OpenRaiser/Intern-Atlas huggingface dataset:
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
Venue: arXiv cs.AI 2018
Year: 2018

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and effective. We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an advanced data structure that allows for fast updates and lookups of the current paths of all agents in an online setting. The resulting MAPD algorithm TP-SIPPwRT takes kinematic constraints of real robots into account directly during planning, computes continuous agent movements with given velocities that work on non-holonomic robots rather than discrete agent movements with uniform velocity, and is complete for well-formed MAPD instances. We demonstrate its benefits for automated warehouses using both an agent simulator and a standard robot simulator. For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.
What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions
Venue: arXiv cs.AI 2018
Year: 2018

One of the core challenges in Visual Dialogue problems is asking the question that will provide the most useful information towards achieving the required objective. Encouraging an agent to ask the right questions is difficult because we don't know a-priori what information the agent will need to achieve its task, and we don't have an explicit model of what it knows already. We propose a solution to this problem based on a Bayesian model of the uncertainty in the implicit model maintained by the visual dialogue agent, and in the function used to select an appropriate output. By selecting the question that minimises the predicted regret with respect to this implicit model the agent actively reduces ambiguity. The Bayesian model of uncertainty also enables a principled method for identifying when enough information has been acquired, and an action should be selected. We evaluate our approach on two goal-oriented dialogue datasets, one for visual-based collaboration task and the other for a negotiation-based task. Our uncertainty-aware information-seeking model outperforms its counterparts in these two challenging problems.
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
Venue: arXiv cs.DC 2018
Year: 2018

Recently, deep neural networks (DNNs) have been widely applied in mobile intelligent applications. The inference for the DNNs is usually performed in the cloud. However, it leads to a large overhead of transmitting data via wireless network. In this paper, we demonstrate the advantages of the cloud-edge collaborative inference with quantization. By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed. We study the effectiveness of mixed-precision collaborative inference of state-of-the-art DNNs by using ImageNet dataset. The experimental results show that our framework can generate reasonable network partitions and reduce the storage on mobile devices with trivial loss of accuracy.
New tab page recommendations cause a strong suppression of exploratory web browsing behaviors
Venue: arXiv cs.HC 2018
Year: 2018

Through a combination of experimental and simulation results, we illustrate that passive recommendations encoded in typical computer user-interfaces (UIs) can subdue users' natural proclivity to access diverse information sources. Inspired by traditional demonstrations of a part-set cueing effect in the cognitive science literature, we performed an online experiment manipulating the operation of the 'New Tab' page for consenting volunteers over a two month period. Examination of their browsing behavior reveals that typical frequency and recency-based methods for displaying websites in these displays subdues users' propensity to access infrequently visited pages compared to a situation wherein no web page icons are displayed on the new tab page. Using a carefully designed simulation study, representing user behavior as a random walk on a graph, we inferred quantitative predictions about the extent to which discovery of new sources of information may be hampered by personalized 'New Tab' recommendations in typical computer UIs. We show that our results are significant at the individual level and explain the potential consequences of the observed suppression in web-exploration.
Multi-Stream LDPC Decoder on GPU of Mobile Devices
Venue: arXiv cs.DC 2018
Year: 2018

Low-density parity check (LDPC) codes have been extensively applied in mobile communication systems due to their excellent error correcting capabilities. However, their broad adoption has been hindered by the high complexity of the LDPC decoder. Although to date, dedicated hardware has been used to implement low latency LDPC decoders, recent advancements in the architecture of mobile processors have made it possible to develop software solutions. In this paper, we propose a multi-stream LDPC decoder designed for a mobile device. The proposed decoder uses graphics processing unit (GPU) of a mobile device to achieve efficient real-time decoding. The proposed solution is implemented on an NVIDIA Tegra board as a system on a chip (SoC), where our results indicate that we can control the load on the central processing units through the multi-stream structure.
PaToPaEM: A Data-Driven Parameter and Topology Joint Estimation Framework for Time Varying System in Distribution Grids
Venue: arXiv cs.SY 2018
Year: 2018

Grid topology and line parameters are essential for grid operation and planning, which may be missing or inaccurate in distribution grids. Existing data-driven approaches for recovering such information usually suffer from ignoring 1) input measurement errors and 2) possible state changes among historical measurements. While using the errors-in-variables (EIV) model and letting the parameter and topology estimation interact with each other (PaToPa) can address input and output measurement error modeling, it only works when all measurements are from a single system state. To solve the two challenges simultaneously, we propose the PaToPaEM framework for joint line parameter and topology estimation with historical measurements from different unknown states. We improve the static framework that only works when measurements are from one single state, by further treating state changes in historical measurements as an unobserved latent variable. We then systematically analyze the new mathematical modeling, decouple the optimization problem, and incorporate the expectation-maximization (EM) algorithm to recover different hidden states in measurements. Combining these, PaToPaEM framework enables joint topology and line parameter estimation using noisy measurements from multiple system states. It lays a solid foundation for data-driven system identification in distribution grids. Superior numerical results validate the practicability of the PaToPaEM framework.
Circular Statistics-based low complexity DOA estimation for hearing aid application
Venue: arXiv cs.SD 2018
Year: 2018

The proposed Circular statistics-based Inter-Microphone Phase difference estimation Localizer (CIMPL) method is tailored toward binaural hearing aid systems with microphone arrays in each unit. The method utilizes the circular statistics (circular mean and circular variance) of inter-microphone phase difference (IPD) across different microphone pairs. These IPDs are firstly mapped to time delays through a variance-weighted linear fit, then mapped to azimuth direction-of-arrival (DoA) and lastly information of different microphone pairs is combined. The variance is carried through the different transformations and acts as a reliability index of the estimated angle. Both the resulting angle and variance are fed into a wrapped Kalman filter, which provides a smoothed estimate of the DoA. The proposed method improves the accuracy of the tracked angle of a single moving source compared with the benchmark method provided by the LOCATA challenge, and it runs approximately 75 times faster.
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Importance of visual context in scene understanding tasks is well recognized in the computer vision community. However, to what extent the computer vision models for image classification and semantic segmentation are dependent on the context to make their predictions is unclear. A model overly relying on context will fail when encountering objects in context distributions different from training data and hence it is important to identify these dependencies before we can deploy the models in the real-world. We propose a method to quantify the sensitivity of black-box vision models to visual context by editing images to remove selected objects and measuring the response of the target models. We apply this methodology on two tasks, image classification and semantic segmentation, and discover undesirable dependency between objects and context, for example that "sidewalk" segmentation relies heavily on "cars" being present in the image. We propose an object removal based data augmentation solution to mitigate this dependency and increase the robustness of classification and segmentation models to contextual variations. Our experiments show that the proposed data augmentation helps these models improve the performance in out-of-context scenarios, while preserving the performance on regular data.
Robust one-bit compressed sensing with partial circulant matrices
Venue: arXiv cs.IT 2018
Year: 2018

We present optimal sample complexity estimates for one-bit compressed sensing problems in a realistic scenario: the procedure uses a structured matrix (a randomly sub-sampled circulant matrix) and is robust to analog pre-quantization noise as well as to adversarial bit corruptions in the quantization process. Our results imply that quantization is not a statistically expensive procedure in the presence of nontrivial analog noise: recovery requires the same sample size one would have needed had the measurement matrix been Gaussian and the noisy analog measurements been given as data.
Secure UAV Communication with Cooperative Jamming and Trajectory Control
Venue: arXiv cs.IT 2018
Year: 2018

This paper presents a new cooperative jamming approach to secure the unmanned aerial vehicle (UAV) communication by leveraging jamming from other nearby UAVs to defend against the eavesdropping. In particular, we consider a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends artificial noise (AN) to confuse the ground eavesdropper for protecting the confidentiality of the data transmission. By exploiting the fully-controllable mobility, the two UAVs can adaptively adjust their locations over time (a.k.a. trajectories) to facilitate the secure communication and cooperative jamming. We assume that the two UAVs perfectly know the GN's location and partially know the eavesdropper's location {\emph{a-priori}}. Under this setup, we maximize the average secrecy rate from the UAV transmitter to the GN over one particular time period, by optimizing the UAVs' trajectories, jointly with their communicating/jamming power allocations. Although the formulated problem is non-convex, we propose an efficient solution by applying the techniques of alternating optimization and successive convex approximation (SCA).
Learning Common Representation from RGB and Depth Images
Venue: arXiv cs.AI 2018
Year: 2018

We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.
Persian Vowel recognition with MFCC and ANN on PCVC speech dataset
Venue: arXiv cs.SD 2018
Year: 2018

In this paper a new method for recognition of consonant-vowel phonemes combination on a new Persian speech dataset titled as PCVC (Persian Consonant-Vowel Combination) is proposed which is used to recognize Persian phonemes. In PCVC dataset, there are 20 sets of audio samples from 10 speakers which are combinations of 23 consonant and 6 vowel phonemes of Persian language. In each sample, there is a combination of one vowel and one consonant. First, the consonant phoneme is pronounced and just after it, the vowel phoneme is pronounced. Each sound sample is a frame of 2 seconds of audio. In every 2 seconds, there is an average of 0.5 second speech and the rest is silence. In this paper, the proposed method is the implementations of the MFCC (Mel Frequency Cepstrum Coefficients) on every partitioned sound sample. Then, every train sample of MFCC vector is given to a multilayer perceptron feed-forward ANN (Artificial Neural Network) for training process. At the end, the test samples are examined on ANN model for phoneme recognition. After training and testing process, the results are presented in recognition of vowels. Then, the average percent of recognition for vowel phonemes are computed.
A Fast Combination of AES Encryption and LZ4 Compression Algorithms
Venue: arXiv cs.CR 2018
Year: 2018

From a long time ago, beside encryption of data and making it secure, compression packing it was also important that could make transmission of data faster. In the past years need for improvement of encryption and compression for a fast and easy transmission is more necessary. In this paper, a new method for combination of LZ4 combination and AES encryption algorithms for a fast and easy packing, securing and compressing of data is presented. Choose of these two algorithms was for some special features of them about aim of this paper. This paper also is introducing a method for Parallelism of compression and encryption in a special way for improvement of speed and security of data.
From Satellite Imagery to Disaster Insights
Venue: arXiv cs.CY 2018
Year: 2018

The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster-affected areas and it's extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results. In order to address these issues, we propose a framework for change detection using Convolutional Neural Networks (CNN) on satellite images which can then be thresholded and clustered together into grids to find areas which have been most severely affected by a disaster. We also present a novel metric called Disaster Impact Index (DII) and use it to quantify the impact of two natural disasters - the Hurricane Harvey flood and the Santa Rosa fire. Our framework achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the gridded fire dataset.
Channel-wise pruning of neural networks with tapering resource constraint
Venue: arXiv cs.CV 2018
Year: 2018

Neural network pruning is an important step in design process of efficient neural networks for edge devices with limited computational power. Pruning is a form of knowledge transfer from the weights of the original network to a smaller target subnetwork. We propose a new method for compute-constrained structured channel-wise pruning of convolutional neural networks. The method iteratively fine-tunes the network, while gradually tapering the computation resources available to the pruned network via a holonomic constraint in the method of Lagrangian multipliers framework. An explicit and adaptive automatic control over the rate of tapering is provided. The trainable parameters of our pruning method are separate from the weights of the neural network, which allows us to avoid the interference with the neural network solver (e.g. avoid the direct dependence of pruning speed on neural network learning rates). Our method combines the `rigoristic' approach by the direct application of constrained optimization, avoiding the pitfalls of ADMM-based methods, like their need to define the target amount of resources for each pruning run, and direct dependence of pruning speed and priority of pruning on the relative scale of weights between layers. For VGG-16 @ ILSVRC-2012, we achieve reduction of 15.47 -> 3.87 GMAC with only 1% top-1 accuracy reduction (68.4% -> 67.4%). For AlexNet @ ILSVRC-2012, we achieve 0.724 -> 0.411 GMAC with 1% top-1 accuracy reduction (56.8% -> 55.8%).
A Novel Communication Paradigm for High Capacity and Security via Programmable Indoor Wireless Environments in Next Generation Wireless Systems
Venue: arXiv cs.NI 2018
Year: 2018

Wireless communication environments comprise passive objects that cause performance degradation and eavesdropping concerns due to anomalous scattering. This paper proposes a new paradigm, where scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Through the proposed programmable wireless environments, the path loss, multi-path fading and interference effects can be controlled and mitigated. Moreover, the eavesdropping can be prevented via novel physical layer security capabilities. The core technology of this new paradigm is the concept of metasurfaces, which are planar intelligent structures whose effects on impinging electromagnetic waves are fully defined by their micro-structure. Their control over impinging waves has been demonstrated to span from 1 GHz to 10 THz. This paper contributes the software-programmable wireless environment, consisting of several HyperSurface tiles (programmable metasurfaces) controlled by a central server. HyperSurfaces are a novel class of metasurfaces whose structure and, hence, electromagnetic behavior can be altered and controlled via a software interface. Multiple networked tiles coat indoor objects, allowing fine-grained, customizable reflection, absorption or polarization overall. A central server calculates and deploys the optimal electromagnetic interaction per tile, to the benefit of communicating devices. Realistic simulations using full 3D ray-tracing demonstrate the groundbreaking performance and security potential of the proposed approach in 2.4 GHz and 60 GHz frequencies.
Towards Ophthalmologist Level Accurate Deep Learning System for OCT Screening and Diagnosis
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we propose an advanced AI based grading system for OCT images. The proposed system is a very deep fully convolutional attentive classification network trained with end to end advanced transfer learning with online random augmentation. It uses quasi random augmentation that outputs confidence values for diseases prevalence during inference. Its a fully automated retinal OCT analysis AI system capable of pathological lesions understanding without any offline preprocessing/postprocessing step or manual feature extraction. We present a state of the art performance on the publicly available Mendeley OCT dataset.
CausalSpartanX: Causal Consistency and Non-Blocking Read-Only Transactions
Venue: arXiv cs.DC 2018
Year: 2018

Causal consistency is an intermediate consistency model that can be achieved together with high availability and performance requirements even in presence of network partitions. In the context of partitioned data stores, it has been shown that implicit dependency tracking using timestamps is more efficient than explicit dependency tracking. Existing time-based solutions depend on monotonic psychical clocks that are closely synchronized. These requirements make current protocols vulnerable to clock anomalies. In this paper, we propose a new time-based algorithm, CausalSpartanX, that instead of physical clocks, utilizes Hybrid Logical Clocks (HLCs). We show that using HLCs, without any overhead, we make the system robust on physical clock anomalies. This improvement is more significant in the context of query amplification, where a single query results in multiple GET/PUT operations. We also show that CausalSpartanX decreases the visibility latency for a given data item compared with existing time-based approaches. In turn, this reduces the completion time of collaborative applications where two clients accessing two different replicas edit same items of the data store. CausalSpartanX also provides causally consistent distributed read-only transactions. CausalSpartanX read-only transactions are non-blocking and require only one round of communication between the client and the servers. Also, the slowdowns of partitions that are unrelated to a transaction do not affect the performance of the transaction. Like previous protocols, CausalSpartanX assumes that a given client does not access more than one replica. We show that in presence of network partitions, this assumption (made in several other works) is essential if one were to provide causal consistency as well as immediate availability to local updates.
Interactive Naming for Explaining Deep Neural Networks: A Formative Study
Venue: arXiv cs.LG 2018
Year: 2018

We consider the problem of explaining the decisions of deep neural networks for image recognition in terms of human-recognizable visual concepts. In particular, given a test set of images, we aim to explain each classification in terms of a small number of image regions, or activation maps, which have been associated with semantic concepts by a human annotator. This allows for generating summary views of the typical reasons for classifications, which can help build trust in a classifier and/or identify example types for which the classifier may not be trusted. For this purpose, we developed a user interface for "interactive naming," which allows a human annotator to manually cluster significant activation maps in a test set into meaningful groups called "visual concepts". The main contribution of this paper is a systematic study of the visual concepts produced by five human annotators using the interactive naming interface. In particular, we consider the adequacy of the concepts for explaining the classification of test-set images, correspondence of the concepts to activations of individual neurons, and the inter-annotator agreement of visual concepts. We find that a large fraction of the activation maps have recognizable visual concepts, and that there is significant agreement between the different annotators about their denotations. Our work is an exploratory study of the interplay between machine learning and human recognition mediated by visualizations of the results of learning.