries relevant to most odometry and SLAM algorithms. In the second part, we detail the three main components of SLAM systems: camera pose tracking, scene mapping and loop closing. For each component, we describe different approaches proposed in the literature. In the final part, we provide a brief discussion on advanced research topics with the references to the state-of-the-art.
Semantics for first-order affine inductive data types via slice categories
Venue: arXiv cs.LO 2020
Year: 2020

Affine type systems are substructural type systems where copying of information is restricted, but discarding of information is permissible at all types. Such type systems are well-suited for describing quantum programming languages, because copying of quantum information violates the laws of quantum mechanics. In this paper, we consider a first-order affine type system with inductive data types and present a novel categorical semantics for it. The most challenging aspect of this interpretation comes from the requirement to construct appropriate discarding maps for our data types which might be defined by mutual/nested recursion. We show how to achieve this for all types by taking models of a first-order linear type system whose atomic types are discardable and then presenting an additional affine interpretation of types within the slice category of the model with the tensor unit. We present some concrete categorical models for the language ranging from classical to quantum. Finally, we discuss potential ways of dualising and extending our methods and using them for interpreting coalgebraic and lazy data types.
Common Conversational Community Prototype: Scholarly Conversational Assistant
Venue: arXiv cs.IR 2020
Year: 2020

This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions. Specifically, we propose to develop and operate a prototype conversational search system for scholarly activities. This Scholarly Conversational Assistant would serve as a useful tool, a means to create datasets, and a platform for running evaluation challenges by groups across the community. This article results from discussions of a working group at Dagstuhl Seminar 19461 on Conversational Search.
Covert Communication in Continuous-Time Systems
Venue: arXiv cs.CR 2020
Year: 2020

Recent works have considered the ability of transmitter Alice to communicate reliably to receiver Bob without being detected by warden Willie. These works generally assume a standard discrete-time model. But the assumption of a discrete-time model in standard communication scenarios is often predicated on its equivalence to a continuous-time model, which has not been established for the covert communications problem. Here, we consider the continuous-time channel directly and study if efficient covert communication can still be achieved. We assume that an uninformed jammer is present to assist Alice, and we consider additive white Gaussian noise (AWGN) channels between all parties. For a channel with approximate bandwidth W, we establish constructions such that O(WT) information bits can be transmitted covertly and reliably from Alice to Bob in T seconds for two separate scenarios: 1) when the path-loss between Alice and Willie is known; and 2) when the path-loss between Alice and Willie is unknown.
Approximation Algorithms and LP Relaxations for Scheduling Problems Related to Min-Sum Set Cover
Venue: arXiv cs.DS 2020
Year: 2020

We consider single-machine scheduling problems that are natural generalizations or variations of the min-sum set cover problem and the min-sum vertex cover problem. For each of these problems, we give new approximation algorithms. Some of these algorithms rely on time-indexed LP relaxations. We show how a variant of alpha-point scheduling leads to the best-known approximation ratios, including a guarantee of 4 for an interesting special case of the so-called generalized min-sum set cover problem. We also make explicit the connection between the greedy algorithm for min-sum set cover and the concept of Sidney decomposition for precedence-constrained single-machine scheduling, and show how this leads to a 4-approximation algorithm for single-machine scheduling with so-called bipartite OR-precedence constraints.
JVS-MuSiC: Japanese multispeaker singing-voice corpus
Venue: arXiv cs.SD 2020
Year: 2020

Thanks to developments in machine learning techniques, it has become possible to synthesize high-quality singing voices of a single singer. An open multispeaker singing-voice corpus would further accelerate the research in singing-voice synthesis. However, conventional singing-voice corpora only consist of the singing voices of a single singer. We designed a Japanese multispeaker singing-voice corpus called "JVS-MuSiC" with the aim to analyze and synthesize a variety of voices. The corpus consists of 100 singers' recordings of the same song, Katatsumuri, which is a Japanese children's song. It also includes another song that is different for each singer. In this paper, we describe the design of the corpus and experimental analyses using JVS-MuSiC. We investigated the relationship between 1) the similarity of singing voices and perceptual oneness of unison singing voices and between 2) the similarity of singing voices and that of speech. The results suggest that 1) there is a positive and moderate correlation between singing-voice similarity and the oneness of unison and that 2) the correlation between singing-voice similarity and speech similarity is weak. This corpus is freely available online.
A 2-Competitive Largest Job on Least Loaded Machine Online Algorithm based on Multi Lists Scheduling Model
Venue: arXiv cs.DS 2020
Year: 2020

Online scheduling in identical machines with makespan minimization has been a well studied research problem in the literature. In online scheduling, the scheduler receives a list of jobs one by one and assigns each incoming job on the fly irrevocably to one of the machines before the arrival of the next job. In this paper, we study a variant of the online scheduling problem, where jobs are requested on the fly from k distinct sources. On simultaneous arrival of jobs from multiple sources, it is a non-trivial research challenge to ensure fairness in scheduling with respect to distinct sources, while optimizing the overall makespan of the schedule. We develop a novel Multi Lists Scheduling Model(MLS) and propose a 2-competitive deterministic online algorithm namely Largest Job on Least Loaded Machine(LJLLM) based on the MLS model to address the above mentioned research challenge.
Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline
Venue: arXiv cs.SE 2020
Year: 2020

To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers' expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop". Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices.
2PS: High-Quality Edge Partitioning with Two-Phase Streaming
Venue: arXiv cs.DC 2020
Year: 2020

Graph partitioning is an important preprocessing step to distributed graph processing. In edge partitioning, the edge set of a given graph is split into $k$ equally-sized partitions, such that the replication of vertices across partitions is minimized. Streaming is a viable approach to partition graphs that exceed the memory capacities of a single server. The graph is ingested as a stream of edges, and one edge at a time is immediately and irrevocably assigned to a partition based on a scoring function. However, streaming partitioning suffers from the uninformed assignment problem: At the time of partitioning early edges in the stream, there is no information available about the rest of the edges. As a consequence, edge assignments are often driven by balancing considerations, and the achieved replication factor is comparably high. In this paper, we propose 2PS, a novel two-phase streaming algorithm for high-quality edge partitioning. In the first phase, vertices are separated into clusters by a lightweight streaming clustering algorithm. In the second phase, the graph is re-streamed and edge partitioning is performed while taking into account the clustering of the vertices from the first phase. Our evaluations show that 2PS can achieve a replication factor that is comparable to heavy-weight random access partitioners while inducing orders of magnitude lower memory overhead.
Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification
Venue: arXiv cs.CV 2020
Year: 2020

Convolutional neural networks (CNN) have made significant advances in hyperspectral image (HSI) classification. However, standard convolutional kernel neglects the intrinsic connections between data points, resulting in poor region delineation and small spurious predictions. Furthermore, HSIs have a unique continuous data distribution along the high dimensional spectrum domain - much remains to be addressed in characterizing the spectral contexts considering the prohibitively high dimensionality and improving reasoning capability in light of the limited amount of labelled data. This paper presents a novel architecture which explicitly addresses these two issues. Specifically, we design an architecture to encode the multiple spectral contextual information in the form of spectral pyramid of multiple embedding spaces. In each spectral embedding space, we propose graph attention mechanism to explicitly perform interpretable reasoning in the spatial domain based on the connection in spectral feature space. Experiments on three HSI datasets demonstrate that the proposed architecture can significantly improve the classification accuracy compared with the existing methods.
High-Quality Hierarchical Process Mapping
Venue: arXiv cs.DS 2020
Year: 2020

Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation when processing graphs on a parallel computer. When a topology of a distributed system is known an important task is then to map the blocks of the partition onto the processors such that the overall communication cost is reduced. We present novel multilevel algorithms that integrate graph partitioning and process mapping. Important ingredients of our algorithm include fast label propagation, more localized local search, initial partitioning, as well as a compressed data structure to compute processor distances without storing a distance matrix. Experiments indicate that our algorithms speed up the overall mapping process and, due to the integrated multilevel approach, also find much better solutions in practice. For example, one configuration of our algorithm yields better solutions than the previous state-of-the-art in terms of mapping quality while being a factor 62 faster. Compared to the currently fastest iterated multilevel mapping algorithm Scotch, we obtain 16% better solutions while investing slightly more running time.
Finding path motifs in large temporal graphs using algebraic fingerprints
Venue: arXiv cs.DS 2020
Year: 2020

We study a family of pattern-detection problems in vertex-colored temporal graphs. In particular, given a vertex-colored temporal graph and a multiset of colors as a query, we search for temporal paths in the graph that contain the colors specified in the query. These types of problems have several applications, for example in recommending tours for tourists or detecting abnormal behavior in a network of financial transactions. For the family of pattern-detection problems we consider, we establish complexity results and design an algebraic-algorithmic framework based on constrained multilinear sieving. We demonstrate that our solution scales to massive graphs with up to a billion edges for a multiset query with five colors and up to hundred million edges for a multiset query with ten colors, despite the problems being NP-hard. Our implementation, which is publicly available, exhibits practical edge-linear scalability and is highly optimized. For instance, in a real-world graph dataset with more than six million edges and a multiset query with ten colors, we can extract an optimum solution in less than eight minutes on a Haswell desktop with four cores.
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
Venue: arXiv cs.NE 2020
Year: 2020

Data-driven methods have recently been developed to discover underlying partial differential equations (PDEs) of physical problems. However, for these methods, a complete candidate library of potential terms in a PDE are usually required. To overcome this limitation, we propose a novel framework combining deep learning and genetic algorithm, called DLGA-PDE, for discovering PDEs. In the proposed framework, a deep neural network that is trained with available data of a physical problem is utilized to generate meta-data and calculate derivatives, and the genetic algorithm is then employed to discover the underlying PDE. Owing to the merits of the genetic algorithm, such as mutation and crossover, DLGA-PDE can work with an incomplete candidate library. The proposed DLGA-PDE is tested for discovery of the Korteweg-de Vries (KdV) equation, the Burgers equation, the wave equation, and the Chaffee-Infante equation, respectively, for proof-of-concept. Satisfactory results are obtained without the need for a complete candidate library, even in the presence of noisy and limited data.
A Hierarchical Location Normalization System for Text
Venue: arXiv cs.CL 2020
Year: 2020

It's natural these days for people to know the local events from massive documents. Many texts contain location information, such as city name or road name, which is always incomplete or latent. It's significant to extract the administrative area of the text and organize the hierarchy of area, called location normalization. Existing detecting location systems either exclude hierarchical normalization or present only a few specific regions. We propose a system named ROIBase that normalizes the text by the Chinese hierarchical administrative divisions. ROIBase adopts a co-occurrence constraint as the basic framework to score the hit of the administrative area, achieves the inference by special embeddings, and expands the recall by the ROI (region of interest). It has high efficiency and interpretability because it mainly establishes on the definite knowledge and has less complex logic than the supervised models. We demonstrate that ROIBase achieves better performance against feasible solutions and is useful as a strong support system for location normalization.
The 2-adic complexity of Yu-Gong sequences with interleaved structure and optimal autocorrelation magnitude
Venue: arXiv cs.IT 2020
Year: 2020

In 2008, a class of binary sequences of period $N=4(2^k-1)(2^k+1)$ with optimal autocorrelation magnitude has been presented by Yu and Gong based on an $m$-sequence, the perfect sequence $(0,1,1,1)$ of period $4$ and interleaving technique. In this paper, we study the 2-adic complexities of these sequences. Our results show that they are larger than $N-2\lceil\mathrm{log}_2N\rceil+4 $ (which is far larger than $N/2$) and could attain the maximum value $N$ if suitable parameters are chosen, i.e., the 2-adic complexity of this class of interleaved sequences is large enough to resist the Rational Approximation Algorithm.
Towards Semantic Clone Detection via Probabilistic Software Modeling
Venue: arXiv cs.SE 2020
Year: 2020

Semantic clones are program components with similar behavior, but different textual representation. Semantic similarity is hard to detect, and semantic clone detection is still an open issue. We present semantic clone detection via Probabilistic Software Modeling (PSM) as a robust method for detecting semantically equivalent methods. PSM inspects the structure and runtime behavior of a program and synthesizes a network of Probabilistic Models (PMs). Each PM in the network represents a method in the program and is capable of generating and evaluating runtime events. We leverage these capabilities to accurately find semantic clones. Results show that the approach can detect semantic clones in the complete absence of syntactic similarity with high precision and low error rates.
On mu-Symmetric Polynomials
Venue: arXiv cs.SC 2020
Year: 2020

In this paper, we study functions of the roots of a univariate polynomial in which the roots have a given multiplicity structure $μ$. Traditionally, root functions are studied via the theory of symmetric polynomials; we extend this theory to $μ$-symmetric polynomials. We were motivated by a conjecture from Becker et al.~(ISSAC 2016) about the $μ$-symmetry of a particular root function $D^+(μ)$, called D-plus. To investigate this conjecture, it was desirable to have fast algorithms for checking if a given root function is $μ$-symmetric. We designed three such algorithms: one based on Gröbner bases, another based on preprocessing and reduction, and the third based on solving linear equations. We implemented them in Maple and experiments show that the latter two algorithms are significantly faster than the first.
Information Leaks via Safari's Intelligent Tracking Prevention
Venue: arXiv cs.CR 2020
Year: 2020

Intelligent Tracking Prevention (ITP) is a privacy mechanism implemented by Apple's Safari browser, released in October 2017. ITP aims to reduce the cross-site tracking of web users by limiting the capabilities of cookies and other website data. As part of a routine security review, the Information Security Engineering team at Google has identified multiple security and privacy issues in Safari's ITP design. These issues have a number of unexpected consequences, including the disclosure of the user's web browsing habits, allowing persistent cross-site tracking, and enabling cross-site information leaks (including cross-site search). This report is a modestly expanded version of our original vulnerability submission to Apple (WebKit bug #201319), providing additional context and edited for clarity. A number of the issues discussed here have been addressed in Safari 13.0.4 and iOS 13.3, released in December 2019.