172 research outputs found
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments
Despite their stellar performance on a wide range of tasks, including
in-context tasks only revealed during inference, vanilla transformers and
variants trained for next-token predictions (a) do not learn an explicit world
model of their environment which can be flexibly queried and (b) cannot be used
for planning or navigation. In this paper, we consider partially observed
environments (POEs), where an agent receives perceptually aliased observations
as it navigates, which makes path planning hard. We introduce a transformer
with (multiple) discrete bottleneck(s), TDB, whose latent codes learn a
compressed representation of the history of observations and actions. After
training a TDB to predict the future observation(s) given the history, we
extract interpretable cognitive maps of the environment from its active
bottleneck(s) indices. These maps are then paired with an external solver to
solve (constrained) path planning problems. First, we show that a TDB trained
on POEs (a) retains the near perfect predictive performance of a vanilla
transformer or an LSTM while (b) solving shortest path problems exponentially
faster. Second, a TDB extracts interpretable representations from text
datasets, while reaching higher in-context accuracy than vanilla sequence
models. Finally, in new POEs, a TDB (a) reaches near-perfect in-context
accuracy, (b) learns accurate in-context cognitive maps (c) solves in-context
path planning problems
Rough set theory applied to pattern recognition of partial discharge in noise affected cable data
This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data
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Base-pair ambiguity and the kinetics of RNA folding
Background
A pairings of nucleotide sequences. Given this forbidding free-energy landscape, mechanisms have evolved that contribute to a directed and efficient folding process, including catalytic proteins and error-detecting chaperones. Among structural RNA molecules we make a distinction between “bound” molecules, which are active as part of ribonucleoprotein (RNP) complexes, and “unbound,” with physiological functions performed without necessarily being bound in RNP complexes. We hypothesized that unbound molecules, lacking the partnering structure of a protein, would be more vulnerable than bound molecules to kinetic traps that compete with native stem structures. We defined an “ambiguity index”—a normalized function of the primary and secondary structure of an individual molecule that measures the number of kinetic traps available to nucleotide sequences that are paired in the native structure, presuming that unbound molecules would have lower indexes. The ambiguity index depends on the purported secondary structure, and was computed under both the comparative (“gold standard”) and an equilibrium-based prediction which approximates the minimum free energy (MFE) structure. Arguing that kinetically accessible metastable structures might be more biologically relevant than thermodynamic equilibrium structures, we also hypothesized that MFE-derived ambiguities would be less effective in separating bound and unbound molecules.
Results
We have introduced an intuitive and easily computed function of primary and secondary structures that measures the availability of complementary sequences that could disrupt the formation of native stems on a given molecule—an ambiguity index. Using comparative secondary structures, the ambiguity index is systematically smaller among unbound than bound molecules, as expected. Furthermore, the effect is lost when the presumably more accurate comparative structure is replaced instead by the MFE structure.
Conclusions
A statistical analysis of the relationship between the primary and secondary structures of non-coding RNA molecules suggests that stem-disrupting kinetic traps are substantially less prevalent in molecules not participating in RNP complexes. In that this distinction is apparent under the comparative but not the MFE secondary structure, the results highlight a possible deficiency in structure predictions when based upon assumptions of thermodynamic equilibrium
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
Probabilistic graphical models (PGMs) provide a compact representation of
knowledge that can be queried in a flexible way: after learning the parameters
of a graphical model once, new probabilistic queries can be answered at test
time without retraining. However, when using undirected PGMS with hidden
variables, two sources of error typically compound in all but the simplest
models (a) learning error (both computing the partition function and
integrating out the hidden variables is intractable); and (b) prediction error
(exact inference is also intractable). Here we introduce query training (QT), a
mechanism to learn a PGM that is optimized for the approximate inference
algorithm that will be paired with it. The resulting PGM is a worse model of
the data (as measured by the likelihood), but it is tuned to produce better
marginals for a given inference algorithm. Unlike prior works, our approach
preserves the querying flexibility of the original PGM: at test time, we can
estimate the marginal of any variable given any partial evidence. We
demonstrate experimentally that QT can be used to learn a challenging
8-connected grid Markov random field with hidden variables and that it
consistently outperforms the state-of-the-art AdVIL when tested on three
undirected models across multiple datasets
Model for the Vaporization of Mixed Organometallic Compounds in the Metalorganic Chemical Vapor Deposition of High Temperature Superconducting Films
A model of the vaporization and mass transport of mixed organometallics from a single source for thin film metalorganic chemical vapor deposition is presented. A stoichiometric gas phase can be obtained from a mixture of the organometallics in the desired mole ratios, in spite of differences in the volatilities of the individual compounds. Proper film composition and growth rates are obtained by controlling the velocity of a carriage containing the organometallics through the heating zone of a vaporizer
Vaporization of a mixed precursors in chemical vapor deposition for YBCO films
Single phase YBa2Cu3O7-delta thin films with T(c) values around 90 K are readily obtained by using a single source chemical vapor deposition technique with a normal precursor mass transport. The quality of the films is controlled by adjusting the carrier gas flow rate and the precursor feed rate
Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming
Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840Ă—2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2Â =Â 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management
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