Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure modes of being unable to propagate patterns of initial conditions. We discover that these failure modes are caused by the simplicity bias of neural networks and the mismatch between PDE's continuity and PINN's discrete sampling. We reveal that the State Space Model (SSM) can be a continuous-discrete articulation allowing initial condition propagation, and that simplicity bias can be eliminated by aligning a sequence of moderate granularity. Accordingly, we propose PINNMamba, a novel framework that introduces sub-sequence modeling with SSM. Experimental results show that PINNMamba can reduce errors by up to 86.3\% compared with state-of-the-art architecture. Our code is available at https://github.com/miniHuiHui/PINNMamba.

This paper copes with the COGERSA waste collection process. Up to now, experts have been manually designed the process using a trial and error mechanism. This process is not globally optimized, since it has been progressively and locally built as council demands appear. Planning optimization algorithms usually solve it, but they need a fitness function to evaluate a route planning quality. The drawback is that even experts are not able to propose one in a straightforward way due to the complexity of the process. Hence, the goal of this paper is to build a fitness function though a preference framework, taking advantage of the available expert knowledge and expertise. Several key performance indicators together with preference judgments are carefully established according to the experts for learning a promising fitness function. Particularly, the additivity property of them makes the task be much more affordable, since it allows to work with routes rather than with route plannings. Besides, a feature selection analysis is performed over such indicators, since the experts suspect of a potential existing (but unknown) redundancy among them. The experiment results confirm this hypothesis, since the best $C-$index ($98\%$ against around $94\%$) is reached when 6 or 8 out of 21 indicators are taken. Particularly, truck load seems to be a highly promising key performance indicator, together to the travelled distance along non-main roads. A comparison with other existing approaches shows that the proposed method clearly outperforms them, since the $C-$index goes from $72\%$ or $90\%$ to $98\%$.

This paper presents a wireless data collection framework that employs an unmanned aerial vehicle (UAV) to efficiently gather data from distributed IoT sensors deployed in a large area. Our approach takes into account the non-zero communication ranges of the sensors to optimize the flight path of the UAV, resulting in a variation of the Traveling Salesman Problem (TSP). We prove mathematically that the optimal waypoints for this TSP-variant problem are restricted to the boundaries of the sensor communication ranges, greatly reducing the solution space. Building on this finding, we develop a low-complexity UAV-assisted sensor data collection algorithm, and demonstrate its effectiveness in a selected use case where we minimize the total energy consumption of the UAV and sensors by jointly optimizing the UAV's travel distance and the sensors' communication ranges.

Global food security is an issue that is fast becoming a critical matter in the world today. Global warming, climate change and a range of other impacts caused by humans, such as carbon emissions, sociopolitical and economical challenges (e.g. war), traditional workforce/labour decline and population growth are straining global food security. The need for high-speed and reliable wireless communication in agriculture is becoming more of a necessity rather than a technological demonstration or showing superiority in the field. Governments and industries around the world are seeing more urgency in establishing communication infrastructure to scale up agricultural activities and improve sustainability, by employing autonomous agri-robotics and agri-technologies. The work presented here evaluates the physical performance of 5G in an agri-robotics application, and the results are compared against 4G and WiFi6 (a newly emerging wireless communication standard), which are typically used in agricultural environments. In addition, a series of simulation experiments were performed to assess the ``real-time'' operational delay in critical tasks that may require a human-in-the-loop to support decision making. The results lead to the conclusion that 4G cannot be used in the agricultural domain for applications that require high throughput and reliable communication between robot and user. Moreover, a single wireless solution does not exist for the agricultural domain, but instead multiple solutions can be combined to meet the necessary telecommunications requirements. Finally, the results show that 5G greatly outperforms 4G in all performance metrics, and on average only 18.2ms slower than WiFi6 making it very reliable.

Bi-directional communication between humans and swarm systems begs for efficient languages to communicate information between the humans and the Artificial Intelligence (AI)-enabled agents in a manner that is most appropriate for the context. We discuss the criteria for effective teaming and functional bi-directional communication between humans and AI, and the design choices required to create effective languages. We then present a human-AI-teaming communication language inspired by the Australian Aboriginal language of Jingulu, which we call JSwarm. We present the motivation and structure of the language. An example is used to demonstrate how the language operates for a shepherding swarm guidance task.

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bah\'ia Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.

This document proposes an alternative proof of the result contained in article [1]. The proof is simpler to understand (I believe) and leads to a more precise statement about the asymptotical distribution of the relative amount of perturbation. Suppose that an artificial intelligent program bases its decision on the collection points neighbouring the query. Suppose that this is not the case for that query q and this collection point x . We are interested in the amount of perturbation to be applied to collection point x so that the program takes it into account.

Amid a severe shortage of manpower, a team comprised of researchers from private companies and a university in Aichi Prefecture is working on developing a self-driving robot that uses cutting-edge technology to support flower-growing farmers. In fiscal 2019 the group hopes to start marketing automated, handcart-type robots that follow pickers of roses and chrysanthemums, carry the cut flowers, and deliver them to collection points. In a laboratory at Toyohashi University of Technology in Toyohashi, a roughly 1-meter-high handcart-type robot -- equipped with three cameras and two infrared radar devices -- moves back and forth, changing direction smoothly. The robot, which recognizes its location through camera footage, can self-drive on the farm grounds or inside greenhouses, follow flower pickers while keeping a certain distance, collect picked flowers, and carry them to designated collection points. Following flower pickers and transporting cut flowers became possible through the use of autonomous driving technology that involves the 3D mapping of farm grounds.

One-third of its global online sales are now picked up in the store, the company says, but that has created long lines in some cities and waits for attendants to retrieve packages, customers say. To speed up the process, Zara said earlier this year it would roll out a robot-run version of click and collect, automating the service. The collection points in brick-and-mortar stores will allow shoppers who have ordered items online to scan or enter a code, triggering a behind-the-scenes robot to search for the customer's package in a small warehouse, and then deliver it quickly to a drop box. The move comes as Zara faces heightened challenges to maintain its momentum and compete with online-only apparel retailers, such as Zalando and ASOS, that sell a variety of brands. Annual sales at both have grown more than 20% in the past couple of years compared with low double-digit percentage growth at Zara's Spanish parent company Inditex SA ITX 0.46% .

One of the most challenging problems on modern urban planning and one of the goals to be solved for smart city design is that of urban waste disposal. Given urban population growth, and that the amount of waste generated by each of us citizens is also growing, the total amount of waste to be collected and treated is growing dramatically (EPA 2011), becoming one sensitive issue for local governments. A modern technique for waste collection that is steadily being adopted is automated vacuum waste collection. This technology uses air suction on a closed network of underground pipes to move waste from the collection points to the processing station, reducing greenhouse gas emissions as well as inconveniences to citizens (odors, noise, . . . ) and allowing better waste reuse and recycling. This technique is open to optimize energy consumption because moving huge amounts of waste by air impulsion requires a lot of electric power. The described problem challenge here is, precisely, that of organizing and scheduling waste collection to minimize the amount of energy per ton of collected waste in such a system via the use of Artificial Intelligence techniques. This kind of problems are an inviting opportunity to showcase the possibilities that AI for Computational Sustainability offers.