OpenRaiser/Intern-Atlas huggingface dataset:
Spacecraft design optimisation for demise and survivability
Venue: arXiv cs.NE 2019
Year: 2019

Among the mitigation measures introduced to cope with the space debris issue there is the de-orbiting of decommissioned satellites. Guidelines for re-entering objects call for a ground casualty risk no higher than 0.0001. To comply with this requirement, satellites can be designed through a design-for-demise philosophy. Still, a spacecraft designed to demise has to survive the debris-populated space environment for many years. The demisability and the survivability of a satellite can both be influenced by a set of common design choices such as the material selection, the geometry definition, and the position of the components. Within this context, two models have been developed to analyse the demise and the survivability of satellites. Given the competing nature of the demisability and the survivability, a multi-objective optimisation framework was developed, with the aim to identify trade-off solutions for the preliminary design of satellites. As the problem is nonlinear and involves the combination of continuous and discrete variables, classical derivative based approaches are unsuited and a genetic algorithm was selected instead. The genetic algorithm uses the developed demisability and survivability criteria as the fitness functions of the multi-objective algorithm. The paper presents a test case, which considers the preliminary optimisation of tanks in terms of material, geometry, location, and number of tanks for a representative Earth observation mission. The configuration of the external structure of the spacecraft is fixed. Tanks were selected because they are sensitive to both design requirements: they represent critical components in the demise process and impact damage can cause the loss of the mission because of leaking and ruptures. The results present the possible trade off solutions, constituting the Pareto front obtained from the multi-objective optimisation.
Single Image BRDF Parameter Estimation with a Conditional Adversarial Network
Venue: arXiv cs.GR 2019
Year: 2019

Creating plausible surfaces is an essential component in achieving a high degree of realism in rendering. To relieve artists, who create these surfaces in a time-consuming, manual process, automated retrieval of the spatially-varying Bidirectional Reflectance Distribution Function (SVBRDF) from a single mobile phone image is desirable. By leveraging a deep neural network, this casual capturing method can be achieved. The trained network can estimate per pixel normal, base color, metallic and roughness parameters from the Disney BRDF. The input image is taken with a mobile phone lit by the camera flash. The network is trained to compensate for environment lighting and thus learned to reduce artifacts introduced by other light sources. These losses contain a multi-scale discriminator with an additional perceptual loss, a rendering loss using a differentiable renderer, and a parameter loss. Besides the local precision, this loss formulation generates material texture maps which are globally more consistent. The network is set up as a generator network trained in an adversarial fashion to ensure that only plausible maps are produced. The estimated parameters not only reproduce the material faithfully in rendering but capture the style of hand-authored materials due to the more global loss terms compared to previous works without requiring additional post-processing. Both the resolution and the quality is improved.
DDTCDR: Deep Dual Transfer Cross Domain Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. However, previously proposed cross-domain models did not take into account bidirectional latent relations between users and items. In addition, they do not explicitly model information of user and item features, while utilizing only user ratings information for recommendations. To address these concerns, in this paper we propose a novel approach to cross-domain recommendations based on the mechanism of dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Combining with autoencoder approach to extract the latent essence of feature information, we propose Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model to provide recommendations in respective domains. We test the proposed method on a large dataset containing three domains of movies, book and music items and demonstrate that it consistently and significantly outperforms several state-of-the-art baselines and also classical transfer learning approaches.
Face Reflectance and Geometry Modeling via Differentiable Ray Tracing
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel strategy to automatically reconstruct 3D faces from monocular images with explicitly disentangled facial geometry (pose, identity and expression), reflectance (diffuse and specular albedo), and self-shadows. The scene lights are modeled as a virtual light stage with pre-oriented area lights used in conjunction with differentiable Monte-Carlo ray tracing to optimize the scene and face parameters. With correctly disentangled self-shadows and specular reflection parameters, we can not only obtain robust facial geometry reconstruction, but also gain explicit control over these parameters, with several practical applications. We can change facial expressions with accurate resultant self-shadows or relight the scene and obtain accurate specular reflection and several other parameter combinations.
Parametric Systems: Verification and Synthesis
Venue: arXiv cs.LO 2019
Year: 2019

In this paper we study possibilities of using hierarchical reasoning, symbol elimination and model generation for the verification of parametric systems, where the parameters can be constants or functions. Our goal is to automatically provide guarantees that such systems satisfy certain safety or invariance conditions. We analyze the possibility of automatically generating such guarantees in the form of constraints on parameters. We illustrate our methods on several examples
The Natural Selection of Conservative Science
Venue: arXiv cs.DL 2019
Year: 2019

Social epistemologists have argued that high risk, high reward science has an important role to play in scientific communities. Recently, though, it has also been argued that various scientific fields seem to be trending towards conservatism -- the increasing production of what Kuhn (1970) would have called `normal science'. This paper will explore a possible explanation for this sort of trend: that the process by which scientific research groups form, grow, and dissolve might be inherently hostile to high risk science. In particular, I employ a paradigm developed by Smaldino and McElreath (2016) that treats a scientific community as a population undergoing selection. As will become clear, perhaps counter-intuitively this sort of process in some ways promotes high risk, high reward science. But, as I will point out, high risk high reward science is, in general, the sort of thing that is hard to repeat. While more conservative scientists will be able to train students capable of continuing their successful projects, and so create thriving lineages, successful risky science may not be the sort of thing one can easily pass on. In such cases, the structure of scientific communities selects against high risk, high rewards projects. More generally, this paper makes clear that there are at least two processes to consider in thinking about how incentives shape scientific communities -- the process by which individual scientists make choices about their careers and research, and the selective process governing the formation of new research groups.
Augmented Hard Example Mining for Generalizable Person Re-Identification
Venue: arXiv cs.CV 2019
Year: 2019

Although the performance of person re-identification (Re-ID) has been much improved by using sophisticated training methods and large-scale labelled datasets, many existing methods make the impractical assumption that information of a target domain can be utilized during training. In practice, a Re-ID system often starts running as soon as it is deployed, hence training with data from a target domain is unrealistic. To make Re-ID systems more practical, methods have been proposed that achieve high performance without information of a target domain. However, they need cumbersome tuning for training and unusual operations for testing. In this paper, we propose augmented hard example mining, which can be easily integrated to a common Re-ID training process and can utilize sophisticated models without any network modification. The method discovers hard examples on the basis of classification probabilities, and to make the examples harder, various types of augmentation are applied to the examples. Among those examples, excessively augmented ones are eliminated by a classification based selection process. Extensive analysis shows that our method successfully selects effective examples and achieves state-of-the-art performance on publicly available benchmark datasets.
Neural Generation for Czech: Data and Baselines
Venue: arXiv cs.CL 2019
Year: 2019

We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.
Two classes of $p$-ary linear codes and their duals
Venue: arXiv cs.IT 2019
Year: 2019

Let $\mathbb{F}_{p^m}$ be the finite field of order $p^m$, where $p$ is an odd prime and $m$ is a positive integer. In this paper, we investigate a class of subfield codes of linear codes and obtain the weight distribution of \begin{equation*} \begin{split} \mathcal{C}_k=\left\{\left(\left( {\rm Tr}_1^m\left(ax^{p^k+1}+bx\right)+c\right)_{x \in \mathbb{F}_{p^m}}, {\rm Tr}_1^m(a)\right) : \, a,b \in \mathbb{F}_{p^m}, c \in \mathbb{F}_p\right\}, \end{split} \end{equation*} where $k$ is a nonnegative integer. Our results generalize the results of the subfield codes of the conic codes in \cite{Hengar}. Among other results, we study the punctured code of $\mathcal{C}_k$, which is defined as $$\mathcal{\bar{C}}_k=\left\{\left( {\rm Tr}_1^m\left(a x^{{p^k}+1}+bx\right)+c\right)_{x \in \mathbb{F}_{p^m}} : \, a,b \in \mathbb{F}_{p^m}, \,\,c \in \mathbb{F}_p\right\}.$$ The parameters of these linear codes are new in some cases. Some of the presented codes are optimal or almost optimal. Moreover, let $v_2(\cdot)$ denote the 2-adic order function and $v_2(0)=\infty$, the duals of $\mathcal{C}_k$ and $\mathcal{\bar{C}}_k$ are optimal with respect to the Sphere Packing bound if $p>3$, and the dual of $\mathcal{\bar{C}}_k$ is an optimal ternary linear code for the case $v_2(m)\leq v_2(k)$ if $p=3$ and $m>1$.
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning
Venue: arXiv cs.LG 2019
Year: 2019

The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. While there exists a large body of literature on the embeddability of canonical graphs, such as lattices or trees, the heterogeneity of the relational data typically encountered in practice limits the applicability of these classical methods. In this paper, we propose a combinatorial approach to evaluating embeddability, i.e., to decide whether a data set is best represented in Euclidean, Hyperbolic or Spherical space. Our method analyzes nearest-neighbor structures and local neighborhood growth rates to identify the geometric priors of suitable embedding spaces. For canonical graphs, the algorithm's prediction provably matches classical results. As for large, heterogeneous graphs, we introduce an efficiently computable statistic that approximates the algorithm's decision rule. We validate our method over a range of benchmark data sets and compare with recently published optimization-based embeddability methods.
On the equivalence of authentication codes and robust (2,2)-threshold schemes
Venue: arXiv cs.CR 2019
Year: 2019

In this paper, we show a "direct" equivalence between certain authentication codes and robust secret sharing schemes. It was previously known that authentication codes and robust secret sharing schemes are closely related to similar types of designs, but direct equivalences had not been considered in the literature. Our new equivalences motivate the consideration of a certain "key-substitution attack." We study this attack and analyze it in the setting of "dual authentication codes." We also show how this viewpoint provides a nice way to prove properties and generalizations of some known constructions.
Optimizing Information Freshness in Computing enabled IoT Networks
Venue: arXiv cs.IT 2019
Year: 2019

Internet of Things (IoT) has emerged as one of the key features of the next generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually needs to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this work, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue, and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a min-max optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and effectiveness of the proposed algorithm are verified with extensive simulation results.
VAIS ASR: Building a conversational speech recognition system using language model combination
Venue: arXiv cs.CL 2019
Year: 2019

Automatic Speech Recognition (ASR) systems have been evolving quickly and reaching human parity in certain cases. The systems usually perform pretty well on reading style and clean speech, however, most of the available systems suffer from situation where the speaking style is conversation and in noisy environments. It is not straight-forward to tackle such problems due to difficulties in data collection for both speech and text. In this paper, we attempt to mitigate the problems using language models combination techniques that allows us to utilize both large amount of writing style text and small number of conversation text data. Evaluation on the VLSP 2019 ASR challenges showed that our system achieved 4.85% WER on the VLSP 2018 and 15.09% WER on the VLSP 2019 data sets.
Reachability and Shortest Paths in the Broadcast CONGEST Model
Venue: arXiv cs.DS 2019
Year: 2019

In this paper we study the time complexity of the single-source reachability problem and the single-source shortest path problem for directed unweighted graphs in the Broadcast CONGEST model. We focus on the case where the diameter $D$ of the underlying network is constant. We show that for the case where $D = 1$ there is, quite surprisingly, a very simple algorithm that solves the reachability problem in $1$(!) round. In contrast, for networks with $D = 2$, we show that any distributed algorithm (possibly randomized) for this problem requires $Ω(\sqrt{n/ \log{n}}\,)$ rounds. Our results therefore completely resolve (up to a small polylogarithmic factor) the complexity of the single-source reachability problem for a wide range of diameters. Furthermore, we show that when $D = 1$, it is even possible to get a $3$-approximation for the all-pairs shortest path problem (for directed unweighted graphs) in just $2$ rounds. We also prove a stronger lower bound of $Ω(\sqrt{n}\,)$ for the single-source shortest path problem for unweighted directed graphs that holds even when the diameter of the underlying network is $2$. As far as we know this is the first lower bound that achieves $Ω(\sqrt{n}\,)$ for this problem.
"Bring Your Own Greedy"+Max: Near-Optimal $1/2$-Approximations for Submodular Knapsack
Venue: arXiv cs.DS 2019
Year: 2019

The problem of selecting a small-size representative summary of a large dataset is a cornerstone of machine learning, optimization and data science. Motivated by applications to recommendation systems and other scenarios with query-limited access to vast amounts of data, we propose a new rigorous algorithmic framework for a standard formulation of this problem as a submodular maximization subject to a linear (knapsack) constraint. Our framework is based on augmenting all partial Greedy solutions with the best additional item. It can be instantiated with negligible overhead in any model of computation, which allows the classic \greedy algorithm and its variants to be implemented. We give such instantiations in the offline (Greedy+Max), multi-pass streaming (Sieve+Max) and distributed (Distributed+Max) settings. Our algorithms give ($1/2-ε$)-approximation with most other key parameters of interest being near-optimal. Our analysis is based on a new set of first-order linear differential inequalities and their robust approximate versions. Experiments on typical datasets (movie recommendations, influence maximization) confirm scalability and high quality of solutions obtained via our framework. Instance-specific approximations are typically in the 0.6-0.7 range and frequently beat even the $(1-1/e) \approx 0.63$ worst-case barrier for polynomial-time algorithms.
Hierarchical Feature-Aware Tracking
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a hierarchical feature-aware tracking framework for efficient visual tracking. Recent years, ensembled trackers which combine multiple component trackers have achieved impressive performance. In ensembled trackers, the decision of results is usually a post-event process, i.e., tracking result for each tracker is first obtained and then the suitable one is selected according to result ensemble. In this paper, we propose a pre-event method. We construct an expert pool with each expert being one set of features. For each frame, several experts are first selected in the pool according to their past performance and then they are used to predict the object. The selection rate of each expert in the pool is then updated and tracking result is obtained according to result ensemble. We propose a novel pre-known expert-adaptive selection strategy. Since the process is more efficient, more experts can be constructed by fusing more types of features which leads to more robustness. Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated. Experiments on several public available datasets demonstrate the superiority of the proposed method and its state-of-the-art performance among ensembled trackers.
Shared E-scooters: Business, Pleasure, or Transit?
Venue: arXiv cs.CY 2019
Year: 2019

Shared e-scooters have become a familiar sight in many cities around the world. Yet the role they play in the mobility space is still poorly understood. This paper presents a study of the use of Bird e-scooters in the city of Atlanta. Starting with raw data which contains the location of available Birds over time, the study identifies trips and leverages the Google Places API to associate each trip origin and destination with a Point of Interest (POI). The resulting trip data is then used to understand the role of e-scooters in mobility by clustering trips using 10 collections of POIs, including business, food and recreation, parking, transit, health, and residential. The trips between these POI clusters reveal some surprising, albeit sensible, findings about the role of e-scooters in mobility, as well as the time of the day where they are most popular.
Modelling Resistive and Phase Change Memory with Passive Selector Arrays -- A Matlab Tool
Venue: arXiv cs.NE 2019
Year: 2019

Memristor devices are crucial for developing neuromorphic computers and next-generation memory technologies. In this work, we provide a comprehensive modelling tool for simulating static DC reading operations of memristor crossbar arrays that use passive selectors with matrix algebra in MATLAB. The software tool was parallel coded and optimized to run with personal computers and distributed computer clusters with minimized CPU and memory consumption. Using the tool, we demonstrate the effect of changing the line resistance, array size, voltage selection scheme, selector diode's ideality factor, reverse saturation current, temperature and sense resistance on the electrical behavior and expected sense margin of one-diode-one-resistor crossbar arrays. We then investigate the effect of single and dual side array biasing and grounding on the dissipated current throughout the array cells. The tool we offer to the memristor community and the studies we present enables the design of larger and more practical memristor arrays for application in data storage and neuromorphic computing.
Parallelized Training of Restricted Boltzmann Machines using Markov-Chain Monte Carlo Methods
Venue: arXiv cs.LG 2019
Year: 2019

Restricted Boltzmann Machine (RBM) is a generative stochastic neural network that can be applied to collaborative filtering technique used by recommendation systems. Prediction accuracy of the RBM model is usually better than that of other models for recommendation systems. However, training the RBM model involves Markov-Chain Monte Carlo (MCMC) method, which is computationally expensive. In this paper, we have successfully applied distributed parallel training using Horovod framework to improve the training time of the RBM model. Our tests show that the distributed training approach of the RBM model has a good scaling efficiency. We also show that this approach effectively reduces the training time to little over 12 minutes on 64 CPU nodes compared to 5 hours on a single CPU node. This will make RBM models more practically applicable in recommendation systems.