Miracle-dz/UniFAR-FTUs huggingface dataset:
Land Use Impacts on Coral Reef Health: A Ridge-to-Reef Perspective

Over 60% of the world’s reefs experience damage from local activities such as overfishing, coastal development, and watershed pollution. Land-based sources of pollution are a critical threat to coral reefs, and understanding “ridge-to-reef” changes is critically needed to improve management and coral survival in the Anthropocene. We review existing literature on spatial-ecological connections between land use and coral health, specifically examining vegetative, agricultural, urban, and other land-use types. In general, forested land use is positively related to metrics of coral condition, while anthropogenic land uses like urban development and agriculture drive a decline in coral cover, diversity, colony size, and structural complexity. However, land-use and land-cover impacts vary across time and space, and small portions of the landscape (e.g., discrete segments of unpaved roads, grazed and scalded hillsides) may have an outsized effect on reef pollution, presenting opportunities for targeted conservation. Some coral species show resilience under land-use and land-cover change, and the impact of land use on coral recovery from bleaching remains an active area of research. Finally, a spatial bibliography of existing literature reveals that most ridge-to-reef studies focus on a handful of regional hotspots, surface water, and watershed-scale dynamics; more research is needed to address groundwater connectivity and to compare land-use impacts across multiple regions and scales. Approaches from landscape ecology that assess spatial patterns of, and synergies between, interlocking land cover may assist conservation managers in designing more resilient reefscapes.
When actions speak louder than clicks: a combined model of purchase probability and long-term customer satisfaction

Maximizing sales and revenue is an important goal of online commercial retailers. Recommender systems are designed to maximize users' click or purchase probability, but often disregard users' eventual satisfaction with purchased items. As result, such systems promote items with high appeal at the selling stage (e.g. an eyecatching presentation) over items that would yield more satisfaction to users in the long run. This work presents a novel unified model that considers both goals and can be tuned to balance between them according to the needs of the business scenario. We propose a multi-task probabilistic matrix factorization model with a dual task objective: predicting binary purchase/no purchase variables combined with predicting continuous satisfaction scores. Model parameters are optimized using Variational Bayes which allows learning a posterior distribution over model parameters. This model allows making predictions that balance the two goals of maximizing the probability for an immediate purchase and maximizing user satisfaction and engagement down the line. These goals lie at the heart of most commercial recommendation scenarios and enabling their balance has the potential to improve value for millions of users worldwide. Finally, we present experimental evaluation on different types of consumer retail datasets that demonstrate the benefits of the model over popular baselines on a number of well-known ranking metrics.
CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations

In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user preferences while CB algorithms are typically based on item profiles. These approaches harness different data sources and therefore the resulting recommended items are generally very different. This paper presents the CB2CF, a deep neural multiview model that serves as a bridge from items content into their CF representations. CB2CF is a "real-world" algorithm designed for Microsoft Store services that handle around a billion users worldwide. CB2CF is demonstrated on movies and apps recommendations, where it is shown to outperform an alternative CB model on completely cold items.
Efficient similarity computation for collaborative filtering in dynamic environments

The problem of computing all pairwise similarities in a large collection of vectors is a well-known and common data mining task. As the number and dimensionality of these vectors keeps increasing, however, currently existing approaches are often unable to meet the strict efficiency requirements imposed by the environments they need to perform in. Real-time neighbourhood-based collaborative filtering (CF) is one example of such an environment in which performance is critical. In this work, we present a novel algorithm for efficient and exact similarity computation between sparse, high-dimensional vectors. Our approach exploits the sparsity that is inherent to implicit feedback data-streams, entailing significant gains compared to other methods. Furthermore, as our model learns incrementally, it is naturally suited for dynamic real-time CF environments. We propose a MapReduce-inspired parallellisation procedure along with our method, and show how even more speed-up can be achieved. Additionally, in many real-world systems, many items are actually not recommendable at any given time, due to recency, stock, seasonality, or enforced business rules. We exploit this fact to further improve the computational efficiency of our approach. Experimental evaluation on both real-world and publicly available datasets shows that our approach scales up to millions of processed user-item interactions per second, and well advances the state-of-the-art.
Effect of age on use of interactive exhibits in a museum context: a large-scale in-the-wild study

We investigate how the age of a visitor affects his/her use of interactive exhibits in a museum context. We conducted a large-scale in-the-wild study using eight interactive museum exhibits (three touch UIs, three tangible UIs, and two gesture UIs) over a period of 31 months. We collected data from a large number of visitors in seven age groups from 10s to 70+ (behavior data on 8,405 visitors and questionnaire answers from 1,203 visitors). For Attractiveness, visitors in their 20s to 50s were most likely to enter the interactive exhibit spaces while visitors in their 10s were least likely to enter them; the higher the age, the longer it took to start interacting with the exhibit after entering the exhibit space. For Engagement, visitors in all age groups stayed in the exhibit spaces for similar lengths of time and interacted with the exhibit with similar frequencies. For Social Interaction, visitors in their 10s and 70+ were more likely to stay when other visitors were in the space. For Preference, visitors in all age groups highly rated interacting with an exhibit, giving an average rating of 4.3 (on a 5-point Likert scale) to the question "The exhibit I just visited was good. "
Spherical Objects as an Opportunity to Investigate Physical Embodiment in Mixed Reality Environments

In this work, we introduce our approach of using current VR and AR technology to explore fully tangible spherical user interfaces. We present three prototypes utilizing this technique. We briefly outline possible challenges, advantages, and fields of application for the presented concepts. Accordingly, we discuss why VR is an interesting tool to investigate interaction with Tangible User Interfaces (TUIs) that are currently not feasible in real world applications such as tangible holographic interfaces or lightweight handheld non-planar displays. This allows for studying various levels of tangible feedback on established use cases such as spherical visualizations. Subsequently, a tangible sphere, due to its natural shape enables an investigation of interaction techniques transferred from the real to the virtual world. Building on these prospects we represent the position that such objects could play a leading role not only in research but in future Virtual Reality (VR) and Augmented Reality (AR) applications that rely on natural interaction based on realistic physical feedback.
How can they know that? A study of factors affecting the creepiness of recommendations

Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users' feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users' expectations of---and their trust in---RS.
Music cold-start and long-tail recommendation: bias in deep representations

Recent advances in deep learning have yielded new approaches for music recommendation in the long tail. The new approaches are based on data related to the music content (i.e. the audio signal) and context (i.e. other textual information), from which it automatically obtains a representation in a latent space that is used to generate the recommendations. The authors of these new approaches have shown improved accuracies, thus becoming the new state-of-the-art for music recommendation in the long tail. One of the drawbacks of these methods is that it is not possible to understand how the recommendations are generated and what the different dimensions of the underlying models represent. The goal of this thesis is to evaluate these models to understand how good are the results from the user perspective and how successful the models are to recommend new artists or less-popular music genres and styles (i.e. the long tail). For example, if a model predicts the latent representation from the audio but a given genre is not well represented in the collection, it is not probable that the songs of this genre are going to be recommended. First, we will focus on defining a measure that could be used to assess how successful a model is recommending new artists or less-popular genres. Then, the state-of-the-art methods will be evaluated offline to understand how they perform under different circumstances and new methods will be proposed. Later, using an online evaluation it will be possible to understand how these recommendations are perceived by the users. Increasingly, algorithms are responsible for the music that we consume, understanding their behavior is fundamental to make sure they give the opportunity to new artists and music styles. This work will contribute in this direction, making it possible to give better recommendations for the users.
The Earconizer - A Tool for constructing hierarchical Earcons

Earcons have become an important part of the human-computer interaction. In addition to the visual presentation of a user interface, the use of the auditory channel enriches software-side entities with information. Additionally, earcons can significantly improve interaction and effectively communicate complex information. Often Earcons are created with the expertise of a sound designer. How can the creation be simplified so that earcons can be created and organized by ordinary users? This paper shows of a tool for creating earcons based on the hierarchical structure of compound earcons to positively enhance the user experience in an interactive environment.
From NoteCards to Notebooks: There and Back Again

Fifty years since the beginning of the Internet, and three decades of the Dexter Hypertext Reference Model and the World Wide Web mark an opportune time to take stock and consider how hypermedia has developed, and in which direction it might be headed. The modern Web has on one hand turned into a place where very few, very large companies control all major platforms with some highly unfortunately consequences. On the other hand, it has also led to the creation of a highly flexible and nigh ubiquitous set of technologies and practices, which can be used as the basis for future hypermedia research with the rise of computational notebooks as a prime example of a new kind of collaborative and highly malleable applications.
Brain Senescence Caused by Elevated Levels of Reactive Metabolite Methylglyoxal on D-Galactose-Induced Aging Mice

Aging is a complex natural phenomenon that is manifested by degenerative changes in the structure and function of cells and tissues. D-Galactose-induced aging mice are an artificial accelerated aging model that causes memory and learning impairment, oxidative stress, and neuroinflammation. In this study, we examined the underlying mechanism of an aging mouse model induced by D-galactose. Our behavioral Morris water maze results revealed that D-galactose administration for 2 months significantly induced memory and learning impairment in C57BL/6J mice. High performance liquid chromatography (HPLC) results showed elevated levels of the metabolite methylglyoxal (MG) in D-galactose-induced aging mice. Whether and how D-galactose induces senescence by elevated levels of reactive metabolite MG remain unclear. In our study, MG mainly accumulated through the following two aspects: to increase its source, namely, the triose phosphate produced by the glycolysis pathway, and to reduce its detoxification system, namely, the glyoxalase system. Therefore, elevated MG levels may be one of the causes of brain senescence in D-galactose-induced mice. However, the molecular mechanism of the increased level of the reaction metabolite methylglyoxal requires further exploration.
Relaxed softmax for PU learning

In recent years, the softmax model and its fast approximations have become the de-facto loss functions for deep neural networks when dealing with multi-class prediction. This loss has been extended to language modeling and recommendation, two fields that fall into the framework of learning from Positive and Unlabeled data. In this paper, we stress the different drawbacks of the current family of softmax losses and sampling schemes when applied in a Positive and Unlabeled learning setup. We propose both a Relaxed Softmax loss (RS) and a new negative sampling scheme based on Boltzmann formulation. We show that the new training objective is better suited for the tasks of density estimation, item similarity and next-event prediction by driving uplifts in performance on textual and recommendation datasets against classical softmax.
Bandit algorithms in recommender systems

The multi-armed bandit problem models an agent that simultaneously attempts to acquire new knowledge (exploration) and optimize his decisions based on existing knowledge (exploitation). The agent attempts to balance these competing tasks in order to maximize his total value over the period of time considered. There are many practical applications of the bandit model, such as clinical trials, adaptive routing or portfolio design. Over the last decade there has been an increased interest in developing bandit algorithms for specific problems in recommender systems, such as news and ad recommendation, the cold start problem in recommendation, personalization, collaborative filtering with bandits, or combining social networks with bandits to improve product recommendation. The aim of this tutorial is to provide an overview of the various applications of bandit algorithms in recommendation.
Malignant Mesothelioma: Has Anything Changed?

Abstract Malignant pleural mesothelioma is a rare cancer associated with asbestos exposure and portends a dismal prognosis. Its worldwide incidence has been increasing, and treatment options are currently suboptimal and noncurative. However, since the turn of the century, several encouraging steps have been made toward improving outcomes for mesothelioma patients. An increased understanding of disease pathophysiology has led to more accurate diagnosis and staging, and the establishment of the standard of care first-line pemetrexed/platin doublet chemotherapy regimen in 2003 initially revolutionized treatment. While significant debate remains regarding the preferred approach to surgical and radiation therapy in the context of multimodal therapy, recent breakthroughs in immunotherapy offer hope for another paradigm shift in the near future. This review will summarize the current clinical approach to diagnosis, staging, and treatment of malignant pleural mesothelioma.
Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints

Prophylactic interventions such as vaccine allocation are some of the most effective public health policy planning tools. The supply of vaccines, however, is limited and an important challenge is to optimally allocate the vaccines to minimize epidemic impact. This resource allocation question (which we refer to as VaccIntDesign) has multiple dimensions: when, where, to whom, etc. Most of the existing literature in this topic deals with the latter (to whom), proposing policies that prioritize individuals by age and disease risk. However, since seasonal influenza spread has a typical spatial trend, and due to the temporal constraints enforced by the availability schedule, the when and where problems become equally, if not more, relevant. In this paper, we study the VaccIntDesign problem in the context of seasonal influenza spread in the United States. We develop a national scale metapopulation model for influenza that integrates both short and long distance human mobility, along with realistic data on vaccine uptake. We also design GreedyAlloc, a greedy algorithm for allocating the vaccine supply at the state level under temporal constraints and show that such a strategy improves over the current baseline of pro-rata allocation, and the improvement is more pronounced for higher vaccine efficacy and moderate flu season intensity. Further, the resulting strategy resembles a ring vaccination applied spatiallyacross the US.
The Genetics and Epigenetics of Sex Change in Fish.

Fish show extraordinary sexual plasticity, changing sex naturally as part of their life cycle or reversing sex because of environmental stressors. This plasticity shows that sexual fate is not an irreversible process but the result of an ongoing tug-of-war for supremacy between male and female signaling networks. The behavioral, gonadal, and morphological changes involved in this process are well described, yet the molecular events that underpin those changes remain poorly understood. Epigenetic modifications emerge as a critical link between environmental stimuli, the onset of sex change, and subsequent maintenance of sexual phenotype. Here we synthesize current knowledge of sex change, focusing on the genetic and epigenetic processes that are likely involved in the initiation and regulation of sex change. We anticipate that better understanding of sex change in fish will shed new light on sex determination and development in vertebrates and on how environmental perturbations affect sexual fate. Expected final online publication date for the Annual Review of Animal Biosciences, Volume 8 is February 15, 2020. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
GPR40 full agonism exerts feeding suppression and weight loss through afferent vagal nerve

GPR40/FFAR1 is a Gq protein-coupled receptor expressed in pancreatic β cells and enteroendocrine cells, and mediates insulin and incretin secretion to regulate feeding behavior. Several GPR40 full agonists have been reported to reduce food intake in rodents by regulating gut hormone secretion in addition to their potent glucose-lowering effects; however, detailed mechanisms of feeding suppression are still unknown. In the present study, we characterized T-3601386, a novel compound with potent full agonistic activity for GPR40, by using in vitro Ca2+ mobilization assay in Chinese hamster ovary (CHO) cells expressing FFAR1 and in vivo hormone secretion assay. We also evaluated feeding suppression and weight loss after the administration of T-3601386 and investigated the involvement of the vagal nerve in these effects. T-3601386, but not a partial agonist fasiglifam, increased intracellular Ca2+ levels in CHO cells with low FFAR1 expression, and single dosing of T-3601386 in diet-induced obese (DIO) rats elevated plasma incretin levels, suggesting full agonistic properties of T-3601386 against GPR40. Multiple doses of T-3601386, but not fasiglifam, in DIO rats showed dose-dependent weight loss accompanied by feeding suppression and durable glucagon-like peptide-1 elevation, all of which were completely abolished in Ffar1-/- mice. Immunohistochemical analysis in the nuclei of the solitary tract demonstrated that T-3601386 increased the number of c-Fos positive cells, which also disappeared in Ffar1-/- mice. Surgical vagotomy and drug-induced deafferentation counteracted the feeding suppression and weight loss induced by the administration of T-3601386. These results suggest that T-3601386 exerts incretin release and weight loss in a GPR40-dependent manner, and that afferent vagal nerves are important for the feeding suppression induced by GPR40 full agonism. Our novel findings raise the possibility that GPR40 full agonist can induce periphery-derived weight reduction, which may provide benefits such as less adverse effects in central nervous system compared to centrally-acting anti-obesity drugs.
Dietary fat associated with insulin concentrations in Native American adolescents

The prevalence of type 2 diabetes among Native American populations is escalating, particularly in children and adolescents. Dietary factors appear to influence the development of diabetes, and therefore, are modifiable risk factors for primary prevention. Studies evaluating the dietary habits of Native American adolescents are sparse. Furthermore, studies examining the relationship between insulin and dietary factors have been limited to adult populations. The objective of this study was to describe dietary intake and food consumption patterns and examine dietary factors associated with fasting and 30-minute postprandial insulin levels among adolescents from the Zuni Pueblo, New Mexico. Dietary intake and plasma insulin concentration data were collected from Zuni male and female adolescents. 15-20 years of age, participating in the Zuni Diabetes Prevention Project (ZDPP). The ZDPP. a 4-year, cross-sectional, school-based intervention study was designed to reduced risk factors of type 2 diabetes among Zuni adolescents. Using 24-hr dietary recalls to estimate dietary intake, dietary data was collected during 1993-1994 (baseline) and spring of 1997 (3 years of intervention exposure). Modified glucose tolerance tests were conducted to measure plasma insulin concentrations. Baseline dietary data was adjusted to remove day-to-day variation and compared to national dietary guidelines. Logistic regression was conducted to examine associations between nutrient intake and insulin concentrations.