Query: Smoothing of Piecewise Linear Paths

Relevant passages:
Elastic Bands: Connecting Path Planning and Control <=SEP=> Elastic bands are proposed as the basis for a new framework to close the gap between global path planning and real-time sensor-based robot control. An elastic band is a deformable collision-free path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. This paper outlines the framework and discusses an efficient implementation based on bubbles.

RRT-Connect: An Efficient Approach to Single-Query Path Planning <=SEP=> A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two Rapidly-exploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.

Rapidly-Exploring Random Trees: A New Tool for Path Planning <=SEP=> 

A Random Sampling Scheme for Path Planning <=SEP=> Several randomized path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot’s configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilistically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the “goodness” of the robot’s free space, we show that the running time only grows as the absolute value of the logarithm of the probability of failure that we are willing to tolerate. We also show that it increases at a reasonable rate as the space goodness degrades. In the last section we suggest directions for future research.

V-Clip: Fast and Robust Polyhedral Collision Detection <=SEP=> This article presents the Voronoi-clip, or V-Clip, collision detection alogrithm for polyhedral objects specified by a boundary representation. V-Clip tracks the closest pair of features between convex polyhedra, using an approach reminiscent of the Lin-Canny closest features algorithm. V-Clip is an improvement over the latter in several respects. Coding complexity is reduced, and robustness is significantly improved; the implementation has no numerical tolerances and does not exhibit cycling problems. The algorithm also handles penetrating polyhedra, and can therefore be used to detect collisions between nonvconvex polyhedra described as hierarchies of convex pieces. The article presents the theoretical principles of V-Clip, and gives a pseudocode description of the algorithm. It also documents various test that compare V-Clip, Lin-Canny, and the Enhanced GJK algorithm, a simplex-based algorithm that is widely used for the same application. The results show V-Clip to be a strong contender in this field, comparing favorably with the other algorithms in most of the tests, in term of both performance and robustness.

Compiling Bayesian Networks Using Variable Elimination <=SEP=> Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of compiling based on variable elimination (VE) and Algebraic Decision Diagrams (ADDs). The approach is important for the following reasons. First, it exploits local structure much more effectively than previous techniques based on VE. Second, the approach allows any of the many VE variants to compute answers to multiple queries simultaneously. Third, the approach makes a large body of research into more structured representations of factors relevant in many more circumstances than it has been previously. Finally, experimental results demonstrate that VE can exploit local structure as effectively as state–of–the–art algorithms based on conditioning on the networks considered, and can sometimes lead to much faster compilation times.

An Examination of Undergraduate Student's Perceptions and Predilections of the Use of YouTube in the Teaching and Learning Process <=SEP=> Pervasive social networking and media sharing technologies have augmented perceptual understanding and information gathering and, while text-based resources have remained the standard for centuries, they do not appeal to the hyper-stimulated visual learners of today. In particular, the research suggests that targeted YouTube videos enhance student engagement, depth of understanding, and overall satisfaction in higher education courses. In order to investigate student perceptions and preferences regarding the implications of YouTube, a study was conducted at a Mid-Atlantic minority serving institution that examined student opinions regarding the usage of YouTube videos to augment instruction in online and classroombased courses. According to the findings, use of YouTube in the teaching and learning process enhances instruction with students most likely to visit video sharing services from mobile devices. Further, length has an impact on student decisions whether or not to watch a video, and course delivery format impacts length and audio preferences. Finally, there is no relationship between personal use of social media and the perceived value of the use of YouTube in the instructional process.

Plant-growth-promoting compounds produced by two agronomically important strains of Azospirillum brasilense, and implications for inoculant formulation <=SEP=> We evaluated phytohormone and polyamine biosynthesis, siderophore production, and phosphate solubilization in two strains (Cd and Az39) of Azospirillum brasilense used for inoculant formulation in Argentina during the last 20 years. Siderophore production and phosphate solubilization were evaluated in a chemically defined medium, with negative results. Indole 3-acetic acid (IAA), gibberellic acid (GA3), and abscisic acid (ABA) production were analyzed by gas chromatography-mass spectrometry. Ethylene, polyamine, and zeatin (Z) biosynthesis were determined by gas chromatography-flame ionization detector and high performance liquid chromatography (HPLC-fluorescence and -UV), respectively. Phytohormones IAA, Z, GA3, ABA, ethylene, and growth regulators putrescine, spermine, spermidine, and cadaverine (CAD) were found in culture supernatant of both strains. IAA, Z, and GA3 were found in all two strains; however, their levels were significantly higher (p < 0.01) in Cd (10.8, 2.32, 0.66 μg ml−1). ABA biosynthesis was significantly higher (p < 0.01) in Az39 (0.077 μg ml−1). Ethylene and polyamine CAD were found in all two strains, with highest production in Cd cultured in NFb plus l-methionine (3.94 ng ml−1 h−1) and Az39 cultured in NFb plus l-lysine (36.55 ng ml−1 h−1). This is the first report on the evaluation of important bioactive molecules in strains of A. brasilense as potentially capable of direct plant growth promotion or agronomic yield increase. Az39 and Cd showed differential capability to produce the five major phytohormones and CAD in chemically defined medium. This fact has important technological implications for inoculant formulation as different concentrations of growth regulators are produced by different strains or culture conditions.

A 1.2–6.6GHz LNA using transformer feedback for wideband input matching and noise cancellation in 0.13µm CMOS <=SEP=> A novel transformer feedback featuring wideband input matching and noise cancellation is proposed and demonstrated in a wideband differential LNA for software-defined-radio (SDR) applications. Implemented in 0.13μm CMOS with an area of 0.32mm2, the LNA prototype measures a wideband input matching S11 of less than -10dB from 1.2GHz to 6.6GHz and minimum NF of 1.8dB while consuming 11mA at 1.2V supply.

Hamming Clustering: A New Approach to Rule Extraction <=SEP=> A new algorithm, called Hamming Clustering (HC), is proposed to extract a set of rules underlying a given classification problem. It is able to reconstruct the and-or expression associated with any Boolean function from a training set of

Extracting and Analyzing Hidden Graphs from Relational Databases <=SEP=> Analyzing interconnection structures among underlying entities or objects in a dataset through the use of graph analytics can provide tremendous value in many application domains. However, graphs are not the primary representation choice for storing most data today, and in order to have access to these analyses, users are forced to manually extract data from their data stores, construct the requisite graphs, and then load them into some graph engine in order to execute their graph analysis task. Moreover, in many cases (especially when the graphs are dense), these graphs can be significantly larger than the initial input stored in the database, making it infeasible to construct or analyze such graphs in memory. In this paper we address both of these challenges by building a system that enables users to declaratively specify graph extraction tasks over a relational database schema and then execute graph algorithms on the extracted graphs. We propose a declarative domain specific language for this purpose, and pair it up with a novel condensed, in-memory representation that significantly reduces the memory footprint of these graphs, permitting analysis of larger-than-memory graphs. We present a general algorithm for creating such a condensed representation for a large class of graph extraction queries against arbitrary schemas. We observe that the condensed representation suffers from a duplication issue, that results in inaccuracies for most graph algorithms. We then present a suite of in-memory representations that handle this duplication in different ways and allow trading off the memory required and the computational cost for executing different graph algorithms. We also introduce several novel deduplication algorithms for removing this duplication in the graph, which are of independent interest for graph compression, and provide a comprehensive experimental evaluation over several real-world and synthetic datasets illustrating these trade-offs.

Debugging Machine Learning Tasks <=SEP=> Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl’s theory of causation, and uses Pearl’s PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.

Interactive cloth rendering of microcylinder appearance model under environment lighting <=SEP=> This paper proposes an interactive rendering method of cloth fabrics under environment lighting. The outgoing radiance from cloth fabrics in the microcylinder model is calculated by integrating the product of the distant environment lighting, the visibility function, the weighting function that includes shadowing/masking effects of threads, and the light scattering function of threads. The radiance calculation at each shading point of the cloth fabrics is simplified to a linear combination of triple product integrals of two circular Gaussians and the visibility function, multiplied by precomputed spherical Gaussian convolutions of the weighting function. We propose an efficient calculation method of the triple product of two circular Gaussians and the visibility function by using the gradient of signed distance function to the visibility boundary where the binary visibility changes in the angular domain of the hemisphere. Our GPU implementation enables interactive rendering of static cloth fabrics with dynamic viewpoints and lighting. In addition, interactive editing of parameters for the scattering function (e.g. thread’s albedo) that controls the visual appearances of cloth fabrics can be achieved.

GPU Accelerated Sub-Sampled Newton's Method <=SEP=> First order methods, which solely rely on gradient information, are commonly used in diverse machine learning (ML) and data analysis (DA) applications. This is attributed to the simplicity of their implementations, as well as low per-iteration computational/storage costs. However, they suffer from significant disadvantages; most notably, their performance degrades with increasing problem ill-conditioning. Furthermore, they often involve a large number of hyper-parameters, and are notoriously sensitive to parameters such as the step-size. By incorporating additional information from the Hessian, second-order methods, have been shown to be resilient to many such adversarial effects. However, these advantages of using curvature information come at the cost of higher per-iteration costs, which in “big data” regimes, can be computationally prohibitive. In this paper, we show that, contrary to conventional belief, second-order methods, when implemented appropriately, can be more efficient than first-order alternatives in many largescale ML/ DA applications. In particular, in convex settings, we consider variants of classical Newton’s method in which the Hessian and/or the gradient are randomly sub-sampled. We show that by effectively leveraging the power of GPUs, such randomized Newton-type algorithms can be significantly accelerated, and can easily outperform state of the art implementations of existing techniques in popular ML/ DA software packages such as TensorFlow. Additionally these randomized methods incur a small memory overhead compared to first-order methods. In particular, we show that for million-dimensional problems, our GPU accelerated sub-sampled Newton’s method achieves a higher test accuracy in milliseconds as compared with tens of seconds for first order alternatives.

Nobody ’ s watching ? Subtle cues affect generosity in an anonymous economic game <=SEP=> Models indicate that opportunities for reputation formation can play an important role in sustaining cooperation and prosocial behavior. Results from experimental economic games support this conclusion, as manipulating reputational opportunities affects prosocial behavior. Noting that some prosocial behavior remains even in anonymous noniterated games, some investigators argue that humans possess a propensity for prosociality independent of reputation management. However, decision-making processes often employ both explicit propositional knowledge and intuitive or affective judgments elicited by tacit cues. Manipulating game parameters alters explicit information employed in overt strategizing but leaves intact cues that may affect intuitive judgments relevant to reputation formation. To explore how subtle cues of observability impact prosocial behavior, we conducted five dictator games, manipulating both auditory cues of the presence of others (via the use of sound-deadening earmuffs) and visual cues (via the presentation of stylized eyespots). Although earmuffs appeared to reduce generosity, this effect was not significant. However, as predicted, eyespots substantially increased generosity, despite no differences in actual anonymity; when using a computer displaying eyespots, almost twice as many participants gave money to their partners compared with the

Personality Traits Recognition on Social Network - Facebook <=SEP=> For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

An Anomaly Detection Method for Spacecraft Using Relevance Vector Learning <=SEP=> This paper proposes a novel anomaly detection system for spacecrafts based on data mining techniques. It constructs a nonlinear probabilistic model w.r.t. behavior of a spacecraft by applying the relevance vector regression and autoregression to massive telemetry data, and then monitors the on-line telemetry data using the model and detects anomalies. A major advantage over conventional anomaly detection methods is that this approach requires little a priori knowledge on the system.

Positron-Emission Tomography and Personality Disorders <=SEP=> This study used positron-emission tomography to examine cerebral metabolic rates of glucose (CMRG) in 17 patients with DSM III-R diagnoses of personality disorder. Within the group of 17 personality disorder patients, there was a significant inverse correlation between a life history of aggressive impulse difficulties and regional CMRG in the frontal cortex of the transaxial plane approximately 40 mm above the canthomeatal line (CML) (r = −.56, p = 0.17). Diagnostic groups included antisocial (n = 6), borderline (n = 6), dependent (n = 2), and narcissistic (n = 3). Regional CMRG in the six antisocial patients and in the six borderline patients was compared to a control group of 43 subjects using an analysis of covariance with age and sex as covariates. In the borderline personality disorder group, there was a significant decrease in frontal cortex metabolism in the transaxial plane approximately 81 mm above the CML and a significant increase in the transaxial plane approximately 53 mm above the CML (F[1,45] = 8.65, p = .005; and F[1,45] = 7.68, p = .008, respectively).

Benchmarking personal cloud storage <=SEP=> Personal cloud storage services are data-intensive applications already producing a significant share of Internet traffic. Several solutions offered by different companies attract more and more people. However, little is known about each service capabilities, architecture and -- most of all -- performance implications of design choices. This paper presents a methodology to study cloud storage services. We apply our methodology to compare 5 popular offers, revealing different system architectures and capabilities. The implications on performance of different designs are assessed executing a series of benchmarks. Our results show no clear winner, with all services suffering from some limitations or having potential for improvement. In some scenarios, the upload of the same file set can take seven times more, wasting twice as much capacity. Our methodology and results are useful thus as both benchmark and guideline for system design.

Multi-Label Classification: An Overview <=SEP=> Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.

On-line Fault Detection of Sensor Measurements <=SEP=> On-line fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classi£cation of faults in sensor networks and the £rst on-line model-based testing technique. The approach is generic in the sense that it can be applied on an arbitrary system of heterogeneous sensors with an arbitrary type of fault model, while it provides a ¤exible tradeoff between accuracy and latency. The key idea is to formulate on-line testing as a set of instances of a non-linear function minimization and consequently apply nonparametric statistical methods to identify the sensors that have the highest probability to be faulty. The optimization is conducted using the Powell nonlinear function minimization method. The effectiveness of the approach is evaluated in the presence of random noise using a system of light sensors.

Development of support system for forward tilting of the upper body <=SEP=> We have been developing a wearable robot that directly and physically supports human movement. A ldquomuscle suitrdquo for the arm that will provide muscular support for the paralyzed or those otherwise unable to move unaided is one example. Whereas low back pain is one of the most serious issue for the manual worker and in this paper, we focus on developing the support system for forward tilting of the upper body. In this paper, the concept and mechanical structure of the support system is shown. From the experimental result to keep forward tilting posture with load indicates our system is very effective for keeping forward tilting posture and it becomes clear that more than 60% reduction of muscular power is possible.

CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites <=SEP=> Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we propose a layered anti-phishing solution that aims at (1) exploiting the expressiveness of a rich set of features with machine learning to achieve a high true positive rate (TP) on novel phish, and (2) limiting the FP to a low level via filtering algorithms.
 Specifically, we proposed CANTINA+, the most comprehensive feature-based approach in the literature including eight novel features, which exploits the HTML Document Object Model (DOM), search engines and third party services with machine learning techniques to detect phish. Moreover, we designed two filters to help reduce FP and achieve runtime speedup. The first is a near-duplicate phish detector that uses hashing to catch highly similar phish. The second is a login form filter, which directly classifies Web pages with no identified login form as legitimate.
 We extensively evaluated CANTINA+ with two methods on a diverse spectrum of corpora with 8118 phish and 4883 legitimate Web pages. In the randomized evaluation, CANTINA+ achieved over 92% TP on unique testing phish and over 99% TP on near-duplicate testing phish, and about 0.4% FP with 10% training phish. In the time-based evaluation, CANTINA+ also achieved over 92% TP on unique testing phish, over 99% TP on near-duplicate testing phish, and about 1.4% FP under 20% training phish with a two-week sliding window. Capable of achieving 0.4% FP and over 92% TP, our CANTINA+ has been demonstrated to be a competitive anti-phishing solution.

Randomized Controlled Caregiver Mediated Joint Engagement Intervention for Toddlers with Autism <=SEP=> This study aimed to determine if a joint attention intervention would result in greater joint engagement between caregivers and toddlers with autism. The intervention consisted of 24 caregiver-mediated sessions with follow-up 1 year later. Compared to caregivers and toddlers randomized to the waitlist control group the immediate treatment (IT) group made significant improvements in targeted areas of joint engagement. The IT group demonstrated significant improvements with medium to large effect sizes in their responsiveness to joint attention and their diversity of functional play acts after the intervention with maintenance of these skills 1 year post-intervention. These are among the first randomized controlled data to suggest that short-term parent-mediated interventions can have important effects on core impairments in toddlers with autism. Clinical Trials #: NCT00065910.

Developmental dyslexia: genetic dissection of a complex cognitive trait <=SEP=> Developmental dyslexia, a specific impairment of reading ability despite adequate intelligence and educational opportunity, is one of the most frequent childhood disorders. Since the first documented cases at the beginning of the last century, it has become increasingly apparent that the reading problems of people with dyslexia form part of a heritable neurobiological syndrome. As for most cognitive and behavioural traits, phenotypic definition is fraught with difficulties and the genetic basis is complex, making the isolation of genetic risk factors a formidable challenge. Against such a background, it is notable that several recent studies have reported the localization of genes that influence dyslexia and other language-related traits. These investigations exploit novel research approaches that are relevant to many areas of human neurogenetics.

Neural Probabilistic Model for Non-projective MST Parsing <=SEP=> In this paper, we propose a probabilistic parsing model that defines a proper conditional probability distribution over nonprojective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTMCNNs, which automatically benefits from both wordand character-level representations, by using a combination of bidirectional LSTMs and CNNs. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over nonprojective trees. By exploiting Kirchhoff’s Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straightforward end-to-end model training procedure via back-propagation. We evaluate our model on 17 different datasets, across 14 different languages. Our parser achieves state-of-the-art parsing performance on nine datasets.

EPBC: Efficient Public Blockchain Client for lightweight users <=SEP=> Public blockchains provide a decentralized method for storing transaction data and have many applications in different sectors. In order for a user to track transactions, a simple method is that every user keeps a local copy of the entire public ledger. Since the size of a ledger keeps growing, this method becomes increasingly less practical, especially for lightweight users such as IoT devices and smartphones. In order to deal with this problem, there have been some proposals. However, existing solutions either achieve a limited storage reduction (e.g., simple payment verification), or rely on some strong security assumption (e.g., the use of trusted server). We propose EPBC, a novel and efficient transaction verification scheme for public ledgers, which only requires lightweight users to store a small amount of data that is independent of the size of the blockchain. We analyze EPBC's performance and security, and discuss its integration with existing public ledger systems. Experimental results confirm that EPBC is practical for lightweight users.

Private memoirs of a smart meter <=SEP=> Household smart meters that measure power consumption in real-time at fine granularities are the foundation of a future smart electricity grid. However, the widespread deployment of smart meters has serious privacy implications since they inadvertently leak detailed information about household activities. In this paper, we show that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods. Our analysis uses two months of data from three homes, which we instrumented to log aggregate household power consumption every second. With the data from our small-scale deployment, we demonstrate the potential for power consumption patterns to reveal a range of information, such as how many people are in the home, sleeping routines, eating routines, etc. We then sketch out the design of a privacy-enhancing smart meter architecture that allows an electric utility to achieve its net metering goals without compromising the privacy of its customers.

Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network <=SEP=> Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual features are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at an output layer. The AVDCNN model is trained in an end-toend manner, and parameters are jointly learned through backpropagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better performance as compared to an audio-only CNN-based SE model, confirming the effectiveness of integrating visual information into the SE process.

Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches <=SEP=> Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like Go. While RL is emerging as a practical component in reallife systems, most successes have been in Single Agent domains. This report will instead specifically focus on challenges that are unique to Multi-Agent Systems interacting in mixed cooperative and competitive environments. The report concludes with advances in the paradigm of training Multi-Agent Systems called Decentralized Actor, Centralized Critic, based on an extension of MDPs called Decentralized Partially Observable MDPs, which has seen a renewed interest lately. 1 ar X iv :1 80 7. 09 42 7v 1 [ cs .A I] 2 5 Ju l 2 01 8