A modified lumped parameter model of distribution transformer winding

This work was supported by the National Key Research and Development Plan of China under Grant (2016YFB0900600XXX).The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction. A distributed parameters model can depict the winding characteristics accurately, but it requires complex calculations. Lumped parameter model requires less calculations, but its applicable frequency range is not wide. This paper studies the amplitude-frequency characteristics of the lightning wave, compares the transformer modelling methods and finally proposes a modified lumped parameter model, based on the above comparison. The proposed model minimizes the errors provoked by the lumped parameter approximation, and the hyperbolic functions of the distributed parameter model. By this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage response. The proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.publishersversionpublishe

Learning with Logical Constraints but without Shortcut Satisfaction

Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the knowledge. In this paper, we present a new framework for learning with logical constraints. Specifically, we address the shortcut satisfaction issue by introducing dual variables for logical connectives, encoding how the constraint is satisfied. We further propose a variational framework where the encoded logical constraint is expressed as a distributional loss that is compatible with the model's original training loss. The theoretical analysis shows that the proposed approach bears salient properties, and the experimental evaluations demonstrate its superior performance in both model generalizability and constraint satisfaction.Comment: Published as a conference paper at ICLR 2023, and code is available at https://github.com/SoftWiser-group/NeSy-without-Shortcut

Softened Symbol Grounding for Neuro-symbolic Systems

Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resulting in an effective and efficient neuro-symbolic learning framework. Technically, the framework features (1) modeling of symbol solution states as a Boltzmann distribution, which avoids expensive state searching and facilitates mutually beneficial interactions between network training and symbolic reasoning;(2) a new MCMC technique leveraging projection and SMT solvers, which efficiently samples from disconnected symbol solution spaces; (3) an annealing mechanism that can escape from %being trapped into sub-optimal symbol groundings. Experiments with three representative neuro symbolic learning tasks demonstrate that, owining to its superior symbol grounding capability, our framework successfully solves problems well beyond the frontier of the existing proposals.Comment: Published as a conference paper at ICLR 2023. Code is available at https://github.com/SoftWiser-group/Soften-NeSy-learnin

In Vivo Blood Glucose Quantification Using Raman Spectroscopy

We here propose a novel Raman spectroscopy method that permits the noninvasive measurement of blood glucose concentration. To reduce the effects of the strong background signals produced by surrounding tissue and to obtain the fingerprint Raman lines formed by blood analytes, a laser was focused on the blood in vessels in the skin. The Raman spectra were collected transcutaneously. Characteristic peaks of glucose (1125 cm(-1)) and hemoglobin (1549 cm(-1)) were observed. Hemoglobin concentration served as an internal standard, and the ratio of the peaks that appeared at 1125 cm(-1) and 1549 cm(-1) peaks was used to calculate the concentration of blood glucose. We studied three mouse subjects whose blood glucose levels became elevated over a period of 2 hours using a glucose test assay. During the test, 25 Raman spectra were collected transcutaneously and glucose reference values were provided by a blood glucose meter. Results clearly showed the relationship between Raman intensity and concentration. The release curves were approximately linear with a correlation coefficient of 0.91. This noninvasive methodology may be useful for the study of blood glucose in vivo

Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey

Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are predominantly pretrained on short text snippets, which compromises their effectiveness in processing the long-context prompts that are frequently encountered in practical scenarios. This article offers a comprehensive survey of the recent advancement in Transformer-based LLM architectures aimed at enhancing the long-context capabilities of LLMs throughout the entire model lifecycle, from pre-training through to inference. We first delineate and analyze the problems of handling long-context input and output with the current Transformer-based models. We then provide a taxonomy and the landscape of upgrades on Transformer architecture to solve these problems. Afterwards, we provide an investigation on wildly used evaluation necessities tailored for long-context LLMs, including datasets, metrics, and baseline models, as well as optimization toolkits such as libraries, frameworks, and compilers to boost the efficacy of LLMs across different stages in runtime. Finally, we discuss the challenges and potential avenues for future research. A curated repository of relevant literature, continuously updated, is available at https://github.com/Strivin0311/long-llms-learning.Comment: 40 pages, 3 figures, 4 table

Protective effects of ginsenosides Rg1 and Rb1 against cognitive impairment induced by simulated microgravity in rats

Microgravity experienced during space flight is known to exert several negative effects on the learning ability and memory of astronauts. Few effective strategies are currently available to counteract these effects. Rg1 and Rb1, the major steroidal components of ginseng, have shown potent neuroprotective effects with a high safety profile. The present study aimed to investigate the effects of Rg1 and Rb1 on simulated microgravity-induced learning and memory dysfunction and its underlying mechanism in the hindlimb suspension (HLS) rat model. Administration of Rg1 (30 and 60 μmol/kg) and Rb1 (30 and 60 μmol/kg) for 2 weeks resulted in a significant amelioration of impaired spatial and associative learning and memory caused by 4-week HLS exposure, measured using the Morris water maze and Reward operating conditioning reflex (ROCR) tests, respectively. Furthermore, Rg1 and Rb1 administration alleviated reactive oxygen species production and enhanced antioxidant enzyme activities in the prefrontal cortex (PFC). Rg1 and Rb1 also assisted in the recovery of mitochondrial complex I (NADH dehydrogenase) activities, increased the expression of Mfn2 and decreased the fission marker dynamin-related protein (Drp)-1expression. Additionally, Rg1 and Rb1 treatment increased the SYN, and PSD95 protein expressions and decreased the ratio of Bax:Bcl-2 and reduced the expression of cleaved caspase-3 and cytochrome C. Besides these, the BDNF-TrkB/PI3K-Akt pathway was also activated by Rg1 and Rb1 treatment. Altogether, Rg1 and Rb1 treatment attenuated cognitive deficits induced by HLS, mitigated mitochondrial dysfunction, attenuated oxidative stress, inhibited apoptosis, increased synaptic plasticity, and restored BDNF-TrkB/PI3K-Akt signaling

Mi-2β promotes immune evasion in melanoma by activating EZH2 methylation

Recent development of new immune checkpoint inhibitors has been particularly successfully in cancer treatment, but still the majority patients fail to benefit. Converting resistant tumors to immunotherapy sensitive will provide a significant improvement in patient outcome. Here we identify Mi-2β as a key melanoma-intrinsic effector regulating the adaptive anti-tumor immune response. Studies in genetically engineered mouse melanoma models indicate that loss of Mi-2β rescues the immune response to immunotherapy in vivo. Mechanistically, ATAC-seq analysis shows that Mi-2β controls the accessibility of IFN-γ-stimulated genes (ISGs). Mi-2β binds to EZH2 and promotes K510 methylation of EZH2, subsequently activating the trimethylation of H3K27 to inhibit the transcription of ISGs. Finally, we develop an Mi-2β-targeted inhibitor, Z36-MP5, which reduces Mi-2β ATPase activity and reactivates ISG transcription. Consequently, Z36-MP5 induces a response to immune checkpoint inhibitors in otherwise resistant melanoma models. Our work provides a potential therapeutic strategy to convert immunotherapy resistant melanomas to sensitive ones