105 research outputs found
Baechi: Fast Device Placement of Machine Learning Graphs
Machine Learning graphs (or models) can be challenging or impossible to train
when either devices have limited memory, or models are large. To split the
model across devices, learning-based approaches are still popular. While these
result in model placements that train fast on data (i.e., low step times),
learning-based model-parallelism is time-consuming, taking many hours or days
to create a placement plan of operators on devices. We present the Baechi
system, the first to adopt an algorithmic approach to the placement problem for
running machine learning training graphs on small clusters of
memory-constrained devices. We integrate our implementation of Baechi into two
popular open-source learning frameworks: TensorFlow and PyTorch. Our
experimental results using GPUs show that: (i) Baechi generates placement plans
654 X - 206K X faster than state-of-the-art learning-based approaches, and (ii)
Baechi-placed model's step (training) time is comparable to expert placements
in PyTorch, and only up to 6.2% worse than expert placements in TensorFlow. We
prove mathematically that our two algorithms are within a constant factor of
the optimal. Our work shows that compared to learning-based approaches,
algorithmic approaches can face different challenges for adaptation to Machine
learning systems, but also they offer proven bounds, and significant
performance benefits.Comment: Extended version of SoCC 2020 paper:
https://dl.acm.org/doi/10.1145/3419111.342130
Multi-dark-state resonances in cold multi-Zeeman-sublevel atoms
We present our experimental and theoretical studies of multi-dark-state
resonances (MDSRs) generated in a unique cold rubidium atomic system with only
one coupling laser beam. Such MDSRs are caused by different transition
strengths of the strong coupling beam connecting different Zeeman sublevels.
Controlling the transparency windows in such electromagnetically induced
transparency system can have potential applications in multi-wavelength optical
communication and quantum information processing.Comment: 11pages, 4figure
SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model
Simulation plays a critical role in the research and development of
autonomous driving and intelligent transportation systems. However, the current
simulation platforms exhibit limitations in the realism and diversity of agent
behaviors, which impede the transfer of simulation outcomes to the real world.
In this paper, we propose a generative driver agent simulation framework based
on large language models (LLMs), capable of perceiving complex traffic
scenarios and providing realistic driving maneuvers. Notably, we conducted
interviews with 24 drivers and used their detailed descriptions of driving
behavior as chain-of-thought prompts to develop a `coach agent' module, which
can evaluate and assist driver agents in accumulating driving experience and
developing human-like driving styles. Through practical simulation experiments
and user experiments, we validate the feasibility of this framework in
generating reliable driver agents and analyze the roles of each module. The
results show that the framework with full architect decreased the collision
rate by 81.04% and increased the human-likeness by 50%. Our research proposes
the first urban context driver agent simulation framework based on LLMs and
provides valuable insights into the future of agent simulation for complex
tasks.Comment: 12 pages, 8 figure
MAS: A versatile Landau-fluid eigenvalue code for plasma stability analysis in general geometry
We have developed a new global eigenvalue code, Multiscale Analysis for
plasma Stabilities (MAS), for studying plasma problems with wave toroidal mode
number n and frequency omega in a broad range of interest in general tokamak
geometry, based on a five-field Landau-fluid description of thermal plasmas.
Beyond keeping the necessary plasma fluid response, we further retain the
important kinetic effects including diamagnetic drift, ion finite Larmor
radius, finite parallel electric field, ion and electron Landau resonances in a
self-consistent and non-perturbative manner without sacrificing the attractive
efficiency in computation. The physical capabilities of the code are evaluated
and examined in the aspects of both theory and simulation. In theory, the
comprehensive Landau-fluid model implemented in MAS can be reduced to the
well-known ideal MHD model, electrostatic ion-fluid model, and drift-kinetic
model in various limits, which clearly delineates the physics validity regime.
In simulation, MAS has been well benchmarked with theory and other gyrokinetic
and kinetic-MHD hybrid codes in a manner of adopting the unified physical and
numerical framework, which covers the kinetic Alfven wave, ion sound wave,
low-n kink, high-n ion temperature gradient mode and kinetic ballooning mode.
Moreover, MAS is successfully applied to model the Alfven eigenmode (AE)
activities in DIII-D discharge #159243, which faithfully captures the frequency
sweeping of RSAE, the tunneling damping of TAE, as well as the polarization
characteristics of KBAE and BAAE being consistent with former gyrokinetic
theory and simulation. With respect to the key progress contributed to the
community, MAS has the advantage of combining rich physics ingredients,
realistic global geometry and high computation efficiency together for plasma
stability analysis in linear regime.Comment: 40 pages, 21 figure
Highly precision carbon dioxide acoustic wave sensor with minimized humidity interference
Extensive applications of carbon dioxide (CO2) in various fields, such as food industry, agricultural production, medical and pharmacological industries, have caused a great demand for high-performance CO2 sensors. However, most existing CO2 sensors suffer from poor performance in a wet environment and often cannot work accurately in a high humidity condition. In this study, a quartz crystal resonator (QCR) coated with a uniform layer of reduced graphene oxide (RGO) is proposed to detect both the concentrations of CO2 and water molecules simultaneously, which can be used to significantly minimize the humidity interference. Unlike the other common gas sensors, the RGO-based CO2 QCR sensor can be operated in different humidity levels and the concentration of CO2 can be quantified precisely and effectively. Moreover, it has a fast response (~0.4 s), which is also suitable for respiration monitoring. Our results showed that before and after a volunteer did a low-intensity exercise, the sensor could detect the differences of concentrations of CO2 in the exhaled breath (i.e., 4.50% and 5.15%, respectively)
Systematic Analysis of Survival-Associated Alternative Splicing Signatures in Thyroid Carcinoma
Alternative splicing (AS) is a key mechanism involved in regulating gene expression and is closely related to tumorigenesis. The incidence of thyroid cancer (THCA) has increased during the past decade, and the role of AS in THCA is still unclear. Here, we used TCGA and to generate AS maps in patients with THCA. Univariate analysis revealed 825 AS events related to the survival of THCA. Five prognostic models of AA, AD, AT, ES, and ME events were obtained through lasso and multivariate analyses, and the final prediction model was established by integrating all the AS events in the five prediction models. Kaplan–Meier survival analysis revealed that the overall survival rate of patients in the high-risk group was significantly shorter than that of patients in the low-risk group. The ROC results revealed that the prognostic capabilities of each model at 3, 5, and 8 years were all greater than 0.7, and the final prognostic capabilities of the models were all greater than 0.9. By reviewing other databases and utilizing qPCR, we verified the established THCA gene model. In addition, gene set enrichment analysis showed that abnormal AS events might play key roles in tumor development and progression of THCA by participating in changes in molecular structure, homeostasis of the cell environment and in cell energy. Finally, a splicing correlation network was established to reveal the potential regulatory patterns between the predicted splicing factors and AS event candidates. In summary, AS should be considered an important prognostic indicator of THCA. Our results will help to elucidate the underlying mechanism of AS in the process of THCA tumorigenesis and broaden the prognostic and clinical application of molecular targeted therapy for THCA
Genome-Wide Association Studies Reveal the Genetic Basis of Ionomic Variation in Rice
Rice (Oryza sativa) is an important dietary source of both essential micronutrients and toxic trace elements for humans. The genetic basis underlying the variations in the mineral composition, the ionome, in rice remains largely unknown. Here, we describe a comprehensive study of the genetic architecture of the variation in the rice ionome performed using genome-wide association studies (GWAS) of the concentrations of 17 mineral elements in rice grain from a diverse panel of 529 accessions, each genotyped at ∼6.4 million single nucleotide polymorphism loci. We identified 72 loci associated with natural ionomic variations, 32 that are common across locations and 40 that are common within a single location. We identified candidate genes for 42 loci and provide evidence for the causal nature of three genes, the sodium transporter gene Os-HKT1;5 for sodium, Os-MOLYBDATE TRANSPORTER1;1 for molybdenum, and Grain number, plant height, and heading date7 for nitrogen. Comparison of GWAS data from rice versus Arabidopsis (Arabidopsis thaliana) also identified well-known as well as new candidates with potential for further characterization. Our study provides crucial insights into the genetic basis of ionomic variations in rice and serves as an important foundation for further studies on the genetic and molecular mechanisms controlling the rice ionome
RAGE Mediates Accelerated Diabetic Vein Graft Atherosclerosis Induced by Combined Mechanical Stress and AGEs via Synergistic ERK Activation
Aims/Hypothesis: Diabetes with hypertension rapidly accelerates vascular disease, but the underlying mechanism remains unclear. We evaluated the hypothesis that the receptor of advanced glycation end products (RAGE) might mediate combined signals initiated by diabetes-related AGEs and hypertension-induced mechanical stress as a common molecular sensor. Methods: In vivo surgical vein grafts created by grafting vena cava segments from C57BL/6J mice into the common carotid arteries of streptozotocin (STZ)-treated and untreated isogenic mice for 4 and 8 weeks were analyzed using morphometric and immunohistochemical techniques. In vitro quiescent mouse vascular smooth muscle cells (VSMCs) with either knockdown or overexpression of RAGE were subjected to cyclic stretching with or without AGEs. Extracellular signalregulated kinase (ERK) phosphorylation and Ki-67 expression were investigated. Results: Significant increases in neointimal formation, AGE deposition, Ki-67 expression, and RAGE were observed in the vein grafts of STZ-induced diabetic mice. The highest levels of ERK phosphorylation and Ki-67 expression in VSMCs were induced by simultaneous stretch stress and AGE exposure. The synergistic activation of ERKs and Ki-67 in VSMCs was significantly inhibited by siRNA-RAGE treatment and enhanced by over-expression of RAGE. Conclusion: RAGE may mediate synergistically increased ERK activation and VSMC proliferation induced by mechanica
- …