OpenRaiser/Intern-Atlas huggingface dataset:
An octree cells occupancy geometric dimensionality descriptor for massive on-server point cloud visualisation and classification
Venue: arXiv cs.CV 2018
Year: 2018

Lidar datasets are becoming more and more common. They are appreciated for their precise 3D nature, and have a wide range of applications, such as surface reconstruction, object detection, visualisation, etc. For all this applications, having additional semantic information per point has potential of increasing the quality and the efficiency of the application. In the last decade the use of Machine Learning and more specifically classification methods have proved to be successful to create this semantic information. In this paradigm, the goal is to classify points into a set of given classes (for instance tree, building, ground, other). Some of these methods use descriptors (also called feature) of a point to learn and predict its class. Designing the descriptors is then the heart of these methods. Descriptors can be based on points geometry and attributes, use contextual information, etc. Furthermore, descriptors can be used by humans for easier visual understanding and sometimes filtering. In this work we propose a new simple geometric descriptor that gives information about the implicit local dimensionality of the point cloud at various scale. For instance a tree seen from afar is more volumetric in nature (3D), yet locally each leaves is rather planar (2D). To do so we build an octree centred on the point to consider, and compare the variation of the occupancy of the cells across the levels of the octree. We compare this descriptor with the state of the art dimensionality descriptor and show its interest. We further test the descriptor for classification within the Point Cloud Server, and demonstrate efficiency and correctness results.
Java & Lambda: a Featherweight Story
Venue: arXiv cs.LO 2018
Year: 2018

We present FJ&$λ$, a new core calculus that extends Featherweight Java (FJ) with interfaces, supporting multiple inheritance in a restricted form, $λ$-expressions, and intersection types. Our main goal is to formalise how lambdas and intersection types are grafted on Java 8, by studying their properties in a formal setting. We show how intersection types play a significant role in several cases, in particular in the typecast of a $λ$-expression and in the typing of conditional expressions. We also embody interface \emph{default methods} in FJ&$λ$, since they increase the dynamism of $λ$-expressions, by allowing these methods to be called on $λ$-expressions. The crucial point in Java 8 and in our calculus is that $λ$-expressions can have various types according to the context requirements (target types): indeed, Java code does not compile when $λ$-expressions come without target types. In particular, in the operational semantics we must record target types by decorating $λ$-expressions, otherwise they would be lost in the runtime expressions. We prove the subject reduction property and progress for the resulting calculus, and we give a type inference algorithm that returns the type of a given program if it is well typed. The design of FJ&$λ$ has been driven by the aim of making it a subset of Java 8, while preserving the elegance and compactness of FJ. Indeed, FJ&$λ$ programs are typed and behave the same as Java programs.
Plane-Casting: 3D Cursor Control with a SmartPhone
Venue: arXiv cs.HC 2018
Year: 2018

We present Plane-Casting, a novel technique for 3D object manipulation from a distance that is especially suitable for smartphones. We describe two variations of Plane-Casting, Pivot and Free Plane-Casting, and present results from a pilot study. Results suggest that Pivot Plane-Casting is more suitable for quick, coarse movements whereas Free Plane-Casting is more suited to slower, precise motion. In a 3D movement task, Pivot Plane-Casting performed better quantitatively, but subjects preferred Free Plane-Casting overall.
Throughput Maximization in Cloud Radio Access Networks using Network Coding
Venue: arXiv cs.IT 2018
Year: 2018

This paper is interested in maximizing the total throughput of cloud radio access networks (CRANs) in which multiple radio remote heads (RRHs) are connected to a central computing unit known as the cloud. The transmit frame of each RRH consists of multiple radio resources blocks (RRBs), and the cloud is responsible for synchronizing these RRBS and scheduling them to users. Unlike previous works that consider allocating each RRB to only a single user at each time instance, this paper proposes to mix the flows of multiple users in each RRB using instantly decodable network coding (IDNC). The proposed scheme is thus designed to jointly schedule the users to different RRBs, choose the encoded file sent in each of them, and the rate at which each of them is transmitted. Hence, the paper maximizes the throughput which is defined as the number of correctly received bits. To jointly fulfill this objective, we design a graph in which each vertex represents a possible user-RRB association, encoded file, and transmission rate. By appropriately choosing the weights of vertices, the scheduling problem is shown to be equivalent to a maximum weight clique problem over the newly introduced graph. Simulation results illustrate the significant gains of the proposed scheme compared to classical coding and uncoded solutions.
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
Venue: arXiv cs.LG 2018
Year: 2018

This paper first answers the question "why do the two most powerful techniques Dropout and Batch Normalization (BN) often lead to a worse performance when they are combined together?" in both theoretical and statistical aspects. Theoretically, we find that Dropout would shift the variance of a specific neural unit when we transfer the state of that network from train to test. However, BN would maintain its statistical variance, which is accumulated from the entire learning procedure, in the test phase. The inconsistency of that variance (we name this scheme as "variance shift") causes the unstable numerical behavior in inference that leads to more erroneous predictions finally, when applying Dropout before BN. Thorough experiments on DenseNet, ResNet, ResNeXt and Wide ResNet confirm our findings. According to the uncovered mechanism, we next explore several strategies that modifies Dropout and try to overcome the limitations of their combination by avoiding the variance shift risks.
Stabilizing Unstable Periodic Orbits with Delayed Feedback Control in Act-and-Wait Fashion
Venue: arXiv cs.SY 2018
Year: 2018

A delayed feedback control framework for stabilizing unstable periodic orbits of linear periodic time-varying systems is proposed. In this framework, act-and-wait approach is utilized for switching a delayed feedback controller on and off alternately at every integer multiples of the period of the system. By analyzing the monodromy matrix of the closed-loop system, we obtain conditions under which the closed-loop system's state converges towards a periodic solution under our proposed control law. We discuss the application of our results in stabilization of unstable periodic orbits of nonlinear systems and present numerical examples to illustrate the efficacy of our approach.
On the characterization of models of H* : The operational aspect
Venue: arXiv cs.LO 2018
Year: 2018

We give a characterization, with respect to a large class of models of untyped $λ$-calculus, of those models that are fully abstract for head-normalization, i.e., whose equational theory is $\mathcal{H}^*$. An extensional K-model $D$ is fully abstract if and only if it is hyperimmune, i.e., non-well founded chains of elements of $D$ cannot be captured by any recursive function. This article share its first title with its companion paper and a short version. It is a standalone paper that present a purely syntactical proof of the result as opposed to its companion paper that present an independent and purely semantical proof of the exact same result.
On the Kernel of $\mathbb{Z}_{2^s}$-Linear Hadamard Codes
Venue: arXiv cs.IT 2018
Year: 2018

The $\mathbb{Z}_{2^s}$-additive codes are subgroups of $\mathbb{Z}^n_{2^s}$, and can be seen as a generalization of linear codes over $\mathbb{Z}_2$ and $\mathbb{Z}_4$. A $\mathbb{Z}_{2^s}$-linear Hadamard code is a binary Hadamard code which is the Gray map image of a $\mathbb{Z}_{2^s}$-additive code. It is known that the dimension of the kernel can be used to give a complete classification of the $\mathbb{Z}_4$-linear Hadamard codes. In this paper, the kernel of $\mathbb{Z}_{2^s}$-linear Hadamard codes and its dimension are established for $s > 2$. Moreover, we prove that this invariant only provides a complete classification for some values of $t$ and $s$. The exact amount of nonequivalent such codes are given up to $t=11$ for any $s\geq 2$, by using also the rank and, in some cases, further computations.
Sequences, yet Functions: The Dual Nature of Data-Stream Processing
Venue: arXiv cs.DB 2018
Year: 2018

Data-stream processing has continuously risen in importance as the amount of available data has been steadily increas- ing over the last decade. Besides traditional domains such as data-center monitoring and click analytics, there is an increasing number of network-enabled production machines that generate continuous streams of data. Due to their continuous nature, queries on data-streams can be more complex, and distinctly harder to understand then database queries. As users have to consider operational details, maintenance and debugging become challenging. Current approaches model data-streams as sequences, be- cause this is the way they are physically received. These models result in an implementation-focused perspective. We explore an alternate way of modeling data-streams by focusing on time-slicing semantics. This focus results in a model based on functions, which is better suited for reasoning about query semantics. By adapting the definitions of relevant concepts in stream processing to our model, we illustrate the practical useful- ness of our approach. Thereby, we link data-streams and query primitives to concepts in functional programming and mathematics. Most noteworthy, we prove that data-streams are monads, and show how to derive monad definitions for current data-stream models. We provide an abstract, yet practical perspective on data- stream related subjects based on a sound, consistent query model. Our work can serve as solid foundation for future data-stream query-languages.
Device-to-Device Aided Multicasting
Venue: arXiv cs.IT 2018
Year: 2018

We consider a device-to-device (D2D) aided multicast channel, where a transmitter wishes to convey a common message to many receivers and these receivers cooperate with each other. We propose a simple computationally efficient scheme requiring only statistical channel knowledge at transmitter. Our analysis in general topologies reveals that, when the number of receivers $K$ grows to infinity, the proposed scheme guarantees a multicast rate of ${1 \over 2} \log_2(1 + β\ln K )$ with high probability for any $β< β^\star$ where $β^\star$ depends on the network topology. This scheme undergoes a phase transition at threshold $β^\star \ln K$ where transmissions are successful/unsuccessful with high probability when the SNR is above/below this threshold. We also analyze the outage rate of the proposed scheme in the same setting.
Solutions to problems with deep learning
Venue: arXiv cs.LG 2018
Year: 2018

Despite the several successes of deep learning systems, there are concerns about their limitations, discussed most recently by Gary Marcus. This paper discusses Marcus's concerns and some others, together with solutions to several of these problems provided by the "P theory of intelligence" and its realisation in the "SP computer model". The main advantages of the SP system are: relatively small requirements for data and the ability to learn from a single experience; the ability to model both hierarchical and non-hierarchical structures; strengths in several kinds of reasoning, including `commonsense' reasoning; transparency in the representation of knowledge, and the provision of an audit trail for all processing; the likelihood that the SP system could not be fooled into bizarre or eccentric recognition of stimuli, as deep learning systems can be; the SP system provides a robust solution to the problem of `catastrophic forgetting' in deep learning systems; the SP system provides a theoretically-coherent solution to the problems of correcting over- and under-generalisations in learning, and learning correct structures despite errors in data; unlike most research on deep learning, the SP programme of research draws extensively on research on human learning, perception, and cognition; and the SP programme of research has an overarching theory, supported by evidence, something that is largely missing from research on deep learning. In general, the SP system provides a much firmer foundation than deep learning for the development of artificial general intelligence.
Deep learning for determining a near-optimal topological design without any iteration
Venue: arXiv cs.LG 2018
Year: 2018

In this study, we propose a novel deep learning-based method to predict an optimized structure for a given boundary condition and optimization setting without using any iterative scheme. For this purpose, first, using open-source topology optimization code, datasets of the optimized structures paired with the corresponding information on boundary conditions and optimization settings are generated at low (32 x 32) and high (128 x 128) resolutions. To construct the artificial neural network for the proposed method, a convolutional neural network (CNN)-based encoder and decoder network is trained using the training dataset generated at low resolution. Then, as a two-stage refinement, the conditional generative adversarial network (cGAN) is trained with the optimized structures paired at both low and high resolutions, and is connected to the trained CNN-based encoder and decoder network. The performance evaluation results of the integrated network demonstrate that the proposed method can determine a near-optimal structure in terms of pixel values and compliance with negligible computational time.
Reinforcement Learning based Recommender System using Biclustering Technique
Venue: arXiv cs.IR 2018
Year: 2018

A recommender system aims to recommend items that a user is interested in among many items. The need for the recommender system has been expanded by the information explosion. Various approaches have been suggested for providing meaningful recommendations to users. One of the proposed approaches is to consider a recommender system as a Markov decision process (MDP) problem and try to solve it using reinforcement learning (RL). However, existing RL-based methods have an obvious drawback. To solve an MDP in a recommender system, they encountered a problem with the large number of discrete actions that bring RL to a larger class of problems. In this paper, we propose a novel RL-based recommender system. We formulate a recommender system as a gridworld game by using a biclustering technique that can reduce the state and action space significantly. Using biclustering not only reduces space but also improves the recommendation quality effectively handling the cold-start problem. In addition, our approach can provide users with some explanation why the system recommends certain items. Lastly, we examine the proposed algorithm on a real-world dataset and achieve a better performance than the widely used recommendation algorithm.
Stable Phaseless Sampling and Reconstruction of Real-Valued Signals with Finite Rate of Innovations
Venue: arXiv cs.IT 2018
Year: 2018

A spatial signal is defined by its evaluations on the whole domain. In this paper, we consider stable reconstruction of real-valued signals with finite rate of innovations (FRI), up to a sign, from their magnitude measurements on the whole domain or their phaseless samples on a discrete subset. FRI signals appear in many engineering applications such as magnetic resonance spectrum, ultra wide-band communication and electrocardiogram. For an FRI signal, we introduce an undirected graph to describe its topological structure. We establish the equivalence between the graph connectivity and phase retrievability of FRI signals, and we apply the graph connected component decomposition to find all FRI signals that have the same magnitude measurements as the original FRI signal has. We construct discrete sets with finite density explicitly so that magnitude measurements of FRI signals on the whole domain are determined by their samples taken on those discrete subsets. In this paper, we also propose a stable algorithm with linear complexity to reconstruct FRI signals from their phaseless samples on the above phaseless sampling set. The proposed algorithm is demonstrated theoretically and numerically to provide a suboptimal approximation to the original FRI signal in magnitude measurements.
Over-the-Air Implementation of Uplink NOMA
Venue: arXiv cs.NI 2018
Year: 2018

Though the concept of non-orthogonal multiple access (NOMA) was proposed several years ago, the performance of uplink NOMA has only been verified in theory, but not in practice. This paper presents an over-the-air implementation of a uplink NOMA system, while providing solutions to most common practical problems, i.e., carrier frequency offset (CFO) synchronization, time synchronization, and channel estimation. The implemented CFO synchronization method adopts the primary synchronization signal (PSS) of LTE. Also, we design a novel preamble for each uplink user, and it is appended to every frame before it is transmitted through the air. This preamble will be used for time synchronization and channel estimation at the BS. Also, a low-complexity, iterative linear minimum mean squared error (LMMSE) detector has been implemented for multi-user decoding. The paper also validates the proposed architecture numerically, as well as experimentally.
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients
Venue: arXiv cs.AI 2018
Year: 2018

In this technical report, we consider an approach that combines the PPO objective and K-FAC natural gradient optimization, for which we call PPOKFAC. We perform a range of empirical analysis on various aspects of the algorithm, such as sample complexity, training speed, and sensitivity to batch size and training epochs. We observe that PPOKFAC is able to outperform PPO in terms of sample complexity and speed in a range of MuJoCo environments, while being scalable in terms of batch size. In spite of this, it seems that adding more epochs is not necessarily helpful for sample efficiency, and PPOKFAC seems to be worse than its A2C counterpart, ACKTR.
Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure
Venue: arXiv cs.CV 2018
Year: 2018

Correctly estimating the discrepancy between two data distributions has always been an important task in Machine Learning. Recently, Cuturi proposed the Sinkhorn distance which makes use of an approximate Optimal Transport cost between two distributions as a distance to describe distribution discrepancy. Although it has been successfully adopted in various machine learning applications (e.g. in Natural Language Processing and Computer Vision) since then, the Sinkhorn distance also suffers from two unnegligible limitations. The first one is that the Sinkhorn distance only gives an approximation of the real Wasserstein distance, the second one is the `divide by zero' problem which often occurs during matrix scaling when setting the entropy regularization coefficient to a small value. In this paper, we introduce a new Brenier approach for calculating a more accurate Wasserstein distance between two discrete distributions, this approach successfully avoids the two limitations shown above for Sinkhorn distance and gives an alternative way for estimating distribution discrepancy.
Packet Routing in 3D Nanonetworks: A Lightweight, Linear-path Scheme
Venue: arXiv cs.NI 2018
Year: 2018

Packet routing in nanonetworks requires novel approaches, which can cope with the extreme limitations posed by the nano-scale. Highly lossy wireless channels, extremely limited hardware capabilities and non-unique node identifiers are among the restrictions. The present work offers an addressing and routing solution for static 3D nanonetworks that find applications in material monitoring and programmatic property tuning. The addressing process relies on virtual coordinates from multiple, alternative anchor point sets that act as \emph{viewports}. Each viewport offers different address granularity within the network space, and its selection is optimized by a packet sending node using a novel heuristic. Regarding routing, each node can deduce whether it is located on the linear segment connecting the sender to the recipient node. This deduction is made using integer calculations, node-local information and in a stateless manner, minimizing the computational and storage overhead of the proposed scheme. Most importantly, the nodes can regulate the width of the linear path, thus trading energy efficiency (redundant transmissions) for increased path diversity. This trait can enable future adaptive routing schemes. Extensive evaluation via simulations highlights the advantages of the novel scheme over related approaches.
Unseen Class Discovery in Open-world Classification
Venue: arXiv cs.LG 2018
Year: 2018

This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject examples from unseen or novel classes that have not appeared in training. Specifically, this paper focuses on discovering the hidden unseen classes of the rejected examples. Clearly, without prior knowledge this is difficult. However, we do have the data from the seen training classes, which can tell us what kind of similarity/difference is expected for examples from the same class or from different classes. It is reasonable to assume that this knowledge can be transferred to the rejected examples and used to discover the hidden unseen classes in them. This paper aims to solve this problem. It first proposes a joint open classification model with a sub-model for classifying whether a pair of examples belongs to the same or different classes. This sub-model can serve as a distance function for clustering to discover the hidden classes of the rejected examples. Experimental results show that the proposed model is highly promising.
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics
Venue: arXiv cs.DB 2018
Year: 2018

We consider methods for learning vector representations of SQL queries to support generalized workload analytics tasks, including workload summarization for index selection and predicting queries that will trigger memory errors. We consider vector representations of both raw SQL text and optimized query plans, and evaluate these methods on synthetic and real SQL workloads. We find that general algorithms based on vector representations can outperform existing approaches that rely on specialized features. For index recommendation, we cluster the vector representations to compress large workloads with no loss in performance from the recommended index. For error prediction, we train a classifier over learned vectors that can automatically relate subtle syntactic patterns with specific errors raised during query execution. Surprisingly, we also find that these methods enable transfer learning, where a model trained on one SQL corpus can be applied to an unrelated corpus and still enable good performance. We find that these general approaches, when trained on a large corpus of SQL queries, provides a robust foundation for a variety of workload analysis tasks and database features, without requiring application-specific feature engineering.