Reasoning about Action: An Argumentation - Theoretic Approach

We present a uniform non-monotonic solution to the problems of reasoning about action on the basis of an argumentation-theoretic approach. Our theory is provably correct relative to a sensible minimisation policy introduced on top of a temporal propositional logic. Sophisticated problem domains can be formalised in our framework. As much attention of researchers in the field has been paid to the traditional and basic problems in reasoning about actions such as the frame, the qualification and the ramification problems, approaches to these problems within our formalisation lie at heart of the expositions presented in this paper

Model-based learning for point pattern data

This article proposes a framework for model-based point pattern learning using point process theory. Likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering, to point pattern data. Furthermore, tractable point pattern models as well as solutions for learning and decision making from point pattern data are developed

On the Stability and the Approximation of Branching Distribution Flows, with Applications to Nonlinear Multiple Target Filtering

We analyse the exponential stability properties of a class of measure-valued equations arising in nonlinear multi-target filtering problems. We also prove the uniform convergence properties w.r.t. the time parameter of a rather general class of stochastic filtering algorithms, including sequential Monte Carlo type models and mean eld particle interpretation models. We illustrate these results in the context of the Bernoulli and the Probability Hypothesis Density filter, yielding what seems to be the first results of this kind in this subject

Varietal effects on methane intensity of paddy fields under different irrigation management

Alternate wetting and drying irrigation (AWD) has been shown to decrease water use and trace gas emissions from paddy fields. Whereas genotypic water use shows little variation, it has been shown that rice varieties differ in the magnitude of their methane emissions. Management and variety-related emission factors have been proposed for modelling the impact of paddy production on climate change; however, the magnitude of a potential reduction in greenhouse gas emissions by changing varieties has not yet been fully assessed. AWD has been shown to affect genotypic yields and high-yielding varieties suffer the greatest loss when grown under AWD. The highest yielding varieties may not have the highest methane emissions; thus, a potential yield loss could be compensated by a larger reduction in methane emissions. However, AWD can only be implemented under full control of irrigation water, leaving the rainy seasons with little scope to reduce methane emissions from paddy fields. Employing low-emitting varieties during the rainy season may be an option to reduce methane emissions but may compromise farmers’ income if such varieties perform less well than the current standard. Methane emissions and rice yields were determined in field trials over two consecutive winter/spring seasons with continuously flooded and AWD irrigation treatments for 20 lowland rice varieties in the Mekong Delta of Vietnam. Based on the results, this paper investigates the magnitude of methane savings through varietal choice for both AWD and continuous flooding in relation to genotypic yields and explores potential options for compensating farmers’ mitigation efforts

Instantaneous frequency measurement system using optical mixing in highly nonlinear fiber

A broadband photonic instantaneous frequency measurement system utilizing four-wave mixing in highly nonlinear fiber is demonstrated. This new approach is highly stable and does not require any high-speed electronics or photodetectors. A first principles model accurately predicts the system response. Frequency measurement responses from 1 to 40 GHz are demonstrated and simple reconfiguration allows the system to operate over multiple bands

A Planarity Test via Construction Sequences

Optimal linear-time algorithms for testing the planarity of a graph are well-known for over 35 years. However, these algorithms are quite involved and recent publications still try to give simpler linear-time tests. We give a simple reduction from planarity testing to the problem of computing a certain construction of a 3-connected graph. The approach is different from previous planarity tests; as key concept, we maintain a planar embedding that is 3-connected at each point in time. The algorithm runs in linear time and computes a planar embedding if the input graph is planar and a Kuratowski-subdivision otherwise

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd

Synthesis of Single Phase Hg-1223 High Tc Superconducting Films With Multistep Electrolytic Process

We report the multistep electrolytic process for the synthesis of high Tc single phase HgBa2Ca2Cu3O8+ (Hg-1223) superconducting films. The process includes : i) deposition of BaCaCu precursor alloy, ii) oxidation of BaCaCu films, iii) electrolytic intercalation of Hg in precursor BaCaCuO films and iv) electrochemical oxidation and annealing of Hg-intercalated BaCaCuO films to convert into Hg1Ba2Ca2Cu3O8+ (Hg-1223). Films were characterized by thermo-gravimetric analysis (TGA) and differential thermal analysis (DTA), X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrolytic intercalation of Hg in BaCaCuO precursor is proved to be a novel alternative to high temperature-high pressure mercuration process. The films are single phase Hg-1223 with Tc = 121.5 K and Jc = 4.3 x 104 A/cm2.Comment: 17 Pages, 10 Figures. Submitted to Superconductor Science and Technolog

Blind symbol identifiability of orthogonal space-time block codes

ABSTRACT This paper addresses the blind symbol identifiability of the orthogonal space-time block code (OSTBC) scheme. That is, the conditions under which OSTBC symbols can be identified without ambiguity when channel state information is not available. In many space-time communication schemes, achieving unique blind symbol identification requires certain assumptions on the number of receiver antennas and the rank of the channel matrix. In this paper we show that unique blind symbol identification of OSTBCs is possible for any number of receiver antennas and for any (nonzero) channel matrix. This attractive unique identifiability result is shown to be achieved by a class of OSTBCs that exhibit certain matrix non-rotational properties. Using these properties, we validate the identifiability of a number of commonly used OSTBCs