Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Efficient Lithography Modeling With Transfer Learning and Active Data Selection

Lithography simulation is one of the key steps in physical verification, enabled by the substantial optical and resist models. A resist model bridges the aerial image simulation to printed patterns. While the effectiveness of learning-based solutions for resist modeling has been demonstrated, they are considerably data-demanding. Meanwhile, a set of manufactured data for a specific lithography configuration is only valid for the training of one single model, indicating low data efficiency. Due to the complexity of the manufacturing process, obtaining enough data for acceptable accuracy becomes very expensive in terms of both time and cost, especially during the evolution of technology generations when the design space is intensively explored. In this paper, we propose a new resist modeling framework for contact layers, utilizing existing data from old technology nodes and active selection of data in a target technology node, to reduce the amount of data required from the target lithography configuration. Our framework based on transfer learning and active learning techniques is effective within a competitive range of accuracy, i.e., $3 \times -10 \times $ reduction on the amount of training data with comparable accuracy to the state-of-the-art learning approach.
The (In)significance of Executive Functions for the Trait of Self-Control: A Psychometric Study

Self-control (SC) is an individual trait defined as the ability to pursue long-distance goals in spite of the obstacles generated by current desires, innate or learned automatisms, and physiological needs of an organism. This trait is relatively stable across the life span and it predicts such important features as level of income, quality of social relationships, and proneness to addictions. It is widely believed that the cognitive substrate of SC involves the executive functions (EFs), such as inhibitory control, shifting of attention, and working memory updating. However, the empirical evidence concerning the relationships between trait SC and EFs is not convincing. The present study aims to address two questions: (1) what is the strength of relationships between trait SC and EFs, and (2) which aspects of SC are predicted by particular EFs, if at all. In order to answer these questions, we carried out a psychometric study with 296 participants (133 men and 163 women, mean age 23.31, SD 3.64), whom we investigated with three types of tools: (1) a battery SC scales and inventories, (2) a battery of EFs tasks, and (3) two general intelligence tests. Structural equation modeling approach was used to analyze the data. We found that the latent variables representing SC and the latent variable representing EFs did not show any relationship. The standardized path coefficient between EFs and general intelligence turned out rather strong. We conclude that the trait of SC, measured with questionnaires, does not depend on the strength of cognitive control, measured with EFs tasks.
Classification of motor imagery eeg using wavelet envelope analysis and LSTM networks

Motor imagery (MI) based brain-computer interface (BCI) facilitates a medium to translate the human motion intentions using Motor imagery electroencephalogram (EEG) into control signals. A major challenge in BCI research is the identification of non-stationary brain electrical signals to categorize human motion intentions. We propose a novel method based on wavelet envelope analysis and long-term short-term memory (LSTM) classifier which consider the amplitude modulation characteristics and time series information of MI-EEG to classify EEG signals into multiple classes. First, the Hilbert transform (HT) and discrete wavelet transform (DWT) are combined to extract significant features which contains the underlying information of both amplitude modulation and frequency modulation of the EEG signals. Then, the wavelet envelope features are input into an LSTM classifier with input gates, forget gates, and output gates for classification. Finally, the experiment was conducted on the 2003 BCI competition data set III with 5-fold cross-validation, and experimental results show that the proposed method helps achieve higher classification accuracy.
ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography

Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography. Despite the success of deep learning-based solutions, this task remains a major challenge in smart healthcare, since it is intrinsically a weakly supervised learning problem. In this paper, we incorporate the attention mechanism into a deep convolutional neural network, and thus propose the ChestNet model to address effective diagnosis of thorax diseases on chest radiography. This model consists of two branches: a classification branch serves as a uniform feature extraction-classification network to free users from troublesome handcrafted feature extraction, and an attention branch exploits the correlation between class labels and the locations of patholog-ical abnormalities and allows the model to concentrate adaptively on the patholog-ically abnormal regions. We evaluated our model against three state-of-the-art deep learning models on the Chest X-ray 14 dataset using the official patient-wise split. The results indicate that our model outperforms other methods, which use no extra training data, in diagnosing 14 thorax diseases on chest radiography.
Expert-Performed Endotracheal Intubation-Related Complications in Trauma Patients: Incidence, Possible Risk Factors, and Outcomes in the Prehospital Setting and Emergency Department

The aim of this study was to determine complication rates and possible risk factors of expert-performed endotracheal intubation (ETI) in patients with trauma, in both the prehospital setting and the emergency department. We also investigated how the occurrence of ETI-related complications affected the survival of trauma patients. This single-center retrospective observational study included all injured patients who underwent anesthesiologist-performed ETI from 2007 to 2017. ETI-related complications were defined as hypoxemia, unrecognized esophageal intubation, regurgitation, cardiac arrest, ETI failure rescued by emergency surgical airway, dental trauma, cuff leak, and mainstem bronchus intubation. Of the 537 patients included, 23.5% experienced at least one complication. Multivariable logistic regression analysis revealed that low Glasgow Coma Scale Score (adjusted odds ratio [AOR], 0.93; 95% confidence interval [CI], 0.88–0.98), elevated heart rate (AOR, 1.01; 95% CI, 1.00–1.02), and three or more ETI attempts (AOR, 15.71; 95% CI, 3.37–73.2) were independent predictors of ETI-related complications. We also found that ETI-related complications decreased the likelihood of survival of trauma patients (AOR, 0.60; 95% CI, 0.38–0.95), independently of age, male sex, Injury Severity Score, Glasgow Coma Scale Score, and off-hours presentation. Our results suggest that airway management in trauma patients carries a very high risk; this finding has implications for the practice of airway management in injured patients.
A new analysis method for parameters retrieving in classifier based on choquet integral

Choquet integral is a powerful aggregation tool in information fusion and classification. It is established with respect to signed fuzzy measure. The nonadditivity of the signed fuzzy measure provides an effective representation to describe the interaction among contributions from the predictive attributes to the objective attributes. Currently, the values of the signed fuzzy measure in most classifiers based on Choquet integral are either preset subjectively by experience, or retrieved by global optimization methods which are time-consuming. In this paper, a new discriminant analysis algorithm to retrieve the values of signed fuzzy measure in classification models based on Choquet integral is proposed via Fisher's linear discriminant. It is the first time to derive the values of the signed fuzzy measure directly in an analytic form. Performance validation on synthetics and benchmark data sets are conducted to reveal the feasibility and effectiveness of the proposed method.
Tight typings and split bounds

Multi types—aka non-idempotent intersection types—have been used to obtain quantitative bounds on higher-order programs, as pioneered by de Carvalho. Notably, they bound at the same time the number of evaluation steps and the size of the result. Recent results show that the number of steps can be taken as a reasonable time complexity measure. At the same time, however, these results suggest that multi types provide quite lax complexity bounds, because the size of the result can be exponentially bigger than the number of steps. Starting from this observation, we refine and generalise a technique introduced by Bernadet & Graham-Lengrand to provide exact bounds for the maximal strategy. Our typing judgements carry two counters, one measuring evaluation lengths and the other measuring result sizes. In order to emphasise the modularity of the approach, we provide exact bounds for four evaluation strategies, both in the λ-calculus (head, leftmost-outermost, and maximal evaluation) and in the linear substitution calculus (linear head evaluation). Our work aims at both capturing the results in the literature and extending them with new outcomes. Concerning the literature, it unifies de Carvalho and Bernadet & Graham-Lengrand via a uniform technique and a complexity-based perspective. The two main novelties are exact split bounds for the leftmost strategy—the only known strategy that evaluates terms to full normal forms and provides a reasonable complexity measure—and the observation that the computing device hidden behind multi types is the notion of substitution at a distance, as implemented by the linear substitution calculus.
Sliced Recurrent Neural Networks

Recurrent neural networks have achieved great success in many NLP tasks. However, they have difficulty in parallelization because of the recurrent structure, so it takes much time to train RNNs. In this paper, we introduce sliced recurrent neural networks (SRNNs), which could be parallelized by slicing the sequences into many subsequences. SRNNs have the ability to obtain high-level information through multiple layers with few extra parameters. We prove that the standard RNN is a special case of the SRNN when we use linear activation functions. Without changing the recurrent units, SRNNs are 136 times as fast as standard RNNs and could be even faster when we train longer sequences. Experiments on six large-scale sentiment analysis datasets show that SRNNs achieve better performance than standard RNNs.
Reversed predator–prey cycles are driven by the amplitude of prey oscillations

Abstract Ecoevolutionary feedbacks in predator–prey systems have been shown to qualitatively alter predator–prey dynamics. As a striking example, defense–offense coevolution can reverse predator–prey cycles, so predator peaks precede prey peaks rather than vice versa. However, this has only rarely been shown in either model studies or empirical systems. Here, we investigate whether this rarity is a fundamental feature of reversed cycles by exploring under which conditions they should be found. For this, we first identify potential conditions and parameter ranges most likely to result in reversed cycles by developing a new measure, the effective prey biomass, which combines prey biomass with prey and predator traits, and represents the prey biomass as perceived by the predator. We show that predator dynamics always follow the dynamics of the effective prey biomass with a classic ¼‐phase lag. From this key insight, it follows that in reversed cycles (i.e., ¾‐lag), the dynamics of the actual and the effective prey biomass must be in antiphase with each other, that is, the effective prey biomass must be highest when actual prey biomass is lowest, and vice versa. Based on this, we predict that reversed cycles should be found mainly when oscillations in actual prey biomass are small and thus have limited impact on the dynamics of the effective prey biomass, which are mainly driven by trait changes. We then confirm this prediction using numerical simulations of a coevolutionary predator–prey system, varying the amplitude of the oscillations in prey biomass: Reversed cycles are consistently associated with regions of parameter space leading to small‐amplitude prey oscillations, offering a specific and highly testable prediction for conditions under which reversed cycles should occur in natural systems.
Design and FE simulation of a novel electromagnetic Blank Holder System in sheet metal drawing process

A DC Electromagnetic Blank Holder System (EBHS) is proposed. First, the application of Blank Holder Force (BHF) in sheet metal drawing is introduced. Then, the structure and working principle of the novel EBHS is illustrated. Furthermore, requirements of the coils and power system are elaborated. Theoretical calculation and FE simulation of Electromagnetic Force (EMF) has also been conducted. With the application of orthogonal test method, more optimal EMF and major effective factors have been obtained. To prove the advantage of EMF acting on the novel Blank Holder System, numerical simulation of circular-cup deep drawing is carried out. By comparing the simulation results derived form traditional and novel Blank Holder System, it can be found that electromagnetic BHF is helpful in drawing sheet metal cylindrical part.
Sex Differences in the Impact of Obstructive Sleep Apnea on Glucose Metabolism

Objectives: Obstructive sleep apnea (OSA) is more prevalent in men and is an independent risk factor for type 2 diabetes. We aimed to determine if there are sex differences in the impact of OSA on glucose metabolism in nondiabetic overweight and obese adults. Methods: One hundred and forty-five men and women (age 33.4 ± 0.6, BMI 37.2 ± 0.7, 70.3% blacks) from the community underwent in-laboratory polysomnography. Severity of OSA was assessed by the apnea-hypopnea index (AHI). Glucose tolerance was assessed using fasting glucose, 1-h glucose, 2-h glucose and the area under the curve (AUC) during the 2-h oral glucose tolerance test (OGTT). Fasting insulin resistance was assessed by HOMA-IR, and insulin sensitivity during the OGTT was assessed by the Matsuda Index. Pancreatic beta-cell function was assessed by fasting HOMA-%B and by AUCinsulin/glucose, insulinogenic index, and oral disposition index (DIoral) during the OGTT. All comparisons were adjusted for age, BMI, race and severity of OSA. Results: There were no significant demographic differences between men and women without OSA. Men and women with OSA were similar in age, BMI, and severity of OSA, but there were more black women with OSA. Compared to women with OSA, men with OSA had significantly higher fasting glucose, 1-h glucose levels, AUCglucose, and AUC for insulin secretion rate (AUCISR) but similar 2-h glucose levels. These differences persisted in adjusted analyses. Men with OSA secreted significantly more insulin than women with OSA in order to achieve similar glucose levels. Men with OSA had significantly worse beta cell function as measured by the DIoral than women with OSA. In contrast, there were no significant sex differences in measures of glucose tolerance and beta-cell function in participants without OSA. Conclusion: Men with OSA secreted more insulin compared to women with OSA in order to maintain glucose homeostasis. The adverse impact of OSA on beta-cell responsiveness was larger in men, which may result in an overall greater risk of type 2 diabetes compared to women.
Enhanced Secure Thresholded Data Deduplication Scheme for Cloud Storage

As more corporate and private users outsource their data to cloud storage, recent data breach incidents make end-to-end encryption increasingly desirable. Unfortunately, semantically secure encryption renders various cost-effective storage optimization techniques, such as data deduplication, ineffective. On this ground Stanek et al. [1] introduced the concept of “data popularity” arguing that data known/owned by many users do not require as strong protection as unpopular data; based on this, Stanek et al. presented an encryption scheme, where the initially semantically secure ciphertext of a file is transparently downgraded to a convergent ciphertext that allows for deduplication as soon as the file becomes popular. In this paper we propose an enhanced version of the original scheme. Focusing on practicality, we modify the original scheme to improve its efficiency and emphasize clear functionality. We analyze the efficiency based on popularity properties of real datasets and provide a detailed performance evaluation, including comparison to alternative schemes in real-like settings. Importantly, the new scheme moves the handling of sensitive decryption shares and popularity state information out of the cloud storage, allowing for improved security notion, simpler security proofs and easier adoption. We show that the new scheme is secure under the Symmetric External Diffie-Hellman assumption in the random oracle model.
Major Risk Factors in Head and Neck Cancer: A Retrospective Analysis of 12-Year Experiences

Background Head and neck cancer (HNC) is the seventh most common type of cancer in the world and constitute 5% of the entire cancers worldwide. The global burden of HNC accounts for 650,000 new cases and 350,000 deaths worldwide every year and a major proportion of regional malignancies in India. More than 70% of squamous cell carcinoma of the head and neck are estimated to be avoidable by lifestyle changes, particularly by effective reduction of exposure to well-known risk factors such as tobacco smoking and alcohol drinking. Methods A retrospective analysis of 12 years (2001 - 2012) of HNC patients attending RCC, PGIMS Rohtak was done. Total numbers of cancer patients seen were 26,295 and out of these 9,950 patients were of HNCs, which were retrospectively analyzed for their associated risk factors in different HNC subtypes. Most of the patients, i.e. 92.3%, were presented as locally advanced HNC (stages III and IV). Results It has been observed that smoking and alcohol are the strongest independent risk factors responsible for increased risk of HNC and are further having synergetic correlations. Conclusion The present study confirms the principal role of alcohol consumption and smoking in HNC carcinogenesis, as well as the differential associations with HNC subtypes, and a significant, positive, multiplicative interaction with different risk factors.
TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces

The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community. In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN. Our experiments demonstrate state-of-the-art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or naturally-supervised approaches.
Emotion Based Music Recommendation System Using Wearable Physiological Sensors

Most of the existing music recommendation systems use collaborative or content based recommendation engines. However, the music choice of a user is not only dependent to the historical preferences or music contents. But also dependent to the mood of that user. This paper proposes an emotion based music recommendation framework that learns the emotion of a user from the signals obtained via wearable physiological sensors. In particular, the emotion of a user is classified by a wearable computing device which is integrated with a galvanic skin response (GSR) and photo plethysmography (PPG) physiological sensors. This emotion information is feed to any collaborative or content based recommendation engine as a supplementary data. Thus, existing recommendation engine performances can be increased using these data. Therefore, in this paper emotion recognition problem is considered as arousal and valence prediction from multi-channel physiological signals. Experimental results are obtained on 32 subjects’ GSR and PPG signal data with/out feature fusion using decision tree, random forest, support vector machine and k-nearest neighbors algorithms. The results of comprehensive experiments on real data confirm the accuracy of the proposed emotion classification system that can be integrated to any recommendation engine.
Infants' Daily Experience With Pets and Their Scanning of Animal Faces

Very little is known about the effect of pet experience on cognitive development in infancy. In Experiment 1, we document in a large sample (N = 1270) that 63% of families with infants under 12 months have at least one household pet. The potential effect on development is significant as the first postnatal year is a critically important time for changes in the brain and cognition. Because research has revealed how experience shapes early development, it is likely that the presence of a companion dog or cat in the home influences infants' development. In Experiment 2, we assess differences between infants who do and do not have pets (N = 171) in one aspect of cognitive development: their processing of animal faces. We examined visual exploration of images of dog, cat, monkey, and sheep faces by 4-, 6-, and 10-month-old infants. Although at the youngest ages infants with and without pets exhibited the same patterns of visual inspection of these animals faces, by 10 months infants with pets spent proportionately more time looking at the region of faces that contained the eyes than did infants without pets. Thus, exposure to pets contributes to how infants look at and learn about animal faces.
A new two-stage object detection network without RoI-Pooling

Two-stage object detection networks often propose a set of candidate boxes in the first stage, and then fine- tune the boxes in the second stage. The original two-stage object detection methods mostly process the features among the candidate boxes in the picture by RoI-Pooling [3]. Due to the overlaps of the candidate boxes proposed in the first stage, the calculation of the second stage is repetitive and the single-frame detection is slow. RoI-Pooling also makes the features of the elongated shape deformed. In this paper, we present a new two-step object detection network, called Spatial Alignment Network(SAN), which does not use the RoI-Pooling layer and reduces the computational repeatability of the second stage. We also use atrous convolution for the network fine-tuning. Our network has a competitive result, and faster than the original two-stage detectors.