OpenRaiser/Intern-Atlas huggingface dataset:
The Role of Communication Technology Across the Life Course: A Field Guide to Social Support in East York
Venue: arXiv cs.CY 2021
Year: 2021

We examine how Canadians living in the East York section of Toronto exchange social support. Just as we have had to deconstruct social support to understand its component parts, we now deconstruct how different types of communication technologies play socially supportive roles. We draw on 101 in-depth interviews conducted in 2013-2014 to shed light on the support networks of a sample of East York residents and discern the role of communication technologies in the exchange of different types of social support across age groups. Our findings show that not much has changed since the 1960s in terms of the social ties that our sample of East Yorkers have, and the types of support mobilized via social networks: companionship, small and large services, emotional aid, and financial support. What has changed is how communication technologies interweave in complex ways with different types of social ties (partners, siblings, friends, etc.) to mobilize social support. We found that with siblings and extended kin communication technologies could boost the frequency of interaction and help exchange support at a distance. With friendship ties, communication technologies provide a continuous, constant flow of interaction. We draw implications for theories of social support and for social policy linked to interventions aimed at helping vulnerable groups during the COVID-19 pandemic.
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
Venue: arXiv cs.CV 2021
Year: 2021

Establishing robust and accurate correspondences between a pair of images is a long-standing computer vision problem with numerous applications. While classically dominated by sparse methods, emerging dense approaches offer a compelling alternative paradigm that avoids the keypoint detection step. However, dense flow estimation is often inaccurate in the case of large displacements, occlusions, or homogeneous regions. In order to apply dense methods to real-world applications, such as pose estimation, image manipulation, or 3D reconstruction, it is therefore crucial to estimate the confidence of the predicted matches. We propose the Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences along with a reliable confidence map. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. In particular, we parametrize the predictive distribution as a constrained mixture model, ensuring better modelling of both accurate flow predictions and outliers. Moreover, we develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction in the context of self-supervised training. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets. We further validate the usefulness of our probabilistic confidence estimation for the tasks of pose estimation, 3D reconstruction, image-based localization, and image retrieval. Code and models are available at https://github.com/PruneTruong/DenseMatching.
Modeling Ephraim Chambers' Knowledge Structure from a Naive Standpoint
Venue: arXiv cs.DL 2021
Year: 2021

In the preface to his Cyclopaedia published in 1728 Ephraim Chambers offers readers a systematized structure of his attempt to produce a universal repository of human knowledge. Divided into an interconnected taxonomic tree and domain vocabulary, this structure forms the basis of one effort from the Metadata Research Center to study historical ontologies. The knowledge structure is being encoded into a Simple Knowledge Organization System (SKOS) form as well as a Web Ontology Language (OWL) version. This paper explores the expressive and functional differences between these SKOS and OWL versions of Chambers' knowledge structure. As part of this goal, the paper research focused on the construction and application of rules in each system to produce a more computationally ready representation of Chambers' structure in SKOS, which is more thesaurus-like, and OWL, which represents additional ontological nuances. First, studying the various textual aspects at the semantic, syntactic, and typographic levels allowed for the relationships between terms to manifest from which rules governing expression of the connections between elements developed. Second, because each language, SKOS and OWL, functionally expresses different logical relationships, their possibilities and limitations offer a ground for further analyzing the resultant knowledge structures; although, each stemmed from the same basic source of Chambers' text. Lastly this paper will examine rule making and expression in light of Paul Grice's theory of conversational implicature to understand how a naive agent formulates and applies these rules to a knowledge structure.
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Venue: arXiv cs.IR 2021
Year: 2021

Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scenarios where the user interactions are extremely sparse. We consider this problem as an automatic identification about whether the user behaviors are rich enough to capture the interests for prediction, and propose an Auto-Quantized Contrastive Learning (AQCL) loss to regularize the model. Different from previous methods, AQCL explores both the instance-instance and the instance-cluster similarity to robustify the latent representation, and automatically reduces the information loss to the active users due to the quantization. The proposed framework is agnostic to different model architectures and can be trained in an end-to-end fashion. Extensive results show that it consistently improves the current state-of-the-art CTR models.
Deep Unrolled Recovery in Sparse Biological Imaging
Venue: arXiv cs.CV 2021
Year: 2021

Deep algorithm unrolling has emerged as a powerful model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization. This framework is well-suited to applications in biological imaging, where physics-based models exist to describe the measurement process and the information to be recovered is often highly structured. Here, we review the method of deep unrolling, and show how it improves source localization in several biological imaging settings.
Local Repair of Neural Networks Using Optimization
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we propose a framework to repair a pre-trained feed-forward neural network (NN) to satisfy a set of properties. We formulate the properties as a set of predicates that impose constraints on the output of NN over the target input domain. We define the NN repair problem as a Mixed Integer Quadratic Program (MIQP) to adjust the weights of a single layer subject to the given predicates while minimizing the original loss function over the original training domain. We demonstrate the application of our framework in bounding an affine transformation, correcting an erroneous NN in classification, and bounding the inputs of a NN controller.
Improving Dialogue State Tracking by Joint Slot Modeling
Venue: arXiv cs.CL 2021
Year: 2021

Dialogue state tracking models play an important role in a task-oriented dialogue system. However, most of them model the slot types conditionally independently given the input. We discover that it may cause the model to be confused by slot types that share the same data type. To mitigate this issue, we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our results show that they are able to alleviate the confusion mentioned above, and they push the state-of-the-art on dataset MultiWoZ 2.1 from 58.7 to 61.3. Our implementation is available at https://github.com/CTinRay/Trippy-Joint.
Improving Arabic Diacritization by Learning to Diacritize and Translate
Venue: arXiv cs.CL 2021
Year: 2021

We propose a novel multitask learning method for diacritization which trains a model to both diacritize and translate. Our method addresses data sparsity by exploiting large, readily available bitext corpora. Furthermore, translation requires implicit linguistic and semantic knowledge, which is helpful for resolving ambiguities in the diacritization task. We apply our method to the Penn Arabic Treebank and report a new state-of-the-art word error rate of 4.79%. We also conduct manual and automatic analysis to better understand our method and highlight some of the remaining challenges in diacritization.
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its practical application in the read-world video surveillance system. The traditional unsupervised Re-Id are mostly based on the method alternating between clustering and fine-tuning with the classification or metric learning objectives on the grouped clusters. However, since person Re-Id is an open-set problem, the clustering based methods often leave out lots of outlier instances or group the instances into the wrong clusters, thus they can not make full use of the training samples as a whole. To solve these problems, we present the hybrid dynamic cluster contrast and probability distillation algorithm. It formulates the unsupervised Re-Id problem into an unified local-to-global dynamic contrastive learning and self-supervised probability distillation framework. Specifically, the proposed method can make the utmost of the self-supervised signals of all the clustered and un-clustered instances, from both the instances' self-contrastive level and the probability distillation respective, in the memory-based non-parametric manner. Besides, the proposed hybrid local-to-global contrastive learning can take full advantage of the informative and valuable training examples for effective and robust training. Extensive experiment results show that the proposed method achieves superior performances to state-of-the-art methods, under both the purely unsupervised and unsupervised domain adaptation experiment settings.
RelicVR: A Virtual Reality Game for Active Exploration of Archaeological Relics
Venue: arXiv cs.HC 2021
Year: 2021

Digitalization is changing how people visit museums and explore the artifacts they house. Museums, as important educational venues outside classrooms, need to actively explore the application of digital interactive media, including games that can balance entertainment and knowledge acquisition. In this paper, we introduce RelicVR, a virtual reality (VR) game that encourages players to discover artifacts through physical interaction in a game-based approach. Players need to unearth artifacts hidden in a clod enclosure by using available tools and physical movements. The game relies on the dynamic voxel deformation technique to allow players to chip away earth covering the artifacts. We added uncertainty in the exploration process to bring it closer to how archaeological discovery happens in real life. Players do not know the shape or features of the hidden artifact and have to take away the earth gradually but strategically without hitting the artifact itself. From playtesting sessions with eight participants, we found that the uncertainty elements are conducive to their engagement and exploration experience. Overall, RelicVR is an innovative game that can improve players' learning motivation and outcomes of ancient artifacts.
On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization
Venue: arXiv cs.LG 2021
Year: 2021

Adam-type methods, the extension of adaptive gradient methods, have shown great performance in the training of both supervised and unsupervised machine learning models. In particular, Adam-type optimizers have been widely used empirically as the default tool for training generative adversarial networks (GANs). On the theory side, however, despite the existence of theoretical results showing the efficiency of Adam-type methods in minimization problems, the reason of their wonderful performance still remains absent in GAN's training. In existing works, the fast convergence has long been considered as one of the most important reasons and multiple works have been proposed to give a theoretical guarantee of the convergence to a critical point of min-max optimization algorithms under certain assumptions. In this paper, we firstly argue empirically that in GAN's training, Adam does not converge to a critical point even upon successful training: Only the generator is converging while the discriminator's gradient norm remains high throughout the training. We name this one-sided convergence. Then we bridge the gap between experiments and theory by showing that Adam-type algorithms provably converge to a one-sided first order stationary points in min-max optimization problems under the one-sided MVI condition. We also empirically verify that such one-sided MVI condition is satisfied for standard GANs after trained over standard data sets. To the best of our knowledge, this is the very first result which provides an empirical observation and a strict theoretical guarantee on the one-sided convergence of Adam-type algorithms in min-max optimization.
Live Visualization of Dynamic Software Cities with Heat Map Overlays
Venue: arXiv cs.SE 2021
Year: 2021

The 3D city metaphor in software visualization is a well-explored rendering method. Numerous tools use their custom variation to visualize offline-analyzed data. Heat map overlays are one of these variants. They introduce a separate information layer in addition to the software city's own semantics. Results show that their usage facilitates program comprehension. In this paper, we present our heat map approach for the city metaphor visualization based on live trace analysis. In comparison to previous approaches, our implementation uses live dynamic analysis of a software system's runtime behavior. At any time, users can toggle the heat map feature and choose which runtime-dependent metric the heat map should visualize. Our approach continuously and automatically renders both software cities and heat maps. It does not require a manual or semi-automatic generation of heat maps and seamlessly blends into the overall software visualization. We implemented this approach in our web-based tool ExplorViz, such that the heat map overlay is also available in our augmented reality environment. ExplorViz is developed as open source software and is continuously published via Docker images. A live demo of ExplorViz is publicly available.
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction
Venue: arXiv cs.LG 2021
Year: 2021

The microstructure is an essential part of materials, storing the genes of materials and having a decisive influence on materials' physical and chemical properties. The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance to realize the reverse design of materials, thereby accelerating the research and development of new materials. However, tissue analysis methods of materials science, such as metallographic analysis, XRD analysis, and EBSD analysis, cannot directly establish a complete quantitative relationship between tissue structure and performance. Therefore, this paper proposes a novel data-knowledge-driven organization representation and performance prediction method to obtain a quantitative structure-performance relationship. First, a knowledge graph based on EBSD is constructed to describe the material's mesoscopic microstructure. Then a graph representation learning network based on graph attention is constructed, and the EBSD organizational knowledge graph is input into the network to obtain graph-level feature embedding. Finally, the graph-level feature embedding is input to a graph feature mapping network to obtain the material's mechanical properties. The experimental results show that our method is superior to traditional machine learning and machine vision methods.
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains
Venue: arXiv cs.LG 2021
Year: 2021

Software-defined networking (SDN) and network function virtualization (NFV) have enabled the efficient provision of network service. However, they also raised new tasks to monitor and ensure the status of virtualized service, and anomaly detection is one of such tasks. There have been many data-driven approaches to implement anomaly detection system (ADS) for virtual network functions in service function chains (SFCs). In this paper, we aim to develop more advanced deep learning models for ADS. Previous approaches used learning algorithms such as random forest (RF), gradient boosting machine (GBM), or deep neural networks (DNNs). However, these models have not utilized sequential dependencies in the data. Furthermore, they are limited as they can only apply to the SFC setting from which they were trained. Therefore, we propose several sequential deep learning models to learn time-series patterns and sequential patterns of the virtual network functions (VNFs) in the chain with variable lengths. As a result, the suggested models improve detection performance and apply to SFCs with varying numbers of VNFs.
Localizing Objects with Self-Supervised Transformers and no Labels
Venue: arXiv cs.CV 2021
Year: 2021

Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns. We propose a simple approach to this problem, that leverages the activation features of a vision transformer pre-trained in a self-supervised manner. Our method, LOST, does not require any external object proposal nor any exploration of the image collection; it operates on a single image. Yet, we outperform state-of-the-art object discovery methods by up to 8 CorLoc points on PASCAL VOC 2012. We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points. Moreover, we show promising results on the unsupervised object discovery task. The code to reproduce our results can be found at https://github.com/valeoai/LOST.
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
Venue: arXiv cs.LG 2021
Year: 2021

Self-supervised learning methods can be used to learn meaningful representations from unlabeled data that can be transferred to supervised downstream tasks to reduce the need for labeled data. In this paper, we propose a 3D self-supervised method that is based on the contrastive (SimCLR) method. Additionally, we show that employing Bayesian neural networks (with Monte-Carlo Dropout) during the inference phase can further enhance the results on the downstream tasks. We showcase our models on two medical imaging segmentation tasks: i) Brain Tumor Segmentation from 3D MRI, ii) Pancreas Tumor Segmentation from 3D CT. Our experimental results demonstrate the benefits of our proposed methods in both downstream data-efficiency and performance.
Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection
Venue: arXiv cs.LG 2021
Year: 2021

Intelligent traffic signal controllers, applying DQN algorithms to traffic light policy optimization, efficiently reduce traffic congestion by adjusting traffic signals to real-time traffic. Most propositions in the literature however consider that all vehicles at the intersection are detected, an unrealistic scenario. Recently, new wireless communication technologies have enabled cost-efficient detection of connected vehicles by infrastructures. With only a small fraction of the total fleet currently equipped, methods able to perform under low detection rates are desirable. In this paper, we propose a deep reinforcement Q-learning model to optimize traffic signal control at an isolated intersection, in a partially observable environment with connected vehicles. First, we present the novel DQN model within the RL framework. We introduce a new state representation for partially observable environments and a new reward function for traffic signal control, and provide a network architecture and tuned hyper-parameters. Second, we evaluate the performances of the model in numerical simulations on multiple scenarios, in two steps. At first in full detection against existing actuated controllers, then in partial detection with loss estimates for proportions of connected vehicles. Finally, from the obtained results, we define thresholds for detection rates with acceptable and optimal performance levels.
Signal power and energy-per-bit optimization problems in systems mMTC
Venue: arXiv cs.IT 2021
Year: 2021

Currently, the issues of the operation of the Internet of Things technology are being actively studied. The operation of a large number of different self-powered sensors is within the framework of a massive machine-type communications scenario using random access methods. Topical issues in this type of communication are: reducing the transmission signal power and increasing the duration of the device by reducing the consumption energy per bit. Formulation and analysis of the tasks of minimizing transmission power and spent energy per bit in systems without retransmissions and with retransmissions to obtain achievability bounds. A model of the system is described, within which four problems of minimizing signal power and energy consumption for given parameters (the number of information bits, the spectral efficiency of the system, and the Packet Delivery Ratio) are formulated and described. The numerical results of solving these optimization problems are presented, which make it possible to obtain the achievability bounds for the considered characteristics in systems with and without losses. The lower bounds obtained by the Shannon formula are presented, assuming that the message length is not limited. The results obtained showed that solving the minimization problem with respect to one of the parameters (signal power or consumption energy per bit) does not minimize the second parameter. This difference is most significant for small information message lengths, which corresponds to IoT scenarios. The results obtained allow assessing the potential for minimizing transmission signal power and consumption energy per bit in random multiple access systems with massive machine-type communications scenarios. The presented problems were solved without taking into account the average delay of message transmission.
EDGAR-CORPUS: Billions of Tokens Make The World Go Round
Venue: arXiv cs.CL 2021
Year: 2021

We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly traded companies in the US spanning a period of more than 25 years. To the best of our knowledge, EDGAR-CORPUS is the largest financial NLP corpus available to date. All the reports are downloaded, split into their corresponding items (sections), and provided in a clean, easy-to-use JSON format. We use EDGAR-CORPUS to train and release EDGAR-W2V, which are WORD2VEC embeddings for the financial domain. We employ these embeddings in a battery of financial NLP tasks and showcase their superiority over generic GloVe embeddings and other existing financial word embeddings. We also open-source EDGAR-CRAWLER, a toolkit that facilitates downloading and extracting future annual reports.