Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System

The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an extension of the well-known Vehicle Routing Problem (VRP), which takes into account the dynamic nature of the problem. This aspect requires the vehicle routes to be updated in an ongoing manner as new customer requests arrive in the system and must be incorporated into an evolving schedule during the working day. Besides the vehicle capacity constraint involved in the classical VRP, DVRPTW considers in addition time windows, which are able to better capture real-world situations. Despite this, so far, few studies have focused on tackling this problem of greater practical importance. To this end, this study devises for the resolution of DVRPTW, an ant colony optimization based algorithm, which resorts to a joint solution construction mechanism, able to construct in parallel the vehicle routes. This method is coupled with a local search procedure, aimed to further improve the solutions built by ants, and with an insertion heuristics, which tries to reduce the number of vehicles used to service the available customers. The experiments indicate that the proposed algorithm is competitive and effective, and on DVRPTW instances with a higher dynamicity level, it is able to yield better results compared to existing ant-based approaches.Comment: 10 pages, 2 figure

Analyzing domain shift when using additional data for the MICCAI KiTS23 Challenge

Using additional training data is known to improve the results, especially for medical image 3D segmentation where there is a lack of training material and the model needs to generalize well from few available data. However, the new data could have been acquired using other instruments and preprocessed such its distribution is significantly different from the original training data. Therefore, we study techniques which ameliorate domain shift during training so that the additional data becomes better usable for preprocessing and training together with the original data. Our results show that transforming the additional data using histogram matching has better results than using simple normalization.Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in [TODO], and is available online at https://doi.org/[TODO

Laser Surface Thermal Treatment Applied to Stainless Steel X5 CrNi 18 10

The paper propose to mark out the influence of different control parameters of laser beam light over the entire surface thermal treatment applied and, also, the physical and technological proprieties of the stainless steel obtained laye

COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB

The paper presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs. The first approach is a volumetric one, involving 3D convolutions, while the other two approaches perform at first slice-wise classification and then aggregate the results at the volume level. The experiments are carried on the COV19-CT-DB dataset, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021. Our best results on the validation subset reach a macro-F1 score of 0.92, which improves considerably the baseline score of 0.70 set by the organizers

Time synchronization for an emulated CAN device on a Multi-Processor System on Chip

The increasing number of applications implemented on modern vehicles leads to the use of multi-core platforms in the automotive field. As the number of I/O interfaces offered by these platforms is typically lower than the number of integrated applications, a solution is needed to provide access to the peripherals, such as the Controller Area Network (CAN), to all applications. Emulation and virtualization can be used to implement and share a CAN bus among multiple applications. Furthermore, cyber-physical automotive applications often require time synchronization. A time synchronization protocol on CAN has been recently introduced by AUTOSAR. In this article we present how multiple applications can share a CAN port, which can be on the local processor tile or on a remote tile. Each application can access a local time base, synchronized over CAN, using the AUTOSAR Application Programming Interface (API). We evaluate our approach with four emulation and virtualization examples, trading the number of applications per core with the speed of the software emulated CAN bus.</p

Your Money or Your Time? Experimental Evidence on Overbidding in All-Pay Auctions

Competition for a prize frequently takes the form of dedicating time toward winning a contest. Those who spend the most time become more likely to obtain the prize. We model this competition as an all-pay auction under incomplete information, and report an experiment in which expenditures and rewards are in terms of time. In the experiment, subjects must stay in the laboratory doing nothing for an initially prespecified length of time. However, they can bid, in terms of time, to leave early. The auction has an allpay structure so that if an individual does not submit the highest bid within her group, she must stay for the additional time that she bid. We correlate behavior in this game with behavior in an isomorphic all-pay auction played with money bids. We also consider how two measures of sophistication, the Cognitive Reflection Test (CRT) score, and performance on a probability calibration task, correlate with behavior. We find strong similarities in overall behavior between the auctions conducted with money and with time. Bidding greater than equilibrium levels is typical, and as a consequence, average earnings are negative in both auctions. Thus, the result that there is overdissipation of rent in all-pay auctions extends to competition in terms of time. Higher CRT score and more accurate probability calibration correlate with better decisions in auctions played for money but not those played for time

Prudence, Emotional State, Personality, and Cognitive Ability

We report an experiment to consider the emotional correlates of prudent decision making. In the experiment, we present subjects with lotteries and measure their emotional response with facial recognition software. They then make binary choices between risky lotteries that distinguish prudent from imprudent individuals. They also perform tasks to measure their cognitive ability and a number of personality characteristics. We find that a more negative emotional state correlates with greater prudence. Higher cognitive ability and less conscientiousness is also associated with greater prudence