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