Miracle-dz/UniFAR-FTUs huggingface dataset:
ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on the stress detection and classification accuracy of machine learning models if genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, the stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&high combined stress conditions, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring, with and without taking genders into account.
Supervised Learning for CT Denoising and Deconvolution Without High-Resolution Reference Images

Purpose: Convolutional neural networks (CNNs) have been proposed for super-resolution in CT, but training of CNNs requires high-resolution reference data. Higher spatial resolution can also be achieved using deconvolution, but conventional deconvolution approaches amplify noise. We develop a CNN that mitigates increasing noise and that does not require higher-resolution reference images. Methods: Our model includes a noise reduction CNN and a deconvolution CNN that are separately trained. The noise reduction CNN is a U-Net, similar to other noise reduction CNNs found in the literature. The deconvolution CNN uses an autoencoder, where the decoder is fixed and provided as a hyperparameter that represents the system point spread function. The encoder is trained to provide a deconvolution that does not amplify noise. Ringing can occur from deconvolution but is controlled with a difference of gradients loss function term. Our technique was demonstrated on a variety of patient images and on ex vivo kidney stones. Results: The noise reduction and deconvolution CNNs produced visually sharper images at low noise. In ex vivo mixed kidney stones, better visual delineation of the kidney stone components could be seen. Conclusions: A noise reduction and deconvolution CNN improves spatial resolution and reduces noise without requiring higher-resolution reference images.
Contrast sensitivity: a fundamental limit to vision restoration after V1 damage

Stroke damage to the primary visual cortex (V1) causes severe visual deficits, which benefit from perceptual retraining. However, whereas training with high-contrast stimuli can locally restore orientation and direction discrimination abilities at trained locations, it only partially restores luminance contrast sensitivity (CS). Recent work revealed that high-contrast discrimination abilities may be preserved in the blind field of some patients early after stroke. Here, we asked if CS for orientation and direction discrimination is similarly preserved inside the blind field, to what extent, and whether it could benefit from a visual training intervention. Thirteen subacute (<3 months post-V1-stroke) and 12 chronic (>6 months post-V1-stroke) participants were pre-tested, then trained to discriminate either orientation or motion direction of Gabor patches of progressively lower contrasts. At baseline, more subacute than chronic participants could correctly discriminate the orientation of high-contrast Gabors in their blind field, but all failed to perform this task at lower contrasts, even when 10Hz flicker or motion direction were added. Training improved CS in a greater portion of subacute than chronic participants, but no-one attained normal CS, even when stimuli contained flicker or motion. We conclude that, unlike the near-complete training-induced restoration of high-contrast orientation and direction discrimination, there is limited capacity for restoring CS after V1 damage in adulthood. Our results suggest that CS involves different neural substrates and computations than those required for orientation and direction discrimination in V1-damaged visual systems.
Associations Between Family Factors, Social Integration, and Suicidal Ideation Across the Life Course of an Urban African American Cohort

Suicide is a problem on the rise but not studied extensively among African Americans. It is critical to identify risk factors for suicidal ideation to reduce risk. This study examines whether family and social factors over the life course predict suicidal ideation among African American adults in midlife. We conducted multiple logistic regression analyses on data from a longitudinal cohort of African Americans first assessed in childhood to identify associations with suicidal ideation in midlife (ages 33–42). Findings suggested living without one’s mother in childhood (vs. living with mother alone; aOR = 3.69, p = .017) and parental rule-setting in adolescence (aOR = 0.79, p = .047) were associated with suicidal ideation. Having a lifetime drug disorder (aOR = 2.19, p = .046) or major depression by young adulthood (aOR = 3.58, p < .001) was also associated with an increased risk of suicidal ideation. Findings highlight the importance of intervention for children in mother-absent homes for improving mental health outcomes. Family interventions that promote parental rule-setting and addressing drug problems and depressive symptoms early in the life course offer an area for intervention to reduce suicide over the long term.
Comparative metabolomics and microbiome analysis of Ethanol vs. OMNImet/gene®•GUT fecal stabilization

Metabolites from feces provide important insights into the functionality of the gut microbiome. As immediate freezing is not always feasible in gut microbiome studies, there is a need for sampling protocols that provide stability of the fecal metabolome and microbiome at room temperature (RT). For this purpose, we investigated the stability of various metabolites and the microbiome (16S ribosomal RNA) in feces collected in 95% ethanol (EtOH) or OMNImet®•GUT/ OMNIgene®•GUT. To simulate in field-collection scenarios, the samples were stored at different temperatures at varying durations (24h +4°C, 24h RT, 36h RT, 48h RT, and 7 days RT), and compared to aliquots immediately frozen at -80°C. We applied several targeted and untargeted metabolomics platforms to measure lipids, polar untargeted metabolites, endocannabinoids, short chain fatty acids (SCFAs), and bile acids (BAs). We found that SCFAs in the non-stabilized samples increased over time, while a stable profile was recorded in sample aliquots stored in 95% EtOH and OMNImet®•GUT. When comparing the metabolite levels between fecal aliquots stored at room temperature and at +4°C, we detected several changes in microbial metabolites, including multiple BAs and SCFAs. Taken together, we found that storing fecal samples at room temperature and stabilizing them in 95% EtOH yielded metabolomic results comparable to flash freezing. We also found that overall composition of the gut microbiome did not vary significantly between different storage types. However, there were notable differences observed in alpha diversity. Taken together, the stability of the metabolome and microbiome in 95 % EtOH provided similar results as the validated commercial collection kits OMNImet®•GUT and OMNIgene®•GUT, respectively. IMPORTANCE The analysis of the gut metabolome and microbiome requires the separate collection of fecal specimens using conventional methods or commercial kits. However, these approaches can potentially introduce sampling errors and biases. In addition, the logistical requirements of studying large human cohorts have driven the need for home collection and transport of human fecal specimens at room temperature. By adopting a unified sampling approach at room temperature, we can enhance sampling convenience and practicality, leading to a more precise and comprehensive understanding of gut microbial function. However, the development and applications of such unified sampling systems still face limitations. The results presented in this study aim to address this knowledge gap by investigating the stability of metabolites and the microbiome (16S ribosomal RNA) from fecal samples collected using 95% EtOH, in comparison to well-established commercial collection kits for fecal metabolome (OMNImet®•GUT) and microbiome (OMNIgene® •GUT) profiling. Additionally, we perform a comparative analysis of various platforms and metabolomic coverage using matrices containing ethanol, evaluating aspects of sensitivity, robustness, and throughput.
Stable Wage Distribution in Japan, 1982-2002: A Counter Example of SBTC?

Wage distribution has been nearly stable in Japan for the last two decades, contrary to findings in the US, the UK, and Canada. The change in wage distribution during this period was almost completely caused by a distributional change in worker attributes. This implies that skill prices were very stable between 1982 and 2002. Both demand and supply for skilled workers have increased because of skill-biased technological change (SBTC), a rise in the number of college-educated workers induced by educational policy changes, and the aging of the population. In the balance of shifts in demand and supply, the skill price has been stable. Industries that experienced rapid computerization also experienced workers’ skill upgrading. Overall, stable wage distribution of Japan is not a counter example of SBTC. JEL Classification: J23; J31
Preconditioned Descent Algorithms in Function Spaces for a Class of Variational Inequalities Involving the p -Laplacian Operator ∗

This paper is concerned with the numerical solution of a class of variational inequalities of the second kind, involving the p -Laplacian operator. This kind of problems arise, for instance, in the mathematical modelling of non-Newtonian ﬂuids. We study these problems by using a regularization approach, based on a Huber smoothing process. Well posedness of the regularized problems is proved, and convergence of the regularized solutions to the solution of the original problem is veriﬁed. We propose a preconditioned descent method for the numerical solution of these problems and analyze the convergence of this method in function spaces. The existence of admissible descent directions is established by variational methods and admissible steps are obtained by a backtracking algorithm which approximates the objective functional by polynomial models. Finally, several numerical experiments are carried out to show the eﬃciency of the methodology here introduced.
Stochastic distribution synchronization and pinning control for complex heterogeneous dynamical networks

This paper investigates the stochastic synchronization and pinning control in the sense of probability distribution for a general model of complex heterogeneous dynamical networks subjected to stochastic disturbances. Some generic stochastic synchronization criteria are established for both cases of undirected and directed topology by using the ergodic theory on stochastic dynamical systems. Compared with most existing studies on the stochastic synchronization in the sense of mean square, it is demonstrated that the concept of stochastic distribution synchronization can well characterize the realistic structure and essential nature of complex practical stochastic systems. Subsequently, two representative examples of complex heterogeneous dynamical networks, namely coupled stochastic Duffing oscillators and coupled FitzHugh‐Nagumo neuron oscillators, are given to illustrate and numerically verify the theoretical results.
MNC Lawsuit Outcomes in Authoritarian Courts: Evidence from New Litigation Data ∗

Existing research on foreign investment risks in emerging economies has focused on foreign investors resorting to rights protection regimes beyond host countries, such as investor-state dispute settlement (ISDS) mechanisms. However, multinational corporations (MNCs) do not sue in supranational legal institutions as often as scholars might expect. We argue that the existing literature has overlooked a platform of investment dispute settlement where foreign ﬁrms are much more frequently involved, that is, host countries’ domestic courts. In this article, we investigate how foreign ﬁrms’ entry modes and political connections with the host country’s government affect litigation outcomes. We argue that MNCs with strong political connections and resources may not need to pursue litigation in order to protect their property rights. Nonetheless, conditional on ﬁling lawsuits, MNCs with stronger political background are more likely to win lawsuits than ﬁrms without such political ties. By constructing a novel dataset on multinationals’ litigation activities, we provide the ﬁrst systematic study of the patterns of lawsuit outcomes involving foreign ﬁrms in China.
Mad About the Mekong : Exploration and Empire in South East Asia

The story of both a dramatic journey retracing the historic voyage of France's greatest 19th-century explorer up the mysterious Mekong river, and a portrait of the river and its peoples today. Any notion of sailing up the Mekong in homage to Francis Garnier has been unthinkable until now. From its delta in Vietnam up through Cambodia, Laos, Thailand, Burma and on into China, the Mekong has been a no-go river, its turbulent waters fouled by ideological barriers as formidable as its natural obstacles. But recently the political obstacles have begun to be dismantled - river traffic is reviving. John Keay describes the world of the Mekong as it is today, rehabilitating a traumatised geography while recreating the thrilling and historic voyage of Garnier in 1866. The French expedition was intended to investigate the 'back door' into China by outflanking the British and American conduits of commerce at Hong Kong and Shanghai. Two naval gunboats headed upriver into the green unknown, bearing crack troops, naturalists, geologists and artists. The two-year expedition's failures and successes, and the tragedy and acrimony that marked it, make riveting reading.
A Stress-Based Smart Retail Service in Shopping Environments: An Adoption Study

Since many years, retailers have tried to leverage the benefits of e-commerce by transferring concepts of online shops into retailing. However, shopping experience in physical stores is different from the one in online shops due to potential stress factors arising in-store. Two previous scientific works focused on unobtrusively detected customers’ perceived stress in order to provide tailored mobile services in real-time. The purpose of this paper is to assess the adoption of the envisioned stress-based smart retail service amongst potential customers. The results show that tailored services are perceived positively and thus, have the potential to contribute to an enhanced shopping experience.
On the Demand for Portfolio Insurance

While insurers manage underwriting risk with various methods including reinsurance, insurers increasingly manage asset risk with options, futures, and other derivatives. Previous research shows that buyers of portfolio insurance pay considerably for downside protection. We add to this literature by providing the first evidence on the cost of portfolio insurance, the payoff to portfolio insurance, and the relative demand for portfolio insurance across VIX levels. We find that the demand for portfolio insurance is relatively high at low levels of VIX, suggesting purchasers demand more downside protection when this protection is cheap on an absolute basis (but expensive on a relative basis). We also provide the first evidence on the hedging behavior of specific investor classes, and show that the demand for portfolio insurance is driven by retail investors (individuals) who buy costly insurance from institutional investors. Results are consistent with other types of paradoxical insurance-buying behavior.
PDiscoNet: Semantically consistent part discovery for fine-grained recognition

Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds. Encouraging a fine-grained classification model to first detect such parts and then using them to infer the class could help us gauge whether the model is indeed looking at the right details better than with interpretability methods that provide a single attribution map. We propose PDiscoNet to discover object parts by using only image-level class labels along with priors encouraging the parts to be: discriminative, compact, distinct from each other, equivariant to rigid transforms, and active in at least some of the images. In addition to using the appropriate losses to encode these priors, we propose to use part-dropout, where full part feature vectors are dropped at once to prevent a single part from dominating in the classification, and part feature vector modulation, which makes the information coming from each part distinct from the perspective of the classifier. Our results on CUB, CelebA, and PartImageNet show that the proposed method provides substantially better part discovery performance than previous methods while not requiring any additional hyper-parameter tuning and without penalizing the classification performance. The code is available at https://github.com/robertdvdk/part_detection
Training Self-supervised Class-conditional GAN with Virtual Labels

Class-conditional GAN is a conditional GAN that can generate class-conditional distribution. Among class-conditional GANs, class-conditional InfoGAN can generate class-conditional data through a self-supervised (unsupervised) method without a labeled dataset. Instead, class-conditional InfoGAN requires optimal categorical latent distribution to train the model. In this paper, we propose a novel GAN that allows the model to perform self-supervised class-conditional data generation and clustering without knowing the optimal categorical latent distribution (prior probability). The proposed model consists of a discriminator, a classifier, and a generator, and uses three losses. The first loss is the cross-entropy classification loss to predict the conditional vector of the fake data. The classifier is trained with the classification loss. The second loss is the CAGAN loss for class-conditional data generation. The conditional vector of the real data predicted by the classifier is used for CAGAN loss. The generator and discriminator are trained with CAGAN loss. The third loss is the classifier gradient penalty loss. The classifier gradient penalty loss regularizes the slope of the classifier’s decision boundary so that the decision boundary converges to a local optimum over a wide region. Additionally, the proposed method updates the categorical latent distribution with a predicted conditional vector of real data. As training progresses, the entropy of the categorical latent distribution gradually decreases and converges to the appropriate value. The converged categorical latent distribution becomes appropriate to represent the discrete part of the data distribution. The proposed method does not require labeled data, optimal categorical latent distribution, and a good metric to measure the distance between data.
Movin' On Up: Interpreting The Earnings-Experience Profile

Human capital theory provides the generally accepted interpretation of the relationship between earnings and labour market experience, namely that general human capital tends to increase with experience. However, there are other plausible interpretations e.g. search models generally predict that more time in the labour market increases the chance of finding a better match and hence tends to be associated with higher earnings. In this paper we show how a simple search model can be used to predict the amount of earnings growth that can be assigned to search with the residual being assigned to the human capital model. We show how a substantial if not the larger part of the rise in earnings over the life-cycle in Britain can be explained by a simple search model and that virtually all the earnings gap between men and women can be explained in this way. Overall, the evidence suggests that we do need to rethink our interpretation of the returns to experience in earnings functions.
Contents of the NASA ocean data system archive, version 11-90

The National Aeronautics and Space Administration (NASA) Ocean Data System (NODS) archive at the Jet Propulsion Laboratory (JPL) includes satellite data sets for the ocean sciences and global-change research to facilitate multidisciplinary use of satellite ocean data. Parameters include sea-surface height, surface-wind vector, sea-surface temperature, atmospheric liquid water, and surface pigment concentration. NODS will become the Data Archive and Distribution Service of the JPL Distributed Active Archive Center for the Earth Observing System Data and Information System (EOSDIS) and will be the United States distribution site for Ocean Topography Experiment (TOPEX)/POSEIDON data and metadata.
The Impact of Maternal Alcohol and Illicit Drug Use on Children's Behavior Problems: Evidence from the Children of the National Longitudinal Survey..

This study uses data from the Children of the National Longitudinal Survey of Youth to test for evidence of a causal relationship between maternal alcohol use, marijuana use and cocaine use, and children's behavior problems. Ordinary least squares results provide strong evidence that maternal substance use is associated with children's behavior problems. Models that account for the potential endogeneity of maternal substance use yield mixed results. Models estimated using instrumental variables (IV) methods are inconsistent with OLS findings. Child-specific and family-specific fixed effects models suggest that maternal alcohol, marijuana and cocaine use are associated with increases in behavior problems.