Ferromagnetic InMnAs on InAs Prepared by Ion Implantation and Pulsed Laser Annealing

Ferromagnetic InMnAs has been prepared by Mn ion implantation and pulsed laser annealing. The InMnAs layer reveals a saturated magnetization of 2.6 mu_B/Mn at 5 K and a perpendicular magnetic anisotropy. The Curie temperature is determined to be 46 K, which is higher than those in previous reports with similar Mn concentrations. Ferromagnetism is further evidenced by the large magnetic circular dichroism.Comment: 9 pages, 3 figure

Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile

The kinematics of human movements and locomotion are closely linked to the activation and contractions of muscles. To investigate this, we present a multimodal dataset with benchmarks collected using a novel pair of Intelligent Knee Sleeves (Texavie MarsWear Knee Sleeves) for human pose estimation. Our system utilizes synchronized datasets that comprise time-series data from the Knee Sleeves and the corresponding ground truth labels from the visualized motion capture camera system. We employ these to generate 3D human models solely based on the wearable data of individuals performing different activities. We demonstrate the effectiveness of this camera-free system and machine learning algorithms in the assessment of various movements and exercises, including extension to unseen exercises and individuals. The results show an average error of 7.21 degrees across all eight lower body joints when compared to the ground truth, indicating the effectiveness and reliability of the Knee Sleeve system for the prediction of different lower body joints beyond the knees. The results enable human pose estimation in a seamless manner without being limited by visual occlusion or the field of view of cameras. Our results show the potential of multimodal wearable sensing in a variety of applications from home fitness to sports, healthcare, and physical rehabilitation focusing on pose and movement estimation.Comment: Accepted by Thirty-seventh Conference on Neural Information Processing Systems (Neurips) D&B Trac

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.Comment: 40 pages, 3 figures, 4 table

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum\u27s de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. CONCLUSIONS: Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients\u27 clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability

Short-term outcomes of robot-assisted versus video-assisted thoracoscopic surgery for non-small cell lung cancer patients with neoadjuvant immunochemotherapy: a single-center retrospective study

BackgroundNeoadjuvant immunochemotherapy has been increasingly applied to treat non-small cell lung cancer (NSCLC). However, the comparison between robotic-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) in the feasibility and oncological efficacy following neoadjuvant immunochemotherapy is scarce. This study aims to assess the superiorities of RATS over (VATS) concerning short-term outcomes in treating NSCLC patients with neoadjuvant immunochemotherapy.MethodsNSCLC patients receiving RATS or VATS lobectomy following neoadjuvant immunochemotherapy at Shanghai Chest Hospital from 2019 to 2022 were retrospectively identified. Baseline clinical characteristics, perioperative outcomes, and survival profiles were analyzed.ResultsForty-six NSCLC patients with neoadjuvant immunochemotherapy were included and divided into the RATS (n=15) and VATS (n=31) groups. The baseline clinical characteristics and induction-related adverse events were comparable between the two groups (all p>0.050). The 30-day mortality in the RATS and VATS groups were 0% and 3.23%, respectively (p=1.000). Patients undergoing RATS were associated with reduced surgical-related intensive unit care (ICU) stay than those receiving VATS (0.0 [0.0-0.0] vs. 0.0 [0.0-1.0] days, p=0.026). Moreover, RATS assessed more N1 LNs (6.27 ± 1.94 vs 4.90 ± 1.92, p=0.042) and LN stations (3.07 ± 1.03 vs 2.52 ± 0.57, p=0.038) compared with VATS. By comparison, no difference was found in surgical outcomes, pathological results, and postoperative complications between the RATS and VATS groups (all p>0.050). Finally, RATS and VATS achieved comparable one-year recurrence-free survival (82.96% vs. 85.23%, p=0.821) and the timing of central nervous system, LN, and bone recurrences (all p>0.050).ConclusionRATS is safe and feasible for NSCLC patients with neoadjuvant immunochemotherapy, reducing surgical-related ICU stay, assessing increased N1 LNs and stations, and achieving similar survival profiles to VATS

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Chemical vapor deposited single layer graphene as transparent electrodes for flexible photovoltaic devices

Graphene has attracted intensive attention for various electronic applications in the past decades given its unique properties. The synthesis of graphene by chemical vapor deposition (CVD) on copper foil provides the opportunity to deliver large-area, high quality, and continuous graphene films. The metal foil can be removed with a wet etching process. The transferred CVD graphene films can be integrated into existing semiconductor device manufacturing platforms, or into low-cost roll-to-roll manufacturing of flexible electronics. Since graphene is a two-dimensional material, the optical, mechanical, and electrical properties can easily be altered with surface modification. Copper etchants used in graphene transfer process can lead to films with different levels of doping and mechanical strength. The topology and temperature dependent electrical properties of transferred graphene using three different etchants were investigated. All of the graphene samples demonstrate a doping level above 10¹³ cm-³. The graphene films prepared with cupric sulfate solution presents the most uniform and continuous layer, with the least density of defects. Metallic and organic residues, defects and grain boundaries, as well as intercalated water molecules, attribute to the variation in conductivity and permittivity of the films. By coating the films with charge selective materials, graphene sheets with improved sheet resistance and transparency of about 90% were fabricated. The hole-selective transparent conductors show about 50% reduction in resistivity. All the samples demonstrate high stability with repeated bending of over 800 cycles. Organic photovoltaic (OPV) devices using the hole-selective graphene transparent conductors as electrodes were fabricated on plastic substrates. Less than 5% fluctuation in power conversion efficiency (PCE) was noticed when the devices were bent up to 130 degrees. As an extension of this work, the photovoltaic characteristics of inverted OPV devices fabricated with AlxZn(1-x)O as an electron transport layer with Al fraction of up to 11% were reported. The light-soaking effect can be eliminated by using more than 4% of Al doping. All devices demonstrate PCE over 3.4% with air-stability of over 150 days. The light-soaking mechanism is investigated by employing a numerical simulation on the devices.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Deep level transient spectroscopy measurements of GaAsBi/GaAs

Bismuth incorporation in GaAs produces a much larger reduction in the band gap than In or Sb alloying, for the same increase in lattice constant. However, Bi is incorporated only at growth temperatures (Tg) < 400 ⁰C, making deep level defects a concern. GaAsBi layers, GaAs layers and p-i-n structures having a 50 nm GaAsBi quantum well with bismide fraction ≤ 5% in the center of the intrinsic layer were grown by molecular beam epitaxy in the temperature range 285-580 ⁰C. Deep level transient spectroscopy (DLTS) measurements of GaAsBi and GaAs Schottky diodes show several different traps. Similarly, DLTS spectra from the p-i-n devices vary with the growth conditions and the bismide fraction. The trap concentrations were found to be ≤ 51015 cm-3, consistent with reported photoluminescence and electroluminescence measurements of the GaAsBi p-i-n structures. The possible identity of some of the traps is discussed