OpenRaiser/Intern-Atlas huggingface dataset:
How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles
Venue: arXiv cs.MA 2019
Year: 2019

We propose an approach of open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of timescales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided in three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.
When redundancy is useful: A Bayesian approach to 'overinformative' referring expressions
Venue: arXiv cs.CL 2019
Year: 2019

Referring is one of the most basic and prevalent uses of language. How do speakers choose from the wealth of referring expressions at their disposal? Rational theories of language use have come under attack for decades for not being able to account for the seemingly irrational overinformativeness ubiquitous in referring expressions. Here we present a novel production model of referring expressions within the Rational Speech Act framework that treats speakers as agents that rationally trade off cost and informativeness of utterances. Crucially, we relax the assumption that informativeness is computed with respect to a deterministic Boolean semantics, in favor of a non-deterministic continuous semantics. This innovation allows us to capture a large number of seemingly disparate phenomena within one unified framework: the basic asymmetry in speakers' propensity to overmodify with color rather than size; the increase in overmodification in complex scenes; the increase in overmodification with atypical features; and the increase in specificity in nominal reference as a function of typicality. These findings cast a new light on the production of referring expressions: rather than being wastefully overinformative, reference is usefully redundant.
Computational Tactile Flow for Anthropomorphic Grippers
Venue: arXiv cs.RO 2019
Year: 2019

Grasping objects requires tight integration between visual and tactile feedback. However, there is an inherent difference in the scale at which both these input modalities operate. It is thus necessary to be able to analyze tactile feedback in isolation in order to gain information about the surface the end-effector is operating on, such that more fine-grained features may be extracted from the surroundings. For tactile perception of the robot, inspired by the concept of the tactile flow in humans, we present the computational tactile flow to improve the analysis of the tactile feedback in robots using a Shadow Dexterous Hand. In the computational tactile flow model, given a sequence of pressure values from the tactile sensors, we define a virtual surface for the pressure values and define the tactile flow as the optical flow of this surface. We provide case studies that demonstrate how the computational tactile flow maps reveal information on the direction of motion and 3D structure of the surface, and feedback regarding the action being performed by the robot.
MP net as Abstract Model of Communication for Message-passing Applications
Venue: arXiv cs.DC 2019
Year: 2019

MP net is a formal model specifically designed for the field of parallel applications that use a message passing interface. The main idea is to use MP net as a comprehensible way of presenting the actual structure of communication within MPI applications. The goal is to provide users with the kind of feedback that can help them to check quickly whether or not the actual communication within their application corresponds to the intended one. This paper introduces MP net that focuses on the communication part of parallel applications and emphasizes its spatial character, which is rather hidden in sequential (textual) form.
Faster Algorithms for the Geometric Transportation Problem
Venue: arXiv cs.DS 2019
Year: 2019

Let $R$ and $B$ be two point sets in $\mathbb{R}^d$, with $|R|+ |B| = n$ and where $d$ is a constant. Next, let $λ: R \cup B \to \mathbb{N}$ such that $\sum_{r \in R } λ(r) = \sum_{b \in B} λ(b)$ be demand functions over $R$ and $B$. Let $\|\cdot\|$ be a suitable distance function such as the $L_p$ distance. The transportation problem asks to find a map $τ: R \times B \to \mathbb{N}$ such that $\sum_{b \in B}τ(r,b) = λ(r)$, $\sum_{r \in R}τ(r,b) = λ(b)$, and $\sum_{r \in R, b \in B} τ(r,b) \|r-b\|$ is minimized. We present three new results for the transportation problem when $\|r-b\|$ is any $L_p$ metric: - For any constant $\varepsilon > 0$, an $O(n^{1+\varepsilon})$ expected time randomized algorithm that returns a transportation map with expected cost $O(\log^2(1/\varepsilon))$ times the optimal cost. - For any $\varepsilon > 0$, a $(1+\varepsilon)$-approximation in $O(n^{3/2}\varepsilon^{-d} \operatorname{polylog}(U) \operatorname{polylog}(n))$ time, where $U = \max_{p\in R\cup B} λ(p)$. - An exact strongly polynomial $O(n^2 \operatorname{polylog}n)$ time algorithm, for $d = 2$.
Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art
Venue: arXiv cs.LG 2019
Year: 2019

The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. We conclude by presenting a discussion of the reviewed literature and outlining the research gaps and remaining challenges for future work.
Contextual Compositionality Detection with External Knowledge Bases andWord Embeddings
Venue: arXiv cs.CL 2019
Year: 2019

When the meaning of a phrase cannot be inferred from the individual meanings of its words (e.g., hot dog), that phrase is said to be non-compositional. Automatic compositionality detection in multi-word phrases is critical in any application of semantic processing, such as search engines; failing to detect non-compositional phrases can hurt system effectiveness notably. Existing research treats phrases as either compositional or non-compositional in a deterministic manner. In this paper, we operationalize the viewpoint that compositionality is contextual rather than deterministic, i.e., that whether a phrase is compositional or non-compositional depends on its context. For example, the phrase `green card' is compositional when referring to a green colored card, whereas it is non-compositional when meaning permanent residence authorization. We address the challenge of detecting this type of contextual compositionality as follows: given a multi-word phrase, we enrich the word embedding representing its semantics with evidence about its global context (terms it often collocates with) as well as its local context (narratives where that phrase is used, which we call usage scenarios). We further extend this representation with information extracted from external knowledge bases. The resulting representation incorporates both localized context and more general usage of the phrase and allows to detect its compositionality in a non-deterministic and contextual way. Empirical evaluation of our model on a dataset of phrase compositionality, manually collected by crowdsourcing contextual compositionality assessments, shows that our model outperforms state-of-the-art baselines notably on detecting phrase compositionality.
How Far are we from Data Mining Democratisation? A Systematic Review
Venue: arXiv cs.DB 2019
Year: 2019

Context: Data mining techniques have demonstrated to be a powerful technique for discovering insights hidden in data from a domain. However, these techniques demand very specialised skills. People willing to analyse data often lack these skills, so they must rely on data scientists, which hinders data mining democratisation. Different approaches have appeared in the last years to address this issue. Objective: Analyse the state of the art to know how far are we from an effective data mining democratisation, what has already been accomplished, and what should be done in the upcoming years. Method: We performed a state-of-the-art review following a systematic and objective procedure, which included works both from the academia and the industry. The reviewed works were grouped in four categories. Each category was then evaluated in detail using a well-defined evaluation criteria to identify its strengths and weaknesses. Results: Around 700 works were initially considered, from which 43 were finally selected for a more in-depth analysis. Only two out of the four identified categories provide effective solutions to data mining democratisation. From these two categories, one always requires a minimum intervention of a data scientist, whereas the other one does not provide support for all the stages of the data mining process, and might exhibit accuracy problems in some contexts. Conclusion: In all analysed approaches, a data scientist is still required to perform some steps of the analysis process. Moreover, automated approaches that do not require data scientists for some steps expose some problems in other quality attributes, such as accuracy. Therefore, although existent work shows some promising initial steps, we are still far from data mining democratisation.
Deep Octonion Networks
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning is a research hot topic in the field of machine learning. Real-value neural networks (Real NNs), especially deep real networks (DRNs), have been widely used in many research fields. In recent years, the deep complex networks (DCNs) and the deep quaternion networks (DQNs) have attracted more and more attentions. The octonion algebra, which is an extension of complex algebra and quaternion algebra, can provide more efficient and compact expression. This paper constructs a general framework of deep octonion networks (DONs) and provides the main building blocks of DONs such as octonion convolution, octonion batch normalization and octonion weight initialization; DONs are then used in image classification tasks for CIFAR-10 and CIFAR-100 data sets. Compared with the DRNs, the DCNs, and the DQNs, the proposed DONs have better convergence and higher classification accuracy. The success of DONs is also explained by multi-task learning.
Topological Data Analysis in Information Space
Venue: arXiv cs.CG 2019
Year: 2019

Various kinds of data are routinely represented as discrete probability distributions. Examples include text documents summarized by histograms of word occurrences and images represented as histograms of oriented gradients. Viewing a discrete probability distribution as a point in the standard simplex of the appropriate dimension, we can understand collections of such objects in geometric and topological terms. Importantly, instead of using the standard Euclidean distance, we look into dissimilarity measures with information-theoretic justification, and we develop the theory needed for applying topological data analysis in this setting. In doing so, we emphasize constructions that enable usage of existing computational topology software in this context.
Sequential Optimization of Speed, Thermal Load, and Power Split in Connected HEVs
Venue: arXiv cs.SY 2019
Year: 2019

The emergence of connected and automated vehicles (CAVs) provides an unprecedented opportunity to capitalize on these technologies well beyond their original designed intents. While abundant evidence has been accumulated showing substantial fuel economy improvement benefits achieved through advanced powertrain control, the implications of the CAV operation on power and thermal management have not been fully investigated. In this paper, in order to explore the opportunities for the coordination between the onboard thermal management and the power split control, we present a sequential optimization solution for eco-driving speed trajectory planning, air conditioning (A/C) thermal load planning (eco-cooling), and powertrain control in hybrid electric CAVs to evaluate the individual as well as the collective energy savings through proactive usage of traffic data for vehicle speed prediction. Simulation results over a real-world driving cycle show that compared to a baseline non-CAV, 11.9%, 14.2%, and 18.8% energy savings can be accumulated sequentially through speed, thermal load, and power split optimizations, respectively.
Single-step Options for Adversary Driving
Venue: arXiv cs.AI 2019
Year: 2019

In this paper, we use reinforcement learning for safety driving in adversary settings. In our work, the knowledge in state-of-art planning methods is reused by single-step options whose action suggestions are compared in parallel with primitive actions. We show two advantages by doing so. First, training this reinforcement learning agent is easier and faster than training the primitive-action agent. Second, our new agent outperforms the primitive-action reinforcement learning agent, human testers as well as the state-of-art planning methods that our agent queries as skill options.
Joint User Association and Resource Allocation in Heterogeneous Cellular Networks: Comparison of Two Modeling Approaches
Venue: arXiv cs.NI 2019
Year: 2019

The performance of different combinations of user association (UA) and resource allocation (RA) in heterogeneous cellular networks has been extensively studied using a classic modeling approach based on system snapshots. There have been also many studies focusing on the dynamics of the system using queueing models. These modeling approaches are rarely compared with each other though they each bring different insights to the design problem. In this paper, we consider a queueing model-based approach to study the interplay of UA and RA, and compare the results to those obtained using snapshot models. Specifically, we formulate three different joint UA and RA optimization problems corresponding to the following three performance metrics: the maximum achievable arrival rate, the average system delay, and the maximum per-user delay. These problems are non-convex integer programs. We have therefore developed numerical techniques to compute either their exact solutions or tight lower bounds. We obtain results for different combinations of RA and UA schemes, and compare the trends with those obtained via the snapshot approach. The trends on RA are very similar, which we take as a cross-validation of the two modeling approaches for this kind of problem. The trends on user association are somewhat different which indicates a lack of robustness of the results and the need for a careful validation of UA models.
Fast learning synapses with molecular spin valves via selective magnetic potentiation
Venue: arXiv cs.ET 2019
Year: 2019

We studied LSMO/Alq3/AlOx/Co molecular spin valves in view of their use as synapses in neuromorphic computing. In neuromorphic computing, the learning ability is embodied in specific changes of the synaptic weight. In this perspective, the relevant parameter is the conductance of the molecular spin valve, which plays the role of the synaptic weight. In this work we demonstrated that the conductance can be changes by the repeated application of voltage pulses. We studied the parameter space of the pulses in order to determine the most effective voltage and duration of the pulses. The conductance could also be modified by aligning the magnetizations of the ferromagnetic electrodes parallel or anti parallel to each other. This phenomenon, known as magnetoresistance, affects high conductance devices while leaving low conductance devices unaffected. We studied how this weight update rule affected the speed of reward-based learning in an actor-critic framework, compared to a linear update rule. This nonlinear update performed significantly better (50 learning trials; Epochs to reach a performance goal of 0.975 was 896+/-301 in the nonlinear case and 1076+/-484 in the nonlinear case; Welch t-test: p<0.05). The linear update resulted in more learning trails with very long convergence times, which was largely absent in the nonlinear update.
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Venue: arXiv cs.LG 2019
Year: 2019

Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a long time scale exhibit strongtemporal patterns, while events within a short time period tend to be disordered co-occurrence. We thus propose differentiated mechanisms to model clinical events at different time scales. Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies. Experimental results on real clinical data show that our model greatly improves over previous state-of-the-art models, achieving AUC scores of 0.94 and 0.90 for predicting death and ICU admission respectively, Our model also successfully identifies important events for different clinical outcome prediction tasks
Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits
Venue: arXiv cs.ET 2019
Year: 2019

The public access to noisy intermediate-scale quantum (NISQ) computers facilitated by IBM, Rigetti, D-Wave, etc., has propelled the development of quantum applications that may offer quantum supremacy in the future large-scale quantum computers. Parameterized quantum circuits (PQC) have emerged as a major driver for the development of quantum routines that potentially improve the circuit's resilience to the noise. PQC's have been applied in both generative (e.g. generative adversarial network) and discriminative (e.g. quantum classifier) tasks in the field of quantum machine learning. PQC's have been also considered to realize high fidelity quantum gates with the available imperfect native gates of a target quantum hardware. Parameters of a PQC are determined through an iterative training process for a target noisy quantum hardware. However, temporal variations in qubit quality metrics affect the performance of a PQC. Therefore, the circuit that is trained without considering temporal variations exhibits poor fidelity over time. In this paper, we present training methodologies for PQC in a completely classical environment that can improve the fidelity of the trained PQC on a target NISQ hardware by as much as 42.5%.
Byzantine Fault Tolerant Distributed Linear Regression
Venue: arXiv cs.LG 2019
Year: 2019

This paper considers the problem of Byzantine fault tolerance in distributed linear regression in a multi-agent system. However, the proposed algorithms are given for a more general class of distributed optimization problems, of which distributed linear regression is a special case. The system comprises of a server and multiple agents, where each agent is holding a certain number of data points and responses that satisfy a linear relationship (could be noisy). The objective of the server is to determine this relationship, given that some of the agents in the system (up to a known number) are Byzantine faulty (aka. actively adversarial). We show that the server can achieve this objective, in a deterministic manner, by robustifying the original distributed gradient descent method using norm based filters, namely 'norm filtering' and 'norm-cap filtering', incurring an additional log-linear computation cost in each iteration. The proposed algorithms improve upon the existing methods on three levels: i) no assumptions are required on the probability distribution of data points, ii) system can be partially asynchronous, and iii) the computational overhead (in order to handle Byzantine faulty agents) is log-linear in number of agents and linear in dimension of data points. The proposed algorithms differ from each other in the assumptions made for their correctness, and the gradient filter they use.
Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks
Venue: arXiv cs.NI 2019
Year: 2019

We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a unique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks. Compared to the existing literature mainly concerned with the node connectivity, we focus on developing effective resource allocation methods to prevent these cascading failures from happening and to mitigate/confine them upon occurrence in the network. To prevent cascading failures, we identify some dangerous stress mechanisms, based on which we quantify the robustness of the network in terms of the resource configuration scheme. Afterward, we identify the optimal resource configuration under two resource/load fluctuations scenarios: uniform and proportional fluctuations. We further investigate the optimal resource configuration problem considering heterogeneous resource sharing costs among the nodes. To mitigate/confine ongoing cascading failures, we propose two network adaptations mechanisms: intentional failure and resource re-adjustment, based on which we propose an algorithm to mitigate an ongoing cascading failure while reinforcing the surviving network with a high robustness to avoid further failures.
Robust Contraction Analysis of Nonlinear Systems via Differential IQC
Venue: arXiv cs.SY 2019
Year: 2019

We present a new approach to verifying contraction and $L_2$-gain of uncertain nonlinear systems, extending the well-known method of integral quadratic constraints. The uncertain system consists of a feedback interconnection of a nonlinear nominal system and uncertainties satisfying differential integral quadratic constraints. A pointwise linear matrix inequality condition is formulated to verify the closed-loop differential $ L_2 $ gain, which can lead to global reference-independent $ L_2 $ gain performance of the nonlinear uncertain system. For a polynomial nominal system, the convex verification conditions can be solved via sum-of-squares programming. A simple computational example based on jet-engine surge with input delays illustrates the approach.