OpenRaiser/Intern-Atlas huggingface dataset:
Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EM
Venue: arXiv cs.CV 2019
Year: 2019

Cryo-electron microscopy (cryo-EM), the subject of the 2017 Nobel Prize in Chemistry, is a technology for determining the 3-D structure of macromolecules from many noisy 2-D projections of instances of these macromolecules, whose orientations and positions are unknown. The molecular structures are not rigid objects, but flexible objects involved in dynamical processes. The different conformations are exhibited by different instances of the macromolecule observed in a cryo-EM experiment, each of which is recorded as a particle image. The range of conformations and the conformation of each particle are not known a priori; one of the great promises of cryo-EM is to map this conformation space. Remarkable progress has been made in determining rigid structures from homogeneous samples of molecules in spite of the unknown orientation of each particle image and significant progress has been made in recovering a few distinct states from mixtures of rather distinct conformations, but more complex heterogeneous samples remain a major challenge. We introduce the ``hyper-molecule'' framework for modeling structures across different states of heterogeneous molecules, including continuums of states. The key idea behind this framework is representing heterogeneous macromolecules as high-dimensional objects, with the additional dimensions representing the conformation space. This idea is then refined to model properties such as localized heterogeneity. In addition, we introduce an algorithmic framework for recovering such maps of heterogeneous objects from experimental data using a Bayesian formulation of the problem and Markov chain Monte Carlo (MCMC) algorithms to address the computational challenges in recovering these high dimensional hyper-molecules. We demonstrate these ideas in a prototype applied to synthetic data.
An Online Topic Modeling Framework with Topics Automatically Labeled
Venue: arXiv cs.IR 2019
Year: 2019

In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic. The proposed framework combines the prior topic distributions in a time window during inferring the topics in current time slice, and introduces a new ranking scheme to select most representative phrases and sentences for the inferred topics in each time slice. Experiments on 7,076 Stack Exchange posts show the effectiveness of IEDL in tracking topic changes and labeling topics.
Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
Venue: arXiv cs.IR 2019
Year: 2019

Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets . However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In this work, we introduce an end-to-end system, trained in a multi-task setting, to filter and re-rank answers in the medical domain. We use task-specific pre-trained models as deep feature extractors. Our model achieves the highest Spearman's Rho and Mean Reciprocal Rank of 0.338 and 0.9622 respectively, on the ACL-BioNLP workshop MediQA Question Answering shared-task.
Machine Reading Comprehension: a Literature Review
Venue: arXiv cs.CL 2019
Year: 2019

Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. This article summarizes recent advances in MRC, mainly focusing on two aspects (i.e., corpus and techniques). The specific characteristics of various MRC corpus are listed and compared. The main ideas of some typical MRC techniques are also described.
Estimating Gaps in Martingales and Applications to Coin-Tossing: Constructions and Hardness
Venue: arXiv cs.DM 2019
Year: 2019

Consider designing a distributed coin-tossing protocol for n processors such that the probability of heads is X0 in [0,1], and an adversary can reset one processor to change the distribution of the final outcome. For X0=1/2, in the non-cryptographic setting, Blum's majority protocol is $\frac1{\sqrt{2πn}}$ insecure. For computationally bounded adversaries and any X0 in [0,1], the protocol of Moran,Naor,Segev (2009) is only O(1/n) insecure. In this paper, we study discrete-time martingales (X0,X1,..,Xn) such that Xi in [0,1], for all i in {0,..,n}, and Xn in {0,1}. In particular, for any X0 in [0,1], we construct martingales that yield $\frac12\sqrt{\frac{X_0(1-X_0)}{n}}$ insecure coin-tossing protocols with n-bit communication; irrespective of the number of bits required to represent the output distribution. Note that for sufficiently small X0, we achieve higher security than Moran et al's protocol even against computationally unbounded adversaries. For X0=1/2, our protocol requires only 40% of the processors to obtain the same security as the majority protocol. We introduce a new inductive technique that uses geometric transformations to estimate the large gaps in these martingales. For any X0 in [0,1], we show that there exists a stopping time $T$ such that $\mathbb{E}[|{X_T-X_{T-1}}|]\geq\frac2{\sqrt{2n-1}}\cdot X_0(1-X_0)$. The inductive technique also constructs martingales that demonstrate the optimality of our bound - we construct optimal martingales such that any $T$ has$\mathbb{E}[|{X_T-X_{T-1}}|]\leq\frac1{\sqrt{n}}\cdot\sqrt{X_0(1-X_0)}$. Our lower-bound holds for all X0 in [0,1]; while the previous bound of Cleve,Impagliazzo (1993) exists only for positive constant X0. Our approach only employs elementary techniques and avoids the complex probabilistic tools inherent to the approaches of Cleve,Impagliazzo (1993) and Beimel,Haitner,Makriyannis,Omri (2018).
End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, a novel deep reinforcement learning (DRL)-based method is proposed to navigate the robot team through unknown complex environments, where the geometric centroid of the robot team aims to reach the goal position while avoiding collisions and maintaining connectivity. Decentralized robot-level policies are derived using a mechanism of centralized learning and decentralized executing. The proposed method can derive end-to-end policies, which map raw lidar measurements into velocity control commands of robots without the necessity of constructing obstacle maps. Simulation and indoor real-world unmanned ground vehicles (UGVs) experimental results verify the effectiveness of the proposed method.
Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification
Venue: arXiv cs.CR 2019
Year: 2019

Preventing organizations from Cyber exploits needs timely intelligence about Cyber vulnerabilities and attacks, referred as threats. Cyber threat intelligence can be extracted from various sources including social media platforms where users publish the threat information in real time. Gathering Cyber threat intelligence from social media sites is a time consuming task for security analysts that can delay timely response to emerging Cyber threats. We propose a framework for automatically gathering Cyber threat intelligence from Twitter by using a novelty detection model. Our model learns the features of Cyber threat intelligence from the threat descriptions published in public repositories such as Common Vulnerabilities and Exposures (CVE) and classifies a new unseen tweet as either normal or anomalous to Cyber threat intelligence. We evaluate our framework using a purpose-built data set of tweets from 50 influential Cyber security related accounts over twelve months (in 2018). Our classifier achieves the F1-score of 0.643 for classifying Cyber threat tweets and outperforms several baselines including binary classification models. Our analysis of the classification results suggests that Cyber threat relevant tweets on Twitter do not often include the CVE identifier of the related threats. Hence, it would be valuable to collect these tweets and associate them with the related CVE identifier for cyber security applications.
Using binary decision diagrams for constraint handling in combinatorial interaction testing
Venue: arXiv cs.SE 2019
Year: 2019

Constraints among test parameters often have substantial effects on the performance of test case generation for combinatorial interaction testing. This paper investigates the effectiveness of the use of Binary Decision Diagrams (BDDs) for constraint handling. BDDs are a data structure used to represent and manipulate Boolean functions. The core role of a constraint handler is to perform a check to determine if a partial test case with unspecified parameter values satisfies the constraints. In the course of generating a test suite, this check is executed a number of times; thus the efficiency of the check significantly affects the overall time required for test case generation. In the paper, we study two different approaches. The first approach performs this check by computing the logical AND of Boolean functions that represent all constraint-satisfying full test cases and a given partial test case. The second approach uses a new technique to construct a BDD that represents all constraint-satisfying partial test cases. With this BDD, the check can be performed by simply traversing the BDD from the root to a sink. We developed a program that incorporates both approaches into IPOG, a well-known test case generation algorithm. Using this program, we empirically evaluate the performance of these BDD-based constraint handling approaches using a total of 62 problem instances. In the evaluation, the two approaches are compared with three different constraint handling approaches, namely, those based on Boolean satisfiability (SAT) solving, Minimum Forbidden Tuples (MFTs), and Constraint Satisfiction Problem (CSP) solving. The results of the evaluation show that the two BDD-based approaches usually outperform the other constraint handling techniques and that the BDD-based approach using the new technique exhibits best performance.
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
Venue: arXiv cs.LG 2019
Year: 2019

Convolutional Neural Networks (CNNs) have had great success in many machine vision as well as machine audition tasks. Many image recognition network architectures have consequently been adapted for audio processing tasks. However, despite some successes, the performance of many of these did not translate from the image to the audio domain. For example, very deep architectures such as ResNet and DenseNet, which significantly outperform VGG in image recognition, do not perform better in audio processing tasks such as Acoustic Scene Classification (ASC). In this paper, we investigate the reasons why such powerful architectures perform worse in ASC compared to simpler models (e.g., VGG). To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models. Using our receptive field analysis, we adapt both ResNet and DenseNet, achieving state-of-the-art performance and eventually outperforming the VGG-based models. We introduce systematic ways of adapting the RF in CNNs, and present results on three data sets that show how changing the RF over the time and frequency dimensions affects a model's performance. Our experimental results show that very small or very large RFs can cause performance degradation, but deep models can be made to generalize well by carefully choosing an appropriate RF size within a certain range.
Challenges in IT Operations Management at a German University Chair -- Ten Years in Retrospect
Venue: arXiv cs.GL 2019
Year: 2019

Over the last two decades, the majority of German universities adopted various characteristics of the prevailing North-American academic system, resulting in significant changes in several key areas that include, e.g., both teaching and research. The universities' internal organizational structures, however, still follow a traditional, decentralized scheme implementing an additional organizational level -- the Chair -- effectively a "mini department" with dedicated staff, budget and infrastructure. Although the Technical University of Munich (TUM) has been establishing a more centralized scheme for many administrative tasks over the past decade, the transition from its distributed to a centralized information technology (IT) administration and infrastructure is still an ongoing process. In case of the authors' chair, this migration so far included handing over all network-related operations to the joint compute center, consolidating the Chair's legacy server system in terms of both hardware architectures and operating systems and, lately, moving selected services to replacements operated by Department or University. With requirements, individuals and organizations constantly shifting, this process, however, is neither close to completion nor particularly unique to TUM. In this paper, we will thus share our experiences w.r.t. this IT migration as we believe both that many of the other German universities might be facing similar challenges and that, in the future, North-American universities - currently not implementing the chair layer and instead relying on a centralized IT infrastructure - could need a more decentralized solution. Hoping that both benefit from this journey, we thus present the design, commissioning and evolution of our infrastructure.
Learning to Predict Robot Keypoints Using Artificially Generated Images
Venue: arXiv cs.CV 2019
Year: 2019

This work considers robot keypoint estimation on color images as a supervised machine learning task. We propose the use of probabilistically created renderings to overcome the lack of labeled real images. Rather than sampling from stationary distributions, our approach introduces a feedback mechanism that constantly adapts probability distributions according to current training progress. Initial results show, our approach achieves near-human-level accuracy on real images. Additionally, we demonstrate that feedback leads to fewer required training steps, while maintaining the same model quality on synthetic data sets.
An Experimental Evaluation of Large Scale GBDT Systems
Venue: arXiv cs.LG 2019
Year: 2019

Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the rapid increase in data volume, efforts have been made to train GBDT in a distributed setting to support large-scale workloads. However, we find it surprising that the existing systems manage the training dataset in different ways, but none of them have studied the impact of data management. To that end, this paper aims to study the pros and cons of different data management methods regarding the performance of distributed GBDT. We first introduce a quadrant categorization of data management policies based on data partitioning and data storage. Then we conduct an in-depth systematic analysis and summarize the advantageous scenarios of the quadrants. Based on the analysis, we further propose a novel distributed GBDT system named Vero, which adopts the unexplored composition of vertical partitioning and row-store and suits for many large-scale cases. To validate our analysis empirically, we implement different quadrants in the same code base and compare them under extensive workloads, and finally compare Vero with other state-of-the-art systems over a wide range of datasets. Our theoretical and experimental results provide a guideline on choosing a proper data management policy for a given workload.
Multitasking with Alexa Multitasking with Alexa: How Using Intelligent Personal Assistants Impacts Language-based Primary Task Performance
Venue: arXiv cs.HC 2019
Year: 2019

Intelligent personal assistants (IPAs) are supposed to help us multitask. Yet the impact of IPA use on multitasking is not clearly quantified, particularly in situations where primary tasks are also language based. Using a dual task paradigm, our study observes how IPA interactions impact two different types of writing primary tasks; copying and generating content. We found writing tasks that involve content generation, which are more cognitively demanding and share more of the resources needed for IPA use, are significantly more disrupted by IPA interaction than less demanding tasks such as copying content. We discuss how theories of cognitive resources, including multiple resource theory and working memory, explain these results. We also outline the need for future work how interruption length and relevance may impact primary task performance as well as the need to identify effects of interruption timing in user and IPA led interruptions.
Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression
Venue: arXiv cs.NE 2019
Year: 2019

The ability to design complex neural network architectures which enable effective training by stochastic gradient descent has been the key for many achievements in the field of deep learning. However, developing such architectures remains a challenging and resourceintensive process full of trial-and-error iterations. All in all, the relation between the network topology and its ability to model the data remains poorly understood. We propose to encode neural networks with a differentiable variant of Cartesian Genetic Programming (dCGPANN) and present a memetic algorithm for architecture design: local searches with gradient descent learn the network parameters while evolutionary operators act on the dCGPANN genes shaping the network architecture towards faster learning. Studying a particular instance of such a learning scheme, we are able to improve the starting feed forward topology by learning how to rewire and prune links, adapt activation functions and introduce skip connections for chosen regression tasks. The evolved network architectures require less space for network parameters and reach, given the same amount of time, a significantly lower error on average.
Intrinsic Image Popularity Assessment
Venue: arXiv cs.MM 2019
Year: 2019

The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet. Here, we aim to single out the contribution of visual content to image popularity, i.e., intrinsic image popularity. Specifically, we first describe a probabilistic method to generate massive popularity-discriminable image pairs, based on which the first large-scale image database for intrinsic IPA (I$^2$PA) is established. We then develop computational models for I$^2$PA based on deep neural networks, optimizing for ranking consistency with millions of popularity-discriminable image pairs. Experiments on Instagram and other social platforms demonstrate that the optimized model performs favorably against existing methods, exhibits reasonable generalizability on different databases, and even surpasses human-level performance on Instagram. In addition, we conduct a psychophysical experiment to analyze various aspects of human behavior in I$^2$PA.
On-Device Neural Net Inference with Mobile GPUs
Venue: arXiv cs.LG 2019
Year: 2019

On-device inference of machine learning models for mobile phones is desirable due to its lower latency and increased privacy. Running such a compute-intensive task solely on the mobile CPU, however, can be difficult due to limited computing power, thermal constraints, and energy consumption. App developers and researchers have begun exploiting hardware accelerators to overcome these challenges. Recently, device manufacturers are adding neural processing units into high-end phones for on-device inference, but these account for only a small fraction of hand-held devices. In this paper, we present how we leverage the mobile GPU, a ubiquitous hardware accelerator on virtually every phone, to run inference of deep neural networks in real-time for both Android and iOS devices. By describing our architecture, we also discuss how to design networks that are mobile GPU-friendly. Our state-of-the-art mobile GPU inference engine is integrated into the open-source project TensorFlow Lite and publicly available at https://tensorflow.org/lite.
Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel method for generating robust adversarial image examples building upon the recent `deep image prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in image synthesis. Adversarial images are commonly generated by perturbing images to introduce high frequency noise that induces image misclassification, but that is fragile to subsequent digital manipulation of the image. We show that using DIP to reconstruct an image under adversarial constraint induces perturbations that are more robust to affine deformation, whilst remaining visually imperceptible. Furthermore we show that our DIP approach can also be adapted to produce local adversarial patches (`adversarial stickers'). We demonstrate robust adversarial examples over a broad gamut of images and object classes drawn from the ImageNet dataset.
Using AI for Economic Upliftment of Handicraft Industry
Venue: arXiv cs.OH 2019
Year: 2019

The handicraft industry is a strong pillar of Indian economy which provides large-scale employment opportunities to artisans in rural and underprivileged communities. However, in this era of globalization, diverse modern designs have rendered traditional designs old and monotonous, causing an alarming decline of handicraft sales. For this age-old industry to survive the global competition, it is imperative to integrate contemporary designs with Indian handicrafts. In this paper, we use novel AI techniques to generate contemporary designs for two popular Indian handicrafts - Ikat and Block Print. These techniques were successfully employed by communities across India to manufacture and sell products with greater appeal and revenue. The designs are evaluated to be significantly more likeable and marketable than the current designs used by artisans.
Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
Venue: arXiv cs.CL 2019
Year: 2019

Factchecking has always been a part of the journalistic process. However with newsroom budgets shrinking it is coming under increasing pressure just as the amount of false information circulating is on the rise. We therefore propose a method to increase the efficiency of the factchecking process, using the latest developments in Natural Language Processing (NLP). This method allows us to compare incoming claims to an existing corpus and return similar, factchecked, claims in a live system-allowing factcheckers to work simultaneously without duplicating their work.