174 research outputs found
Sparse Complementary Pairs with Additional Aperiodic ZCZ Property
This paper presents a novel class of complex-valued sparse complementary
pairs (SCPs), each consisting of a number of zero values and with additional
zero-correlation zone (ZCZ) property for the aperiodic autocorrelations and
crosscorrelations of the two constituent sequences. Direct constructions of
SCPs and their mutually-orthogonal mates based on restricted generalized
Boolean functions are proposed. It is shown that such SCPs exist with arbitrary
lengths and controllable sparsity levels, making them a disruptive sequence
candidate for modern low-complexity, low-latency, and low-storage signal
processing applications
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Oscillation-specific nodal alterations in early to middle stages Parkinsons disease.
Background: Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinsons disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods: Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results: Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions: Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages
OV-VG: A Benchmark for Open-Vocabulary Visual Grounding
Open-vocabulary learning has emerged as a cutting-edge research area,
particularly in light of the widespread adoption of vision-based foundational
models. Its primary objective is to comprehend novel concepts that are not
encompassed within a predefined vocabulary. One key facet of this endeavor is
Visual Grounding, which entails locating a specific region within an image
based on a corresponding language description. While current foundational
models excel at various visual language tasks, there's a noticeable absence of
models specifically tailored for open-vocabulary visual grounding. This
research endeavor introduces novel and challenging OV tasks, namely
Open-Vocabulary Visual Grounding and Open-Vocabulary Phrase Localization. The
overarching aim is to establish connections between language descriptions and
the localization of novel objects. To facilitate this, we have curated a
comprehensive annotated benchmark, encompassing 7,272 OV-VG images and 1,000
OV-PL images. In our pursuit of addressing these challenges, we delved into
various baseline methodologies rooted in existing open-vocabulary object
detection, VG, and phrase localization frameworks. Surprisingly, we discovered
that state-of-the-art methods often falter in diverse scenarios. Consequently,
we developed a novel framework that integrates two critical components:
Text-Image Query Selection and Language-Guided Feature Attention. These modules
are designed to bolster the recognition of novel categories and enhance the
alignment between visual and linguistic information. Extensive experiments
demonstrate the efficacy of our proposed framework, which consistently attains
SOTA performance across the OV-VG task. Additionally, ablation studies provide
further evidence of the effectiveness of our innovative models. Codes and
datasets will be made publicly available at https://github.com/cv516Buaa/OV-VG
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision
Visual Grounding (VG) aims at localizing target objects from an image based
on given expressions and has made significant progress with the development of
detection and vision transformer. However, existing VG methods tend to generate
false-alarm objects when presented with inaccurate or irrelevant descriptions,
which commonly occur in practical applications. Moreover, existing methods fail
to capture fine-grained features, accurate localization, and sufficient context
comprehension from the whole image and textual descriptions. To address both
issues, we propose an Iterative Robust Visual Grounding (IR-VG) framework with
Masked Reference based Centerpoint Supervision (MRCS). The framework introduces
iterative multi-level vision-language fusion (IMVF) for better alignment. We
use MRCS to ahieve more accurate localization with point-wised feature
supervision. Then, to improve the robustness of VG, we also present a
multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of
false-alarm objects when presented with inaccurate expressions. The proposed
framework is evaluated on five regular VG datasets and two newly constructed
robust VG datasets. Extensive experiments demonstrate that IR-VG achieves new
state-of-the-art (SOTA) results, with improvements of 25\% and 10\% compared to
existing SOTA approaches on the two newly proposed robust VG datasets.
Moreover, the proposed framework is also verified effective on five regular VG
datasets. Codes and models will be publicly at
https://github.com/cv516Buaa/IR-VG
Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification
In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses
Chiral Antioxidant-based Gold Nanoclusters Reprogram DNA Epigenetic Patterns
Epigenetic modifications sit ‘on top of’ the genome and influence DNA transcription, which can force a significant impact on cellular behavior and phenotype and, consequently human development and disease. Conventional methods for evaluating epigenetic modifications have inherent limitations and, hence, new methods based on nanoscale devices are needed. Here, we found that antioxidant (glutathione) chiral gold nanoclusters induce a decrease of 5-hydroxymethylcytosine (5hmC), which is an important epigenetic marker that associates with gene transcription regulation. This epigenetic change was triggered partially through ROS activation and oxidation generated by the treatment with glutathione chiral gold nanoclusters, which may inhibit the activity of TET proteins catalyzing the conversion of 5-methylcytosine (5mC) to 5hmC. In addition, these chiral gold nanoclusters can downregulate TET1 and TET2 mRNA expression. Alteration of TET-5hmC signaling will then affect several downstream targets and be involved in many aspects of cell behavior. We demonstrate for the first time that antioxidant-based chiral gold nanomaterials have a direct effect on epigenetic process of TET-5hmC pathways and reveal critical DNA demethylation patterns
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