OpenRaiser/Intern-Atlas huggingface dataset:
A Privacy Preserved and Cost Efficient Control Scheme for Coronavirus Outbreak Using Call Data Record and Contact Tracing
Venue: arXiv cs.SI 2020
Year: 2020

Coronavirus or COVID-19, which has been declared pandemic by the World Health Organization, has incurred huge losses to the lives of people throughout the world. Although, the scientists, researchers and doctors are working round the clock to develop a vaccine for COVID-19, it may take a year or two to make a safe and effective vaccine available for the world. In current circumstances, a solution must be developed to control or stop the spread of the virus. For this purpose, a novel technique based on call data record analysis (CDRA)and contact tracing is proposed that can effectively control the coronavirus outbreak. A positive coronavirus patient can be traced through CDRA and contact tracing. The technique can track the path traversed by the patient and collect the cell numbers of all those people who have met with the patient. Keeping in tact the privacy of this group of people, who are contacted through their cell numbers so that they can isolate themselves till the result of their coronavirus test arrives. If a test result of a person comes positive among the group, then he/she must be isolated and same CDRA and contact tracing procedures are adopted for that person. A COVID-19 patient is geo tagged and alerts are sent if any violation of isolation is done by the patient. Moreover, the general public is informed in advance to avoid the path followed by the patients. This cost effective mechanism is not only capable to control the coronavirus outbreak but also helps in isolating the patient in his/her house.
RODE: Learning Roles to Decompose Multi-Agent Tasks
Venue: arXiv cs.LG 2020
Year: 2020

Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents. Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces. We further integrate information about action effects into the role policies to boost learning efficiency and policy generalization. By virtue of these advances, our method (1) outperforms the current state-of-the-art MARL algorithms on 10 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark and (2) achieves rapid transfer to new environments with three times the number of agents. Demonstrative videos are available at https://sites.google.com/view/rode-marl .
iPregel: Strategies to Deal with an Extreme Form of Irregularity in Vertex-Centric Graph Processing
Venue: arXiv cs.DC 2020
Year: 2020

Over the last decade, the vertex-centric programming model has attracted significant attention in the world of graph processing, resulting in the emergence of a number of vertex-centric frameworks. Its simple programming interface, where computation is expressed from a vertex point of view, offers both ease of programming to the user and inherent parallelism for the underlying framework to leverage. However, vertex-centric programs represent an extreme form of irregularity, both inter and intra core. This is because they exhibit a variety of challenges from a workload that may greatly vary across supersteps, through fine-grain synchronisations, to memory accesses that are unpredictable both in terms of quantity and location. In this paper, we explore three optimisations which address these irregular challenges; a hybrid combiner carefully coupling lock-free and lock-based combinations, the partial externalisation of vertex structures to improve locality and the shift to an edge-centric representation of the workload. The optimisations were integrated into the iPregel vertex-centric framework, enabling the evaluation of each optimisation in the context of graph processing across three general purpose benchmarks common in the vertex-centric community, each run on four publicly available graphs covering all orders of magnitude from a million to a billion edges. The result of this work is a set of techniques which we believe not only provide a significant performance improvement in vertex-centric graph processing, but are also applicable more generally to irregular applications.
Feature Whitening via Gradient Transformation for Improved Convergence
Venue: arXiv cs.LG 2020
Year: 2020

Feature whitening is a known technique for speeding up training of DNN. Under certain assumptions, whitening the activations reduces the Fisher information matrix to a simple identity matrix, in which case stochastic gradient descent is equivalent to the faster natural gradient descent. Due to the additional complexity resulting from transforming the layer inputs and their corresponding gradients in the forward and backward propagation, and from repeatedly computing the Eigenvalue decomposition (EVD), this method is not commonly used to date. In this work, we address the complexity drawbacks of feature whitening. Our contribution is twofold. First, we derive an equivalent method, which replaces the sample transformations by a transformation to the weight gradients, applied to every batch of B samples. The complexity is reduced by a factor of S=(2B), where S denotes the feature dimension of the layer output. As the batch size increases with distributed training, the benefit of using the proposed method becomes more compelling. Second, motivated by the theoretical relation between the condition number of the sample covariance matrix and the convergence speed, we derive an alternative sub-optimal algorithm which recursively reduces the condition number of the latter matrix. Compared to EVD, complexity is reduced by a factor of the input feature dimension M. We exemplify the proposed algorithms with ResNet-based networks for image classification demonstrated on the CIFAR and Imagenet datasets. Parallelizing the proposed algorithms is straightforward and we implement a distributed version thereof. Improved convergence, in terms of speed and attained accuracy, can be observed in our experiments.
Intermittent Demand Forecasting with Renewal Processes
Venue: arXiv cs.LG 2020
Year: 2020

Intermittency is a common and challenging problem in demand forecasting. We introduce a new, unified framework for building intermittent demand forecasting models, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but also for an natural inclusion of neural network based models---by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios that compares favorably to the state of the art.
The FaceChannelS: Strike of the Sequences for the AffWild 2 Challenge
Venue: arXiv cs.CV 2020
Year: 2020

Predicting affective information from human faces became a popular task for most of the machine learning community in the past years. The development of immense and dense deep neural networks was backed by the availability of numerous labeled datasets. These models, most of the time, present state-of-the-art results in such benchmarks, but are very difficult to adapt to other scenarios. In this paper, we present one more chapter of benchmarking different versions of the FaceChannel neural network: we demonstrate how our little model can predict affective information from the facial expression on the novel AffWild2 dataset.
The Accordiatron: A MIDI Controller For Interactive Music
Venue: arXiv cs.HC 2020
Year: 2020

The Accordiatron is a new MIDI controller for real-time performance based on the paradigm of a conventional squeeze box or concertina. It translates the gestures of a performer to the standard communication protocol of MIDI, allowing for flexible mappings of performance data to sonic parameters. When used in conjunction with a realtime signal processing environment, the Accordiatron becomes an expressive, versatile musical instrument. A combination of sensory outputs providing both discrete and continuous data gives the subtle expressiveness and control necessary for interactive music.
Body, Clothes, Water, and Toys: Media Towards Natural Music Expressions with Digital Sounds
Venue: arXiv cs.HC 2020
Year: 2020

In this paper, we introduce our research challenges for creating new musical instruments using everyday-life media with intimate interfaces, such as the self-body, clothes, water and stuffed toys. Various sensor technologies including image processing and general touch sensitive devices are employed to exploit these interaction media. The focus of our effort is to provide user-friendly and enjoyable experiences for new music and sound performances. Multimodality of musical instruments is explored in each attempt. The degree of controllability in the performance and the richness of expressions are also discussed for each installation.
Mining and modeling complex leadership-followership dynamics of movement data
Venue: arXiv cs.SI 2020
Year: 2020

Leadership and followership are essential parts of collective decision and organization in social animals, including humans. In nature, relationships of leaders and followers are dynamic and vary with context or temporal factors. Understanding dynamics of leadership and followership, such as how leaders and followers change, emerge, or converge, allows scientists to gain more insight into group decision-making and collective behavior in general. However, given only data of individual activities, it is challenging to infer the dynamics of leaders and followers. In this paper, we focus on mining and modeling frequent patterns of leading and following. We formalize new computational problems and propose a framework that can be used to address several questions regarding group movement. We use the leadership inference framework, mFLICA, to infer the time series of leaders and their factions from movement datasets and then propose an approach to mine and model frequent patterns of both leadership and followership dynamics. We evaluate our framework performance by using several simulated datasets, as well as the real-world dataset of baboon movement to demonstrate the applications of our framework. These are novel computational problems and, to the best of our knowledge, there are no existing comparable methods to address them. Thus, we modify and extend an existing leadership inference framework to provide a non-trivial baseline for comparison. Our framework performs better than this baseline in all datasets. Our framework opens the opportunities for scientists to generate testable scientific hypotheses about the dynamics of leadership in movement data.
Latency optimal storage and scheduling of replicated fragments for memory-constrained servers
Venue: arXiv cs.IT 2020
Year: 2020

We consider the setting of distributed storage system where a single file is subdivided into smaller fragments of same size which are then replicated with a common replication factor across servers of identical cache size. An incoming file download request is sent to all the servers, and the download is completed whenever request gathers all the fragments. At each server, we are interested in determining the set of fragments to be stored, and the sequence in which fragments should be accessed, such that the mean file download time for a request is minimized. We model the fragment download time as an exponential random variable independent and identically distributed for all fragments across all servers, and show that the mean file download time can be lower bounded in terms of the expected number of useful servers summed over all distinct fragment downloads. We present deterministic storage schemes that attempt to maximize the number of useful servers. We show that finding the optimal sequence of accessing the fragments is a Markov decision problem, whose complexity grows exponentially with the number of fragments. We propose heuristic algorithms that determine the sequence of access to the fragments which are empirically shown to perform well.
When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised
Venue: arXiv cs.CL 2020
Year: 2020

Question Answering (QA) is key for making possible a robust communication between human and machine. Modern language models used for QA have surpassed the human-performance in several essential tasks; however, these models require large amounts of human-generated training data which are costly and time-consuming to create. This paper studies augmenting human-made datasets with synthetic data as a way of surmounting this problem. A state-of-the-art model based on deep transformers is used to inspect the impact of using synthetic answerable and unanswerable questions to complement a well-known human-made dataset. The results indicate a tangible improvement in the performance of the language model (measured in terms of F1 and EM scores) trained on the mixed dataset. Specifically, unanswerable question-answers prove more effective in boosting the model: the F1 score gain from adding to the original dataset the answerable, unanswerable, and combined question-answers were 1.3%, 5.0%, and 6.7%, respectively. [Link to the Github repository: https://github.com/lnikolenko/EQA]
Multi-Modal Retrieval using Graph Neural Networks
Venue: arXiv cs.IR 2020
Year: 2020

Most real world applications of image retrieval such as Adobe Stock, which is a marketplace for stock photography and illustrations, need a way for users to find images which are both visually (i.e. aesthetically) and conceptually (i.e. containing the same salient objects) as a query image. Learning visual-semantic representations from images is a well studied problem for image retrieval. Filtering based on image concepts or attributes is traditionally achieved with index-based filtering (e.g. on textual tags) or by re-ranking after an initial visual embedding based retrieval. In this paper, we learn a joint vision and concept embedding in the same high-dimensional space. This joint model gives the user fine-grained control over the semantics of the result set, allowing them to explore the catalog of images more rapidly. We model the visual and concept relationships as a graph structure, which captures the rich information through node neighborhood. This graph structure helps us learn multi-modal node embeddings using Graph Neural Networks. We also introduce a novel inference time control, based on selective neighborhood connectivity allowing the user control over the retrieval algorithm. We evaluate these multi-modal embeddings quantitatively on the downstream relevance task of image retrieval on MS-COCO dataset and qualitatively on MS-COCO and an Adobe Stock dataset.
A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games
Venue: arXiv cs.LG 2020
Year: 2020

We consider a pursuit-evasion game [11] played between two agents, 'Blue' (the pursuer) and 'Red' (the evader), over $T$ time steps. Red aims to attack Blue's territory. Blue's objective is to intercept Red by time $T$ and thereby limit the success of Red's attack. Blue must plan its pursuit trajectory by choosing parameters that determine its course of movement (speed and angle in our setup) such that it intercepts Red by time $T$. We show that Blue's path-planning problem in pursuing Red, can be posed as a sequential decision making problem under uncertainty. Blue's unawareness of Red's action policy renders the analytic dynamic programming approach intractable for finding the optimal action policy for Blue. In this work, we are interested in exploring data-driven approaches to the policy optimization problem that Blue faces. We apply generative machine learning (ML) approaches to learn optimal action policies for Blue. This highlights the ability of generative ML model to learn the relevant implicit representations for the dynamics of simulated pursuit-evasion games. We demonstrate the effectiveness of our modeling approach via extensive statistical assessments. This work can be viewed as a preliminary step towards further adoption of generative modeling approaches for addressing policy optimization problems that arise in the context of multi-agent learning and planning [1].
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation
Venue: arXiv cs.CL 2020
Year: 2020

Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
Venue: arXiv cs.CL 2020
Year: 2020

Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.
On the Effects of Knowledge-Augmented Data in Word Embeddings
Venue: arXiv cs.CL 2020
Year: 2020

This paper investigates techniques for knowledge injection into word embeddings learned from large corpora of unannotated data. These representations are trained with word cooccurrence statistics and do not commonly exploit syntactic and semantic information from linguistic knowledge bases, which potentially limits their transferability to domains with differing language distributions or usages. We propose a novel approach for linguistic knowledge injection through data augmentation to learn word embeddings that enforce semantic relationships from the data, and systematically evaluate the impact it has on the resulting representations. We show our knowledge augmentation approach improves the intrinsic characteristics of the learned embeddings while not significantly altering their results on a downstream text classification task.
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior
Venue: arXiv cs.CL 2020
Year: 2020

Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero. In this work, we explore spectral-normalized identity priors (SNIP), a structured pruning approach that penalizes an entire residual module in a Transformer model toward an identity mapping. Our method identifies and discards unimportant non-linear mappings in the residual connections by applying a thresholding operator on the function norm. It is applicable to any structured module, including a single attention head, an entire attention block, or a feed-forward subnetwork. Furthermore, we introduce spectral normalization to stabilize the distribution of the post-activation values of the Transformer layers, further improving the pruning effectiveness of the proposed methodology. We conduct experiments with BERT on 5 GLUE benchmark tasks to demonstrate that SNIP achieves effective pruning results while maintaining comparable performance. Specifically, we improve the performance over the state-of-the-art by 0.5 to 1.0% on average at 50% compression ratio.
Off-the-grid data-driven optimization of sampling schemes in MRI
Venue: arXiv cs.LG 2020
Year: 2020

We propose a novel learning based algorithm to generate efficient and physically plausible sampling patterns in MRI. This method has a few advantages compared to recent learning based approaches: i) it works off-the-grid and ii) allows to handle arbitrary physical constraints. These two features allow for much more versatility in the sampling patterns that can take advantage of all the degrees of freedom offered by an MRI scanner. The method consists in a high dimensional optimization of a cost function defined implicitly by an algorithm. We propose various numerical tools to address this numerical challenge.
Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure
Venue: arXiv cs.CV 2020
Year: 2020

Estimating absolute camera orientations is essential for attitude estimation tasks. An established approach is to first carry out visual odometry (VO) or visual SLAM (V-SLAM), and retrieve the camera orientations (3 DOF) from the camera poses (6 DOF) estimated by VO or V-SLAM. One drawback of this approach, besides the redundancy in estimating full 6 DOF camera poses, is the dependency on estimating a map (3D scene points) jointly with the 6 DOF poses due to the basic constraint on structure-and-motion. To simplify the task of absolute orientation estimation, we formulate the monocular rotational odometry problem and devise a fast algorithm to accurately estimate camera orientations with 2D-2D feature matches alone. Underpinning our system is a new incremental rotation averaging method for fast and constant time iterative updating. Furthermore, our system maintains a view-graph that 1) allows solving loop closure to remove camera orientation drift, and 2) can be used to warm start a V-SLAM system. We conduct extensive quantitative experiments on real-world datasets to demonstrate the accuracy of our incremental camera orientation solver. Finally, we showcase the benefit of our algorithm to V-SLAM: 1) solving the known rotation problem to estimate the trajectory of the camera and the surrounding map, and 2)enabling V-SLAM systems to track pure rotational motions.