SEMICONDUCTOR-BASED NANOMATERIALS FOR PHOTOCATALYTIC ORGANIC DEGRADATION

Ph.DDOCTOR OF PHILOSOPH

Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

In this paper, a resource-efficient FPGA-based neural network regression model is developed for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned upgrades, the entirely new FPGA-based hardware muon trigger system will be installed in 2025-2026 that will process full muon detector data within a 10 ${\mu}s$ latency window. The planned large FPGA devices should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our model promises to improve the rejection of the dominant source of background events in the central detector region, which are due to muon candidates with low transverse momenta. This neural network was implemented in the hardware description language using 65 digital signal processors and about 10,000 lookup tables. The simulated network latency and deadtime are 245 and 60 ns, respectively, when implemented in the FPGA device using a 400 MHz clock frequency. These results are well within the requirements of the future ATLAS muon trigger system, therefore opening a possibility for deploying machine learning methods for data taking by the ATLAS experiment at the High Luminosity LHC.Comment: 12 pages, 17 figure

Kelangsungan industri lada hitam

In current Cloud computing environments, management of data reliability has become a challenge. For data-intensive scientific applications, storing data in the Cloud with the typical 3-replica replication strategy for managing the data reliability would incur huge storage cost. To address this issue, in this paper we present a novel cost-effective data reliability management mechanism named PRCR, which proactively checks the availability of replicas for maintaining the reliability. Our simulation indicates that, comparing with the typical 3 replica replication strategy, PRCR can significantly reduce the storage space consumption, hence storage cost in the Cloud

An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation

Widely available healthcare services are now getting popular because of advancements in wearable sensing techniques and mobile edge computing. People's health information is collected by edge devices such as smartphones and wearable bands for further analysis on servers, then send back suggestions and alerts for abnormal conditions. The recent emergence of federated learning allows users to train private data on local devices while updating models collaboratively. However, the heterogeneous distribution of the health condition data may lead to significant risks to model performance due to class imbalance. Meanwhile, as FL training is powered by sharing gradients only with the server, training data is almost inaccessible. The conventional solutions to class imbalance do not work for federated learning. In this work, we propose a new federated learning framework FedImT, dedicated to addressing the challenges of class imbalance in federated learning scenarios. FedImT contains an online scheme that can estimate the data composition during each round of aggregation, then introduces a self-attenuating iterative equivalent to track variations of multiple estimations and promptly tweak the balance of the loss computing for minority classes. Experiments demonstrate the effectiveness of FedImT in solving the imbalance problem without extra energy consumption and avoiding privacy risks.Comment: submitted to IEEE OJCS in Oct. 2023, under revie

Molecular mechanisms of exercise intervention in alleviating the symptoms of autism spectrum disorder: Targeting the structural alterations of synapse

Autism spectrum disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder characterized by stereotyped behaviors, specific interests, and impaired social and communication skills. Synapses are fundamental structures for transmitting information between neurons. It has been reported that synaptic deficits, such as the increased or decreased density of synapses, may contribute to the onset of ASD, which affects the synaptic function and neuronal circuits. Therefore, targeting the recovery of the synaptic normal structure and function may be a promising therapeutic strategy to alleviate ASD symptoms. Exercise intervention has been shown to regulate the structural plasticity of synapses and improve ASD symptoms, but the underlying molecular mechanisms require further exploration. In this review, we highlight the characteristics of synaptic structural alterations in the context of ASD and the beneficial effects of an exercise intervention on improving ASD symptoms. Finally, we explore the possible molecular mechanisms of improving ASD symptoms through exercise intervention from the perspective of regulating synaptic structural plasticity, which contributes to further optimizing the related strategies of exercise intervention promoting ASD rehabilitation in future

A New Type of Crumb Rubber Asphalt Mixture: A Dry Process Design and Performance Evaluation

To obtain a crumb rubber asphalt mixture with excellent performance, this study combined trans-polyoctenamer rubber (TOR), crumb rubber, and other additives to establish a new type of crumb rubber (CRT). The objective of this study was to design and evaluate the road performance of the new type of crumb rubber asphalt mixture (CRTAM) with a skeleton dense texture through a dry process. First, the skeleton intrusion compact volume method was used to optimize the grading of coarse and fine aggregates, and the design of the CRTAM gradation was carried out through the same and unequal volume replacement grading method. Then, three types of road performance were analyzed: high-temperature stability, low-temperature crack resistance, and water stability. The results showed that 2% and 2.5% CRT met a low-temperature index with equal volume substitution, and the six gradations obtained by unequal volume replacement with 2% CRT complied with the requirements of a skeleton dense texture. When the substitution ratio was 1.5 and 0.5, the high-temperature performance was better. In addition, when the substitution ratio was 0.5, the flexural strain energy density was the highest and the low-temperature performance was the best. Including considerations of economic benefits, it is recommended that the CRT content be 2% and the substitution ratio be 0.5

Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models

Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveraging external knowledge sources. However, the reliability of the retrieved information is not always guaranteed. The retrieval of irrelevant data can lead to misguided responses, and potentially causing the model to overlook its inherent knowledge, even when it possesses adequate information to address the query. Moreover, standard RALMs often struggle to assess whether they possess adequate knowledge, both intrinsic and retrieved, to provide an accurate answer. In situations where knowledge is lacking, these systems should ideally respond with "unknown" when the answer is unattainable. In response to these challenges, we introduces Chain-of-Noting (CoN), a novel approach aimed at improving the robustness of RALMs in facing noisy, irrelevant documents and in handling unknown scenarios. The core idea of CoN is to generate sequential reading notes for retrieved documents, enabling a thorough evaluation of their relevance to the given question and integrating this information to formulate the final answer. We employed ChatGPT to create training data for CoN, which was subsequently trained on an LLaMa-2 7B model. Our experiments across four open-domain QA benchmarks show that RALMs equipped with CoN significantly outperform standard RALMs. Notably, CoN achieves an average improvement of +7.9 in EM score given entirely noisy retrieved documents and +10.5 in rejection rates for real-time questions that fall outside the pre-training knowledge scope.Comment: Preprin

LASER: LLM Agent with State-Space Exploration for Web Navigation

Large language models (LLMs) have been successfully adapted for interactive decision-making tasks like web navigation. While achieving decent performance, previous methods implicitly assume a forward-only execution mode for the model, where they only provide oracle trajectories as in-context examples to guide the model on how to reason in the environment. Consequently, the model could not handle more challenging scenarios not covered in the in-context examples, e.g., mistakes, leading to sub-optimal performance. To address this issue, we propose to model the interactive task as state space exploration, where the LLM agent transitions among a pre-defined set of states by performing actions to complete the task. This formulation enables flexible backtracking, allowing the model to recover from errors easily. We evaluate our proposed LLM Agent with State-Space ExploRation (LASER) on both the WebShop task and amazon.com. Experimental results show that LASER significantly outperforms previous methods and closes the gap with human performance on the web navigation task.Comment: 4 pages, 2 figure

Fructose-1,6-bisphosphatase deficiency: estimation of prevalence in the Chinese population and analysis of genotype-phenotype association

ObjectiveFructose-1,6-bisphosphatase deficiency (FBP1D) is a rare inborn error due to mutations in the FBP1 gene. The genetic spectrum of FBP1D in China is unknown, also nonspecific manifestations confuse disease diagnosis. We systematically estimated the FBP1D prevalence in Chinese and explored genotype-phenotype association.MethodsWe collected 101 FBP1 variants from our cohort and public resources, and manually curated pathogenicity of these variants. Ninety-seven pathogenic or likely pathogenic variants were used in our cohort to estimate Chinese FBP1D prevalence by three methods: 1) carrier frequency, 2) permutation and combination, 3) Bayesian framework. Allele frequencies (AFs) of these variants in our cohort, China Metabolic Analytics Project (ChinaMAP) and gnomAD were compared to reveal the different hotspots in Chinese and other populations. Clinical and genetic information of 122 FBP1D patients from our cohort and published literature were collected to analyze the genotype-phenotypes association. Phenotypes of 68 hereditary fructose intolerance (HFI) patients from our previous study were used to compare the phenotypic differences between these two fructose metabolism diseases.ResultsThe estimated Chinese FBP1D prevalence was 1/1,310,034. In the Chinese population, c.490G>A and c.355G>A had significantly higher AFs than in the non-Finland European population, and c.841G>A had significantly lower AF value than in the South Asian population (all p values < 0.05). The genotype-phenotype association analyses showed that patients carrying homozygous c.841G>A were more likely to present increased urinary glycerol, carrying two CNVs (especially homozygous exon1 deletion) were often with hepatic steatosis, carrying compound heterozygous variants were usually with lethargy, and carrying homozygous variants were usually with ketosis and hepatic steatosis (all p values < 0.05). By comparing to phenotypes of HFI patients, FBP1D patients were more likely to present hypoglycemia, metabolic acidosis, and seizures (all p-value < 0.05).ConclusionThe prevalence of FBP1D in the Chinese population is extremely low. Genetic sequencing could effectively help to diagnose FBP1D