Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Wafermap Patterns Clustering via Variational Autoencoders

Semiconductor manufacturing processes are prone to process deviations or other production issues. Quality assurance of every processing step and measuring wafer test values is crucial for finding possible root causes of these problems. Automated visual inspection and recognition of patterns in wafermap data obtained during different processing steps has a potential to signifficantly improve the efficiency of finding early production issues and even help with adjustment of the production parameters to automatically resolve them. In this paper, we present a machine learning approach for unsupervised clustering of spatial patterns in wafermap measurement data. Measured test values are first pre-processed using some computer vision techniques, followed by a feature extraction based on variational autoencoders to decompose high-dimensional wafermaps into a low-dimensional latent representation. Final step is to detect the structure of this latent space and assign individual wafers into clusters. We experimentally evaluate the performance of the proposed method over a real dataset.
“The most inspiring bikini photos you’ll see this summer”: A thematic analysis of mass audiences’ interpretations of ostomy selfies

How people with ostomies—a surgically created opening in the body that expels bodily wastes—use social media to challenge ostomy stigma represents a growing area of research, especially the creation, posting, and circulation of ostomy selfies within online health communities. This project contributes to this research by examining reactions by a mass audience to news stories about a viral ostomy selfie posted by ostomate Bethany Townsend to a Crohn’s disease Facebook page. By analyzing the user-generated comments associated with this news coverage, this study illuminates how ostomy selfies are interpreted outside the highly sympathetic audiences that populate online health communities. Analysis reveals positive and negative reactions, posted by ostomates and non-ostomates alike, coexist within the comments. Implications of the conflicting reactions to ostomies, in general, and ostomy selfies, in particular, are discussed with regard to the effort to destigmatize ostomies in society.
Deep Video Dehazing With Semantic Segmentation

Recent research have shown the potential of using convolutional neural networks (CNNs) to accomplish single image dehazing. In this paper, we take one step further to explore the possibility of exploiting a network to perform haze removal for videos. Unlike single image dehazing, video-based approaches can take advantage of the abundant information that exists across neighboring frames. In this paper, assuming that a scene point yields highly correlated transmission values between adjacent video frames, we develop a deep learning solution for video dehazing, where a CNN is trained end-to-end to learn how to accumulate information across frames for transmission estimation. The estimated transmission map is subsequently used to recover a haze-free frame via atmospheric scattering model. In addition, as the semantic information of a scene provides a strong prior for image restoration, we propose to incorporate global semantic priors as input to regularize the transmission maps so that the estimated maps can be smooth in the regions of the same object and only discontinuous across the boundaries of different objects. To train this network, we generate a dataset consisted of synthetic hazy and haze-free videos for supervision based on the NYU depth dataset. We show that the features learned from this dataset are capable of removing haze that arises in outdoor scenes in a wide range of videos. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world videos.
Functional Diversity of Thalamic Reticular Subnetworks

The activity of the GABAergic neurons of the thalamic reticular nucleus (TRN) has long been known to play important roles in modulating the flow of information through the thalamus and in generating changes in thalamic activity during transitions from wakefulness to sleep. Recently, technological advances have considerably expanded our understanding of the functional organization of TRN. These have identified an impressive array of functionally distinct subnetworks in TRN that participate in sensory, motor, and/or cognitive processes through their different functional connections with thalamic projection neurons. Accordingly, “first order” projection neurons receive “driver” inputs from subcortical sources and are usually connected to a densely distributed TRN subnetwork composed of multiple elongated neural clusters that are topographically organized and incorporate spatially corresponding electrically connected neurons—first order projection neurons are also connected to TRN subnetworks exhibiting different state-dependent activity profiles. “Higher order” projection neurons receive driver inputs from cortical layer 5 and are mainly connected to a densely distributed TRN subnetwork composed of multiple broad neural clusters that are non-topographically organized and incorporate spatially corresponding electrically connected neurons. And projection neurons receiving “driver-like” inputs from the superior colliculus or basal ganglia are connected to TRN subnetworks composed of either elongated or broad neural clusters. Furthermore, TRN subnetworks that mediate interactions among neurons within groups of thalamic nuclei are connected to all three types of thalamic projection neurons. In addition, several TRN subnetworks mediate various bottom-up, top-down, and internuclear attentional processes: some bottom-up and top-down attentional mechanisms are specifically related to first order projection neurons whereas internuclear attentional mechanisms engage all three types of projection neurons. The TRN subnetworks formed by elongated and broad neural clusters may act as templates to guide the operations of the TRN subnetworks related to attentional processes. In this review article, the evidence revealing the functional TRN subnetworks will be evaluated and will be discussed in relation to the functions of the various sensory and motor thalamic nuclei with which these subnetworks are connected.
The FuzzyLog: A Partially Ordered Shared Log

The FuzzyLog is a partially ordered shared log abstraction. Distributed applications can concurrently append to the partial order and play it back. FuzzyLog applications obtain the benefits of an underlying shared log — extracting strong consistency, durability, and failure atomicity in simple ways — without suffering from its drawbacks. By exposing a partial order, the FuzzyLog enables three key capabilities for applications: linear scaling for throughput and capacity (without sacrificing atomicity), weaker consistency guarantees, and tolerance to network partitions. We present Dapple, a distributed implementation of the FuzzyLog abstraction that stores the partial order compactly and supports efficient appends/playback via a new ordering protocol. We implement several data structures and applications over the FuzzyLog, including several map variants as well as a ZooKeeper implementation. Our evaluation shows that these applications are compact, fast, and flexible: they retain the simplicity (100s of lines of code) and strong semantics (durability and failure atomicity) of a shared log design while exploiting the partial order of the Fuzzy- Log for linear scalability, flexible consistency guarantees (e.g., causal+ consistency), and network partition tolerance. On a 6-node Dapple deployment, our FuzzyLog-based ZooKeeper supports 3M/sec single-key writes, and 150K/sec atomic cross-shard renames.
Long‐term prognostic value of the FibroTest in patients with non‐alcoholic fatty liver disease, compared to chronic hepatitis C, B, and alcoholic liver disease

Although the FibroTest has been validated as a biomarker to determine the stage of fibrosis in non‐alcoholic fatty liver disease (NAFLD) with results similar to those in chronic hepatitis C (CHC), B (CHB), and alcoholic liver disease (ALD), it has not yet been confirmed for the prediction of liver‐related death.
Hospitalizations for Influenza-Associated Severe Acute Respiratory Infection, Beijing, China, 2014–2016

We analyzed surveillance data for 2 sentinel hospitals to estimate the influenza-associated severe acute respiratory infection hospitalization rate in Beijing, China. The rate was 39 and 37 per 100,000 persons during the 2014–15 and 2015–16 influenza seasons, respectively. Rates were highest for children <5 years of age.
Human-Robot Interfaces for Interactive Receptionist Systems and Wayfinding Applications

Service robots are playing an increasingly relevant role in society. Humanoid robots, especially those equipped with social skills, could be used to address a number of people’s daily needs. Knowing how these robots are perceived and potentially accepted by ordinary users when used in common tasks and what the benefits brought are in terms, e.g., of tasks’ effectiveness, is becoming of primary importance. This paper specifically focuses on receptionist scenarios, which can be regarded as a good benchmark for social robotics applications given their implications on human-robot interaction. Precisely, the goal of this paper is to investigate how robots used as direction-giving systems can be perceived by human users and can impact on their wayfinding performance. A comparative analysis is performed, considering both solutions from the literature and new implementations which use different types of interfaces to ask for and give directions (voice, in-the-air arm pointing gestures, route tracing) and various embodiments (physical robot, virtual agent, interactive audio-map). Experimental results showed a marked preference for a physical robot-based system showing directions on a map over solutions using gestures, as well as a positive effect of embodiment and social behaviors. Moreover, in the comparison, physical robots were generally preferred to virtual agents.
Robust 2D Joint Sparse Principal Component Analysis With F-Norm Minimization For Sparse Modelling: 2D-RJSPCA

Principal component analysis (PCA) is widely used methods for dimensionality reduction and Lots of variants have been proposed to improve the robustness of algorithm, however, these methods suffer from the fact that PCA is linear combination which makes it difficult to interpret complex nonlinear data, and sensitive to outliers or cannot extract features consistently, i.e., collectively; PCA may still require measuring all input features. 2DPCA based on $\ell _{1} -norm$ has been recently used for robust dimensionality reduction in the image domain but still sensitive to noise. In this paper, we introduce robust formation of 2DPCA by centering the data using the optimized mean for two-dimensional joint sparse as well as effectively combining the robustness of 2DPCA and the sparsity-inducing lasso regularization. Optimal mean helps to improve the robustness of joint sparse PCA further. The distance in spatial dimension is measure in F-norm and sum of different datapoint uses 1-norm. 2DR-JSPCA imposes joint sparse constraints on its objective function whereas additional plenty term help to deal with outliers efficiently. Both theoretical and empirical results on six publicly available benchmark datasets shows that Optimal mean 2DR-JSPCA provides better performance for dimensionality reduction as compare to non-sparse (2DPCA and 2DPCA-L1) and sparse (SPCA, JSPCA).
In utero priming of highly functional effector T cell responses to human malaria

Fetal malaria-responsive effector CD4+ T cells provide protection from childhood malaria in infants born to mothers with placental malaria. Prenatal Plasmodium reactivity Fetal immunity is generally thought to be skewed toward tolerance. Odorizzi et al. used samples from a study in Uganda to determine whether placental malaria infection modulated fetal immune responses to malaria. They stimulated cord blood cells in vitro and found that the fetal cells from cases of placental malaria were more reactive to Plasmodium antigens. Moreover, clinical follow-up revealed that this increased T cell response correlated with protection from childhood malaria. Their results demonstrate that protective immune responses in humans are able to develop even before birth. These findings not only contribute to our knowledge of human fetal immunity but also have implications for malaria control programs. Malaria remains a significant cause of morbidity and mortality worldwide, particularly in infants and children. Some studies have reported that exposure to malaria antigens in utero results in the development of tolerance, which could contribute to poor immunity to malaria in early life. However, the effector T cell response to pathogen-derived antigens encountered in utero, including malaria, has not been well characterized. Here, we assessed the frequency, phenotype, and function of cord blood T cells from Ugandan infants born to mothers with and without placental malaria. We found that infants born to mothers with active placental malaria had elevated frequencies of proliferating effector memory fetal CD4+ T cells and higher frequencies of CD4+ and CD8+ T cells that produced inflammatory cytokines. Fetal CD4+ and CD8+ T cells from placental malaria–exposed infants exhibited greater in vitro proliferation to malaria antigens. Malaria-specific CD4+ T cell proliferation correlated with prospective protection from malaria during childhood. These data demonstrate that placental malaria is associated with the generation of proinflammatory malaria-responsive fetal T cells. These findings add to our current understanding of fetal immunity and indicate that a functional and protective pathogen-specific T cell response can be generated in utero.
Low Hartmann’s procedure or intersphincteric abdominoperineal resection in the primary treatment of low rectal cancer; a survey among surgeons evaluating current practice

Abstract Background: Low Hartmann's procedure (LHP) and intersphincteric abdominoperineal resection (iAPR) are both surgical options in the treatment of distal rectal cancer when there is no intention to restore bowel continuity. This study aimed to evaluate current practice among members of the Dutch Association of Coloproctology (WCP). Methods: An online survey among members of the WCP who represent 66 Dutch hospitals was conducted. The survey consisted of 15 questions addressing indications for surgical procedures and complications. Results: Surgeons from 37 hospitals (56%) responded. Thirty-six percent does not distinguish low from high Hartmann's procedures based on estimated length of the rectal remnant. Overall, iAPR was the preferred technique in 86%. If asking whether operative approach would be different in tumours at 1 cm from the pelvic floor compared to 5 cm distance, 62% stated that they would consider a different technique. The incidence of pelvic abscess after LHP was thought to be higher, equal or lower than iAPR in 36%, 36% and 21%, respectively, with the remaining respondents not answering this question. Conclusions: The vast majority of the respondents considers iAPR as the preferred non-restorative procedure for rectal cancer not invading the sphincter complex, which contradicts with population based data from 2011.
The Changing Epidemiology of Gastroesophageal Reflux Disease: Are Patients Getting Younger?

Background/Aims Gastroesophageal reflux disease (GERD) is a common disease globally with increasing prevalence and consequently greater burden on the Healthcare system. Traditionally, GERD has been considered a disease of middle-aged and older people. Since risk factors for GERD affect a growing number of the adult population, concerns have been raised that increasingly younger people may develop GERD. We aim to determine if the proportion of younger patients has increased among the GERD population. Methods The incidence of GERD as well as several variables were evaluated during an 11-year period. Explorys was used to evaluate datasets at a “Universal” and Healthcare system in northern Ohio to determine if trends at a local level reflected those at a universal level. GERD patients were classified into 7 age groups (15–19, 20–29, 30–39, 40–49, 50–59, 60–69, and ≥70 years). Results The proportion of patients with GERD increased in all age groups, except for those who were ≥70 years in the universal dataset (P < 0.001) and those who were ≥60 years in the Healthcare system (P < 0.001). The greatest rise was seen in 30–39 years in both datasets (P < 0.001). Similarly, the proportion of GERD patients who were using proton pump inhibitors increased in all age groups except for those who were ≥70 years in both datasets (P < 0.001), with the greatest increase being the group 30–39 years (P < 0.001). Conclusion Over the last decade, there has been a significant increase in the proportion of younger patients with GERD, especially those within the age range of 30–39 years.
Edge-Based Finite-Time Protocol Analysis With Final Consensus Value and Settling Time Estimations

The objective of this paper is to design the protocols with a final consensus value and settling time estimations for finite-time consensus of multiagent systems. A couple of new edge-based protocols are developed for multiple second-order nonlinear agents under bounded or Lipschitz-type nonlinear functions, respectively. The final consensus value of the multiagent system is obtained as an average expression. Further, to obtain the estimation of the finite settling time, a special Lyapunov function is constructed. Through the construction processes, both the final consensus value and the settling time are obtained. Finally, as applications, a finite-time formation controller based on the first protocol is designed for multiple mini-spacecraft, verified by simulations.
Novelty Detection in Passive Sonar Systems using Stacked AutoEncoders

Modern military ships have become more silent in the recent years, so that their detection, tracking, and classification have become even more challenging. Submarines rely very much on passive sonar systems, which might operate in a multi-class scenario. The training data for (semi) automatic decision may not contain all possible classes; i.e., classifiers will face unknown classes in operation. Here, Stacked AutoEncoders (SAE) are applied for such novelty detection. Beyond that, the impact on classification efficiency is estimated, and a comparison with a shallow learning algorithm is performed.
Caregiving, Employment and Social Isolation: Challenges for Rural Carers in Australia

Australia has one of the world’s highest life expectancy rates, and there is a rapidly growing need for informal caregivers to support individuals who are ageing, have chronic illness or a lifelong disability. These informal carers themselves face numerous physical and psychological stressors in attempting to balance the provision of care with their personal life, their work commitments and family responsibilities. However, little is known about the specific challenges facing rural carers and the barriers that limit their capacity to provide ongoing support. A cross-sectional survey composed of open-ended responses and demographic/socioeconomic measures used routinely by the Australian Bureau of Statistics (ABS) and the Australian Institute of Health & Welfare (AIHW) was used with a cohort of 225 rurally-based carers within New South Wales, Australia. Demographic questions specified the respondents’ age, gender, employment, caregiving status, condition of and relationship to the care recipient, postcode, residency status, and distance and frequency travelled to provide care. Open-ended comments sections were provided to allow participants to describe any issues and problems associated with caregiving including employment, travel, residency, carer support groups and any other general information. The results show that most rural carers were middle-aged women supporting a spouse or a child. Unpredictability associated with providing care exacerbated demands on carers’ time, with many reporting significant employment consequences associated with inflexibility and limited job options in rural locations. Specific issues associated with travel requirements to assist with care were reported, as were the impacts of care provision on the respondents’ own personal health. The majority of carers were aware of the social supports available in their local rural community, but did not access them, leaving the carers vulnerable to marginalisation. Problems associated with employment were noted as resulting in financial pressures and associated personal stress and anxiety for the caregivers. While this issue is not necessarily limited to rural areas, it would appear that the lack of opportunity and flexibility evident in rural areas would exacerbate this problem for non-metropolitan residents. The participants also identified specific barriers to the provision of care in rural areas, including the significant impact of travel. Access to support services, such as carer groups, were rarely accessed due to a mix of factors including inaccessibility, poor timing and a lack of anonymity. Financially, there was considerable evidence of hardship, and there is an urgent need for a comprehensive review of government and community-based support to better meet the needs of rural carers.
Rural Broadband Access via Clustered Collaborative Communication

Broadband penetration in rural areas of the developing countries is significantly low. The unique challenges in enabling rural connectivity are sparsity of population locations and modest income of the villagers, which induce low return on investment to the conventional cellular network providers. In this paper, we propose a novel cluster-based network architecture and protocols for efficient rural broadband coverage which requires minimal infrastructure setup by the service provider. Multiple customer premise equipments (CPEs) in a village form clusters and transmit collaboratively over unused television bands to the base station. A two-tier uplink access protocol is proposed and its performance in terms of network throughput and energy efficiency are obtained analytically. The cluster size is optimized to maximize the uplink network throughput and energy-efficiency. A distributed clustering algorithm is proposed for the CPEs to form clusters, while channels are allocated to the clusters using the proposed channel allocation algorithm to minimize inter-cluster interference. Via network simulation studies we demonstrate that the proposed architecture can be cost-effective and energy-efficient, while being scalable at the same time.