Miracle-dz/UniFAR-FTUs huggingface dataset:
Indications of habitat use patterns among small cetaceans in the central North Pacific based on fisheries observer data

Biological specimens and environmental data collected by observers monitoring Japanese squid driftnet fishing operations during the summers of 1990 and 1991 in the central North Pacific (37°N-46°N, and 170°E-150°W) were used to explore habitat use patterns among three small cetacean species common to that area: the Dall’s porpoise (Phocoenoides dalli), Pacific white-sided dolphin (Lagenorhynchus obliquidens) and northern right whale dolphin (Lissodelphis borealis). Sex and maturity status were determined for 805 northern right whale dolphins, 421 Pacific white-sided dolphins and 206 Dall’s porpoises incidentally taken in 800 observed gillnet sets, allowing sub-taxon comparisons of habitat use patterns. Habitat variables were based on observer records of sea surface temperature (SST), wind velocity and direction, and swell height. Current velocity and direction and SST gradients were also derived. Canonical Correspondence Analysis (CCA) was used to relate the species categories to the habitat conditions recorded for the gillnet operations in which entanglements occurred. The samples collected from the southern, middle and northern latitudes within the overall study area were examined separately to account for northward movement of the fishing fleets across the summer months. SST was the most dominant and consistent feature; northern right whale dolphins occupied the warmest waters, Dall’s porpoises the coldest; Pacific white-sided dolphins were found in-between, but more similar to the latter. Wind velocity and swell height also reflected potentially important habitat features. Young-of-the-year northern right whale dolphin showed a preference for the warmest waters observed in the middle latitude band, coincident with that species summer calving mode.
THE POLITICS OF THE SHI'A-SUNNI DIVIDE IN THE PERSIAN GULF: POWER AND GROUP IDENTIFICATION IN BAHRAIN

This research attempts to convey the importance of the political usage of the Shi’a-Sunni Divide. After demonstrating the motivations behind the Divide, this paper discusses the impact it has on the relationship between the al-Khalifah family and the citizens in Bahrain. In particular, the paper focuses on the formation of groups and their identity, and how group’s identification reacts with the majoritarian nationalism promoted by the state. Bahrain is a small island that can be examined in terms of how the image of the Shi’a-Sunni Divide gets manipulated by the state in order to maintain power. Nationalism, a central concern of the state, is the other -ing that has the ability to demonize the Shi’a-Sunni Divide in the attempt to form and retain certain networks of power. The goal of showing the fragility of the perceived Shi’a-Sunni Divide is to demonstrate the ability of Bahrain to move forward without sectarian images that have created a political stalemate in the process toward changing the application of the Shi’a-Sunni Divide. There are differences between the Shi’a and Sunni communities, but this paper describes how it is possible better understand them and begin to make them manageable.
Emerging Applications for Intelligent Diabetes Management

Type 1 diabetes (T1D) is an autoimmune disease in which the pancreas fails to produce insulin, an essential hormone needed to convert food into energy. It is more severe and less common than type 2 diabetes, but still affects approximately 20 million people worldwide. T1D is a chronic disease, which cannot be cured, but which must be treated and managed over time. At our diabetes center, more than 600 T1D patients are treated with insulin pump therapy. A mechanical pump infuses the patient with insulin, attempting to mimic and replace normal pancreatic function. The management goal is for the person with diabetes to achieve and maintain blood glucose (BG) levels close to those of a person without diabetes. It has been experimentally determined that good BG control can help delay or prevent serious long-term diabetic complications, including blindness, amputations, kidney failure, strokes, and heart attacks (Diabetes Control and Complications Trial Research Group 1993). Avoiding complications improves quality of life for patients, while reducing the financial burden of health-care cost expenditures. Diabetes management is a challenging task for patients, who must monitor their BG levels and daily activities, and for physicians, who recommend therapeutic adjustments based on the monitoring data. (1) Task complexity stems from a wide variability among individual patients in terms of sensitivity to insulin, response to lifestyle factors, propensity for complications, adherence to physician recommendations, and response to treatment; and (2) voluminous BG data, which is automatiArticles
A Formal Instruction-level GPU Model for Scalable Verification

GPUs have been widely used to accelerate big-data inference applications and scientific computing through their parallelized hardware resources and programming model. Their extreme parallelism increases the possibility of bugs such as data races and un-coalesced memory accesses, and thus verifying program correctness is critical. State-of-the-art GPU program verification efforts mainly focus on analyzing application-level programs, e.g., in C, and suffer from the following limitations: (1) high false-positive rate due to coarse-grained abstraction of synchronization primitives, (2) high complexity of reasoning about pointer arithmetic, and (3) keeping up with an evolving API for developing application-level programs. In this paper, we address these limitations by modeling GPUs and reasoning about programs at the instruction level. We formally model the Nvidia GPU at the parallel execution thread (PTX) level using the recently proposed Instruction-Level Abstraction (ILA) model for accelerators. PTX is analogous to the Instruction-Set Architecture (ISA) of a general-purpose processor. Our formal ILA model of the GPU includes non-synchronization instructions as well as all synchronization primitives, enabling us to verify multithreaded programs. We demonstrate the applicability of our ILA model in scalable GPU program verification of data-race checking. The evaluation shows that our checker outperforms state-of-the-art GPU data race checkers with fewer false-positives and improved scalability.
The co-occurrence of mtDNA mutations on different oxidative phosphorylation subunits, not detected by haplogroup analysis, affects human longevity and is population specific

Raule N, Sevini F, Li ST, Barbieri A, Tallaro F, Lomartire L, Vianello D, Montesanto A, Moilanen JS, Bezrukov V, Blanche H, Hervonen A, Christensen K, Deiana L, Gonos ES, Kirkwood TBL, Kristensen P, Leon A, Pelicci PG, Poulain M, Rea IM, Remacle J, Robine JM, Schreiber S, Sikora E, Slagboom PE, Spazzafumo L, Stazi MA, Toussaint O, Vaupel JW, Rose G, Majamaa K, Perola M, Johnson TE, Bolund L, Yang HM, Passarino G, Franceschi C. The cooccurrence of mtDNA mutations on different oxidative phosphorylation subunits, not detected by haplogroup analysis, affects human longevity and is population specific. Aging Cell 2014, 13(3), 401-407.
The Hippocampus

The hippocampus (named after its resemblance to the seahorse, from the Greek ἱππόκαμπος, "seahorse" from ἵππος hippos, "horse" and κάμπος kampos, "sea monster") is a major component of the brains of humans and other vertebrates. Humans and other mammals have two hippocampi, one in each side of the brain. It belongs to the limbic system and plays important roles in the consolidation of information from short-term memory to long-term memory and spatial memory that enables navigation. The hippocampus is located under the cerebral cortex; (allocortical)[1][2][3] and in primates it is located in the medial temporal lobe, underneath the cortical surface. It contains two main interlocking parts: the hippocampus proper (also called Ammon's horn)[4] and the dentate gyrus. In Alzheimer's disease (and other forms of dementia), the hippocampus is one of the first regions of the brain to suffer damage; short-term memory loss and disorientation are included among the early symptoms. Damage to the hippocampus can also result from oxygen starvation (hypoxia), encephalitis, or medial temporal lobe epilepsy. People with extensive, bilateral hippocampal damage may experience anterograde amnesia (the inability to form and retain new memories). In rodents as model organisms, the hippocampus has been studied extensively as part of a brain system responsible for spatial memory and navigation. Many neurons in the rat and mouse hippocampus respond as place cells: that is, they fire bursts of action potentials when the animal passes through a specific part of its environment. Hippocampal place cells interact extensively with head direction cells, whose activity acts as an inertial compass, and conjecturally with grid cells in the neighboring entorhinal cortex. Since different neuronal cell types are neatly organized into layers in the hippocampus, it has frequently been used as a model system for studying neurophysiology. The form of neural plasticity known as long-term potentiation (LTP) was first discovered to occur in the hippocampus and has often been studied in this structure. LTP is widely believed to be one of the main neural mechanisms by which memories are stored in the brain.
Pattern recognition and neural networks

From the Publisher: Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.
AI-Based Software Defect Predictors : Applications and Benefits in a Case Study

the later stages of the development life cycle versus during production (Brooks 1995). Therefore, testing is one of the most critical and time-consuming phases of the software development life cycle, which accounts for 50 percent of the total cost of development (Brooks 1995). The testing phase should be planned carefully in order to save time and effort while detecting as many defects as possible. Different verification, validation, and testing strategies have been proposed so far to optimize the time and effort utilized during the testing phase: code reviews (Adrian, Branstad, and Cherniavsky 1982; Shull et al. 2002), inspections (Fagan 1976), and automated tools (Menzies, Greenwald, and Frank 2007; Nagappan, Ball, and Murphy 2006; Ostrand, Weyuker, and Bell 2005). Defect predictors improve the efficiency of the testing phase in addition to helping developers assess the quality and defectproneness of their software product (Fenton and Neil 1999). They also help managers in allocating resources. Most defect prediction models combine well-known methodologies and algorithms such as statistical techniques (Nagappan, Ball, and Murphy 2006; Ostrand, Weyuker, and Bell 2005; Zimmermann et al. 2004) and machine learning (Munson and Khoshgoftaar 1992; Fenton and Neil 1999; Lessmann et al. 2008; Moser, Pedrycz, and Succi 2008). They require historical data in terms of software metrics and actual defect rates, and combine these metrics and defect information as training data to learn which modules seem to be defect prone. Based on the knowledge from training data and software metrics acquired from a recently completed project, such tools can estimate defect-prone modules of that project. IAAI Articles
On the Computation of Paracoherent Answer Sets

Fuzzy Answer Set Programming (FASP) combines the non-monotonic reasoning typical of Answer Set Programming with the capability of Fuzzy Logic to deal with imprecise information and paraconsistent reasoning. In the context of paraconsistent reasoning, the fundamental principle of minimal undeﬁnedness states that truth degrees close to 0 and 1 should be preferred to those close to 0.5, to minimize the ambiguity of the scenario. The aim of this paper is to enforce such a principle in FASP through the minimization of a measure of undeﬁnedness. Algorithms that minimize undeﬁned-ness of fuzzy answer sets are presented, and implemented.
Gibt es entitätsspezifische Kriterien für den Beginn der spezialisierten Palliativversorgung für nicht heilbare Krebserkrankungen? Ein Scoping Review Are There Entity-Specific Criteria for Initiation of Specialized Palliative Care for Incurable Cancer? A Scoping Review

Background/Objective Finding the right time for offering specialized palliative care to patients with incurable cancer remains challenging. The aim of the scoping review was to identify appropriate entity-specific criteria. Method A scoping review was conducted in the online databases PubMed and Scopus in February 2020.The aim of the literature search was to identify original English- and German-language papers published between 2009 and February 2020 providing evidence on which entity-specific and entity-independent criteria of non-curable cancers are used to integrate affected patients into specialized palliative care in a timely manner. A total of 13 relevant articles were identified. In accordance with the method of the scoping review, a formal assessment of the methodological quality of the included literature was not performed. Results The relevant publications included six reviews and seven original articles. None of the publications analysed explicitly cited characteristic criteria related to specific cancer entities. Incurability/advanced tumour disease,
Multi-Scale Bidirectional FCN for Object Skeleton Extraction

Object skeleton detection is a challenging problem with wide application. Recently, deep Convolutional Neural Networks (CNNs) have substantially improved the performance of the state-of-the-art in this task. However, most of the existing CNN-Based methods are based on a skip-layer structure where low-level and high-level features are combined and learned so as to gather multi-level contextual information. As shallow features are too messy and lack semantic knowledge, they may cause errors and inaccuracy. Therefore, we propose a novel network architecture, Multi-Scale Bidirectional Fully Convolutional Network (MSB-FCN), to better capture and consolidate multi-scale high-level context information for object skeleton detection. Our network uses only deep features to build multi-scale feature representations, and employs a bidirectional structure to collect contextual knowledge. Hence the proposed MSB-FCN has the ability to learn the semantic-level information from different sub-regions. Furthermore, we introduce dense connections into the bidirectional structure of our MSB-FCN to ensure that the learning process at each scale can directly encode information from all other scales. Extensive experiments on various commonly used benchmarks demonstrate that the proposed MSB-FCN has achieved significant improvements over the state-of-the-art algorithms.
Self-rewards and personal motivation

Self-administered rewards are ubiquitous. They serve as incentives for personal accomplishments and are widely recommended as tools for overcoming self-control problems. However, it seems puzzling why self-rewards can work: the prospect of a reward has a motivating force only if the threat of self-denial of the reward after low performance is credible, and the reward is sufficiently enticing. By combining well-received theories of reference-dependent preferences and hyperbolic discounting we explain how a rational forward-looking individual may achieve commitment to self-rewards. Our results show why and when self-regulation built on self-rewards can be successful and thus illustrate the power, but also the limits, of self-rewards. JEL Classification: A12, C70, D86
Conflicting Interactions Among Protections Mechanisms for Machine Learning Models

—Nowadays, systems based on machine learning (ML) are widely used in different domains. Given their popularity, ML models have become targets for various attacks. As a result, research at the intersection of security and privacy, and ML has ﬂourished. The research community has been exploring the attack vectors and potential mitigations separately. However, a practitioner will likely need to deploy defences against several threats simultaneously. A solution that is optimal for a speciﬁc concern may interact negatively with solutions intended to address other concerns. In this work, we explore the potential for conﬂicting interactions between different solutions that enhance the security/privacy of ML-base systems. We focus on model and data ownership; exploring how ownership veriﬁcation techniques interact with other ML security/privacy techniques like differentially private training, and robustness against model evasion. We provide a framework, and conduct systematic analysis of pairwise interactions. We show that many pairs are incompatible. Where possible, we provide relaxations to the hyperparameters or the techniques themselves that allow for the simultaneous deployment. Lastly, we discuss the implications and provide guidelines for future work.
Evolution of rotifers in saline and subsaline lakes: A molecular phylogenetic approach

Evolutionary relationships within the phylum Rotifera are poorly understood despite the important role that they play in freshwater ecosystems. Phylogenetic analyses of DNA sequences from two mitochondrial genes, 16S rDNA and cytochrome oxidase I (COI), were employed to examine the extent of genetic divergence within populations of several common taxa. This work sought to verify the role of phenotypic plasticity versus genetic variation in explaining the morphological variation in some taxa. Deep genetic divergence (4.4% COI nucleotide sequence divergence) was detected between spined and unspined forms of Keratella cochlearis, which suggests that they represent different species. However, morphological variation in K. hiemalis appeared to be environmentally induced. The study also sought to ascertain the role of one environmental variable, salinity, in the patterning of sequence variation. Greater haplotype diversity and genetic divergence were observed among populations of halophilic Brachionus plicatilis than among freshwater Keratella quadrata populations from northern Canada. When COI DNA sequences for B. plicatilis haplotypes were compared with those from Spain, there was evidence for considerable genetic diversity within this species among closely located saline lakes in northern Canada.
The Socio-Economic Effects of Cross-Border Migration on Zimbabwe's Border Lying Communities - A Case Study of Beitbridge and Plumtree

Zimbabwe has in the past decade (since the year 2000) undergone what some commentators have called ‘the largest migration event in the region’s recent history…’ driven by deteriorating economic conditions. This paper seeks to assess the nature and magnitude of the emigration patterns and the implications that this migration has on the demographic structure of the communities and on various socio-economic sectors. A specific focus is made on education and agriculture. The study uses a mixed-methods approach to investigate the effect of migration on border lying communities in Beitbridge and Plumtree specifically focussing on the demographic structure of the communities, agriculture and education. The paper concludes that outward migration in the areas studied in this paper has fundamentally altered the age structure of the population with important implications for population growth rates, business, politics, agriculture and education. The paper argues that chronic food insecurity, high failure rates in schools and declining population growth rates may be partly attributed to the migration phenomenon. Contrary to common belief that migrants improve their former households’ welfare through remittances, the paper concludes that most of the non-skilled migrants originating from these rural areas often struggle to meet their own survival needs.
Understanding and Reducing the Spread of Misinformation Online (Extended Abstract)

I will give an overview of our work assessing various interventions against mis-information and “fake news” on social media (for a review, see [7]). I will start by brieﬂy discussing the limitations of two of the most commonly discussed approaches: warnings based on professional fact-checking, which are not scalable and which we ﬁnd may increase belief in, and sharing, of misinformation which is not ﬂagged [4]; and emphasizing publishers, which is (surprisingly) ineﬀective because untrusted outlets typically produce headlines that are judged as inaccurate even without knowing the source [2]. I will then focus on two more promising approaches. First, we show that most users do not want to share misinformation, but may wind up doing so anyway because the social media context directs their attention towards other, more salient factors. Therefore, we show using survey experiments and a Twitter ﬁeld experiment that shifting users’ attention towards accuracy increases the quality of news they subsequently share [5, 7, 3]. Second, we show that crowds of laypeople produce judgments that are highly aligned with professional fact-checkers when assessing the trustworthiness of news sources [6, 3] and the accuracy of individual articles [1], indicating that using crowdsourcing to identify misinformation is a promising approach.
Darwin’s Theory of Evolution by Natural Selection

The common ancestor and evolution by natural selection, concepts introduced by Charles Darwin, constitute the central core of biology research and education. However, students generally struggle to understand these concepts and commonly form misconceptions about them. To help teachers select the most revelant portions of Darwin’s work, I suggest some sentences from On the Origin of Species and briefly discuss their implications. I also suggest a teaching strategy that uses history of science and curriculum crosscutting concepts (cause and effect) that constitute the framework to explain the evolutionary history of ratites (flightless birds) as described by Darwin, starting in the Jurassic, with the breakup of Gondwanaland, as first described by Alfred Wegener in The Origin of Continents and Oceans.
Application of the biosphere reserve concept to coastal marine areas : papers presented at the UNESCO/IUCN San Francisco Workshop of 14-20 August 1989

A straight forward presentation of the keynote papers and case studies prepared for the workshop on coastal marine biosphere serves held in 1989. The keynote papers together provide an excellent digest of the different factors to be considered in biosphere reserve planning, examining constraints across the full range of physical to socio-economic spheres. These are supplemented by case studies illustrating the practicalities of applying the biosphere reserve concept in a wide range of coastal environments.