OpenRaiser/Intern-Atlas huggingface dataset:
Analytical models of Energy and Throughput for Caches in MPSoCs
Venue: arXiv cs.AR 2019
Year: 2019

General trends in computer architecture are shifting more towards parallelism. Multicore architectures have proven to be a major step in processor evolution. With the advancement in multicore architecture, researchers are focusing on finding different solutions to fully utilize the power of multiple cores. With an ever-increasing number of cores on a chip, the role of cache memory has become pivotal. An ideal memory configuration should be both large and fast, however, in fact, system architects have to strike a balance between the size and access time of the memory hierarchy. It is important to know the impact of a particular cache configuration on the throughput and energy consumption of the system at design time. This paper presents an enhanced version of previously proposed cache energy and throughput models for multicore systems. These models use significantly a smaller number of input parameters as compared to other models. This paper also validates the proposed models through cycle accurate simulator and a renowned processor power estimator. The results show that the proposed energy models provide accuracy within a maximum error range of 10% for single-core processors and around 5% for MPSoCs, and the throughput models result in a maximum error of up to 11.5% for both single and multicore architectures.
Maximum-Area Rectangles in a Simple Polygon
Venue: arXiv cs.CG 2019
Year: 2019

We study the problem of finding maximum-area rectangles contained in a polygon in the plane. There has been a fair amount of work for this problem when the rectangles have to be axis-aligned or when the polygon is convex. We consider this problem in a simple polygon with $n$ vertices, possibly with holes, and with no restriction on the orientation of the rectangles. We present an algorithm that computes a maximum-area rectangle in $O(n^3\log n)$ time using $O(kn^2)$ space, where $k$ is the number of reflex vertices of $P$. Our algorithm can report all maximum-area rectangles in the same time using $O(n^3)$ space. We also present a simple algorithm that finds a maximum-area rectangle contained in a convex polygon with $n$ vertices in $O(n^3)$ time using $O(n)$ space.
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Credit investigation is critical for financial services. Whereas, traditional methods are often restricted as the employed data hardly provide sufficient, timely and reliable information. With the prevalence of smart mobile devices, peoples' geographic footprints can be automatically and constantly collected nowadays, which provides an unprecedented opportunity for credit investigations. Inspired by the observation that locations are somehow related to peoples' credit level, this research aims to enhance credit investigation with users' geographic footprints. To this end, a two-stage credit investigation framework is designed, namely CreditPrint. In the first stage, CreditPrint explores regions' credit characteristics and learns a credit-aware embedding for each region by considering both each region's individual characteristics and cross-region relationships with graph convolutional networks. In the second stage, a hierarchical attention-based credit assessment network is proposed to aggregate the credit indications from a user's multiple trajectories covering diverse regions. The results on real-life user mobility datasets show that CreditPrint can increase the credit investigation accuracy by up to 10% compared to baseline methods.
Reverse Experience Replay
Venue: arXiv cs.LG 2019
Year: 2019

This paper describes an improvement in Deep Q-learning called Reverse Experience Replay (also RER) that solves the problem of sparse rewards and helps to deal with reward maximizing tasks by sampling transitions successively in reverse order. On tasks with enough experience for training and enough Experience Replay memory capacity, Deep Q-learning Network with Reverse Experience Replay shows competitive results against both Double DQN, with a standard Experience Replay, and vanilla DQN. Also, RER achieves significantly increased results in tasks with a lack of experience and Replay memory capacity.
Deck.gl: Large-scale Web-based Visual Analytics Made Easy
Venue: arXiv cs.HC 2019
Year: 2019

In this paper, we demonstrate how deck.gl, an open-source project born out of data-heavy visual analytics applications, has grown into the robust visualization framework it is today. We begin by explaining why we built another data visualization framework in the first place. Then, we summarize our design goals (distilled from our interactions with users) and discuss how they guided the development of the framework's main features. We use two real-world applications of deck.gl to showcase how it can be applied to simplify the creation of data-heavy visualizations. We also discuss our lessons learned as we continue to improve the framework for the larger visualization community.
Processing Large Datasets of Fined Grained Source Code Changes
Venue: arXiv cs.SE 2019
Year: 2019

In the era of Big Code, when researchers seek to study an increasingly large number of repositories to support their findings, the data processing stage may require manipulating millions and more of records. In this work we focus on studies involving fine-grained AST level source code changes. We present how we extended the CodeDistillery source code mining framework with data manipulation capabilities, aimed to alleviate the processing of large datasets of fine grained source code changes. The capabilities we have introduced allow researchers to highly automate their repository mining process and streamline the data acquisition and processing phases. These capabilities have been successfully used to conduct a number of studies, in the course of which dozens of millions of fine-grained source code changes have been processed.
The exact complexity of the Tutte polynomial
Venue: arXiv cs.CC 2019
Year: 2019

This is a survey on the exact complexity of computing the Tutte polynomial. It is the longer 2017 version of Chapter 25 of the CRC Handbook on the Tutte polynomial and related topics, edited by J. Ellis-Monaghan and I. Moffatt, which is due to appear in the first quarter of 2020. In the version to be published in the Handbook the Sections 5 and 6 are shortened and made into a single section.
Computing Branching Distances Using Quantitative Games
Venue: arXiv cs.LO 2019
Year: 2019

We lay out a general method for computing branching distances between labeled transition systems. We translate the quantitative games used for defining these distances to other, path-building games which are amenable to methods from the theory of quantitative games. We then show for all common types of branching distances how the resulting path-building games can be solved. In the end, we achieve a method which can be used to compute all branching distances in the linear-time--branching-time spectrum.
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)
Venue: arXiv cs.LG 2019
Year: 2019

Despite the significant advances in recent years, Generative Adversarial Networks (GANs) are still notoriously hard to train. In this paper, we propose three novel curriculum learning strategies for training GANs. All strategies are first based on ranking the training images by their difficulty scores, which are estimated by a state-of-the-art image difficulty predictor. Our first strategy is to divide images into gradually more difficult batches. Our second strategy introduces a novel curriculum loss function for the discriminator that takes into account the difficulty scores of the real images. Our third strategy is based on sampling from an evolving distribution, which favors the easier images during the initial training stages and gradually converges to a uniform distribution, in which samples are equally likely, regardless of difficulty. We compare our curriculum learning strategies with the classic training procedure on two tasks: image generation and image translation. Our experiments indicate that all strategies provide faster convergence and superior results. For example, our best curriculum learning strategy applied on spectrally normalized GANs (SNGANs) fooled human annotators in thinking that generated CIFAR-like images are real in 25.0% of the presented cases, while the SNGANs trained using the classic procedure fooled the annotators in only 18.4% cases. Similarly, in image translation, the human annotators preferred the images produced by the Cycle-consistent GAN (CycleGAN) trained using curriculum learning in 40.5% cases and those produced by CycleGAN based on classic training in only 19.8% cases, 39.7% cases being labeled as ties.
Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example
Venue: arXiv cs.SD 2019
Year: 2019

Unlike melody extraction and other aspects of music transcription, research on playing technique detection is still in its early stages. Compared to existing work mostly focused on playing technique detection for individual single notes, we propose a general end-to-end method based on Sound Event Detection by FCN for musical instrument playing technique detection. In our case, we choose Erhu, a well-known Chinese bowed-stringed instrument, to experiment with our method. Because of the limitation of FCN, we present an algorithm to detect on variable length audio. The effectiveness of the proposed framework is tested on a new dataset, its categorization of techniques is similar to our training dataset. The highest accuracy of our 3 experiments on the new test set is 87.31%. Furthermore, we also evaluate the performance of the proposed framework on 10 real-world studio music (produced by midi) and 7 real-world recording samples to address the ability of generalization on our model.
Automated Reasoning with Restricted Intensional Sets
Venue: arXiv cs.LO 2019
Year: 2019

Intensional sets, i.e., sets given by a property rather than by enumerating elements, are widely recognized as a key feature to describe complex problems (see, e.g., specification languages such as B and Z). Notwithstanding, very few tools exist supporting high-level automated reasoning on general formulas involving intensional sets. In this paper we present a decision procedure for a first-order logic language offering both extensional and (a restricted form of) intensional sets (RIS). RIS are introduced as first-class citizens of the language and set-theoretical operators on RIS are dealt with as constraints. Syntactic restrictions on RIS guarantee that the denoted sets are finite, though unbounded. The language of RIS, called L_RIS , is parametric with respect to any first-order theory X providing at least equality and a decision procedure for X-formulas. In particular, we consider the instance of L_RIS when X is the theory of hereditarily finite sets and binary relations. We also present a working implementation of this instance as part of the {log} tool and we show through a number of examples and two case studies that, although RIS are a subclass of general intensional sets, they are still sufficiently expressive as to encode and solve many interesting problems. Finally, an extensive empirical evaluation provides evidence that the tool can be used in practice.
Generative Hierarchical Models for Parts, Objects, and Scenes
Venue: arXiv cs.LG 2019
Year: 2019

Compositional structures between parts and objects are inherent in natural scenes. Modeling such compositional hierarchies via unsupervised learning can bring various benefits such as interpretability and transferability, which are important in many downstream tasks. In this paper, we propose the first deep latent variable model, called RICH, for learning Representation of Interpretable Compositional Hierarchies. At the core of RICH is a latent scene graph representation that organizes the entities of a scene into a tree structure according to their compositional relationships. During inference, taking top-down approach, RICH is able to use higher-level representation to guide lower-level decomposition. This avoids the difficult problem of routing between parts and objects that is faced by bottom-up approaches. In experiments on images containing multiple objects with different part compositions, we demonstrate that RICH is able to learn the latent compositional hierarchy and generate imaginary scenes.
A Comparison of Semantic Similarity Methods for Maximum Human Interpretability
Venue: arXiv cs.IR 2019
Year: 2019

The inclusion of semantic information in any similarity measures improves the efficiency of the similarity measure and provides human interpretable results for further analysis. The similarity calculation method that focuses on features related to the text's words only, will give less accurate results. This paper presents three different methods that not only focus on the text's words but also incorporates semantic information of texts in their feature vector and computes semantic similarities. These methods are based on corpus-based and knowledge-based methods, which are: cosine similarity using tf-idf vectors, cosine similarity using word embedding and soft cosine similarity using word embedding. Among these three, cosine similarity using tf-idf vectors performed best in finding similarities between short news texts. The similar texts given by the method are easy to interpret and can be used directly in other information retrieval applications.
Modulo: Drive-by Sensing at City-scale on the Cheap
Venue: arXiv cs.NI 2019
Year: 2019

Drive-by sensing is gaining popularity as an inexpensive way to perform fine-grained, city-scale, spatiotemporal monitoring of physical phenomena. Prior work explores several challenges in the design of low-cost sensors, the reliability of these sensors, and their application for specific use-cases like pothole detection and pollution monitoring. However, the process of deployment of a drive-by sensing network at a city-scale is still unexplored. Despite the rise of ride-sharing services, there is still no way to optimally select vehicles from a fleet that can accomplish the sensing task by providing enough coverage of the city. In this paper, we propose Modulo -- a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints. Further, Modulo is well-suited to satisfy unique deployment constraints such as colocations with other sensors (needed for gas and PM sensor calibration), etc. We compare Modulo with two baseline algorithms on real-world taxi and bus datasets. We find that Modulo marginally outperforms the two baselines for datasets with just random-routes vehicles such as taxis. However, it significantly outperforms the baselines when a fleet comprises of both taxis and fixed-route vehicles such as public transport buses. Finally, we present a real-deployment that uses Modulo to select vehicles for an air pollution sensing application.
Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow
Venue: arXiv cs.LG 2019
Year: 2019

Recent studies incorporate Nesterov's accelerated gradient method for the acceleration of gradient based training. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to drastically improve the convergence speed compared to the conventional quasi-Newton method. This paper implements NAQ for non-convex optimization on Tensorflow. Two modifications have been proposed to the original NAQ algorithm to ensure global convergence and eliminate linesearch. The performance of the proposed algorithm - mNAQ is evaluated on standard non-convex function approximation benchmark problems and microwave circuit modelling problems. The results show that the improved algorithm converges better and faster compared to first order optimizers such as AdaGrad, RMSProp, Adam, and the second order methods such as the quasi-Newton method.
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Venue: arXiv cs.CL 2019
Year: 2019

Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations. Modeling the semantic interactions between the two arguments of a relation has proven useful for detecting implicit discourse relations. However, most previous approaches model such semantic interactions from a shallow interactive level, which is inadequate on capturing enough semantic information. In this paper, we propose a novel and effective Semantic Graph Convolutional Network (SGCN) to enhance the modeling of inter-argument semantics on a deeper interaction level for implicit discourse relation classification. We first build an interaction graph over representations of the two arguments, and then automatically extract in-depth semantic interactive information through graph convolution. Experimental results on the English corpus PDTB and the Chinese corpus CDTB both demonstrate the superiority of our model to previous state-of-the-art systems.
Who wants accurate models? Arguing for a different metrics to take classification models seriously
Venue: arXiv cs.LG 2019
Year: 2019

With the increasing availability of AI-based decision support, there is an increasing need for their certification by both AI manufacturers and notified bodies, as well as the pragmatic (real-world) validation of these systems. Therefore, there is the need for meaningful and informative ways to assess the performance of AI systems in clinical practice. Common metrics (like accuracy scores and areas under the ROC curve) have known problems and they do not take into account important information about the preferences of clinicians and the needs of their specialist practice, like the likelihood and impact of errors and the complexity of cases. In this paper, we present a new accuracy measure, the H-accuracy (Ha), which we claim is more informative in the medical domain (and others of similar needs) for the elements it encompasses. We also provide proof that the H-accuracy is a generalization of the balanced accuracy and establish a relation between the H-accuracy and the Net Benefit. Finally, we illustrate an experimentation in two user studies to show the descriptive power of the Ha score and how complementary and differently informative measures can be derived from its formulation (a Python script to compute Ha is also made available).
Learning Hierarchical Feature Space Using CLAss-specific Subspace Multiple Kernel -- Metric Learning for Classification
Venue: arXiv cs.LG 2019
Year: 2019

Metric learning for classification has been intensively studied over the last decade. The idea is to learn a metric space induced from a normed vector space on which data from different classes are well separated. Different measures of the separation thus lead to various designs of the objective function in the metric learning model. One classical metric is the Mahalanobis distance, where a linear transformation matrix is designed and applied on the original dataset to obtain a new subspace equipped with the Euclidean norm. The kernelized version has also been developed, followed by Multiple-Kernel learning models. In this paper, we consider metric learning to be the identification of the best kernel function with respect to a high class separability in the corresponding metric space. The contribution is twofold: 1) No pairwise computations are required as in most metric learning techniques; 2) Better flexibility and lower computational complexity is achieved using the CLAss-Specific (Multiple) Kernel - Metric Learning (CLAS(M)K-ML). The proposed techniques can be considered as a preprocessing step to any kernel method or kernel approximation technique. An extension to a hierarchical learning structure is also proposed to further improve the classification performance, where on each layer, the CLASMK is computed based on a selected "marginal" subset and feature vectors are constructed by concatenating the features from all previous layers.
A parallel dynamic overset grid framework for immersed boundary methods
Venue: arXiv cs.CE 2019
Year: 2019

A parallel dynamic overset framework has been developed for the curvilinear immersed boundary (overset-CURVIB) method to enable tackling a wide range of challenging flow problems. The dynamic overset grids are used to locally increase the grid resolution near complex immersed bodies, which are handled using a sharp interface immersed boundary method, undergoing large movements as well as arbitrary relative motions. The new framework extends the previous overset-CURVIB method with fixed overset grids and a sequential grid assembly to moving overset grids with an efficient parallel grid assembly. In addition, a new method for the interpolation of variables at the grid boundaries is developed which can drastically decrease the execution time and increase the parallel efficiency of our framework compared to the previous strategy. The moving/rotating overset grids are solved in a non-inertial frame of reference to avoid recalculating the curvilinear metrics of transformation while the background/stationary grids are solved in the inertial frame. The new framework is verified and validated against experimental data, and analytical/benchmark solutions. In addition, the results of the overset grid are compared with results over a similar single grid. The method is shown to be 2nd order accurate, decrease the computational cost relative to a single grid, and good overall parallel speedup. The grid assembly takes less than 7% of the total CPU time even at the highest number of CPUs tested in this work. The capabilities of our method are demonstrated by simulating the flow past a school of self-propelled aquatic swimmers arranged initially in a diamond pattern.
Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning
Venue: arXiv cs.CV 2019
Year: 2019

Eltwise layer is a commonly used structure in the multi-branch deep learning network. In a filter-wise pruning procedure, due to the specific operation of the eltwise layer, all its previous convolutional layers should vote for which filters by index should be pruned. Since only an intersection of the voted filters is pruned, the compression rate is limited. This work proposes a method called Directed-Weighting Group Lasso (DWGL), which enforces an index-wise incremental (directed) coefficient on the filterlevel group lasso items, so that the low index filters getting high activation tend to be kept while the high index ones tend to be pruned. When using DWGL, much fewer filters are retained during the voting process and the compression rate can be boosted. The paper test the proposed method on the ResNet series networks. On CIFAR-10, it achieved a 75.34% compression rate on ResNet-56 with a 0.94% error increment, and a 52.06% compression rate on ResNet-20 with a 0.72% error increment. On ImageNet, it achieved a 53% compression rate with ResNet-50 with a 0.6% error increment, speeding up the network by 2.23 times. Furthermore, it achieved a 75% compression rate on ResNet-50 with a 1.2% error increment, speeding up the network by 4 times.