OpenRaiser/Intern-Atlas huggingface dataset:
Continuous Histograms for Anisotropy of 2D Symmetric Piece-wise Linear Tensor Fields
Venue: arXiv cs.GR 2019
Year: 2019

The analysis of contours of scalar fields plays an important role in visualization. For example the contour tree and contour statistics can be used as a means for interaction and filtering or as signatures. In the context of tensor field analysis, such methods are also interesting for the analysis of derived scalar invariants. While there are standard algorithms to compute and analyze contours, they are not directly applicable to tensor invariants when using component-wise tensor interpolation. In this chapter we present an accurate derivation of the contour spectrum for invariants with quadratic behavior computed from two-dimensional piece-wise linear tensor fields. For this work, we are mostly motivated by a consistent treatment of the anisotropy field, which plays an important role as stability measure for tensor field topology. We show that it is possible to derive an analytical expression for the distribution of the invariant values in this setting, which is exemplary given for the anisotropy in all details. Our derivation is based on a topological sub-division of the mesh in triangles that exhibit a monotonic behavior. This triangulation can also directly be used to compute the accurate contour tree with standard algorithms. We compare the results to a naïve approach based on linear interpolation on the original mesh or the subdivision.
Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor
Venue: arXiv cs.RO 2019
Year: 2019

Tactile sensors can provide detailed contact in-formation that can facilitate robots to perform dexterous, in-hand manipulation tasks. One of the primitive but important tasks is surface following that is a key feature for robots while exploring unknown environments or workspace of inaccurate modeling. In this paper, we propose a novel end-to-end learning strategy, by directly mapping the raw tactile data acquired from a GelSight tactile sensor to the motion of the robot end-effector.Experiments on a KUKA youBot platform equipped with theGelSight sensor show that 80% of the actions generated by a fully trained SFDQN model are proper surface following actions; the autonomous surface following test also indicates that the proposed solution works well on a test surface.
A Synergistic Approach for Internet of Things and Cloud Integration: Current Research and Future Direction
Venue: arXiv cs.CY 2019
Year: 2019

Cloud computing and Internet of Things have independently changed the course of technological development. The use of a synergistic approach that amalgamates the benefits of both these path breaking technologies into a single package is expected to have flourishing benefits. However, such an integration is faced with numerous limitations and challenges. This paper surveys the different aspects of each of these technologies and explores the possibilities, benefits, limitations and challenges that rise from the development of a convergent approach. We have also investigated the current research and future direction.
Three Orthogonal Dimensions for Psychoacoustic Sonification
Venue: arXiv cs.SD 2019
Year: 2019

Objective: Three perceptually orthogonal auditory dimensions for multidimensional and multivariate data sonification are identified and experimentally validated. Background: Psychoacoustic investigations have shown that orthogonal acoustical parameters may interfere perceptually. The literature hardly offers any solutions to this problem, and previous auditory display approaches have failed to implement auditory dimensions that are perceived orthogonally by a user. In this study we demonstrate how a location in three-dimensional space can be sonified unambiguously by the implementation of perceptually orthogonal psychoacoustic attributes in monophonic playback. Method: Perceptually orthogonal auditory attributes are identified from literature research and experience in music and psychoacoustic research. We carried out an experiment with 21 participants who identified sonified locations in two-dimensional space. Results: With just 5 minutes of explanation and exploration, naive users can interpret our multidimensional sonification with high accuracy. Conclusion: We identified a set of perceptually orthogonal auditory dimensions suitable for three-dimensional data sonification. Application: Three-dimensional data sonification promises blind navigation, e.g. for unmanned vehicles, and reliable real-time monitoring of multivariate data, e.g., in the patient care sector.
The relationship between trust in AI and trustworthy machine learning technologies
Venue: arXiv cs.CY 2019
Year: 2019

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.
CNNs, LSTMs, and Attention Networks for Pathology Detection in Medical Data
Venue: arXiv cs.LG 2019
Year: 2019

For the weakly supervised task of electrocardiogram (ECG) rhythm classification, convolutional neural networks (CNNs) and long short-term memory (LSTM) networks are two increasingly popular classification models. This work investigates whether a combination of both architectures to so-called convolutional long short-term memory (ConvLSTM) networks can improve classification performances by explicitly capturing morphological as well as temporal features of raw ECG records. In addition, various attention mechanisms are studied to localize and visualize record sections of abnormal morphology and irregular rhythm. The resulting saliency maps are supposed to not only allow for a better network understanding but to also improve clinicians' acceptance of automatic diagnosis in order to avoid the technique being labeled as a black box. In further experiments, attention mechanisms are actively incorporated into the training process by learning a few additional attention gating parameters in a CNN model. An 8-fold cross validation is finally carried out on the PhysioNet Computing in Cardiology (CinC) challenge 2017 to compare the performances of standard CNN models, ConvLSTMs, and attention gated CNNs.
ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network
Venue: arXiv cs.CL 2019
Year: 2019

Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field. We evaluated the effectiveness of our model on the widely-used MIMIC dataset. On the full code set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of 6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set of MIMIC-II, our model outperformed all the existing and state-of-the-art models in all evaluation metrics. The code is available at https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network.
Independent language modeling architecture for end-to-end ASR
Venue: arXiv cs.CL 2019
Year: 2019

The attention-based end-to-end (E2E) automatic speech recognition (ASR) architecture allows for joint optimization of acoustic and language models within a single network. However, in a vanilla E2E ASR architecture, the decoder sub-network (subnet), which incorporates the role of the language model (LM), is conditioned on the encoder output. This means that the acoustic encoder and the language model are entangled that doesn't allow language model to be trained separately from external text data. To address this problem, in this work, we propose a new architecture that separates the decoder subnet from the encoder output. In this way, the decoupled subnet becomes an independently trainable LM subnet, which can easily be updated using the external text data. We study two strategies for updating the new architecture. Experimental results show that, 1) the independent LM architecture benefits from external text data, achieving 9.3% and 22.8% relative character and word error rate reduction on Mandarin HKUST and English NSC datasets respectively; 2)the proposed architecture works well with external LM and can be generalized to different amount of labelled data.
Augmented Reality for Human-Swarm Interaction in a Swarm-Robotic Chemistry Simulation
Venue: arXiv cs.RO 2019
Year: 2019

We present a method to register individual members of a robotic swarm in an augmented reality display while showing relevant information about swarm dynamics to the user that would be otherwise hidden. Individual swarm members and clusters of the same group are identified by their color, and by blinking at a specific time interval that is distinct from the time interval at which their neighbors blink. We show that this problem is an instance of the graph coloring problem, which can be solved in a distributed manner in O(log(n)) time. We demonstrate our approach using a swarm chemistry simulation in which robots simulate individual atoms that form molecules following the rules of chemistry. Augmented reality is then used to display information about the internal state of individual swarm members as well as their topological relationship, corresponding to molecular bonds.
Morphological Tagging and Lemmatization of Albanian: A Manually Annotated Corpus and Neural Models
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we present the first publicly available part-of-speech and morphologically tagged corpus for the Albanian language, as well as a neural morphological tagger and lemmatizer trained on it. There is currently a lack of available NLP resources for Albanian, and its complex grammar and morphology present challenges to their development. We have created an Albanian part-of-speech corpus based on the Universal Dependencies schema for morphological annotation, containing about 118,000 tokens of naturally occuring text collected from different text sources, with an addition of 67,000 tokens of artificially created simple sentences used only in training. On this corpus, we subsequently train and evaluate segmentation, morphological tagging and lemmatization models, using the Turku Neural Parser Pipeline. On the held-out evaluation set, the model achieves 92.74% accuracy on part-of-speech tagging, 85.31% on morphological tagging, and 89.95% on lemmatization. The manually annotated corpus, as well as the trained models are available under an open license.
Low-Latency High-Level Data Sharing for Connected and Autonomous Vehicular Networks
Venue: arXiv cs.NI 2019
Year: 2019

Autonomous vehicles can combine their own data with that of other vehicles to enhance their perceptive ability, and thus improve detection accuracy and driving safety. Data sharing among autonomous vehicles, however, is a challenging problem due to the sheer volume of data generated by various types of sensors on the vehicles. In this paper, we propose a low-latency, high-level (L3) data sharing protocol for connected and autonomous vehicular networks. Based on the L3 protocol, sensing results generated by individual vehicles will be broadcasted simultaneously within a limited sensing zone. The L3 protocol reduces the networking latency by taking advantage of the capture effect of the wireless transmissions occurred among vehicles. With the proposed design principles, we implement and test the L3 protocol in a simulated environment. Simulation results demonstrate that the L3 protocol is able to achieve reliable and fast data sharing among autonomous vehicles.
Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling
Venue: arXiv cs.LG 2019
Year: 2019

In sequence learning tasks such as language modelling, Recurrent Neural Networks must learn relationships between input features separated by time. State of the art models such as LSTM and Transformer are trained by backpropagation of losses into prior hidden states and inputs held in memory. This allows gradients to flow from present to past and effectively learn with perfect hindsight, but at a significant memory cost. In this paper we show that it is possible to train high performance recurrent networks using information that is local in time, and thereby achieve a significantly reduced memory footprint. We describe a predictive autoencoder called bRSM featuring recurrent connections, sparse activations, and a boosting rule for improved cell utilization. The architecture demonstrates near optimal performance on a non-deterministic (stochastic) partially-observable sequence learning task consisting of high-Markov-order sequences of MNIST digits. We find that this model learns these sequences faster and more completely than an LSTM, and offer several possible explanations why the LSTM architecture might struggle with the partially observable sequence structure in this task. We also apply our model to a next word prediction task on the Penn Treebank (PTB) dataset. We show that a 'flattened' RSM network, when paired with a modern semantic word embedding and the addition of boosting, achieves 103.5 PPL (a 20-point improvement over the best N-gram models), beating ordinary RNNs trained with BPTT and approaching the scores of early LSTM implementations. This work provides encouraging evidence that strong results on challenging tasks such as language modelling may be possible using less memory intensive, biologically-plausible training regimes.
Deep Bayesian Active Learning for Multiple Correct Outputs
Venue: arXiv cs.CV 2019
Year: 2019

Typical active learning strategies are designed for tasks, such as classification, with the assumption that the output space is mutually exclusive. The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least confidence, which operate over a model's outputs. Unfortunately, many real-world vision tasks, like visual question answering and image captioning, have multiple correct answers, causing these measurements to overestimate uncertainty and sometimes perform worse than a random sampling baseline. In this paper, we propose a new paradigm that estimates uncertainty in the model's internal hidden space instead of the model's output space. We specifically study a manifestation of this problem for visual question answer generation (VQA), where the aim is not to classify the correct answer but to produce a natural language answer, given an image and a question. Our method overcomes the paraphrastic nature of language. It requires a semantic space that structures the model's output concepts and that enables the usage of techniques like dropout-based Bayesian uncertainty. We build a visual-semantic space that embeds paraphrases close together for any existing VQA model. We empirically show state-of-art active learning results on the task of VQA on two datasets, being 5 times more cost-efficient on Visual Genome and 3 times more cost-efficient on VQA 2.0.
BERT for Large-scale Video Segment Classification with Test-time Augmentation
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents our approach to the third YouTube-8M video understanding competition that challenges par-ticipants to localize video-level labels at scale to the pre-cise time in the video where the label actually occurs. Ourmodel is an ensemble of frame-level models such as GatedNetVLAD and NeXtVLAD and various BERT models withtest-time augmentation. We explore multiple ways to ag-gregate BERT outputs as video representation and variousways to combine visual and audio information. We proposetest-time augmentation as shifting video frames to one leftor right unit, which adds variety to the predictions and em-pirically shows improvement in evaluation metrics. We firstpre-train the model on the 4M training video-level data, andthen fine-tune the model on 237K annotated video segment-level data. We achieve MAP@100K 0.7871 on private test-ing video segment data, which is ranked 9th over 283 teams.
A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification
Venue: arXiv cs.CV 2019
Year: 2019

Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56% in the test set.
The Polynomial Transform
Venue: arXiv cs.DS 2019
Year: 2019

We explore a new form of DFT, which we call the Polynomial Transform. It functions over finite fields, and a size $n$ transform takes $O(n)$ operations. In the multitape Turing machine model, it allows us to multiply two $n$ bit numbers in time $n(k^{\log^*{n}} + \log{p})$, where $k$ is a constant and $\log^*{n}$ is the iterated logarithm. One important consequence is that the Network Coding Conjecture is false.
Exact Polynomial Time Algorithm for the Response Time Analysis of Harmonic Tasks with Constrained Release Jitter
Venue: arXiv cs.OS 2019
Year: 2019

In some important application areas of hard real-time systems, preemptive sporadic tasks with harmonic periods and constraint deadlines running upon a uni-processor platform play an important role. We propose a new algorithm for determining the exact worst-case response time for a task that has a lower computational complexity (linear in the number of tasks) than the known algorithm developed for the same system class. We also allow the task executions to start delayed due to release jitter if they are within certain value ranges. For checking if these constraints are met we define a constraint programming problem that has a special structure and can be solved with heuristic components in a time that is linear in the task number. If the check determines the admissibility of the jitter values, the linear time algorithm can be used to determine the worst-case response time also for jitter-aware systems.
Value-laden Disciplinary Shifts in Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values. However, little attention thus far has been given to how values influence the machine learning discipline as a whole. How do values influence what the discipline focuses on and the way it develops? If undesirable values are at play at the level of the discipline, then intervening on particular models will not suffice to address the problem. Instead, interventions at the disciplinary-level are required. This paper analyzes the discipline of machine learning through the lens of philosophy of science. We develop a conceptual framework to evaluate the process through which types of machine learning models (e.g. neural networks, support vector machines, graphical models) become predominant. The rise and fall of model-types is often framed as objective progress. However, such disciplinary shifts are more nuanced. First, we argue that the rise of a model-type is self-reinforcing--it influences the way model-types are evaluated. For example, the rise of deep learning was entangled with a greater focus on evaluations in compute-rich and data-rich environments. Second, the way model-types are evaluated encodes loaded social and political values. For example, a greater focus on evaluations in compute-rich and data-rich environments encodes values about centralization of power, privacy, and environmental concerns.
Range-only Collaborative Localization for Ground Vehicles
Venue: arXiv cs.RO 2019
Year: 2019

High-accuracy absolute localization for a team of vehicles is essential when accomplishing various kinds of tasks. As a promising approach, collaborative localization fuses the individual motion measurements and the inter-vehicle measurements to collaboratively estimate the states. In this paper, we focus on the range-only collaborative localization, which specifies the inter-vehicle measurements as inter-vehicle ranging measurements. We first investigate the observability properties of the system and derive that to achieve bounded localization errors, two vehicles are required to remain static like external infrastructures. Under the guide of the observability analysis, we then propose our range-only collaborative localization system which categorize the ground vehicles into two static vehicles and dynamic vehicles. The vehicles are connected utilizing a UWB network that is capable of both producing inter-vehicle ranging measurements and communication. Simulation results validate the observability analysis and demonstrate that collaborative localization is capable of achieving higher accuracy when utilizing the inter-vehicle measurements. Extensive experimental results are performed for a team of 3 and 5 vehicles. The real-world results illustrate that our proposed system enables accurate and real-time estimation of all vehicles' absolute poses.
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Transfer learning between different language pairs has shown its effectiveness for Neural Machine Translation (NMT) in low-resource scenario. However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side. To address this challenge, we propose an effective transfer learning approach based on cross-lingual pre-training. Our key idea is to make all source languages share the same feature space and thus enable a smooth transition for zero-shot translation. To this end, we introduce one monolingual pre-training method and two bilingual pre-training methods to obtain a universal encoder for different languages. Once the universal encoder is constructed, the parent model built on such encoder is trained with large-scale annotated data and then directly applied in zero-shot translation scenario. Experiments on two public datasets show that our approach significantly outperforms strong pivot-based baseline and various multilingual NMT approaches.