Vision transformers have shown great success due to their high model capabilities. However, remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision named Efficient ViT. We find that the speed existing transformer models commonly bounded memory inefficient operations, especially tensor reshaping and element-wise functions in MHSA. Therefore, design new building block with...

Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With advancements technologies such as chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed LLMs are becoming increasingly lengthy, even exceeding tens of thousands tokens. To accelerate model inference reduce cost, this paper presents LLMLingua, a coarse-to-fine prompt compression method that involves budget controller maintain semantic integrity under...

Deep learning-based models have achieved remarkable performance in video super-resolution (VSR) recent years, but most of these are less applicable to online applications. These methods solely consider the distortion quality and ignore crucial requirements for applications, e.g., low latency model complexity. In this paper, we focus on transmission which VSR algorithms required generate high-resolution sequences frame by real time. To address such challenges, propose an extremely low-latency...

The growing ubiquity of the metaverse among small and medium-sized enterprises (SMEs) offers a game-changing chance for creative business plans improved communication. This adoption reduces consumption physical resources promotes an environmentally responsible model in besides increasing operational efficiency. In accordance with Sustainable Development Goals (SDGs) UNDP, our study attempts to clarify how might improve sustainable performance SMEs. We developed comprehensive based on...

Abstract In the coming decades, it is urgent to find a way power future while keeping clean environment and strong economic growth. Thus, expected transition from using fossil fuels renewable energy. Materials based on cellulose are abundant, low cost, sustainable, possess high surface area, good thermal stability, biocompatibility, mechanical flexibility, inherit unique layered fiber structure cellulose, which can act as an ideal electrolyte matrix for sustainable energy density lithium‐ion...

In vision-language modeling, image token removal is an efficient augmentation technique to reduce the cost of encoding features. The CLIP-style models, however, have been found be negatively impacted by this technique. We hypothesize that removing a large portion tokens may inadvertently destroy semantic information associated given text description, resulting in misaligned paired data CLIP training. To address issue, we propose attentive approach, which retains small number strong...

Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usually with assumption that targeted time are well-formatted and complete. However, real-world (RTS) far away from this utopia, e.g., long sequences variable lengths informative missing raise intractable challenges for designing powerful algorithms. In paper, we propose a novel framework RTS – RTSGAN aforementioned...

Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of complex semantic structures and helps to capture long-distance dependency. However, in these is used only implicitly, instance, as additional features or training signals. Motivated by the fact that all event can be inferred from AMR, this work reformulates EAE link prediction problem on graphs. Since generic structure...

We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks. evaluate their zero-shot across synthetic images, real-world distributional shifts, specialized datasets like medical molecular imagery. Empirical results indicate that MLLMs struggle with beyond common training domains, limiting direct application without adaptation. To understand cause unreliable...

A multibit delta-sigma audio stereo analog-to-digital converter has been developed. It employs a fifth-order single-loop 17-level modulator with an input feedforward gain stage. second-order mismatch shaping (DEM) circuit is utilized to remove tones and nonlinearities caused by capacitor of the feedback digital-to-analog converter. The implementation DEM block introduces minimum latency into loop. Chopper stabilization applied first integrator eliminate 1/f noise. achieves 114-dB dynamic...

With the recent developments in robotics research, autonomous robot systems have grown rapidly to meet increasing demands. The Robot Operating System (ROS) is commonly used for building systems. Due high latency communication, ROS has been upgraded 2 using Data Distribution Service (DDS) as a transport system. In 2, an Executor concept was introduced support execution management and ensure real-time performance. However, it difficult improve performance of 2. A executor called...

Large Language Models (LLMs) have revolutionized Natural Processing (NLP) but demand massive GPU resources for training. Lowering the threshold LLMs training would encourage greater participation from researchers, benefiting both academia and society. While existing approaches focused on parameter-efficient fine-tuning, which tunes or adds a small number of parameters, few addressed challenge tuning full parameters with limited resources. In this work, we propose new optimizer, LOw-Memory...

Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity super-resolution is a promising potential solution. However, applying existing methods to online non-trivial. Existing codecs protocols (e.g., WebRTC) dynamically change quality both spatially temporally, which leads diverse dynamic degradations. Furthermore, strict requirement for latency that most are less applicable. As result, this paper focuses rarely exploited problem setting of...

Agricultural production frequently encounters challenges, including soil nitrogen pollution and imbalances resulting from improper irrigation fertilization practices. This study focuses on wolfberry farmland, analyzing the effects of four levels [full (W0, 75%−85% θ f ), mild water deficit (W1, 65%−75% moderate (W2, 55%−65% severe (W3, 45%−55% )] application [no (N0, 0 kg·ha −1 low (N1, 150 medium (N2, 300 high (N3, 450 uptake by plants, loss, plant-soil balance, use efficiency. The results...

This article aims to explore the potential categories of college students' sports behavior motivation and differences between different family social classes on categories.In total, 1,092 students were investigated in this study.This used "College Students' Sports Behavior Motivation Questionnaire" survey using whole group sampling method. The profile analysis method was applied classify types further analyze characteristics motivation.College can be divided into following four categories:...

Deep learning training on cloud platforms usually follows the tradition of separation storage and computing. The executes a compute cluster equipped with GPUs/TPUs while reading data from separate hosting service. To alleviate potential bottleneck, leverages its local as cache to reduce remote IO cluster. However, existing deep schedulers do not manage resources thus fail consider diverse caching effects across different jobs. This could degrade scheduling quality significantly.

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware learning solutions restricted pre-defined, static due overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces misalignment between GPU-friendly tile configuration for efficient and shape that minimizes coverage wastes (non-zero values in tensor).

Accurate oil and gas production forecasting is essential for optimizing field development operational efficiency. Steady-state capacity prediction models based on machine learning techniques, such as Linear Regression, Support Vector Machines, Random Forest, Extreme Gradient Boosting, effectively address complex nonlinear relationships through feature selection, hyperparameter tuning, hybrid integration, achieving high accuracy reliability. These maintain relative errors within acceptable...