OpenRaiser/Intern-Atlas huggingface dataset:
RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images
Venue: arXiv cs.CV 2018
Year: 2018

Semantic segmentation in high resolution remote sensing images is a fundamental and challenging task. Convolutional neural networks (CNNs), such as fully convolutional network (FCN) and SegNet, have shown outstanding performance in many segmentation tasks. One key pillar of these successes is mining useful information from features in convolutional layers for producing high resolution segmentation maps. For example, FCN nonlinearly combines high-level features extracted from last convolutional layers; whereas SegNet utilizes a deconvolutional network which takes as input only coarse, high-level feature maps of the last convolutional layer. However, how to better fuse multi-level convolutional feature maps for semantic segmentation of remote sensing images is underexplored. In this work, we propose a novel bidirectional network called recurrent network in fully convolutional network (RiFCN), which is end-to-end trainable. It has a forward stream and a backward stream. The former is a classification CNN architecture for feature extraction, which takes an input image and produces multi-level convolutional feature maps from shallow to deep; while in the later, to achieve accurate boundary inference and semantic segmentation, boundary-aware high resolution feature maps in shallower layers and high-level but low-resolution features are recursively embedded into the learning framework (from deep to shallow) to generate a fused feature representation that draws a holistic picture of not only high-level semantic information but also low-level fine-grained details. Experimental results on two widely-used high resolution remote sensing data sets for semantic segmentation tasks, ISPRS Potsdam and Inria Aerial Image Labeling Data Set, demonstrate competitive performance obtained by the proposed methodology compared to other studied approaches.
Revisiting Temporal Modeling for Video-based Person ReID
Venue: arXiv cs.CV 2018
Year: 2018

Video-based person reID is an important task, which has received much attention in recent years due to the increasing demand in surveillance and camera networks. A typical video-based person reID system consists of three parts: an image-level feature extractor (e.g. CNN), a temporal modeling method to aggregate temporal features and a loss function. Although many methods on temporal modeling have been proposed, it is hard to directly compare these methods, because the choice of feature extractor and loss function also have a large impact on the final performance. We comprehensively study and compare four different temporal modeling methods (temporal pooling, temporal attention, RNN and 3D convnets) for video-based person reID. We also propose a new attention generation network which adopts temporal convolution to extract temporal information among frames. The evaluation is done on the MARS dataset, and our methods outperform state-of-the-art methods by a large margin. Our source codes are released at https://github.com/jiyanggao/Video-Person-ReID.
Estimation and Tracking of AP-diameter of the Inferior Vena Cava in Ultrasound Images Using a Novel Active Circle Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

Medical research suggests that the anterior-posterior (AP)-diameter of the inferior vena cava (IVC) and its associated temporal variation as imaged by bedside ultrasound is useful in guiding fluid resuscitation of the critically-ill patient. Unfortunately, indistinct edges and gaps in vessel walls are frequently present which impede accurate estimation of the IVC AP-diameter for both human operators and segmentation algorithms. The majority of research involving use of the IVC to guide fluid resuscitation involves manual measurement of the maximum and minimum AP-diameter as it varies over time. This effort proposes using a time-varying circle fitted inside the typically ellipsoid IVC as an efficient, consistent and novel approach to tracking and approximating the AP-diameter even in the context of poor image quality. In this active-circle algorithm, a novel evolution functional is proposed and shown to be a useful tool for ultrasound image processing. The proposed algorithm is compared with an expert manual measurement, and state-of-the-art relevant algorithms. It is shown that the algorithm outperforms other techniques and performs very close to manual measurement.
Smoothing and Mapping using Multiple Robots
Venue: arXiv cs.RO 2018
Year: 2018

Mapping expansive regions is an arduous and often times incomplete when performed by a single agent. In this paper we illustrate an extension of \texttt{Full SLAM} \cite{Dellaert06ijrr} and \cite{dong}, which ensures smooth maps with loop-closure for multi-robot settings. The current development and the associated mathematical formulation ensure without loss of generality the applicability of full bundle adjustment approach for multiple robots operating in relatively static environments. We illustrate the efficacy of this system by presenting relevant results from experiments performed in an indoor setting. In addition to end-to-end description of the pipeline for performing smoothing and mapping \texttt{SAM} with a fleet of robots, we discuss a one-time prior estimation technique that ensures the incremental concatenation of measurements from respective robots in order to generate one smooth global map - thus emulating large scale mapping with single robot. Along with an interpretation of the non-linear estimates, we present necessary implementation details for adopting this SAM system.
RealCertify: a Maple package for certifying non-negativity
Venue: arXiv cs.SC 2018
Year: 2018

Let $\mathbb{Q}$ (resp. $\mathbb{R}$) be the field of rational (resp. real) numbers and $X = (X_1, \ldots, X_n)$ be variables. Deciding the non-negativity of polynomials in $\mathbb{Q}[X]$ over $\mathbb{R}^n$ or over semi-algebraic domains defined by polynomial constraints in $\mathbb{Q}[X]$ is a classical algorithmic problem for symbolic computation. The Maple package \textsc{RealCertify} tackles this decision problem by computing sum of squares certificates of non-negativity for inputs where such certificates hold over the rational numbers. It can be applied to numerous problems coming from engineering sciences, program verification and cyber-physical systems. It is based on hybrid symbolic-numeric algorithms based on semi-definite programming.
MIMO-OTFS in High-Doppler Fading Channels: Signal Detection and Channel Estimation
Venue: arXiv cs.IT 2018
Year: 2018

Orthogonal time frequency space (OTFS) modulation is a recently introduced multiplexing technique designed in the 2-dimensional (2D) delay-Doppler domain suited for high-Doppler fading channels. OTFS converts a doubly-dispersive channel into an almost non-fading channel in the delay-Doppler domain through a series of 2D transformations. In this paper, we focus on MIMO-OTFS which brings in the high spectral and energy efficiency benefits of MIMO and the robustness of OTFS in high-Doppler fading channels. The OTFS channel-symbol coupling and the sparse delay-Doppler channel impulse response enable efficient MIMO channel estimation in high Doppler environments. We present an iterative algorithm for signal detection based on message passing and a channel estimation scheme in the delay-Doppler domain suited for MIMO-OTFS. The proposed channel estimation scheme uses impulses in the delay-Doppler domain as pilots for estimation. We also compare the performance of MIMO-OTFS with that of MIMO-OFDM under high Doppler scenarios.
On Facility Location with General Lower Bounds
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we give the first constant approximation algorithm for the lower bounded facility location (LBFL) problem with general lower bounds. Prior to our work, such algorithms were only known for the special case where all facilities have the same lower bound: Svitkina \cite{Svi10} gave a $448$-approximation for the special case, and subsequently Ahmadian and Swamy \cite{AS13} improved the approximation factor to 82.6. As in \cite{Svi10} and \cite{AS13}, our algorithm for LBFL with general lower bounds works by reducing the problem to the capacitated facility location (CFL) problem. To handle some challenges caused by the general lower bounds, our algorithm involves more reduction steps. One main complication is that after aggregation of clients and facilities at a few locations, each of these locations may contain many facilities with different opening costs and lower bounds. To handle this issue, we introduce and reduce our LBFL problem to an intermediate problem called the transportation with configurable supplies and demands (TCSD) problem, which in turn can be reduced to the CFL problem.
Multidimensional Realization Theory and Polynomial System Solving
Venue: arXiv cs.SY 2018
Year: 2018

Multidimensional systems are becoming increasingly important as they provide a promising tool for estimation, simulation and control, while going beyond the traditional setting of one-dimensional systems. The analysis of multidimensional systems is linked to multivariate polynomials, and is therefore more difficult than the well-known analysis of one-dimensional systems, which is linked to univariate polynomials. In the current paper we relate the realization theory for overdetermined autonomous multidimensional systems to the problem of solving a system of polynomial equations. We show that basic notions of linear algebra suffice to analyze and solve the problem. The difference equations are associated with a Macaulay matrix formulation, and it is shown that the null space of the Macaulay matrix is a multidimensional observability matrix. Application of the classical shift trick from realization theory allows for the computation of the corresponding system matrices in a multidimensional state-space setting. This reduces the task of solving a system of polynomial equations to computing an eigenvalue decomposition. We study the occurrence of multiple solutions, as well as the existence and analysis of solutions at infinity, which allow for an interpretation in terms of multidimensional descriptor systems.
S4ND: Single-Shot Single-Scale Lung Nodule Detection
Venue: arXiv cs.CV 2018
Year: 2018

The state of the art lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature. The whole detection pipeline is designed as a single $3D$ Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publically available $888$ CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of $0.897$. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.
Day-ahead Trading of Aggregated Energy Flexibility - Full Version
Venue: arXiv cs.CE 2018
Year: 2018

Flexibility of small loads, in particular from Electric Vehicles (EVs), has recently attracted a lot of interest due to their possibility of participating in the energy market and the new commercial potentials. Different from existing work, the aggregation techniques proposed in this paper produce flexible aggregated loads from EVs taking into account technical market requirements. They can be further transformed into the so-called flexible orders and be traded in the day-ahead market by a Balance Responsible Party (BRP). As a result, the BRP can achieve at least 20% cost reduction on average in energy purchase compared to traditional charging based on 2017 real electricity prices from the Danish electricity market.
Domination Cover Number of Graphs
Venue: arXiv cs.DM 2018
Year: 2018

A set $D \subseteq V$ for the graph $G=(V, E)$ is called a dominating set if any vertex $v\in V\setminus D$ has at least one neighbor in $D$. Fomin et al.[9] gave an algorithm for enumerating all minimal dominating sets with $n$ vertices in $O(1.7159^n)$ time. It is known that the number of minimal dominating sets for interval graphs and trees on $n$ vertices is at most $3^{n/3} \approx 1.4422^n$. In this paper, we introduce the domination cover number as a new criterion for evaluating the dominating sets in graphs. The domination cover number of a dominating set $D$, denoted by $\mathcal{C}_D(G)$, is the summation of the degrees of the vertices in $D$. Maximizing or minimizing this parameter among all minimal dominating sets have interesting applications in many real-world problems, such as the art gallery problem. Moreover, we investigate this concept for different graph classes and propose some algorithms for finding the domination cover number in trees, block graphs.
Billion-scale Network Embedding with Iterative Random Projection
Venue: arXiv cs.SI 2018
Year: 2018

Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently. However, the existing methods are incapable of handling billion-scale networks, because they are computationally expensive and, at the same time, difficult to be accelerated by distributed computing schemes. To address these problems, we propose RandNE (Iterative Random Projection Network Embedding), a novel and simple billion-scale network embedding method. Specifically, we propose a Gaussian random projection approach to map the network into a low-dimensional embedding space while preserving the high-order proximities between nodes. To reduce the time complexity, we design an iterative projection procedure to avoid the explicit calculation of the high-order proximities. Theoretical analysis shows that our method is extremely efficient, and friendly to distributed computing schemes without any communication cost in the calculation. We also design a dynamic updating procedure which can efficiently incorporate the dynamic changes of the networks without error aggregation. Extensive experimental results demonstrate the efficiency and efficacy of RandNE over state-of-the-art methods in several tasks including network reconstruction, link prediction and node classification on multiple datasets with different scales, ranging from thousands to billions of nodes and edges.
Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
Venue: arXiv cs.CR 2018
Year: 2018

Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this work (char-LSTM ) has one drawback: it has difficulties staying in context, i.e. when it generates a review for specific target entity, the resulting review may contain phrases that are unrelated to the target, thus increasing its detectability. In this work, we present and evaluate a more sophisticated technique based on neural machine translation (NMT) with which we can generate reviews that stay on-topic. We test multiple variants of our technique using native English speakers on Amazon Mechanical Turk. We demonstrate that reviews generated by the best variant have almost optimal undetectability (class-averaged F-score 47%). We conduct a user study with skeptical users and show that our method evades detection more frequently compared to the state-of-the-art (average evasion 3.2/4 vs 1.5/4) with statistical significance, at level α = 1% (Section 4.3). We develop very effective detection tools and reach average F-score of 97% in classifying these. Although fake reviews are very effective in fooling people, effective automatic detection is still feasible.
Deep Ordinal Hashing with Spatial Attention
Venue: arXiv cs.CV 2018
Year: 2018

Hashing has attracted increasing research attentions in recent years due to its high efficiency of computation and storage in image retrieval. Recent works have demonstrated the superiority of simultaneous feature representations and hash functions learning with deep neural networks. However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images. The loss of local spatial structure makes the performance bottleneck of hash functions, therefore limiting its application for accurate similarity retrieval. In this work, we propose a novel Deep Ordinal Hashing (DOH) method, which learns ordinal representations by leveraging the ranking structure of feature space from both local and global views. In particular, to effectively build the ranking structure, we propose to learn the rank correlation space by exploiting the local spatial information from Fully Convolutional Network (FCN) and the global semantic information from the Convolutional Neural Network (CNN) simultaneously. More specifically, an effective spatial attention model is designed to capture the local spatial information by selectively learning well-specified locations closely related to target objects. In such hashing framework,the local spatial and global semantic nature of images are captured in an end-to-end ranking-to-hashing manner. Experimental results conducted on three widely-used datasets demonstrate that the proposed DOH method significantly outperforms the state-of-the-art hashing methods.
Characterization and Classification of Human Body Channel as a function of Excitation and Termination Modalities
Venue: arXiv cs.HC 2018
Year: 2018

Human Body Communication (HBC) has recently emerged as an alternative to radio frequency transmission for connecting devices on and in the human body with order(s) of magnitude lower energy. The communication between these devices can give rise to different scenarios, which can be classified as wearable-wearable, wearable-machine, machine-machine interactions. In this paper, for the first time, the human body channel characteristics is measured for a wide range of such possible scenarios (14 vs. a few in previous literature) and classified according to the form-factor of the transmitter and receiver. The effect of excitation/termination configurations on the channel loss is also explored, which helps explain the previously unexplained wide variation in HBC Channel measurements. Measurement results show that wearable-wearable interaction has the maximum loss (upto -50 dB) followed by wearable-machine and machinemachine interaction (min loss of 0.5 dB), primarily due to the small ground size of the wearable devices. Among the excitation configurations, differential excitation is suitable for small channel length whereas single ended is better for longer channel.
A Generic Self-Evolving Neuro-Fuzzy Controller based High-performance Hexacopter Altitude Control System
Venue: arXiv cs.SY 2018
Year: 2018

Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics, a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the system's dynamic characteristics. In this work, such an evolving controller namely Generic-controller (G-controller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent parameters are derived from the sliding mode control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain a uniform asymptotic convergence of tracking error to zero. Finally, the G-controller's performance evaluation is observed through the altitude tracking of a UAV namely hexacopter for various trajectories.
Detecting Traffic Lights by Single Shot Detection
Venue: arXiv cs.CV 2018
Year: 2018

Recent improvements in object detection are driven by the success of convolutional neural networks (CNN). They are able to learn rich features outperforming hand-crafted features. So far, research in traffic light detection mainly focused on hand-crafted features, such as color, shape or brightness of the traffic light bulb. This paper presents a deep learning approach for accurate traffic light detection in adapting a single shot detection (SSD) approach. SSD performs object proposals creation and classification using a single CNN. The original SSD struggles in detecting very small objects, which is essential for traffic light detection. By our adaptations it is possible to detect objects much smaller than ten pixels without increasing the input image size. We present an extensive evaluation on the DriveU Traffic Light Dataset (DTLD). We reach both, high accuracy and low false positive rates. The trained model is real-time capable with ten frames per second on a Nvidia Titan Xp. Code has been made available at https://github.com/julimueller/tl_ssd.
Distributed Capacity of A Multiple Access Channel
Venue: arXiv cs.IT 2018
Year: 2018

In distributed communication, each transmitter prepares an ensemble of channel codes. To encode a message, a transmitter chooses a channel code individually without sharing the coding choice with other transmitters or with the receiver. Upon receiving the block of channel output symbols, the receiver either decodes the messages of interest if a pre-determined reliability requirement can be met, or reports collision otherwise. Revised from the distributed channel coding theorems in the literature, distributed capacity of a discrete-time memoryless multiple access channel is defined and derived under the assumption that codeword length can be taken to infinity. An improved achievable error performance bound is presented for the case when codeword length is finite.
Evaluation of Visualization by Demonstration and Manual View Specification
Venue: arXiv cs.HC 2018
Year: 2018

We present an exploratory study comparing the visualization construction and data exploration processes of people using two visualization tools, each implementing a different interaction paradigm. One of the visualization tools implements the manual view specification paradigm (Polestar) and another implements the visualization by demonstration paradigm (VisExemplar). Findings of our study indicate that the interaction paradigms implemented in these tools influence: 1) approaches used for constructing visualizations, 2) how users form goals, 3) how many visualization alternatives are considered and created, and 4) the feeling of control during the visualization construction process.
Real-time regression analysis with deep convolutional neural networks
Venue: arXiv cs.LG 2018
Year: 2018

We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.