Impact analysis of lateral damper on the ride quality of metro vehicle

In view of the lateral and vertical vibration problem of car body in actual operation, dynamic simulation and relevant line tests are carried out to study the impact of lateral damper on the ride quality. To facilitate comparative analysis, dynamic models of metro vehicle are set up and simulation results indicate that ride quality when using a single lateral damper is better than when using double dampers. On this basis, line comparison tests are conducted, with ride index in time domain as an indicator. Acceleration sensors are utilized to conduct lateral and vertical acceleration tests when using a single lateral damper and double dampers, respectively. Ride index of every 5 seconds at normal operating velocity is obtained after post-processing of data collected from the tests. Comparison of ride index of two adjacent stations obtained by statistics has found that, in actual operation, metro vehicle with a single lateral damper mounted on the bogie has a better ride quality both laterally and vertically than that with double dampers. Single lateral damper model can also effectively solve abnormal vibration problem of the metro vehicle

EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Mixture-of-experts (MoE) is becoming popular due to its success in improving the model quality, especially in Transformers. By routing tokens with a sparse gate to a few experts (i.e., a small pieces of the full model), MoE can easily increase the model parameters to a very large scale while keeping the computation cost in a constant level. Most existing works just initialize some random experts, set a fixed gating strategy (e.g., Top-k), and train the model from scratch in an ad-hoc way. We identify that these MoE models are suffering from the immature experts and unstable sparse gate, which are harmful to the convergence performance. In this paper, we propose an efficient end-to-end MoE training framework called EvoMoE. EvoMoE starts from training one single expert and gradually evolves into a large and sparse MoE structure. EvoMoE mainly contains two phases: the expert-diversify phase to train the base expert for a while and spawn multiple diverse experts from it, and the gate-sparsify phase to learn an adaptive sparse gate and activate a dynamic number of experts. EvoMoE naturally decouples the joint learning of both the experts and the sparse gate and focuses on learning the basic knowledge with a single expert at the early training stage. Then it diversifies the experts and continues to train the MoE with a novel Dense-to-Sparse gate (DTS-Gate). Specifically, instead of using a permanent sparse gate, DTS-Gate begins as a dense gate that routes tokens to all experts, then gradually and adaptively becomes sparser while routes to fewer experts. Evaluations are conducted on three popular models and tasks, including RoBERTa for masked language modeling task, GPT for language modeling task and Transformer for machine translation task. The results show that EvoMoE outperforms existing baselines, including Switch, BASE Layer, Hash Layer and StableMoE

PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor). In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity. PIT proposes a novel tiling mechanism that leverages Permutation Invariant Transformation (PIT), a mathematically proven property, to transform multiple sparsely located micro-tiles into a GPU-efficient dense tile without changing the computation results, thus achieving both high GPU utilization and low coverage waste. Given a model, PIT first finds feasible PIT rules for all its operators and generates efficient GPU kernels accordingly. At runtime, with the novel SRead and SWrite primitives, PIT rules can be executed extremely fast to support dynamic sparsity in an online manner. Extensive evaluation on diverse models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x (average 2.43x) over state-of-the-art compilers

A knowledge-based design advisory system for collaborative design for micromanufacturing

The manufacture of microproducts differs from that of conventional products in many ways, not only in the sizes, but also in issues concerning the effects of material properties, tools, and manufacturing equipment. There was a need for a new design methodology and associated design tools to aid designers in assessing the design of their microproducts by considering new micromanufacturing capabilities and constraints. A knowledge-based design advisory system (DAS) was, therefore, developed in MASMICRO in which the knowledge-based system with dedicated assessment modules and knowledge representatives based on the ontology was created to implement the distributed design and manufacturing assessment for micromanufacturing. The modules address the assessment on geometrical features relating to manufacturability, manufacturing processes, selection of materials, tools, and machines, as well as manufacturing cost. The Microsoft C# programming language, ASP.NET web technology, Prolog, and Microsoft Access database were used to develop the DAS. The test on the DAS prototype system was found to provide an increase of design efficiency due to more efficient use of design and manufacturing knowledge and afforded a web-based collaborative design environment

FXR Acts as a Metastasis Suppressor in Intrahepatic Cholangiocarcinoma by Inhibiting IL-6-Induced Epithelial-Mesenchymal Transition

Background/Aims: Intrahepatic cholangiocarcinoma (ICC) is a complicated condition, with difficult diagnosis and poor prognosis. The expression and clinical significance of the farnesoid X receptor (FXR), an endogenous receptor of bile acids, in ICC is not well understood. Methods: Western blotting and immunochemical analyses were used to determine the levels of FXR in 4 cholangiocarcinoma cell lines, a human intrahepatic biliary epithelial cell line (HIBEpic) and 322 ICC specimens, respectively, while quantitative reverse transcription polymerase chain reaction was used to detect the mRNA levels of FXR in cholangiocarcinoma cell lines. We evaluated the prognostic value of FXR expression and its association with clinical parameters. We determined the biological significance of FXR in ICC cell lines by agonist-mediated activation and lentivirus-mediated silence. IL-6 expression was tested by an enzyme-linked immunosorbent assay and flow cytometry. In vitro, cell proliferation was examined by Cell Counting Kit-8, migration and invasion were examined by wound healing and transwell assays; in vivo, tumor migration and invasion were explored in NOD-SCID mice. Results: FXR was downregulated in ICC cell lines and clinical ICC specimens. Loss of FXR was markedly correlated with aggressive tumor phenotypes and poor prognosis in patients with ICC. Moreover, FXR expression also had significant prognostic value in carbohydrate antigen 19-9 (CA19-9) negative patients. The expression of FXR was negatively correlated with IL-6 levels in clinical ICC tissues. FXR inhibited the proliferation, migration, invasion and epithelial mesenchymal transition (EMT) of ICC cells via suppression of IL-6 in vitro. Obeticholic acid, an agonist of FXR, inhibited IL-6 production, tumor growth and lung metastasis of ICC in vivo. Conclusions: FXR could be a promising ICC prognostic biomarker, especially in CA19-9 negative patients with ICC. FXR inhibits the tumor growth and metastasis of ICC via IL-6 suppression

Rapidly progressive interstitial lung disease risk prediction in anti-MDA5 positive dermatomyositis: the CROSS model

BackgroundThe prognosis of anti-melanoma differentiation-associated gene 5 positive dermatomyositis (anti-MDA5+DM) is poor and heterogeneous. Rapidly progressive interstitial lung disease (RP-ILD) is these patients’ leading cause of death. We sought to develop prediction models for RP-ILD risk in anti-MDA5+DM patients.MethodsPatients with anti-MDA5+DM were enrolled in two cohorts: 170 patients from the southern region of Jiangsu province (discovery cohort) and 85 patients from the northern region of Jiangsu province (validation cohort). Cox proportional hazards models were used to identify risk factors of RP-ILD. RP-ILD risk prediction models were developed and validated by testing every independent prognostic risk factor derived from the Cox model.ResultsThere are no significant differences in baseline clinical parameters and prognosis between discovery and validation cohorts. Among all 255 anti-MDA5+DM patients, with a median follow-up of 12 months, the incidence of RP-ILD was 36.86%. Using the discovery cohort, four variables were included in the final risk prediction model for RP-ILD: C-reactive protein (CRP) levels, anti-Ro52 antibody positivity, short disease duration, and male sex. A point scoring system was used to classify anti-MDA5+DM patients into moderate, high, and very high risk of RP-ILD. After one-year follow-up, the incidence of RP-ILD in the very high risk group was 71.3% and 85.71%, significantly higher than those in the high-risk group (35.19%, 41.69%) and moderate-risk group (9.54%, 6.67%) in both cohorts.ConclusionsThe CROSS model is an easy-to-use prediction classification system for RP-ILD risk in anti-MDA5+DM patients. It has great application prospect in disease management

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks