Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion

This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets. Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities. To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged. In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered. Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.Comment: 11 pages, 14 figures. IEEE Transactions on Aerospace and Electronics Systems, 202

Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR

<p>Abstract</p> <p>Background</p> <p>Real-time quantitative reverse transcription PCR (RT-qPCR) data needs to be normalized for its proper interpretation. Housekeeping genes are routinely employed for this purpose, but their expression level cannot be assumed to remain constant under all possible experimental conditions. Thus, a systematic validation of reference genes is required to ensure proper normalization. For soybean, only a small number of validated reference genes are available to date.</p> <p>Results</p> <p>A systematic comparison of 14 potential reference genes for soybean is presented. These included seven commonly used (<it>ACT2, ACT11, TUB4, TUA5, CYP, UBQ10, EF1b</it>) and seven new candidates (<it>SKIP16, MTP, PEPKR1, HDC, TIP41, UKN1, UKN2</it>). Expression stability was examined by RT-qPCR across 116 biological samples, representing tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars. Expression of all 14 genes was variable to some extent, but that of <it>SKIP16, UKN1 </it>and <it>UKN2 </it>was overall the most stable. A combination of <it>ACT11, UKN1 </it>and <it>UKN2 </it>would be appropriate as a reference panel for normalizing gene expression data among different tissues, whereas the combination SKIP16, UKN1 and MTP was most suitable for developmental stages. <it>ACT11, TUA5 </it>and <it>TIP41 </it>were the most stably expressed when the photoperiod was altered, and <it>TIP41, UKN1 </it>and <it>UKN2 </it>when the light quality was changed. For six different cultivars in long day (LD) and short day (SD), their expression stability did not vary significantly with <it>ACT11, UKN2 </it>and <it>TUB4 </it>being the most stable genes. The relative gene expression level of <it>GmFTL3</it>, an ortholog of Arabidopsis <it>FT </it>(<it>FLOWERING LOCUS T</it>) was detected to validate the reference genes selected in this study.</p> <p>Conclusion</p> <p>None of the candidate reference genes was uniformly expressed across all experimental conditions, and the most suitable reference genes are conditional-, tissue-specific-, developmental-, and cultivar-dependent. Most of the new reference genes performed better than the conventional housekeeping genes. These results should guide the selection of reference genes for gene expression studies in soybean.</p

Learning to Behave Like Clean Speech: Dual-Branch Knowledge Distillation for Noise-Robust Fake Audio Detection

Most research in fake audio detection (FAD) focuses on improving performance on standard noise-free datasets. However, in actual situations, there is usually noise interference, which will cause significant performance degradation in FAD systems. To improve the noise robustness, we propose a dual-branch knowledge distillation fake audio detection (DKDFAD) method. Specifically, a parallel data flow of the clean teacher branch and the noisy student branch is designed, and interactive fusion and response-based teacher-student paradigms are proposed to guide the training of noisy data from the data distribution and decision-making perspectives. In the noise branch, speech enhancement is first introduced for denoising, which reduces the interference of strong noise. The proposed interactive fusion combines denoising features and noise features to reduce the impact of speech distortion and seek consistency with the data distribution of clean branch. The teacher-student paradigm maps the student's decision space to the teacher's decision space, making noisy speech behave as clean. In addition, a joint training method is used to optimize the two branches to achieve global optimality. Experimental results based on multiple datasets show that the proposed method performs well in noisy environments and maintains performance in cross-dataset experiments

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

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Location Choice of the Five Mekong Countries’ Ability to Undertake China’s Industrial Transfer

The core of “building a community of destiny of the Mekong countries” is to promote sustainable economic development in the Mekong basin, and a comprehensive understanding of the ability of the five Mekong countries to take over Chinese industries is of great significance to promote Mekong cooperation. The study analyzes the overall take-up capacity of the five Mekong countries by constructing an index system for evaluating the take-up capacity of industries. The study shows that the industrial transfer capacity of the five Lan Mekong countries from 2011 to 2017 shows a stable trend, with Vietnam declining slowly and Laos growing faster, and the industrial transfer capacity of the five countries shows obvious spatial differentiation characteristics

Identification of major environmental factors driving phytoplankton community succession before and after the regime shift of Erhai Lake, China

In lake ecosystems, regime shifts can induce changes in physical and biological components, particularly phytoplankton community structure, as well as across multiple trophic levels. Phytoplankton community succession may be driven by different environmental factors before and after regime shift; however, owing to the abruptness of the regime shift, long-term observations encompassing both periods are scarce, limiting our ability to identify the shift of key drivers of phytoplankton community succession. Here, an analysis was conducted based on long-term observations (involving phytoplankton community and environmental variables from 1997 to 2008) in Erhai Lake, a eutrophic lake in China that may have undergone a regime shift in 2001–2003. The dynamics of hydrochemical parameters and phytoplankton community composition indicated a distinct regime shift between 2001 and 2003. The phytoplankton community evolved over a decadal period toward community structure simplification, dominance of individual species, and homogenization of species composition. Prior to the regime shift (1997–2000), the eutrophic status of Erhai Lake was oligotrophic, and the phytoplankton community was characterized by high biodiversity and low biomass, with Cyanophyta, Bacillariophyta, and Cryptophyta being the dominant species. Nutrient concentrations had a significant limiting effect on phytoplankton, with total nitrogen concentration being the primary limiting factor. Following the regime shift (2005–2008), the phytoplankton community exhibited low biodiversity and high biomass, with the dominant taxa shifting to Cyanophyta, Chlorophyta, and Bacillariophyta. Light became the primary driver of phytoplankton community succession. Our study was based on long-term observations covering regime shifts in Erhai Lake, providing strong evidence that the phytoplankton community is influenced by nutrients in oligotrophic status. However, as nutrient concentrations increase, especially once the threshold is exceeded, species interactions may shift from competition for nutrients to competition for light. This implies that the resource competition theory, which integrates nutrient- and light-based approaches, provides an effective approach to predicting phytoplankton community succession, and that measures to improve underwater light environments should be considered as an additional option for nutrient load reduction in eutrophic lakes

Bio-inspired Chemical Space Exploration of Terpenoids

Many computational methods are used to expand the open-ended border of chemical spaces. Natural products and their derivatives are an important source for drug discovery, and some algorithms are devoted to rapidly generating pseudo-natural products, while their accessibility and chemical interpretation were often ignored or underestimated, thus hampering experimental synthesis in practice. Herein, a bio-inspired strategy (named TeroGen) is proposed, in which the cyclization and decoration stage of terpenoid biosynthesis were mimicked by meta-dynamics simulations and deep learning models respectively, to explore their chemical space. In the protocol of TeroGen, the synthetic accessibility is validated by reaction energetics (reaction barrier and reaction heat) based on the GFN2-xTB methods. Chemical interpretation is an intrinsic feature as the reaction pathway is bioinspired and triggered by the RMSD-PP method in conjunction with an encoder-decoder architecture. This is quite distinct from conventional library/fragment-based or rule-based strategies, by using TeroGen, new reaction routes are feasibly explored to increase the structural diversity. For example, only a rather limited number of sesterterpenoids in our training set is included in this work, but our TeroGen would predict more than 30000 sesterterpenoids and map out the reaction network with super efficiency, ten times as many as the known sesterterpenoids (less than 2500). In sum, TeroGen not only greatly expands the chemical space of terpenoids but also provides various plausible biosynthetic pathways, which are crucial clues for heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids