1,481 research outputs found
Single-index quantile regression
This is the post-print version of the final paper published in Journal of Multivariate Analysis. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.Nonparametric quantile regression with multivariate covariates is a difficult estimation problem due to the “curse of dimensionality”. To reduce the dimensionality while still retaining the flexibility of a nonparametric model, we propose modeling the conditional quantile by a single-index function View the MathML sourceg0(xTγ0), where a univariate link function g0(⋅)g0(⋅) is applied to a linear combination of covariates View the MathML sourcexTγ0, often called the single-index. We introduce a practical algorithm where the unknown link function g0(⋅)g0(⋅) is estimated by local linear quantile regression and the parametric index is estimated through linear quantile regression. Large sample properties of estimators are studied, which facilitate further inference. Both the modeling and estimation approaches are demonstrated by simulation studies and real data applications
Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
Anticipating human intention by observing one's actions has many
applications. For instance, picking up a cellphone, then a charger (actions)
implies that one wants to charge the cellphone (intention). By anticipating the
intention, an intelligent system can guide the user to the closest power
outlet. We propose an on-wrist motion triggered sensing system for anticipating
daily intentions, where the on-wrist sensors help us to persistently observe
one's actions. The core of the system is a novel Recurrent Neural Network (RNN)
and Policy Network (PN), where the RNN encodes visual and motion observation to
anticipate intention, and the PN parsimoniously triggers the process of visual
observation to reduce computation requirement. We jointly trained the whole
network using policy gradient and cross-entropy loss. To evaluate, we collect
the first daily "intention" dataset consisting of 2379 videos with 34
intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%,
97.56% accuracy on three users while processing only 29% of the visual
observation on average
ProTeCt: Prompt Tuning for Hierarchical Consistency
Large visual-language models, like CLIP, learn generalized representations
and have shown promising zero-shot performance. Few-shot adaptation methods,
based on prompt tuning, have also been shown to further improve performance on
downstream datasets. However, these models are not hierarchically consistent.
Frequently, they infer incorrect labels at coarser taxonomic class levels, even
when the inference at the leaf level (original class labels) is correct. This
is problematic, given their support for open set classification and, in
particular, open-grained classification, where practitioners define label sets
at various levels of granularity. To address this problem, we propose a prompt
tuning technique to calibrate the hierarchical consistency of model
predictions. A set of metrics of hierarchical consistency, the Hierarchical
Consistent Accuracy (HCA) and the Mean Treecut Accuracy (MTA), are first
proposed to benchmark model performance in the open-granularity setting. A
prompt tuning technique, denoted as Prompt Tuning for Hierarchical Consistency
(ProTeCt), is then proposed to calibrate classification across all possible
label set granularities. Results show that ProTeCt can be combined with
existing prompt tuning methods to significantly improve open-granularity
classification performance without degradation of the original classification
performance at the leaf level
Single-Stage Visual Relationship Learning using Conditional Queries
Research in scene graph generation (SGG) usually considers two-stage models,
that is, detecting a set of entities, followed by combining them and labeling
all possible relationships. While showing promising results, the pipeline
structure induces large parameter and computation overhead, and typically
hinders end-to-end optimizations. To address this, recent research attempts to
train single-stage models that are computationally efficient. With the advent
of DETR, a set based detection model, one-stage models attempt to predict a set
of subject-predicate-object triplets directly in a single shot. However, SGG is
inherently a multi-task learning problem that requires modeling entity and
predicate distributions simultaneously. In this paper, we propose Transformers
with conditional queries for SGG, namely, TraCQ with a new formulation for SGG
that avoids the multi-task learning problem and the combinatorial entity pair
distribution. We employ a DETR-based encoder-decoder design and leverage
conditional queries to significantly reduce the entity label space as well,
which leads to 20% fewer parameters compared to state-of-the-art single-stage
models. Experimental results show that TraCQ not only outperforms existing
single-stage scene graph generation methods, it also beats many
state-of-the-art two-stage methods on the Visual Genome dataset, yet is capable
of end-to-end training and faster inference.Comment: Accepted to NeurIPS 202
Erratum to: Metformin improved health-related quality of life in ethnic Chinese women with polycystic ovary syndrome
Phylometrics: a pipeline for inferring phylogenetic trees from a sequence relationship network perspective
<p>Abstract</p> <p>Background</p> <p>Comparative sequence analysis of the 16S rRNA gene is frequently used to characterize the microbial diversity of environmental samples. However, sequence similarities do not always imply functional or evolutionary relatedness due to many factors, including unequal rates of change and convergence. Thus, relying on top BLASTN hits for phylogenetic studies may misrepresent the diversity of these constituents. Furthermore, attempts to circumvent this issue by including a large number of BLASTN hits per sequence in one tree to explore their relatedness presents other problems. For instance, the multiple sequence alignment will be poor and computationally costly if not relying on manual alignment, and it may be difficult to derive meaningful relationships from the resulting tree. Analyzing sequence relationship networks within collective BLASTN results, however, reveal sequences that are closely related despite low rank.</p> <p>Results</p> <p>We have developed a web application, Phylometrics, that relies on networks of collective BLASTN results (rather than single BLASTN hits) to facilitate the process of building phylogenetic trees in an automated, high-throughput fashion while offering novel tools to find sequences that are of significant phylogenetic interest with minimal human involvement. The application, which can be installed locally in a laboratory or hosted remotely, utilizes a simple wizard-style format to guide the user through the pipeline without necessitating a background in programming. Furthermore, Phylometrics implements an independent job queuing system that enables users to continue to use the system while jobs are run with little or no degradation in performance. </p> <p>Conclusions</p> <p>Phylometrics provides a novel data mining method to screen supplied DNA sequences and to identify sequences that are of significant phylogenetic interest using powerful analytical tools. Sequences that are identified as being similar to a number of supplied sequences may provide key insights into their functional or evolutionary relatedness. Users require the same basic computer skills as for navigating most internet applications.</p
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