Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing a model of auditory hallucinations: the role of negative emotions and cognitive resources

ABSTRACT Introduction: Models of auditory hallucinations (AH) state that AH arise through an interaction between negative emotions and limited available cognitive resources. However, this hypothesis has never been directly tested. Methods: A two-by-two factorial design was used to examine the effect of emotions (neutral VS negative) and available cognitive resources (high VS low) on the elicitation of false alarms in an auditory signal detection paradigm. One hundred and seventy four healthy participants were assigned to one of the four experimental conditions. While participants were listening to white noise, their emotional state was manipulated using affective pictures and the level of available cognitive resources was manipulated using a visual N-back task. Results: Results revealed significant interaction effects between emotions and cognitive resources on the number of false alarms. In particular, participants with fewer available cognitive resources and at the same time who were in a negative emotional state, tended to hear significantly more false alarms. In addition, the degree of certitude was significantly correlated with a higher degree of hallucination proneness. Conclusions: Such results are in agreement with models of AH and they provide new data for the understanding of the emotional and cognitive mechanisms that underpin AH.
The inositol hexakisphosphate kinases IP6K1 and -2 regulate human cellular phosphate homeostasis, including XPR1-mediated phosphate export

Phosphate's central role in most biochemical reactions in a living organism requires carefully maintained homeostasis. Although phosphate homeostasis in mammals has long been studied at the organismal level, the intracellular mechanisms controlling phosphate metabolism are not well-understood. Inositol pyrophosphates have emerged as important regulatory elements controlling yeast phosphate homeostasis. To verify whether inositol pyrophosphates also regulate mammalian cellular phosphate homeostasis, here we knocked out inositol hexakisphosphate kinase (IP6K) 1 and IP6K2 to generate human HCT116 cells devoid of any inositol pyrophosphates. Using PAGE and HPLC analysis, we observed that the IP6K1/2-knockout cells have nondetectable levels of the IP6-derived IP7 and IP8 and also exhibit reduced synthesis of the IP5-derived PP-IP4. Nucleotide analysis showed that the knockout cells contain increased amounts of ATP, whereas the Malachite green assay found elevated levels of free intracellular phosphate. Furthermore, [32Pi] pulse labeling experiments uncovered alterations in phosphate flux, with both import and export of phosphate being decreased in the knockout cells. Functional analysis of the phosphate exporter xenotropic and polytropic retrovirus receptor 1 (XPR1) revealed that it is regulated by inositol pyrophosphates, which can bind to its SPX domain. We conclude that IP6K1 and -2 together control inositol pyrophosphate metabolism and thereby physiologically regulate phosphate export and other aspects of mammalian cellular phosphate homeostasis.
The Power of Metabolism for Predicting Microbial Community Dynamics

Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. ABSTRACT Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. However, metabolic models have many constraints and often serve best as null models to identify additional processes at play. We anticipate that major advances in metabolic systems biology will involve scaling bottom-up approaches to complex communities and expanding the processes that are incorporated in a metabolic perspective. Ultimately, cellular metabolism will inform predictive ecology that enables precision management of microbial systems.
Healthy diet is associated with gene expression in blood: the Framingham Heart Study.

BACKGROUND Genes in metabolic and nutrient signaling pathways play important roles in lifespan in model organisms and human longevity. OBJECTIVE The aim of this study was to examine the relation of a quantitative measure of healthy diet to gene expression in a community-based cohort. METHODS We used the 2015 Dietary Guidelines for Americans Adherence Index (DGAI) score to quantify key dietary recommendations of an overall healthy diet. Our current analyses included 2220 Offspring participants (mean age 66 ± 9 y, 55.4% women) and 2941 Third-Generation participants (mean age 46 ± 9 y, 54.5% women) from the Framingham Heart Study. Gene expression was profiled in blood through the use of the Affymetrix Human Exon 1.0 ST Array. We conducted a transcriptome-wide association study of DGAI adjusting for age, sex, smoking, cell counts, and technical covariates. We also constructed a combined gene score from genes significantly associated with DGAI. RESULTS The DGAI was significantly associated with the expression of 19 genes (false discovery rate <0.05). The most significant gene, ARRDC3, is a member of the arrestin family of proteins, and evidence in animal models and human data suggests that this gene is a regulator of obesity and energy expenditure. The DGAI gene score was associated with body mass index (P = 1.4 × 10-50), fasting glucose concentration (P = 2.5 × 10-11), type 2 diabetes (P = 1.1 × 10-5), and metabolic syndrome (P = 1.8 × 10-32). CONCLUSIONS Healthier diet was associated with genes involved in metabolic function. Further work is needed to replicate our findings and investigate the relation of a healthy diet to altered gene regulation.
Latent Allocation Spatiotemporal Models For Indoor Human Mobility

The ability to understand, model and predict human mobility in high-occupancy buildings like corporate offices and campuses can fundamentally change the way buildings are managed. For example, energy efficiency can be improved using more accurate models of the temporal and spatial aspects of building occupancy. Similarly, responding to emergency situations is more effective and less intrusive if the building system has better knowledge not just of where occupants are, but also of their likely next locations and when they will get there. We propose a novel approach to learn a spatiotemporal model of human mobility from observed trajectories. Our approach posits the existence of different mobility profiles that reflect the heterogeneity in the way people move between locations. Our proposed latent allocation model describes the probabilistic relationships between the observed trajectory data and the latent (unobserved) mobility profiles and their parameters. To tackle the problem of inferring these parameters efficiently, we frame the model as a neural network. We engineer the layers of the network to enforce appropriate constraints on the learned spatial and temporal parameters of each profile to best explain the data. We demonstrate our model and learning approach on synthetic data and give initial results from a subset of real data collected from our corporate building. ACM Reference Format: Yiming Gu, Hala Mostafa and B. Aditya Prakash. 2018. Latent Allocation Spatiotemporal Models For Indoor Human Mobility . In UrbComp 2018, August 2018, London, United Kingdom. ACM, New York, NY, USA, Article 4, 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn
Real-time studies of ferroelectric domain switching: a review

Ferroelectric materials have been utilized in a broad range of electronic, optical, and electromechanical applications and hold the promise for the design of future high-density nonvolatile memories and multifunctional nano-devices. The applications of ferroelectric materials stem from the ability to switch polarized domains by applying an electric field, and therefore a fundamental understanding of the switching dynamics is critical for design of practical devices. In this review, we summarize the progress in the study of the microscopic process of ferroelectric domain switching using recently developed in situ transmission electron microscopy (TEM). We first briefly introduce the instrumentation, experimental procedures, imaging mechanisms, and analytical methods of the state-of-the-art in situ TEM techniques. The application of these techniques to studying a wide range of complex switching phenomena, including domain nucleation, domain wall motion, domain relaxation, domain-defect interaction, and the interplay between different types of domains, is demonstrated. The underlying physics of these dynamic processes are discussed.
The emerging role of chemokine receptor CXCR2 in cancer progression

Chemokines and their receptors form a complex network controlling leukocyte migration, particularly during infection and inflammation. Besides their critical contributions to the immune system, they are being increasingly implicated in tumour development and metastasis. Chemokines also mediate other tumour-related processes, including angiogenesis and even chemoresistance. There are four subgroups of chemokines based on the position of the first two N-terminal cysteine residues: CXC, CC, CX3C and C. The CXC subgroup is further divided into ELR+ and ELR− chemokines. Chemokines with the ELR motif have angiogenic properties and bind mainly to CXC chemokine receptor 2 (CXCR2). CXCR2 and its ligands are intimately involved in tumour regulation and growth, and its functional inhibition shows promising results in several cancer types. This review discusses the contribution of CXCR2 to the progression of the most frequently occurring cancers including lung, prostate, breast and colorectal cancer, in addition to recent relevant data on ovarian and pancreatic cancer. Pharmacological intervention against CXCR2 and its potential therapeutic benefits are also addressed.
Metacommunities: Spatial Community Ecology

Ecology explains the distribution and abundance of species from small to large spatial scales. Explanations based only on processes operating at local scales do not fully account for patterns of diversity at regional scales. Recent decades have seen the unification of local and regional processes as explanations for the maintenance of diversity within the metacommunity concept. A metacommunity is a set of local communities connected by dispersal of multiple potentially interacting species. Metacommunity ecology studies the interactions among species as they occur across a network of patches. The rate and frequency of dispersal mediates the spatial distribution of diversity, abundance and the flux of energy across the metacommunity. The metacommunity concept also provides a deeper understanding of the causes and consequences of species loss, and suggests solutions to mitigate these effects. Key concepts Concept: Summary Colonization: A spatial process in which a population becomes established in an area where it was previously absent. Open community: A community that experiences immigration and emigration. Metacommunity dynamics: The dynamics of species abundances and distribution within a metacommunity due to the compound effects of species interactions and dispersal. Local versus regional processes: Local processes occur within a community and include density-dependent growth and interspecific interactions, whereas regional processes occur across communities and include dispersal. Source–sink system: A network of patches of a single species connected by dispersal containing two categories of population types: (1) source populations, in which the birth rate exceeds the death rate and emigration is common and (2) sink populations, in which the death rate exceeds the birth rate and frequent immigration is required to sustain the presence of individuals. Rescue effect: A spatial process in which local extinction is prevented by immigration from other patches in the metacommunity. The rate of immigration per patch increases as the proportion of patches occupied increases. Neutral model: All species are assumed to be identical with respect to their birth and death rates (fitness). Stabilizing mechanisms are absent, and the abundances of all species drift randomly to extinction. Diversity is maintained in the long-term by speciation. Extinction debt: The delayed loss of species that occurs long after the initial loss of habitat connectivity. Spatial insurance hypothesis: The dispersal-dependent maintenance of biodiversity and ecosystem processes within a metacommunity. Keywords: diversity; dispersal; species interactions; extinction; colonization
A Comprehensive Survey of Knowledge Graph Embeddings with Literals: Techniques and Applications

Knowledge Graphs are organized to describe entities from any discipline and the interrelations between them. Apart from facilitat-ing the inter-connectivity of datasets in the LOD cloud, KGs have been used in a variety of applications such as Web search or entity linking, and recently are part of popular search systems and Q&A applications etc. However, the KG applications suﬀer from high computational and storage cost. Hence, there arises the necessity of having a representation learning of the high dimensional KGs into low dimensional spaces preserving structural as well as relational information. In this study, we conduct a comprehensive survey based on techniques of KG embedding models which consider the structured information of the graph as well as the unstructured information in form of literals such as text, numerical values etc. Furthermore, we address the challenges in their embedding models followed by a discussion on diﬀerent application scenarios.
SEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia

Event Detection has been one of the research areas in Text Mining that has attracted attention during this decade due to the widespread availability of social media data specifically twitter data. Twitter has become a major source for information about real-world events because of the use of hashtags and the small word limit of Twitter that ensures concise presentation of events. Previous works on event detection from tweets are either applicable to detect localized events or breaking news only or miss out on many important events. This paper presents the problems associated with event detection from tweets and a tweet-segmentation based system for event detection called SEDTWik, an extension to a previous work, that is able to detect newsworthy events occurring at different locations of the world from a wide range of categories. The main idea is to split each tweet and hash-tag into segments, extract bursty segments, cluster them, and summarize them. We evaluated our results on the well-known Events2012 corpus and achieved state-of-the-art results. Keywords: Event detection, Twitter, Social Media, Microblogging, Tweet segmentation, Text Mining, Wikipedia, Hashtag.
A Richer Policy Language for GDPR Compliance

We illustrate an OWL2 fragment properly tailored for the specification of both companies’ data protection policies and data subjects’ consent to data processing. Assessing a company’s policy for compliance with data subjects’ consent is then reduced to a subsumption checking. We provide a complete subsumption algorithm which combines tableaux-based and structural-subsumption techniques and show that, under some conditions, it performs in polynomial time.
The Strength of the Weakest Supervision: Topic Classification Using Class Labels

When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels. Ideally, an intelligent classifier can understand these labels right away and start classifying documents. Indeed, a human can confidently tell if an article is about science, politics, sports, or none of the above, after knowing just the class labels. We study the problem of training an initial topic classifier using only class labels. We investigate existing techniques for solving this problem and propose a simple but effective approach. Experiments on a variety of topic classification data sets show that learning from class labels can save significant initial labeling effort, essentially providing a ”free” warm start to the topic classifier.
Probably Approximately Correct Completion of Description Logic Knowledge Bases

We propose an approach for approximately completing a TBox w.r. t. a ﬁxed model. By asking implication questions to a domain expert, our method approximates the subsumption relationships that hold in expert’s model and enriches the TBox with the newly discovered relationships between a given set of concept names. Our approach is based on Angluin’s exact learning framework and on the attribute exploration method from Formal Concept Analysis. It brings together the best of both approaches to ask only polynomially many questions to the domain expert.
Efficient Indoor Positioning with Visual Experiences via Lifelong Learning

Positioning with visual sensors in indoor environments has many advantages: it doesn’t require infrastructure or accurate maps, and is more robust and accurate than other modalities such as WiFi. However, one of the biggest hurdles that prevents its practical application on mobile devices is the time-consuming visual processing pipeline. To overcome this problem, this paper proposes a novel lifelong learning approach to enable efficient and real-time visual positioning. We explore the fact that when following a previous visual experience for multiple times, one could gradually discover clues on how to traverse it with much less effort, e.g., which parts of the scene are more informative, and what kind of visual elements we should expect. Such second-order information is recorded as parameters, which provide key insights of the context and empower our system to dynamically optimise itself to stay localised with minimum cost. We implement the proposed approach on an array of mobile and wearable devices, and evaluate its performance in two indoor settings. Experimental results show our approach can reduce the visual processing time up to two orders of magnitude, while achieving sub-metre positioning accuracy.
“When Numbers Matter!”: Detecting Sarcasm in Numerical Portions of Text

Research in sarcasm detection spans almost a decade. However a particular form of sarcasm remains unexplored: sarcasm expressed through numbers, which we estimate, forms about 11% of the sarcastic tweets in our dataset. The sentence ‘Love waking up at 3 am’ is sarcastic because of the number. In this paper, we focus on detecting sarcasm in tweets arising out of numbers. Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier. The rule-based classifier conveys the crux of the numerical sarcasm problem, namely, incongruity arising out of numbers. The statistical ML classifier uncovers the indicators i.e., features of such sarcasm. The actual system in place, however, are two deep learning (DL) models, CNN and attention network that obtains an F-score of 0.93 and 0.91 on our dataset of tweets containing numbers. To the best of our knowledge, this is the first line of research investigating the phenomenon of sarcasm arising out of numbers, culminating in a detector thereof.
Learning Description Logic Concepts: When can Positive and Negative Examples be Separated? (Abstract)

Learning description logic (DL) concepts from positive and negative examples given in the form of labeled data items in a KB has received significant attention in the literature. We study the fundamental question of when a separating DL concept exists and provide useful model-theoretic characterizations as well as complexity results for the associated decision problem. For expressive DLs such as ALC and ALCQI, our characterizations show a surprising link to the evaluation of ontology-mediated conjunctive queries. We exploit this to determine the combined complexity (between ExpTime and NExpTime) and data complexity (second level of the polynomial hierarchy) of separability. For the Horn DL EL, separability is ExpTime-complete both in combined and in data complexity while for its modest extension ELI it is even undecidable. Separability is also undecidable when the KB is formulated in ALC and the separating concept is required to be in EL or ELI.
On Queries with Inequalities in DL-LiteR≠

It is well-known that answering conjunctive queries with inequalities (CQ6=s) over DL-LiteR ontologies is in general undecidable. In this paper we consider the subclass of CQ 6=s, called CQ6=,bs, where inequalities involve only distinguished variables or individuals. In particular, we tackle the problem of answering CQ 6=,bs and unions thereof (UCQ 6=,bs) over DL-LiteR ontologies, where DL-LiteR corresponds to DL-LiteR without the Unique Name Assumption, and with the possibility of asserting inequalities between individuals, as in OWL2QL. As a first contribution, we show that answering CQ6=,bs over DL-LiteR ontologies has the same computational complexity as the UCQ case over DL-LiteR, i.e., it is in AC in data complexity, in PTIME in TBox complexity, and NP-complete in combined complexity. We then deal with the UCQ6=,b case, and prove that answering UCQ 6=,bs over DL-LiteR ontologies is still in AC 0 in data complexity and in PTIME in TBox complexity, but is Π 2 -hard in combined complexity.
VIDEO-SURVEILLANCE SYSTEM FOR TRACKING A PERSON BY ESTIMATING THE TRAJECTORY

This paper we propose a simple and efficient video surveillance system which detects and tracks a person in a video stream. Object detection is the most important and crucial step for any video surveillance system. In this paper, we separate foreground and background by using the statistical model of Gaussian Mixture (GMM). Interests points are identified in the detected regions (foreground) using the Harris detector. The analysis of connected components detected by subtracting the background allows grouped the pixels of moving objects in order to extract the center of gravity. Then, a boundary box is used to limit the area of connected components in order to detect the coordinates of the gravity center of the moving person. Finally, by projection in Euclidean plan we can get the trajectory of the person in motion and compute the Euclidean distance crossed in the scene.
An Overview of User Feedback Classification Approaches

Online user feedback about software products is a promising source of user requirements. To allow scaling analyses to large amounts of user feedback, research on Crowd-based Requirements Engineering (CrowdRE) seeks to tailor natural language processing (NLP) techniques to Requirements Engineering (RE). Various frameworks have been proposed, but it remains largely unclear why particular NLP techniques are better suited for CrowdRE than others, which makes it hard to make a well-founded choice for a technique. We found that CrowdRE research most often uses machine learning (ML) and has so far applied twelve clusters of ML algorithms and seven clusters of ML features. The prevalent algorithm–feature pair is Näıve Bayes with Bag of Words – Term Frequency (BOW-TF), followed by Support Vector Machines (SVM) with BOW-TF. An initial comparison of the reported precision and recall suggests that classifications in RE need further improvement. Our research presents a preliminary overview of the current landscape of automated classification techniques for RE whose results may inspire researchers to apply new strategies to advance research in this field, or to include ML models they had not considered previously in their benchmarks.