OpenRaiser/Intern-Atlas huggingface dataset:
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Using touch devices to navigate in virtual 3D environments such as computer assisted design (CAD) models or geographical information systems (GIS) is inherently difficult for humans, as the 3D operations have to be performed by the user on a 2D touch surface. This ill-posed problem is classically solved with a fixed and handcrafted interaction protocol, which must be learned by the user. We propose to automatically learn a new interaction protocol allowing to map a 2D user input to 3D actions in virtual environments using reinforcement learning (RL). A fundamental problem of RL methods is the vast amount of interactions often required, which are difficult to come by when humans are involved. To overcome this limitation, we make use of two collaborative agents. The first agent models the human by learning to perform the 2D finger trajectories. The second agent acts as the interaction protocol, interpreting and translating to 3D operations the 2D finger trajectories from the first agent. We restrict the learned 2D trajectories to be similar to a training set of collected human gestures by first performing state representation learning, prior to reinforcement learning. This state representation learning is addressed by projecting the gestures into a latent space learned by a variational auto encoder (VAE).
Predicting GNSS satellite visibility from dense point clouds
Venue: arXiv cs.RO 2019
Year: 2019

To help future mobile agents plan their movement in harsh environments,a predictive model has been designed to determine what areas would be favorable for Global Navigation Satellite System (GNSS) positioning. The model is able to predict the number of viable satellites for a GNSS receiver, based on a 3D point cloud map and a satellite constellation. Both occlusion and absorption effects of the environment are considered. A rugged mobile platform was designed to collect data in order to generate the point cloud maps. It was deployed during the Canadian winter known for large amounts of snow and extremely low temperatures. The test environments include a highly dense boreal forest and a university campus with high buildings. The experiment results indicate that the model performs well in both structured and unstructured environments
Objects as Points
Venue: arXiv cs.CV 2019
Year: 2019

Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.
AllocTC-Sharing: A New Bandwidth Allocation Model for DS-TE Networks
Venue: arXiv cs.NI 2019
Year: 2019

DiffServ-aware MPLS-TE (DS-TE) allows bandwidth reservation for Traffic Classes (TCs) in MPLS-based engineered networks and, as such, improves the basic MPLS-TE model. In DS-TE networks, per-Class quality of service guarantees are provided while being possible to achieve improved network utilization. DS-TE requires the use of a Bandwidth Allocation Model (BAM) that establishes the amount of bandwidth per-Class and any eventual sharing among them. This paper proposes a new bandwidth allocation model (AllocTC-Sharing) in which the higher priority traffic classes are allowed to use non allocated resources of lower priority traffic classes and vice versa. By adopting this dual sense allocation strategy for dynamic bandwidth allocation, it is shown that AllocTC-Sharing model preserves bandwidth constraints for traffic classes and improves overall link utilization.
Histopathologic Image Processing: A Review
Venue: arXiv cs.CV 2019
Year: 2019

Histopathologic Images (HI) are the gold standard for evaluation of some tumors. However, the analysis of such images is challenging even for experienced pathologists, resulting in problems of inter and intra observer. Besides that, the analysis is time and resource consuming. One of the ways to accelerate such an analysis is by using Computer Aided Diagnosis systems. In this work we present a literature review about the computing techniques to process HI, including shallow and deep methods. We cover the most common tasks for processing HI such as segmentation, feature extraction, unsupervised learning and supervised learning. A dataset section show some datasets found during the literature review. We also bring a study case of breast cancer classification using a mix of deep and shallow machine learning methods. The proposed method obtained an accuracy of 91% in the best case, outperforming the compared baseline of the dataset.
PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization
Venue: arXiv cs.LG 2019
Year: 2019

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including topic modeling, recommender systems and bioinformatics. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have been developed in the past. However, existing parallel NMF algorithms have not addressed data locality optimizations, which are critical for high performance since data movement costs greatly exceed the cost of arithmetic/logic operations on current computer systems. In this paper, we devise a parallel NMF algorithm based on the HALS (Hierarchical Alternating Least Squares) scheme that incorporates algorithmic transformations to enhance data locality. Efficient realizations of the algorithm on multi-core CPUs and GPUs are developed, demonstrating significant performance improvement over existing state-of-the-art parallel NMF algorithms.
An Efficient Framework for Network Code based Multimedia Content Distribution in a Hybrid P2P Network
Venue: arXiv cs.NI 2019
Year: 2019

Most of the existing P2P content distribution schemes implement a random or rarest piece first dissemination procedure to avoid duplicate transmission of the same pieces of data and rare pieces of data occurring in the network. This problem can be solved using peer-to-peer content distribution based on network coding scheme. Network coding scheme uses random linear combination of coded pieces. Hence the above stated problem can be solved ease and simple. Our proposed mechanism uses network coding mechanism in which several contents of same message is grouped into different group and coding operation is performed only within the same group. The interested peers are also divided into several groups with each group have the responsibility to spread one set of contents of some message. The coding system is designed to assure the property that any subset of the messages can be utilized to decode the original content as long as the size of the subset is suitably large. To meet this condition, dynamic smart network coding scheme is defined which assures the preferred property, then peers are connected in the same group to send the corresponding message, and connect peers in different groups to disseminate messages for carrying out decoding operation. Moreover, the proposed system can be readily expanded to support topology change to get better system performance further in terms of reliability, link stress and throughput. The simulation results prove that the proposed system can attain 20 to 25% higher throughput than existing systems, it also further attains good reliability, link failure and robustness to peer churn.
DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World Dataset
Venue: arXiv cs.CV 2019
Year: 2019

Is cognition a collection of loosely connected functions tuned to different tasks, or can there be a general learning algorithm? If such an hypothetical general algorithm did exist, tuned to our world, could it adapt seamlessly to a world with different laws of nature? We consider the theory that predictive coding is such a general rule, and falsify it for one specific neural architecture known for high-performance predictions on natural videos and replication of human visual illusions: PredNet. Our results show that PredNet's high performance generalizes without retraining on a completely different natural video dataset. Yet PredNet cannot be trained to reach even mediocre accuracy on an artificial video dataset created with the rules of the Game of Life (GoL). We also find that a submodule of PredNet, a Convolutional Neural Network trained alone, reaches perfect accuracy on the GoL while being mediocre for natural videos, showing that PredNet's architecture itself is responsible for both the high performance on natural videos and the loss of performance on the GoL. Just as humans cannot predict the dynamics of the GoL, our results suggest that there might be a trade-off between high performance on sensory inputs with different sets of rules.
Economic Analysis of Rollover and Shared Data Plans
Venue: arXiv cs.NI 2019
Year: 2019

In today's growing data market, wireless service providers (WSPs) compete severely to attract users by announcing innovative data plans. Two of the most popular innovative data plans are rollover and shared data plans, where the former plan allows a user to keep his unused data quota to next month and the latter plan allows users in a family to share unused data. As a pioneer to provide such data plans, a WSP faces immediate revenue loss from existing users who pay less overage charges due to less data over-usage, but his market share increases gradually by attracting new users and those under the other WSPs. In some countries, WSPs have asymmetric timing for providing such innovative data plans, while some other markets' WSPs have symmetric timing or no planning. This raises the question of why and when the competitive WSPs should offer the new data plans. This paper provides game theoretic modelling and analysis of the WSPs' timing of offering innovative data plans, by considering new user arrival and dynamic user churn between WSPs. Our equilibrium analysis shows that the WSP with small market share prefers to announce the innovative data plan first to attract more users, while the WSP with large market share prefers to announce later to avoid the immediate revenue loss. In a market with many new users, WSPs with similar market shares will offer the data plans simultaneously, but these WSPs facing few new users may not offer any new plan. Perhaps surprisingly, WSPs' profits can decrease with new user number and they may not benefit from the option of innovative data plans. Finally, unlike rollover data plan, we show that the timing of shared data plan further depends on the composition of users.
Experimental Demonstration of Staggered CAP Modulation for Low Bandwidth Red-Emitting Polymer-LED based Visible Light Communications
Venue: arXiv cs.NI 2019
Year: 2019

In this paper we experimentally demonstrate, for the first time, staggered carrier-less amplitude and phase (sCAP) modulation for visible light communication systems based on polymer light-emitting diodes emitting at ~639 nm. The key advantage offered by sCAP in comparison to conventional multiband CAP is its full use of the available spectrum. In this work, we compare sCAP, which utilises four orthogonal filters to generate the signal, with a conventional 4-band multi-CAP system and on-off keying (OOK). We transmit each modulation format with equal energy and present a record un-coded transmission speed of ~6 Mb/s. This represents gains of 25% and 65% over the achievable rate using 4-CAP and OOK, respectively.
Fast Uplink Grant for NOMA: a Federated Learning based Approach
Venue: arXiv cs.NI 2019
Year: 2019

Recently, non-orthogonal multiple access (NOMA) technique have emerged and is being considered as a building block of 5G systems and beyond. In this paper, we focus on the resource allocation for NOMA-based systems and we investigate how Machine Type Devices (MTDs) can be arranged into clusters. Specifically , we propose three allocation technique to enable the integration of massive NOMA-based MTD in the 5G. Firstly, we propose a low-complexity schema where the BS assigns an MTD to a cluster based on its CSI and transmit power in order to ensure that the SIC can be performed in the uplink as well as the downlink. In the second framework, we propose a federated-learning based traffic model estimation in order to extend the capacity of the system. In fact, the BS take into account the traffic model of the MTDs in order to use time multiplexing in addition to the power multiplexing to separate MTDs. After the BS allocation, we propose a learning algorithm to allow contending MTDs synchronize their transmissions. Finally, we focus on the Quality of Service (QoS) for NOMA based devices and we propose in the third framework a QoS-aware technique, where MTDs are served differently based on their mode, i.e. regular or alarm modes. Simulation results show that the proposed techniques outperform existing technique in the literature.
Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader
Venue: arXiv cs.IT 2019
Year: 2019

Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource-limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BC) at the tags. Since, this joint problem is nonconvex, we first present individually-optimal designs for the TRX and BC. We show that with precoder and {combiner} designs at the reader respectively targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low and high signal-to-noise-ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly-suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings like wireless powered communication networks and BSC with imperfect channel state information. Lastly, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.
Reducing Adversarial Example Transferability Using Gradient Regularization
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples transfer between different architectures trained on different datasets. We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models. To demonstrate the relevance of this approach, we perform case studies that involve jointly training pairs of models. These case studies empirically justify the theoretical intuitions for why the angle between gradients is a fundamental quantity in AdvX transferability. Furthermore, we consider the asymmetry of AdvX transferability between two models of the same architecture and explain it in terms of differences in gradient norms between the models. Lastly, we provide a simple modification to existing training setups that reduces transferability of adversarial examples between pairs of models.
An Axiomatic Approach to Liveness for Differential Equations
Venue: arXiv cs.LO 2019
Year: 2019

This paper presents an approach for deductive liveness verification for ordinary differential equations (ODEs) with differential dynamic logic. Numerous subtleties complicate the generalization of well-known discrete liveness verification techniques, such as loop variants, to the continuous setting. For example, ODE solutions may blow up in finite time or their progress towards the goal may converge to zero. Our approach handles these subtleties by successively refining ODE liveness properties using ODE invariance properties which have a well-understood deductive proof theory. This approach is widely applicable: we survey several liveness arguments in the literature and derive them all as special instances of our axiomatic refinement approach. We also correct several soundness errors in the surveyed arguments, which further highlights the subtlety of ODE liveness reasoning and the utility of our deductive approach. The library of common refinement steps identified through our approach enables both the sound development and justification of new ODE liveness proof rules from our axioms.
People infer recursive visual concepts from just a few examples
Venue: arXiv cs.LG 2019
Year: 2019

Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the limits of this human ability to infer causal "programs" -- latent generating processes with nontrivial algorithmic properties -- from one, two, or three visual examples. People were asked to extrapolate the programs in several ways, for both classifying and generating new examples. As a theory of these inductive abilities, we present a Bayesian program learning model that searches the space of programs for the best explanation of the observations. Although variable, people's judgments are broadly consistent with the model and inconsistent with several alternatives, including a pre-trained deep neural network for object recognition, indicating that people can learn and reason with rich algorithmic abstractions from sparse input data.
In Search of a Key Value Store with High Performance and High Availability
Venue: arXiv cs.DB 2019
Year: 2019

In recent year, the write-heavy applications is more and more prevalent. How to efficiently handle this sort of workload is one of intensive research direction in the field of database system. The overhead caused by write operation is mainly issued by two reasons: 1) the hardware level, i.e., the IO cost caused by logging. We can't remove this cost in short period 2) the dual-copy software architecture and serial replay. The born of log as database architecture is originated to overcome the software defect. But existing systems treating log as database either are built on top of special infrastructure such as infiniband or NVRam (Non-Volatile Random access memory) which is far from widely available or are constructed with the help of other system such as Dynamo which is lack of flexibility. In this paper we build only write-once key-value system called LogStore from scratch to support our instant messenger business. The key features of LogStore include: 1) a single thread per partition executing mode, which eliminates the concurrency overhead; 2) log as database to enable write-once feature and freshness on the standby. We achieve high availability by embedding replication protocol other than dependent on other infrastructure; 3) fine-grained and low overhead data buffer pool management to effectively minimize IO cost. According to our empirical evaluations LogStore has good performance in write operation, recovery and replication
Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries
Venue: arXiv cs.SI 2019
Year: 2019

In the densest subgraph problem, given a weighted undirected graph $G(V,E,w)$, with non-negative edge weights, we are asked to find a subset of nodes $S\subseteq V$ that maximizes the degree density $w(S)/|S|$, where $w(S)$ is the sum of the edge weights induced by $S$. This problem is a well studied problem, known as the {\em densest subgraph problem}, and is solvable in polynomial time. But what happens when the edge weights are negative? Is the problem still solvable in polynomial time? Also, why should we care about the densest subgraph problem in the presence of negative weights? In this work we answer the aforementioned question. Specifically, we provide two novel graph mining primitives that are applicable to a wide variety of applications. Our primitives can be used to answer questions such as "how can we find a dense subgraph in Twitter with lots of replies and mentions but no follows?", "how do we extract a dense subgraph with high expected reward and low risk from an uncertain graph"? We formulate both problems mathematically as special instances of dense subgraph discovery in graphs with negative weights. We study the hardness of the problem, and we prove that the problem in general is NP-hard. We design an efficient approximation algorithm that works well in the presence of small negative weights, and also an effective heuristic for the more general case. Finally, we perform experiments on various real-world uncertain graphs, and a crawled Twitter multilayer graph that verify the value of the proposed primitives, and the practical value of our proposed algorithms. The code and the data are available at \url{https://github.com/negativedsd}.
Authenticated Preambles for Denial of Service Mitigation in LPWANs
Venue: arXiv cs.NI 2019
Year: 2019

In this article we introduce authentication preambles as a mechanism to mitigate battery exhaustion attacks in LPWAN networks. We focus on the LoRaWAN technology as an exponent of industrial LPWANs. We analyze the impact of DoS attacks in Class B deployments and implement authentication preambles to limit attacker options when forcing nodes to overhear class B beacons. The article presents realistic results demonstrating significant energy savings (91% energy saving when a network is attacked) versus a 4% energy overhead of the mechanism in normally operating networks.
The intersection of two vertex coloring problems
Venue: arXiv cs.DM 2019
Year: 2019

A hole is an induced cycle with at least four vertices. A hole is even if its number of vertices is even. Given a set L of graphs, a graph G is L-free if G does not contain any graph in L as an induced subgraph. Currently, the following two problems are unresolved: the complexity of coloring even hole-free graphs, and the complexity of coloring {4K1, C4}-free graphs. The intersection of these two problems is the problem of coloring {4K1, C4, C6}-free graphs. In this paper we present partial results on this problem.
Computational Complexity of Stable Marriage and Stable Roommates and Their Variants
Venue: arXiv cs.GT 2019
Year: 2019

This paper gives an overview on and summarizes existing complexity and algorithmic results of some variants of the Stable Marriage and the Stable Roommates problems. The last section defines a list of stable matching problems mentioned in the paper. If you find any corrections, suggestions, new or missing results, please send them to jiehua.chen2@gmail.com.