First-order logic learning in artificial neural networks

Artificial Neural Networks have previously been applied in neuro-symbolic learning to learn ground logic program rules. However, there are few results of learning relations using neuro-symbolic learning. This paper presents the system PAN, which can learn relations. The inputs to PAN are one or more atoms, representing the conditions of a logic rule, and the output is the conclusion of the rule. The symbolic inputs may include functional terms of arbitrary depth and arity, and the output may include terms constructed from the input functors. Symbolic inputs are encoded as an integer using an invertible encoding function, which is used in reverse to extract the output terms. The main advance of this system is a convention to allow construction of Artificial Neural Networks able to learn rules with the same power of expression as first order definite clauses. The system is tested on three examples and the results are discussed

Osteochondroma of the proximal humerus with frictional bursitis and secondary synovial osteochondromatosis

We report a case of multiple hereditary exostosis in a 33-year old patient with clinical symptoms of pain and impression of a growing mass of the left shoulder alerting potential risk of malignant transformation of an osteochondroma. Imaging studies illustrated perilesional bursitis surrounding an osteochondroma of the proximal humerus. Malignant transformation was excluded with MRI. Fragments of the osteochondroma were dislocated in the inflammatory synovial bursa illustrating a case of secondary synovial osteochondromatosis

Alien Registration- Gogan, Bert K. (Presque Isle, Aroostook County)

https://digitalmaine.com/alien_docs/33697/thumbnail.jp

Clustering outdoor soundscapes using fuzzy ants

A classification algorithm for environmental sound recordings or "soundscapes" is outlined. An ant clustering approach is proposed, in which the behavior of the ants is governed by fuzzy rules. These rules are optimized by a genetic algorithm specially designed in order to achieve the optimal set of homogeneous clusters. Soundscape similarity is expressed as fuzzy resemblance of the shape of the sound pressure level histogram, the frequency spectrum and the spectrum of temporal fluctuations. These represent the loudness, the spectral and the temporal content of the soundscapes. Compared to traditional clustering methods, the advantages of this approach are that no a priori information is needed, such as the desired number of clusters, and that a flexible set of soundscape measures can be used. The clustering algorithm was applied to a set of 1116 acoustic measurements in 16 urban parks of Stockholm. The resulting clusters were validated against visitor's perceptual measurements of soundscape quality

Ground State of the Easy-Axis Rare-Earth Kagom\'e Langasite Pr3_3Ga5_5SiO14_{14}

We report muon spin relaxation ($\mu$SR) and $^{69,71}$Ga nuclear quadrupolar resonance (NQR) local-probe investigations of the kagom\'e compound Pr$_3$Ga$_5$SiO$_{14}$. Small quasi-static random internal fields develop below 40 K and persist down to our base temperature of 21 mK. They originate from hyperfine-enhanced $^{141}$Pr nuclear magnetism which requires a non-magnetic Pr$^{3+}$ crystal-field (CF) ground state. Besides, we observe a broad maximum of the relaxation rate at $\simeq 10$ K which we attribute to the population of the first excited magnetic CF level. Our results yield a Van-Vleck paramagnet picture, at variance with the formerly proposed spin-liquid ground state.Comment: minor change