OpenRaiser/Intern-Atlas huggingface dataset:
Brand > Logo: Visual Analysis of Fashion Brands
Venue: arXiv cs.CV 2018
Year: 2018

While lots of people may think branding begins and ends with a logo, fashion brands communicate their uniqueness through a wide range of visual cues such as color, patterns and shapes. In this work, we analyze learned visual representations by deep networks that are trained to recognize fashion brands. In particular, the activation strength and extent of neurons are studied to provide interesting insights about visual brand expressions. The proposed method identifies where a brand stands in the spectrum of branding strategy, i.e., from trademark-emblazoned goods with bold logos to implicit no logo marketing. By quantifying attention maps, we are able to interpret the visual characteristics of a brand present in a single image and model the general design direction of a brand as a whole. We further investigate versatility of neurons and discover "specialists" that are highly brand-specific and "generalists" that detect diverse visual features. A human experiment based on three main visual scenarios of fashion brands is conducted to verify the alignment of our quantitative measures with the human perception of brands. This paper demonstrate how deep networks go beyond logos in order to recognize clothing brands in an image.
Preprocessor Selection for Machine Learning Pipelines
Venue: arXiv cs.LG 2018
Year: 2018

Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing. Yet, it is generally well accepted that machine learning applications require not only model building, but also data preprocessing. In other words, practical solutions consist of pipelines of machine learning operators rather than single algorithms. Interestingly, our experiments suggest that, on average, data preprocessing hinders accuracy, while the best performing pipelines do actually make use of preprocessors. Here, we conduct an extensive empirical study over a wide range of learning algorithms and preprocessors, and use metalearning to determine when one should make use of preprocessors in ML pipeline design.
A Fusion Approach for Multi-Frame Optical Flow Estimation
Venue: arXiv cs.CV 2018
Year: 2018

To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.
On the log-normality of the degree distribution in large homogeneous binary multiplicative attribute graph models
Venue: arXiv cs.SI 2018
Year: 2018

The muliplicative attribute graph (MAG) model was introduced by Kim and Leskovec as a mathematically tractable model for networks where network structure is believed to be shaped by features or attributes associated with individual nodes. For large homogeneous binary MAGs, they argued through approximation arguments that the "tail of [the] degree distribution follows a log-normal distribution" as the number of nodes becomes unboundedly large and the number of attributes scales logarithmically with the number of nodes. Under the same limiting regime, we revisit the asymptotic behavior of the degree distribution: Under weaker conditions we obtain a precise convergence result to log-normality, develop from it reasoned log-normal approximations to the degree distribution and derive various rates of convergence. In particular, we show that a certain transformation of the node degree converges in distribution to a log-normal distribution, and give its convergence rate in the form of a Berry-Esseen type estimate.
Joint Transaction Transmission and Channel Selection in Cognitive Radio Based Blockchain Networks: A Deep Reinforcement Learning Approach
Venue: arXiv cs.NI 2018
Year: 2018

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.
A Whole-Body Model Predictive Control Scheme Including External Contact Forces and CoM Height Variations
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we present an approach for generating a variety of whole-body motions for a humanoid robot. We extend the available Model Predictive Control (MPC) approaches for walking on flat terrain to plan for both vertical motion of the Center of Mass (CoM) and external contact forces consistent with a given task. The optimization problem is comprised of three stages, i. e. the CoM vertical motion, joint angles, and contact forces planning. The choice of external contact (e. g. hand contact with the object or environment) among all available locations and the appropriate time to reach and maintain a contact are all computed automatically within the algorithm. The presented algorithm benefits from the simplicity of the Linear Inverted Pendulum Model (LIPM), while it overcomes the common limitations of this model and enables us to generate a variety of whole-body motions through external contacts. Simulation and experimental implementation of several whole-body actions in multi-contact scenarios on a humanoid robot show the capability of the proposed algorithm.
TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets
Venue: arXiv cs.IR 2018
Year: 2018

Publicly available social media archives facilitate research in a variety of fields, such as data science, sociology or the digital humanities, where Twitter has emerged as one of the most prominent sources. However, obtaining, archiving and annotating large amounts of tweets is costly. In this paper, we describe TweetsKB, a publicly available corpus of currently more than 1.5 billion tweets, spanning almost 5 years (Jan'13-Nov'17). Metadata information about the tweets as well as extracted entities, hashtags, user mentions and sentiment information are exposed using established RDF/S vocabularies. Next to a description of the extraction and annotation process, we present use cases to illustrate scenarios for entity-centric information exploration, data integration and knowledge discovery facilitated by TweetsKB.
From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
Venue: arXiv cs.CV 2018
Year: 2018

Previous approaches using deep learning algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using spectral-domain optical coherence tomography (SDOCT) data to train a deep learning algorithm to quantify glaucomatous structural damage on optic disc photographs. The dataset included 32,820 pairs of optic disc photos and SDOCT retinal nerve fiber layer (RNFL) scans from 2,312 eyes of 1,198 subjects. A deep learning convolutional neural network was trained to assess optic disc photographs and predict SDOCT average RNFL thickness. The performance of the algorithm was evaluated in an independent test sample. The mean prediction of average RNFL thickness from all 6,292 optic disc photos in the test set was 83.3$\pm$14.5 $μ$m, whereas the mean average RNFL thickness from all corresponding SDOCT scans was 82.5$\pm$16.8 $μ$m (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (r = 0.832; P<0.001), with mean absolute error of the predictions of 7.39 $μ$m. The areas under the receiver operating characteristic curves for discriminating glaucoma from healthy eyes with the deep learning predictions and actual SDOCT measurements were 0.944 (95$\%$ CI: 0.912- 0.966) and 0.940 (95$\%$ CI: 0.902 - 0.966), respectively (P = 0.724). In conclusion, we introduced a novel deep learning approach to assess optic disc photographs and provide quantitative information about the amount of neural damage. This approach could potentially be used to diagnose and stage glaucomatous damage from optic disc photographs.
Computing control invariant sets in high dimension is easy
Venue: arXiv cs.SY 2018
Year: 2018

In this paper we consider the problem of computing control invariant sets for linear controlled high-dimensional systems with constraints on the input and on the states. Set inclusions conditions for control invariance are presented that involve the N-step sets and are posed in form of linear programming problems. Such conditions allow to overcome the complexity limitation inherent to the set addition and vertices enumeration and can be applied also to high dimensional systems. The efficiency and scalability of the method are illustrated by computing approximations of the maximal control invariant set, based on the 10-step operator, for a system whose state and input dimensions are 30 and 15, respectively.
Preserving Both Privacy and Utility in Network Trace Anonymization
Venue: arXiv cs.CR 2018
Year: 2018

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. In this paper, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces; those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We present the general approach and instantiate it based on CryptoPAn. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The results show that our approach can significantly reduce the level of information leakage (e.g., less than 1\% of the information leaked by CryptoPAn) with comparable utility.
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either accurate predictions of patients prognoses or clinically interpretable representations of disease pathophysiology, but not both. In this paper, we develop the phased attentive state space (PASS) model of disease progression, a deep probabilistic model that captures complex representations for disease progression while maintaining clinical interpretability. Unlike Markovian state space models which assume memoryless dynamics, PASS uses an attention mechanism to induce "memoryful" state transitions, whereby repeatedly updated attention weights are used to focus on past state realizations that best predict future states. This gives rise to complex, non-stationary state dynamics that remain interpretable through the generated attention weights, which designate the relationships between the realized state variables for individual patients. PASS uses phased LSTM units (with time gates controlled by parametrized oscillations) to generate the attention weights in continuous time, which enables handling irregularly-sampled and potentially missing medical observations. Experiments on data from a realworld cohort of patients show that PASS successfully balances the tradeoff between accuracy and interpretability: it demonstrates superior predictive accuracy and learns insightful individual-level representations of disease progression.
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning
Venue: arXiv cs.LG 2018
Year: 2018

This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces. The constitutive models are conceptualized as information flow in directed graphs. The process of writing constitutive models are simplified as a sequence of forming graph edges with the goal of maximizing the model score (a function of accuracy, robustness and forward prediction quality). Thus meta-modeling can be formulated as a Markov decision process with well-defined states, actions, rules, objective functions, and rewards. By using neural networks to estimate policies and state values, the computer agent is able to efficiently self-improve the constitutive model it generated through self-playing, in the same way AlphaGo Zero (the algorithm that outplayed the world champion in the game of Go)improves its gameplay. Our numerical examples show that this automated meta-modeling framework not only produces models which outperform existing cohesive models on benchmark traction-separation data but is also capable of detecting hidden mechanisms among micro-structural features and incorporating them in constitutive models to improve the forward prediction accuracy, which are difficult tasks to do manually.
Fast and accurate object detection in high resolution 4K and 8K video using GPUs
Venue: arXiv cs.CV 2018
Year: 2018

Machine learning has celebrated a lot of achievements on computer vision tasks such as object detection, but the traditionally used models work with relatively low resolution images. The resolution of recording devices is gradually increasing and there is a rising need for new methods of processing high resolution data. We propose an attention pipeline method which uses two staged evaluation of each image or video frame under rough and refined resolution to limit the total number of necessary evaluations. For both stages, we make use of the fast object detection model YOLO v2. We have implemented our model in code, which distributes the work across GPUs. We maintain high accuracy while reaching the average performance of 3-6 fps on 4K video and 2 fps on 8K video.
Clinical Concept Extraction with Contextual Word Embedding
Venue: arXiv cs.CL 2018
Year: 2018

Automatic extraction of clinical concepts is an essential step for turning the unstructured data within a clinical note into structured and actionable information. In this work, we propose a clinical concept extraction model for automatic annotation of clinical problems, treatments, and tests in clinical notes utilizing domain-specific contextual word embedding. A contextual word embedding model is first trained on a corpus with a mixture of clinical reports and relevant Wikipedia pages in the clinical domain. Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model. We tested our proposed model on the I2B2 2010 challenge dataset. Our proposed model achieved the best performance among reported baseline models and outperformed the state-of-the-art models by 3.4% in terms of F1-score.
Visual Rendering of Shapes on 2D Display Devices Guided by Hand Gestures
Venue: arXiv cs.HC 2018
Year: 2018

Designing of touchless user interface is gaining popularity in various contexts. Using such interfaces, users can interact with electronic devices even when the hands are dirty or non-conductive. Also, user with partial physical disability can interact with electronic devices using such systems. Research in this direction has got major boost because of the emergence of low-cost sensors such as Leap Motion, Kinect or RealSense devices. In this paper, we propose a Leap Motion controller-based methodology to facilitate rendering of 2D and 3D shapes on display devices. The proposed method tracks finger movements while users perform natural gestures within the field of view of the sensor. In the next phase, trajectories are analyzed to extract extended Npen++ features in 3D. These features represent finger movements during the gestures and they are fed to unidirectional left-to-right Hidden Markov Model (HMM) for training. A one-to-one mapping between gestures and shapes is proposed. Finally, shapes corresponding to these gestures are rendered over the display using MuPad interface. We have created a dataset of 5400 samples recorded by 10 volunteers. Our dataset contains 18 geometric and 18 non-geometric shapes such as "circle", "rectangle", "flower", "cone", "sphere" etc. The proposed methodology achieves an accuracy of 92.87% when evaluated using 5-fold cross validation method. Our experiments revel that the extended 3D features perform better than existing 3D features in the context of shape representation and classification. The method can be used for developing useful HCI applications for smart display devices.
Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance
Venue: arXiv cs.SY 2018
Year: 2018

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.
Learning to Route Efficiently with End-to-End Feedback: The Value of Networked Structure
Venue: arXiv cs.LG 2018
Year: 2018

We introduce efficient algorithms which achieve nearly optimal regrets for the problem of stochastic online shortest path routing with end-to-end feedback. The setting is a natural application of the combinatorial stochastic bandits problem, a special case of the linear stochastic bandits problem. We show how the difficulties posed by the large scale action set can be overcome by the networked structure of the action set. Our approach presents a novel connection between bandit learning and shortest path algorithms. Our main contribution is an adaptive exploration algorithm with nearly optimal instance-dependent regret for any directed acyclic network. We then modify it so that nearly optimal worst case regret is achieved simultaneously. Driven by the carefully designed Top-Two Comparison (TTC) technique, the algorithms are efficiently implementable. We further conduct extensive numerical experiments to show that our proposed algorithms not only achieve superior regret performances, but also reduce the runtime drastically.
Multi-Channel Auto-Encoder for Speech Emotion Recognition
Venue: arXiv cs.SD 2018
Year: 2018

Inferring emotion status from users' queries plays an important role to enhance the capacity in voice dialogues applications. Even though several related works obtained satisfactory results, the performance can still be further improved. In this paper, we proposed a novel framework named multi-channel auto-encoder (MTC-AE) on emotion recognition from acoustic information. MTC-AE contains multiple local DNNs based on different low-level descriptors with different statistics functions that are partly concatenated together, by which the structure is enabled to consider both local and global features simultaneously. Experiment based on a benchmark dataset IEMOCAP shows that our method significantly outperforms the existing state-of-the-art results, achieving $64.8\%$ leave-one-speaker-out unweighted accuracy, which is $2.4\%$ higher than the best result on this dataset.
Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health
Venue: arXiv cs.LG 2018
Year: 2018

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation. The classifier achieved an AUC of 0.677 with precision and recall of 0.271 and 0.429, respectively, indicating a learned association between disease signatures in collected images. Periodontal diseases were more prevalent among males (p=0.0012) and older subjects (p=0.0224) in the screened population. Physicians independently examined the collected images, assigning localized modified gingival indices (MGIs). MGIs and periodontal disease were then cross-correlated with responses to a medical history questionnaire, blood pressure and body mass index measurements, and optic nerve, tympanic membrane, neurological, and cardiac rhythm imaging examinations. Gingivitis and early periodontal disease were associated with subjects diagnosed with optic nerve abnormalities (p <0.0001) in their retinal scans. We also report significant co-occurrences of periodontal disease in subjects reporting swollen joints (p=0.0422) and a family history of eye disease (p=0.0337). These results indicate cross-correlation of poor periodontal health with systemic health outcomes and stress the importance of oral health screenings at the primary care level. Our screening process and analysis method, using images and machine learning, can be generalized for automated diagnoses and systemic health screenings for other diseases.