80 research outputs found
InstMove: Instance Motion for Object-centric Video Segmentation
Despite significant efforts, cutting-edge video segmentation methods still
remain sensitive to occlusion and rapid movement, due to their reliance on the
appearance of objects in the form of object embeddings, which are vulnerable to
these disturbances. A common solution is to use optical flow to provide motion
information, but essentially it only considers pixel-level motion, which still
relies on appearance similarity and hence is often inaccurate under occlusion
and fast movement. In this work, we study the instance-level motion and present
InstMove, which stands for Instance Motion for Object-centric Video
Segmentation. In comparison to pixel-wise motion, InstMove mainly relies on
instance-level motion information that is free from image feature embeddings,
and features physical interpretations, making it more accurate and robust
toward occlusion and fast-moving objects. To better fit in with the video
segmentation tasks, InstMove uses instance masks to model the physical presence
of an object and learns the dynamic model through a memory network to predict
its position and shape in the next frame. With only a few lines of code,
InstMove can be integrated into current SOTA methods for three different video
segmentation tasks and boost their performance. Specifically, we improve the
previous arts by 1.5 AP on OVIS dataset, which features heavy occlusions, and
4.9 AP on YouTubeVIS-Long dataset, which mainly contains fast-moving objects.
These results suggest that instance-level motion is robust and accurate, and
hence serving as a powerful solution in complex scenarios for object-centric
video segmentation.Comment: Accepted to CVPR 202
WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning
Although deep learning has revolutionized music generation, existing methods
for structured melody generation follow an end-to-end left-to-right
note-by-note generative paradigm and treat each note equally. Here, we present
WuYun, a knowledge-enhanced deep learning architecture for improving the
structure of generated melodies, which first generates the most structurally
important notes to construct a melodic skeleton and subsequently infills it
with dynamically decorative notes into a full-fledged melody. Specifically, we
use music domain knowledge to extract melodic skeletons and employ sequence
learning to reconstruct them, which serve as additional knowledge to provide
auxiliary guidance for the melody generation process. We demonstrate that WuYun
can generate melodies with better long-term structure and musicality and
outperforms other state-of-the-art methods by 0.51 on average on all subjective
evaluation metrics. Our study provides a multidisciplinary lens to design
melodic hierarchical structures and bridge the gap between data-driven and
knowledge-based approaches for numerous music generation tasks
Gold nanoplasmonic particles in tunable porous silicon 3D scaffolds for ultra-low concentration detection by SERS
A composite material of plasmonic nanoparticles embedded in the scaffold of nanoporous Silicon offers unmatched capabilities to use it as a SERS substrate. The marriage of these components presents an exclusive combination of tightly focused amplification of Localised Surface Plasmon (LSP) fields inside the material with extremely high surface-to-volume ratio. This provides favourable conditions for a single molecule or extremely low concentration detection by SERS. In this work the advantage of the composite is demonstrated by SERS detection of Methylene Blue at the concentration as low a few picomolars. We systematically investigate the plasmonic properties of the material by imaging its morphology, establishing composition and their effect on the LSP resonance optical spectra
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
The rapid development of large language models has revolutionized code
intelligence in software development. However, the predominance of
closed-source models has restricted extensive research and development. To
address this, we introduce the DeepSeek-Coder series, a range of open-source
code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion
tokens. These models are pre-trained on a high-quality project-level code
corpus and employ a fill-in-the-blank task with a 16K window to enhance code
generation and infilling. Our extensive evaluations demonstrate that
DeepSeek-Coder not only achieves state-of-the-art performance among open-source
code models across multiple benchmarks but also surpasses existing
closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models
are under a permissive license that allows for both research and unrestricted
commercial use
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been
truly remarkable. However, the scaling law described in previous literature
presents varying conclusions, which casts a dark cloud over scaling LLMs. We
delve into the study of scaling laws and present our distinctive findings that
facilitate scaling of large scale models in two commonly used open-source
configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek
LLM, a project dedicated to advancing open-source language models with a
long-term perspective. To support the pre-training phase, we have developed a
dataset that currently consists of 2 trillion tokens and is continuously
expanding. We further conduct supervised fine-tuning (SFT) and Direct
Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the
creation of DeepSeek Chat models. Our evaluation results demonstrate that
DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in
the domains of code, mathematics, and reasoning. Furthermore, open-ended
evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance
compared to GPT-3.5
Albisporachelin, a New Hydroxamate Type Siderophore from the Deep Ocean Sediment-Derived Actinomycete Amycolatopsis albispora WP1T
Marine actinobacteria continue to be a rich source for the discovery of structurally diverse secondary metabolites. Here we present a new hydroxymate siderophore produced by Amycolatopsis albispora, a recently described species of this less explored actinomycete genus. Strain WP1T was isolated from sediments collected at −2945 m in the Indian Ocean. The new siderophore, designated albisporachelin, was isolated from iron depleted culture broths and the structure was established by 1D and 2D NMR and MS/MS experiments, and application of a modified Marfey’s method. Albisporachelin is composed of one N-methylated-formylated/hydroxylated l-ornithine (N-Me-fh-l-Orn), one l-serine (l-Ser), one formylated/hydroxylated l-ornithine (fh-l-Orn) and a cyclo-N-methylated-hydroxylated l-ornithine (cyclo-N-Me-h-l-Orn)
The Soft Coral <i>Sarcophyton trocheliophorum</i>: A Warehouse of Terpenoids with Structural and Pharmacological Diversity
The soft coral Sarcophyton trocheliophorum, which was frequently encountered on Indo-Pacific and Red Sea coral reefs, furnished a wealth of secondary metabolites. Notably, terpenoids dominated the chemical profile of this species. In this review, we summarized the discovery of 156 terpenoids from the soft coral S. trocheliophorum specimens in different geographical areas. The structures comprised 13 terpenoidal classes with various functionalities. We covered the era from the first report of S. trocheliophorum-derived metabolites in 1976 up to October 2022. The biological effects of these chemical compositions on a vast array of potential pharmacological activities such as protein tyrosine phosphatase 1B (PTP1B) inhibitory, neuroprotective, cytotoxic, anti-inflammatory, antibacterial, antivirus, and immunomodulatory activities were also presented. This review also revealed an immense demand to explore the terpene biosynthetic gene clusters of this species besides the chemo- and bio-investigations
Numerical Investigation on the Combustion and Emission Characteristics of Diesel Engine with Flexible Fuel Injection
As the main engineering power plant, diesel engines are irreplaceable in the future. However, the stringent emission regulations impose many tough requirements to their developments. Recently, flexible fuel injection strategy has been recognized as an effective technology in creating an advanced spray and mixture formation and improving combustion efficiency indirectly. However, the detailed combustion and emission behaviors under flexible fuel injection are still unknown. Therefore, this paper aims to investigate the combustion and emission characteristics under flexible fuel injection and explore an optimal injection strategy for high-efficiency combustion. A numerical simulation method is conducted by coupling the large-eddy simulation (LES) model and the SAGE combustion model. Then, the spray mixing, combustion flame propagation and emissions formation under various multiple-injection strategies are investigated. Results reveal that initial an ultrahigh injection pressure has a significant influence on the spray’s axial penetration while dwell time mainly affects the spray’s radial expansion. Under an initial ultrahigh injection pressure, the turbulence kinetic energy (TKE) becomes larger, and the vortex motions are stronger, contributing to a better spray turbulent mixing. Meanwhile, a snatchier flame structure with a favorable level of equivalence ratio and a homogeneous temperature distribution is obtained. In this way, the peak heat release rate (HRR) could increase by 46.7% with a 16.7% reduction in soot formation and a 31.4% reduction in NOx formation
Theoretical Model for the Stress–Strain Curve of CNT-Reinforced Concrete under Uniaxial Compression
The incorporation of carbon nanotubes (CNTs) can enhance the mechanical properties of concrete. The stress–strain curves of CNT-reinforced concrete under uniaxial compression are investigated through an experimental program with different CNT and steel fiber proportions considered. The test results demonstrate that CNTs can increase both peak stress and peak strain, and steel fibers can further enhance the effect of CNTs. Additionally, steel fibers can effectively enhance both the strength and ductility. Theoretical models for the peak strain, initial elastic modulus, toughness index and relative absorbed energy are established. A theoretical model for the uniaxial compressive constitutive relationship of CNT-reinforced concrete considering CNT and steel fiber content is developed. Finite element (FE) modelling is developed to simulate the axial compression behavior of CNT-reinforced concrete
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