This work advances and substantiates the thesis that the resolution of this crisis lies in the domain of possibility theory, specifically in the axiomatic approach developed in Bychkovs article. Unlike numerous attempts to fix Dempster rule, this approach builds from scratch a logically consistent and mathematically rigorous foundation for working with uncertainty, using the dualistic apparatus of possibility and necessity measures. The aim of this work is to demonstrate that possibility theory is not merely an alternative, but provides a fundamental resolution to DST paradoxes. A comparative analysis of three paradigms will be conducted probabilistic, evidential, and possibilistic. Using a classic medical diagnostic dilemma as an example, it will be shown how possibility theory allows for correct processing of contradictory data, avoiding the logical traps of DST and bringing formal reasoning closer to the logic of natural intelligence.

Deep reinforcement learning (DRL) has had success in virtual and simulated domains, but due to key differences between simulated and real-world environments, DRL-trained policies have had limited success in real-world applications. To assist researchers to bridge the \textit{sim-to-real gap}, in this paper, we describe a low-cost physical inverted pendulum apparatus and software environment for exploring sim-to-real DRL methods. In particular, the design of our apparatus enables detailed examination of the delays that arise in physical systems when sensing, communicating, learning, inferring and actuating. Moreover, we wish to improve access to educational systems, so our apparatus uses readily available materials and parts to reduce cost and logistical barriers. Our design shows how commercial, off-the-shelf electronics and electromechanical and sensor systems, combined with common metal extrusions, dowel and 3D printed couplings provide a pathway for affordable physical DRL apparatus. The physical apparatus is complemented with a simulated environment implemented using a high-fidelity physics engine and OpenAI Gym interface.

When Hans Hartman, a civil engineer, attempted to film the ocean depths in 1917, he pioneered what would become the first deep-sea ROV, or remotely operated vehicle. During an era of silent movies and wartime U-boats, Hartman's ambitious invention--a 1,500-pound electric, submarine camera--could be lowered to a depth of 1,000 feet to capture images of sunken ships and submerged treasures. Despite featuring a gyroscope for stability, a motorized propeller for controlled rotation, and an innovative light source, as Popular Science explained, it had a serious limitation: The hulking apparatus had to be operated blindly from a ship's deck, which meant it was impossible for the camera's operator to see what they were filming until the footage was viewed later. In 1925, Popular Science showcased his next breakthrough--a cylindrical apparatus (seen above) attached to a ship by a cable, housing a submersible, motor-driven camera, as well as enough room for a person who could control the camera, or communicate with crew members nearby to aid with various underwater missions, such as salvaging. The vertical, tin-can-like submarine, equipped with porthole windows and a powerful spotlight, allowed "the operator to go down into the water with a camera and photograph whatever he chooses."

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. What's wrong with these darn institutions, and why does nobody trust them? That's the question lurking behind every postmortem about why Democrats lost the 2024 presidential election and what they could do to start winning future ones. The thinking goes like this: Donald Trump, as a political figure, represents blowing up the status quo; Trump won and the incumbent vice president lost; ergo, a majority of voters are unhappy with the people and groups responsible for the status quo. But the evidence that residents of the United States don't trust their institutions goes beyond election results.

Scientists have brought science fiction one step closer to reality by achieving the first two-way communication between individuals during lucid dreaming. In an experiment that sounds like a scene out of the movie'Inception,' REMspace - a California-based startup that designs technology to enhance sleep and lucid dreaming - reportedly exchanged a message between two people who were asleep. The company used'specially designed equipment' which included a'server,' an'apparatus,' 'Wifi' and'sensors,' but did not specify the exact technology they used. The study participants were asleep in separate homes when REMspace researchers beamed a word created through a unique language between them. REMspace CEO and founder Michael Raduga said: 'Yesterday, communicating in dreams seemed like science fiction.

In this contribution, we define (and test) a pipeline to perform virtual painting recolouring using raw data of X-Ray Fluorescence (XRF) analysis on pictorial artworks. To circumvent the small dataset size, we generate a synthetic dataset, starting from a database of XRF spectra; furthermore, to ensure a better generalisation capacity (and to tackle the issue of in-memory size and inference time), we define a Deep Variational Embedding network to embed the XRF spectra into a lower dimensional, K-Means friendly, metric space. We thus train a set of models to assign coloured images to embedded XRF images. We report here the devised pipeline performances in terms of visual quality metrics, and we close on a discussion on the results.

The nasopharyngeal (NP) swab sample test, commonly used to detect COVID-19 and other respiratory illnesses, involves moving a swab through the nasal cavity to collect samples from the nasopharynx. While typically this is done by human healthcare workers, there is a significant societal interest to enable robots to do this test to reduce exposure to patients and to free up human resources. The task is challenging from the robotics perspective because of the dexterity and safety requirements. While other works have implemented specific hardware solutions, our research differentiates itself by using a ubiquitous rigid robotic arm. This work presents a case study where we investigate the strengths and challenges using compliant control system to accomplish NP swab tests with such a robotic configuration. To accomplish this, we designed a force sensing end-effector that integrates with the proposed torque controlled compliant control loop. We then conducted experiments where the robot inserted NP swabs into a 3D printed nasal cavity phantom. Ultimately, we found that the compliant control system outperformed a basic position controller and shows promise for human use. However, further efforts are needed to ensure the initial alignment with the nostril and to address head motion.

Large language models (LLMs) have demonstrated remarkable capabilities in learning patterns from massive text corpora, including word relationships, sentence structures, and even complex semantic and pragmatic information. However, it remains challenging to induce pre-trained language models to generate structured content that strictly follows specific conventions. We propose a scheme for guiding LLMs to generate highly usable content for computers without the need for fine-tuning and additional neural network inference, by introducing coroutine-based content generation constraints through a pre-agreed context-free grammar (CFG), which guides the autoregressive model Transformer to sample the correct tokens during its decoding phase to form a program-compliant form in the decoding phase of the autoregressive model Transformer to form a formal language that conforms to the program conventions. This will effectively improve the stability and consistency of LLMs in generating target data structures, types or instructions, and reduce the difficulty of application development and integration. We first conducted the matching bracket pairs experiment to verify that the error rate of models like GPT-2 and Gemma reaches 95% when the generated DSLs exceed lengths of 36 and 282 characters, respectively.

The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($\delta$ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dynamical systems. We show that suitable sufficient conditions on the weights of the network can be leveraged to setup a training procedure able to learn provenly-$\delta$ISS LSTM models from data. The proposed approach is tested on a real brake-by-wire apparatus to identify a model of the system from input-output experimentally collected data. Results show satisfactory modeling performances.

Dents on the aircraft skin are frequent and may easily go undetected during airworthiness checks, as their inspection process is tedious and extremely subject to human factors and environmental conditions. Nowadays, 3D scanning technologies are being proposed for more reliable, human-independent measurements, yet the process of inspection and reporting remains laborious and time consuming because data acquisition and validation are still carried out by the engineer. For full automation of dent inspection, the acquired point cloud data must be analysed via a reliable segmentation algorithm, releasing humans from the search and evaluation of damage. This paper reports on two developments towards automated dent inspection. The first is a method to generate a synthetic dataset of dented surfaces to train a fully convolutional neural network. The training of machine learning algorithms needs a substantial volume of dent data, which is not readily available. Dents are thus simulated in random positions and shapes, within criteria and definitions of a Boeing 737 structural repair manual. The noise distribution from the scanning apparatus is then added to reflect the complete process of 3D point acquisition on the training. The second proposition is a surface fitting strategy to convert 3D point clouds to 2.5D. This allows higher resolution point clouds to be processed with a small amount of memory compared with state-of-the-art methods involving 3D sampling approaches. Simulations with available ground truth data show that the proposed technique reaches an intersection-over-union of over 80%. Experiments over dent samples prove an effective detection of dents with a speed of over 500 000 points per second.