30,227 research outputs found
Discontinuous grammar as a foreign language
[Abstract] In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy,
coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2020/11We acknowledge the European Research Council (ERC), which has funded this research under the European Unionâs Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150) and the Horizon Europe research and innovation programme (SALSA, grant agreement No 101100615), ERDF/ MICINN-AEI (SCANNER-UDC, PID2020-113230RB-C21), Xunta de Galicia (ED431C 2020/11), and Centro de InvestigaciĂłn de Galicia ââCITICâ, funded by Xunta de Galicia and the European Union (ERDF - Galicia 2014â2020 Program), by grant ED431G 2019/01. Funding for open access charge: Universidade da Coruña/CISUG
Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects
Robotic manipulation, in particular in-hand object manipulation, often
requires an accurate estimate of the object's 6D pose. To improve the accuracy
of the estimated pose, state-of-the-art approaches in 6D object pose estimation
use observational data from one or more modalities, e.g., RGB images, depth,
and tactile readings. However, existing approaches make limited use of the
underlying geometric structure of the object captured by these modalities,
thereby, increasing their reliance on visual features. This results in poor
performance when presented with objects that lack such visual features or when
visual features are simply occluded. Furthermore, current approaches do not
take advantage of the proprioceptive information embedded in the position of
the fingers. To address these limitations, in this paper: (1) we introduce a
hierarchical graph neural network architecture for combining multimodal (vision
and touch) data that allows for a geometrically informed 6D object pose
estimation, (2) we introduce a hierarchical message passing operation that
flows the information within and across modalities to learn a graph-based
object representation, and (3) we introduce a method that accounts for the
proprioceptive information for in-hand object representation. We evaluate our
model on a diverse subset of objects from the YCB Object and Model Set, and
show that our method substantially outperforms existing state-of-the-art work
in accuracy and robustness to occlusion. We also deploy our proposed framework
on a real robot and qualitatively demonstrate successful transfer to real
settings
Convergence of Dynamics on Inductive Systems of Banach Spaces
Many features of physical systems, both qualitative and quantitative, become
sharply defined or tractable only in some limiting situation. Examples are
phase transitions in the thermodynamic limit, the emergence of classical
mechanics from quantum theory at large action, and continuum quantum field
theory arising from renormalization group fixed points. It would seem that few
methods can be useful in such diverse applications. However, we here present a
flexible modeling tool for the limit of theories: soft inductive limits
constituting a generalization of inductive limits of Banach spaces. In this
context, general criteria for the convergence of dynamics will be formulated,
and these criteria will be shown to apply in the situations mentioned and more.Comment: Comments welcom
Classification of multiplicity free quasi-Hamiltonian manifolds
A quasi-Hamiltonian manifold is called multiplicity free if all of its
symplectic reductions are 0-dimensional. In this paper, we classify compact,
multiplicity free, twisted quasi-Hamiltonian manifolds for simply connected,
compact Lie groups. Thereby, we recover old and find new examples of these
structures.Comment: v1: 35 pages, this is a complete revision of arxiv:1612.03843. Since
some omitted parts have already been cited, I opted for a new submission
under a new title. v2: 39 pages, revised according to the advice of a very
helpful refere
Boundedness, Ultracontractive Bounds and Optimal Evolution of the Support for Doubly Nonlinear Anisotropic Diffusion
We investigate some regularity properties of a class of doubly nonlinear
anisotropic evolution equations whose model case is \begin{align*}
\partial_t \big(|u|^{\alpha -1}u \big) - \sum^N_{i=1} \partial_i \big(
|\partial_i u|^{p_i - 2} \partial_i u \big) = 0, \end{align*} where and . We obtain super and ultracontractive bounds,
and global boundedness in space for solutions to the Cauchy problem with
initial data in , and show that the mass is
nonincreasing over time. As a consequence, compactly supported evolution is
shown for optimal exponents. We introduce a seemingly new paradigm, by showing
that Caccioppoli estimates, local boundedness and semicontinuity are
consequences of the membership to a suitable energy class. This membership is
proved by first establishing the continuity of the map permitting us to use
a suitable mollified weak formulation along with an appropriate test function
A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges
Measuring and evaluating source code similarity is a fundamental software
engineering activity that embraces a broad range of applications, including but
not limited to code recommendation, duplicate code, plagiarism, malware, and
smell detection. This paper proposes a systematic literature review and
meta-analysis on code similarity measurement and evaluation techniques to shed
light on the existing approaches and their characteristics in different
applications. We initially found over 10000 articles by querying four digital
libraries and ended up with 136 primary studies in the field. The studies were
classified according to their methodology, programming languages, datasets,
tools, and applications. A deep investigation reveals 80 software tools,
working with eight different techniques on five application domains. Nearly 49%
of the tools work on Java programs and 37% support C and C++, while there is no
support for many programming languages. A noteworthy point was the existence of
12 datasets related to source code similarity measurement and duplicate codes,
of which only eight datasets were publicly accessible. The lack of reliable
datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm
languages are the main challenges in the field. Emerging applications of code
similarity measurement concentrate on the development phase in addition to the
maintenance.Comment: 49 pages, 10 figures, 6 table
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection
Fabric defect segmentation is integral to textile quality control. Despite
this, the scarcity of high-quality annotated data and the diversity of fabric
defects present significant challenges to the application of deep learning in
this field. These factors limit the generalization and segmentation performance
of existing models, impeding their ability to handle the complexity of diverse
fabric types and defects. To overcome these obstacles, this study introduces an
innovative method to infuse specialized knowledge of fabric defects into the
Segment Anything Model (SAM), a large-scale visual model. By introducing and
training a unique set of fabric defect-related parameters, this approach
seamlessly integrates domain-specific knowledge into SAM without the need for
extensive modifications to the pre-existing model parameters. The revamped SAM
model leverages generalized image understanding learned from large-scale
natural image datasets while incorporating fabric defect-specific knowledge,
ensuring its proficiency in fabric defect segmentation tasks. The experimental
results reveal a significant improvement in the model's segmentation
performance, attributable to this novel amalgamation of generic and
fabric-specific knowledge. When benchmarking against popular existing
segmentation models across three datasets, our proposed model demonstrates a
substantial leap in performance. Its impressive results in cross-dataset
comparisons and few-shot learning experiments further demonstrate its potential
for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table
Predicting extreme events in a data-driven model of turbulent shear flow using an atlas of charts
Dynamical systems with extreme events are difficult to capture with
data-driven modeling, due to the relative scarcity of data within extreme
events compared to the typical dynamics of the system, and the strong
dependence of the long-time occurrence of extreme events on short-time
conditions.A recently developed technique [Floryan, D. & Graham, M. D.
Data-driven discovery of intrinsic dynamics. Nat Mach Intell ,
1113-1120 (2022)], here denoted as , or CANDyMan, overcomes these difficulties
by decomposing the time series into separate charts based on data similarity,
learning dynamical models on each chart via individual time-mapping neural
networks, then stitching the charts together to create a single atlas to yield
a global dynamical model. We apply CANDyMan to a nine-dimensional model of
turbulent shear flow between infinite parallel free-slip walls under a
sinusoidal body force [Moehlis, J., Faisst, H. & Eckhardt, B. A low-dimensional
model for turbulent shear flows. New J Phys , 56 (2004)], which
undergoes extreme events in the form of intermittent quasi-laminarization and
long-time full laminarization. We demonstrate that the CANDyMan method allows
the trained dynamical models to more accurately forecast the evolution of the
model coefficients, reducing the error in the predictions as the model evolves
forward in time. The technique exhibits more accurate predictions of extreme
events, capturing the frequency of quasi-laminarization events and predicting
the time until full laminarization more accurately than a single neural
network.Comment: 9 pages, 7 figure
Focused Decoding Enables 3D Anatomical Detection by Transformers
Detection Transformers represent end-to-end object detection approaches based
on a Transformer encoder-decoder architecture, exploiting the attention
mechanism for global relation modeling. Although Detection Transformers deliver
results on par with or even superior to their highly optimized CNN-based
counterparts operating on 2D natural images, their success is closely coupled
to access to a vast amount of training data. This, however, restricts the
feasibility of employing Detection Transformers in the medical domain, as
access to annotated data is typically limited. To tackle this issue and
facilitate the advent of medical Detection Transformers, we propose a novel
Detection Transformer for 3D anatomical structure detection, dubbed Focused
Decoder. Focused Decoder leverages information from an anatomical region atlas
to simultaneously deploy query anchors and restrict the cross-attention's field
of view to regions of interest, which allows for a precise focus on relevant
anatomical structures. We evaluate our proposed approach on two publicly
available CT datasets and demonstrate that Focused Decoder not only provides
strong detection results and thus alleviates the need for a vast amount of
annotated data but also exhibits exceptional and highly intuitive
explainability of results via attention weights. Our code is available at
https://github.com/bwittmann/transoar.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2023:00
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures
In this work we present a non-parametric online market regime detection
method for multidimensional data structures using a path-wise two-sample test
derived from a maximum mean discrepancy-based similarity metric on path space
that uses rough path signatures as a feature map. The latter similarity metric
has been developed and applied as a discriminator in recent generative models
for small data environments, and has been optimised here to the setting where
the size of new incoming data is particularly small, for faster reactivity.
On the same principles, we also present a path-wise method for regime
clustering which extends our previous work. The presented regime clustering
techniques were designed as ex-ante market analysis tools that can identify
periods of approximatively similar market activity, but the new results also
apply to path-wise, high dimensional-, and to non-Markovian settings as well as
to data structures that exhibit autocorrelation.
We demonstrate our clustering tools on easily verifiable synthetic datasets
of increasing complexity, and also show how the outlined regime detection
techniques can be used as fast on-line automatic regime change detectors or as
outlier detection tools, including a fully automated pipeline. Finally, we
apply the fine-tuned algorithms to real-world historical data including
high-dimensional baskets of equities and the recent price evolution of crypto
assets, and we show that our methodology swiftly and accurately indicated
historical periods of market turmoil.Comment: 65 pages, 52 figure
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