Miracle-dz/UniFAR-FTUs huggingface dataset:
Meaning-Based Coping and Spirituality During the COVID-19 Pandemic: Mediating Effects on Subjective Well-Being

The coronavirus pandemic has significantly affected the Turkish population. During the pandemic, people with high coronavirus stress are at risk of experiencing poor subjective well-being. There is no research investigating the role of meaning-based coping and spirituality in explaining the link between coronavirus stress and subjective well-being. This study examined the mediating roles of meaning-based coping and spiritual well-being in the link between coronavirus stress and subjective well-being in young adults during the COVID-19 pandemic. The sample included 427 young adults (71% female), ranging in age between 18 and 48 years (M = 21.06; SD = 2.62). Turkish young adults completed an online survey, including measures of coronavirus stress, subjective well-being, meaning-based coping, and spiritual well-being. The results indicate that greater meaning-based coping and spiritual well-being mediated decreases in the adverse impacts of coronavirus stress on subjective well-being. These results suggest that the importance of a combination of meaning-based coping and spirituality processes mitigate the adverse effects of stress on well-being during the coronavirus pandemic. Interventions focusing on meaning-based coping and spirituality in those experiencing high coronavirus stress are urgently needed to improve the mental health and well-being of young adults.
Case Report: Hyperplastic Callus of the Femur Mimicking Osteosarcoma in Osteogenesis Imperfecta Type V

Background Osteogenesis imperfecta (OI) type V is a rare form of OI which is often characterized by hyperplastic callus. Misdiagnosis is a possibility due to its rarity and because patients involved are mostly in adolescence, a predisposing age for osteosarcoma. Here, we report this case and aim to improve understanding of patients with OI type V and avoid misdiagnosis. Case Presentation A male, 14-year-old patient was admitted to Jiangxi Provincial People’s Hospital affiliated to Nanchang University in August 2020 due to repeated fractures for more than 11 years and swelling in his right leg for more than 4 years. The patient was diagnosed with OI in 2014 due to repeated fracture and was treated with bisphosphonates. The swelling was accompanied by huge callus formation. Prior to admission to our hospital in 2016 osteosarcoma was suspected by imaging and pathology, and amputation was recommended. OI-V was confirmed after more than four years of follow-up and genetic diagnosis, and the affected limb was preserved. Conclusion The history of OI and lack of rapid progression suggested OI-V with a hyperplastic callus. Combined with genetic testing, the diagnosis was OI-V. Although the patient was at a predisposing age for osteosarcoma, diagnosis and treatment should be based on the medical history of the patient, imaging,and genetic testing, and sometimes even time-consuming retrospective observation.
Snow Depth and Air Temperature Seasonality on Sea Ice Derived From Snow Buoy Measurements

Snow depth on sea ice is an essential state variable of the polar climate system and yet one of the least known and most difficult to characterize parameters of the Arctic and Antarctic sea ice systems. Here, we present a new type of autonomous platform to measure snow depth, air temperature, and barometric pressure on drifting Arctic and Antarctic sea ice. “Snow Buoys” are designed to withstand the harshest environmental conditions and to deliver high and consistent data quality with minimal impact on the surface. Our current dataset consists of 79 time series (47 Arctic, 32 Antarctic) since 2013, many of which cover entire seasonal cycles and with individual observation periods of up to 3 years. In addition to a detailed introduction of the platform itself, we describe the processing of the publicly available (near real time) data and discuss limitations. First scientific results reveal characteristic regional differences in the annual cycle of snow depth: in the Weddell Sea, annual net snow accumulation ranged from 0.2 to 0.9 m (mean 0.34 m) with some regions accumulating snow in all months. On Arctic sea ice, the seasonal cycle was more pronounced, showing accumulation from synoptic events mostly between August and April and maxima in autumn. Strongest ablation was observed in June and July, and consistently the entire snow cover melted during summer. Arctic air temperature measurements revealed several above-freezing temperature events in winter that likely impacted snow stratigraphy and thus preconditioned the subsequent spring snow cover. The ongoing Snow Buoy program will be the basis of many future studies and is expected to significantly advance our understanding of snow on sea ice, also providing invaluable in situ validation data for numerical simulations and remote sensing techniques.
Determinants of Revolving Door in an Acute Psychiatric Ward for Prison Inmates

Among the different types of heavy use of mental health services, frequent inpatient admission in acute care units of individuals unable to return to their usual environment refers to as revolving-door (RD). RD in prisoners is related to increased violence (acted and supported) and suicidal recidivism. We explored the determinants of RD in 200 inmates from the Swiss-French speaking areas who were admitted to the sole acute psychiatric care unit for all of the Swiss-French counties, located in Geneva. The Cuzick's test for trend across ordered groups, Kruskal-Wallis test and oneway ANOVA were used to compare demographic and clinical variables between single (one admission, N = 100), frequent (3–7, N = 69) and RD (more than 8, N = 31) during a 12 months period. In addition, univariate and multivariable ordered logistic regression modes were built to examine the determinants of RD. The sample included 27 women (mean age: 31.2 years) and 173 men (34.5 years) who were admitted during the period 2014–2019. The vast majority were single (65%) with low level of education (<6 years, 78%). Suicidal behavior was the more frequent reason for admission (57%). Psychiatric history was positive in 77.5% of cases and in 54.5% of cases there was at least one episode of inpatient psychiatric care. The more frequent ICD-10 psychiatric diagnosis in the last admission were psychotic disorder (38%), personality disorder (29.5%) and adjustment disorder (19.5%). In contrast, depressive episodes (7%) and bipolar disorder (4.5%) were rare. Group comparison showed that the presence of court-ordered treatments, suicidal behavior, personality and psychotic disorders was associated with significantly increased frequency of RD use. In univariate models, the same factors were positively associated with RD, the highest odds ratio being found for court-ordered treatments (5.77) and personality disorders (2.14). In contrast, the diagnosis of adjustment disorders was related to decreased RD use (OR 0.25). Court-ordered treatments and personality disorders were the only factors to predict RD in multivariable regression models. These findings suggest that acute psychiatric care in these patients did not depend of environmental stressors but rather represents the expression of a long-lasting vulnerability related to their psychological profile and criminal status.
Factors affecting in vitro gastric mucoadhesion. I: Test conditions and instrumental parameters

A new method to test the in vitro gastric mucoadhesion of polymers has been developed. In order to validate the testing system, and to investigate its compatibility with previously published results, experiments have been undertaken to elucidate which test conditions and instrumental parameters influence the observed results. It was found that the region of pig stomach chosen influenced the observed mucoadhesion, in terms of the work of adhesion, and the results correlated with the amount of mucus on the tissue, as found by alcian blue staining. It was subsequently found that the contact time and force between tissue and mucoadhesive polymer, the speed of removal of the test probe and degree of prehydration of the polymer tablet significantly affected the results obtained. Although some of the parameters which constitute a mucoadhesive test method have been studied none is the subject of a universal standard which all experimenters utilize. The influence of the composition of the test medium in which the experiments are carried out, in terms of pH and sodium chloride content, is also assessed for to assess any possible influence on the strength of the observed mucoadhesive bond. It is found that a wide range of factors, not standardized in previous mucoadhesive testing systems, can crucially influence the strength of the observed mucoadhesive bond.
Large Cell Neuro-Endocrine Carcinoma of the Lung: Current Treatment Options and Potential Future Opportunities

Large-cell neuroendocrine carcinomas of the lung (LCNECs) are rare tumors representing 1–3% of all primary lung cancers. Patients with LCNEC are predominantly male, older, and heavy smokers. Histologically, these tumors are characterized by large cells with abundant cytoplasm, high mitotic rate, and neuroendocrine immunohistochemistry-detected markers (chromogranin-A, synaptophysin, and CD56). In 2015 the World Health Organization classified LCNEC as a distinct subtype of pulmonary large-cell carcinoma and, therefore, as a subtype of non-small cell lung carcinoma (NSCLC). Because of the small-sized tissue samples and the likeness to other neuroendocrine tumors, the histological diagnosis of LCNEC remains difficult. Clinically, the prognosis of metastatic LCNECs is poor, with high rates of recurrence after surgery alone and overall survival of approximately 35% at 5 years, even for patients with early stage disease that is dramatically shorter compared with other NSCLC subtypes. First-line treatment options have been largely discussed but with limited data based on phase II studies with small sample sizes, and there are no second-line well defined treatments. To date, no standard treatment regimen has been developed, and how to treat LCNEC is still on debate. In the immunotherapy and targeted therapy era, in which NSCLC treatment strategies have been radically reshaped, a few data are available regarding these opportunities in LCNEC. Due to lack of knowledge in this field, many efforts have been done for a deeper understanding of the biological and molecular characteristics of LCNEC. Next generation sequencing analyses have identified subtypes of LCNEC that may be relevant for prognosis and response to therapy, but further studies are needed to better define the clinical impact of these results. Moreover, scarce data exist about PD-L1 expression in LCNEC and its predictive value in this histotype with regard to immunotherapy efficacy. In the literature some cases are reported concerning LCNEC metastatic patients carrying driver mutations, especially EGFR alterations, showing targeted therapy efficacy in this setting of disease. Due to the rarity and the challenging understanding of LCNEC, in this review we aim to summarize the management options currently available for treatment of LCNEC.
Youth Can Promote Marine Debris Concern and Policy Support Among Local Voters and Political Officials

Many of the most sweeping social movements throughout history have been youth-led, including those related to environmental challenges. Emerging research suggests youth can build environmental concern among parents via intergenerational learning, in some cases overcoming socio-ideological differences that normally stymie attempts at collective action. What has not been studied is the potential for youth to also influence adults outside their immediate families. This study based in North Carolina, USA, explores the potential of today's young people as environmental change-agents in their communities on the topic of marine debris. Specifically, this evaluation examines responses from voters and local officials after participating in youth-led civic engagement events. After engaging with a youth-led civic engagement event, voters, and local officials completed a retrospective pretest survey that asked questions about levels of marine debris concern and their likelihood of supporting a local marine debris ordinance. Young people encouraged both concern and policy support among both voters and officials, and that concern and policy support increased independently of whether adults were voters or officials, liberals or conservatives, or knew the students personally. Further, participation in the youth-led engagement event reduced political differences in marine debris concern. This study suggests youth can play a critical role addressing marine debris challenges by promoting support for marine debris management policy, and doing so across political barriers.
A LSGDM method based on social network and IVIFN's geometric characteristics for evaluating the collaborative innovation problem

During the development of regional economy, introducing collaborative innovation is an important policy. Constructing a scientific and effective measurement for evaluating the collaborative innovation degree is essential to determine an optimum collaborative innovation plan. As this problem is complex and has a long-lasting impact, this paper will propose a novel large scale group decision making (LSGDM) method both considering decision makers’ social network and their evaluation quality. Firstly, the decision makers will be detected based on their social connections and aggregated into different subgroups by an optimization algorithm. Secondly, decision makers are weighted according to their important degree and decision information, where the information is carried by interval valued intuitionistic fuzzy number (IVIFN). During the information processing, IVIFN is put in rectangular coordinate system considering its geometric meaning. And some related novel concept are given based on the barycenter of rectangle region determined by IVIFN. Meanwhile, the criteria’s weights are calculated by the accurate degree and deviation degree. A classical example is used to illustrate the effect of weighting methods. In summary, a large scale group decision making method based on the geometry characteristics of IVIFN (GIVIFN-LSGDM) is proposed. The scientific and practicability of GIVIFN-LSGDM method is illustrated through evaluating four different projects based on the constructed criteria system. Comparisons with the other methods are discussed, followed by conclusions and further research.
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions

Randomized smoothing is currently the most competitive technique for providing provable robustness guarantees. Since this approach is model-agnostic and inherently scalable we can certify arbitrary classiﬁers. Despite its success, recent works show that for a small class of i.i. d. distributions, the largest l p radius that can be certiﬁed using randomized smoothing decreases as O (1 /d 1 / 2 − 1 /p ) with dimension d for p > 2. We complete the picture and show that similar no-go results hold for the l 2 norm for a much more general family of distributions which are continuous and symmetric about the origin. Speciﬁcally, we calculate two diﬀerent upper bounds of the l 2 certiﬁed radius which have a constant multiplier of order Θ(1 /d 1 / 2 ). Moreover, we extend our results to l p ( p > 2) certiﬁcation with spherical symmetric distributions solidifying the limitations of randomized smoothing. We discuss the implications of our results for how accuracy and robustness are related, and why robust training with noise augmentation can alleviate some of the limitations in practice. We also show that on real-world data the gap between the certiﬁed radius and our upper bounds is small.
Deep Generative Missingness Pattern-Set Mixture Models

We propose a variational autoencoder architecture to model both ignorable and nonig-norable missing data using pattern-set mixtures as proposed by Little (1993). Our model explicitly learns to cluster the missing data into missingness pattern sets based on the observed data and missingness masks. Underpinning our approach is the assumption that the data distribution under miss-ingness is probabilistically semi-supervised by samples from the observed data distribution. Our setup trades oﬀ the characteristics of ignorable and nonignorable missing-ness and can thus be applied to data of both types. We evaluate our method on a wide range of data sets with diﬀerent types of miss-ingness and achieve state-of-the-art imputation performance. Our model outperforms many common imputation algorithms, especially when the amount of missing data is high and the missingness mechanism is non-ignorable.
Learning to Defend by Learning to Attack

Adversarial training provides a principled approach for training robust neural networks. From an optimization perspective, adversarial training is essentially solving a bilevel optimization problem. The leader problem is trying to learn a robust classifier, while the follower problem is trying to generate adversarial samples. Unfortunately, such a bilevel problem is difficult to solve due to its highly complicated structure. This work proposes a new adversarial training method based on a generic learning-to-learn (L2L) framework. Specifically, instead of applying existing hand-designed algorithms for the inner problem, we learn an optimizer, which is parametrized as a convolutional neural network. At the same time, a robust classifier is learned to defense the adversarial attack generated by the learned optimizer. Experiments over CIFAR-10 and CIFAR-100 datasets demonstrate that L2L outperforms existing adversarial training methods in both classification accuracy and computational efficiency. Moreover, our L2L framework can be extended to generative adversarial imitation learning and stabilize the training.
Understanding Gradient Clipping In Incremental Gradient Methods

We provide a theoretical analysis on how gradient clipping aﬀects the convergence of the incremental gradient methods on minimizing an objective function that is the sum of a large number of component functions. We show that clipping on gradients of component functions leads to bias on the descent direction, which is aﬀected by the clipping threshold, the norms of gradients of component functions, together with the angles be-tween gradients of component functions and the full gradient. We then propose some suﬃcient conditions under which the increment gradient methods with gradient clipping can be shown to be convergent under the more general relaxed smoothness assumption. We also empirically observe that the angles between gradients of component functions and the full gradient generally decrease as the batchsize increases, which may help to explain why larger batchsizes generally lead to faster convergence in training deep neural networks with gradient clipping.
Adaptive Sequential Sample Average Approximation for Solving Two-Stage Stochastic Linear Programs

We present adaptive sequential SAA (sample average approximation) algorithms to solve large-scale two-stage stochastic linear programs. The iterative algorithm framework we propose is organized into outer and inner iterations as follows: during each outer iteration, a sample-path problem is implicitly generated using a sample of observations or “scenarios,” and solved only imprecisely , to within a tolerance that is chosen adaptively , by balancing the estimated statistical error against solution error. The solutions from prior iterations serve as warm starts to aid eﬃcient solution of the (piecewise linear convex) sample-path optimization problems generated on subsequent iterations. The generated scenarios can be independent and identically distributed, or dependent, as in Monte Carlo generation using Latin-hypercube sampling, antithetic variates, or randomized quasi-Monte Carlo. We ﬁrst characterize the almost-sure convergence (and convergence in mean) of the optimality gap and the distance of the generated stochastic iterates to the true solution set. We then characterize the corresponding iteration complexity and work complexity rates as a function of the sample size schedule, demonstrating that the best achievable work complexity rate is Monte Carlo canonical and analogous to the generic O ( (cid:15) − 2 ) optimal complexity for nonsmooth convex optimization. We report extensive numerical tests that indicate favorable performance, due primarily to the use of a sequential framework with an optimal sample size schedule, and the use of warm starts. The proposed algorithm can be stopped in ﬁnite time to return a solution endowed with a probabilistic guarantee on quality.
Adaptive wavelet pooling for convolutional neural networks

Convolutional neural networks (CNN)s have become the go-to choice for most image and video processing tasks. Most CNN architectures rely on pooling layers to reduce the resolution along spatial dimensions. The reduction allows subsequent deep convolution layers to operate with greater eﬃciency. This paper introduces adaptive wavelet pooling layers, which employ fast wavelet transforms (FWT) to reduce the feature resolution. The FWT decomposes the input features into multiple scales reducing the feature dimensions by removing the ﬁne-scale subbands. Our approach adds extra ﬂex-ibility through wavelet-basis function optimization and coeﬃcient weighting at diﬀer-ent scales. The adaptive wavelet layers integrate directly into well-known CNNs like the LeNet, Alexnet, or Densenet architectures. Using these networks, we validate our approach and ﬁnd competitive performance on the MNIST, CIFAR-10, and SVHN (street view house numbers) data-sets.
A comparative study on sampling with replacement vs Poisson sampling in optimal subsampling

Faced with massive data, subsampling is a commonly used technique to improve computational eﬃciency, and using nonuniform subsampling probabilities is an eﬀec-tive approach to improve estimation eﬃ-ciency. For computational eﬃciency, subsampling is often implemented with replacement or through Poisson subsampling. However, no rigorous investigation has been performed to study the diﬀerence between the two sub-sampling procedures such as their estimation eﬃciency and computational convenience. In the context of maximizing a general target function, this paper derives optimal subsampling probabilities for both subsampling with replacement and Poisson subsampling. The optimal subsampling probabilities minimize variance functions of the subsampling estimators. Furthermore, they provide deep insights on the theoretical similarities and diﬀer-ences between subsampling with replacement and Poisson subsampling. Practically implementable algorithms are proposed based on the optimal structural results, which are evaluated by both theoretical and empirical analysis.
A Theoretical Characterization of Semi-supervised Learning with Self-training for Gaussian Mixture Models

Self-training is a classical approach in semi-supervised learning which is successfully applied to a variety of machine learning problems. Self-training algorithms generate pseudo-labels for the unlabeled examples and progressively reﬁne these pseudo-labels which hopefully coincides with the actual labels. This work provides theoretical insights into self-training algorithms with a focus on linear classiﬁers. First, we provide a sample complexity analysis for Gaussian mixture models with two components. This is established by sharp non-asymptotic characterization of the self-training iterations which captures the evolution of the model accuracy in terms of a ﬁxed-point iteration. Our analysis reveals the provable beneﬁts of rejecting samples with low conﬁdence and demonstrates how self-training iterations can gracefully improve the model accuracy. Secondly, we study a generalized GMM where the component means follow a distribution. We demonstrate that ridge regularization and class margin (i.e. separation between the component means) is crucial for the success and lack of regularization may prevent self-training from identifying the core features in the data.