240 research outputs found
A new oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of southern China
No full textWang, Shuo, Sun, Chengkai, Sullivan, Corwin, Xu, Xing (2013): A new oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of southern China. Zootaxa 3640 (2): 242-257, DOI: 10.11646/zootaxa.3640.2.
FIGURE 4 in A new oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of southern China
No full textFIGURE 4. Strict consensus of oviraptorosaur interrelationships. The phylogenetic analysis resulted in 9 most parsimonious trees of 338 steps, each with a consistency index of 0.645 and a retention index of 0.701. Values above nodes represent bootstrap percentages (%), values below nodes represent Bremer support values. Bootstrap values lower than 20 are not shown.Published as part of Wang, Shuo, Sun, Chengkai, Sullivan, Corwin & Xu, Xing, 2013, A new oviraptorid (Dinosauria: Theropoda) from the Upper Cretaceous of southern China, pp. 242-257 in Zootaxa 3640 (2) on page 250, DOI: 10.11646/zootaxa.3640.2.7, http://zenodo.org/record/28367
New stratigraphic data from the Erlian Basin : implications for the division, correlation, and definition of Paleogene lithological units in Nei Mongol (Inner Mongolia)
No full textFig. 5. Stratigraphic correlations of the Nuhetingboerhe-Huheboerhe area (NHA). See Fig. 1B for the locations of the sections.Published as part of MENG, JIN, WANG, YUANQING, NI, XIJUN, BEARD, K. CHRISTOPHER, SUN, CHENGKAI, LI, QIAN, JIN, XUN & BAI, BIN, 2007, New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia), pp. 1-32 in American Museum Novitates 3570 (1) on page 12, DOI: 10.1206/0003-0082(2007)526[1:NSDFTE]2.0.CO;2, http://zenodo.org/record/538787
Fig. 7. Sketch profile from the locality 10 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
No full textFig. 7. Sketch profile from the locality 10 miles southwest of the Camp Margetts (Granger, 1930).Published as part of MENG, JIN, WANG, YUANQING, NI, XIJUN, BEARD, K. CHRISTOPHER, SUN, CHENGKAI, LI, QIAN, JIN, XUN & BAI, BIN, 2007, New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia), pp. 1-32 in American Museum Novitates 3570 (1) on page 17, DOI: 10.1206/0003-0082(2007)526[1:NSDFTE]2.0.CO;2, http://zenodo.org/record/538787
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
No full textSequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven to be effective for sequential recommendation, they suffer from the inference inefficiency problem stemming from the quadratic computational complexity of attention operators, especially for long behavior sequences. Inspired by the recent success of state space models (SSMs), we propose Mamba4Rec, which is the first work to explore the potential of selective SSMs for efficient sequential recommendation. Built upon the basic Mamba block which is a selective SSM with an efficient hardware-aware parallel algorithm, we design a series of sequential modeling techniques to further promote model performance while maintaining inference efficiency. Through experiments on public datasets, we demonstrate how Mamba4Rec effectively tackles the effectiveness-efficiency dilemma, outperforming both RNN- and attention-based baselines in terms of both effectiveness and efficiency. The code is available at https://github.com/chengkai-liu/Mamba4Rec
A Study on the Evacuation of an Extra-Long Highway Tunnel Fire—A Case Study of Chengkai Tunnel
No full textThe smoke from tunnel fires spreads over long distances and is difficult to vent. Smoke accumulation leads to high temperatures, low visibility, and high concentrations of toxic gases, which greatly hinders the evacuation of people inside the tunnel. In this paper, a representative extra-long highway tunnel—Chengkai Tunnel—is selected as the engineering background, and a tunnel model is built using FDS and Pathfinder software to simulate the fire scenario and evacuation scenario under different longitudinal wind speeds. The concept of safe evacuation reliability is proposed to describe the relationship between the ASET (available safe egress time) and the RSET (required safe egress time). The simulation results show that with the increase in longitudinal wind speed, the ASET upstream of fire source increases first and then remains unchanged, while ASET downstream of fire source increases first and then decreases. The ASET upstream of the fire source is affected by visibility, while the ASET downstream of the fire source is affected by visibility when the wind speed is low, and is affected by temperature as the wind speed increases. The bottleneck effect is an important reason for the long evacuation time of people. The blockage time is a power function of the evacuation movement time, and increasing the width of the cross passage can improve the evacuation efficiency of the tunnel. The increase in the number of evacuees will reduce the reliability of the safe evacuation of personnel. Among all simulated scenarios, a longitudinal wind speed of 2.5 m/s has the highest safe evacuation reliability, with 0.79, 0.92, and 0.99 for scenarios R1, R2, and R3, respectively. Excessive wind speed reduces the safe evacuation reliability downstream of the fire source
Generative adversarial modeling of three-dimensional shapes
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 53-59).Given a 3D shape, humans are capable of telling whether it looks natural. This shape priors, namely the perception of whether a shape looks realistic, are formed over years of our interactions with surrounding 3D objects, and go beyond simple definition of objects. In this thesis, we propose two models, 3D Generative Adversarial Network and ShapeHD, to learn shape priors from existing 3D shapes via generative-adversarial modeling, pushing the limits of shape generation, single-view shape completion and reconstruction. For shape generation, we demonstrate that our 3D-GAN generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods; for single-view shape completion and reconstruction, we show that ShapeHD recovers fine details for 3D shapes, and outperforms state-of-the-art by a large margin on both tasks.by Chengkai Zhang.M. Eng
ThIPK1 regulates lignocellulolytic enzyme expression during wood degradation in white-rot fungi
No full textABSTRACT White-rot fungi play a crucial role in terrestrial carbon cycling by decomposing lignocellulose, particularly lignin, in plant cell walls. The degradation process is initiated by the fungal perception of lignocellulosic signals, which trigger a complex regulatory network controlling lignocellulolytic enzyme expression. However, the ecological and molecular mechanisms underlying how these fungi sense lignocellulosic signals and regulate their degradation capacity remain unclear. In this study, a degenerated Trametes hirsuta AH28-2 that lost lignocellulolytic degradation capacity was generated through successive subcultures. Phenotypic stability, transcriptomic analyses, and functional validation revealed that ThIPK1, a key gene in the inositol polyphosphate pathway, regulates the orchestrated expression of lignocellulolytic enzymes in response to distinct lignin monomers. Epigenetic modifications, particularly 5mC methylation, were identified as mediators of signal transduction and regulation for ThIPK1 and its downstream Zn2Cys6 transcription factors, which differentially control lignocellulolytic gene transcription. IPK1 exhibits a closer relationship among white-rot fungi compared to their phylogenetic relationships. These findings provide novel insights into the molecular basis of white-rot fungal adaptation to lignocellulosic environments, contributing to our understanding of microbial-driven lignin turnover in forest ecosystems.IMPORTANCEWhite-rot fungi are among the most efficient lignocellulose degraders in nature. Understanding how white-rot fungi sense and respond to lignocellulose is critical for deciphering microbial contributions to forest carbon turnover. Despite their ecological importance, the molecular mechanisms underlying lignin signal perception remain elusive. In this study, we uncover a regulatory axis involving inositol polyphosphate signaling and epigenetic modulation that connects environmental lignin cues to the transcriptional control of lignocellulolytic enzymes. By identifying ThIPK1 as a crucial regulator and revealing 5mC methylation and Zn2Cys6 transcription factors as downstream effectors, we demonstrate how fungi integrate chemical signals from lignin monomers into adaptive gene expression. These findings not only reveal a novel lignin-responsive regulatory mechanism but also provide a framework for understanding fungal adaptation and function in dynamic, lignin-rich environments
Modeling on the dynamic mechanical response of single-crystalline Ni–Mn–Ga alloys based on Hamilton’s principle
No full textLong 3D-POT: A Long-Term 3D Drosophila-Tracking Method for Position and Orientation with Self-Attention Weighted Particle Filters
No full textThe study of the intricate flight patterns and behaviors of swarm insects, such as drosophilas, has long been a subject of interest in both the biological and computational realms. Tracking drosophilas is an essential and indispensable method for researching drosophilas’ behaviors. Still, it remains a challenging task due to the highly dynamic nature of these drosophilas and their partial occlusion in multi-target environments. To address these challenges, particularly in environments where multiple targets (drosophilas) interact and overlap, we have developed a long-term Trajectory 3D Position and Orientation Tracking Method (Long 3D-POT) that combines deep learning with particle filtering. Our approach employs a detection model based on an improved Mask-RCNN to accurately detect the position and state of drosophilas from frames, even when they are partially occluded. Following detection, improved particle filtering is used to predict and update the motion of the drosophilas. To further enhance accuracy, we have introduced a prediction module based on the self-attention backbone that predicts the drosophila’s next state and updates the particles’ weights accordingly. Compared with previous methods by Ameni, Cheng, and Wang, our method has demonstrated a higher degree of accuracy and robustness in tracking the long-term trajectories of drosophilas, even those that are partially occluded. Specifically, Ameni employs the Interacting Multiple Model (IMM) combined with the Global Nearest Neighbor (GNN) assignment algorithm, primarily designed for tracking larger, more predictable targets like aircraft, which tends to perform poorly with small, fast-moving objects like drosophilas. The method by Cheng then integrates particle filtering with LSTM networks to predict particle weights, enhancing trajectory prediction under kinetic uncertainties. Wang’s approach builds on Cheng’s by incorporating an estimation of the orientation of drosophilas in order to refine tracking further. Compared with those methods, our method performs with higher accuracy on detection, which increases by more than 10% on the F1 Score, and tracks more long-term trajectories, showing stability
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