Large bandwidth and high accuracy photonic-assisted instantaneous microwave frequency estimation system based on an integrated silicon micro-resonator

We demonstrate two instantaneous frequency measurement systems based on silicon photonics. One has a large bandwidth from 0.5 GHz to 35 GHz, while the other one exhibits a small RMS error of 63 MHz

Adaptation Speed Analysis for Fairness-aware Causal Models

For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models, respectively, exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.Comment: CIKM 202

Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains

Recognizing the prevalence of domain shift as a common challenge in machine learning, various domain generalization (DG) techniques have been developed to enhance the performance of machine learning systems when dealing with out-of-distribution (OOD) data. Furthermore, in real-world scenarios, data distributions can gradually change across a sequence of sequential domains. While current methodologies primarily focus on improving model effectiveness within these new domains, they often overlook fairness issues throughout the learning process. In response, we introduce an innovative framework called Counterfactual Fairness-Aware Domain Generalization with Sequential Autoencoder (CDSAE). This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features. This concurrent separation not only greatly improves model generalization across diverse and unfamiliar domains but also effectively addresses challenges related to unfair classification. Our strategy is rooted in the principles of causal inference to tackle these dual issues. To examine the intricate relationship between semantic information, sensitive attributes, and environmental cues, we systematically categorize exogenous uncertainty factors into four latent variables: 1) semantic information influenced by sensitive attributes, 2) semantic information unaffected by sensitive attributes, 3) environmental cues influenced by sensitive attributes, and 4) environmental cues unaffected by sensitive attributes. By incorporating fairness regularization, we exclusively employ semantic information for classification purposes. Empirical validation on synthetic and real-world datasets substantiates the effectiveness of our approach, demonstrating improved accuracy levels while ensuring the preservation of fairness in the evolving landscape of continuous domains

Effects of different extracts of Cremastra appendiculata (D. Don) Makino Cremastra appendiculata (D. Don) Makino on apoptosis of A549 cells

Purpose: To investigate the effect of different extracts of Cremastra appendiculata (D. Don) Makino onapoptosis of A549 cells, and the underlying mechanism.Methods: The contents of colchicine in ethyl acetate and n-butanol extracts of Cremastra appendiculata(D. Don) Makino were determined using high performance liquid chromatography (HPLC). Lung cancerA549 cells cultured in vitro were divided into blank control, standard colchicine and Cremastra appendiculata (D. Don) Makino extract groups. The effect of different extract concentrations on proliferation of the cells was determined using methyl thiazolyl diphenyl-tetrazolium (MTT) assay, while apoptosis of A549 cells induced by the extracts was evaluated by flow cytometry (FC).Results: Compared with the standard colchicine group, there was no colchicine in the n-butanol and ethyl acetate extracts of Cremastra appendiculata. Results from MTT assay showed that the extract inhibited the proliferation of A549 cells (p < 0.05). Flow cytometry results showed that ethyl acetate extract significantly enhanced apoptosis in A549 cells (p < 0.05). However, n-butanol extract had no significant effect on the apoptosis of A549 cells (p < 0.05).Conclusion: The ethyl acetate extract of Cremastra appendiculata (D. Don) Makino induces apoptosis in lung cancer A549 cells. Therefore, there is a need for further research and development of antitumor drugs from the extract of Cremastra appendiculata (D. Don) Makino. Keywords: Cremastra appendiculata (D. Don) Makino, Colchicine, A549 cells, Apoptosi

Direct hydrodeoxygenation of raw woody biomass into liquid alkanes

Being the only sustainable source of organic carbon, biomass is playing an ever-increasingly important role in our energy landscape. The conversion of renewable lignocellulosic biomass into liquid fuels is particularly attractive but extremely challenging due to the inertness and complexity of lignocellulose. Here we describe the direct hydrodeoxygenation of raw woods into liquid alkanes with mass yields up to 28.1 wt% over a multifunctional Pt/NbOPO(4) catalyst in cyclohexane. The superior performance of this catalyst allows simultaneous conversion of cellulose, hemicellulose and, more significantly, lignin fractions in the wood sawdust into hexane, pentane and alkylcyclohexanes, respectively. Investigation on the molecular mechanism reveals that a synergistic effect between Pt, NbO(x) species and acidic sites promotes this highly efficient hydrodeoxygenation of bulk lignocellulose. No chemical pretreatment of the raw woody biomass or separation is required for this one-pot process, which opens a general and energy-efficient route for converting raw lignocellulose into valuable alkanes

Analysis on Safety Measures of Substation Maintenance

As an important node in the power grid, substation plays a very important role in the whole power grid. The equipment operation of the substation is carried out in order to timely and effectively detect the operation status of the equipment, find the latent fault of the equipment, and the maintenance of the substation is necessary for safe operation. However, there are some safety problems in the operation of the substation, which need to analyze the status quo of its safe operation and formulate the corresponding improvement measures. Based on this, this article on the substation maintenance work safety measures for a brief analysis, hoping to provide for future reference