8,730 research outputs found
Case-based reasoning approach to estimating the strength of sustainable concrete
Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete
Superfluid-insulator transition of the Josephson junction array model with commensurate frustration
We have studied the rationally frustrated Josephson-junction array model in
the square lattice through Monte Carlo simulations of D XY-model. For
frustration , the model at zero temperature shows a continuous
superfluid-insulator transition. From the measurement of the correlation
function and the superfluid stiffness, we obtain the dynamical critical
exponent and the correlation length critical exponent . While the dynamical critical exponent is the same as that for cases
, 1/2, and 1/3, the correlation length critical exponent is surprisingly
quite different. When , we have the nature of a first-order transition.Comment: RevTex 4, to appear in PR
From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding
Current state-of-the-art models for natural language understanding require a
preprocessing step to convert raw text into discrete tokens. This process known
as tokenization relies on a pre-built vocabulary of words or sub-word
morphemes. This fixed vocabulary limits the model's robustness to spelling
errors and its capacity to adapt to new domains. In this work, we introduce a
novel open-vocabulary language model that adopts a hierarchical two-level
approach: one at the word level and another at the sequence level. Concretely,
we design an intra-word module that uses a shallow Transformer architecture to
learn word representations from their characters, and a deep inter-word
Transformer module that contextualizes each word representation by attending to
the entire word sequence. Our model thus directly operates on character
sequences with explicit awareness of word boundaries, but without biased
sub-word or word-level vocabulary. Experiments on various downstream tasks show
that our method outperforms strong baselines. We also demonstrate that our
hierarchical model is robust to textual corruption and domain shift.Comment: Accepted to ACL 2023 Main Conferenc
Spondylodiscitis with Epidural and Psoas Muscle Abscesses as Complications After Transrectal Ultrasound-guided Prostate Biopsy: Report of a Rare Case
A 71-year-old man presented with spondylodiscitis with epidural and psoas muscle abscesses following transrectal ultrasound (TRUS)-
guided prostate biopsy. These rare complications were detected by computed tomography of the abdomen and magnetic resonance imaging
of the lumbar spine. The patient was successfully treated with antibiotics and underwent neurosurgery. Awareness of presentations such
as backache and weakness of the lower limbs after prostate biopsy is important as these symptoms are usually mistaken for bone or muscle
problems and often not recognized as being related to infection
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