We study Granger Causality in the context of wide-sense stationary time series. The focus of the analysis is to understand how the underlying topological structure of the causality graph affects graph recovery by means of the pairwise testing heuristic. Our main theoretical result establishes a sufficient condition (in particular, the graph must satisfy a polytree assumption we refer to as strong causality) under which the graph can be recovered by means of unconditional and binary pairwise causality testing. Examples from the gene regulatory network literature are provided which establish that graphs which are strongly causal, or very nearly so, can be expected to arise in practice. We implement finite sample heuristics derived from our theory, and use simulation to compare our pairwise testing heuristic against LASSO-based methods. These simulations show that, for graphs which are strongly causal (or small perturbations thereof) the pairwise testing heuristic is able to more accurately recover the underlying graph. We show that the algorithm is scalable to graphs with thousands of nodes, and that, as long as structural assumptions are met, exhibits similar high-dimensional scaling properties as the LASSO. That is, performance degrades slowly while the system size increases and the number of available samples is held fixed. Finally, a proof-of-concept application example shows, by attempting to classify alcoholic individuals using only Granger causality graphs inferred from EEG measurements, that the inferred Granger causality graph topology carries identifiable features.

Named entity recognition (NER) aims to identify mentions of named entities in an unstructured text and classify them into predefined named entity classes. While deep learning-based pre-trained language models help to achieve good predictive performances in NER, many domain-specific NER applications still call for a substantial amount of labeled data. Active learning (AL), a general framework for the label acquisition problem, has been used for NER tasks to minimize the annotation cost without sacrificing model performance. However, the heavily imbalanced class distribution of tokens introduces challenges in designing effective AL querying methods for NER. We propose several AL sentence query evaluation functions that pay more attention to potential positive tokens and evaluate these proposed functions with both sentence-based and token-based cost evaluation strategies. We also propose a better data-driven normalization approach to penalize sentences that are too long or too short. Our experiments on three datasets from different domains reveal that the proposed approach reduces the number of annotated tokens while achieving better or comparable prediction performance with conventional methods.

This paper presents backstepping control and backstepping constraint control approaches for a quadrotor unmanned aerial vehicle (UAV) control system. The proposed methods are applied to a Parrot Mambo drone model to control rotational motion along the $x$, $y$, and $z$ axes during hovering and trajectory tracking. In the backstepping control approach, each state of the system controls the previous state and is called “virtual control.” The last state is controlled by the real control input. The idea is to compute, in several steps, a control law that ensures the asymptotic stability of the system. The backstepping constraint control method, based on barrier Lyapunov functions (BLFs), is designed not only to track the desired trajectory but also to guarantee no violation of the position and angle constraints. Symmetric BLFs are introduced in the design of the controller. A nonlinear mathematical model is considered in this study. Based on Lyapunov stability theory, it can be concluded that the proposed controllers can guarantee the stability of the UAV system and the state converges asymptotically to the desired trajectory. To make the control robust, an adaptation law is applied to the backstepping control that estimates the unknown parameters and ensures their convergence to their respective values. Validation of the proposed controllers was performed by simulation on a flying UAV system.

The paper investigates from a proof-theoretic perspective various non-contractive logical systems, which circumvent logical and semantic paradoxes. Until recently, such systems only displayed additive quantifiers (Grišin and Cantini). Systems with multiplicative quantifiers were proposed in the 2010s (Zardini), but they turned out to be inconsistent with the naive rules for truth or comprehension. We start by presenting a first-order system for disquotational truth with additive quantifiers and compare it with Grišin set theory. We then analyze the reasons behind the inconsistency phenomenon affecting multiplicative quantifiers. After interpreting the exponentials in affine logic as vacuous quantifiers, we show how such a logic can be simulated within a truth-free fragment of a system with multiplicative quantifiers. Finally, we establish that the logic for these multiplicative quantifiers (but without disquotational truth) is consistent, by proving that an infinitary version of the cut rule can be eliminated. This paves the way to a syntactic approach to the proof theory of infinitary logic with infinite sequents.

This article uses data from several publicly available databases to show that the distribution of intellectual property for frontier technologies, including those useful for sustainable development, is very highly skewed in favor of a handful of developed countries. The intellectual property rights (IPR) regime as it exists does not optimize the global flow of technology and know-how for the attainment of the sustainable development goals and is in need of updating. Some features of the Fourth Industrial Revolution imply that the current system of patents is even more in need of reform than before. COVID-19 vaccines and therapies and the vast inequality in access to these has highlighted the costs of inaction. We recommend several policy changes for the international IPR regime. Broadly, these fall into three categories: allowing greater flexibility for developing countries, reassessing the appropriateness of patents for technologies that may be considered public goods, and closing loopholes that allow for unreasonable intellectual property protections.

This paper presents the technical specifications of a lightweight humanoid robot platform named Robinion Sr. including its mechanical and electrical design. We describe a versatile and robust mechatronic system, efficient walking gait, and software architecture of the humanoid robot. The humanoid robot platform is targeted for use in a range of applications, including research and development, competitions, and the service industry. A reduced platform cost was an essential consideration in our design. We introduce a specialized and inexpensive mechanical design, which includes a parallel-kinematics leg design, external gears, and low-cost controllers and sensors. The humanoid robot is equipped with an efficient electronic structure and a tablet computer for task scheduling, control, and perception, as well as an embedded controller for solving forward & inverse kinematics and low-level actuator control. The perception system recognizes objects at real-time inference with Deep Learning-based detection algorithms without a dedicated GPU. We present and evaluate the capabilities of our newly developed advanced humanoid robot and believe it is a suitable platform for the academic and industrial robotics community.

This paper proposed an elastodynamic modeling method combined with independent displacement coordinates and substructure synthesis technology. Firstly, the connecting rod was discretized, and the elastodynamic control equation for each element was established. The multipoint constraint element theory, linear algebra, and singularity analysis were used to identify the globally independent displacement coordinates of the manipulator. On this basis, the elastodynamic model using the substructure synthesis for the 3-PRS parallel manipulator (PM) was developed, with its natural frequencies distribution in the regular workspace discussed. The comparison with the finite-element results showed that the maximum error of the first three-order natural frequencies was within 1.03%, which verified the correctness of the analytical model. The proposed elastodynamic model included all the kinematic constraints of the manipulator without increasing the Lagrangian multiplier. The method is computationally efficient and assesses the dynamic behaviors of the mechanism at the predesign phase.

Collective decision-making by a swarm of robots is of paramount importance. In particular, the problem of collective perception wherein a swarm of robots aims to achieve consensus on the prevalent feature in the environment. Recently, this problem has been formulated as a discrete collective estimation scenario to estimate their proportion rather than deciding about the prevalent one. Nevertheless, the performance of the existing strategies to resolve this scenario is either poor or depends on higher communication bandwidth. In this work, we propose a novel decision-making strategy based on maximum likelihood estimate sharing (MLES) to resolve the discrete collective estimation scenario. Experimentally, we compare the tradeoff speed versus accuracy of MLES with state-of-the-art methods in the literature, such as direct comparison (DC) and distributed Bayesian belief sharing (DBBS). Interestingly, MLES achieves an accurate consensus nearly 20% faster than DBBS, its communication bandwidth requirement is the same as DC but six times less than DBBS, and its computational complexity is $O(1)$. Furthermore, we investigate how noisy sensors affect the effectiveness of the strategies under consideration, with MLES showing better sustainability.

This article reports on designing and implementing a multiclass sentiment classification approach to handle the imbalanced class distribution of Arabic documents. The proposed approach, sentiment classification of Arabic documents (SCArD), combines the advantages of a clustering-based undersampling (CBUS) method and an ensemble learning model to aid machine learning (ML) classifiers in building accurate models against highly imbalanced datasets. The CBUS method applies two standard clustering algorithms: K-means and expectation–maximization, to balance the ratio between the major and the minor classes by decreasing the number of the major class instances and maintaining the number of the minor class instances at the cluster level. The merits of the proposed approach are that it does not remove the majority class instances from the dataset nor injects the dataset with artificial minority class instances. The resulting balanced datasets are used to train two ML classifiers, random forest and updateable Naïve Bayes, to develop prediction data models. The best prediction data models are selected based on F1-score rates. We applied two techniques to test SCArD and generate new predictions from the imbalanced test dataset. The first technique uses the best prediction data models. The second technique uses the majority voting ensemble learning model, which combines the best prediction data models to generate the final predictions. The experimental results showed that SCArD is promising and outperformed the other comparative classification models based on the F1-score rates.

Which patterns must a two-colouring of $K_n$ contain if each vertex has at least $\varepsilon n$ red and $\varepsilon n$ blue neighbours? We show that when $\varepsilon \gt 1/4$, $K_n$ must contain a complete subgraph on $\Omega (\log n)$ vertices where one of the colours forms a balanced complete bipartite graph.

When $\varepsilon \leq 1/4$, this statement is no longer true, as evidenced by the following colouring $\chi$ of $K_n$. Divide the vertex set into $4$ parts nearly equal in size as $V_1,V_2,V_3, V_4$, and let the blue colour class consist of the edges between $(V_1,V_2)$, $(V_2,V_3)$, $(V_3,V_4)$, and the edges contained inside $V_2$ and inside $V_3$. Surprisingly, we find that this obstruction is unique in the following sense. Any two-colouring of $K_n$ in which each vertex has at least $\varepsilon n$ red and $\varepsilon n$ blue neighbours (with $\varepsilon \gt 0$) contains a vertex set $S$ of order $\Omega _{\varepsilon }(\log n)$ on which one colour class forms a balanced complete bipartite graph, or which has the same colouring as $\chi$.

Bending and elongation have been some of the most studied motions in soft actuators due to the variety of their applications. For that matter, multi-DOF actuators have been developed with the purpose to generate different movements in a single actuator, mainly bending.

However, these actuators are still limited in mobility range, and some of them do not perform continuous curvatures. This paper presents the design, characterisation and implementations of a multi-DOF soft pneumatic module. The internal structure of the proposed module is composed of four channels, which generate bending in several directions. The finite element method analysis demonstrates that the actuator performs continuous curvatures for different pressure values. We present a repeatable and easy manufacturing process using the casting technique, considering the material Ecoflex 00-50; as well as the kinematic model of the actuator, taking into consideration two bending Degrees of Freedom (DOFs). Furthermore, we performed bending characterisation for all possible combinations of the four channels via computer vision, demonstrating a wide mobility range and performing continuous curvatures. Additionally, we evaluated the kinematic model with characterisation data, obtaining the angular and cartesian relationship between the pressure and continuous curvatures. On the other hand, the authors propose the design of a modular soft manipulator based on two multi-DOF modules. The kinematic model is reported. In addition, we implement a motion sequence in the manipulator to pick and place tasks.