2,227 research outputs found
Silicon nitride metalenses for unpolarized high-NA visible imaging
As one of nanoscale planar structures, metasurface has shown excellent
superiorities on manipulating light intensity, phase and/or polarization with
specially designed nanoposts pattern. It allows to miniature a bulky optical
lens into the chip-size metalens with wavelength-order thickness, playing an
unprecedented role in visible imaging systems (e.g. ultrawide-angle lens and
telephoto). However, a CMOS-compatible metalens has yet to be achieved in the
visible region due to the limitation on material properties such as
transmission and compatibility. Here, we experimentally demonstrate a divergent
metalens based on silicon nitride platform with large numerical aperture
(NA~0.98) and high transmission (~0.8) for unpolarized visible light,
fabricated by a 695-nm-thick hexagonal silicon nitride array with a minimum
space of 42 nm between adjacent nanoposts. Nearly diffraction-limit virtual
focus spots are achieved within the visible region. Such metalens enables to
shrink objects into a micro-scale size field of view as small as a single-mode
fiber core. Furthermore, a macroscopic metalens with 1-cm-diameter is also
realized including over half billion nanoposts, showing a potential application
of wide viewing-angle functionality. Thanks to the high-transmission and
CMOS-compatibility of silicon nitride, our findings may open a new door for the
miniaturization of optical lenses in the fields of optical fibers,
microendoscopes, smart phones, aerial cameras, beam shaping, and other
integrated on-chip devices.Comment: 16 pages, 7 figure
Practical Distributed Control for VTOL UAVs to Pass a Tunnel
Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to
amateur and commercial users alike. An air traffic management (ATM) system is
needed to help ensure that this newest entrant into the skies does not collide
with others. In an ATM, airspace can be composed of airways, intersections and
nodes. In this paper, for simplicity, distributed coordinating the motions of
Vertical TakeOff and Landing (VTOL) UAVs to pass an airway is focused. This is
formulated as a tunnel passing problem, which includes passing a tunnel,
inter-agent collision avoidance and keeping within the tunnel. Lyapunov-like
functions are designed elaborately, and formal analysis based on invariant set
theorem is made to show that all UAVs can pass the tunnel without getting
trapped, avoid collision and keep within the tunnel. What is more, by the
proposed distributed control, a VTOL UAV can keep away from another VTOL UAV or
return back to the tunnel as soon as possible, once it enters into the safety
area of another or has a collision with the tunnel during it is passing the
tunnel. Simulations and experiments are carried out to show the effectiveness
of the proposed method and the comparison with other methods
Different Peripheral Tissue Injury Induces Differential Phenotypic Changes of Spinal Activated Microglia
The purpose of this study is to investigate the possible different cellular marker expression associated with spinal cord microglial activation in different pain models. Immunohistochemistry and western blotting analysis of CD45, CD68, and MHC class I antigen as well as CD11b and Iba-1 in the spinal cord were quantitatively compared among widely used three pain animal models, complete Freund's adjuvant (CFA) injection, formalin injection, and chronic constriction injury (CCI) models. The results showed that significant upregulated expressions of CD45 and MHC class I antigen in spinal microglia as well as morphological changes with increased staining with CD11b and Iba-1 were seen in CCI and formalin models and not found in CFA-induced inflammatory pain model. CD68 expression was only detected in CCI model. Our findings suggested that different peripheral tissue injuries produced differential phenotypic changes associated with spinal microglial activation; peripheral nerve injury might induce spinal microglia to acquire these immunomolecular phenotypic changes
SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
Recently, Segmenting Anything has taken an important step towards general
artificial intelligence. At the same time, its reliability and fairness have
also attracted great attention, especially in the field of health care. In this
study, we propose multi-box prompts triggered uncertainty estimation for SAM
cues to demonstrate the reliability of segmented lesions or tissues. We
estimate the distribution of SAM predictions via Monte Carlo with prior
distribution parameters, which employs different prompts as formulation of
test-time augmentation. Our experimental results found that multi-box prompts
augmentation improve the SAM performance, and endowed each pixel with
uncertainty. This provides the first paradigm for a reliable SAM
Metrical analysis of disc-condyle relation with different splint treatment positions in patients with TMJ disc displacement
Objective: To evaluate the effect of bite positions characterizing different splint treatments (anterior repositioning and stabilization splints) on the disc-condyle relation in patients with TMJ disc displacement with reduction (DDwR), using magnetic resonance imaging (MRI). Material and Methods: 37 patients, with a mean age of 18.8±4.3 years (7 male and 30 females) and diagnosed with DDwR based on the RDC/TMD, were recruited. MRI metrical analysis of the spatial changes of the disc/condyle, as well as their relationships, was done in three positions: maximum intercuspation (Position 1), anterior repositioning splint position (Position 2), and stabilization splint position (Position 3). Disc/condyle coordinate measurements and disc condyle angles were determined and compared. Results: In Position 1, the average disc-condyle angle was 53.4° in the 60 joints with DDwR, while it was −13.3° with Position 2 and 30.1° with Position 3. The frequency of successful "disc recapture" with Position 2 was significantly higher (58/60, 96.7%) than Position 3 (20/60, 33.3%). In Positions 2 and 3, the condyle moved forward and downward while the disc moved backward. The movements were, however, more remarkable with Position 2. Conclusions: Anterior repositioning of the mandible improves the spatial relationship between the disc and condyle in patients with DDwR. In addition to anterior and inferior movement of the condyle, transitory posterior movement of the disc also occurred
ent-Kaurane diterpenoids from the plant Wedelia trilobata
Four new ent-kaurane diterpenoids, namely, 3α-tigloyloxypterokaurene L(3) (1), ent-17-hydroxy-kaura-9(11),15-dien-19-oic acid (2), and wedelobatins A (3) and B (4), together with 11 known ent-kaurane diterpenoids (5-15), were isolated from the ethanol extract of Wedelia trilobata. All the structures of 1–15 were elucidated on the basis of spectroscopic studies. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at 10.1007/s13659-013-0029-4 and is accessible for authorized users
Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation
Classification and segmentation are crucial in medical image analysis as they
enable accurate diagnosis and disease monitoring. However, current methods
often prioritize the mutual learning features and shared model parameters,
while neglecting the reliability of features and performances. In this paper,
we propose a novel Uncertainty-informed Mutual Learning (UML) framework for
reliable and interpretable medical image analysis. Our UML introduces
reliability to joint classification and segmentation tasks, leveraging mutual
learning with uncertainty to improve performance. To achieve this, we first use
evidential deep learning to provide image-level and pixel-wise confidences.
Then, an Uncertainty Navigator Decoder is constructed for better using mutual
features and generating segmentation results. Besides, an Uncertainty
Instructor is proposed to screen reliable masks for classification. Overall,
UML could produce confidence estimation in features and performance for each
link (classification and segmentation). The experiments on the public datasets
demonstrate that our UML outperforms existing methods in terms of both accuracy
and robustness. Our UML has the potential to explore the development of more
reliable and explainable medical image analysis models. We will release the
codes for reproduction after acceptance.Comment: 13 page
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following
End-to-end Transformers have demonstrated an impressive success rate for
Embodied Instruction Following when the environment has been seen in training.
However, they tend to struggle when deployed in an unseen environment. This
lack of generalizability is due to the agent's insensitivity to subtle changes
in natural language instructions. To mitigate this issue, we propose explicitly
aligning the agent's hidden states with the instructions via contrastive
learning. Nevertheless, the semantic gap between high-level language
instructions and the agent's low-level action space remains an obstacle.
Therefore, we further introduce a novel concept of meta-actions to bridge the
gap. Meta-actions are ubiquitous action patterns that can be parsed from the
original action sequence. These patterns represent higher-level semantics that
are intuitively aligned closer to the instructions. When meta-actions are
applied as additional training signals, the agent generalizes better to unseen
environments. Compared to a strong multi-modal Transformer baseline, we achieve
a significant 4.5% absolute gain in success rate in unseen environments of
ALFRED Embodied Instruction Following. Additional analysis shows that the
contrastive objective and meta-actions are complementary in achieving the best
results, and the resulting agent better aligns its states with corresponding
instructions, making it more suitable for real-world embodied agents. The code
is available at: https://github.com/joeyy5588/LACMA.Comment: EMNLP 202
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