UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt

Recent research has shown that multi-task pre-training greatly improves the model's robustness and transfer ability, which is crucial for building a high-quality dialog system. However, most previous works on multi-task pre-training rely heavily on human-defined input format or prompt, which is not optimal in quality and quantity. In this work, we propose to use Task-based Automatic Prompt generation (TAP) to automatically generate high-quality prompts. Using the high-quality prompts generated, we scale the corpus of the pre-trained conversation model to 122 datasets from 15 dialog-related tasks, resulting in Universal Pre-trained Conversation Model (UniPCM), a powerful foundation model for various conversational tasks and different dialog systems. Extensive experiments have shown that UniPCM is robust to input prompts and capable of various dialog-related tasks. Moreover, UniPCM has strong transfer ability and excels at low resource scenarios, achieving SOTA results on 9 different datasets ranging from task-oriented dialog to open-domain conversation. Furthermore, we are amazed to find that TAP can generate prompts on par with those collected with crowdsourcing. The code is released with the paper

A coordinated investigation of the gravity wave breaking and the associated dynamical instability by a Na lidar and an Advanced Mesosphere Temperature Mapper over Logan, UT (41.7°N, 111.8°W)

The impacts of gravity wave (GW) on the thermal and dynamic characteristics within the mesosphere/lower thermosphere, especially on the atmospheric instabilities, are still not fully understood. In this paper, we conduct a comprehensive and detailed investigation on one GW breaking event during a collaborative campaign between the Utah State University Na lidar and the Advanced Mesospheric Temperature Mapper (AMTM) on 9 September 2012. The AMTM provides direct evidence of the GW breaking as well as the horizontal parameters of the GWs involved, while the Na lidar\u27s full diurnal cycle observations are utilized to uncover the roles of tide and GWs in generating a dynamical instability layer. By studying the changes of the OH layer peak altitude, we located the wave breaking altitude as well as the significance of a 2 h wave that are essential to this instability formation. By reconstructing the mean fields, tidal and GW variations during the wave breaking event, we find that the large-amplitude GWs significantly changed the Brunt–Vaisala frequency square and the horizontal wind shear when superimposed on the tidal wind, producing a transient dynamic unstable region between 84 km and 87 km around 11:00 UT that caused a subsequent small-scale GW breaking

Coordinated investigation of mid-latitude upper mesospheric temperature inversion layers and the associated gravity wave forcing by Na lidar and Advanced Mesospheric Temperature Mapper at Logan, Utah (42°N)

Mesospheric inversion layers (MIL) are well studied in the literature but their relationship to the dynamic feature associated with the breaking of atmospheric waves in the mesosphere/lower thermosphere (MLT) region are not well understood. Two strong MIL events (ΔT ~30 K) were observed above 90 km during a 6 day full diurnal cycle Na lidar campaign conducted from 6 August to 13 August Logan, Utah (42°N, 112°W). Colocated Advanced Mesospheric Temperature Mapper observations provided key information on concurrent gravity wave (GW) events and their characteristics during the nighttime observations. The study found both MILs were well correlated with the development and presence of an unstable region ~2 km above the MIL peak altitudes and a highly stable region below, implicating the strengthening of MIL is likely due to the increase of downward heat flux by the enhanced saturation of gravity wave, when it propagates through a highly stable layer. Each MIL event also exhibited distinct features: one showed a downward progression most likely due to tidal-GW interaction, while the peak height of the other event remained constant. During further investigation of atmospheric stability surrounding the MIL structure, lidar measurements indicate a sharp enhancement of the convective stability below the peak altitude of each MIL. We postulate that the sources of these stable layers were different; one was potentially triggered by concurrent large tidal wave activity and the other during the passage of a strong mesospheric bore

High-Sensitivity C-Reactive Protein: An Independent Risk Factor for Left Ventricular Hypertrophy in Patients with Lupus Nephritis

Objective. To determine the prevalence of left ventricular hypertrophy (LVH) and its associated risk factors in lupus nephritis (LN) patients. Methods. 287 LN patients (age: 38.54 ± 13.31, 262 female) were recruited. Echocardiography and serum high-sensitivity C-reactive protein (hs-CRP) were measured. Their relationship was evaluated by univariate correlation analysis and multivariate regression analysis. Results. The prevalence of LVH in this cohort was 21.25% (n = 61). Serum hs-CRP level was significantly elevated in patients with LVH compared to those without (8.03 (3.22–30.95) versus 3.93 (1.48–9.48) mg/L, P < .01), and correlated with left ventricular mass index (LVMI) (r = 0.314, P = .001). Multivariate regression analysis further confirmed that hs-CRP was an independent risk factor (β = 0.338, P = .002) for LVH in patients with LN. Conclusions. Our findings demonstrated that serum hs-CRP level is independently correlated with LVMI and suggested that measurement of hs-CRP may provide important clinical information to investigate LVH in LN patients

Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization

Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leading to different organs/tissues captured. To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space. Our experiments on 2608 volumetric CT data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas Abdomen Labeling Challenge dataset (BTCV) Challenge demonstrate that our model can generate high-quality images that are realistic and similar. We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area. This approach provides a promising direction for mapping slices from different vertebral levels to a target slice and reducing positional variance for single-slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen

The First Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a new wide-field legacy imaging survey in the northern Galactic cap using the 2.3m Bok telescope. The survey will cover about 5400 deg$^2$ in the $g$ and $r$ bands, and the expected 5$\sigma$ depths (corrected for the Galactic extinction) in the two bands are 24.0 and 23.4 mag, respectively. BASS started observations in January 2015, and has completed about 41% of the whole area as of July 2016. The first data release contains both calibrated images and photometric catalogs obtained in 2015 and 2016. The depths of single-epoch images in the two bands are 23.4 and 22.9 mag, and the full depths of three epochs are about 24.1 and 23.5 mag, respectively.Comment: 16 pages, published by A

Sulforaphane Attenuation of Type 2 Diabetes-Induced Aortic Damage Was Associated with the Upregulation of Nrf2 Expression and Function

Type 2 diabetes mellitus (T2DM) significantly increases risk for vascular complications. Diabetes-induced aorta pathological changes are predominantly attributed to oxidative stress. Nuclear factor E2-related factor-2 (Nrf2) is a transcription factor orchestrating antioxidant and cytoprotective responses to oxidative stress. Sulforaphane protects against oxidative damage by increasing Nrf2 expression and its downstream target genes. Here we explored the protective effect of sulforaphane on T2DM-induced aortic pathogenic changes in C57BL/6J mice which were fed with high-fat diet for 3 months, followed by a treatment with streptozotocin at 100 mg/kg body weight. Diabetic and nondiabetic mice were randomly divided into groups with and without 4-month sulforaphane treatment. Aorta of T2DM mice exhibited significant increases in the wall thickness and structural derangement, along with significant increases in fibrosis (connective tissue growth factor and transforming growth factor), inflammation (tumor necrosis factor-α and vascular cell adhesion molecule 1), oxidative/nitrative stress (3-nitrotyrosine and 4-hydroxy-2-nonenal), apoptosis, and cell proliferation. However, these pathological changes were significantly attenuated by sulforaphane treatment that was associated with a significant upregulation of Nrf2 expression and function. These results suggest that sulforaphane is able to upregulate aortic Nrf2 expression and function and to protect the aorta from T2DM-induced pathological changes

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challenges and inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting of multiple modalities, anatomies, and a wide range of tissue classes, including 133 structures in the brain, 14 organs in the abdomen, 4 hierarchical components in the kidneys, inter-connected kidney tumors and brain tumors. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in a single network, outperforming prior state-of-the-art method SLANT27 ensembled with 27 networks.Comment: 19 pages, 17 figures. arXiv admin note: text overlap with arXiv:2203.0243