The increase in global population and urbanization is presenting significant challenges to society: space is becoming increasingly scarce, demand is exceeding capacity for deteriorating infrastructure, transportation is fraught with congestion, and environmental impacts are accelerating. Underground space, and particularly tunnels, has a key role to play in tackling these challenges. However, the cost, risk, uncertainty, and complexity of the tunneling process have impeded its growth. In this paper, we envision several technological advancements that can potentially innovate and transform the mechanized tunneling industry, including artificial intelligence (AI), autonomous, and bio-inspired systems. The proliferation of AI may assist human engineers and operators in making informed decisions systematically and quantitatively based on massive real-time data during tunneling. Autonomous tunneling systems may enable precise and predictable tunneling operations with minimal human intervention and facilitate the construction of massive and large-scale underground infrastructure projects that were previously challenging or unfeasible using conventional methods. Bio-inspired systems may provide valuable references and strategies for more efficient tunneling design and construction concepts. While these technological advancements can offer great promise, they also face considerable challenges, such as improving accessibility to and shareability of tunneling data, developing robust, reliable, and explainable machine learning systems, as well as scaling the mechanics and ensuring the applicability of bio-inspired systems from the prototype level to real-world applications. Addressing these challenges is imperative to ensure the successful implementation of these innovations for future tunneling.

Given a fixed graph $H$ and a constant $c \in [0,1]$, we can ask what graphs $G$ with edge density $c$ asymptotically maximise the homomorphism density of $H$ in $G$. For all $H$ for which this problem has been solved, the maximum is always asymptotically attained on one of two kinds of graphs: the quasi-star or the quasi-clique. We show that for any $H$ the maximising $G$ is asymptotically a threshold graph, while the quasi-clique and the quasi-star are the simplest threshold graphs, having only two parts. This result gives us a unified framework to derive a number of results on graph homomorphism maximisation, some of which were also found quite recently and independently using several different approaches. We show that there exist graphs $H$ and densities $c$ such that the optimising graph $G$ is neither the quasi-star nor the quasi-clique (Day and Sarkar, SIAM J. Discrete Math. 35(1), 294–306, 2021). We also show that for $c$ large enough all graphs $H$ maximise on the quasi-clique (Gerbner et al., J. Graph Theory 96(1), 34–43, 2021), and for any $c \in [0,1]$ the density of $K_{1,2}$ is always maximised on either the quasi-star or the quasi-clique (Ahlswede and Katona, Acta Math. Hung. 32(1–2), 97–120, 1978). Finally, we extend our results to uniform hypergraphs.

Artificial barriers in Learning Automata (LA) is a powerful and yet under-explored concept although it was first proposed in the 1980s. Introducing artificial non-absorbing barriers makes the LA schemes resilient to being trapped in absorbing barriers, a phenomenon which is often referred to as lock in probability leading to an exclusive choice of one action after convergence. Within the field of LA and reinforcement learning in general, there is a sacristy of theoretical works and applications of schemes with artificial barriers. In this paper, we devise a LA with artificial barriers for solving a general form of stochastic bimatrix game. Classical LA systems possess properties of absorbing barriers and they are a powerful tool in game theory and were shown to converge to game’s of Nash equilibrium under limited information. However, the stream of works in LA for solving game theoretical problems can merely solve the case where the Saddle Point of the game exists in a pure strategy and fail to reach mixed Nash equilibrium when no Saddle Point exists for a pure strategy.

Furthermore, we provide experimental results that are in line with our theoretical findings.

Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

To answer database queries over incomplete data, the gold standard is finding certain answers: those that are true regardless of how incomplete data is interpreted. Such answers can be found efficiently for conjunctive queries and their unions, even in the presence of constraints. With negation added, the problem becomes intractable however. We concentrate on the complexity of certain answers under constraints and on effficiently answering queries outside the usual classes of (unions) of conjunctive queries by means of rewriting as Datalog and first-order queries. We first notice that there are three different ways in which query answering can be cast as a decision problem. We complete the existing picture and provide precise complexity bounds on all versions of the decision problem, for certain and best answers. We then study a well-behaved class of queries that extends unions of conjunctive queries with a mild form of negation. We show that for them, certain answers can be expressed in Datalog with negation, even in the presence of functional dependencies, thus making them tractable in data complexity. We show that in general, Datalog cannot be replaced by first-order logic, but without constraints such a rewriting can be done in first order.

Ultrafilters play a significant role in model theory to characterize logics having various compactness and interpolation properties. They also provide a general method to construct extensions of first-order logic having these properties. A main result of this paper is that every class $\Omega $ of uniform ultrafilters generates a $\Delta $-closed logic ${\mathcal {L}}_\Omega $. ${\mathcal {L}}_\Omega $ is $\omega $-relatively compact iff some $D\in \Omega $ fails to be $\omega _1$-complete iff ${\mathcal {L}}_\Omega $ does not contain the quantifier “there are uncountably many.” If $\Omega $ is a set, or if it contains a countably incomplete ultrafilter, then ${\mathcal {L}}_\Omega $ is not generated by Mostowski cardinality quantifiers. Assuming $\neg 0^\sharp $ or $\neg L^{\mu }$, if $D\in \Omega $ is a uniform ultrafilter over a regular cardinal $\nu $, then every family $\Psi $ of formulas in ${\mathcal {L}}_\Omega $ with $|\Phi |\leq \nu $ satisfies the compactness theorem. In particular, if $\Omega $ is a proper class of uniform ultrafilters over regular cardinals, ${\mathcal {L}}_\Omega $ is compact.

In this paper, we propose an enhanced version of the vanilla transformer for data-to-text generation and then use it as the generator of a conditional generative adversarial model to improve the semantic quality and diversity of output sentences. Specifically, by adding a diagonal mask matrix to the attention scores of the encoder and using the history of the attention weights in the decoder, this enhanced version of the vanilla transformer prevents semantic defects in the output text. Also, using this enhanced transformer along with a triplet network, respectively, as the generator and discriminator of conditional generative adversarial network, diversity and semantic quality of sentences are guaranteed. To prove the effectiveness of the proposed model, called conditional generative adversarial with enhanced transformer (CGA-ET), we performed experiments on three different datasets and observed that our proposed model is able to achieve better results than the baselines models in terms of BLEU, METEOR, NIST, ROUGE-L, CIDEr, BERTScore, and SER automatic evaluation metrics as well as human evaluation.

Lower limb exoskeletons (LLEs) have demonstrated their potential in delivering quantified repetitive gait training for individuals afflicted with gait impairments. A critical concern in robotic gait training pertains to fostering active patient engagement, and a viable solution entails harnessing the patient’s intrinsic effort to govern the control of LLEs. To address these challenges, this study presents an innovative online gait learning approach with an appropriate control strategy for rehabilitation exoskeletons based on dynamic movement primitives (DMP) and an Assist-As-Needed (AAN) control strategy, denoted as DMP-AAN. Specifically tailored for post-stroke patients, this approach aims to acquire the gait trajectory from the unaffected leg and subsequently generate the reference gait trajectory for the affected leg, leveraging the acquired model and the patient’s personal exertion. Compared to conventional AAN methodologies, the proposed DMP-AAN approach exhibits adaptability to diverse scenarios encompassing varying gait patterns. Experimental validation has been performed using the lower limb rehabilitation exoskeleton HemiGo. The findings highlight the ability to generate suitable control efforts for LLEs with reduced human-robot interactive force, thereby enabling highly patient-controlled gait training sessions to be achieved.

Accurate geospatial information about the causes and consequences of climate change, including energy systems infrastructure, is critical to planning climate change mitigation and adaptation strategies. When up-to-date spatial data on infrastructure is lacking, one approach to fill this gap is to learn from overhead imagery using deep-learning-based object detection algorithms. However, the performance of these algorithms can suffer when applied to diverse geographies, which is a common case. We propose a technique to generate realistic synthetic overhead images of an object (e.g., a generator) to enhance the ability of these techniques to transfer across diverse geographic domains. Our technique blends example objects into unlabeled images from the target domain using generative adversarial networks. This requires minimal labeled examples of the target object and is computationally efficient such that it can be used to generate a large corpus of synthetic imagery. We show that including these synthetic images in the training of an object detection model improves its ability to generalize to new domains (measured in terms of average precision) when compared to a baseline model and other relevant domain adaptation techniques.

This paper examines Smalley’s preliminary taxonomy of the sound shape and the subsequent application of graphical notation in electroacoustic music. It will demonstrate ways in which spatial categorisations of the morphological sound shape have remained relatively untouched in academia, despite a codependency of frequency, space and time. Theoretical examples and existing visualisations of the sound shape will be considered as a starting point, to determine why the holistic visualisation of space is warranted. A notational system addressing the codependency between spatial and spectral sound shapes will be presented, with reference to its context in Cartesian-coordinate sound environments. This method of electroacoustic notation will incorporate the visualisation of Smalley’s categorisation of spatial sound shapes and ideas of spatial gesture, texture and distribution within Smalley’s composed and listening spaces. This visualisation and notation of composed and listening spaces will demonstrate that audio technologies are imperative drivers in the future analysis and understanding of the sound shape. It will measure the modulation of spatial sound shape properties for Cartesian (height, width, depth) and spherical (azimuth and altitude) across linear temporality, to better represent the complete form of Smalley’s sound shape. This spatial notation will aid the rounded visualisation of Smalley’s morphology, motion, texture, gesture, structure and form. Use of this notational framework will illustrate ways in which a new tool to score electroacoustic sound shapes can inform new practices in computer music composition.

Several governmental organizations all over the world aim for algorithmic accountability of artificial intelligence systems. However, there are few specific proposals on how exactly to achieve it. This article provides an extensive overview of possible transparency and inspectability mechanisms that contribute to accountability for the technical components of an algorithmic decision-making system. Following the different phases of a generic software development process, we identify and discuss several such mechanisms. For each of them, we give an estimate of the cost with respect to time and money that might be associated with that measure.