OpenRaiser/Intern-Atlas huggingface dataset:
Segmentation Approach for Coreference Resolution Task
Venue: arXiv cs.CL 2020
Year: 2020

In coreference resolution, it is important to consider all members of a coreference cluster and decide about all of them at once. This technique can help to avoid losing precision and also in finding long-distance relations. The presented paper is a report of an ongoing study on an idea which proposes a new approach for coreference resolution which can resolve all coreference mentions to a given mention in the document in one pass. This has been accomplished by defining an embedding method for the position of all members of a coreference cluster in a document and resolving all of them for a given mention. In the proposed method, the BERT model has been used for encoding the documents and a head network designed to capture the relations between the embedded tokens. These are then converted to the proposed span position embedding matrix which embeds the position of all coreference mentions in the document. We tested this idea on CoNLL 2012 dataset and although the preliminary results from this method do not quite meet the state-of-the-art results, they are promising and they can capture features like long-distance relations better than the other approaches.
A Novel BGCapsule Network for Text Classification
Venue: arXiv cs.CL 2020
Year: 2020

Several text classification tasks such as sentiment analysis, news categorization, multi-label classification and opinion classification are challenging problems even for modern deep learning networks. Recently, Capsule Networks (CapsNets) are proposed for image classification. It has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while their validity in the domain of text has been less explored. In this paper, we propose a novel hybrid architecture viz., BGCapsule, which is a Capsule model preceded by an ensemble of Bidirectional Gated Recurrent Units (BiGRU) for several text classification tasks. We employed an ensemble of Bidirectional GRUs for feature extraction layer preceding the primary capsule layer. The hybrid architecture, after performing basic pre-processing steps, consists of five layers: an embedding layer based on GloVe, a BiGRU based ensemble layer, a primary capsule layer, a flatten layer and fully connected ReLU layer followed by a fully connected softmax layer. In order to evaluate the effectiveness of BGCapsule, we conducted extensive experiments on five benchmark datasets (ranging from 10,000 records to 700,000 records) including Movie Review (MR Imdb 2005), AG News dataset, Dbpedia ontology dataset, Yelp Review Full dataset and Yelp review polarity dataset. These benchmarks cover several text classification tasks such as news categorization, sentiment analysis, multiclass classification, multi-label classification and opinion classification. We found that our proposed architecture (BGCapsule) achieves better accuracy compared to the existing methods without the help of any external linguistic knowledge such as positive sentiment keywords and negative sentiment keywords. Further, BGCapsule converged faster compared to other extant techniques.
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
Venue: arXiv cs.CV 2020
Year: 2020

During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blood flow in both fetuses. The procedure is challenging due to the mobility of the environment, poor visibility in amniotic fluid, occasional bleeding, and limitations in the fetoscopic field-of-view and image quality. Ideally, anastomotic placental vessels would be automatically identified, segmented and registered to create expanded vessel maps to guide laser ablation, however, such methods have yet to be clinically adopted. We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos. The obtained vessel probability maps provide sufficient cues for mosaicking alignment by registering consecutive vessel maps using the direct intensity-based technique. Experiments on 6 different in vivo fetoscopic videos demonstrate that the vessel intensity-based registration outperformed image intensity-based registration approaches showing better robustness in qualitative and quantitative comparison. We additionally reduce drift accumulation to negligible even for sequences with up to 400 frames and we incorporate a scheme for quantifying drift error in the absence of the ground-truth. Our paper provides a benchmark for fetoscopy placental vessel segmentation and registration by contributing the first in vivo vessel segmentation and fetoscopic videos dataset.
Information, communication and music: Recognition of musical dissonance and consonance in a simple reservoir computing system
Venue: arXiv cs.SD 2020
Year: 2020

Reservoir computing is an emerging, but very successful approach towards processing and classification of various signals. It can be described as a model of a transient computation, where influence of input changes internal dynamics of chosen computational reservoir. Trajectory of these changes represents computation performed by the system. The selection of a suitable computational substrate capable of non-linear response and rich internal dynamics ensures the implementation of simple readout protocols. Signal evolution based on the rich dynamics of the reservoir layer helps to emphasize differences between given signals thus enabling their easier classification. Here we present a simple reservoir computing system (single node echo-state machine) implemented on Multisim platform as a tool for classification of musical intervals according to their consonant or dissonant character. The result of this classification closely resembled sensory dissonance curve, with some significant differences. A deeper analysis of the received signals indicates the geometric relationships between the consonant and dissonant intervals, enabling their classification.
Discourse Coherence, Reference Grounding and Goal Oriented Dialogue
Venue: arXiv cs.CL 2020
Year: 2020

Prior approaches to realizing mixed-initiative human--computer referential communication have adopted information-state or collaborative problem-solving approaches. In this paper, we argue for a new approach, inspired by coherence-based models of discourse such as SDRT \cite{asher-lascarides:2003a}, in which utterances attach to an evolving discourse structure and the associated knowledge graph of speaker commitments serves as an interface to real-world reasoning and conversational strategy. As first steps towards implementing the approach, we describe a simple dialogue system in a referential communication domain that accumulates constraints across discourse, interprets them using a learned probabilistic model, and plans clarification using reinforcement learning.
Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery
Venue: arXiv cs.CV 2020
Year: 2020

Segmentation of surgical instruments is an important problem in robot-assisted surgery: it is a crucial step towards full instrument pose estimation and is directly used for masking of augmented reality overlays during surgical procedures. Most applications rely on accurate real-time segmentation of high-resolution surgical images. While previous research focused primarily on methods that deliver high accuracy segmentation masks, majority of them can not be used for real-time applications due to their computational cost. In this work, we design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images. To account for reduced accuracy of the discovered light-weight deep residual network and avoid adding any additional computational burden, we perform a differentiable search over dilation rates for residual units of our network. We test our discovered architecture on the EndoVis 2017 Robotic Instruments dataset and verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff with a speed of up to 125 FPS on high resolution images.
Just in Time: Personal Temporal Insights for Altering Model Decisions
Venue: arXiv cs.DB 2020
Year: 2020

The interpretability of complex Machine Learning models is coming to be a critical social concern, as they are increasingly used in human-related decision-making processes such as resume filtering or loan applications. Individuals receiving an undesired classification are likely to call for an explanation -- preferably one that specifies what they should do in order to alter that decision when they reapply in the future. Existing work focuses on a single ML model and a single point in time, whereas in practice, both models and data evolve over time: an explanation for an application rejection in 2018 may be irrelevant in 2019 since in the meantime both the model and the applicant's data can change. To this end, we propose a novel framework that provides users with insights and plans for changing their classification in particular future time points. The solution is based on combining state-of-the-art algorithms for (single) model explanations, ones for predicting future models, and database-style querying of the obtained explanations. We propose to demonstrate the usefulness of our solution in the context of loan applications, and interactively engage the audience in computing and viewing suggestions tailored for applicants based on their unique characteristic.
FPT and kernelization algorithms for the k-in-a-tree problem
Venue: arXiv cs.DS 2020
Year: 2020

The three-in-a-tree problem asks for an induced tree of the input graph containing three mandatory vertices. In 2006, Chudnovsky and Seymour [Combinatorica, 2010] presented the first polynomial time algorithm for this problem, which has become a critical subroutine in many algorithms for detecting induced subgraphs, such as beetles, pyramids, thetas, and even and odd-holes. In 2007, Derhy and Picouleau [Discrete Applied Mathematics, 2009] considered the natural generalization to $k$ mandatory vertices, proving that, when $k$ is part of the input, the problem is $\mathsf{NP}$-complete, and ask what is the complexity of four-in-a-tree. Motivated by this question and the relevance of the original problem, we study the parameterized complexity of $k$-in-a-tree. We begin by showing that the problem is $\mathsf{W[1]}$-hard when jointly parameterized by the size of the solution and minimum clique cover and, under the Exponential Time Hypothesis, does not admit an $n^{o(k)}$ time algorithm. Afterwards, we use Courcelle's Theorem to prove fixed-parameter tractability under cliquewidth, which prompts our investigation into which parameterizations admit single exponential algorithms; we show that such algorithms exist for the unrelated parameterizations treewidth, distance to cluster, and distance to co-cluster. In terms of kernelization, we present a linear kernel under feedback edge set, and show that no polynomial kernel exists under vertex cover nor distance to clique unless $\mathsf{NP} \subseteq \mathsf{coNP}/\mathsf{poly}$. Along with other remarks and previous work, our tractability and kernelization results cover many of the most commonly employed parameters in the graph parameter hierarchy.
EPI-based Oriented Relation Networks for Light Field Depth Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Light field cameras record not only the spatial information of observed scenes but also the directions of all incoming light rays. The spatial and angular information implicitly contain geometrical characteristics such as multi-view or epipolar geometry, which can be exploited to improve the performance of depth estimation. An Epipolar Plane Image (EPI), the unique 2D spatial-angular slice of the light field, contains patterns of oriented lines. The slope of these lines is associated with the disparity. Benefiting from this property of EPIs, some representative methods estimate depth maps by analyzing the disparity of each line in EPIs. However, these methods often extract the optimal slope of the lines from EPIs while ignoring the relationship between neighboring pixels, which leads to inaccurate depth map predictions. Based on the observation that an oriented line and its neighboring pixels in an EPI share a similar linear structure, we propose an end-to-end fully convolutional network (FCN) to estimate the depth value of the intersection point on the horizontal and vertical EPIs. Specifically, we present a new feature-extraction module, called Oriented Relation Module (ORM), that constructs the relationship between the line orientations. To facilitate training, we also propose a refocusing-based data augmentation method to obtain different slopes from EPIs of the same scene point. Extensive experiments verify the efficacy of learning relations and show that our approach is competitive to other state-of-the-art methods. The code and the trained models are available at https://github.com/lkyahpu/EPI_ORM.git.
Auxiliary Tasks Speed Up Learning PointGoal Navigation
Venue: arXiv cs.CV 2020
Year: 2020

PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment. Wijmans et al. showed that this task is solvable but their method is computationally prohibitive, requiring 2.5 billion frames and 180 GPU-days. In this work, we develop a method to significantly increase sample and time efficiency in learning PointNav using self-supervised auxiliary tasks (e.g. predicting the action taken between two egocentric observations, predicting the distance between two observations from a trajectory,etc.).We find that naively combining multiple auxiliary tasks improves sample efficiency,but only provides marginal gains beyond a point. To overcome this, we use attention to combine representations learnt from individual auxiliary tasks. Our best agent is 5.5x faster to reach the performance of the previous state-of-the-art, DD-PPO, at 40M frames, and improves on DD-PPO's performance at 40M frames by 0.16 SPL. Our code is publicly available at https://github.com/joel99/habitat-pointnav-aux.
On the Reliability and Generalizability of Brain-inspired Reinforcement Learning Algorithms
Venue: arXiv cs.AI 2020
Year: 2020

Although deep RL models have shown a great potential for solving various types of tasks with minimal supervision, several key challenges remain in terms of learning from limited experience, adapting to environmental changes, and generalizing learning from a single task. Recent evidence in decision neuroscience has shown that the human brain has an innate capacity to resolve these issues, leading to optimism regarding the development of neuroscience-inspired solutions toward sample-efficient, and generalizable RL algorithms. We show that the computational model combining model-based and model-free control, which we term the prefrontal RL, reliably encodes the information of high-level policy that humans learned, and this model can generalize the learned policy to a wide range of tasks. First, we trained the prefrontal RL, and deep RL algorithms on 82 subjects' data, collected while human participants were performing two-stage Markov decision tasks, in which we manipulated the goal, state-transition uncertainty and state-space complexity. In the reliability test, which includes the latent behavior profile and the parameter recoverability test, we showed that the prefrontal RL reliably learned the latent policies of the humans, while all the other models failed. Second, to test the ability to generalize what these models learned from the original task, we situated them in the context of environmental volatility. Specifically, we ran large-scale simulations with 10 Markov decision tasks, in which latent context variables change over time. Our information-theoretic analysis showed that the prefrontal RL showed the highest level of adaptability and episodic encoding efficacy. This is the first attempt to formally test the possibility that computational models mimicking the way the brain solves general problems can lead to practical solutions to key challenges in machine learning.
Monocular Vision based Crowdsourced 3D Traffic Sign Positioning with Unknown Camera Intrinsics and Distortion Coefficients
Venue: arXiv cs.CV 2020
Year: 2020

Autonomous vehicles and driver assistance systems utilize maps of 3D semantic landmarks for improved decision making. However, scaling the mapping process as well as regularly updating such maps come with a huge cost. Crowdsourced mapping of these landmarks such as traffic sign positions provides an appealing alternative. The state-of-the-art approaches to crowdsourced mapping use ground truth camera parameters, which may not always be known or may change over time. In this work, we demonstrate an approach to computing 3D traffic sign positions without knowing the camera focal lengths, principal point, and distortion coefficients a priori. We validate our proposed approach on a public dataset of traffic signs in KITTI. Using only a monocular color camera and GPS, we achieve an average single journey relative and absolute positioning accuracy of 0.26 m and 1.38 m, respectively.
Establishing Secrecy Region for Directional Modulation Scheme with Random Frequency Diverse Array
Venue: arXiv cs.IT 2020
Year: 2020

Random frequency diverse array (RFDA) based directional modulation (DM) was proposed as a promising technology in secure communications to achieve a precise transmission of confidential messages, and artificial noise (AN) was considered as an important helper in RFDA-DM. Compared with previous works that only focus on the spot of the desired receiver, in this work, we investigate a secrecy region around the desired receiver, that is, a specific range and angle resolution around the desired receiver. Firstly, the minimum number of antennas and the bandwidth needed to achieve a secrecy region are derived. Moreover, based on the lower bound of the secrecy capacity in RFDA-DM-AN scheme, we investigate the performance impact of AN on the secrecy capacity. From this work, we conclude that: 1) AN is not always beneficial to the secure transmission. Specifically, when the number of antennas is sufficiently large and the transmit power is smaller than a specified value, AN will reduce secrecy capacity due to the consumption of limited transmit power. 2) Increasing bandwidth will enlarge the set for randomly allocating frequencies and thus lead to a higher secrecy capacity. 3) The minimum number of antennas increases as the predefined secrecy transmission rate increases.
Serverless Electronic Mail
Venue: arXiv cs.CY 2020
Year: 2020

We describe a simple approach to peer-to-peer electronic mail that would allow users of ordinary workstations and mobile devices to exchange messages without relying upon third-party mail server operators. Crucially, the system allows participants to establish and use multiple unlinked identities for communication with each other. The architecture leverages ordinary SMTP for message delivery and Tor for peer-to-peer communication. The design offers a robust, unintrusive method to use self-certifying Tor onion service names to bootstrap a web of trust based on public keys for end-to-end authentication and encryption, which in turn can be used to facilitate message delivery when the sender and recipient are not online simultaneously. We show how the system can interoperate with existing email systems and paradigms, allowing users to hold messages that others can retrieve via IMAP or to operate as a relay between system participants and external email users. Finally, we show how it is possible to use a broadcast protocol to implement mailing lists and how distributed ledger technology might be used to bootstrap consensus about shared knowledge among list members.
Treewidth-Aware Complexity in ASP: Not all Positive Cycles are Equally Hard
Venue: arXiv cs.LO 2020
Year: 2020

It is well-know that deciding consistency for normal answer set programs (ASP) is NP-complete, thus, as hard as the satisfaction problem for classical propositional logic (SAT). The best algorithms to solve these problems take exponential time in the worst case. The exponential time hypothesis (ETH) implies that this result is tight for SAT, that is, SAT cannot be solved in subexponential time. This immediately establishes that the result is also tight for the consistency problem for ASP. However, accounting for the treewidth of the problem, the consistency problem for ASP is slightly harder than SAT: while SAT can be solved by an algorithm that runs in exponential time in the treewidth k, it was recently shown that ASP requires exponential time in k \cdot log(k). This extra cost is due checking that there are no self-supported true atoms due to positive cycles in the program. In this paper, we refine the above result and show that the consistency problem for ASP can be solved in exponential time in k \cdot log(λ) where λ is the minimum between the treewidth and the size of the largest strongly-connected component in the positive dependency graph of the program. We provide a dynamic programming algorithm that solves the problem and a treewidth-aware reduction from ASP to SAT that adhere to the above limit.
Logic of computational semi-effects and categorical gluing for equivariant functors
Venue: arXiv cs.LO 2020
Year: 2020

In this paper, we revisit Moggi's celebrated calculus of computational effects from the perspective of logic of monoidal action (actegory). Our development takes the following steps. Firstly, we perform proof-theoretic reconstruction of Moggi's computational metalanguage and obtain a type theory with a modal type $\rhd$ as a refinement. Through the proposition-as-type paradigm, its logic can be seen as a decomposition of lax logic via Benton's adjoint calculus. This calculus models as a programming language a weaker version of effects, which we call \emph{semi-effects}. Secondly, we give its semantics using actegories and equivariant functors. Compared to previous studies of effects and actegories, our approach is more general in that models are directly given by equivariant functors, which include Freyd categories (hence strong monads) as a special case. Thirdly, we show that categorical gluing along equivariant functors is possible and derive logical predicates for $\rhd$-modality. We also show that this gluing, under a natural assumption, gives rise to logical predicates that coincide with those derived by Katsumata's categorical $\top\top$-lifting for Moggi's metalanguage.
ESA-ReID: Entropy-Based Semantic Feature Alignment for Person re-ID
Venue: arXiv cs.CV 2020
Year: 2020

Person re-identification (re-ID) is a challenging task in real-world. Besides the typical application in surveillance system, re-ID also has significant values to improve the recall rate of people identification in content video (TV or Movies). However, the occlusion, shot angle variations and complicated background make it far away from application, especially in content video. In this paper we propose an entropy based semantic feature alignment model, which takes advantages of the detailed information of the human semantic feature. Considering the uncertainty of semantic segmentation, we introduce a semantic alignment with an entropy-based mask which can reduce the negative effects of mask segmentation errors. We construct a new re-ID dataset based on content videos with many cases of occlusion and body part missing, which will be released in future. Extensive studies on both existing datasets and the new dataset demonstrate the superior performance of the proposed model.