Search

Single infrared (IR) image super‐resolution methods can help to reduce the cost and difficulty in manufacturing IR sensors for the imaging system. However, the deep learning‐based image SR methods need to build a complex network and thus consume a lot of computational power, which limits the application of SR technology on devices with low computing resources in practice. To solve this problem, the authors present a lightweight multi‐path feature fusion network (MFFN) for the single infrared (IR) image SR. A multi‐path feature fusion block (MFFB) is developed to extract and fuse multiple and discriminative features in a recursive feedback way. Specifically, the multiple features are refined via the linear feature extraction branch, shared‐source residual feature extraction branch, and channel attention branch in MFFB. Finally, the authors reconstruct the high‐resolution IR images from the low‐resolution counterpart based on the refined multiple features. The experimental results demonstrate that MFFN achieves high‐quality single infrared image SR and shows superiority over previous methods for several scale factors (e.g. ×2, ×3, and ×4). MFFN has potential applications in the mobile infrared imaging system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hardware Trojan detection method based on sweep‐frequency test since Trojan horse threatens information security seriously. An equivalent parasitic and load circuit model, and analysis method are depicted in detail. Experimental results of the amplitude‐frequency responses between the input and output accord with the simulation results. 11 golden chips and 9 Trojan chips are used for the sweep‐frequency tests, and the golden chips can be distinguished from Trojan chips accurately based on the relationship between the peak and valley resonant frequencies, which indicates that the proposed model and detection method are effective. [ABSTRACT FROM AUTHOR]

Power consumption reduction is the primary problem for the design and implementation of heterogeneous parallel systems. As it is difficult to make progress in the low-power optimization in the hardware layer to meet the increasing need for power optimization, more attention has been paid to low-power optimization in the hardware layer. The relationship between the execution time and dynamic power consumption of programs divided between homogeneous and heterogeneous computing sections is analysed. In addition, the communication power consumption for data transmission and dynamic multi-task allocation are described. Afterwards, this study establishes a power model for the whole procedure of heterogeneous parallel systems. By using this model, a selection algorithm is designed for the optimal frequency of processors with optimal power consumption under time constraints, optimal descent-based time allocation algorithms in multiple computing sections, and profiling dynamic analysis-based integral linear programming at algorithm-level, separately. Finally, the validity of the power optimization algorithm is ascertained using typical applications. [ABSTRACT FROM AUTHOR]

A device for detecting the microorganisms in water with a freeform lens is developed. The main components of the device include a water channel, a light-emitting diode (LED) light source, and a freeform lens. The freeform lens which plays a key role in the device is designed with a predefined light intensity distribution using a double freeform surface design method. Different from the traditional methods, during the construction of the lens, the mapping is established by an incident-emergent angle relation. As an example, a model of the device is designed. Monte Carlo ray tracing simulations are implemented to verify optical performance of the device. Installation error analyses on the LED source are also conducted. The results demonstrate that the device can generate a uniform illumination pattern at the arbitrary depth of the water and is feasible for the microorganism detection in water. Additionally, the results also show that the lens can achieve the uniform illumination without the influence of the illumination distance. An optical efficiency greater than 82% is obtained for the designed model. [ABSTRACT FROM AUTHOR]

Knowledge graph technology plays an important role in knowledge supporting for efficient supply chain management (SCM) of manufacturing enterprises, a SCM knowledge graph can be constructed based on the relevant case corpus. In order to solve the problems of coarse concept granularity of event ontology knowledge in SCM cases, potential words of characters in the existing entity recognition models may not be matched or matched incorrectly, key features of entities with different lengths may interfere with each other in the expression of attention mechanism, and lack of a large number of annotation training samples, a top-down construction method of SCM event logic knowledge graph (ELKG) is proposed. Firstly, SCM event argument classes and class relations are defined, an event logic ontology model is built, and the event argument entities according to event logic ontology are labeled. Then, an active learning event argument entity recognition (EAER) model based on two-stage generative adversarial network (GAN) is proposed. In GAN generator, an EAER model based on binocular attention-based stacked BiLSTM with CNN (BACSBN) is proposed, which combines word-level character feature attention mechanism and n -gram pooling feature attention mechanism to improve the attention to character features of constituent words and highlight entity key information with different lengths, respectively. Two-stage GAN adversarial training and label space attention mechanism are introduced to select the correct predicted label samples for active learning training. The experimental results show that BACSBN can improve the entity recognition accuracy, and the two-stage GAN's active learning can further improve the recognition effect on the basis of full annotation sample training, and can still maintain a high accuracy in the absence of a large number of manual annotation data. Further, according to the sentence pattern and keyword matching, the matching relations of argument entities are completed, and the SCM ELKG is constructed to provide knowledge support for autonomous SCM. [ABSTRACT FROM AUTHOR]

Fault diagnosis is the key concern in the operation and maintenance of industrial assets. A fault diagnosis knowledge graph (KG) can provide decision support to the engineers to efficiently conduct maintenance tasks. However, as a type of domain KG, it would be time-consuming to manually label the corpus collected from the multi-source including the maintenance log, handbook and article. Meanwhile, the existence of the noisy sentence in the multi-source corpus jeopardises the performance of relation extraction modelling. In order to address this issue, this paper proposes a distant supervision relation extraction (DSRE)-based approach to construct a fault diagnosis KG. In this approach, the ontology of the fault diagnosis KG is firstly designed. Subsequently, a DSRE algorithm named relation-aware-based sentence-level attention enhanced piecewise convolutional neural network with reinforcement learning strategy (PCNN-ATTRA-RL) is proposed. The algorithm can effectively lower the impact of noisy sentences and accurately label the relation of different entities when the labelled data is insufficient. In this algorithm, PCNN-ATTRA is designed as the DSRE classifier to effectively extract the relation between entity pairs. RL is conducted to remove the noisy sentence so as to further improve the performance. An experimental study based on the multi-source corpus collected from the real world reveals that the proposed approach shows merits in comparison with the state-of-the-art algorithms. Meanwhile, a fault diagnosis KG, which can greatly support the decision-making of the engineers in the fault diagnosis, is established via the proposed approach. [ABSTRACT FROM AUTHOR]

Microwave oven is widely used around the world since the functions of reheating and defrosting are convenient and efficient. However, these functions in traditional microwave oven are not intelligent for the control effects deeply rely on the users' experience. Besides, heating without food is very dangerous since there is no no-load detection. Therefore, in this paper, infrared array sensor and humidity sensor are combined together to achieve a smart microwave oven. Besides, no-load detection method, detection method for humidity sensor, pre-processing method for infrared sensor, location identification method for the food, and intelligent control methods for defrosting and reheating are proposed in detail. Experimental results demonstrate that the eigenvalues of load tests are all smaller than 1.5, while they are all larger than 1.5 in the no-load tests. The detection accuracy is 100% in the 200 test cases. In addition, the final temperatures of the meats (i.e. pork, fish, drumstick and chicken) after intelligent defrosting control are all between −2 °C and 25 °C, and the mean temperature ranges from 1 °C to 2.5 °C, which meets the expected target. Besides, the maximum control error of "Heating Target" is 3.5 °C, and the maximum difference between the mean temperature of multiple measurements and the corresponding target is 1.56 °C. The control error and control deviation of the temperature range for "Milk" are both less than 1 °C. As for "Reheating" control, the maximum control error and control deviation of the temperature range for the food with less moisture content are 0.18 °C and 2.5 °C, while they are 2.7 °C and 3.2 °C for the food with more moisture content. Moreover, the food quality after reheating or defrosting with the proposed smart microwave oven is better than that with the traditional microwave oven. In general, the proposed no-load detection method, and the intelligent control methods for defrosting and reheating are effective. • A really smart microwave oven is achieved with infrared array sensor and humidity sensor. • Fuzzy classification, energy estimation and temperature-rise control are used for the automatic reheating or defrosting control. • The reconstruction of characteristic envelope is proposed to advance the no-load detection sensitivity. • The no-load detection accuracy is 100%, and the maximum control error or control deviation is less than 3.5 ℃. • The proposed smart microwave oven is low cost and easy to realize. [ABSTRACT FROM AUTHOR]

• A deep learning denoising computational ghost imaging method is proposed. • A deep neural network is developed for ghost imaging image reconstruction. • The object image is restored directly from the one-dimensional bucket signals. • The proposed scheme have wide potential applications. • The proposed scheme can obtain clear images with sub-Nyquist sampling ratios. We propose a deep learning denoising computational ghost imaging (CGI) method to obtain a clear object image with a sub-Nyquist sampling ratio. We develop an end-to-end deep neural network (DDANet) for CGI image reconstruction. DDANet uses a one-dimensional (1-D) bucket signals (BSs) and multiple tunable noise-level maps as input, and outputs a clear image. We train DDANet with simulated BSs and ground-truth pairs, and then retrieve the object image directly from an experimental obtained 1-D BSs. The effectiveness of the proposed method is experimentally investigated. The proposed method has practical applications in image denoising and enhancement of the CGI and single-pixel computational imaging. [ABSTRACT FROM AUTHOR]

• A compact computational ghost imaging system is proposed. • A 4-connected-region-based Hadamard pattern sequence optimization method is proposed. • The system can achieve image reconstructions with a low sampling rate. • The proposed method possesses excellent imaging performance. • The proposed system and method have wide potential applications. We propose a computational ghost imaging system based on the Hadamard patterns. A 4-connected-region-based method is proposed to optimize the Hadamard pattern sequence and a differential decomposition method is developed to construct a sequence that can be shown on the light modulation devices with the optimized pattern sequence. Finally, the image is reconstructed by the second-order correlation algorithm. Both numerical model and experimental setup are established and a series of experiments are implemented. The imaging performance of the proposed method is validated by comparing with the results from the default order of the Hadamard pattern sequence, Russian-dolls ordering and fast Walsh-Hadamard transform. The proposed system can realize high quality imaging with a low sampling ratio (5%–10%), and it requires much less measurement numbers for imaging. [ABSTRACT FROM AUTHOR]

• An online computational ghost imaging scheme is proposed. • An adaptive measurement termination method is proposed. • The object image is recovered immediately after the light intensity is acquired. • The proposed scheme have wide potential applications. • The proposed scheme can achieve image reconstructions with few measurement numbers. We propose an online adaptive computational ghost imaging (CGI) scheme that offers high quality imaging with few measurement numbers. Different from the conventional second-order correlation CGI, the proposed scheme utilizes an online imaging method for image reconstruction, where the object image is recovered immediately after the light intensity is acquired. An adaptive measurement termination method is proposed to terminate the online imaging when the quality of the recovered image is satisfied. The effectiveness of the proposed scheme is verified by numerical simulations and experiments and its imaging performance is assessed by comparing the results with state-of-the-art FWHTGI (fast Walsh-Hadamard transform CGI) and RDCGI (Russian dolls ordering CGI). The results demonstrate that the proposed scheme can achieve high-quality imaging with few measurements, and imaging quality is better than that with FWHTGI and RDCGI under low measurement number conditions. The proposed scheme may be helpful for high-resolution and real-time CGI. [ABSTRACT FROM AUTHOR]

We propose a computational ghost imaging (CGI) scheme based on a rearranged Hadamard matrix (HCGI). A decomposition method is first introduced to construct a speckle pattern sequence that can be displayed on the digital micromirror device. Then a speckle pattern sequence rearrangement method is proposed, which makes the large intensity fluctuation values concentrate on the front part of the intensity fluctuation sequence. Finally, a HCGI model is established and a series of numerical experiments are conducted. The influence of intensity fluctuations on HCGI is studied and analyzed by comparing the numerical results which are respectively recovered by the proposed method and fast Walsh–Hadamard transform. The results demonstrate that large intensity fluctuation values play the key role in HCGI and provide maximum contribution to high quality imaging. The proposed method provides a way to improve the imaging quality and speed of CGI from the perspective of intensity fluctuations. • A Hadamard-based computational ghost imaging scheme is proposed. • A speckle pattern sequence rearrangement method is proposed. • The scheme can achieve image reconstructions with a low sampling rate. • Influence of intensity fluctuations on Hadamard-based ghost imaging is studied. • The technique possesses excellent performances and wide potential applications. [ABSTRACT FROM AUTHOR]

Objective: Histological classification is a challenging task due to the diverse appearances, unpredictable variations, and blurry edges of histological tissues. Recently, many approaches based on large networks have achieved satisfactory performance. However, most of these methods rely heavily on substantial computational resources and large high-quality datasets, limiting their practical application. Knowledge Distillation (KD) offers a promising solution by enabling smaller networks to achieve performance comparable to that of larger networks. Nonetheless, KD is hindered by the problem of high-dimensional characteristics, which makes it difficult to capture tiny scattered features and often leads to the loss of edge feature relationships., Methods: A novel cross-domain visual prompting distillation approach is proposed, compelling the teacher network to facilitate the extraction of significant high-dimensional features into low-dimensional feature maps, thereby aiding the student network in achieving superior performance. Additionally, a dynamic learnable temperature module based on novel vector-based spatial proximity is introduced to further encourage the student to imitate the teacher., Results: Experiments conducted on widely accepted histological datasets, NCT-CRC-HE-100K and LC25000, demonstrate the effectiveness of the proposed method and validate its robustness on the popular dermoscopic dataset ISIC-2019. Compared to state-of-the-art knowledge distillation methods, the proposed method achieves better performance and greater robustness with optimal domain adaptation., Conclusion: A novel distillation architecture, termed VPSP, tailored for histological classification, is proposed. This architecture achieves superior performance with optimal domain adaptation, enhancing the clinical application of histological classification. The source code will be released at https://github.com/xiaohongji/VPSP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

About a quarter of the world's population suffers from insomnia, and the number of the insomniacs is gradually increasing. However, the current drug therapy and non-drug therapy sleep-aid methods have certain limitations. In general, the sleep-aid effect of drug therapy is better than that of Non-drug therapy, but western medicine may lead to some side effects and drug abuse. Although the side effects of Chinese Herbal Medicine (CHM) are relatively small, making the herbal decoction is complex and time-consuming. Therefore, exploring a novel sleep-aid method is very significant. In this paper, a flexible and dissolving Traditional Chinese Medicine (TCM) microneedle patch is proposed for sleep-aid intervention. The TCM microneedle patch is a micrometer-scale intrusive object, and the herbal extracts are carried by the patch. The materials, design method, and fabrication process of the microneedle patch have been described in detail. Besides, the mechanical characteristics of the microneedle patch, sleep-aid effect evaluation method, and experimental scheme have been presented. Three microneedle tips with radii of 5 μm, 15 μm, and 22 μm are selected for simulation analysis. Abaqus simulation results indicate that the smaller the radius of the microneedle tip, the smaller the piercing force. Considering that the microneedle should easily penetrate the skin without buckling, that is, the piercing force should be larger than the buckling force, thus 15 μm, instead of 5 μm or 22 μm, is more suitable to be adopted as the radius of the microneedle tip. For the microneedle with the radius of 15 μm, the piercing force is 0.033 N, and the difference between the piercing force and buckling force is 0.036 N. Experimental results demonstrate that the fracture force of the microneedle is about 0.29 N, which is far larger than the piercing force and buckling force. The single-lead EEG signals of the frontal lobe are used to evaluate the sleep-aid effect of the TCM microneedle patch. After sleep-aid intervention on the Anmian and Yintang acupoints using the patches, for most subjects, the ratios of the low-frequency brain wave energies to the high-frequency brain wave energies are increased obviously, indicating that the proposed sleep-aid method is effective., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Cashmere and wool are both natural animal fibers used in the textile industry, but cashmere is of superior quality, is rarer, and more precious. It is therefore important to distinguish the two fibers accurately and effectively. However, challenges due to their similar appearance, morphology, and physical and chemical properties remain. Herein, a terahertz electromagnetic inductive transparency (EIT) metasurface biosensor is introduced for qualitative and quantitative identification of cashmere and wool. The periodic unit structure of the metasurface consists of four rotationally symmetric resonators and two cross-arranged metal secants to form toroidal dipoles and electric dipoles, respectively, so that its effective sensing area can be greatly improved by 1075% compared to the traditional dipole mode, and the sensitivity will be up to 342 GHz/RIU. The amplitude and frequency shift changes of the terahertz transmission spectra caused by the different refractive indices of cashmere/wool can achieve highly sensitive label-free qualitative and quantitative identification of both. The experimental results show that the terahertz metasurface biosensor can work at a concentration of 0.02 mg/mL. It provides a new way to achieve high sensitivity, precision, and trace detection of cashmere/wool, and would be a valuable application for the cashmere industry.

The reliability of MEMS inertial devices applied in complex environments involves interdisciplinary fields, such as structural mechanics, material mechanics and multi-physics field coupling. Nowadays, MEMS inertial devices are widely used in the fields of automotive industry, consumer electronics, aerospace and missile guidance, and a variety of reliability issues induced by complex environments arise subsequently. Hence, reliability analysis and design of MEMS inertial devices are becoming increasingly significant. Since the reliability issues of MEMS inertial devices are mainly caused by complex mechanical and thermal environments with intricate failure mechanisms, there are fewer reviews of related research in this field. Therefore, this paper provides an extensive review of the research on the reliability of typical failure modes and mechanisms in MEMS inertial devices under high temperature, temperature cycling, vibration, shock, and multi-physical field coupling environments in the last five to six years. It is found that though multiple studies exist examining the reliability of MEMS inertial devices under single stress, there is a dearth of research conducted under composite stress and a lack of systematic investigation. Through analyzing and summarizing the current research progress in reliability design, it is concluded that multi-physical field coupling simulation, theoretical modeling, composite stress experiments, and special test standards are important directions for future reliability research on MEMS inertial devices., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.

MEMS gyroscopes are widely applied in consumer electronics, aerospace, missile guidance, and other fields. Reliable packaging is the foundation for ensuring the survivability and performance of the sensor in harsh environments, but gas leakage models of wafer-level MEMS gyroscopes are rarely reported. This paper proposes a gas leakage model for evaluating the packaging reliability of wafer-level MEMS gyroscopes. Based on thermodynamics and hydromechanics, the relationships between the quality factor, gas molecule number, and a quality factor degradation model are derived. The mechanism of the effect of gas leakage on the quality factor is explored at wafer-level packaging. The experimental results show that the reciprocal of the quality factor is exponentially related to gas leakage time, which is in accordance with the theoretical analysis. The coefficients of determination ( R 2 ) are all greater than 0.95 by fitting the curves in Matlab R2022b. The stable values of the quality factor for drive mode and sense mode are predicted to be 6609.4 and 1205.1, respectively, and the average degradation characteristic time is 435.84 h. The gas leakage time is at least eight times the average characteristic time, namely 3486.72 h, before a stable condition is achieved in the packaging chamber of the MEMS gyroscopes.

Bread and soup are two of the most important foods in daily life, thus dough fermentation and nutrient soup elaboration are more and more popular, but there is a lack of relevant low-cost and high-reliable household appliances on the market. Therefore, this paper proposes automatic control methods for dough fermentation and nutrient soup elaboration based on a special microwave oven. Fermentation theory, run-up microwave fermentation principle, microwave extraction principle, NTC temperature probe design and scalable fuzzy control algorithm are described in detail. Besides, the experimental platform is set up with a temperature chamber, an optical fiber thermometer and a power meter. Experimental results demonstrate that the relationship between the heating time and flour's mass is linear. For different ambient temperature tests, the volume ratios of the fermented dough to unfermented dough of different cases range from 2.2 to 2.62, and the inside of the dough after fermentation is fluffy, with small and dense cavities. Meanwhile, there is no acid taste and skin dryness, and the power consumption of microwave fermentation is less than half of that induced by grill, convection or steam fermentation. The detection error of the NTC temperature probe with microwave shielded is 0.48 °C, and the control error of the closed loop system is less than 0.5 °C. The temperature-rise slope of water is lower than that of ingredient, and the water's temperature is about 1 °C less than that of the ingredient. The soup after microwave elaboration is amber and clear, the ingredients are intact, the water loss is less than 50 g, and the total power consumption is 684 Wh. In short, microwave-based control methods for dough fermentation and nutrient soup elaboration are effective., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Objective . Histopathology image segmentation can assist medical professionals in identifying and diagnosing diseased tissue more efficiently. Although fully supervised segmentation models have excellent performance, the annotation cost is extremely expensive. Weakly supervised models are widely used in medical image segmentation due to their low annotation cost. Nevertheless, these weakly supervised models have difficulty in accurately locating the boundaries between different classes of regions in pathological images, resulting in a high rate of false alarms Our objective is to design a weakly supervised segmentation model to resolve the above problems. Approach . The segmentation model is divided into two main stages, the generation of pseudo labels based on class residual attention accumulation network (CRAANet) and the semantic segmentation based on pixel feature space construction network (PFSCNet). CRAANet provides attention scores for each class through the class residual attention module, while the Attention Accumulation (AA) module overlays the attention feature maps generated in each training epoch. PFSCNet employs a network model containing an inflated convolutional residual neural network and a multi-scale feature-aware module as the segmentation backbone, and proposes dense energy loss and pixel clustering modules are based on contrast learning to solve the pseudo-labeling-inaccuracy problem. Main results . We validate our method using the lung adenocarcinoma (LUAD-HistoSeg) dataset and the breast cancer (BCSS) dataset. The results of the experiments show that our proposed method outperforms other state-of-the-art methods on both datasets in several metrics. This suggests that it is capable of performing well in a wide variety of histopathological image segmentation tasks. Significance . We propose a weakly supervised semantic segmentation network that achieves approximate fully supervised segmentation performance even in the case of incomplete labels. The proposed AA and pixel-level contrast learning also make the edges more accurate and can well assist pathologists in their research., (© 2023 Institute of Physics and Engineering in Medicine.)

The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F 1 -Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.

Artificial-intelligence-assisted decision-making is appearing increasingly more frequently in our daily lives; however, it has been shown that biased data can cause unfairness in decision-making. In light of this, computational techniques are needed to limit the inequities in algorithmic decision-making. In this letter, we present a framework to join fair feature selection and fair meta-learning to do few-shot classification, which contains three parts: (1) a pre-processing component acts as an intermediate bridge between fair genetic algorithm (FairGA) and fair few-shot (FairFS) to generate the feature pool; (2) the FairGA module considers the presence or absence of words as gene expression, and filters out key features by a fairness clustering genetic algorithm; (3) the FairFS part carries out the task of representation and fairness constraint classification. Meanwhile, we propose a combinatorial loss function to cope with fairness constraints and hard samples. Experiments show that the proposed method achieves strong competitive performance on three public benchmarks.

Background: Population stratification modeling is essential in Genome-Wide Association Studies., Objective: In this paper, we aim to build a fine-scale population stratification model to efficiently infer individual genetic ancestry., Methods: Kernel Principal Component Analysis (PCA) and random forest are adopted to build the population stratification model, together with parameter optimization. We explore different PCA methods, including standard PCA and kernel PCA to extract relevant features from the genotype data that is transformed by vcf2geno, a pipeline from LASER software. These extracted features are fed into a random forest for ensemble learning. Parameter tuning is performed to jointly find the optimal number of principal components, kernel function for PCA and parameters of the random forest., Results: Experiments based on HGDP dataset show that kernel PCA with Sigmoid function and Gaussian function can achieve higher prediction accuracy than the standard PCA. Compared to standard PCA with the two principal components, the accuracy by using KPCA-Sigmoid with the optimal number of principal components can achieve around 100% and 200% improvement for East Asian and European populations, respectively., Conclusion: With the optimal parameter configuration on both PCA and random forest, our proposed method can infer the individual genetic ancestry more accurately, given their variants., (© 2021. The Genetics Society of Korea.)