OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Frame Content Integration with a Spatio-Temporal Attention Mechanism for Person Video Motion Transfer
Venue: arXiv cs.CV 2019
Year: 2019

Existing person video generation methods either lack the flexibility in controlling both the appearance and motion, or fail to preserve detailed appearance and temporal consistency. In this paper, we tackle the problem of motion transfer for generating person videos, which provides controls on both the appearance and the motion. Specifically, we transfer the motion of one person in a target video to another person in a source video, while preserving the appearance of the source person. Besides only relying on one source frame as the existing state-of-the-art methods, our proposed method integrates information from multiple source frames based on a spatio-temporal attention mechanism to preserve rich appearance details. In addition to a spatial discriminator employed for encouraging the frame-level fidelity, a multi-range temporal discriminator is adopted to enforce the generated video to resemble temporal dynamics of a real video in various time ranges. A challenging real-world dataset, which contains about 500 dancing video clips with complex and unpredictable motions, is collected for the training and testing. Extensive experiments show that the proposed method can produce more photo-realistic and temporally consistent person videos than previous methods. As our method decomposes the syntheses of the foreground and background into two branches, a flexible background substitution application can also be achieved.
Regular Separability and Intersection Emptiness are Independent Problems
Venue: arXiv cs.FL 2019
Year: 2019

The problem of \emph{regular separability} asks, given two languages $K$ and $L$, whether there exists a regular language $S$ with $K\subseteq S$ and $S\cap L=\emptyset$. This problem has recently been studied for various classes of languages. All the results on regular separability obtained so far exhibited a noteworthy correspondence with the intersection emptiness problem: In eachcase, regular separability is decidable if and only if intersection emptiness is decidable. This raises the question whether under mild assumptions, regular separability can be reduced to intersection emptiness and vice-versa. We present counterexamples showing that none of the two problems can be reduced to the other. More specifically, we describe language classes $\mathcal{C_1}$, $\mathcal{D_1}$, $\mathcal{C_2}$, $\mathcal{D_2}$ such that (i)~intersection emptiness is decidable for $\mathcal{C_1}$ and $\mathcal{D_1}$, but regular separability is undecidable for $\mathcal{C_1}$ and $\mathcal{D_1}$ and (ii)~regular separability is decidable for $\mathcal{C_2}$ and $\mathcal{D_2}$, but intersection emptiness is undecidable for $\mathcal{C_2}$ and $\mathcal{D_2}$.
SIP Server Load Balancing Based on SDN
Venue: arXiv cs.NI 2019
Year: 2019

Session Initiation Protocol (SIP) grows for VoIP applications, and faces challenges including security and overload. On the other hand, the new concept of Software-defined Networking (SDN) has made great changes in the networked world. SDN is the idea of separating the control plane from the network infrastructure that can bring several benefits. We used this idea to provide a new architecture for SIP networks. Moreover, for the load distribution challenge in these networks, a framework based on SDN was offered, in which the load balancing and network management can be easily done by a central controller considering the network status. Unlike the traditional methods, in this framework, there is no need to change the infrastructures like SIP servers or SIP load balancer to implement the distribution method. Also, several types of load distribution algorithms can be performed as software in the controller. We were able to achieve the desired results by simulating the three methods based on the proposed framework in Mininet.
Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming
Venue: arXiv cs.IT 2019
Year: 2019

Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve the propagation environment and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS's passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To tackle the formulated non-convex problem, we divide it into two subproblems, namely, passive beamforming and trajectory optimization. We first derive a closed-form phase-shift solution for any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the optimal phase-shift solution, we obtain a suboptimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed algorithm can considerably improve the average achievable rate of the system.
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
Venue: arXiv cs.ET 2019
Year: 2019

One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow improving the energy efficiency of Artificial Intelligence by orders of magnitude with regards to its implementation on computers and graphics cards. In particular, ST-MRAM could be ideal for implementing Binarized Neural Networks (BNNs), a type of deep neural networks discovered in 2016, which can achieve state-of-the-art performance with a highly reduced memory footprint with regards to conventional artificial intelligence approaches. The challenge of ST-MRAM, however, is that it is prone to write errors and usually requires the use of error correction. In this work, we show that these bit errors can be tolerated by BNNs to an outstanding level, based on examples of image recognition tasks (MNIST, CIFAR-10 and ImageNet): bit error rates of ST-MRAM up to 0.1% have little impact on recognition accuracy. The requirements for ST-MRAM are therefore considerably relaxed for BNNs with regards to traditional applications. By consequence, we show that for BNNs, ST-MRAMs can be programmed with weak (low-energy) programming conditions, without error correcting codes. We show that this result can allow the use of low energy and low area ST-MRAM cells, and show that the energy savings at the system level can reach a factor two.
Multimodal Unified Attention Networks for Vision-and-Language Interactions
Venue: arXiv cs.CV 2019
Year: 2019

Learning an effective attention mechanism for multimodal data is important in many vision-and-language tasks that require a synergic understanding of both the visual and textual contents. Existing state-of-the-art approaches use co-attention models to associate each visual object (e.g., image region) with each textual object (e.g., query word). Despite the success of these co-attention models, they only model inter-modal interactions while neglecting intra-modal interactions. Here we propose a general `unified attention' model that simultaneously captures the intra- and inter-modal interactions of multimodal features and outputs their corresponding attended representations. By stacking such unified attention blocks in depth, we obtain the deep Multimodal Unified Attention Network (MUAN), which can seamlessly be applied to the visual question answering (VQA) and visual grounding tasks. We evaluate our MUAN models on two VQA datasets and three visual grounding datasets, and the results show that MUAN achieves top-level performance on both tasks without bells and whistles.
3-D Positioning and Environment Mapping for mmWave Communication Systems
Venue: arXiv cs.IT 2019
Year: 2019

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths, large antenna arrays, and increased densities of base stations allow for their unparalleled delay and angular resolution. Combining localization into communications and designing simultaneous localization and mapping (SLAM) algorithms are challenging problems. This study considers the joint position and velocity estimation and environment mapping problem in a three-dimensional mmWave CRAN architecture. We first embed cooperative localization into communications and establish the joint estimation and mapping model with hybrid delay and angle measurements. Then, we propose a closed-form weighted least square (WLS) solution for the joint estimation and mapping problems. The proposed WLS estimator is proven asymptotically unbiased and confirmed by simulations as effective in achieving the Cramer-Rao lower bound (CRLB) and the desired decimeter-level accuracy. Furthermore, we propose a WLS-Net-based SLAM algorithm by embedding neural networks into the proposed WLS estimators to replace the linear approximations. The solution possesses both powerful learning ability of the neural network and robustness of the proposed geometric model, and the ensemble learning is applied to further improve positioning accuracy. A public ray-tracing dataset is used in the simulations to test the performance of the WLS-Net-based SLAM algorithm, which is proven fast and effective in attaining the centimeter-level accuracy.
On simplified NP-complete variants of Not-All-Equal 3-Sat and 3-Sat
Venue: arXiv cs.CC 2019
Year: 2019

We consider simplified, monotone versions of Not-All-Equal 3-Sat and 3-Sat, variants of the famous Satisfiability Problem where each clause is made up of exactly three distinct literals. We show that Not-All-Equal 3-Sat remains NP-complete even if (1) each variable appears exactly four times, (2) there are no negations in the formula, and (3) the formula is linear, i.e., each pair of distinct clauses shares at most one variable. Concerning 3-Sat we prove several hardness results for monotone formulas with respect to a variety of restrictions imposed on the variable appearances. Monotone 3-Sat is the restriction of 3-Sat to monotone formulas, i.e. to formulas in which each clause contains only unnegated variables or only negated variables, respectively. In particular, we show that, for any $k\geq 5$, Monotone 3-Sat is NP-complete even if each variable appears exactly $k$ times unnegated and exactly once negated. In addition, we show that Monotone 3-Sat is NP-complete even if each variable appears exactly three times unnegated and three times negated, respectively. In fact, we provide a complete analysis of Monotone 3-Sat with exactly six appearances per variable. Further, we prove that the problem remains NP-complete when restricted to instances in which each variable appears either exactly once unnegated and three times negated or the other way around. Thereby, we improve on a result by Darmann et al. [DDD18] showing NP-completeness for four appearances per variable. Our stronger result also implies that 3-Sat remains NP-complete even if each variable appears exactly three times unnegated and once negated, therewith complementing a result by Berman et al. [BKS03].
Recursion, Probability, Convolution and Classification for Computations
Venue: arXiv cs.PL 2019
Year: 2019

The main motivation of this work was practical, to offer computationally and theoretical scalable ways to structuring large classes of computation. It started from attempts to optimize R code for machine learning/artificial intelligence algorithms for huge data sets, that due to their size, should be handled into an incremental (online) fashion. Our target are large classes of relational (attribute based), mathematical (index based) or graph computations. We wanted to use powerful computation representations that emerged in AI (artificial intelligence)/ML (machine learning) as BN (Bayesian networks) and CNN (convolution neural networks). For the classes of computation addressed by us, and for our HPC (high performance computing) needs, the current solutions for translating computations into such representation need to be extended. Our results show that the classes of computation targeted by us, could be tree-structured, and a probability distribution (defining a DBN, i.e. Dynamic Bayesian Network) associated with it. More ever, this DBN may be viewed as a recursive CNN (Convolution Neural Network). Within this tree-like structure, classification in classes with size bounded (by a parameterizable may be performed. These results are at the core of very powerful, yet highly practically algorithms for restructuring and parallelizing the computations. The mathematical background required for an in depth presentation and exposing the full generality of our approach) is the subject of a subsequent paper. In this paper, we work in an limited (but important) framework that could be understood with rudiments of linear algebra and graph theory. The focus is in applicability, most of this paper discuss the usefulness of our approach for solving hard compilation problems related to automatic parallelism.
Convolutional Humanoid Animation via Deformation
Venue: arXiv cs.GR 2019
Year: 2019

In this paper we present a new deep learning-driven approach to image-based synthesis of animations involving humanoid characters. Unlike previous deep approaches to image-based animation our method makes no assumptions on the type of motion to be animated nor does it require dense temporal input to produce motion. Instead we generate new animations by interpolating between user chosen keyframes, arranged sparsely in time. Utilizing a novel configuration manifold learning approach we interpolate suitable motions between these keyframes. In contrast to previous methods, ours requires less data (animations can be generated from a single youtube video) and is broadly applicable to a wide range of motions including facial motion, whole body motion and even scenes with multiple characters. These improvements serve to significantly reduce the difficulty in producing image-based animations of humanoid characters, allowing even broader audiences to express their creativity.
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report. To automate this process, we propose a multitask universal lesion analysis network (MULAN) for joint detection, tagging, and segmentation of lesions in a variety of body parts, which greatly extends existing work of single-task lesion analysis on specific body parts. MULAN is based on an improved Mask R-CNN framework with three head branches and a 3D feature fusion strategy. It achieves the state-of-the-art accuracy in the detection and tagging tasks on the DeepLesion dataset, which contains 32K lesions in the whole body. We also analyze the relationship between the three tasks and show that tag predictions can improve detection accuracy via a score refinement layer.
Fine-Tuning Models Comparisons on Garbage Classification for Recyclability
Venue: arXiv cs.CV 2019
Year: 2019

In this study, it is aimed to develop a deep learning application which detects types of garbage into trash in order to provide recyclability with vision system. Training and testing will be performed with image data consisting of several classes on different garbage types. The data set used during training and testing will be generated from original frames taken from garbage images. The data set used for deep learning structures has a total of 2527 images with 6 different classes. Half of these images in the data set were used for training process and remaining part were used for testing procedure. Also, transfer learning was used to obtain shorter training and test procedures with and higher accuracy. As fine-tuned models, Alexnet, VGG16, Googlenet and Resnet structures were carried. In order to test performance of classifiers, two different classifiers are used as Softmax and Support Vector Machines. 6 different type of trash images were correctly classified the highest accuracy with GoogleNet+SVM as 97.86%.
Cyclic Oritatami Systems Cannot Fold Infinite Fractal Curves
Venue: arXiv cs.CC 2019
Year: 2019

RNA cotranscriptional folding is the phenomenon in which an RNA transcript folds upon itself while being synthesized out of a gene. The oritatami system (OS) is a computation model of this phenomenon, which lets its sequence (transcript) of beads (abstract molecules) fold cotranscriptionally by the interactions between beads according to the binding ruleset. The OS is an useful computational model for predicting and simulating an RNA folding as well as constructing a biological structure. A fractal is an infinite pattern that is self-similar across different scales, and is an important structure in nature. Therefore, the fractal construction using self-assembly is one of the most important problems. We focus on the problem of generating an infinite fractal instead of a partial finite fractal, which is much more challenging. We use a cyclic OS, which has an infinite periodic transcript, to generate an infinite structure. We prove a negative result that it is impossible to make a Koch curve or a Minkowski curve, both of which are fractals, using a cyclic OS. We then establish sufficient conditions of infinite aperiodic curves that a cyclic OS cannot fold.
Context-Aware Information Lapse for Timely Status Updates in Remote Control Systems
Venue: arXiv cs.IT 2019
Year: 2019

Emerging applications in Internet of Things (IoT), such as remote monitoring and control, extensively rely on timely status updates. Age of Information (AoI) has been proposed to characterize the freshness of information in status update systems. However, it only considers the time elapsed since the generation of the latest packet, and is incapable of capturing other critical information in remote control systems, such as the stochastic evolution and the importance of the source status. In order to evaluate the timeliness of status updates in remote control systems, we propose a context-aware metric, namely the context-aware information lapse. The context-aware information lapse characterizes both the stochastic evolution of the source status and the context-aware importance of the status. In this paper, the minimization of average context-aware lapse in a multi-user system is considered, and a corresponding user scheduling policy is proposed based on Lyapunov optimization. Numerical results show that compared to AoI-based policy, the context-aware-lapse-based policy can achieve a substantial improvement in terms of error-threshold violation probability and control performance.
Linking Graph Entities with Multiplicity and Provenance
Venue: arXiv cs.DB 2019
Year: 2019

Entity linking and resolution is a fundamental database problem with applications in data integration, data cleansing, information retrieval, knowledge fusion, and knowledge-base population. It is the task of accurately identifying multiple, differing, and possibly contradicting representations of the same real-world entity in data. In this work, we propose an entity linking and resolution system capable of linking entities across different databases and mentioned-entities extracted from text data. Our entity linking/resolution solution, called Certus, uses a graph model to represent the profiles of entities. The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values. Provenance descriptions of a value provide the settings of the value, such as validity periods, sources, security requirements, etc. This paper presents the architecture for the entity linking system, the logical, physical, and indexing models used in the system, and the general linking process. Furthermore, we demonstrate the performance of update operations of the physical storage models when the system is implemented in two state-of-the-art database management systems, HBase and Postgres.
Industrial Control via Application Containers: Migrating from Bare-Metal to IAAS
Venue: arXiv cs.DC 2019
Year: 2019

We explore the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or cloud computing platforms using off the shelf technologies. In particular, we demonstrate that executing time-critical applications on cloud platforms is viable based on a series of dedicated latency tests targeting relevant real-time configurations.
Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization
Venue: arXiv cs.NI 2019
Year: 2019

Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. Optimizing the runtime performance of a Network-on-Chip (NoC) necessitates a continuous learning framework. In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance. We present three RL-based methods for learning optimal routing algorithms. The experimental results show the algorithms can successfully learn a near-optimal solution across different environment states. Reproducible Code: github.com/huckiyang/interconnect-routing-gym
Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning
Venue: arXiv cs.CL 2019
Year: 2019

We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL). We annotated a total of 2000 chest X-ray reports with 4 spatial roles corresponding to the common radiology entities. Our focus is on extracting detailed information of a radiologist's interpretation containing a radiographic finding, its anatomical location, corresponding probable diagnoses, as well as associated hedging terms. For this, we propose a deep learning-based natural language processing (NLP) method involving both word and character-level encodings. Specifically, we utilize a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) model for extracting the spatial roles. The model achieved average F1 measures of 90.28 and 94.61 for extracting the Trajector and Landmark roles respectively whereas the performance was moderate for Diagnosis and Hedge roles with average F1 of 71.47 and 73.27 respectively. The corpus will soon be made available upon request.
A Simple and Intuitive Algorithm for Preventing Directory Traversal Attacks
Venue: arXiv cs.CR 2019
Year: 2019

With web applications becoming a preferred method of presenting graphical user interfaces to users, software vulnerabilities affecting web applications are becoming more and more prevalent and devastating. Some of these vulnerabilities, such as directory traversal attacks, have varying defense mechanisms and mitigations that can be difficult to understand, analyze, and test. Gaps in the testing of these directory traversal defense mechanisms can lead to vulnerabilities that allow attackers to read sensitive data from files or even execute malicious code. This paper presents an analysis of some currently used directory traversal attack defenses and presents a new, stack-based algorithm to help prevent these attacks by safely canonicalizing user-supplied path strings. The goal of this algorithm is to be small, easy to test, cross-platform compatible, and above all, intuitive. We provide a proof of correctness and verification strategies using symbolic execution for the algorithm. We hope that the algorithm is simple and effective enough to help move developers towards a unified defense against directory traversal attacks.