OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Classification of Bright Retinal Lesions via Deep Network Features
Venue: arXiv cs.CV 2017
Year: 2017

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.
External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the mTOR pathway
Venue: arXiv cs.CL 2017
Year: 2017

This paper evaluates the impact of various event extraction systems on automatic pathway curation using the popular mTOR pathway. We quantify the impact of training data sets as well as different machine learning classifiers and show that some improve the quality of automatically extracted pathways.
Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks
Venue: arXiv cs.LG 2017
Year: 2017

We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning. Our DAN approach builds upon generative adversarial networks (GANs) and conditional GANs but includes the key differentiator of using two discriminators instead of a generator and a discriminator. DAN can be seen as a framework to learn loss functions for predictors that also implements semi-supervised learning in a straightforward manner. We propose instantiations of DAN for two different prediction tasks: classification and ranking. Our experimental results on three datasets of different tasks demonstrate that DAN is a promising framework for both semi-supervised learning and learning loss functions for predictors. For all tasks, the semi-supervised capability of DAN can significantly boost the predictor performance for small labeled sets with minor architecture changes across tasks. Moreover, the loss functions automatically learned by DANs are very competitive and usually outperform the standard pairwise and negative log-likelihood loss functions for both semi-supervised and supervised learning.
A Lower Bound Technique for Communication in BSP
Venue: arXiv cs.DC 2017
Year: 2017

Communication is a major factor determining the performance of algorithms on current computing systems; it is therefore valuable to provide tight lower bounds on the communication complexity of computations. This paper presents a lower bound technique for the communication complexity in the bulk-synchronous parallel (BSP) model of a given class of DAG computations. The derived bound is expressed in terms of the switching potential of a DAG, that is, the number of permutations that the DAG can realize when viewed as a switching network. The proposed technique yields tight lower bounds for the fast Fourier transform (FFT), and for any sorting and permutation network. A stronger bound is also derived for the periodic balanced sorting network, by applying this technique to suitable subnetworks. Finally, we demonstrate that the switching potential captures communication requirements even in computational models different from BSP, such as the I/O model and the LPRAM.
Measured Multiseries and Integration
Venue: arXiv cs.SC 2017
Year: 2017

A paper by Bruno Salvy and the author introduced measured multiseries and gave an algorithm to compute these for a large class of elementary functions, modulo a zero-equivalence method for constants. This gave a theoretical background for the implementation that Salvy was developing at that time. The main result of the present article is an algorithm to calculate measured multiseries for integrals of functions of the form h*sin G, where h and G belong to a Hardy field. The process can reiterated with the resulting algebra, and also applied to solutions of a second order differential equation of a particular form.
Generative Adversarial Models for People Attribute Recognition in Surveillance
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing ...) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often occur in surveillance footages by enhancing the images by means of Deep Convolutional Generative Adversarial Networks (DCGAN). Experiments show that by combining both our Generative Reconstruction and Deep Attribute Classification Network we can effectively extract attributes even when resolution is poor and in presence of strong occlusions up to 80\% of the whole person figure.
Fair Personalization
Venue: arXiv cs.CY 2017
Year: 2017

Personalization is pervasive in the online space as, when combined with learning, it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studies suggest that such personalization can propagate societal or systemic biases, which has led to calls for regulatory mechanisms and algorithms to combat inequality. Here we propose a rigorous algorithmic framework that allows for the possibility to control biased or discriminatory personalization with respect to sensitive attributes of users without losing all of the benefits of personalization.
Text Summarization Techniques: A Brief Survey
Venue: arXiv cs.CL 2017
Year: 2017

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.
Emergence of Locomotion Behaviours in Rich Environments
Venue: arXiv cs.AI 2017
Year: 2017

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully hand-design the reward function to encourage a particular solution, or to derive it from demonstration data. In this paper explore how a rich environment can help to promote the learning of complex behavior. Specifically, we train agents in diverse environmental contexts, and find that this encourages the emergence of robust behaviours that perform well across a suite of tasks. We demonstrate this principle for locomotion -- behaviours that are known for their sensitivity to the choice of reward. We train several simulated bodies on a diverse set of challenging terrains and obstacles, using a simple reward function based on forward progress. Using a novel scalable variant of policy gradient reinforcement learning, our agents learn to run, jump, crouch and turn as required by the environment without explicit reward-based guidance. A visual depiction of highlights of the learned behavior can be viewed following https://youtu.be/hx_bgoTF7bs .
Limitations on the Achievable Repair Bandwidth of Piggybacking Codes with Low Substriping
Venue: arXiv cs.IT 2017
Year: 2017

The piggybacking framework for designing erasure codes for distributed storage has empirically proven to be very useful, and has been used to design codes with desirable properties, such as low repair bandwidth and complexity. However, the theoretical properties of this framework remain largely unexplored. We address this by adapting a general characterization of repair schemes (previously used for Reed Solomon codes) to analyze piggybacking codes with low substriping. With this characterization, we establish a separation between piggybacking and general erasure codes, and several impossibility results for subcategories of piggybacking codes; for certain parameters, we also present explicit, optimal constructions of piggybacking codes.
Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures
Venue: arXiv cs.CV 2017
Year: 2017

We present a simple dynamic batching approach applicable to a large class of dynamic architectures that consistently yields speedups of over 10x. We provide performance bounds when the architecture is not known a priori and a stronger bound in the special case where the architecture is a predetermined balanced tree. We evaluate our approach on Johnson et al.'s recent visual question answering (VQA) result of his CLEVR dataset by Inferring and Executing Programs (IEP). We also evaluate on sparsely gated mixture of experts layers and achieve speedups of up to 1000x over the naive implementation.
Translation-based Recommendation
Venue: arXiv cs.IR 2017
Year: 2017

Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is predicting users' personalized sequential behavior (or `next-item' recommendation), where the challenges mainly lie in modeling `third-order' interactions between a user, her previously visited item(s), and the next item to consume. Existing methods typically decompose these higher-order interactions into a combination of pairwise relationships, by way of which user preferences (user-item interactions) and sequential patterns (item-item interactions) are captured by separate components. In this paper, we propose a unified method, TransRec, to model such third-order relationships for large-scale sequential prediction. Methodologically, we embed items into a `transition space' where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets. Data and code are available at https://sites.google.com/a/eng.ucsd.edu/ruining-he/.
Efficient Dynamic Approximate Distance Oracles for Vertex-Labeled Planar Graphs
Venue: arXiv cs.DS 2017
Year: 2017

Let $G$ be a graph where each vertex is associated with a label. A Vertex-Labeled Approximate Distance Oracle is a data structure that, given a vertex $v$ and a label $λ$, returns a $(1+\varepsilon)$-approximation of the distance from $v$ to the closest vertex with label $λ$ in $G$. Such an oracle is dynamic if it also supports label changes. In this paper we present three different dynamic approximate vertex-labeled distance oracles for planar graphs, all with polylogarithmic query and update times, and nearly linear space requirements.
Stability, Fairness and Random Walks in the Bargaining Problem
Venue: arXiv cs.GT 2017
Year: 2017

We study the classical bargaining problem and its two canonical solutions, (Nash and Kalai-Smorodinsky), from a novel point of view: we ask for stability of the solution if both players are able distort the underlying bargaining process by reference to a third party (e.g. a court). By exploring the simplest case, where decisions of the third party are made randomly we obtain a stable solution, where players do not have any incentive to refer to such a third party. While neither the Nash nor the Kalai-Smorodinsky solution are able to ensure stability in case reference to a third party is possible, we found that the Kalai-Smorodinsky solution seems to always dominate the stable allocation which constitutes novel support in favor of the latter.
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Venue: arXiv cs.CL 2017
Year: 2017

The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual information. However, such a model could still make mistakes if its features favor a wrong entity type. In this paper, we utilize Wikipedia as an open knowledge base to improve multilingual NER systems. Central to our approach is the construction of high-accuracy, high-coverage multilingual Wikipedia entity type mappings. These mappings are built from weakly annotated data and can be extended to new languages with no human annotation or language-dependent knowledge involved. Based on these mappings, we develop several approaches to improve an NER system. We evaluate the performance of the approaches via experiments on NER systems trained for 6 languages. Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18.3 F1 score improvement).
Exploiting Active Subspaces in Global Optimization: How Complex is your Problem?
Venue: arXiv cs.NE 2017
Year: 2017

When applying optimization method to a real-world problem, the possession of prior knowledge and preliminary analysis on the landscape of a global optimization problem can give us an insight into the complexity of the problem. This knowledge can better inform us in deciding what optimization method should be used to tackle the problem. However, this analysis becomes problematic when the dimensionality of the problem is high. This paper presents a framework to take a deeper look at the global optimization problem to be tackled: by analyzing the low-dimensional representation of the problem through discovering the active subspaces of the given problem. The virtue of this is that the problem's complexity can be visualized in a one or two-dimensional plot, thus allow one to get a better grip about the problem's difficulty. One could then have a better idea regarding the complexity of their problem to determine the choice of global optimizer or what surrogate-model type to be used. Furthermore, we also demonstrate how the active subspaces can be used to perform design exploration and analysis.
Multiantenna Quantum Backscatter Communications
Venue: arXiv cs.IT 2017
Year: 2017

Quantum illumination (QI) is a revolutionary photonic quantum sensing paradigm that enhances the sensitivity of photodetection in noisy and lossy environments. The QI concept has been recently used to propose a quantum backscatter communication (QBC), with the aim of increasing the receiver sensitivity beyond the limits of its classical counterpart. One of the practical challenges in microwave QI is the slow rate at which the entangled microwave modes can be generated. Here, we propose to mitigate this problem by using a multiple-input multiple-output antenna system to synthetically increase the number of efficiently-distinguishable modes in the QBC context.
Dynamic clustering to minimize the sum of radii
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we study the problem of opening centers to cluster a set of clients in a metric space so as to minimize the sum of the costs of the centers and of the cluster radii, in a dynamic environment where clients arrive and depart, and the solution must be updated efficiently while remaining competitive with respect to the current optimal solution. We call this dynamic sum-of-radii clustering problem. We present a data structure that maintains a solution whose cost is within a constant factor of the cost of an optimal solution in metric spaces with bounded doubling dimension and whose worst-case update time is logarithmic in the parameters of the problem.
Mathematical Models of Adaptation in Human-Robot Collaboration
Venue: arXiv cs.RO 2017
Year: 2017

A robot operating in isolation needs to reason over the uncertainty in its model of the world and adapt its own actions to account for this uncertainty. Similarly, a robot interacting with people needs to reason over its uncertainty over the human internal state, as well as over how this state may change, as humans adapt to the robot. This paper summarizes our own work in this area, which depicts the different ways that probabilistic planning and game-theoretic algorithms can enable such reasoning in robotic systems that collaborate with people. We start with a general formulation of the problem as a two-player game with incomplete information. We then articulate the different assumptions within this general formulation, and we explain how these lead to exciting and diverse robot behaviors in real-time interactions with actual human subjects, in a variety of manufacturing, personal robotics and assistive care settings.
Exploiting the Tradeoff between Program Accuracy and Soft-error Resiliency Overhead for Machine Learning Workloads
Venue: arXiv cs.DC 2017
Year: 2017

To protect multicores from soft-error perturbations, resiliency schemes have been developed with high coverage but high power and performance overheads. Emerging safety-critical machine learning applications are increasingly being deployed on these platforms. Moreover, these systems are exposed to harsh environments, such as unmanned aerial vehicles (UAVs) and self-driving cars. Due to the unique structure and computational behavior of such applications, research has been done on relaxing their accuracy for performance benefits. We observe that not all transient errors affect program correctness, some errors only affect program accuracy, i.e., the program completes with certain acceptable deviations from error free outcome. This paper illustrates the idea of cross-layer soft-error resilience using machine learning workloads, where program accuracy is introduced as a tradeoff to deliver resilient yet efficient execution on futuristic large-scale multicores.
Vision-Based Classification of Social Gestures in Videochat Sessions
Venue: arXiv cs.HC 2017
Year: 2017

This paper describes the design and evaluation of the vision-based classification of social gestures, such as handshake, hug, high-five, etc. This is a component of the mediated social touch systems, which can be incorporated into ShareTable and SqueezeBands system to achieve automated gestures recognition and transmission of the touch between the users in real time. The results from our pilot study show the recognition accuracy of each gestures, and they indicate that significant future work is necessary to improve its practical feasibility in the mediated social touch applications.
Case For Static AMSDU Aggregation in WLANs
Venue: arXiv cs.NI 2017
Year: 2017

Frame aggregation is a mechanism by which multiple frames are combined into a single transmission unit over the air. Frames aggregated at the AMSDU level use a common CRC check to enforce integrity. For longer aggregated AMSDU frames, the packet error rate increases significantly for the same bit error rate. Hence, multiple studies have proposed doing AMSDU aggregation adaptively based on the error rate. This study evaluates if there is a \emph{practical} advantage in doing adaptive AMSDU aggregation based on the link bit error rate. Evaluations on a model show that instead of implementing a complex adaptive AMSDU frame aggregation mechanism which impact queuing and other implementation aspects, it is easier to influence packet error rate with traditional mechanisms while keeping the AMSDU aggregation logic simple.
Synthesis-based Robust Low Resolution Face Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Recognition of low resolution face images is a challenging problem in many practical face recognition systems. Methods have been proposed in the face recognition literature for the problem which assume that the probe is low resolution, but a high resolution gallery is available for recognition. These attempts have been aimed at modifying the probe image such that the resultant image provides better discrimination. We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a dictionary learning approach for classifying the low-resolution probe image. An important feature of our algorithm is that it can handle resolution change along with illumination variations. Furthermore, we also kernelize the algorithm to handle non-linearity in data and present a joint dictionary learning technique for robust recognition at low resolutions. The effectiveness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset. It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms.
Improving speaker turn embedding by crossmodal transfer learning from face embedding
Venue: arXiv cs.CV 2017
Year: 2017

Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has been proven very successful for face verification and clustering tasks. Assuming that face and voices from the same identities share some latent properties (like age, gender, ethnicity), we propose three transfer learning approaches to leverage the knowledge from the face domain (learned from thousands of images and identities) for tasks in the speaker domain. These approaches, namely target embedding transfer, relative distance transfer, and clustering structure transfer, utilize the structure of the source face embedding space at different granularities to regularize the target speaker turn embedding space as optimizing terms. Our methods are evaluated on two public broadcast corpora and yield promising advances over competitive baselines in verification and audio clustering tasks, especially when dealing with short speaker utterances. The analysis of the results also gives insight into characteristics of the embedding spaces and shows their potential applications.
Model Identification and Controller Parameter Optimization for an Autopilot Design for Autonomous Underwater Vehicles
Venue: arXiv cs.RO 2017
Year: 2017

Nowadays an accurate modeling of the system to be controlled is essential for reliable autopilot. This paper presents a non-linear model of the autonomous underwater vehicle 'CWolf'. Matrices and the corresponding coefficients generate a parameterized representation for added mass, Coriolis and centripetal forces, damping, gravity and buoyancy, using the equations of motion, for all six degrees of freedom. The determination of actuator behaviour by surge tests allows the conversion of propeller revolutions to the respective forces and moments. Based on geometric approximations, the coefficients of the model can be specified by optimization algorithms in 'open loop' sea trials. The realistic model is the basis for the subsequent design of the autopilot. The reference variables used in the four decoupled adaptive PID controllers for surge, heading, pitch and heave are provided a 'Line of Sight' - guidance system. A constraint criteria optimization determines the required controller parameters. The verification by 'closed loop' sea trials ensures the results.
Adaptive Binarization for Weakly Supervised Affordance Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

The concept of affordance is important to understand the relevance of object parts for a certain functional interaction. Affordance types generalize across object categories and are not mutually exclusive. This makes the segmentation of affordance regions of objects in images a difficult task. In this work, we build on an iterative approach that learns a convolutional neural network for affordance segmentation from sparse keypoints. During this process, the predictions of the network need to be binarized. In this work, we propose an adaptive approach for binarization and estimate the parameters for initialization by approximated cross validation. We evaluate our approach on two affordance datasets where our approach outperforms the state-of-the-art for weakly supervised affordance segmentation.