250 research outputs found
Deciphering Charging Status, Absolute Quantum Efficiency, and Absorption Cross Section of MultiCarrier States in Single Colloidal Quantum Dot
Upon photo- or electrical-excitation, colloidal quantum dots (QDs) are often
found in multi-carrier states due to multi-photon absorption and photo-charging
of the QDs. While many of these multi-carrier states are observed in single-dot
spectroscopy, their properties are not well studied due to random
charging/discharging, emission intensity intermittency, and uncontrolled
surface defects of single QD. Here we report in-situ deciphering the charging
status, and precisely assessing the absorption cross section, and determining
the absolute emission quantum yield of mono-exciton and biexciton states for
neutral, positively-charged, and negatively-charged single core/shell CdSe/CdS
QD. We uncover very different photon statistics of the three charge states in
single QD and unambiguously identify their charge sign together with the
information of their photoluminescence decay dynamics. We then show their
distinct photoluminescence saturation behaviors and evaluated the absolute
values of absorption cross sections and quantum efficiencies of monoexcitons
and biexcitons. We demonstrate that addition of an extra hole or electron in a
QD changes not only its emission properties but also varies its absorption
cross section
Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
Implicit neural representation has opened up new avenues for dynamic scene
reconstruction and rendering. Nonetheless, state-of-the-art methods of dynamic
neural rendering rely heavily on these implicit representations, which
frequently struggle with accurately capturing the intricate details of objects
in the scene. Furthermore, implicit methods struggle to achieve real-time
rendering in general dynamic scenes, limiting their use in a wide range of
tasks. To address the issues, we propose a deformable 3D Gaussians Splatting
method that reconstructs scenes using explicit 3D Gaussians and learns
Gaussians in canonical space with a deformation field to model monocular
dynamic scenes. We also introduced a smoothing training mechanism with no extra
overhead to mitigate the impact of inaccurate poses in real datasets on the
smoothness of time interpolation tasks. Through differential gaussian
rasterization, the deformable 3D Gaussians not only achieve higher rendering
quality but also real-time rendering speed. Experiments show that our method
outperforms existing methods significantly in terms of both rendering quality
and speed, making it well-suited for tasks such as novel-view synthesis, time
synthesis, and real-time rendering
SHAPFUZZ: Efficient Fuzzing via Shapley-Guided Byte Selection
Mutation-based fuzzing is popular and effective in discovering unseen code
and exposing bugs. However, only a few studies have concentrated on quantifying
the importance of input bytes, which refers to the degree to which a byte
contributes to the discovery of new code. They often focus on obtaining the
relationship between input bytes and path constraints, ignoring the fact that
not all constraint-related bytes can discover new code. In this paper, we
conduct Shapely analysis to understand the effect of byte positions on fuzzing
performance, and find that some byte positions contribute more than others and
this property often holds across seeds. Based on this observation, we propose a
novel fuzzing solution, ShapFuzz, to guide byte selection and mutation.
Specifically, ShapFuzz updates Shapley values (importance) of bytes when each
input is tested during fuzzing with a low overhead, and utilizes contextual
multi-armed bandit to trade off between mutating high Shapley value bytes and
low-frequently chosen bytes. We implement a prototype of this solution based on
AFL++, i.e., ShapFuzz. We evaluate ShapFuzz against ten state-of-the-art
fuzzers, including five byte schedule-reinforced fuzzers and five commonly used
fuzzers. Compared with byte schedule-reinforced fuzzers, ShapFuzz discovers
more edges and exposes more bugs than the best baseline on three different sets
of initial seeds. Compared with commonly used fuzzers, ShapFuzz exposes 20 more
bugs than the best comparison fuzzer, and discovers 6 more CVEs than the best
baseline on MAGMA. Furthermore, ShapFuzz discovers 11 new bugs on the latest
versions of programs, and 3 of them are confirmed by vendors
Characterization of photosystem II in transgenic tobacco plants with decreased iron superoxide dismutase
AbstractIron superoxide dismutases (FeSODs) play an important role in preventing the oxidative damage associated with photosynthesis. To investigate the mechanisms of FeSOD in protection against photooxidative stress, we obtained transgenic tobacco (Nicotiana tabacum) plants with severely decreased FeSOD by using a gene encoding tobacco chloroplastic FeSOD for the RNAi construct. Transgenic plants were highly sensitive to photooxidative stress and accumulated increased levels of O2•− under normal light conditions. Spectroscopic analysis and electron transport measurements showed that PSII activity was significantly reduced in transgenic plants. Flash-induced fluorescence relaxation and thermoluminescence measurements revealed that there was a slow electron transfer between QA and QB and decreased redox potential of QB in transgenic plants, whereas the donor side function of PSII was not affected. Immunoblot and blue native gel analyses showed that PSII protein accumulation was also decreased in transgenic plants. PSII photodamage and D1 protein degradation under high light treatment was increased in transgenic plants, whereas the PSII repair was not affected, indicating that the stability of the PSII complex was decreased in transgenic plants. The results in this study suggest that FeSOD plays an important role in maintaining PSII function by stabilizing PSII complexes in tobacco plants
Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
In recent years, the task of weakly supervised audio-visual violence
detection has gained considerable attention. The goal of this task is to
identify violent segments within multimodal data based on video-level labels.
Despite advances in this field, traditional Euclidean neural networks, which
have been used in prior research, encounter difficulties in capturing highly
discriminative representations due to limitations of the feature space. To
overcome this, we propose HyperVD, a novel framework that learns snippet
embeddings in hyperbolic space to improve model discrimination. Our framework
comprises a detour fusion module for multimodal fusion, effectively alleviating
modality inconsistency between audio and visual signals. Additionally, we
contribute two branches of fully hyperbolic graph convolutional networks that
excavate feature similarities and temporal relationships among snippets in
hyperbolic space. By learning snippet representations in this space, the
framework effectively learns semantic discrepancies between violent and normal
events. Extensive experiments on the XD-Violence benchmark demonstrate that our
method outperforms state-of-the-art methods by a sizable margin.Comment: 8 pages, 5 figure
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