OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots
Venue: arXiv cs.RO 2021
Year: 2021

Robotic catheterization is typically used for percutaneous coronary intervention procedures nowadays and it involves steering flexible endovascular tools to open up occlusion in the coronaries. In this study, a sample-efficient deep reinforcement learning with episodic policy transfer is, for the first time, used for motion control during robotic catheterization with fully adaptive PID tuning strategy. The reinforcement model aids the agent to continuously learn from its interactions in its environment and adaptively tune PID control gains for axial navigation of endovascular tool. The model was validated for axial motion control of a robotic system designed for intravascular catheterization. Simulation and experimental trials were done to validate the application of the model, and results obtained shows it could self-tune PID gains appropriately for motion control of a robotic catheter system. Performance comparison with conventional methods in average of 10 trials shows the agent tunes the gain better with error of 0.003 mm. Thus, the proposed model would offer more stable set-point motion control robotic catheterization.
On the explainability of hospitalization prediction on a large COVID-19 patient dataset
Venue: arXiv cs.LG 2021
Year: 2021

We develop various AI models to predict hospitalization on a large (over 110$k$) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models range from Random Forest to Neural Network (NN) and Time Convolutional NN, where combination of the data modalities (tabular and time dependent) are performed at different stages (early vs. model fusion). Despite high data unbalance, the models reach average precision 0.96-0.98 (0.75-0.85), recall 0.96-0.98 (0.74-0.85), and $F_1$-score 0.97-0.98 (0.79-0.83) on the non-hospitalized (or hospitalized) class. Performances do not significantly drop even when selected lists of features are removed to study model adaptability to different scenarios. However, a systematic study of the SHAP feature importance values for the developed models in the different scenarios shows a large variability across models and use cases. This calls for even more complete studies on several explainability methods before their adoption in high-stakes scenarios.
Engineering Uniform Sampling of Graphs with a Prescribed Power-law Degree Sequence
Venue: arXiv cs.DS 2021
Year: 2021

We consider the following common network analysis problem: given a degree sequence $\mathbf{d} = (d_1, \dots, d_n) \in \mathbb N^n$ return a uniform sample from the ensemble of all simple graphs with matching degrees. In practice, the problem is typically solved using Markov Chain Monte Carlo approaches, such as Edge-Switching or Curveball, even if no practical useful rigorous bounds are known on their mixing times. In contrast, Arman et al. sketch Inc-Powerlaw, a novel and much more involved algorithm capable of generating graphs for power-law bounded degree sequences with $γ\gtrapprox 2.88$ in expected linear time. For the first time, we give a complete description of the algorithm and add novel switchings. To the best of our knowledge, our open-source implementation of Inc-Powerlaw is the first practical generator with rigorous uniformity guarantees for the aforementioned degree sequences. In an empirical investigation, we find that for small average-degrees Inc-Powerlaw is very efficient and generates graphs with one million nodes in less than a second. For larger average-degrees, parallelism can partially mitigate the increased running-time.
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC
Venue: arXiv cs.CL 2021
Year: 2021

Machine translation (MT) system aims to translate source language into target language. Recent studies on MT systems mainly focus on neural machine translation (NMT). One factor that significantly affects the performance of NMT is the availability of high-quality parallel corpora. However, high-quality parallel corpora concerning Korean are relatively scarce compared to those associated with other high-resource languages, such as German or Italian. To address this problem, AI Hub recently released seven types of parallel corpora for Korean. In this study, we conduct an in-depth verification of the quality of corresponding parallel corpora through Linguistic Inquiry and Word Count (LIWC) and several relevant experiments. LIWC is a word-counting software program that can analyze corpora in multiple ways and extract linguistic features as a dictionary base. To the best of our knowledge, this study is the first to use LIWC to analyze parallel corpora in the field of NMT. Our findings suggest the direction of further research toward obtaining the improved quality parallel corpora through our correlation analysis in LIWC and NMT performance.
Orientation Probabilistic Movement Primitives on Riemannian Manifolds
Venue: arXiv cs.RO 2021
Year: 2021

Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Probabilistic movement primitives (ProMPs) stand out as a principled approach that models trajectory distributions learned from demonstrations. ProMPs allow for trajectory modulation and blending to achieve better generalization to novel situations. However, when ProMPs are employed in operational space, their original formulation does not directly apply to full-pose movements including rotational trajectories described by quaternions. This paper proposes a Riemannian formulation of ProMPs that enables encoding and retrieving of quaternion trajectories. Our method builds on Riemannian manifold theory, and exploits multilinear geodesic regression for estimating the ProMPs parameters. This novel approach makes ProMPs a suitable model for learning complex full-pose robot motion patterns. Riemannian ProMPs are tested on toy examples to illustrate their workflow, and on real learning-from-demonstration experiments.
The chemical space of terpenes: insights from data science and AI
Venue: arXiv cs.LG 2021
Year: 2021

Terpenes are a widespread class of natural products with significant chemical and biological diversity and many of these molecules have already made their way into medicines. Given the thousands of molecules already described, the full characterization of this chemical space can be a challenging task when relying in classical approaches. In this work we employ a data science-based approach to identify, compile and characterize the diversity of terpenes currently known in a systematic way. We worked with a natural product database, COCONUT, from which we extracted information for nearly 60000 terpenes. For these molecules, we conducted a subclass-by-subclass analysis in which we highlight several chemical and physical properties relevant to several fields, such as natural products chemistry, medicinal chemistry and drug discovery, among others. We were also interested in assessing the potential of this data for clustering and classification tasks. For clustering, we have applied and compared k-means with agglomerative clustering, both to the original data and following a step of dimensionality reduction. To this end, PCA, FastICA, Kernel PCA, t-SNE and UMAP were used and benchmarked. We also employed a number of methods for the purpose of classifying terpene subclasses using their physico-chemical descriptors. Light gradient boosting machine, k-nearest neighbors, random forests, Gaussian naiive Bayes and Multilayer perceptron, with the best-performing algorithms yielding accuracy, F1 score, precision and other metrics all over 0.9, thus showing the capabilities of these approaches for the classification of terpene subclasses.
Improving Causal Effect Estimation of Weighted RegressionBased Estimator using Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Estimating causal effects from observational data informs us about which factors are important in an autonomous system, and enables us to take better decisions. This is important because it has applications in selecting a treatment in medical systems or making better strategies in industries or making better policies for our government or even the society. Unavailability of complete data, coupled with high cardinality of data, makes this estimation task computationally intractable. Recently, a regression-based weighted estimator has been introduced that is capable of producing solution using bounded samples of a given problem. However, as the data dimension increases, the solution produced by the regression-based method degrades. Against this background, we introduce a neural network based estimator that improves the solution quality in case of non-linear and finitude of samples. Finally, our empirical evaluation illustrates a significant improvement of solution quality, up to around $55\%$, compared to the state-of-the-art estimators.
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance, one pays a significant price in terms of other important metrics: users get stuck in a "filter bubble" and their array of options is significantly reduced, hence degrading the quality of the user experience and leading to churn. Recommendation, and in particular session-based/sequential recommendation, is a complex task with multiple - and often conflicting objectives - that existing state-of-the-art approaches fail to address. In this work, we take on the aforementioned challenge and introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the RS setting, a novel Reinforcement Learning (RL) framework that can effectively address multi-objective recommendation tasks. The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations. We integrate this framework with four state-of-the-art session-based recommendation models and compare it with a single-objective RL agent that only focuses on accuracy. Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.
A Framework for Parameterized Subexponential Algorithms for Generalized Cycle Hitting Problems on Planar Graphs
Venue: arXiv cs.DS 2021
Year: 2021

Subexponential parameterized algorithms are known for a wide range of natural problems on planar graphs, but the techniques are usually highly problem specific. The goal of this paper is to introduce a framework for obtaining $n^{O(\sqrt{k})}$ time algorithms for a family of graph modification problems that includes problems that can be seen as generalized cycle hitting problems. Our starting point is the Node Unique Label Cover problem (that is, given a CSP instance where each constraint is a permutation of values on two variables, the task is to delete $k$ variables to make the instance satisfiable). We introduce a variant of the problem where $k$ vertices have to be deleted such that every 2-connected component of the remaining instance is satisfiable. Then we extend the problem with cardinality constraints that restrict the number of times a certain value can be used (globally or within a 2-connected component of the solution). We show that there is an $n^{O(\sqrt{k})}$ time algorithm on planar graphs for any problem that can be formulated this way, which includes a large number of well-studied problems, for example, Odd Cycle Transversal, Subset Feedback Vertex Set, Group Feedback Vertex Set, Subset Group Feedback Vertex Set, Vertex Multiway Cut, and Component Order Connectivity. For those problems that admit appropriate (quasi)polynomial kernels (that increase the parameter only linearly and preserve planarity), our results immediately imply $2^{O(\sqrt{k}\cdot\operatorname{polylog}(k))}n^{O(1)}$ time parameterized algorithms on planar graphs. In particular, we use or adapt known kernelization results to obtain $2^{O(\sqrt{k}\cdot \operatorname{polylog}(k))} n^{O(1)}$ time (randomized) algorithms for Vertex Multiway Cut, Group Feedback Vertex Set, and Subset Feedback Vertex Set.
Be Lean -- How to Fit a Model-Based System Architecture Development Process Based on ARP4754 Into an Agile Environment
Venue: arXiv cs.SE 2021
Year: 2021

An emerging service is moving the known aviation sector in terms of technology, paradigms, and key players - the Urban Air Mobility. The reason: new developments in non-aviation industries are driving technological progress in aviation. For instance electrical motors, modern sensor technologies and better energy storage expand the possibilities and enable novel vehicle concepts which require also novel system architectures for flight control systems. Their development is governed by aviation authority and industry recognized standards, guidelines and recommended practices. Comprehensive methods for Model-Based Systems Engineering exist which address these guidance materials but their setup and their application can be quite resource-demanding. Especially the new and rather small key players - start-ups and development teams in an educational environment - can be overwhelmed to setup such development processes. For these clients, the authors propose a custom workflow for the development of system architectures. It shall ensure development rigor, quality and consistency. The authors show how the custom workflow has been established based on the ARP4754A and its level of compliance to the standard's process objectives. Based on automation of life cycle activities, manual effort can be reduced to allow the application even in small teams. The custom workflow's activities are explained and demonstrated within a case study of an Experimental Autopilot system architecture.
Learning Feasibility to Imitate Demonstrators with Different Dynamics
Venue: arXiv cs.RO 2021
Year: 2021

The goal of learning from demonstrations is to learn a policy for an agent (imitator) by mimicking the behavior in the demonstrations. Prior works on learning from demonstrations assume that the demonstrations are collected by a demonstrator that has the same dynamics as the imitator. However, in many real-world applications, this assumption is limiting -- to improve the problem of lack of data in robotics, we would like to be able to leverage demonstrations collected from agents with different dynamics. This can be challenging as the demonstrations might not even be feasible for the imitator. Our insight is that we can learn a feasibility metric that captures the likelihood of a demonstration being feasible by the imitator. We develop a feasibility MDP (f-MDP) and derive the feasibility score by learning an optimal policy in the f-MDP. Our proposed feasibility measure encourages the imitator to learn from more informative demonstrations, and disregard the far from feasible demonstrations. Our experiments on four simulated environments and on a real robot show that the policy learned with our approach achieves a higher expected return than prior works. We show the videos of the real robot arm experiments on our website (https://sites.google.com/view/learning-feasibility).
Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes
Venue: arXiv cs.NI 2021
Year: 2021

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay. Specifically, a deep Q-network (DQN) is conceived for capturing the relationship between the optimal routing decision and the local geographic information observed by the forwarding node. The DQN is trained in an offline manner based on historical flight data and then stored by each airplane for assisting their routing decisions during flight. To boost the learning efficiency and the online adaptability of the proposed DQN-routing, we further exploit the knowledge concerning the system's dynamics by using a deep value network (DVN) conceived with a feedback mechanism. Our simulation results show that both DQN-routing and DVN-routing achieve lower E2E delay than the benchmark protocol, and DVN-routing performs similarly to the optimal routing that relies on perfect global information.
Privacy Aware Person Detection in Surveillance Data
Venue: arXiv cs.CV 2021
Year: 2021

Crowd management relies on inspection of surveillance video either by operators or by object detection models. These models are large, making it difficult to deploy them on resource constrained edge hardware. Instead, the computations are often offloaded to a (third party) cloud platform. While crowd management may be a legitimate application, transferring video from the camera to remote infrastructure may open the door for extracting additional information that are infringements of privacy, like person tracking or face recognition. In this paper, we use adversarial training to obtain a lightweight obfuscator that transforms video frames to only retain the necessary information for person detection. Importantly, the obfuscated data can be processed by publicly available object detectors without retraining and without significant loss of accuracy.
An Efficient Multi-Link Channel Model for LiFi
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we report for the first time on a new channel modelling technique for multi-link LiFi scenarios. By considering simplified numerical calculation, it models the links between multiple optical frontends and multiple mobile devices much faster than previous approaches. For the first two diffuse reflections, we replace ray-tracing method by frequency domain channel modelling technique. For the other higher order diffuse reflections, we use a well-established model based on the integrating sphere. For validation of our new approach, we performed distributed 4x2 MIMO channel measurements. Comparison of simulation and measurement yields a relative mean square error below 5 percent for the signals with a free line-of-sight. Our new technique enables efficient modeling of mobile scenarios and analyzing statistical properties of the LiFi channels.
Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems
Venue: arXiv cs.CL 2021
Year: 2021

Clarification resolution plays an important role in various information retrieval tasks such as interactive question answering and conversational search. In such context, the user often formulates their information needs as short and ambiguous queries, some popular search interfaces then prompt the user to confirm her intent (e.g. "Did you mean ... ?") or to rephrase if needed. When it comes to dialogue systems, having fluid user-bot exchanges is key to good user experience. In the absence of such clarification mechanism, one of the following responses is given to the user: 1) A direct answer, which can potentially be non-relevant if the intent was not clear, 2) a generic fallback message informing the user that the retrieval tool is incapable of handling the query. Both scenarios might raise frustration and degrade the user experience. To this end, we propose a multi-stage clarification mechanism for prompting clarification and query selection in the context of a question answering dialogue system. We show that our proposed mechanism improves the overall user experience and outperforms competitive baselines with two datasets, namely the public in-scope out-of-scope dataset and a commercial dataset based on real user logs.
Multi-Pair Two-Way Massive MIMO DF Relaying Over Rician Fading Channels Under Imperfect CSI
Venue: arXiv cs.IT 2021
Year: 2021

We investigate a multi-pair two-way decode-andforward relaying aided massive multiple-input multiple-output antenna system under Rician fading channels, in which multiple pairs of users exchange information through a relay station having multiple antennas. Imperfect channel state information is considered in the context of maximum-ratio processing. Closedform expressions are derived for approximating the sum spectral efficiency (SE) of the system. Moreover, we obtain the powerscaling laws at the users and the relay station to satisfy a certain SE requirement in three typical scenarios. Finally, simulations validate the accuracy of the derived results.
On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming
Venue: arXiv cs.SE 2021
Year: 2021

Well structured and readable source code is a pre-requisite for maintainable software and successful collaboration among developers. Static analysis enables the automated extraction of code complexity and readability metrics which can be leveraged to highlight potential improvements in code to both attain software of high quality and reinforce good practices for developers as an educational tool. This assumes reliable readability metrics which are not trivial to obtain since code readability is somewhat subjective. Recent research has resulted in increasingly sophisticated models for predicting readability as perceived by humans primarily with a procedural and object oriented focus, while functional and declarative languages and language extensions advance as they often are said to lead to more concise and readable code. In this paper, we investigate whether the existing complexity and readability metrics reflect that wisdom or whether the notion of readability and its constituents requires overhaul in the light of programming language changes. We therefore compare traditional object oriented and reactive programming in terms of code complexity and readability in a case study. Reactive programming is claimed to increase code quality but few studies have substantiated these claims empirically. We refactored an object oriented open source project into a reactive candidate and compare readability with the original using cyclomatic complexity and two state-of-the-art readability metrics. More elaborate investigations are required, but our findings suggest that both cyclomatic complexity and readability decrease significantly at the same time in the reactive candidate, which seems counter-intuitive. We exemplify and substantiate why readability metrics may require adjustment to better suit popular programming styles other than imperative and object-oriented to better match human expectations.
CIIA:A New Algorithm for Community Detection
Venue: arXiv cs.SI 2021
Year: 2021

In this paper, through thinking on the modularity function that measures the standard of community division, a new algorithm for dividing communities is proposed, called the Connect Intensity Iteration algorithm, or CIIA for short. In this algorithm, a new indicator is proposed.This indicator is the difference between the actual number of edges between two nodes and the number of edges when the edges are randomly placed. It can reflect more information between the nodes. The larger the value of this index, the greater the possibility that the two nodes are divided into the same community, and vice versa. This paper also verifies the algorithm through numerical simulations and real cases, and the results show the feasibility of the algorithm.