Multiple Losses Research Articles

Synthesis of porous carbon-encapsulated MoxC/Co based on NaCl template and ZIF-67 towards enhancing electromagnetic wave absorption

Although molybdenum carbide (MoxC) has emerged as an effective absorber of electromagnetic (EM) waves, its practical application is hindered by impedance mismatch and single loss mechanism. Herein, we developed a one-step method for creating NaCl template precursors assembled with ZIF-67 to produce porous multiple components of MoxC composites. The MoxC-Co/C composites exhibit a minimum reflection loss (RLmin) of −59.69 dB with a bandwidth of 3.12 GHz at a thickness of 1.50 mm. The RLmin reaches −71.39 dB with a bandwidth of 4.08 GHz at a thickness of 2.09 mm. The flower-like MoxC-Co/C composites are capable of improving impedance matching and offering multiple loss mechanisms such as interfacial polarization, dipolar polarization, conductive loss and magnetic loss, resulting in enhanced EM wave absorption. Thus, our work featured a simple strategy to provide a unique insight into fabricating high-performance EM wave absorbers on molybdenum carbon basis.

HDNet: Human-like discrimination with visual key for few-shot cross-domain object detection

Most existing few-shot object detection (FSD) methods implicitly assume that the target domain data with few samples conform to the same statistical distribution as the source domain. However, this assumption is impractical, especially when dealing with unconstrained scenarios. Also, the decline of fine-grained hidden samples caused by scene switching, significant morphological changes, etc. has brought great challenges to object detection. To solve the above few-shot cross-domain detection (FS-CDD) problems, in this work, we propose a novel and flexible Human-like Discrimination Network (HDNet), which is composed of four modules. Firstly, multi-level key generation (MKG) fully mining multi-level comprehensive abstract representation. The precious knowledge that contains diverse low and high levels is obtained by three parallel branches. After decoupling the hidden space, the rich patches from each pair of target and source domains are closely matched in the Embedded Space Implicit Association (ESIA) using degree description. In order to enhance the discriminative capability of the model for a few samples, the instances are additionally encoded and corrected at the classification end using the Prediction head with instance embedding (PHIE). Finally, the Adaptive Reweighted Module (ARM) is redesigned to determine coefficient of multiple loss functions, which avoids the improper operation of setting coefficients according to experience in the past. Extensive experiments demonstrate that despite the dual challenges of limited samples and cross-domain scenarios, the proposed HDNet exhibits remarkable performance.

Does losing family members in midlife matter for late-life mental and cognitive health? A longitudinal study of older Swedes spanning 30 years

Objectives Mental and cognitive health is crucial to ensure well-being in older age. However, prolonged periods of stress, grief, and bereavement might compromise mental health balance, leading to profound changes. This study investigated the sex-stratified associations between midlife bereavement experiences (e.g. sibling loss, spousal loss, and multiple losses) and late-life depression (LLD) and cognitive impairment. Method Linked data from the Swedish Level-of-Living Survey and the Swedish Panel Study of Living Conditions of the Oldest Old (SWEOLD) were used. Multiple logistic regressions were performed to examine the associations between midlife bereavement and LLD (n = 1078) and cognitive impairment (n = 995), separately. Results Sibling loss and multiple losses in midlife were associated with lower odds of LLD, especially among women. Among men, sibling loss in midlife was associated with lower odds of cognitive impairment, while the experience of two losses among women suggested an increased (but non-significant) risk of cognitive impairment. Interaction analyses did not show significant effects between bereavement and gender on LLD and cognitive impairment. Conclusion Midlife bereavement might have gendered implications on LLD and cognitive impairment, but associations need to be confirmed by well-powered studies. Further research is warranted to elucidate the association between multiple midlife losses and reduced LLD risk.

Proximity Testing with Logarithmic Randomness

A fundamental result dating to Ligero (Des. Codes Cryptogr. '23) establishes that each fixed linear block code exhibits proximity gaps with respect to the collection of affine subspaces, in the sense that each given subspace either resides entirely close to the code, or else contains only a small portion which resides close to the code. In particular, any given subspace's failure to reside entirely close to the code is necessarily witnessed, with high probability, by a uniformly randomly sampled element of that subspace. We investigate a variant of this phenomenon in which the witness is not sampled uniformly from the subspace, but rather from a much smaller subset of it. We show that a logarithmic number of random field elements (in the dimension of the subspace) suffice to effect an analogous proximity test, with moreover only a logarithmic (multiplicative) loss in the possible prevalence of false witnesses. We discuss applications to recent noninteractive proofs based on linear codes, including Brakedown (CRYPTO '23).

Engineering robust ZnO/Au decorated biomass-derived carbonaceous platforms for integrated antibacterial and electromagnetic wave absorbing properties

In consideration of the issues of electromagnetic pollution in medical settings, the function integration of high antibacterial activity and efficient wave absorption is becoming the preferred solution. In this work, spinach-derived carbon coupled with antibacterial assembly of ZnO nanorods decorated by Au nanoparticles were fabricated to form a hierarchical nanocomposite (CS–ZnO/Au) to meet the dual demands. Such an active platform guaranteed a pronounced bactericidal performance upon visible light illumination. 99.9 % of E. coli and 99.7 % of S. aureus were effectively eradicated in a lean period of 30 min, which was attributed to that ZnO/Au nanohybrids promoted the production of reactive oxygen species (ROS) and inhibited the charge recombination. Meanwhile, the anticipated wave attenuation ability was available owing to the multiple loss responses in the hierarchical CS-ZnO/Au. The ternary nanocomposites achieved the maximum absorbing bandwidth of 4.8 GHz and the strongest absorption of −54.61 dB. The mechanisms for wave absorption were ascribed to the improved dielectric loss and optimized impedance matching performance. It proposed an engineering robust platform, in sum, to tackle the dual challenges of antibacterial and wave absorption with these sustainable materials obtained from a green route.

Lightweight, flexible, heat resistant and thermal insulating aramid nanofiber/magnetic carbon nanotube interpenetrating network aerogel for microwave absorption in complex environments

Microwave absorbing (MA) materials used in complex environments not only need efficient MA performance, but also should have comprehensive properties of being lightweight, flexible, heat-resistant and insulating. Here, we grew CoFe2O4 nanoparticles (CFO NPs) in situ on oxidized carbon nanotubes (CNTs) by a wet chemical method. Oxidized CNTs limit the aggregation of CFO NPs and form rich heterogeneous interfaces. Then CNT@CFO was mixed with aramid nanofibers (ANFs) dispersions and prepared lightweight and flexible ANF/CNT@CFO interpenetrating network aerogels through a freeze-drying process. Under the action of multiple loss mechanisms, the ANF/CNT@CFO aerogel shows −53.2 dB of minimum reflection loss, and 4.76 GHz of effective absorption bandwidth (1.59 mm). Meanwhile, the composite aerogel inherits the high heat resistance of each component, with the quality and shape remaining stable below 400 °C, and TG5% occurs at 460 °C. In addition, effective MA properties can be maintained from room temperature to 300 °C. ANF/CNT@CFO is expected to become a MA material that can be used in complex environments.

Chromophobe Renal Cell Carcinoma With Extensive Retraction Artifact: A Potential Diagnostic Pitfall From Micropapillary Urothelial Carcinoma.

In addition to "classic" and eosinophilic subtype, chromophobe renal cell carcinoma (RCC) is well-known to demonstrate various morphological patterns including adenomatoid, microcystic, pigmented, multicystic, papillary, neuroendocrine-like, and small cell-like, all of which are important to appreciate for accurate diagnosis. Herein, we expand on a unique chromophobe RCC morphology not previously described consisting of tumor cells with extensive stromal retraction, mimicking upper urothelial tract micropapillary carcinoma (MPC). Twelve MPC-like chromophobe RCC nephrectomies were reviewed with clinicopathological features recorded; molecular testing was performed on 7 of 12 tumors. Patients were mostly men (n=10) with a mean age of 65 years. Mean tumor size was 6.4 cm with pathological stage distribution as follows: 4 (33%) T1a, 2 (17%) T1b, 1 (8%) T2b, and 3 (25%) T3a. The extent of MPC-like chromophobe RCC foci ranged from 10% to 40% (mean=26%; there was no correlation between the extent of MPC-like chromophobe RCC foci and tumor stage). Other chromophobe RCC morphological patterns were not identified. When performed, all (100%) tumors depicted prototypic chromophobe RCC staining pattern of KIT positivity/KRT7 positivity. Molecular showed 6 of 7 (86%) with multiple chromosomal losses. Clinically significant mutations were identified in NF1, TP53, FLCN (likely somatic), CHEK2, and ZFHX3 genes. Follow up available in 9 patients showed no evidence of disease (mean=23 months). Although the etiology behind the extensive stromal retraction in our tumors is unknown, this may likely be artifactual in nature. Nonetheless, it is important to include MPC-like chromophobe RCC in the spectrum of "variant" morphologies to avoid diagnostic pitfalls from micropapillary carcinoma.

Superior microwave absorption properties of carbon-coated FeSiCr micro flakes

Carbon-coated ferromagnetic alloys can protect from microwave radiation due to their tunable electromagnetic parameters and multiple loss mechanisms. We prepared carbon-coated flaky FeSiCr (FFSC@C) powders by the encapsulation and thermal decomposition of epoxy resin. The crystallization degree and thickness of carbon coating were tuned to optimize the microwave absorption of FFSC@C. The phase composition, morphology, magnetic properties, electromagnetic parameters, and microwave absorption properties of FFSC@C were comprehensively investigated. The oriented flaky FeSiCr possessed strong attenuation capacity, the amorphous carbon improved the impedance matching of flaky FeSiCr. The sample carbonized at 500 ℃, with the epoxy addition amount of 45 wt% exhibited a broad effective absorption bandwidth of 5.60 GHz at a thickness of 2.4 mm. Compared with other absorbents, the FFSC@C exhibited stronger absorption capacity at a lower thickness and filler loading in the X-band (8−12 GHz). This work demonstrated the facile fabrication of a high-performance carbon-coated ferromagnetic microwave absorber.

Effects of parental job loss on psychotropic drug use in children: Long-term effects, timing, and cumulative exposure

Intra-family crossover effects triggered by job losses have received growing attention across scientific disciplines, but existing research has reached discrepant conclusions concerning if, and if so how, parental job losses affect child mental health. Drawing on sociological models of stress and life course epidemiology, we ask if parental job losses have long-term effects on child mental health, and if these effects are conditional on the timing of, or the cumulative exposure to, job losses. We use intergenerationally linked Swedish register data combined with entropy balance and structural nested mean models for the analyses. The data allow us to track 400,000 children over 14 years and thereby test different life-course models of cross-over effects. We identify involuntary job losses using information on workplace closures, thus reducing the risk of confounding. Results show that paternal but not maternal job loss significantly increases the risk of psychotropic drug use among children, that the average effects are modest in size (less than 4% in relative terms), that they may persist for up to five years, and that they are driven by children aged 6–10 years. Moreover, cumulative exposure to multiple job losses are more harmful than zero or one job loss.

Automatic crack detection on concrete and asphalt surfaces using semantic segmentation network with hierarchical Transformer

In recent studies, deep learning methodologies have shown significant promise in crack detection. However, their practical implementation faces challenges due to the intricate diversity of structural surfaces and the inherent narrowness of cracks. To mitigate these problems, this paper introduces SegFormer, an efficient semantic segmentation model with hierarchical Transformer, for crack detection on concrete and asphalt surfaces in multiple scenarios. The combination of Cross-Entropy (CE) and Dice loss functions is employed to enhance the detection of fine cracks. Additionally, the paper presents an evaluation framework and discusses metrics for assessing crack segmentation results to provide a more precise and comprehensive analysis of model performance. Experimental results indicate that SegFormer outperforms Convolutional Neural Networks (CNNs) such as FCN, U-Net, and DeepLabV3 utilizing different backbones. Notably, the integration of multiple loss functions contributes to a more stable training process, expedites convergence, and yields enhanced results compared to models utilizing individual loss functions.

Bead-like Co/C@MoS2Composites connected by Multi-Walled carbon nanotubes for high-efficiency electromagnetic wave absorption

This study presents a novel approach to synthesize a composite material composed of bead-like Co/C nanoparticles anchored on MWCNTs, encapsulated by MoS2 nanosheets through a combination of carbonization and hydrothermal treatment using ZIF-67 as the precursor. The resulting composite demonstrates exceptional electromagnetic wave (EMW) absorption performance, with a minimum reflection loss (RL) of −63 dB at a thickness of 2.44 mm and an effective absorption bandwidth (EAB) of 7.2 GHz when RL is below 10 dB together with an expanded EAB of 12.3 GHz (d = 5 mm).The superior absorption capabilities arise from a well-thought-out design of material composition and structure. The synergy of Co/C and MWCNTs enhances ferromagnetic resonance, conductivity, and polarization loss, while sulfur vacancies in MoS2 act as polarization centers, facilitating dipole polarization and efficient conversion of electromagnetic wave energy. The core–shell structure and heterogeneous interfaces introduce various energy loss mechanisms, including multiple polarizations, scattering effects, and dielectric loss. This work introduces a facile method for synthesizing a composite material with outstanding EMW absorption performance. The synergistic combination of unique material components and the carefully engineered structure make this composite a promising candidate for advanced applications in electromagnetic wave absorption technology.

Health-risk behaviours co-occur among adults with tooth loss.

Health-risk behaviours tend to co-occur among the same sectors of the population. The aim of this study is to examine the association between an aggregate of multiple health-risk behaviours and tooth loss among American Adults. We used data from the Behavioural Risk Factor Surveillance System (BRFSS) 2022, a nationally representative survey of non-institutionalized American. We included participants aged 18 years and older. An aggregate variable of health-risk behaviours which included smoking, heavy alcohol consumption, lack of physical activities, overweight/obesity, infrequent dental visits and infrequent medical check-up was created. Tooth loss was indicated by losing one tooth or more. Logistic Regression analysis was conducted to test the association between the aggregate of behaviours and tooth loss adjusting for income, education, ethnicity and health insurance. The analysis included 326,561 participants. The mean number of health-risk behaviours was 2.13 and 1.72 among participants with tooth loss and without tooth loss, respectively. The aggregate of health-risk behaviours was significantly associated with tooth loss with odds ratios 1.23 (95% CI, 1.21, 1.26) in a model adjusting for age, gender, education, income ethnicity and health insurance. This study demonstrated that an aggregate of health-risk behaviours, with some not directly linked to oral health, is associated with tooth loss among American adults. The study highlights the importance of considering different risk factors when planning health promotion policies to tackle oral health.

Ti3C2Tx/PANI/CoNi as a ternary composite material unveiling excellent microwave absorption performance

Ti3C2Tx MXene material exhibits exceptional dielectric properties and a distinct layered structure, making it a noteworthy microwave absorbing material. However, its microwave absorption performance is limited due to poor impedance matching and a single loss mechanism. To address these issues, this work develops a solvothermal method to introduce the magnetic component CoNi alloy and generate organic polymer PANI through in-situ polymerization, resulting in the preparation of Ti3C2Tx/PANI/CoNi ternary composites that significantly improve impedance matching and microwave absorption performance. The microwave absorption properties of the binary and ternary composites are compared, and the superior performance of the ternary composites is emphasized. Among the ternary composites, M-P-CN 3:1:8 exhibited an excellent microwave absorption performance, demonstrating a minimum RL value of −55.48 dB and an effective absorption bandwidth of 5.1 GHz at a thickness of 2.0 mm. The synergistic effect of multiple loss mechanisms, multilayer structures, and multiple interfacial polarization methods further contribute to the enhanced microwave absorption performance of ternary composites. This study provides a straightforward method for synthesizing and designing multi-component composite absorbing materials based on MXene.

MFHOD: Multi-modal image fusion method based on the higher-order degradation model

The task of multimodal image fusion aims to preserve the respective advantages of each modality, such as the detailed texture information from visible light images and the salient target information from infrared images. However, images in real environments are often not perfect high-quality images but are affected by various degradation factors such as noise, blur, and glare. Existing unsupervised multimodal image fusion algorithms use undegraded source images as input to fusion networks and generate high-quality fusion images through complex fusion strategies. This single linear learning approach from high-quality to high-quality cannot be applied to the fusion task of degraded multimodal images in real environments. To address this issue, this paper proposes an end-to-end multimodal image fusion network based on a high-order local degradation model (MFHOD). Firstly, inspired by the idea of probabilistic degradation, we propose a high-order local random degradation model (HODM), which inputs the source multimodal images into the degradation model to obtain degraded images before feeding them into the network. Secondly, we design a simple and efficient dual-branch feature extraction encoder to extract deep features from images. Then, from the perspectives of image pixels, brightness, and gradients, we propose an improved composite loss composed of multiple loss functions to constrain network training. Finally, we propose a L2-norm fusion strategy for preserving brightness information in low-light nighttime images. Our MFHOD demonstrates good performance on infrared and visible light image datasets as well as medical image datasets. Experimental results show that MFHOD can effectively suppress the effects of glare, noise, and smoke in adverse environments, and also improve the quality of fusion images in low-light and nighttime environments.

Uncertainty-Aware GAN for Single Image Super Resolution

Generative adversarial network (GAN) has become a popular tool in the perceptual-oriented single image super-resolution (SISR) for its excellent capability to hallucinate details. However, the performance of most GAN-based SISR methods is impeded due to the limited discriminative ability of their discriminators. In specific, these discriminators only focus on the global image reconstruction quality and ignore the more fine-grained reconstruction quality for constraining the generator, as they predict the overall realness of an image instead of the pixel-level realness. Here, we first introduce the uncertainty into the GAN and propose an Uncertainty-aware GAN (UGAN) to regularize SISR solutions, where the challenging pixels with large reconstruction uncertainty and importance (e.g., texture and edge) are prioritized for optimization. The uncertainty-aware adversarial training strategy enables the discriminator to capture the pixel-level SR uncertainty, which constrains the generator to focus on image areas with high reconstruction difficulty, meanwhile, it improves the interpretability of the SR. To balance weights of multiple training losses, we introduce an uncertainty-aware loss weighting strategy to adaptively learn the optimal loss weights. Extensive experiments demonstrate the effectiveness of our approach in extracting the SR uncertainty and the superiority of the UGAN over the state-of-the-arts in terms of the reconstruction accuracy and perceptual quality.

More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function

Gas turbines play a crucial role in absorbing the volatility of new energy sources such as wind and photovoltaic. Continuous degradation trend prediction for gas turbines is vital for rationalizing maintenance schedules and improving power system stability. Current prediction techniques do not consider the practicality of the prediction results. To address this issue, a prediction framework based on factor analysis and Multiple Penalty Mechanisms (MPM) loss function is proposed. Firstly, factor analysis is used to assess the health index of gas turbines. Secondly, an innovative loss function that incorporates penalties for prediction errors, lag prediction, and fluctuation prediction is proposed to improve forecast usability. A range-adjustable and asymmetric Hyperbolic Cosine with Exponential (CoshE) function is first proposed to address the prediction lag problem. Finally, Long Short Term Memory network is chosen as the predictive model, and dynamic weights are used to optimize the loss function. Experiments on the combustion chamber degradation dataset and C-MAPSS dataset show that the framework proposed performs optimally than the conventional loss functions and the CoshE function is more efficient in the MPM framework. Meanwhile, MPM significantly improves gate recurrent unit and convolutional neural network performance. The method proposed is noteworthy for its superiority and applicability.

An adaptive threshold based gait authentication by incorporating quality measures

In this paper, an adaptive threshold-based gait authentication model is proposed, which incorporates the quality measure in the distance domain and maps them into the gradient domain to realize the optimal threshold of each gait sample, in contrast to the fixed threshold, as most of the authentication model utilizes. For accessing the quality measure of each gait, a gait covariate invariant generative adversarial network (GCI-GAN) is proposed to generate normal gait (canonical condition) irrespective of covariates (carrying, and viewing conditions) while preserving the subject identity. In particular, GCI-GAN connects to gradient weighted class activation mapping (Grad-CAMs) to obtain an attention mask from the significant components of input features, employs blending operation to manipulate specific regions of the input, and finally, multiple losses are employed to constrain the quality of generated samples. We validate the approach on gait datasets of CASIA-B and OU-ISIR and show a substantial increase in authentication rate over other state-of-the-art techniques.

The evolution of the gliotoxin biosynthetic gene cluster in Penicillium fungi.

Fungi biosynthesize diverse secondary metabolites, small organic bioactive molecules with key roles in fungal ecology. Fungal secondary metabolites are often encoded by physically clustered genes known as biosynthetic gene clusters (BGCs). Fungi in the genus Penicillium produce a cadre of secondary metabolites, some of which are useful (e.g. the antibiotic penicillin and the cholesterol-lowering drug mevastatin) and others harmful (e.g. the mycotoxin patulin and the immunosuppressant gliotoxin) to human affairs. Fungal genomes often also encode resistance genes that confer protection against toxic secondary metabolites. Some Penicillium species, such as Penicillium decumbens, are known to produce gliotoxin, a secondary metabolite with known immunosuppressant activity. To investigate the evolutionary conservation of homologs of the gliotoxin BGC and of genes involved in gliotoxin resistance in Penicillium, we analyzed 35 Penicillium genomes from 23 species. Homologous, lesser fragmented gliotoxin BGCs were found in 12 genomes, mostly fragmented remnants of the gliotoxin BGC were found in 21 genomes, whereas the remaining 2 Penicillium genomes lacked the gliotoxin BGC altogether. In contrast, broad conservation of homologs of resistance genes that reside outside the BGC across Penicillium genomes was observed. Evolutionary rate analysis revealed that BGCs with higher numbers of genes evolve slower than BGCs with few genes, suggestive of constraint and potential functional significance or more recent decay. Gene tree-species tree reconciliation analyses suggested that the history of homologs in the gliotoxin BGC across the genus Penicillium likely involved multiple duplications, losses, and horizontal gene transfers. Our analyses suggest that genes encoded in BGCs can have complex evolutionary histories and be retained in genomes long after the loss of secondary metabolite biosynthesis.

Abundant vacancies induced high polarization-attenuation effects in flower-like WS2 microwave absorbers

Defect engineering could provide new ideas for the design of transition metal disulfide electromagnetic wave (EMW) absorbers with high performance. Since the effects of dipoles on impedance matching and EMW absorption are crucial for the development of novel absorbers, the polarization attenuation dependence on defect engineering should be understood at micro- and macro-scales. In this paper, it is found that the defect-rich WS2 nanoflowers synthesized by the cold plasma method possess excellent EMW absorption properties. Cold plasma treatment of materials is easy to perform and maintains the original shape of the material to a high degree. The formation of defects results in abundant electrochemically active sites, increased multiple reflection losses, improved dielectric properties and impedance matching in the materials. The RLmin of the defect-rich material with a thickness of 3.19 mm is as high as −54.36 dB at 8.16 GHz, and the effective absorption bandwidth is 4.72 GHz. The results reveal that the formation of defective vacancies enhances the effects of dipole polarization of the material on improving its EMW absorption properties. Thus, this work provides not only a facile preparation route for novel EMW-absorbing materials, but also a new strategy for tunning defects in transition metal disulfides.

DSG-GAN:A dual-stage-generator-based GAN for cross-modality synthesis from PET to CT

PET/CT devices typically use CT images for PET attenuation correction, leading to additional radiation exposure. Alternatively, in a standalone PET imaging system, attenuation and scatter correction cannot be performed due to the absence of CT images. Therefore, it is necessary to explore methods for generating pseudo-CT images from PET images. However, traditional PET-to-CT synthesis models encounter conflicts in multi-objective optimization, leading to disparities between synthetic and real images in overall structure and texture. To address this issue, we propose a staged image generation model. Firstly, we construct a dual-stage generator, which synthesizes the overall structure and texture details of images by decomposing optimization objectives and employing multiple loss functions constraints. Additionally, in each generator, we employ improved deep perceptual skip connections, which utilize cross-layer information interaction and deep perceptual selection to effectively and selectively leverage multi-level deep information and avoid interference from redundant information. Finally, we construct a context-aware local discriminator, which integrates context information and extracts local features to generate fine local details of images and reasonably maintain the overall coherence of the images. Experimental results demonstrate that our approach outperforms other methods, with SSIM, PSNR, and FID metrics reaching 0.8993, 29.6108, and 29.7489, respectively, achieving the state-of-the-art. Furthermore, we conduct visual experiments on the synthesized pseudo-CT images in terms of image structure and texture. The results indicate that the pseudo-CT images synthesized in this study are more similar to real CT images, providing accurate structure information for clinical disease analysis and lesion localization.