260 research outputs found
Slot-VLM: SlowFast Slots for Video-Language Modeling
Video-Language Models (VLMs), powered by the advancements in Large Language
Models (LLMs), are charting new frontiers in video understanding. A pivotal
challenge is the development of an efficient method to encapsulate video
content into a set of representative tokens to align with LLMs. In this work,
we introduce Slot-VLM, a novel framework designed to generate semantically
decomposed video tokens, in terms of object-wise and event-wise visual
representations, to facilitate LLM inference. Particularly, we design a
SlowFast Slots module, i.e., SF-Slots, that adaptively aggregates the dense
video tokens from the CLIP vision encoder to a set of representative slots. In
order to take into account both the spatial object details and the varied
temporal dynamics, SF-Slots is built with a dual-branch structure. The
Slow-Slots branch focuses on extracting object-centric slots from features at
high spatial resolution but low (slow) frame sample rate, emphasizing detailed
object information. Conversely, Fast-Slots branch is engineered to learn
event-centric slots from high temporal sample rate but low spatial resolution
features. These complementary slots are combined to form the vision context,
serving as the input to the LLM for efficient question answering. Our
experimental results demonstrate the effectiveness of our Slot-VLM, which
achieves the state-of-the-art performance on video question-answering.Comment: 16 pages, 10 figure
Modeling and simulation of sintering process across scales
Sintering, as a thermal process at elevated temperature below the melting
point, is widely used to bond contacting particles into engineering products
such as ceramics, metals, polymers, and cemented carbides. Modelling and
simulation as important complement to experiments are essential for
understanding the sintering mechanisms and for the optimization and design of
sintering process. We share in this article a state-to-the-art review on the
major methods and models for the simulation of sintering process at various
length scales. It starts with molecular dynamics simulations deciphering
atomistic diffusion process, and then moves to microstructure-level approaches
such as discrete element method, Monte--Carlo method, and phase-field models,
which can reveal subtle mechanisms like grain coalescence, grain rotation,
densification, grain coarsening, etc. Phenomenological/empirical models on the
macroscopic scales for estimating densification, porosity and average grain
size are also summarized. The features, merits, drawbacks, and applicability of
these models and simulation technologies are expounded. In particular, the
latest progress on the modelling and simulation of selective and direct-metal
laser sintering based additive manufacturing is also reviewed. Finally, a
summary and concluding remarks on the challenges and opportunities are given
for the modelling and simulations of sintering process.Comment: 45 pages, 38 figure
Transcriptome Analysis and Ultrastructure Observation Reveal that Hawthorn Fruit Softening Is due to Cellulose/Hemicellulose Degradation
Softening, a common phenomenon in many fruits, is a well coordinated and genetically determined process. However, the process of flesh softening during ripening has rarely been described in hawthorn. In this study, we found that âRuanrou Shanlihong 3 Haoâ fruits became softer during ripening, whereas âQiu JinXingâ fruits remained hard. At late developmental stages, the firmness of âRuanrou Shanlihong 3 Haoâ fruits rapidly declined, and that of âQiu JinXingâ fruits remained essentially unchanged. According to transmission electron microscopy (TEM), the middle lamella of âQiu JinXingâ and âRuanrou Shanlihong 3 Haoâ fruit flesh was largely degraded as the fruits matured. Microfilaments in âQiu JinXingâ flesh were arranged close together and were deep in color, whereas those in âRuanrou Shanlihong 3 Haoâ fruit flesh were arranged loosely, partially degraded and light in color. RNA-Seq analysis yielded approximately 46.72 Gb of clean data and 72,837 unigenes. Galactose metabolism and pentose and glucuronate interconversions are involved in cell wall metabolism, play an important role in hawthorn texture. We identified 85 unigenes related to the cell wall between hard- and soft-fleshed hawthorn fruits. Based on data analysis and real-time PCR, we suggest that ÎČ-GAL and PE4 have important functions in early fruit softening. The genes Ffase, Gns, α-GAL, PE63, XTH and CWP, which are involved in cell wall degradation, are responsible for the different textures of hawthorn fruits. Thus, we hypothesize that the different textures of âQiu JinXingâ and âRuanrou Shanlihong 3 Haoâ fruits at maturity mainly result from cellulose/hemicelluloses degradation rather than from lamella degradation. Overall, we propose that different types of hydrolytic enzymes in cells interact to degrade the cell wall, resulting in ultramicroscopic Structure changes in the cell wall and, consequently, fruit softening. These results provide fundamental insight regarding the mechanisms by which hawthorn fruits acquire different textures and also lay a solid foundation for further research
Current status and future challenges for khulan (Equus hemionus) conservation in China
publishedVersio
From Clozing to Comprehending: Retrofitting Pre-trained Language Model to Pre-trained Machine Reader
We present Pre-trained Machine Reader (PMR), a novel method to retrofit
Pre-trained Language Models (PLMs) into Machine Reading Comprehension (MRC)
models without acquiring labeled data. PMR is capable of resolving the
discrepancy between model pre-training and downstream fine-tuning of existing
PLMs, and provides a unified solver for tackling various extraction tasks. To
achieve this, we construct a large volume of general-purpose and high-quality
MRC-style training data with the help of Wikipedia hyperlinks and design a Wiki
Anchor Extraction task to guide the MRC-style pre-training process. Although
conceptually simple, PMR is particularly effective in solving extraction tasks
including Extractive Question Answering and Named Entity Recognition, where it
shows tremendous improvements over previous approaches especially under
low-resource settings. Moreover, viewing sequence classification task as a
special case of extraction task in our MRC formulation, PMR is even capable to
extract high-quality rationales to explain the classification process,
providing more explainability of the predictions
An integrated control and protection scheme based on FBSM-MMC active current limiting strategy for DC distribution network
DC faults can easily lead to overcurrent in DC distribution networks; these faults pose serious threats to the safe operation of the system. The blocking of modular multilevel converters based on the full-bridge sub-modules (FBSM-MMC) is mostly utilized to cut off the fault current. However, the blocking causes short-term blackouts in the entire DC distribution network and there are presently no effective solutions to address this problem. In this study, an integrated control and protection scheme based on the FBSM-MMC active current limiting strategy is proposed. The project includes three stages: first, MMC active current limiting strategy is used to limit the output current of the converter to about 1.2 p.u. after the occurrence of the fault (Stage 1); next, faulty lines are identified based on the asynchronous zero-crossing features of the DC currents of the two ends of the line (Stage 2); then, a fault isolation scheme based on the cooperation of converters, DC circuit breakers, and high-speed switches is proposed to isolate the faulty line (Stage 3). The distribution network can restart quickly via control of the converters. Finally, the simulation of a four-terminal flexible DC distribution network in PSCAD/EMTDC demonstrates the effectiveness of the proposed integrated scheme
CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering
The task of zero-shot commonsense question answering evaluates models on
their capacity to reason about general scenarios beyond those presented in
specific datasets. Existing approaches for tackling this task leverage external
knowledge from CommonSense Knowledge Bases (CSKBs) by pretraining the model on
synthetic QA pairs constructed from CSKBs. In these approaches, negative
examples (distractors) are formulated by randomly sampling from CSKBs using
fairly primitive keyword constraints. However, two bottlenecks limit these
approaches: the inherent incompleteness of CSKBs limits the semantic coverage
of synthetic QA pairs, and the lack of human annotations makes the sampled
negative examples potentially uninformative and contradictory. To tackle these
limitations above, we propose Conceptualization-Augmented Reasoner (CAR), a
zero-shot commonsense question-answering framework that fully leverages the
power of conceptualization. Specifically, CAR abstracts a commonsense knowledge
triple to many higher-level instances, which increases the coverage of CSKB and
expands the ground-truth answer space, reducing the likelihood of selecting
false-negative distractors. Extensive experiments demonstrate that CAR more
robustly generalizes to answering questions about zero-shot commonsense
scenarios than existing methods, including large language models, such as
GPT3.5 and ChatGPT. Our codes, data, and model checkpoints are available at
https://github.com/HKUST-KnowComp/CAR
Circadian rhythm of plasminogen activator inhibitor-1 and cardiovascular complications in type 2 diabetes
Cardiovascular complications are a common death cause in type 2 diabetes patients, as they are often combined. Plasminogen-activator Inhibitor 1 (PAI-1) participates in the development and progression of cardiovascular complications in diabetes. Insulin resistance increases PAI-1 production, and high PAI-1 levels lead to an environment conducive to thrombosis and earlier and more severe vascular disease. Current evidence also suggests that PAI-1 has a rhythmic profile of circadian fluctuations and acrophase in the morning within a single day, which might explain the high morning incidence of cardiovascular events. Thus, PAI-1 is a possible drug target. Although several PAI-1 inhibitors have been developed, none have yet been allowed for clinical use. Research on rhythm has also led to the concept of âchronotherapyâ, a rhythm-based drug regimen expected to improve the treatment of cardiovascular complications in diabetic patients. Herein, we searched several databases and reviewed relevant articles to describe the circadian rhythm characteristics and endogenous molecular mechanisms of PAI-1, its relationship with insulin resistance, the causes of cardiovascular complications caused by PAI-1, and the current development of PAI-1 inhibitors. We also summarized the possibility of using the circadian rhythm of PAI-1 to treat cardiovascular complications in diabetic patients
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