Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs

The Efficiency Spectrum of Large Language Models: An Algorithmic Survey

The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape. However, the increasing computational and memory demands of these models present substantial challenges, hindering both academic research and practical applications. To address these issues, a wide array of methods, including both algorithmic and hardware solutions, have been developed to enhance the efficiency of LLMs. This survey delivers a comprehensive review of algorithmic advancements aimed at improving LLM efficiency. Unlike other surveys that typically focus on specific areas such as training or model compression, this paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs. Specifically, it covers various topics related to efficiency, including scaling laws, data utilization, architectural innovations, training and tuning strategies, and inference techniques. This paper aims to serve as a valuable resource for researchers and practitioners, laying the groundwork for future innovations in this critical research area. Our repository of relevant references is maintained at url{https://github.com/tding1/Efficient-LLM-Survey}

Pancreatic cancer mortality trends attributable to high fasting blood sugar over the period 1990–2019 and projections up to 2040

BackgroundPancreatic cancer (PC) is a prevalent malignancy within the digestive system, with diabetes recognized as one of its well-established risk factors.MethodsData on PC mortality attributed to high fasting blood sugar were retrieved from the Global Burden of Disease (GBD) study 2019 online database. To assess the temporal trends of PC burden attributable to high fasting plasma glucose (HFPG), estimated annual percentage changes (EAPCs) for age-standardized death rates (ASDRs) between 1990 and 2019 were determined using a generalized linear model. Furthermore, a Bayesian age-period-cohort (BAPC) model using the integrated nested Laplacian approximation algorithm was employed to project the disease burden over the next 20 years.ResultsGlobally, the crude death number of PC attributable to HFPG almost tripled (from 13,065.7 in 1990 to 48,358.5 in 2019) from 1990 to 2019, and the ASDR increased from 0.36/100,000 to 0.61/100,000 with an EAPC of 2.04 (95% CI 1.91–2.16). The population aged ≥70 years accounted for nearly 60% of total deaths in 2019 and experienced a more significant increase, with the death number increasing approximately fourfold and the ASDR increasing annually by 2.65%. In regions with different sociodemographic indexes (SDIs), the highest disease burden was observed in the high-SDI region, whereas more pronounced increasing trends in ASDR were observed in the low to middle-SDI, low-SDI, and middle-SDI regions. Additionally, a significantly negative association was found between EAPCs and ASDRs of PC attributable to HFPG from 1990 to 2019. Moreover, the BAPC model predicts that ASDR and age-standardized disability-adjusted life-years (DALYs) rate for PC attributed to HFPG was projected to increase obviously for men and women from 2019 to 2040.ConclusionsThe burden of PC attributed to HFPG has increased globally over the past three decades, with the elderly population and high-SDI regions carrying a relatively greater disease burden, but more adverse trends observed in low-SDI areas. Furthermore, the burden is projected to continue increasing over the next 20 years. Hence, more tailored prevention methodologies should be established to mitigate this increasing trend

Polydatin ameliorates early brain injury after subarachnoid hemorrhage through up-regulating SIRT1 to suppress endoplasmic reticulum stress

ObjectiveThis study aims to investigate the inhibitory effect of Polydatin (PD) on endoplasmic reticulum (ER) stress following subarachnoid hemorrhage (SAH) and to elucidate the underlying mechanisms.MethodsA standard intravascular puncture model was established to mimic SAH in mice. Neurological functions were assessed using neurological scoring, Grip test, and Morris water maze. Brain edema and Evans blue extravasation were measured to evaluate blood-brain barrier permeability. Western blot and quantitative real-time polymerase chain reaction (PCR) analyses were performed to examine protein and mRNA expressions related to ER stress. Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining was used to detect cell apoptosis, and transmission electron microscopy was used to observe the ultrastructure of the endoplasmic reticulum.ResultsThe results indicated that PD significantly reduced brain edema and Evans blue extravasation after SAH, improving neurological function. Compared to the SAH group, the expression levels of ER stress-related proteins including glucose-regulated protein 78 (GRP78), phosphorylated protein kinase R-like endoplasmic reticulum kinase (p-PERK), phosphorylated eukaryotic initiation factor 2α (p-eIF2α), activating transcription factor 4 (ATF4), and C/EBP homologous protein (CHOP), were significantly lower in the PD-treated group. Moreover, PD significantly enhances the protein expression of Sirtuin 1 (SIRT1). Validation with sh-SIRT1 confirmed the critical role of SIRT1 in ER stress, with PD’s inhibitory effect on ER stress being dependent on SIRT1 expression. Additionally, PD attenuated ER stress-mediated neuronal apoptosis and SAH-induced ferroptosis through upregulation of SIRT1.ConclusionPD alleviates ER stress following SAH by upregulating SIRT1 expression, thereby mitigating early brain injury. The protective effects of PD are mediated through SIRT1, which inhibits ER stress and reduces neuronal apoptosis and ferroptosis

High-efficiency 100-W Kerr-lens mode-locked Yb:YAG thin-disk oscillator

We demonstrate a Kerr-lens mode-locked femtosecond Yb:YAG thin-disk oscillator and investigate the approach to increase the optical-to-optical efficiency based on the scheme of direct multiple passes of the laser beam through the thin-disk medium. With twelve passes through the thin disk, 266-fs pulses were delivered from the oscillator with an average power of 105.6 W at a repetition rate of 20 MHz. The corresponding optical-to-optical efficiency is 31.1%, which is, to the best of our knowledge, the highest efficiency of any mode-locked thin-disk oscillator with pulse duration below 300 fs. This demonstration paves the way to even more efficient mode-locked femtosecond thin-disk oscillators, and provides an excellent laser source for the applications such as non-linear frequency conversion and high-precision industrial processing

TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio's development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance