OpenRaiser/Intern-Atlas huggingface dataset:
A Survey on Generative Adversarial Networks: Variants, Applications, and Training
Venue: arXiv cs.CV 2020
Year: 2020

The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have proposed, and several practical applications emerged in various domains of computer vision and machine learning. Despite GAN's excellent success, there are still obstacles to stable training. The problems are due to Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GAN. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We survey, (I) the original GAN model and its modified classical versions, (II) detail analysis of various GAN applications in different domains, (III) detail study about the various GAN training obstacles as well as training solutions. Finally, we discuss several new issues as well as research outlines to the topic.
Precise Robot Localization in Architectural 3D Plans
Venue: arXiv cs.RO 2020
Year: 2020

This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel image-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. We evaluate the proposed approach on a mobile robot in a challenging real world building construction site. It consistently outperforms the traditional ICP-based alingment, reducing localization error by at least 30%.
Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data
Venue: arXiv cs.LG 2020
Year: 2020

Dynamic Distribution Decomposition (DDD) was introduced in Taylor-King et. al. (PLOS Comp Biol, 2020) as a variation on Dynamic Mode Decomposition. In brief, by using basis functions over a continuous state space, DDD allows for the fitting of continuous-time Markov chains over these basis functions and as a result continuously maps between distributions. The number of parameters in DDD scales by the square of the number of basis functions; we reformulate the problem and restrict the method to compact basis functions which leads to the inference of sparse matrices only -- hence reducing the number of parameters. Finally, we demonstrate how DDD is suitable to integrate both trajectory time series (paired between subsequent time points) and snapshot time series (unpaired time points). Methods capable of integrating both scenarios are particularly relevant for the analysis of biomedical data, whereby studies observe population at fixed time points (snapshots) and individual patient journeys with repeated follow ups (trajectories).
Physically constrained short-term vehicle trajectory forecasting with naive semantic maps
Venue: arXiv cs.CV 2020
Year: 2020

Urban environments manifest a high level of complexity, and therefore it is of vital importance for safety systems embedded within autonomous vehicles (AVs) to be able to accurately predict the short-term future motion of nearby agents. This problem can be further understood as generating a sequence of future coordinates for a given agent based on its past motion data e.g. position, velocity, acceleration etc, and whilst current approaches demonstrate plausible results they have a propensity to neglect a scene's physical constrains. In this paper we propose the model based on a combination of the CNN and LSTM encoder-decoder architecture that learns to extract a relevant road features from semantic maps as well as general motion of agents and uses this learned representation to predict their short-term future trajectories. We train and validate the model on the publicly available dataset that provides data from urban areas, allowing us to examine it in challenging and uncertain scenarios. We show that our model is not only capable of anticipating future motion whilst taking into consideration road boundaries, but can also effectively and precisely predict trajectories for a longer time horizon than initially trained for.
A note on anonymous credentials using BLS signatures
Venue: arXiv cs.CR 2020
Year: 2020

In this note, we remark that the aggregation property of the BLS signature scheme yields an efficient Content Extraction Signature (CES). This construction can be used to build digital credentials that support selective disclosure in various settings. Interestingly, this construction is efficient and well suited to build credential issuance schemes with various applications in the client-server or in the distributed ledger models. Finally, we sketch a protocol that combines the CES with the use of a NIZK which allows to prove predicate satisfaction on claims extracted from a credential, while keeping the data secret.
A bio-inspired bistable recurrent cell allows for long-lasting memory
Venue: arXiv cs.NE 2020
Year: 2020

Recurrent neural networks (RNNs) provide state-of-the-art performances in a wide variety of tasks that require memory. These performances can often be achieved thanks to gated recurrent cells such as gated recurrent units (GRU) and long short-term memory (LSTM). Standard gated cells share a layer internal state to store information at the network level, and long term memory is shaped by network-wide recurrent connection weights. Biological neurons on the other hand are capable of holding information at the cellular level for an arbitrary long amount of time through a process called bistability. Through bistability, cells can stabilize to different stable states depending on their own past state and inputs, which permits the durable storing of past information in neuron state. In this work, we take inspiration from biological neuron bistability to embed RNNs with long-lasting memory at the cellular level. This leads to the introduction of a new bistable biologically-inspired recurrent cell that is shown to strongly improves RNN performance on time-series which require very long memory, despite using only cellular connections (all recurrent connections are from neurons to themselves, i.e. a neuron state is not influenced by the state of other neurons). Furthermore, equipping this cell with recurrent neuromodulation permits to link them to standard GRU cells, taking a step towards the biological plausibility of GRU.
Creating a Robot Coach for Mindfulness and Wellbeing: A Longitudinal Study
Venue: arXiv cs.HC 2020
Year: 2020

Social robots are starting to become incorporated into daily lives by assisting in the promotion of physical and mental wellbeing. This paper investigates the use of social robots for delivering mindfulness sessions. We created a teleoperated robotic platform that enables an experienced human coach to conduct the sessions in a virtual manner by replicating upper body and head pose in real time. The coach is also able to view the world from the robot's perspective and make a conversation with participants by talking and listening through the robot. We studied how participants interacted with a teleoperated robot mindfulness coach over a period of 5 weeks and compared with the interactions another group of participants had with a human coach. The mindfulness sessions delivered by both types of coaching invoked positive responses from the participants for all the sessions. We found that the participants rated the interactions with human coach consistently high in all aspects. However, there is a longitudinal change in the ratings for the interaction with the teleoperated robot for the aspects of motion and conversation. We also found that the participants' personality traits -- conscientiousness and neuroticism influenced the perceptions of the robot coach.
Social Media and COVID-19: Can Social Distancing be Quantified without Measuring Human Movements?
Venue: arXiv cs.SI 2020
Year: 2020

The COVID-19 outbreak has posed significant threats to international health and the economy. In the absence of treatment for this virus, public health officials asked the public to practice social distancing to reduce the number of physical contacts. However, quantifying social distancing is a challenging task and current methods are based on human movements. We propose a time and cost-effective approach to measure how people practice social distancing. This study proposes a new method based on utilizing the frequency of hashtags supporting and encouraging social distancing for measuring social distancing. We have identified 18 related hashtags and tracked their trends between Jan and May 2020. Our evaluation results show that there is a strong correlation (P<0.05) between our findings and the Google social distancing report.
A Hybrid Framework for Matching Printing Design Files to Product Photos
Venue: arXiv cs.CV 2020
Year: 2020

We propose a real-time image matching framework, which is hybrid in the sense that it uses both hand-crafted features and deep features obtained from a well-tuned deep convolutional network. The matching problem, which we concentrate on, is specific to a certain application, that is, printing design to product photo matching. Printing designs are any kind of template image files, created using a design tool, thus are perfect image signals. However, photographs of a printed product suffer many unwanted effects, such as uncontrolled shooting angle, uncontrolled illumination, occlusions, printing deficiencies in color, camera noise, optic blur, et cetera. For this purpose, we create an image set that includes printing design and corresponding product photo pairs with collaboration of an actual printing facility. Using this image set, we benchmark various hand-crafted and deep features for matching performance and propose a framework in which deep learning is utilized with highest contribution, but without disabling real-time operation using an ordinary desktop computer.
Sustainability of ICT hardware procurement in Switzerland -- A status-quo analysis of the public procurement sector
Venue: arXiv cs.CY 2020
Year: 2020

Sustainable procurement requires organizations to align their purchasing behavior with regard to broader goals linked to resource efficiency, climate change, social responsibility and other sustainability criteria. The level of sustainability in Information and Communication Technology (ICT) hardware procurement is analyzed for two reasons: First, ICT hardware belongs to the six key product groups in sustainable procurement. Second, ICT in general is expected to be an important enabler for low-carbon economies, providing solutions to reduce Green-House Gas (GHG) emissions. While the advantages of sustainable procurement are obvious, certain barriers hinder the adoption in day-to-day procurement. This case study on ICT hardware discusses the three important barriers "lack of clear definitions per product group", "missing market intelligence about sustainable products" and "inflexible procedures and attitudes as barriers for innovative approaches" based on an in-depth analysis of sustainable procurement of ICT hardware by the public sector in Switzerland. To this end, tender data published on the national procurement platform simap.ch is screened for sustainability criteria using the Common Procurement Vocabulary (CPV) nomenclature to identify relevant ICT projects. The results reveal to which extent such criteria as well as their determinants are currently included in public tenders. Using two different approaches, only a small number of tenders were found containing sustainability criteria of a wide range from basic to comprehensive. The overall performance of Swiss public procurement is benchmarked by comparing the identified sustainability criteria with available criteria from international key actors in sustainable procurement. Thus, this analysis provides novel insights on how public agencies today take sustainability into account when procuring ICT hardware.
Variational Model-based Policy Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, designing such algorithms is often challenging because the bias in simulated data may overshadow the ease of data generation. A potential solution to this challenge is to jointly learn and improve model and policy using a universal objective function. In this paper, we leverage the connection between RL and probabilistic inference, and formulate such an objective function as a variational lower-bound of a log-likelihood. This allows us to use expectation maximization (EM) and iteratively fix a baseline policy and learn a variational distribution, consisting of a model and a policy (E-step), followed by improving the baseline policy given the learned variational distribution (M-step). We propose model-based and model-free policy iteration (actor-critic) style algorithms for the E-step and show how the variational distribution learned by them can be used to optimize the M-step in a fully model-based fashion. Our experiments on a number of continuous control tasks show that despite being more complex, our model-based (E-step) algorithm, called {\em variational model-based policy optimization} (VMBPO), is more sample-efficient and robust to hyper-parameter tuning than its model-free (E-step) counterpart. Using the same control tasks, we also compare VMBPO with several state-of-the-art model-based and model-free RL algorithms and show its sample efficiency and performance.
Sublinear Algorithms and Lower Bounds for Metric TSP Cost Estimation
Venue: arXiv cs.DS 2020
Year: 2020

We consider the problem of designing sublinear time algorithms for estimating the cost of a minimum metric traveling salesman (TSP) tour. Specifically, given access to a $n \times n$ distance matrix $D$ that specifies pairwise distances between $n$ points, the goal is to estimate the TSP cost by performing only sublinear (in the size of $D$) queries. For the closely related problem of estimating the weight of a metric minimum spanning tree (MST), it is known that for any $\varepsilon > 0$, there exists an $\tilde{O}(n/\varepsilon^{O(1)})$ time algorithm that returns a $(1 + \varepsilon)$-approximate estimate of the MST cost. This result immediately implies an $\tilde{O}(n/\varepsilon^{O(1)})$ time algorithm to estimate the TSP cost to within a $(2 + \varepsilon)$ factor for any $\varepsilon > 0$. However, no $o(n^2)$ time algorithms are known to approximate metric TSP to a factor that is strictly better than $2$. On the other hand, there were also no known barriers that rule out the existence of $(1 + \varepsilon)$-approximate estimation algorithms for metric TSP with $\tilde{O}(n)$ time for any fixed $\varepsilon > 0$. In this paper, we make progress on both algorithms and lower bounds for estimating metric TSP cost. We also show that the problem of estimating metric TSP cost is closely connected to the problem of estimating the size of a maximum matching in a graph.
Stochastic Automata Network for Performance Evaluation of Heterogeneous SoC Communication
Venue: arXiv cs.PF 2020
Year: 2020

To meet ever increasing demand for performance of emerging System-on-Chip (SoC) applications, designer employ techniques for concurrent communication between components. Hence communication architecture becomes complex and major performance bottleneck. An early performance evaluation of communication architecture is the key to reduce design time, time-to-market and consequently cost of the system. Moreover, it helps to optimize system performance by selecting appropriate communication architecture. However, performance model of concurrent communication is complex to describe and hard to solve. In this paper, we propose methodology for performance evaluation of bus based communication architectures, modeling for which is based on modular Stochastic Automata Network (SAN). We employ Generalized Semi Markov Process (GSMP) model for each module of the SAN that emulates dynamic behavior of a Processing Element (PE) of an SoC architecture. The proposed modeling approach provides an early estimation of performance parameters viz. memory bandwidth, average queue length at memory and average waiting time seen by a processing element; while we provide parameters viz. number of processing elements, the mean computation time of processing elements and the first and second moments of connection time between processing elements and memories, as input to the model.
Deep Adversarial Koopman Model for Reaction-Diffusion systems
Venue: arXiv cs.CE 2020
Year: 2020

Reaction-diffusion systems are ubiquitous in nature and in engineering applications, and are often modeled using a non-linear system of governing equations. While robust numerical methods exist to solve them, deep learning-based reduced ordermodels (ROMs) are gaining traction as they use linearized dynamical models to advance the solution in time. One such family of algorithms is based on Koopman theory, and this paper applies this numerical simulation strategy to reaction-diffusion systems. Adversarial and gradient losses are introduced, and are found to robustify the predictions. The proposed model is extended to handle missing training data as well as recasting the problem from a control perspective. The efficacy of these developments are demonstrated for two different reaction-diffusion problems: (1) the Kuramoto-Sivashinsky equation of chaos and (2) the Turing instability using the Gray-Scott model.
Hermitian-Lifted Codes
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we construct codes for local recovery of erasures with high availability and constant-bounded rate from the Hermitian curve. These new codes, called Hermitian-lifted codes, are evaluation codes with evaluation set being the set of $\mathbb{F}_{q^2}$-rational points on the affine curve. The novelty is in terms of the functions to be evaluated; they are a special set of monomials which restrict to low degree polynomials on lines intersected with the Hermitian curve. As a result, the positions corresponding to points on any line through a given point act as a recovery set for the position corresponding to that point.
Fast Subtrajectory Similarity Search in Road Networks under Weighted Edit Distance Constraints
Venue: arXiv cs.DB 2020
Year: 2020

In this paper, we address a similarity search problem for spatial trajectories in road networks. In particular, we focus on the subtrajectory similarity search problem, which involves finding in a database the subtrajectories similar to a query trajectory. A key feature of our approach is that we do not focus on a specific similarity function; instead, we consider weighted edit distance (WED), a class of similarity functions which allows user-defined cost functions and hence includes several important similarity functions such as EDR and ERP. We model trajectories as strings, and propose a generic solution which is able to deal with any similarity function belonging to the class of WED. By employing the filter-and-verify strategy, we introduce subsequence filtering to efficiently prunes trajectories and find candidates. In order to choose a proper subsequence to optimize the candidate number, we model the choice as a discrete optimization problem (NP-hard) and compute it using a 2-approximation algorithm. To verify candidates, we design bidirectional tries, with which the verification starts from promising positions and leverage the shared segments of trajectories and the sparsity of road networks for speed-up. Experiments are conducted on large datasets to demonstrate the effectiveness of WED and the efficiency of our method for various similarity functions under WED.
Adversarial Attacks on Brain-Inspired Hyperdimensional Computing-Based Classifiers
Venue: arXiv cs.LG 2020
Year: 2020

Being an emerging class of in-memory computing architecture, brain-inspired hyperdimensional computing (HDC) mimics brain cognition and leverages random hypervectors (i.e., vectors with a dimensionality of thousands or even more) to represent features and to perform classification tasks. The unique hypervector representation enables HDC classifiers to exhibit high energy efficiency, low inference latency and strong robustness against hardware-induced bit errors. Consequently, they have been increasingly recognized as an appealing alternative to or even replacement of traditional deep neural networks (DNNs) for local on device classification, especially on low-power Internet of Things devices. Nonetheless, unlike their DNN counterparts, state-of-the-art designs for HDC classifiers are mostly security-oblivious, casting doubt on their safety and immunity to adversarial inputs. In this paper, we study for the first time adversarial attacks on HDC classifiers and highlight that HDC classifiers can be vulnerable to even minimally-perturbed adversarial samples. Concretely, using handwritten digit classification as an example, we construct a HDC classifier and formulate a grey-box attack problem, where an attacker's goal is to mislead the target HDC classifier to produce erroneous prediction labels while keeping the amount of added perturbation noise as little as possible. Then, we propose a modified genetic algorithm to generate adversarial samples within a reasonably small number of queries. Our results show that adversarial images generated by our algorithm can successfully mislead the HDC classifier to produce wrong prediction labels with a high probability (i.e., 78% when the HDC classifier uses a fixed majority rule for decision). Finally, we also present two defense strategies -- adversarial training and retraining-- to strengthen the security of HDC classifiers.
Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption
Venue: arXiv cs.LG 2020
Year: 2020

We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter. In this work, we propose an indicator to measure the robustness of neural network parameters by exploiting their vulnerability via parameter corruption. The proposed indicator describes the maximum loss variation in the non-trivial worst-case scenario under parameter corruption. For practical purposes, we give a gradient-based estimation, which is far more effective than random corruption trials that can hardly induce the worst accuracy degradation. Equipped with theoretical support and empirical validation, we are able to systematically investigate the robustness of different model parameters and reveal vulnerability of deep neural networks that has been rarely paid attention to before. Moreover, we can enhance the models accordingly with the proposed adversarial corruption-resistant training, which not only improves the parameter robustness but also translates into accuracy elevation.