OpenRaiser/Intern-Atlas huggingface dataset:
An Enhanced Ad Event-Prediction Method Based on Feature Engineering
Venue: arXiv cs.LG 2019
Year: 2019

In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance. As a result, ad event prediction systems are vital and widely used for sponsored search and display advertising as well as Real-Time Bidding (RTB). In this work, we introduce an enhanced method for ad event prediction (i.e. clicks, conversions) by proposing a new efficient feature engineering approach. A large real-world event-based dataset of a running marketing campaign is used to evaluate the efficiency of the proposed prediction algorithm. The results illustrate the benefits of the proposed ad event prediction approach, which significantly outperforms the alternative ones.
Using Bi-Directional Information Exchange to Improve Decentralized Schedule-Driven Traffic Control
Venue: arXiv cs.AI 2019
Year: 2019

Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. In order to achieve network level coordination in a scalable manner, scheduling agents communicate only with their direct neighbors. Each time an agent generates a new intersection schedule it communicates its expected outflows to its downstream neighbors as a prediction of future demand and these outflows are appended to the downstream agent's locally perceived demand. In this paper, we extend this basic coordination algorithm to additionally incorporate the complementary flow of information reflective of an intersection's current congestion level to its upstream neighbors. We present an asynchronous decentralized algorithm for updating intersection schedules and congestion level estimates based on these bi-directional information flows. By relating this algorithm to the self-optimized decision making of the basic operation, we are able to approach network-wide optimality and reduce inefficiency due to strictly self-interested intersection control decisions.
Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval
Venue: arXiv cs.IR 2019
Year: 2019

The Q&A community has become an important way for people to access knowledge and information from the Internet. However, the existing translation based on models does not consider the query specific semantics when assigning weights to query terms in question retrieval. So we improve the term weighting model based on the traditional topic translation model and further considering the quality characteristics of question and answer pairs, this paper proposes a communitybased question retrieval method that combines question and answer on quality and question relevance (T2LM+). We have also proposed a question retrieval method based on convolutional neural networks. The results show that Compared with the relatively advanced methods, the two methods proposed in this paper increase MAP by 4.91% and 6.31%.
Sensing Volume Coverage of Robot Workspace using On-Robot Time-of-Flight Sensor Arrays for Safe Human Robot Interaction
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, an analysis of the sensing volume coverage of robot workspace as well as the shared human-robot collaborative workspace for various configurations of on-robot Time-of-Flight (ToF) sensor array rings is presented. A methodology for volumetry using octrees to quantify the detection/sensing volume of the sensors is proposed. The change in sensing volume coverage by increasing the number of sensors per ToF sensor array ring and also increasing the number of rings mounted on robot link is also studied. Considerations of maximum ideal volume around the robot workspace that a given ToF sensor array ring placement and orientation setup should cover for safe human robot interaction are presented. The sensing volume coverage measurements in this maximum ideal volume are tabulated and observations on various ToF configurations and their coverage for close and far zones of the robot are determined.
Repairing Timed Automata Clock Guards through Abstraction and Testing
Venue: arXiv cs.LO 2019
Year: 2019

Timed automata (TAs) are a widely used formalism to specify systems having temporal requirements. However, exactly specifying the system may be difficult, as the user may not know the exact clock constraints triggering state transitions. In this work, we assume the user already specified a TA, and (s)he wants to validate it against an oracle that can be queried for acceptance. Under the assumption that the user only wrote wrong guard transitions (i.e., the structure of the TA is correct), the search space for the correct TA can be represented by a Parametric Timed Automaton (PTA), i.e., a TA in which some constants are parametrized. The paper presents a process that i) abstracts the initial (faulty) TA tainit in a PTA pta; ii) generates some test data (i.e., timed traces) from pta; iii) assesses the correct evaluation of the traces with the oracle; iv) uses the IMITATOR tool for synthesizing some constraints phi on the parameters of pta; v) instantiate from phi a TA tarep as final repaired model. Experiments show that the approach is successfully able to partially repair the initial design of the user.
Learning Blended, Precise Semantic Program Embeddings
Venue: arXiv cs.SE 2019
Year: 2019

Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application of deep models to a wide range of program analysis tasks. Existing approaches predominately learn to embed programs from their source code, and, as a result, they do not capture deep, precise program semantics. On the other hand, models learned from runtime information critically depend on the quality of program executions, thus leading to trained models with highly variant quality. This paper tackles these inherent weaknesses of prior approaches by introducing a new deep neural network, \liger, which learns program representations from a mixture of symbolic and concrete execution traces. We have evaluated \liger on \coset, a recently proposed benchmark suite for evaluating neural program embeddings. Results show \liger (1) is significantly more accurate than the state-of-the-art syntax-based models Gated Graph Neural Network and code2vec in classifying program semantics, and (2) requires on average 10x fewer executions covering 74\% fewer paths than the state-of-the-art dynamic model \dypro. Furthermore, we extend \liger to predict the name for a method from its body's vector representation. Learning on the same set of functions (more than 170K in total), \liger significantly outperforms code2seq, the previous state-of-the-art for method name prediction.
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model
Venue: arXiv cs.LG 2019
Year: 2019

Integration of reinforcement learning and imitation learning is an important problem that has been studied for a long time in the field of intelligent robotics. Reinforcement learning optimizes policies to maximize the cumulative reward, whereas imitation learning attempts to extract general knowledge about the trajectories demonstrated by experts, i.e., demonstrators. Because each of them has their own drawbacks, methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of the methods are heuristic and do not have a solid theoretical basis. In this paper, we present a new theory for integrating reinforcement and imitation learning by extending the probabilistic generative model framework for reinforcement learning, {\it plan by inference}. We develop a new probabilistic graphical model for reinforcement learning with multiple types of rewards and a probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of reinforcement learning and imitation learning can be formulated as a probabilistic inference of policies on pMDP-MO by considering the output of the discriminator in generative adversarial imitation learning as an additional optimal emission observation. We adapt the generative adversarial imitation learning and task-achievement reward to our proposed framework, achieving significantly better performance than agents trained with reinforcement learning or imitation learning alone. Experiments demonstrate that our framework successfully integrates imitation and reinforcement learning even when the number of demonstrators is only a few.
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs
Venue: arXiv cs.DC 2019
Year: 2019

Modern deep learning applications urge to push the model inference taking place at the edge devices for multiple reasons such as achieving shorter latency, relieving the burden of the network connecting to the cloud, and protecting user privacy. The Convolutional Neural Network (\emph{CNN}) is one of the most widely used model family in the applications. Given the high computational complexity of the CNN models, it is favorable to execute them on the integrated GPUs at the edge devices, which are ubiquitous and have more power and better energy efficiency than the accompanying CPUs. However, programming on integrated GPUs efficiently is challenging due to the variety of their architectures and programming interfaces. This paper proposes an end-to-end solution to execute CNN model inference on the integrated GPUs at the edge, which uses a unified IR to represent and optimize vision-specific operators on integrated GPUs from multiple vendors, as well as leverages machine learning-based scheduling search schemes to optimize computationally-intensive operators like convolution. Our solution even provides a fallback mechanism for operators not suitable or convenient to run on GPUs. The evaluation results suggest that compared to state-of-the-art solutions backed up by the vendor-provided high-performance libraries on Intel Graphics, ARM Mali GPU, and Nvidia integrated Maxwell GPU, our solution achieves similar, or even better (up to 1.62$\times$), performance on a number of popular image classification and object detection models. In addition, our solution has a wider model coverage and is more flexible to embrace new models. Our solution has been adopted in production services in AWS and is open-sourced.
TicToc: Enabling Bandwidth-Efficient DRAM Caching for both Hits and Misses in Hybrid Memory Systems
Venue: arXiv cs.AR 2019
Year: 2019

This paper investigates bandwidth-efficient DRAM caching for hybrid DRAM + 3D-XPoint memories. 3D-XPoint is becoming a viable alternative to DRAM as it enables high-capacity and non-volatile main memory systems; however, 3D-XPoint has 4-8x slower read, and worse writes. As such, effective DRAM caching in front of 3D-XPoint is important to enable a high-capacity, low-latency, and high-write-bandwidth memory. There are two major approaches for DRAM cache design: (1) a Tag-Inside-Cacheline (TIC) organization that optimizes for hits, by storing tag next to each line such that one access gets both tag and data, and (2) a Tag-Outside-Cacheline (TOC) organization that optimizes for misses, by storing tags from multiple data-lines together such that one tag-access gets info for several data-lines. Ideally, we desire the low hit-latency of TIC, and the low miss-bandwidth of TOC. To this end, we propose TicToc, an organization that provisions both TIC and TOC to get hit and miss benefits of both. However, we find that naively combining both actually performs worse than TIC, because one needs to pay bandwidth to maintain both metadata. The main contribution of this work is developing architectural techniques to reduce the bandwidth of maintaining both TIC and TOC metadata. We find the majority of the bandwidth cost is due to maintaining TOC dirty bits. We propose DRAM Cache Dirtiness Bit, which carries DRAM cache dirty info to last-level caches, to prune repeated dirty-bit checks for known dirty lines. We then propose Preemptive Dirty Marking, which predicts which lines will be written and proactively marks dirty bit at install time, to amortize the initial dirty-bit update. Our evaluations on a 4GB DRAM cache with 3D-XPoint memory show that TicToc enables 10% speedup over baseline TIC, nearing 14% speedup possible with an idealized DRAM cache w/ 64MB of SRAM tags, while needing only 34KB SRAM.
An Item Recommendation Approach by Fusing Images based on Neural Networks
Venue: arXiv cs.IR 2019
Year: 2019

There are rich formats of information in the network, such as rating, text, image, and so on, which represent different aspects of user preferences. In the field of recommendation, how to use those data effectively has become a difficult subject. With the rapid development of neural network, researching on multi-modal method for recommendation has become one of the major directions. In the existing recommender systems, numerical rating, item description and review are main information to be considered by researchers. However, the characteristics of the item may affect the user's preferences, which are rarely used for recommendation models. In this work, we propose a novel model to incorporate visual factors into predictors of people's preferences, namely MF-VMLP, based on the recent developments of neural collaborative filtering (NCF). Firstly, we get visual presentation via a pre-trained convolutional neural network (CNN) model. To obtain the nonlinearities interaction of latent vectors and visual vectors, we propose to leverage a multi-layer perceptron (MLP) to learn. Moreover, the combination of MF and MLP has achieved collaborative filtering recommendation between users and items. Our experiments conduct Amazon's public dataset for experimental validation and root-mean-square error (RMSE) as evaluation metrics. To some extent, experimental result on a real-world data set demonstrates that our model can boost the recommendation performance.
Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches
Venue: arXiv cs.LG 2019
Year: 2019

Even though it is well known that for most relevant computational problems different algorithms may perform better on different classes of problem instances, most researchers still focus on determining a single best algorithmic configuration based on aggregate results such as the average. In this paper, we propose Integer Programming based approaches to build decision trees for the Algorithm Selection Problem. These techniques allow automate three crucial decisions: (i) discerning the most important problem features to determine problem classes; (ii) grouping the problems into classes and (iii) select the best algorithm configuration for each class. To evaluate this new approach, extensive computational experiments were executed using the linear programming algorithms implemented in the COIN-OR Branch & Cut solver across a comprehensive set of instances, including all MIPLIB benchmark instances. The results exceeded our expectations. While selecting the single best parameter setting across all instances decreased the total running time by 22%, our approach decreased the total running time by 40% on average across 10-fold cross validation experiments. These results indicate that our method generalizes quite well and does not overfit.
Attention based Convolutional Recurrent Neural Network for Environmental Sound Classification
Venue: arXiv cs.SD 2019
Year: 2019

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose an convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and achieved the state-of-the-art performance in terms of classification accuracy.
Optimizing micro-tiles in micro-structures as a design paradigm
Venue: arXiv cs.CG 2019
Year: 2019

In recent years, new methods have been developed to synthesize complex porous and micro-structured geometry in a variety of ways. In this work, we take these approaches one step further and present these methods as an efficacious design paradigm. Specifically, complex micro-structure geometry can be synthesized while optimizing certain properties such as maximal heat exchange in heat exchangers, or minimal weight under stress specifications. By being able to adjust the geometry, the topology and/or the material properties of individual tiles in the micro-structure, possibly in a gradual way, a porous object can be synthesized that is optimal with respect to the design specifications. As part of this work, we exemplify this paradigm on a variety of diverse applications.
A New Insight into GAMP and AMP
Venue: arXiv cs.IT 2019
Year: 2019

A concise expectation propagation (EP) based message passing algorithm (MPA) is derived for the general measurement channel. By neglecting some high-order infinitesimal terms, the EP-MPA is proven to be equivalent to the Generalized Approximate Message Passing (GAMP), which exploits central limit theorem and Taylor expansion to simplify the belief propagation process. Furthermore, for additive white gaussian noise measurement channels, EP-MPA is proven to be equivalent to the AMP. Such intrinsic equivalence between EP and GAMP/AMP offers a new insight into GAMP and AMP via a unified message passing rule for non-linear processing, and may provide clues towards building new MPAs in solving more general non-linear problems.
LumièreNet: Lecture Video Synthesis from Audio
Venue: arXiv cs.LG 2019
Year: 2019

We present LumièreNet, a simple, modular, and completely deep-learning based architecture that synthesizes, high quality, full-pose headshot lecture videos from instructor's new audio narration of any length. Unlike prior works, LumièreNet is entirely composed of trainable neural network modules to learn mapping functions from the audio to video through (intermediate) estimated pose-based compact and abstract latent codes. Our video demos are available at [22] and [23].
Supervised Symbolic Music Style Translation Using Synthetic Data
Venue: arXiv cs.SD 2019
Year: 2019

Research on style transfer and domain translation has clearly demonstrated the ability of deep learning-based algorithms to manipulate images in terms of artistic style. More recently, several attempts have been made to extend such approaches to music (both symbolic and audio) in order to enable transforming musical style in a similar manner. In this study, we focus on symbolic music with the goal of altering the 'style' of a piece while keeping its original 'content'. As opposed to the current methods, which are inherently restricted to be unsupervised due to the lack of 'aligned' data (i.e. the same musical piece played in multiple styles), we develop the first fully supervised algorithm for this task. At the core of our approach lies a synthetic data generation scheme which allows us to produce virtually unlimited amounts of aligned data, and hence avoid the above issue. In view of this data generation scheme, we propose an encoder-decoder model for translating symbolic music accompaniments between a number of different styles. Our experiments show that our models, although trained entirely on synthetic data, are capable of producing musically meaningful accompaniments even for real (non-synthetic) MIDI recordings.
Wireless Caching Helper System with Heterogeneous Traffic and Random Availability
Venue: arXiv cs.NI 2019
Year: 2019

Multimedia content streaming from Internet-based sources emerges as one of the most high demanded services by wireless users. In order to alleviate excessive traffic due to multimedia content transmission, many architectures (e.g., small cells, femtocells, etc.) have been proposed to offload such traffic to the nearest (or strongest) access point also called "helper". The deployment of more helpers is not necessarily beneficial due to their potential of increasing interference. In this work, we evaluate a wireless system in which we distinguish between cachable and non-cachable traffic. More specifically, we consider a general system in which a wireless user with limited cache storage requests cachable content from a data center that can be directly accessed through a base station. The user can be assisted by a pair of wireless helpers that exchange non-cachable content as well. Packets arrive at the queue of the source helper in bursts. Each helper has its own cache to assist the user's requests for cachable content. Files not available from the helpers are transmitted by the base station. We analyze the system throughput and the delay experienced by the user and show how they are affected by the packet arrival rate at the source helper, the availability of caching helpers, the caches' parameters, and the user's request rate by means of numerical results.
infotheory: A C++/Python package for multivariate information theoretic analysis
Venue: arXiv cs.IT 2019
Year: 2019

This paper introduces \texttt{infotheory}: a package written in C++ and usable from Python and C++, for multivariate information theoretic analyses of discrete and continuous data. This package allows the user to study the relationship between components of a complex system simply from the data recorded during its operation, using the tools of information theory. It implements widely used measures such as entropy and mutual information, as well as more recent measures that arise from multivariate extensions to information theory, specifically Partial Information Decomposition. It provides an easy-to-use and flexible tool for use in research as well as pedgogical purposes to introduce students to information theory. Website: http://mcandadai.com/infotheory/ Source: https://git.io/infot