A Nonlinear Projection Neural Network for Solving Interval Quadratic Programming Problems and Its Stability Analysis

This paper presents a nonlinear projection neural network for solving interval quadratic programs subject to box-set constraints in engineering applications. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the interval quadratic optimization problems. By employing Lyapunov function approach, the global exponential stability of the proposed neural network is analyzed. Two illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper

On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions

As Federated Learning (FL) has gained increasing attention, it has become widely acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the overall framework when learning over a sequence of tasks results in the phenomenon known as ``catastrophic forgetting''. Consequently, much FL research has centered on devising federated increasing learning methods to alleviate forgetting while augmenting knowledge. On the other hand, forgetting is not always detrimental. The selective amnesia, also known as federated unlearning, which entails the elimination of specific knowledge, can address privacy concerns and create additional ``space'' for acquiring new knowledge. However, there is a scarcity of extensive surveys that encompass recent advancements and provide a thorough examination of this issue. In this manuscript, we present an extensive survey on the topic of knowledge editing (augmentation/removal) in Federated Learning, with the goal of summarizing the state-of-the-art research and expanding the perspective for various domains. Initially, we introduce an integrated paradigm, referred to as Federated Editable Learning (FEL), by reevaluating the entire lifecycle of FL. Secondly, we provide a comprehensive overview of existing methods, evaluate their position within the proposed paradigm, and emphasize the current challenges they face. Lastly, we explore potential avenues for future research and identify unresolved issues.Comment: 7 pages, 1 figure, 2 tabel

QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation

Federated Unlearning (FU) aims to delete specific training data from an ML model trained using Federated Learning (FL). We introduce QuickDrop, an efficient and original FU method that utilizes dataset distillation (DD) to accelerate unlearning and drastically reduces computational overhead compared to existing approaches. In QuickDrop, each client uses DD to generate a compact dataset representative of the original training dataset, called a distilled dataset, and uses this compact dataset during unlearning. To unlearn specific knowledge from the global model, QuickDrop has clients execute Stochastic Gradient Ascent with samples from the distilled datasets, thus significantly reducing computational overhead compared to conventional FU methods. We further increase the efficiency of QuickDrop by ingeniously integrating DD into the FL training process. By reusing the gradient updates produced during FL training for DD, the overhead of creating distilled datasets becomes close to negligible. Evaluations on three standard datasets show that, with comparable accuracy guarantees, QuickDrop reduces the duration of unlearning by 463.8x compared to model retraining from scratch and 65.1x compared to existing FU approaches. We also demonstrate the scalability of QuickDrop with 100 clients and show its effectiveness while handling multiple unlearning operations

Study on the spatial variability of thermal landscape in Xi’an based on OSM road network and POI data

As the primary contributor to the urban heat island effect, the construction land can be used to understand the spatial variation characteristics of the thermal landscape within the city at the microscopic scale. Taking the main urban area of Xi’an as the study area, this paper divides the minimum urban land unit by using OpenStreetMap (OSM) road network data and employs the kernel density analysis method based on Point of interest (POI) data to construct seven types of urban functional blocks. Furthermore, this paper also establishes a thermal landscape footprint characterization model to investigate the impact range of thermal landscape footprint for various types of functional blocks and quantitatively evaluate the spatial variation characteristics of urban thermal landscape, which is of great significance to the enhancement of urban ecological environment. The study indicates that: (1) The spatial distribution of urban functional blocks presents highly coupled characteristics with POI kernel density. (2) The surface thermal landscapes of seven types of urban functional blocks are predominantly medium-temperature and sub-high-temperature pixels, with the mean values of thermal fields ranked as logistics and storage blocks > industrial development blocks > transportation hub blocks > comprehensive service blocks > residential and living blocks > commercial and business blocks > strategic reserved blocks. (3) Apart from the strategic reserved blocks, the remaining urban functional blocks can produce thermal diffusion phenomena to the surrounding areas, and their thermal landscape footprints are obviously differentiated, with their influence ranges ranked as industrial development blocks > logistics and storage blocks > comprehensive service blocks > residential and living blocks > commercial and business blocks > transportation hub blocks. The findings of the study can provide scientific guidance for both the enhancement of urban ecological environment as well as the rational planning and layout of the city

A Nonlinear Projection Neural Network for Solving Interval Quadratic Programming Problems and Its Stability Analysis

This paper presents a nonlinear projection neural network for solving interval quadratic programs subject to box-set constraints in engineering applications. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the interval quadratic optimization problems. By employing Lyapunov function approach, the global exponential stability of the proposed neural network is analyzed. Two illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper

A Nonlinear Projection Neural Network for Solving Interval Quadratic Programming Problems and Its Stability Analysis

This paper presents a nonlinear projection neural network for solving interval quadratic programs subject to box-set constraints in engineering applications. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the interval quadratic optimization problems. By employing Lyapunov function approach, the global exponential stability of the proposed neural network is analyzed. Two illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Fast Bubble Detection Method in Microtubes Based on Pulsed Ultrasound

In the process of biological microfluidic manipulation, the bubbles generated in the tube will seriously reduce the gauging accuracy. This paper introduces an improving method that can estimate the size of microbubbles in real time. Hence, the measurement data of the liquid volume can be modified according to this method. A microbubble detector based on the pulsed-ultrasound method was studied, including the device structure and the working principle. The assessment formula of the microbubbles in the tube was derived from the simulation results, which adopted the two-phase theory. The digital image processing method was applied to fulfill the microbubble calibration. This detection method was applied to measure the microbubbles in the tube and to modify the flow volume in a timely manner. The results of the experiments showed that this method is effective at improving the microflow gauging accuracy

Identifying molecular subtypes and tumor microenvironment infiltration signatures in kidney renal clear cell carcinoma based on stemness-associated disulfidptosis genes by integrating machine learning, single-cell analyses and experimental validation

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignant tumor. Disulfidptosis is a new programmed cell death mechanism, which is characterized by the abnormal accumulation of intracellular disulfides that are highly toxic to cells. However, the contribution of disulfidptosis to ccRCC progression has not been fully clarified. In this study, two different molecular subtypes related to disulfidptosis were identified in ccRCC patients by the non-negative matrix factorization (NMF) algorithm. The cluster 1 was characterized by a worse prognosis and higher mRNAsi levels. Then, difference analysis and weighted gene co-expression network analysis (WGCNA) were conducted to search modular genes that are highly associated with tumor stemness and tumor microenvironment. Subsequently, a SADG signature containing nine genes was constructed stepwise by WGCNA and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The high-risk score group had a worse outcome, and immune regulation and metabolic signatures might be responsible for cancer progression in the high-risk group. After that, a predictive nomogram was constructed, and the predicting power of the risk model was verified using inter and three independent external validation datasets. Nine SADGs were shown to significantly correlate with immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) and immune checkpoint. In addition, based on the single-cell RNA sequencing dataset (GSE139555), the distribution and expression of nine hub genes in various types of immune cells were analyzed. Finally, the expression level of the nine genes was verified in clinical samples by qRT-PCR

Comparative Analysis of Growth, Survival, and Virulence Characteristics of <i>Listeria monocytogenes</i> Isolated from Imported Meat

Listeria monocytogenes is an important foodborne pathogen with worldwide prevalence. Understanding the variability in the potential pathogenicity among strains of different subtypes is crucial for risk assessment. In this study, the growth, survival, and virulence characteristics of 16 L. monocytogenes strains isolated from imported meat in China (2018–2020) were investigated. The maximum specific growth rate (μmax) and lag phase (λ) were evaluated using the time-to-detection (TTD) method and the Baranyi model at different temperatures (25, 30, and 37 °C). Survival characteristics were determined by D-values and population reduction after exposure to heat (60, 62.5, and 65 °C) and acid (HCl, pH = 2.5, 3.5, and 4.5). The potential virulence was evaluated via adhesion and invasion to Caco-2 cells, motility, and lethality to Galleria mellonella. The potential pathogenicity was compared among strains of different lineages and subtypes. The results indicate that the lineage I strains exhibited a higher growth rate than the lineage II strains at three growth temperatures, particularly serotype 4b within lineage I. At all temperatures tested, serotypes 1/2a and 1/2b consistently demonstrated higher heat resistance than the other subtypes. No significant differences in the log reduction were observed between the lineage I and lineage II strains at pH 2.5, 3.5, and 4.5. However, the serotype 1/2c strains exhibited significantly low acid resistance at pH 2.5. In terms of virulence, the lineage I strains outperformed the lineage II strains. The invasion rate to Caco-2 cells and lethality to G. mellonella exhibited by the serotype 4b strains were higher than those observed in the other serotypes. This study provides meaningful insights into the growth, survival, and virulence of L. monocytogenes, offering valuable information for understanding the correlation between the pathogenicity and subtypes of L. monocytogenes