692 research outputs found
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
Short-term passenger demand forecasting is of great importance to the
on-demand ride service platform, which can incentivize vacant cars moving from
over-supply regions to over-demand regions. The spatial dependences, temporal
dependences, and exogenous dependences need to be considered simultaneously,
however, which makes short-term passenger demand forecasting challenging. We
propose a novel deep learning (DL) approach, named the fusion convolutional
long short-term memory network (FCL-Net), to address these three dependences
within one end-to-end learning architecture. The model is stacked and fused by
multiple convolutional long short-term memory (LSTM) layers, standard LSTM
layers, and convolutional layers. The fusion of convolutional techniques and
the LSTM network enables the proposed DL approach to better capture the
spatio-temporal characteristics and correlations of explanatory variables. A
tailored spatially aggregated random forest is employed to rank the importance
of the explanatory variables. The ranking is then used for feature selection.
The proposed DL approach is applied to the short-term forecasting of passenger
demand under an on-demand ride service platform in Hangzhou, China.
Experimental results, validated on real-world data provided by DiDi Chuxing,
show that the FCL-Net achieves better predictive performance than traditional
approaches including both classical time-series prediction models and neural
network based algorithms (e.g., artificial neural network and LSTM). This paper
is one of the first DL studies to forecast the short-term passenger demand of
an on-demand ride service platform by examining the spatio-temporal
correlations.Comment: 39 pages, 10 figure
Exact Transcript Quantification Over Splice Graphs
The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quantifier with exactly the set of all compatible transcripts. The key to our method is constructing an extension of the splice graph based on Aho-Corasick automata. The proof of equivalence is based on a novel reparameterization of the read generation model of a state-of-art transcript quantification method. This new approach is useful for modeling scenarios where reference transcriptome is incomplete or not available and can be further used in transcriptome assembly or alternative splicing analysis
Bias detection and correction in RNA-Sequencing data
<p>Abstract</p> <p>Background</p> <p>High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates.</p> <p>Results</p> <p>In this paper, by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes, we showed that commonly used estimates of gene expression levels from RNA-Seq data, such as reads per kilobase of gene length per million reads (RPKM), are biased in terms of gene length, GC content and dinucleotide frequencies. We directly examined the biases at the gene-level, and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously. Compared to previously proposed base level correction methods, our method reduces bias in gene-level expression estimates more effectively.</p> <p>Conclusions</p> <p>Our method identifies and corrects different sources of biases in gene-level expression measures from RNA-Seq data, and provides more accurate estimates of gene expression levels from RNA-Seq. This method should prove useful in meta-analysis of gene expression levels using different platforms or experimental protocols.</p
Enhanced Microscale Hydrodynamic Near-cloaking using Electro-osmosis
In this paper, we develop a general mathematical framework for enhanced
hydrodynamic near-cloaking of electro-osmotic flow for more complex shapes,
which is obtained by simultaneously perturbing the inner and outer boundaries
of the perfect cloaking structure. We first derive the asymptotic expansions of
perturbed fields and obtain a first-order coupled system. We then establish the
representation formula of the solution to the first-order coupled system using
the layer potential techniques. Based on the asymptotic analysis, the enhanced
hydrodynamic near-cloaking conditions are derived for the control region with
general cross-sectional shape. The conditions reveal the inner relationship
between the shapes of the object and the control region. Especially, for the
shape of a deformed annulus or confocal ellipses cylinder, the cloaking
conditions and relationship of shapes are quantified more accurately. Our
theoretical findings are validated and supplemented by a variety of numerical
results. The results in this paper also provide a mathematical foundation for
more complex hydrodynamic cloaking
Toward Unified Controllable Text Generation via Regular Expression Instruction
Controllable text generation is a fundamental aspect of natural language
generation, with numerous methods proposed for different constraint types.
However, these approaches often require significant architectural or decoding
modifications, making them challenging to apply to additional constraints or
resolve different constraint combinations. To address this, our paper
introduces Regular Expression Instruction (REI), which utilizes an
instruction-based mechanism to fully exploit regular expressions' advantages to
uniformly model diverse constraints. Specifically, our REI supports all popular
fine-grained controllable generation constraints, i.e., lexical, positional,
and length, as well as their complex combinations, via regular expression-style
instructions. Our method only requires fine-tuning on medium-scale language
models or few-shot, in-context learning on large language models, and requires
no further adjustment when applied to various constraint combinations.
Experiments demonstrate that our straightforward approach yields high success
rates and adaptability to various constraints while maintaining competitiveness
in automatic metrics and outperforming most previous baselines.Comment: Accepted on IJCNLP-AACL 202
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