OpenRaiser/Intern-Atlas huggingface dataset:
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers
Venue: arXiv cs.CV 2020
Year: 2020

Neural Memory Networks (NMNs) have received increased attention in recent years compared to deep architectures that use a constrained memory. Despite their new appeal, the success of NMNs hinges on the ability of the gradient-based optimiser to perform incremental training of the NMN controllers, determining how to leverage their high capacity for knowledge retrieval. This means that while excellent performance can be achieved when the training data is consistent and well distributed, rare data samples are hard to learn from as the controllers fail to incorporate them effectively during model training. Drawing inspiration from the human cognition process, in particular the utilisation of neuromodulators in the human brain, we propose to decouple the learning process of the NMN controllers to allow them to achieve flexible, rapid adaptation in the presence of new information. This trait is highly beneficial for meta-learning tasks where the memory controllers must quickly grasp abstract concepts in the target domain, and adapt stored knowledge. This allows the NMN controllers to quickly determine which memories are to be retained and which are to be erased, and swiftly adapt their strategy to the new task at hand. Through both quantitative and qualitative evaluations on multiple public benchmarks, including classification and regression tasks, we demonstrate the utility of the proposed approach. Our evaluations not only highlight the ability of the proposed NMN architecture to outperform the current state-of-the-art methods, but also provide insights on how the proposed augmentations help achieve such superior results. In addition, we demonstrate the practical implications of the proposed learning strategy, where the feedback path can be shared among multiple neural memory networks as a mechanism for knowledge sharing.
List Decoding of Direct Sum Codes
Venue: arXiv cs.DS 2020
Year: 2020

We consider families of codes obtained by "lifting" a base code $\mathcal{C}$ through operations such as $k$-XOR applied to "local views" of codewords of $\mathcal{C}$, according to a suitable $k$-uniform hypergraph. The $k$-XOR operation yields the direct sum encoding used in works of [Ta-Shma, STOC 2017] and [Dinur and Kaufman, FOCS 2017]. We give a general framework for list decoding such lifted codes, as long as the base code admits a unique decoding algorithm, and the hypergraph used for lifting satisfies certain expansion properties. We show that these properties are satisfied by the collection of length $k$ walks on an expander graph, and by hypergraphs corresponding to high-dimensional expanders. Instantiating our framework, we obtain list decoding algorithms for direct sum liftings on the above hypergraph families. Using known connections between direct sum and direct product, we also recover the recent results of Dinur et al. [SODA 2019] on list decoding for direct product liftings. Our framework relies on relaxations given by the Sum-of-Squares (SOS) SDP hierarchy for solving various constraint satisfaction problems (CSPs). We view the problem of recovering the closest codeword to a given word, as finding the optimal solution of a CSP. Constraints in the instance correspond to edges of the lifting hypergraph, and the solutions are restricted to lie in the base code $\mathcal{C}$. We show that recent algorithms for (approximately) solving CSPs on certain expanding hypergraphs also yield a decoding algorithm for such lifted codes. We extend the framework to list decoding, by requiring the SOS solution to minimize a convex proxy for negative entropy. We show that this ensures a covering property for the SOS solution, and the "condition and round" approach used in several SOS algorithms can then be used to recover the required list of codewords.
Multi-Label Classification Using Link Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Solving classification with graph methods has gained huge popularity in recent years. This is due to the fact that the data can be intuitively modeled with graphs to utilize high level features to aid in solving the classification problem. CULP which is short for Classification Using Link Prediction is a graph-based classifier. This classifier utilizes the graph representation of the data and transforms the problem to that of link prediction where we try to find the link between an unlabeled node and the proper class node for it. CULP proved to be highly accurate classifier and it has the power to predict the labels in near constant time. A variant of the classification problem is multi-label classification which tackles this problem for multi-label data where an instance can have multiple labels associated to it. In this work, we extend the CULP algorithm to address this problem. Our proposed extensions conveys the powers of CULP and its intuitive representation of the data in to the multi-label domain and in comparison to some of the cutting edge multi-label classifiers, yield competitive results.
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
Venue: arXiv cs.CV 2020
Year: 2020

Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpreters. We use the dataset to train and validate the models and demonstrate that ForestNet substantially outperforms other standard driver classification approaches. In order to support future research on automated approaches to deforestation driver classification, the dataset curated in this study is publicly available at https://stanfordmlgroup.github.io/projects/forestnet .
A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus
Venue: arXiv cs.LG 2020
Year: 2020

Introduction: The discovery of causal mechanisms underlying diseases enables better diagnosis, prognosis and treatment selection. Clinical trials have been the gold standard for determining causality, but they are resource intensive, sometimes infeasible or unethical. Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging them due to the special characteristics of the EHR data. We propose a new data transformation method and a novel CSD algorithm to overcome the challenges posed by these characteristics. Materials and methods: We demonstrated the proposed methods on an application to type-2 diabetes mellitus. We used a large EHR data set from Mayo Clinic to internally evaluate the proposed transformation and CSD methods and used another large data set from an independent health system, Fairview Health Services, as external validation. We compared the performance of our proposed method to Fast Greedy Equivalence Search (FGES), a state-of-the-art CSD method in terms of correctness, stability and completeness. We tested the generalizability of the proposed algorithm through external validation. Results and conclusions: The proposed method improved over the existing methods by successfully incorporating study design considerations, was robust in face of unreliable EHR timestamps and inferred causal effect directions more correctly and reliably. The proposed data transformation successfully improved the clinical correctness of the discovered graph and the consistency of edge orientation across bootstrap samples. It resulted in superior accuracy, stability, and completeness.
NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents the model submitted by the NIT_COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as the location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14% in the F1-score metric.
Some new Features and Algorithms for the Study of DFA
Venue: arXiv cs.FL 2020
Year: 2020

The work presents some new algorithms realized recently in the package TESTAS. They decide whether or not deterministic finite automaton (DFA) is synchronizing, several procedures find relatively short synchronizing words and a synchronizing word of the minimal length. We check the existence of a coloring of directed graph that turns the graph into a synchronizing DFA. The algorithm finds the coloring (better known as the road coloring) if it exists. Otherwise, the k-synchronizing road coloring can be found. We use a linear visualization of the graph of an automaton based on its structural properties.
An Investigation of Potential Function Designs for Neural CRF
Venue: arXiv cs.CL 2020
Year: 2020

The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the contextual words as input. Our extensive experiments show that the decomposed quadrilinear potential function based on the vector representations of two neighboring labels and two neighboring words consistently achieves the best performance.
Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace
Venue: arXiv cs.CV 2020
Year: 2020

Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Many approaches have been proposed. Typically, they handle this problem to either introduce a well-labeled dataset from another related task, turn to iterative refinement and post-processing with the graphical model, or manipulate the scribble label. This work aims to achieve semantic segmentation supervised by scribble label directly without auxiliary information and other intermediate manipulation. Specifically, we impose diffusion on neural representation by random walk and consistency on neural eigenspace by self-supervision, which forces the neural network to produce dense and consistent predictions over the whole dataset. The random walk embedded in the network will compute a probabilistic transition matrix, with which the neural representation diffused to be uniform. Moreover, given the probabilistic transition matrix, we apply the self-supervision on its eigenspace for consistency in the image's main parts. In addition to comparing the common scribble dataset, we also conduct experiments on the modified datasets that randomly shrink and even drop the scribbles on image objects. The results demonstrate the superiority of the proposed method and are even comparable to some full-label supervised ones. The code and datasets are available at https://github.com/panzhiyi/RW-SS.
Toward Edge-Centric Network Embeddings
Venue: arXiv cs.SI 2020
Year: 2020

Existing network embedding approaches tackle the problem of learning low-dimensional node representations. However, networks can also be seen in the light of edges interlinking pairs of nodes. The broad goal of this paper is to introduce edge-centric network embeddings. We present an approach called ECNE, which instead of computing node embeddings directly, computes edge embeddings by relying on the notion of line graph coupled with an edge weighting mechanism to preserve the dynamic of the original graph in the line graph. We also present a link prediction framework called ECNE-LP, which given a target link (u,v) first collects paths between nodes u and v, then directly embeds the edges in these paths, and finally aggregates them toward predicting the existence of a link. We show that both ECNE and ECNE-LP bring benefit wrt the state-of-the-art.
VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions
Venue: arXiv cs.AI 2020
Year: 2020

Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large amount of offices, companies employ distributed video streaming solutions which rely on prior knowledge of the underlying evolving enterprise network. However, such networks are highly complex and dynamic. Hence, to optimally coordinate the live video distribution, the available network capacity between viewers has to be accurately predicted. In this paper we propose a graph representation learning technique on weighted and dynamic graphs to predict the network capacity, that is the weights of connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events. VStreamDRLS employs the graph convolutional network (GCN) model over the duration of a live video streaming event and introduces a self-attention mechanism to evolve the GCN parameters. In doing so, our model focuses on the GCN weights that are relevant to the evolution of the graph and generate the node representation, accordingly. We evaluate our proposed approach on the link prediction task on two real-world datasets, generated by enterprise live video streaming events. The duration of each event lasted an hour. The experimental results demonstrate the effectiveness of VStreamDRLS when compared with state-of-the-art strategies. Our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/vstreamdrls
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Venue: arXiv cs.LG 2020
Year: 2020

The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such large-scale data sets with precise annotations is very expensive and time-consuming, and the cheap alternatives often yield data sets that have noisy labels. The field has addressed this problem by focusing on training models under two types of label noise: 1) closed-set noise, where some training samples are incorrectly annotated to a training label other than their known true class; and 2) open-set noise, where the training set includes samples that possess a true class that is (strictly) not contained in the set of known training labels. In this work, we study a new variant of the noisy label problem that combines the open-set and closed-set noisy labels, and introduce a benchmark evaluation to assess the performance of training algorithms under this setup. We argue that such problem is more general and better reflects the noisy label scenarios in practice. Furthermore, we propose a novel algorithm, called EvidentialMix, that addresses this problem and compare its performance with the state-of-the-art methods for both closed-set and open-set noise on the proposed benchmark. Our results show that our method produces superior classification results and better feature representations than previous state-of-the-art methods. The code is available at https://github.com/ragavsachdeva/EvidentialMix.
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high-resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization, we reach an AP up to 0.84 for class person on High-Definition Analytics dataset.
DeepSim: Semantic similarity metrics for learned image registration
Venue: arXiv cs.CV 2020
Year: 2020

We propose a semantic similarity metric for image registration. Existing metrics like euclidean distance or normalized cross-correlation focus on aligning intensity values, giving difficulties with low intensity contrast or noise. Our semantic approach learns dataset-specific features that drive the optimization of a learning-based registration model. Comparing to existing unsupervised and supervised methods across multiple image modalities and applications, we achieve consistently high registration accuracy and faster convergence than state of the art, and the learned invariance to noise gives smoother transformations on low-quality images.
SPRITE: Stewart Platform Robot for Interactive Tabletop Engagement
Venue: arXiv cs.RO 2020
Year: 2020

We present the design of the Stewart Platform Robot for Interactive Tabletop Engagement (SPRITE). This robot is designed for use in socially assistive robotics, a field focusing on non-contact social interaction to help people achieve goals relating to health, wellness, and education. We describe a series of design goals for a tabletop, socially assistive robot, including expressive movement, affective communication, a friendly, nonthreatening, and customizable appearance, and a safe, robust, and easily-repaired mechanical design.
Energy Concealment based Compressive Sensing Encryption for Perfect Secrecy for IoT
Venue: arXiv cs.IT 2020
Year: 2020

Recent study has shown that compressive sensing (CS) based computationally secure scheme using Gaussian or Binomial sensing matrix in resource-constrained IoT devices is vulnerable to ciphertext-only attack. Although the CS-based perfectly secure scheme has no such vulnerabilities, the practical realization of the perfectly secure scheme is challenging, because it requires an additional secure channel to transmit the measurement norm. In this paper, we devise a practical realization of a perfectly secure scheme by concealing energy in which the requirement of an additional secure channel is removed. Since the generation of Gaussian sensing matrices is not feasible in resource-constrained IoT devices, approximate Gaussian sensing matrices are generated using linear feedback shift registers. We also demonstrate the implementation feasibility of the proposed perfectly secure scheme in practice without additional complexity. Furthermore, the security analysis of the proposed scheme is performed and compared with the state-of-the-art compressive sensing based energy obfuscation scheme.
Transferred Fusion Learning using Skipped Networks
Venue: arXiv cs.CV 2020
Year: 2020

Identification of an entity that is of interest is prominent in any intelligent system. The visual intelligence of the model is enhanced when the capability of recognition is added. Several methods such as transfer learning and zero shot learning help to reuse the existing models or augment the existing model to achieve improved performance at the task of object recognition. Transferred fusion learning is one such mechanism that intends to use the best of both worlds and build a model that is capable of outperforming the models involved in the system. We propose a novel mechanism to amplify the process of transfer learning by introducing a student architecture where the networks learn from each other.
Error-correcting Codes for Short Tandem Duplication and Substitution Errors
Venue: arXiv cs.IT 2020
Year: 2020

Due to its high data density and longevity, DNA is considered a promising medium for satisfying ever-increasing data storage needs. However, the diversity of errors that occur in DNA sequences makes efficient error-correction a challenging task. This paper aims to address simultaneously correcting two types of errors, namely, short tandem duplication and substitution errors. We focus on tandem repeats of length at most 3 and design codes for correcting an arbitrary number of duplication errors and one substitution error. Because a substituted symbol can be duplicated many times (as part of substrings of various lengths), a single substitution can affect an unbounded substring of the retrieved word. However, we show that with appropriate preprocessing, the effect may be limited to a substring of finite length, thus making efficient error-correction possible. We construct a code for correcting the aforementioned errors and provide lower bounds for its rate. Compared to optimal codes correcting only duplication errors, numerical results show that the asymptotic cost of protecting against an additional substitution is only 0.003 bits/symbol when the alphabet has size 4, an important case corresponding to data storage in DNA.
I-BOT: Interference-Based Orchestration of Tasks for Dynamic Unmanaged Edge Computing
Venue: arXiv cs.DC 2020
Year: 2020

In recent years, edge computing has become a popular choice for latency-sensitive applications like facial recognition and augmented reality because it is closer to the end users compared to the cloud. Although infrastructure providers are working toward creating managed edge networks, personal devices such as laptops and tablets, which are widely available and are underutilized, can also be used as potential edge devices. We call such devices Unmanaged Edge Devices (UEDs). Scheduling application tasks on such an unmanaged edge system is not straightforward because of three fundamental reasons-heterogeneity in the computational capacity of the UEDs, uncertainty in the availability of the UEDs (due to devices leaving the system), and interference among multiple tasks sharing a UED. In this paper, we present I-BOT, an interference-based orchestration scheme for latency-sensitive tasks on an Unmanaged Edge Platform (UEP). It minimizes the completion time of applications and is bandwidth efficient. I-BOT brings forth three innovations. First, it profiles and predicts the interference patterns of the tasks to make scheduling decisions. Second, it uses a feedback mechanism to adjust for changes in the computational capacity of the UEDs and a prediction mechanism to handle their sporadic exits. Third, it accounts for input dependence of tasks in its scheduling decision (such as, two tasks requiring the same input data). To evaluate I-BOT, we run end-to-end simulations with applications representing autonomous driving, composed of multiple tasks. We compare to two basic baselines (random and round-robin) and two state-of-the-arts, Lavea [SEC-2017] and Petrel [MSN-2018]. Compared to these baselines, I-BOT significantly reduces the average service time of application tasks. This reduction is more pronounced in dynamic heterogeneous environments, which would be the case in a UEP.