OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Unsupervised Domain Translation
Venue: arXiv cs.LG 2019
Year: 2019

Domain Translation is the problem of finding a meaningful correspondence between two domains. Since in a majority of settings paired supervision is not available, much work focuses on Unsupervised Domain Translation (UDT) where data samples from each domain are unpaired. Following the seminal work of CycleGAN for UDT, many variants and extensions of this model have been proposed. However, there is still little theoretical understanding behind their success. We observe that these methods yield solutions which are approximately minimal w.r.t. a given transportation cost, leading us to reformulate the problem in the Optimal Transport (OT) framework. This viewpoint gives us a new perspective on Unsupervised Domain Translation and allows us to prove the existence and uniqueness of the retrieved mapping, given a large family of transport costs. We then propose a novel framework to efficiently compute optimal mappings in a dynamical setting. We show that it generalizes previous methods and enables a more explicit control over the computed optimal mapping. It also provides smooth interpolations between the two domains. Experiments on toy and real world datasets illustrate the behavior of our method.
Vision-Based Autonomous UAV Navigation and Landing for Urban Search and Rescue
Venue: arXiv cs.RO 2019
Year: 2019

Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natural disasters such as earthquakes or gas explosions. However, these UAVs have to be able to autonomously navigate in disaster struck environments and land on debris piles in order to accurately locate the survivors. This problem is extremely challenging as pre-existing maps cannot be leveraged for navigation due to structural changes that may have occurred. Furthermore, existing landing site detection algorithms are not suitable to identify safe landing regions on debris piles. In this work, we present a computationally efficient system for autonomous UAV navigation and landing that does not require any prior knowledge about the environment. We propose a novel landing site detection algorithm that computes costmaps based on several hazard factors including terrain flatness, steepness, depth accuracy, and energy consumption information. We also introduce a first-of-a-kind synthetic dataset of over 1.2 million images of collapsed buildings with groundtruth depth, surface normals, semantics and camera pose information. We demonstrate the efficacy of our system using experiments from a city scale hyperrealistic simulation environment and in real-world scenarios with collapsed buildings.
Pegadas: A Portal for Management and Activities Planning with Games and Environments for Education in Health
Venue: arXiv cs.HC 2019
Year: 2019

Applications for learning and training have been developed and highlighted as important tools in health education. Despite the several approaches and initiatives, these tools have not been used in an integrated way. The specific skills approached by each application, the absence of a consensus about how to integrate them in the curricula, and the necessity of evaluation tools that standardize their utilization are the main difficulties. Considering these issues, Portal of Games and Environments Management for Designing Activities in Health (Pegadas) was designed and developed as a web portal that offers the services of organizing and sequencing serious games and virtual environments and evaluating the performance of the user in these activities. This article presents the structure of Pegadas, including the proposal of an evaluation model based on learning objectives. The results indicate its potential to collaborate with human resources training from the proposal of the sequencing, allowing a linked composition of activities and providing the reinforcement or complement of tasks and contents in a progressive scale with planned educational objective-based evaluation. These results can contribute to expand the discussions about ways to integrate the use of these applications in health curricula.
RL4health: Crowdsourcing Reinforcement Learning for Knee Replacement Pathway Optimization
Venue: arXiv cs.LG 2019
Year: 2019

Joint replacement is the most common inpatient surgical treatment in the US. We investigate the clinical pathway optimization for knee replacement, which is a sequential decision process from onset to recovery. Based on episodic claims from previous cases, we view the pathway optimization as an intelligence crowdsourcing problem and learn the optimal decision policy from data by imitating the best expert at every intermediate state. We develop a reinforcement learning-based pipeline that uses value iteration, state compression and aggregation learning, kernel representation and cross validation to predict the best treatment policy. It also provides forecast of the clinical pathway under the optimized policy. Empirical validation shows that the optimized policy reduces the overall cost by 7 percent and reduces the excessive cost premium by 33 percent.
A Comparative Study of Milestones for Featuring GUI Prototyping Tools
Venue: arXiv cs.HC 2019
Year: 2019

Prototyping is one of the core activities of User-Centered Design (UCD) processes and an integral component of Human-Computer Interaction (HCI) research. For many years, prototyping was synonym of paper-based mockups and only more recently we can say that dedicated tools for supporting prototyping activities really reach the market. In this paper, we propose to analyze the evolution of prototyping tools for supporting the development process of interactive systems. For that, this paper presents a review of the literature. We analyze the tools proposed by academic community as a proof of concepts and/or support to research activities. Moreover, we also analyze prototyping tools that are available in the market. We report our observation in terms of features that appear over time and constitute milestones for understating the evolution of concerns related to the development and use of prototyping tools. This survey covers publications published since 1988 in some of the main HCI conferences and 118 commercial tools available on the web. The results enable a brief comparison of characteristics present in both academic and commercial tools, how they have evolved, and what are the gaps that can provide insights for future research and development.
A Dynamical System-based Key Equation for Decoding One-Point Algebraic-Geometry Codes
Venue: arXiv cs.IT 2019
Year: 2019

A closer look at linear recurring sequences allowed us to define the multiplication of a univariate polynomial and a sequence, viewed as a power series with another variable, resulting in another sequence. Extending this operation, one gets the multiplication of matrices of multivariate polynomials and vectors of powers series. A dynamical system, according to U. Oberst is then the kernel of the linear mapping of modules defined by a polynomial matrix by this operation. Applying these tools in the decoding of the so-called one point algebraic-geometry codes, after showing that the syndrome array, which is the general transform of the error in a received word is a linear recurring sequence, we construct a dynamical system. We then prove that this array is the solution of Cauchy's homogeneous equations with respect to the dynamical system. The aim of the Berlekamp-Massey-Sakata Algorithm in the decoding process being the determination of the syndrome array, we have proved that in fact, this algorithm solves the Cauchy's homogeneous equations with respect to a dynamical system.
Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914
Venue: arXiv cs.CL 2019
Year: 2019

We investigate some aspects of the history of antisemitism in France, one of the cradles of modern antisemitism, using diachronic word embeddings. We constructed a large corpus of French books and periodicals issues that contain a keyword related to Jews and performed a diachronic word embedding over the 1789-1914 period. We studied the changes over time in the semantic spaces of 4 target words and performed embedding projections over 6 streams of antisemitic discourse. This allowed us to track the evolution of antisemitic bias in the religious, economic, socio-politic, racial, ethic and conspiratorial domains. Projections show a trend of growing antisemitism, especially in the years starting in the mid-80s and culminating in the Dreyfus affair. Our analysis also allows us to highlight the peculiar adverse bias towards Judaism in the broader context of other religions.
Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, the problem of describing visual contents of a video sequence with natural language is addressed. Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) in a novel encoder-decoder-reconstructor architecture, which leverages both forward (video to sentence) and backward (sentence to video) flows for video captioning. Specifically, the encoder-decoder component makes use of the forward flow to produce a sentence description based on the encoded video semantic features. Two types of reconstructors are subsequently proposed to employ the backward flow and reproduce the video features from local and global perspectives, respectively, capitalizing on the hidden state sequence generated by the decoder. Moreover, in order to make a comprehensive reconstruction of the video features, we propose to fuse the two types of reconstructors together. The generation loss yielded by the encoder-decoder component and the reconstruction loss introduced by the reconstructor are jointly cast into training the proposed RecNet in an end-to-end fashion. Furthermore, the RecNet is fine-tuned by CIDEr optimization via reinforcement learning, which significantly boosts the captioning performance. Experimental results on benchmark datasets demonstrate that the proposed reconstructor can boost the performance of video captioning consistently.
Bridging the Gap between Unit Test Generation and System Test Generation
Venue: arXiv cs.SE 2019
Year: 2019

Common test generators fall into two categories. Generating test inputs at the unit level is fast, but can lead to false alarms when a function is called with inputs that would not occur in a system context. If a generated input at the system level causes a failure, this is a true alarm, as the input could also have come from the user or a third party; but system testing is much slower. In this paper, we introduce the concept of a test generation bridge, which joins the accuracy of system testing with the speed of unit testing. A Test Generation Bridge allows to combine an arbitrary system test generator with an arbitrary unit test generator. It does so by carving parameterized unit tests from system (test) executions. These unit tests run in a context recorded from the system test, but individual parameters are left free for the unit test generator to systematically explore. This allows symbolic test generators such as KLEE to operate on individual functions in the recorded system context. If the test generator detects a failure, we lift the failure-inducing parameter back to the system input; if the failure can be reproduced at the system level, it is reported as a true alarm. Our BASILISK prototype can extract and test units out of complex systems such as a Web/Python/SQLite/C stack; in its evaluation, it achieves a higher coverage than a state-of-the-art system test generator.
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence
Venue: arXiv cs.LG 2019
Year: 2019

The `Internet of Things' has brought increased demand for AI-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles. Quantization is a powerful tool to address the growing computational cost of such applications, and yields significant compression over full-precision networks. However, quantization can result in substantial loss of performance for complex image classification tasks. To address this, we propose a Principal Component Analysis (PCA) driven methodology to identify the important layers of a binary network, and design mixed-precision networks. The proposed Hybrid-Net achieves a more than 10% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving up to 94% of the energy-efficiency of XNOR-Nets. This work furthers the feasibility of using highly compressed neural networks for energy-efficient neural computing in edge devices.
Cross-Domain Cascaded Deep Feature Translation
Venue: arXiv cs.CV 2019
Year: 2019

In recent years we have witnessed tremendous progress in unpaired image-to-image translation methods, propelled by the emergence of DNNs and adversarial training strategies. However, most existing methods focus on transfer of style and appearance, rather than on shape translation. The latter task is challenging, due to its intricate non-local nature, which calls for additional supervision. We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation from and between these deep features. Specifically, we leverage VGG, which is a classification network, pre-trained with large-scale semantic supervision. Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we first translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones. We show that our method is able to translate between different domains, which exhibit significantly different shapes. We evaluate our method both qualitatively and quantitatively and compare it to state-of-the-art image-to-image translation methods. Our code and trained models will be made available.
Natural Vocabulary Emerges from Free-Form Annotations
Venue: arXiv cs.CV 2019
Year: 2019

We propose an approach for annotating object classes using free-form text written by undirected and untrained annotators. Free-form labeling is natural for annotators, they intuitively provide very specific and exhaustive labels, and no training stage is necessary. We first collect 729 labels on 15k images using 124 different annotators. Then we automatically enrich the structure of these free-form annotations by discovering a natural vocabulary of 4020 classes within them. This vocabulary represents the natural distribution of objects well and is learned directly from data, instead of being an educated guess done before collecting any labels. Hence, the natural vocabulary emerges from a large mass of free-form annotations. To do so, we (i) map the raw input strings to entities in an ontology of physical objects (which gives them an unambiguous meaning); and (ii) leverage inter-annotator co-occurrences, as well as biases and knowledge specific to individual annotators. Finally, we also automatically extract natural vocabularies of reduced size that have high object coverage while remaining specific. These reduced vocabularies represent the natural distribution of objects much better than commonly used predefined vocabularies. Moreover, they feature more uniform sample distribution over classes.
Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images
Venue: arXiv cs.CV 2019
Year: 2019

We show that generative models can be used to capture visual geometry constraints statistically. We use this fact to infer the 3D shape of object categories from raw single-view images. Differently from prior work, we use no external supervision, nor do we use multiple views or videos of the objects. We achieve this by a simple reconstruction task, exploiting the symmetry of the objects' shape and albedo. Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder. Our experiments show that this method can recover the 3D shape of human faces, cat faces, and cars from single view images, without supervision. On benchmarks, we demonstrate superior accuracy compared to other methods that use supervision at the level of 2D image correspondences.
Finding Syntactic Representations in Neural Stacks
Venue: arXiv cs.CL 2019
Year: 2019

Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable. In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure. Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives. We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure.
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction
Venue: arXiv cs.CL 2019
Year: 2019

Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits. At the same time, advancements in language modeling have managed to generate linguistic output, which is almost indistinguishable from that of human-generated text. In this paper, we up the ante by exploring the potential of more sophisticated language models in GEC and offer some key insights on their strengths and weaknesses. We show that, in line with recent results in other NLP tasks, Transformer architectures achieve consistently high performance and provide a competitive baseline for future machine learning models.
Locally Differentially Private Data Collection and Analysis
Venue: arXiv cs.CR 2019
Year: 2019

Local differential privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data. Due to its powerfulness, LDP has been widely adopted to protect privacy in various tasks (e.g., heavy hitters discovery, probability estimation) and systems (e.g., Google Chrome, Apple iOS). Although $ε$-LDP has been proposed for many years, the more general notion of $(ε, δ)$-LDP has only been studied in very few papers, which mainly consider mean estimation for numeric data. Besides, prior solutions achieve $(ε, δ)$-LDP by leveraging Gaussian mechanism, which leads to low accuracy of the aggregated results. In this paper, we propose novel mechanisms that achieve $(ε, δ)$-LDP with high utility in data analytics and machine learning. Specifically, we first design $(ε, δ)$-LDP algorithms for collecting multi-dimensional numeric data, which can ensure higher accuracy than the optimal Gaussian mechanism while guaranteeing strong privacy for each user. Then, we investigate different local protocols for categorical attributes under $(ε, δ)$-LDP. Furthermore, we conduct theoretical analysis on the error bound and variance of the proposed algorithms. Experimental results on real and synthetic datasets demonstrate the high data utility of our proposed algorithms on both simple data statistics and complex machine learning models.
Owner-centric sharing of physical resources, data, and data-driven insights in digital ecosystems
Venue: arXiv cs.CR 2019
Year: 2019

We are living in an age in which digitization will connect more and more physical assets with IT systems and where IoT endpoints will generate a wealth of valuable data. Companies, individual users, and organizations alike therefore have the need to control their own physical or non-physical assets and data sources. At the same time, they recognize the need for, and opportunity to, share access to such data and digitized physical assets. This paper sets out our technology vision for such sharing ecosystems, reports initial work in that direction, identifies challenges for realizing this vision, and seeks feedback and collaboration from the academic access-control community in that R\&D space.
Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (CNN). In the first stage, a U-Net is trained for the down-sampled 3D volume segmentation. Then a candidate region covering the pancreas is extracted from the estimated labels. Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage. We evaluated the performance of the proposed method on the NIH computed tomography (CT) dataset, and verified its superiority over other state-of-the-art 2D and 3D approaches for pancreas segmentation in terms of dice-sorensen coefficient (DSC) accuracy in testing. The mean DSC of the proposed method is 85.99%.
Practical Byte-Granular Memory Blacklisting using Califorms
Venue: arXiv cs.CR 2019
Year: 2019

Recent rapid strides in memory safety tools and hardware have improved software quality and security. While coarse-grained memory safety has improved, achieving memory safety at the granularity of individual objects remains a challenge due to high performance overheads which can be between ~1.7x-2.2x. In this paper, we present a novel idea called Califorms, and associated program observations, to obtain a low overhead security solution for practical, byte-granular memory safety. The idea we build on is called memory blacklisting, which prohibits a program from accessing certain memory regions based on program semantics. State of the art hardware-supported memory blacklisting while much faster than software blacklisting creates memory fragmentation (of the order of few bytes) for each use of the blacklisted location. In this paper, we observe that metadata used for blacklisting can be stored in dead spaces in a program's data memory and that this metadata can be integrated into microarchitecture by changing the cache line format. Using these observations, Califorms based system proposed in this paper reduces the performance overheads of memory safety to ~1.02x-1.16x while providing byte-granular protection and maintaining very low hardware overheads. The low overhead offered by Califorms enables always on, memory safety for small and large objects alike, and the fundamental idea of storing metadata in empty spaces, and microarchitecture can be used for other security and performance applications.