Machine learning reconstruction of depth-dependent thermal conductivity profile from frequency-domain thermoreflectance signals

Characterizing materials with spatially varying thermal conductivities is significant to unveil the structure-property relationship for a wide range of functional materials, such as chemical-vapor-deposited diamonds, ion-irradiated materials, nuclear materials under radiation, and battery electrode materials. Although the development of thermal conductivity microscopy based on time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning of thermal conductivity profile, measuring depth-dependent thermal conductivity remains challenging. This work proposed a machine-learning-based reconstruction method for extracting depth-dependent thermal conductivity K(z) directly from frequency-domain phase signals. We demonstrated that the simple supervised-learning algorithm kernel ridge regression (KRR) can reconstruct K(z) without requiring pre-knowledge about the functional form of the profile. The reconstruction method can not only accurately reproduce typical K(z) distributions such as the pre-assumed exponential profile of chemical-vapor-deposited (CVD) diamonds and Gaussian profile of ion-irradiated materials, but also complex profiles artificially constructed by superimposing Gaussian, exponential, polynomial, and logarithmic functions. In addition, the method also shows excellent performances of reconstructing K(z) of ion-irradiated semiconductors from Fourier-transformed TDTR signals. This work demonstrates that combining machine learning with pump-probe thermoreflectance is an effective way for depth-dependent thermal property mapping

Frequency Chirping of Electromagnetic Ion Cyclotron Waves in Earth's Magnetosphere

Electromagnetic ion cyclotron waves are known to exhibit frequency chirping, contributing to the rapid scattering and acceleration of energetic particles. However, the physical mechanism of chirping remains elusive. Here, we propose a new model to explain the chirping and provide direct observational evidence for validation. Our results relate the frequency chirping of the wave to both the wave amplitude and magnetic field inhomogeneity for the first time. The general applicability of the model's underlying principle opens a new path toward understanding the frequency chirping of other waves.Comment: 8 pages, 3 figure

Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models

Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalization has gained increasing popularity due to its practical value. However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e.g., CLIP-ViT-B/16. This paper, for the first time, explores the collaborative potential of leveraging much weaker VLMs to enhance the generalization of a robust single model. The affirmative findings motivate us to address the generalization problem from a novel perspective, i.e., ensemble of pre-trained VLMs. We introduce three customized ensemble strategies, each tailored to one specific scenario. Firstly, we introduce the zero-shot ensemble, automatically adjusting the logits of different models based on their confidence when only pre-trained VLMs are available. Furthermore, for scenarios with extra few-shot samples, we propose the training-free and tuning ensemble, offering flexibility based on the availability of computing resources. The proposed ensemble strategies are evaluated on zero-shot, base-to-new, and cross-dataset generalization, achieving new state-of-the-art performance. Notably, this work represents an initial stride toward enhancing the generalization performance of VLMs via ensemble. The code is available at https://github.com/zhiheLu/Ensemble_VLM.git.Comment: Technical repor

Droplet Deposition Pattern Affected by Different Heating Directions

The coffee ring effect commonly exists in droplet deposition patterns, which fundamentally affects scientific research and industrial applications, like pharmaceutical purification, salt manufacturing, etc. Some researchers have tried different solutions to control the distributions of droplet deposition patterns, but most control deposits by adjusting droplet characteristics. In this work, droplet deposition patterns with different wettability are investigated by both localized and substrate heating. A whole process of droplet evaporation is recorded. The droplet generally evaporates from constant contact radius (CCR) mode to constant contact angle (CCA) mode, and CCR stage occupies the most of time. Experimental results show that, without any chemicals, laser induced local heating transitions particle deposition patterns from ring-like structure to dot-like patterns on a hydrophilic surface, driving most saline solvent to the center. Meanwhile, a hydrophobic surface is also investigated showing that the particles tend to assemble at the central area, but the pattern is slightly different compared to that on hydrophilic surface. In addition, physical mechanisms of local heating and heating from substrate are also explored in the present study

Transcriptome and Physiological Analyses for Revealing Genes Involved in Wheat Response to Endoplasmic Reticulum Stress.

BACKGROUND: Wheat production is largely restricted by adverse environmental stresses. Under many undesirable conditions, endoplasmic reticulum (ER) stress can be induced. However, the physiological and molecular responses of wheat to ER stress remain poorly understood. We used dithiothreitol (DTT) and tauroursodeoxycholic acid (TUDCA) to induce or suppress ER stress in wheat cells, respectively, with the aim to reveal the molecular background of ER stress responses using a combined approach of transcriptional profiling and morpho-physiological characterization. METHODS: To understand the mechanism of wheat response to ER stress, three wheat cultivars were used in our pre-experiments. Among them, the cultivar with a moderate stress tolerance, Yunong211 was used in the following experiments. We used DTT (7.5 mM) to induce ER stress and TUDCA (25 μg·mL RESULTS: Morpho-physiological results showed DTT significantly reduced plant height and biomass, decreased contents of chlorophyll and water, increased electrolyte leakage rate and antioxidant enzymes activity, and accelerated the cell death ratio, whereas these changes were all remarkably alleviated after TUDCA co-treatment. Therefore, RNA sequencing was performed to determine the genes involved in regulating wheat response to stress. Transcriptomic analysis revealed that 8204 genes were differentially expressed in three treatment groups. Among these genes, 158 photosynthesis-related genes, 42 antioxidant enzyme genes, 318 plant hormone-related genes and 457 transcription factors (TFs) may play vital roles in regulating wheat response to ER stress. Based on the comprehensive analysis, we propose a hypothetical model to elucidate possible mechanisms of how plants adapt to environmental stresses. CONCLUSIONS: We identified several important genes that may play vital roles in wheat responding to ER stress. This work should lay the foundations of future studies in plant response to environmental stresses

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model

IntroductionTo enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data.MethodsThree cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment.Results(1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model’s high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model’s p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model’s credibility in estimating health risks under varied urban environments.DiscussionThese results showcase the model’s ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning

Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models

Large Language Models (LLMs) have seen great advance in both academia and industry, and their popularity results in numerous open-source frameworks and techniques in accelerating LLM pre-training, fine-tuning, and inference. Training and deploying LLMs are expensive as it requires considerable computing resources and memory, hence many efficient approaches have been developed for improving system pipelines as well as operators. However, the runtime performance can vary significantly across hardware and software stacks, which makes it difficult to choose the best configuration. In this work, we aim to benchmark the performance from both macro and micro perspectives. First, we benchmark the end-to-end performance of pre-training, fine-tuning, and serving LLMs in different sizes , i.e., 7, 13, and 70 billion parameters (7B, 13B, and 70B) on three 8-GPU platforms with and without individual optimization techniques, including ZeRO, quantization, recomputation, FlashAttention. Then, we dive deeper to provide a detailed runtime analysis of the sub-modules, including computing and communication operators in LLMs. For end users, our benchmark and findings help better understand different optimization techniques, training and inference frameworks, together with hardware platforms in choosing configurations for deploying LLMs. For researchers, our in-depth module-wise analyses discover potential opportunities for future work to further optimize the runtime performance of LLMs

Evolutionary origin of a tetraploid allium species in the Qinghai-Tibet Plateau

Extinct taxa may be detectable if they were ancestors to extant hybrid species, which retain their genetic signature. In this study, we combined phylogenomics, population genetics and fluorescence in situ hybridization (GISH and FISH) analyses to trace the origin of the alpine tetraploid Allium tetraploideum (2n = 4x = 32), one of the five known members in the subgenus Cyathophora. We found that A. tetraploideum was an obvious allotetrapoploid derived from ancestors including at least two closely related diploid species, A. farreri and A. cyathophorum, from which it differs by multiple ecological and genomic attributes. However, these two species cannot account for the full genome of A. tetraploideum, indicating that at least one extinct diploid is also involved in its ancestry. Furthermore, A. tetraploideum appears to have arisen via homoploid hybrid speciation (HHS) from two extinct allotetraploid parents, which derived in turn from the aforementioned diploids. Other modes of origin were possible, but all were even more complex and involved additional extinct ancestors. Our study together highlights how some polyploid species might have very complex origins, involving both HHS and polyploid speciation and also extinct ancestors.</p

Gujin Dan is a Chinese medicine formulation that stimulates cell proliferation and differentiation by controlling multiple genes involved in MC3T3-E1 cells

Background: With the development of Traditional Chinese medicine (TCM) in recent years, the use of TCM in the treatment of osteoporosis has received much attention and research. Gujin Dan (GJD) is one of the representative Chinese medicine formulations that work synergistically with 19 herbs and has been used for decades to treat cervical spondylosis, lumbar disc herniation, osteoarthritis and osteoporosis. However, the exact molecular mechanism by which GJD is used to strengthen bones in the treatment of osteoporosis remains largely unknown. / Methods: In this study, an aqueous extract of GJD was prepared and its components were identified by high-performance liquid chromatography (HPLC). The effect of GJD aqueous extract on MC3T3-E1 cells was determined by Cell Counting Kit-8 (CCK-8) assay, alkaline phosphatase (ALP), and alizarin red S staining (ARS), combined with RNA sequencing (RNA-seq) and qRT-PCR. / Results: Our study showed that GJD significantly promoted the proliferation of MC3T3-E1 cells, as well as the synthesis and mineralisation of the extracellular matrix. GJD significantly increased the expression levels of genes that promote cell proliferation such as Adamts1, Mcam, Cyr61, Fos, Cebpd, Fosl2, Sirt1, Nipbl, Sema3c and Kcnq1ot1, up-regulated genes that inhibit apoptosis such as Gadd45a, Birc3, up-regulated genes that inhibit osteoclastogenesis such as Bcl6, Nfkbiz, Clcf1, Bcl3, Lgals3, Wisp1, Dusp1 and Fblim1, up-regulated genes that promote MC3T3-E1 cell differentiation such as Junb, Egr1, Klf10, Atf6, Malat1, Btg2, Sertad4, Zfyve16, Tet2, Creb5, Snai2, Fam46a, Calcrl and Pdzrn3. In addition, GJD mildly upregulated the expression levels of gene markers such as Atf4, Fn1, Usp7, Sox4, Col16a1, Spp1, Bmp1, Runx2, Bglap, Col12a1, and Alpl in osteoblasts. / Conclusions: Our results show that GJD promotes the differentiation and proliferation of MC3T3-E1 cells, inhibits osteoclast formation, and prevents osteoblast apoptosis. The present study significantly improves the current understanding of the molecular effects of GJD on MC3T3-E1 cells. This study also provides a new strategy for the further use of Chinese medicinal preparations against bone metabolism-related diseases