ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos

Phase recognition plays an essential role for surgical workflow analysis in computer assisted intervention. Transformer, originally proposed for sequential data modeling in natural language processing, has been successfully applied to surgical phase recognition. Existing works based on transformer mainly focus on modeling attention dependency, without introducing auto-regression. In this work, an Auto-Regressive Surgical Transformer, referred as ARST, is first proposed for on-line surgical phase recognition from laparoscopic videos, modeling the inter-phase correlation implicitly by conditional probability distribution. To reduce inference bias and to enhance phase consistency, we further develop a consistency constraint inference strategy based on auto-regression. We conduct comprehensive validations on a well-known public dataset Cholec80. Experimental results show that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and achieves an inference rate of 66 frames per second (fps).Comment: 11 Pages, 3 figure

Lactobacillus gasseri LA39 Activates the Oxidative Phosphorylation Pathway in Porcine Intestinal Epithelial Cells

Intestinal microbial interactions with the host epithelium have important roles in host health. Our previous data have suggested that Lactobacillus gasseri LA39 is the predominant intestinal Lactobacillus in weaned piglets. However, the regulatory role of L. gasseri LA39 in the intestinal epithelial protein expression in piglets remains unclear. In the present study, we conducted comparative proteomics approach to investigate the intestinal epithelial protein profile alteration caused by L. gasseri LA39 in piglets. The expressions of 15 proteins significantly increased, whereas the expressions of 13 proteins significantly decreased in the IPEC-J2 cells upon L. gasseri LA39 treatment. Bioinformatics analyses, including COG function annotation, GO annotation, and KEGG pathway analysis for the differentially expressed proteins revealed that the oxidative phosphorylation (OXPHOS) pathway in IPEC-J2 cells was significantly activated by L. gasseri LA39 treatment. Further data indicated that two differentially expressed proteins UQCRC2 and TCIRG1, associated with the OXPHOS pathway, and cellular ATP levels in IPEC-J2 cells were significantly up-regulated by L. gasseri LA39 treatment. Importantly, the in vivo data indicated that oral gavage of L. gasseri LA39 significantly increased the expression of UQCRC2 and TCIRG1 and the cellular ATP levels in the intestinal epithelial cells of weaned piglets. Our results, both in vitro and in vivo, reveal that L. gasseri LA39 activates the OXPHOS pathway and increases the energy production in porcine intestinal epithelial cells. These findings suggest that L. gasseri LA39 may be a potential probiotics candidate for intestinal energy production promotion and confers health-promoting functions in mammals

Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis

Training a high performance end-to-end speech (E2E) processing model requires an enormous amount of labeled speech data, especially in the era of data-centric artificial intelligence. However, labeled speech data are usually scarcer and more expensive for collection, compared to textual data. We propose Latent Synthesis (LaSyn), an efficient textual data utilization framework for E2E speech processing models. We train a latent synthesizer to convert textual data into an intermediate latent representation of a pre-trained speech model. These pseudo acoustic representations of textual data augment acoustic data for model training. We evaluate LaSyn on low-resource automatic speech recognition (ASR) and spoken language understanding (SLU) tasks. For ASR, LaSyn improves an E2E baseline trained on LibriSpeech train-clean-100, with relative word error rate reductions over 22.3% on different test sets. For SLU, LaSyn improves our E2E baseline by absolute 4.1% for intent classification accuracy and 3.8% for slot filling SLU-F1 on SLURP, and absolute 4.49% and 2.25% for exact match (EM) and EM-Tree accuracies on STOP respectively. With fewer parameters, the results of LaSyn are competitive to published state-of-the-art works. The results demonstrate the quality of the augmented training data.Comment: 15 pages, 8 figures, 8 tables, Accepted to EMNLP 2023 Finding

First Spectroscopic Confirmations of z ~ 7.0 Lya Emitting Galaxies in the LAGER Survey

Narrowband imaging is a highly successful approach for finding large numbers of high redshift Lya emitting galaxies (LAEs) up to z~6.6. However, at z>~7 there are as yet only 3 narrowband selected LAEs with spectroscopic confirmations (two at z~6.9-7.0, one at z~7.3), which hinders extensive studies on cosmic reionization and galaxy evolution at this key epoch. We have selected 23 candidate z~6.9 LAEs in COSMOS field with the large area narrowband survey LAGER (Lyman-Alpha Galaxies at the End of Reionization). In this work we present spectroscopic followup observations of 12 candidates using IMACS on Magellan. For 9 of these, the observations are sufficiently deep to detect the expected lines. Lya emission lines are identified in six sources (yielding a success rate of 2/3), including 3 luminous LAEs with Lya luminosities of L(Lya) ~ 10^{43.5} erg/s, the highest among known spectroscopically confirmed galaxies at >~7.0. This triples the sample size of spectroscopically confirmed narrowband selected LAEs at z>~7, and confirms the bright end bump in the Lya luminosity function we previously derived based on the photometric sample, supporting a patchy reionization scenario. Two luminous LAEs appear physically linked with projected distance of 1.1 pMpc and velocity difference of ~ 170 km/s. They likely sit in a common ionized bubble produced by themselves or with close neighbors, which reduces the IGM attenuation of Lya. A tentative narrow NV${\lambda}$1240 line is seen in one source, hinting at activity of a central massive black hole with metal rich line emitting gas.Comment: 6 pages, 3 figures, 2 tables, accepted by ApJ

Digital Twin Brain: a simulation and assimilation platform for whole human brain

In this work, we present a computing platform named digital twin brain (DTB) that can simulate spiking neuronal networks of the whole human brain scale and more importantly, a personalized biological brain structure. In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive. We utilize a number of optimization techniques to balance and integrate the computation loads and communication traffics from the heterogeneous biological structure to the general GPU-based HPC and achieve leading simulation performance for the whole human brain-scaled spiking neuronal networks. On the other hand, the biological structure, equipped with a mesoscopic data assimilation, enables the DTB to investigate brain cognitive function by a reverse-engineering method, which is demonstrated by a digital experiment of visual evaluation on the DTB. Furthermore, we believe that the developing DTB will be a promising powerful platform for a large of research orients including brain-inspiredintelligence, rain disease medicine and brain-machine interface.Comment: 12 pages, 11 figure

Interaction of MSE Abutments with Bridge Superstructures under Seismic Loading \u2013 Shaking Table Tests

65A0556This report presents results from shaking table tests on half-scale mechanically-stabilized earth (MSE) bridge abutments. The testing program consists of five tests where the direction of shaking is in the longitudinal direction of the bridge beam, and one test where the direction of shaking is perpendicular to the bridge beam. The longitudinal shaking tests include a baseline configuration and a parametric study of different configurations to investigate the effects of bridge surcharge stress, reinforcement spacing, reinforcement stiffness, and steel reinforcement on the seismic response of MSE bridge abutments. Experimental design of the scale model followed established similitude relationships for shaking table testing in a 1g gravitational field, including scaling for of geometry, reinforcement stiffness, backfill soil modulus, bridge surcharge stress, and characteristics of the earthquake motions. Facing displacements, bridge seat settlements, accelerations, vertical and lateral stresses, reinforcement strains, and contact forces between the bridge beam and bridge seat were measured for different instrumented sections to evaluate the three-dimensional dynamic response during a series of applied shaking motions. Results indicate that reinforcement spacing and reinforcement stiffness have the most significant effects on the facing displacements and bridge seat settlements for dynamic loading conditions