OpenRaiser/Intern-Atlas huggingface dataset:
Mature GAIL: Imitation Learning for Low-level and High-dimensional Input using Global Encoder and Cost Transformation
Venue: arXiv cs.LG 2019
Year: 2019

Recently, GAIL framework and various variants have shown remarkable possibilities for solving practical MDP problems. However, detailed researches of low-level, and high-dimensional state input in this framework, such as image sequences, has not been conducted. Furthermore, the cost function learned in the traditional GAIL frame-work only lies on a negative range, acting as a non-penalized reward and making the agent difficult to learn the optimal policy. In this paper, we propose a new algorithm based on the GAIL framework that includes a global encoder and the reward penalization mechanism. The global encoder solves two issues that arise when applying GAIL framework to high-dimensional image state. Also, it is shown that the penalization mechanism provides more adequate reward to the agent, resulting in stable performance improvement. Our approach's potential can be backed up by the fact that it is generally applicable to variants of GAIL framework. We conducted in-depth experiments by applying our methods to various variants of the GAIL framework. And, the results proved that our method significantly improves the performances when it comes to low-level and high-dimensional tasks.
Streaming Message Interface: High-Performance Distributed Memory Programming on Reconfigurable Hardware
Venue: arXiv cs.DC 2019
Year: 2019

Distributed memory programming is the established paradigm used in high-performance computing (HPC) systems, requiring explicit communication between nodes and devices. When FPGAs are deployed in distributed settings, communication is typically handled either by going through the host machine, sacrificing performance, or by streaming across fixed device-to-device connections, sacrificing flexibility. We present Streaming Message Interface (SMI), a communication model and API that unifies explicit message passing with a hardware-oriented programming model, facilitating minimal-overhead, flexible, and productive inter-FPGA communication. Instead of bulk transmission, messages are streamed across the network during computation, allowing communication to be seamlessly integrated into pipelined designs. We present a high-level synthesis implementation of SMI targeting a dedicated FPGA interconnect, exposing runtime-configurable routing with support for arbitrary network topologies, and implement a set of distributed memory benchmarks. Using SMI, programmers can implement distributed, scalable HPC programs on reconfigurable hardware, without deviating from best practices for hardware design.
{Networks of Uniform Splicing Processors
Venue: arXiv cs.FL 2019
Year: 2019

In this note we consider a new variant of network of splicing processors which simplifies the general model such that filters remain associated with nodes but the input and output filters of every node coincide. This variant is called {\it network of uniform splicing processors}. Although the communication in the new variant seems less powerful, being based on simpler filters, the new variant is sufficiently powerful to be computationally complete. The main result is that nondeterministic Turing machines can be simulated by networks of uniform splicing processors. Furthermore, the simulation is time efficient.
Hard properties with (very) short PCPPs and their applications
Venue: arXiv cs.CC 2019
Year: 2019

We show that there exist properties that are maximally hard for testing, while still admitting PCPPs with a proof size very close to linear. Specifically, for every fixed $\ell$, we construct a property $\mathcal{P}^{(\ell)}\subseteq\{0,1\}^n$ satisfying the following: Any testing algorithm for $\mathcal{P}^{(\ell)}$ requires $Ω(n)$ many queries, and yet $\mathcal{P}^{(\ell)}$ has a constant query PCPP whose proof size is $O(n\cdot \log^{(\ell)}n)$, where $\log^{(\ell)}$ denotes the $\ell$ times iterated log function (e.g., $\log^{(2)}n = \log \log n$). The best previously known upper bound on the PCPP proof size for a maximally hard to test property was $O(n \cdot \mathrm{poly}\log{n})$. As an immediate application, we obtain stronger separations between the standard testing model and both the tolerant testing model and the erasure-resilient testing model: for every fixed $\ell$, we construct a property that has a constant-query tester, but requires $Ω(n/\log^{(\ell)}(n))$ queries for every tolerant or erasure-resilient tester.
Automatic Image Pixel Clustering based on Mussels Wandering Optimiz
Venue: arXiv cs.CV 2019
Year: 2019

Image segmentation as a clustering problem is to identify pixel groups on an image without any preliminary labels available. It remains a challenge in machine vision because of the variations in size and shape of image segments. Furthermore, determining the segment number in an image is NP-hard without prior knowledge of the image content. This paper presents an automatic color image pixel clustering scheme based on mussels wandering optimization. By applying an activation variable to determine the number of clusters along with the cluster centers optimization, an image is segmented with minimal prior knowledge and human intervention. By revising the within- and between-class sum of squares ratio for random natural image contents, we provide a novel fitness function for image pixel clustering tasks. Comprehensive empirical studies of the proposed scheme against other state-of-the-art competitors on synthetic data and the ASD dataset have demonstrated the promising performance of the proposed scheme.
Characterizing the interplay between information and strength in Blotto games
Venue: arXiv cs.GT 2019
Year: 2019

In this paper, we investigate informational asymmetries in the Colonel Blotto game, a game-theoretic model of competitive resource allocation between two players over a set of battlefields. The battlefield valuations are subject to randomness. One of the two players knows the valuations with certainty. The other knows only a distribution on the battlefield realizations. However, the informed player has fewer resources to allocate. We characterize unique equilibrium payoffs in a two battlefield setup of the Colonel Blotto game. We then focus on a three battlefield setup in the General Lotto game, a popular variant of the Colonel Blotto game. We characterize the unique equilibrium payoffs and mixed equilibrium strategies. We quantify the value of information - the difference in equilibrium payoff between the asymmetric information game and complete information game. We find information strictly improves the informed player's performance guarantee. However, the magnitude of improvement varies with the informed player's strength as well as the game parameters. Our analysis highlights the interplay between strength and information in adversarial environments.
L_DMI: An Information-theoretic Noise-robust Loss Function
Venue: arXiv cs.LG 2019
Year: 2019

Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly damages the performance of trained models when using such dataset without particular treatment. Various methods have been proposed for learning with noisy labels. However, most methods only handle limited kinds of noise patterns, require auxiliary information or steps (e.g. , knowing or estimating the noise transition matrix), or lack theoretical justification. In this paper, we propose a novel information-theoretic loss function, $\mathcal{L}_{DMI}$, for training deep neural networks robust to label noise. The core of $\mathcal{L}_{DMI}$ is a generalized version of mutual information, termed Determinant based Mutual Information (DMI), which is not only information-monotone but also relatively invariant. \emph{To the best of our knowledge, $\mathcal{L}_{DMI}$ is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information}. In addition to theoretical justification, we also empirically show that using $\mathcal{L}_{DMI}$ outperforms all other counterparts in the classification task on both image dataset and natural language dataset include Fashion-MNIST, CIFAR-10, Dogs vs. Cats, MR with a variety of synthesized noise patterns and noise amounts, as well as a real-world dataset Clothing1M. Codes are available at https://github.com/Newbeeer/L_DMI .
Living near the edge: A lower-bound on the phase transition of total variation minimization
Venue: arXiv cs.IT 2019
Year: 2019

This work is about the total variation (TV) minimization which is used for recovering gradient-sparse signals from compressed measurements. Recent studies indicate that TV minimization exhibits a phase transition behavior from failure to success as the number of measurements increases. In fact, in large dimensions, TV minimization succeeds in recovering the gradient-sparse signal with high probability when the number of measurements exceeds a certain threshold; otherwise, it fails almost certainly. Obtaining a closed-form expression that approximates this threshold is a major challenge in this field and has not been appropriately addressed yet. In this work, we derive a tight lower-bound on this threshold in case of any random measurement matrix whose null space is distributed uniformly with respect to the Haar measure. In contrast to the conventional TV phase transition results that depend on the simple gradient-sparsity level, our bound is highly affected by generalized notions of gradient-sparsity. Our proposed bound is very close to the true phase transition of TV minimization confirmed by simulation results.
Back to the Future -- Sequential Alignment of Text Representations
Venue: arXiv cs.CL 2019
Year: 2019

Language evolves over time in many ways relevant to natural language processing tasks. For example, recent occurrences of tokens 'BERT' and 'ELMO' in publications refer to neural network architectures rather than persons. This type of temporal signal is typically overlooked, but is important if one aims to deploy a machine learning model over an extended period of time. In particular, language evolution causes data drift between time-steps in sequential decision-making tasks. Examples of such tasks include prediction of paper acceptance for yearly conferences (regular intervals) or author stance prediction for rumours on Twitter (irregular intervals). Inspired by successes in computer vision, we tackle data drift by sequentially aligning learned representations. We evaluate on three challenging tasks varying in terms of time-scales, linguistic units, and domains. These tasks show our method outperforming several strong baselines, including using all available data. We argue that, due to its low computational expense, sequential alignment is a practical solution to dealing with language evolution.
Self-driving scale car trained by Deep reinforcement learning
Venue: arXiv cs.LG 2019
Year: 2019

The self-driving based on deep reinforcement learning, as the most important application of artificial intelligence, has become a popular topic. Most of the current self-driving methods focus on how to directly learn end-to-end self-driving control strategy from the raw sensory data. Essentially, this control strategy can be considered as a mapping between images and driving behavior, which usually faces a problem of low generalization ability. To improve the generalization ability for the driving behavior, the reinforcement learning method requires extrinsic reward from the real environment, which may damage the car. In order to obtain a good generalization ability in safety, a virtual simulation environment that can be constructed different driving scene is designed by Unity. A theoretical model is established and analyzed in the virtual simulation environment, and it is trained by double Deep Q-network. Then, the trained model is migrated to a scale car in real world. This process is also called a sim2real method. The sim2real training method efficiently handle the these two problems. The simulations and experiments are carried out to evaluate the performance and effectiveness of the proposed algorithm. Finally, it is demonstrated that the scale car in real world obtain the capability for autonomous driving.
How much research output from India gets social media attention?
Venue: arXiv cs.DL 2019
Year: 2019

Scholarly articles are now increasingly being mentioned and discussed in social media platforms, sometimes even as pre- or post-print version uploads. Measures of social media mentions and coverage are now emerging as an alternative indicator of impact of scholarly articles. This article aims to explore how much scholarly research output from India is covered in different social media platforms, and how similar or different it is from the world average. It also analyses the discipline-wise variations in coverage and altmetric attention for Indian research output, including a comparison with the world average. Results obtained show interesting patterns. Only 28.5% of the total research output from India is covered in social media platforms, which is about 18% less than the world average. ResearchGate and Mendeley are the most popular social media platforms in India for scholarly article coverage. In terms of discipline-wise variation, medical sciences and biological sciences have relatively higher coverage across different platforms compared to disciplines like information science and engineering.
MIDI-Sandwich2: RNN-based Hierarchical Multi-modal Fusion Generation VAE networks for multi-track symbolic music generation
Venue: arXiv cs.LG 2019
Year: 2019

Currently, almost all the multi-track music generation models use the Convolutional Neural Network (CNN) to build the generative model, while the Recurrent Neural Network (RNN) based models can not be applied in this task. In view of the above problem, this paper proposes a RNN-based Hierarchical Multi-modal Fusion Generation Variational Autoencoder (VAE) network, MIDI-Sandwich2, for multi-track symbolic music generation. Inspired by VQ-VAE2, MIDI-Sandwich2 expands the dimension of the original hierarchical model by using multiple independent Binary Variational Autoencoder (BVAE) models without sharing weights to process the information of each track. Then, with multi-modal fusion technology, the upper layer named Multi-modal Fusion Generation VAE (MFG-VAE) combines the latent space vectors generated by the respective tracks, and uses the decoder to perform the ascending dimension reconstruction to simulate the inverse operation of multi-modal fusion, multi-modal generation, so as to realize the RNN-based multi-track symbolic music generation. For the multi-track format pianoroll, we also improve the output binarization method of MuseGAN, which solves the problem that the refinement step of the original scheme is difficult to differentiate and the gradient is hard to descent, making the generated song more expressive. The model is validated on the Lakh Pianoroll Dataset (LPD) multi-track dataset. Compared to the MuseGAN, MIDI-Sandwich2 can not only generate harmonious multi-track music, the generation quality is also close to the state of the art level. At the same time, by using the VAE to restore songs, the semi-generated songs reproduced by the MIDI-Sandwich2 are more beautiful than the pure autogeneration music generated by MuseGAN. Both the code and the audition audio samples are open source on https://github.com/LiangHsia/MIDI-S2.
Performance considerations on execution of large scale workflow applications on cloud functions
Venue: arXiv cs.DC 2019
Year: 2019

Function-as-a-Service is a novel type of cloud service used for creating distributed applications and utilizing computing resources. Application developer supplies source code of cloud functions, which are small applications or application components, while the service provider is responsible for provisioning the infrastructure, scaling and exposing a REST style API. This environment seems to be adequate for running scientific workflows, which in recent years, have become an established paradigm for implementing and preserving complex scientific processes. In this paper, we present work done on evaluating three major FaaS providers (Amazon, Google, IBM) as a platform for running scientific workflows. The experiments were performed with a dedicated benchmarking framework, which consisted of instrumented workflow execution engine. The testing load was implemented as a large scale bag-of-tasks style workflow, where task count reached 5120 running in parallel. The studied parameters include raw performance, efficiency of infrastructure provisioning, overhead introduced by the API and network layers, as well as aspects related to run time accounting. Conclusions include insights into available performance, expressed as raw GFlops values and charts depicting relation of performance to function size. The infrastructure provisioning proved to be governed by parallelism and rate limits, which can be deducted from included charts. The overhead imposed by using a REST API proved to be a significant contribution to overall run time of individual tasks, and possibly the whole workflow. The paper ends with pointing out possible future work, which includes building performance models and designing a dedicated scheduling algorithms for running scientific workflows on FaaS.
A Privacy-Preserving Longevity Study of Tor's Hidden Services
Venue: arXiv cs.CR 2019
Year: 2019

Tor and hidden services have emerged as a practical solution to protect user privacy against tracking and censorship. At the same time, little is known about the lifetime and nature of hidden services. Data collection and study of Tor hidden services is challenging due to its nature of providing privacy. Studying the lifetime of hidden services provides several benefits. For example, it allows investigation of the maliciousness of domains based on their lifetime. Short-lived hidden services are more likely not to be legitimate domains, e.g., used by ransomware, as compared to long-lived domains. In this work, we investigate the lifetime of hidden services by collecting data from a small (2%) subset of all Tor HSDir relays in a privacy-preserving manner. Based on the data collected, we devise protocols and extrapolation techniques to infer the lifetime of hidden services. Moreover we show that, due to Tor's specifics, our small subset of HSDir relays is sufficient to extrapolate lifetime with high accuracy, while respecting Tor user and service privacy and following Tor's research safety guidelines. Our results indicate that a large majority of the hidden services have a very short lifetime. In particular, 50% of all current Tor hidden services have an estimate lifetime of only 10 days or less, and 80% have a lifetime of less than a month.
Unified Underwater Structure-from-Motion
Venue: arXiv cs.CV 2019
Year: 2019

This paper shows that accurate underwater 3D shape reconstruction is possible using a single camera, observing a target through a refractive interface. We provide unified reconstruction techniques for a variety of scenarios such as single static camera and moving refractive interface, single moving camera and static refractive interface, and single moving camera and moving refractive interface. In our basic setup, we assume that the refractive interface is planar, and simultaneously estimate the unknown transformations of the planar interface and the camera, and the unknown target shape using bundle adjustment. We also extend it to relax the planarity assumption, which enables us to use waves of the refractive interface for the reconstruction task. Experiments with real data show the superiority of our method to existing methods.
Open Data Chatbot
Venue: arXiv cs.IR 2019
Year: 2019

Recently, chatbots received an increased attention from industry and diverse research communities as a dialogue-based interface providing advanced human-computer interactions. On the other hand, Open Data continues to be an important trend and a potential enabler for government transparency and citizen participation. This paper shows how these two paradigms can be combined to help non-expert users find and discover open government datasets through dialogue.
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Venue: arXiv cs.CL 2019
Year: 2019

State-of-the-art models often make use of superficial patterns in the data that do not generalize well to out-of-domain or adversarial settings. For example, textual entailment models often learn that particular key words imply entailment, irrespective of context, and visual question answering models learn to predict prototypical answers, without considering evidence in the image. In this paper, we show that if we have prior knowledge of such biases, we can train a model to be more robust to domain shift. Our method has two stages: we (1) train a naive model that makes predictions exclusively based on dataset biases, and (2) train a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize. Experiments on five datasets with out-of-domain test sets show significantly improved robustness in all settings, including a 12 point gain on a changing priors visual question answering dataset and a 9 point gain on an adversarial question answering test set.
On Domination Coloring in Graphs
Venue: arXiv cs.DM 2019
Year: 2019

A domination coloring of a graph $G$ is a proper vertex coloring of $G$ such that each vertex of $G$ dominates at least one color class, and each color class is dominated by at least one vertex. The minimum number of colors among all domination colorings is called the domination chromatic number, denoted by $χ_{dd}(G)$. In this paper, we study the complexity of this problem by proving its NP-Completeness for arbitrary graphs, and give general bounds and characterizations on several classes of graphs. We also show the relation between dominator chromatic number $χ_{d}(G)$, dominated chromatic number $χ_{dom}(G)$, chromatic number $χ(G)$, and domination number $γ(G)$. We present several results on graphs with $χ_{dd}(G)=χ(G)$.
Optimal Function Approximation with Relu Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

We consider in this paper the optimal approximations of convex univariate functions with feed-forward Relu neural networks. We are interested in the following question: what is the minimal approximation error given the number of approximating linear pieces? We establish the necessary and sufficient conditions and uniqueness of optimal approximations, and give lower and upper bounds of the optimal approximation errors. Relu neural network architectures are then presented to generate these optimal approximations. Finally, we propose an algorithm to find the optimal approximations, as well as prove its convergence and validate it with experimental results.
Off-Policy Evaluation in Partially Observable Environments
Venue: arXiv cs.LG 2019
Year: 2019

This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments. Off-policy evaluation under partial observability is inherently prone to bias, with risk of arbitrarily large errors. We define the problem of off-policy evaluation for Partially Observable Markov Decision Processes (POMDPs) and establish what we believe is the first off-policy evaluation result for POMDPs. In addition, we formulate a model in which observed and unobserved variables are decoupled into two dynamic processes, called a Decoupled POMDP. We show how off-policy evaluation can be performed under this new model, mitigating estimation errors inherent to general POMDPs. We demonstrate the pitfalls of off-policy evaluation in POMDPs using a well-known off-policy method, Importance Sampling, and compare it with our result on synthetic medical data.