213 research outputs found
Multi-Domain Learning From Insufficient Annotations
Multi-domain learning (MDL) refers to simultaneously constructing a model or
a set of models on datasets collected from different domains. Conventional
approaches emphasize domain-shared information extraction and domain-private
information preservation, following the shared-private framework (SP models),
which offers significant advantages over single-domain learning. However, the
limited availability of annotated data in each domain considerably hinders the
effectiveness of conventional supervised MDL approaches in real-world
applications. In this paper, we introduce a novel method called multi-domain
contrastive learning (MDCL) to alleviate the impact of insufficient annotations
by capturing both semantic and structural information from both labeled and
unlabeled data.Specifically, MDCL comprises two modules: inter-domain semantic
alignment and intra-domain contrast. The former aims to align annotated
instances of the same semantic category from distinct domains within a shared
hidden space, while the latter focuses on learning a cluster structure of
unlabeled instances in a private hidden space for each domain. MDCL is readily
compatible with many SP models, requiring no additional model parameters and
allowing for end-to-end training. Experimental results across five textual and
image multi-domain datasets demonstrate that MDCL brings noticeable improvement
over various SP models.Furthermore, MDCL can further be employed in
multi-domain active learning (MDAL) to achieve a superior initialization,
eventually leading to better overall performance.Comment: This paper has been accepted to ECAI-2
On symbology and differential equations of Feynman integrals from Schubert analysis
We take the first step in generalizing the so-called "Schubert analysis",
originally proposed in twistor space for four-dimensional kinematics, to the
study of symbol letters and more detailed information on canonical differential
equations for Feynman integral families in general dimensions with general
masses. The basic idea is to work in embedding space and compute possible
cross-ratios built from (Lorentz products of) maximal cut solutions for all
integrals in the family. We demonstrate the power of the method using the most
general one-loop integrals, as well as various two-loop planar integral
families (such as sunrise, double-triangle and double-box) in general
dimensions. Not only can we obtain all symbol letters as cross-ratios from
maximal-cut solutions, but we also reproduce entries in the canonical
differential equations satisfied by a basis of dlog integrals.Comment: 51 pages, many figure
When the Differences in Frequency Domain are Compensated: Understanding and Defeating Modulated Replay Attacks on Automatic Speech Recognition
Automatic speech recognition (ASR) systems have been widely deployed in
modern smart devices to provide convenient and diverse voice-controlled
services. Since ASR systems are vulnerable to audio replay attacks that can
spoof and mislead ASR systems, a number of defense systems have been proposed
to identify replayed audio signals based on the speakers' unique acoustic
features in the frequency domain. In this paper, we uncover a new type of
replay attack called modulated replay attack, which can bypass the existing
frequency domain based defense systems. The basic idea is to compensate for the
frequency distortion of a given electronic speaker using an inverse filter that
is customized to the speaker's transform characteristics. Our experiments on
real smart devices confirm the modulated replay attacks can successfully escape
the existing detection mechanisms that rely on identifying suspicious features
in the frequency domain. To defeat modulated replay attacks, we design and
implement a countermeasure named DualGuard. We discover and formally prove that
no matter how the replay audio signals could be modulated, the replay attacks
will either leave ringing artifacts in the time domain or cause spectrum
distortion in the frequency domain. Therefore, by jointly checking suspicious
features in both frequency and time domains, DualGuard can successfully detect
various replay attacks including the modulated replay attacks. We implement a
prototype of DualGuard on a popular voice interactive platform, ReSpeaker Core
v2. The experimental results show DualGuard can achieve 98% accuracy on
detecting modulated replay attacks.Comment: 17 pages, 24 figures, In Proceedings of the 2020 ACM SIGSAC
Conference on Computer and Communications Security (CCS' 20
Adaptive Spot-Guided Transformer for Consistent Local Feature Matching
Local feature matching aims at finding correspondences between a pair of
images. Although current detector-free methods leverage Transformer
architecture to obtain an impressive performance, few works consider
maintaining local consistency. Meanwhile, most methods struggle with large
scale variations. To deal with the above issues, we propose Adaptive
Spot-Guided Transformer (ASTR) for local feature matching, which jointly models
the local consistency and scale variations in a unified coarse-to-fine
architecture. The proposed ASTR enjoys several merits. First, we design a
spot-guided aggregation module to avoid interfering with irrelevant areas
during feature aggregation. Second, we design an adaptive scaling module to
adjust the size of grids according to the calculated depth information at fine
stage. Extensive experimental results on five standard benchmarks demonstrate
that our ASTR performs favorably against state-of-the-art methods. Our code
will be released on https://astr2023.github.io.Comment: Accepted to CVPR 2023. Project page: https://astr2023.github.io
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds
Current 3D single object tracking methods are typically based on VoteNet, a
3D region proposal network. Despite the success, using a single seed point
feature as the cue for offset learning in VoteNet prevents high-quality 3D
proposals from being generated. Moreover, seed points with different importance
are treated equally in the voting process, aggravating this defect. To address
these issues, we propose a novel global-local transformer voting scheme to
provide more informative cues and guide the model pay more attention on
potential seed points, promoting the generation of high-quality 3D proposals.
Technically, a global-local transformer (GLT) module is employed to integrate
object- and patch-aware prior into seed point features to effectively form
strong feature representation for geometric positions of the seed points, thus
providing more robust and accurate cues for offset learning. Subsequently, a
simple yet effective training strategy is designed to train the GLT module. We
develop an importance prediction branch to learn the potential importance of
the seed points and treat the output weights vector as a training constraint
term. By incorporating the above components together, we exhibit a superior
tracking method GLT-T. Extensive experiments on challenging KITTI and NuScenes
benchmarks demonstrate that GLT-T achieves state-of-the-art performance in the
3D single object tracking task. Besides, further ablation studies show the
advantages of the proposed global-local transformer voting scheme over the
original VoteNet. Code and models will be available at
https://github.com/haooozi/GLT-T.Comment: Accepted to AAAI 2023. The source code and models will be available
at https://github.com/haooozi/GLT-
Salient Object Detection in RGB-D Videos
Given the widespread adoption of depth-sensing acquisition devices, RGB-D
videos and related data/media have gained considerable traction in various
aspects of daily life. Consequently, conducting salient object detection (SOD)
in RGB-D videos presents a highly promising and evolving avenue. Despite the
potential of this area, SOD in RGB-D videos remains somewhat under-explored,
with RGB-D SOD and video SOD (VSOD) traditionally studied in isolation. To
explore this emerging field, this paper makes two primary contributions: the
dataset and the model. On one front, we construct the RDVS dataset, a new RGB-D
VSOD dataset with realistic depth and characterized by its diversity of scenes
and rigorous frame-by-frame annotations. We validate the dataset through
comprehensive attribute and object-oriented analyses, and provide training and
testing splits. Moreover, we introduce DCTNet+, a three-stream network tailored
for RGB-D VSOD, with an emphasis on RGB modality and treats depth and optical
flow as auxiliary modalities. In pursuit of effective feature enhancement,
refinement, and fusion for precise final prediction, we propose two modules:
the multi-modal attention module (MAM) and the refinement fusion module (RFM).
To enhance interaction and fusion within RFM, we design a universal interaction
module (UIM) and then integrate holistic multi-modal attentive paths (HMAPs)
for refining multi-modal low-level features before reaching RFMs. Comprehensive
experiments, conducted on pseudo RGB-D video datasets alongside our RDVS,
highlight the superiority of DCTNet+ over 17 VSOD models and 14 RGB-D SOD
models. Ablation experiments were performed on both pseudo and realistic RGB-D
video datasets to demonstrate the advantages of individual modules as well as
the necessity of introducing realistic depth. Our code together with RDVS
dataset will be available at https://github.com/kerenfu/RDVS/
Ontology-based systematic representation and analysis of traditional Chinese drugs against rheumatism
Abstract
Background
Rheumatism represents any disease condition marked with inflammation and pain in the joints, muscles, or connective tissues. Many traditional Chinese drugs have been used for a long time to treat rheumatism. However, a comprehensive information source for these drugs is still missing, and their anti-rheumatism mechanisms remain unclear. An ontology for anti-rheumatism traditional Chinese drugs would strongly support the representation, analysis, and understanding of these drugs.
Results
In this study, we first systematically collected reported information about 26 traditional Chinese decoction pieces drugs, including their chemical ingredients and adverse events (AEs). By mostly reusing terms from existing ontologies (e.g., TCMDPO for traditional Chinese medicines, NCBITaxon for taxonomy, ChEBI for chemical elements, and OAE for adverse events) and making semantic axioms linking different entities, we developed the Ontology of Chinese Medicine for Rheumatism (OCMR) that includes over 3000 class terms. Our OCMR analysis found that these 26 traditional Chinese decoction pieces are made from anatomic entities (e.g., root and stem) from 3 Bilateria animals and 23 Mesangiospermae plants. Anti-inflammatory and antineoplastic roles are important for anti-rheumatism drugs. Using the total of 555 unique ChEBI chemical entities identified from these drugs, our ChEBI-based classification analysis identified 18 anti-inflammatory, 33 antineoplastic chemicals, and 9 chemicals (including 3 diterpenoids and 3 triterpenoids) having both anti-inflammatory and antineoplastic roles. Furthermore, our study detected 22 diterpenoids and 23 triterpenoids, including 16 pentacyclic triterpenoids that are likely bioactive against rheumatism. Six drugs were found to be associated with 184 unique AEs, including three AEs (i.e., dizziness, nausea and vomiting, and anorexia) each associated with 5 drugs. Several chemical entities are classified as neurotoxins (e.g., diethyl phthalate) and allergens (e.g., eugenol), which may explain the formation of some TCD AEs. The OCMR could be efficiently queried for useful information using SPARQL scripts.
Conclusions
The OCMR ontology was developed to systematically represent 26 traditional anti-rheumatism Chinese drugs and their related information. The OCMR analysis identified possible anti-rheumatism and AE mechanisms of these drugs. Our novel ontology-based approach can also be applied to systematic representation and analysis of other traditional Chinese drugs.https://deepblue.lib.umich.edu/bitstream/2027.42/140395/1/12918_2017_Article_510.pd
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