Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecology of Gymnodinium aureolum. II. Predation by common heterotrophic dinoflagellates and a ciliate

We investigated whether the common heterotrophic dinoflagellates Gyrodinium domi- nans, Oxyrrhis marina, Pfiesteria piscicida, Polykrikos kofoidii, Protoperidinium bipes and Stoecke- ria algicida, and the naked ciliate Strombidinopsis sp. , were able to feed on the mixotrophic red-tide dinoflagellate Gymnodinium aureolum (GenBank accession no. FN392226). We also measured the growth and ingestion rates of G. dominans, O. marina, P. kofoidii, and Strombidinopsis sp. on G. aureolum as a function of prey concentration. We calculated grazing coefficients by combining field data on abundance of small Gyrodinium spp. (25 to 35 µm in cell length) and Strombidinopsis spp. (>70 µm) and co-occurring G. aureolum with laboratory data on ingestion rates obtained in this study. G. dominans, O. marina, P. kofoidii, and Strombidinopsis sp. were able to feed on G. aureolum, whereas P. piscicida, P. bipes, and S. algicida were not. The maximum growth rates of G. dominans, O. marina, Strombidinopsis sp. and P. kofoidii on G. aureolum were 0.92, 0.71, 0.44 and 0.11 d -1 , respectively. However, the maximum ingestion rates of G. dominans on G. aureolum (2.0 ng C preda- tor -1 d -1 ) were comparable with that of P. kofoidii (2.3 ng C predator -1 d -1 ), but much lower than that of Strombidinopsis sp. (69.7 ng C predator -1 d -1 ). Calculated grazing coefficients for small heterotro- phic Gyrodinium spp. and large Strombidinopsis spp. on G. aureolum were up to 0.40 d -1 and 0.25 d -1 , respectively (i.e. up to 33 and 22% of G. aureolum populations were removed by small Gyro- dinium and Strombidinopsis populations in 1 d, respectively). The results of the present study suggest that small Gyrodinium spp. and Strombidinopsis sp. sometimes have considerable grazing effect on populations of G. aureolum.
Location-Centered House Price Prediction: A Multi-Task Learning Approach

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, and investors. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we make an important observation as follows – besides the in-house features such as floor area, the location plays a critical role in house price prediction. Unfortunately, existing work either overlooked it or had a coarse grained measurement of locations. Thereby, we define and capture a fine-grained location profile powered by a diverse range of location data sources, including transportation profile, education profile, suburb profile based on census data, and facility profile. Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire data for modeling, or split the entire house data and model each partition independently. However, such modeling ignores the relatedness among partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and select specific MTL-based methods with different regularization terms to capture and exploit the relatedness among tasks. Based on real-world house transaction data collected in Melbourne, Australia, we design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.
Decades of native bee biodiversity surveys at Pinnacles National Park highlight the importance of monitoring natural areas over time

Thousands of species of bees are in global decline, yet research addressing the ecology and status of these wild pollinators lags far behind work being done to address similar impacts on the managed honey bee. This knowledge gap is especially glaring in natural areas, despite knowledge that protected habitats harbor and export diverse bee communities into nearby croplands where their pollination services have been valued at over $3 billion per year. Surrounded by ranches and farmlands, Pinnacles National Park in the Inner South Coast Range of California contains intact Mediterranean chaparral shrubland. This habitat type is among the most valuable for bee biodiversity worldwide, as well as one of the most vulnerable to agricultural conversion, urbanization and climate change. Pinnacles National Park is also one of a very few locations where extensive native bee inventory efforts have been repeated over time. This park thus presents a valuable and rare opportunity to monitor long-term trends and baseline variability of native bees in natural habitats. Fifteen years after a species inventory marked Pinnacles as a biodiversity hotspot for native bees, we resurveyed these native bee communities over two flowering seasons using a systematic, plot-based design. Combining results, we report a total of 450 bee species within this 109km2 natural area of California, including 48 new species records as of 2012 and 95 species not seen since 1999. As far as we are aware, this species richness marks Pinnacles National Park as one of the most densely diverse places known for native bees. We explore patterns of bee diversity across this protected landscape, compare results to other surveyed natural areas, and highlight the need for additional repeated inventories in protected areas over time amid widespread concerns of bee declines.
Cinematic discourse generation

Narrative is one of the fundamental ways in which humans organize information. 3D virtual environments provide a compelling new medium for creating and sharing narratives. In pre-rendered virtual environments like animated movies, directors communicate complex narratives by carefully constructing them shot-by-shot. To do this, a film's director exploits the viewer's familiarity with narrative patterns and cinematic idioms to effectively convey a structured story. In real-time environments like games and training simulations, however, a system has much less control over the stories that need to be told, since often novel stories are constructed on demand and tailored to a specific session or user's needs. In many contexts, stories are built not solely by the system, but collaboratively with many users whose choices for action contribute to the construction of unanticipated narrative structure. In the past, intelligent cinematography systems have been developed to automatically record the actions of users within a virtual world and then to construct coherent visualizations that communicate these action sequences. While these systems generate coherent visualizations, they do not attempt to address the careful construction of narrative discourse based on established and identifiable patterns of narrative communication. Current automated camera systems take into account local coherence of shots and transitions but do not address the rhetorical coherence of the communication across multiple shots. I describe an end-to-end camera planning system - Darshak - that constructs visual narrative discourse of a given story in a 3D virtual environment. Darshak uses a hierarchical partial order causal link planning algorithm to generate narrative plans that contain both story and camera actions. Dramatic situation patterns commonly used by writers of fictional narratives and endorsed by narrative theorists are formalized as communicative plan operators that provide a basis for structuring the cinematic content of the story's visualization. The dramatic patterns are realized through abstract communicative operators that represent operations on a viewer's beliefs about the story and its telling. Camera shots and transitions are defined in this plan-based framework as execution primitives. Representation of narrative discourse as a hierarchical plan structure enables us to utilize (1) the hierarchical nature of narrative patterns and film idioms through the hierarchy in decompositional plan operators, and (2) explicit representation of causal motivation for selection of shots through causal links. I present an empirical evaluation of the algorithm, based on cognitive metrics, for three properties of cinematic discourse: Saliency, Coherence and Temporal Consistency.
Heavy Metal and the Oratorio

This paper explores different approaches to the sonification and visualisation of two environmental projects:-”Heavy Metal” is focussed upon the real-time analysis and sonification of the chemical elements in a painting via a camera vision system, whilst “Oratorio for a Million Souls” concerns the behaviour and acoustic properties of live bee colonies manifest in the creation of real-time multi-channel sound compositions and associated sound architectures.Whilst these two projects differ in terms of methodology, aesthetics and technical approach they both share a direct concern with a deep analysis of the underlying environmental structures and perceptual frameworks that emerge in direct ‘live’ encounters - one one hand the discover of what lies behind the surface of a painted image in terms of chemical and colour structures - and in the case of Oratorio, a compelling immersion into the acoustic environment of Bees.
Uncommon Understanding: Development and Disorders of Language Comprehension in Children

Preface to the Classic Edition. Foreword. 1. From Sound to Meaning: A Framework for Analysing Comprehension. 2. Specific Language Impairment. 3. Speech Perception. 4. Understanding Word Meaning. 5. Grammatical Knowledge in Sentence Comprehension. 6. Understanding Sentences in Real Time. 7. Understanding Discourse: Integrating Language and Context. 8. Understanding Intended Meaning: Social Aspects of Comprehension. 9. Modularity and Interaction in Language Development and Disorders.
Path Reasoning over Knowledge Graph: A Multi-agent and Reinforcement Learning Based Method

Relation reasoning over knowledge graphs is an important research problem in the fields of knowledge engineering and artificial intelligence, because of its extensive applications (e.g., knowledge graph completion and question answering). Recently, reinforcement learning has been successfully applied to multi-hop relation reasoning (i.e., path reasoning). And, a kind of practical path reasoning, in the form of query answering (e.g., (entity, relation, ? )), has been proposed and attracted much attention. However, existing methods for such type of path reasoning focus on relation selection and underestimate the importance of entity selection during the reasoning process. To solve this problem, we propose a Multi-Agent and Reinforcement Learning based method for Path Reasoning, thus called MARLPaR, where two agents are employed to carry out relation selection and entity selection, respectively, in an iterative manner, so as to implement complex path reasoning. Experimental comparison with the state-of-the-art baselines on two benchmark datasets validates the effectiveness and merits of the proposed method.
An Instruction Set Architecture for Machine Learning

Machine Learning (ML) are a family of models for learning from the data to improve performance on a certain task. ML techniques, especially recent renewed neural networks (deep neural networks), have proven to be efficient for a broad range of applications. ML techniques are conventionally executed on general-purpose processors (such as CPU and GPGPU), which usually are not energy efficient, since they invest excessive hardware resources to flexibly support various workloads. Consequently, application-specific hardware accelerators have been proposed recently to improve energy efficiency. However, such accelerators were designed for a small set of ML techniques sharing similar computational patterns, and they adopt complex and informative instructions (control signals) directly corresponding to high-level functional blocks of an ML technique (such as layers in neural networks) or even an ML as a whole. Although straightforward and easy to implement for a limited set of similar ML techniques, the lack of agility in the instruction set prevents such accelerator designs from supporting a variety of different ML techniques with sufficient flexibility and efficiency. In this article, we first propose a novel domain-specific Instruction Set Architecture (ISA) for NN accelerators, called Cambricon, which is a load-store architecture that integrates scalar, vector, matrix, logical, data transfer, and control instructions, based on a comprehensive analysis of existing NN techniques. We then extend the application scope of Cambricon from NN to ML techniques. We also propose an assembly language, an assembler, and runtime to support programming with Cambricon, especially targeting large-scale ML problems. Our evaluation over a total of 16 representative yet distinct ML techniques have demonstrated that Cambricon exhibits strong descriptive capacity over a broad range of ML techniques and provides higher code density than general-purpose ISAs such as x86, MIPS, and GPGPU. Compared to the latest state-of-the-art NN accelerator design DaDianNao [7] (which can only accommodate three types of NN techniques), our Cambricon-based accelerator prototype implemented in TSMC 65nm technology incurs only negligible latency/power/area overheads, with a versatile coverage of 10 different NN benchmarks and 7 other ML benchmarks. Compared to the recent prevalent ML accelerator PuDianNao, our Cambricon-based accelerator is able to support all the ML techniques as well as the 10 NNs but with only approximate 5.1% performance loss.
Methods for Polyp Detection in Methods for Polyp Detection in Colonoscopy Videos: A Review Colonoscopy Videos: A Review

Abstract – Colonoscopy is one of the best methods for screening coloncancer. As the automatic detection of polyps in endoscopic images is a chal-lenging task for image processing, a variety of research groups have proposedmethods that try to fulﬁll this task to develop a system which supports thedoctors during examination. However, the problem is still – at least partially– not solved. This paper gives a summary of 16 diﬀerent polyp detectionmethods published in the last ten years. We found out that the major draw-back of many approaches is the lack of representative video data, whichhinders comparison and evaluation of the published methods. 1 Introduction Colon cancer isone ofthe leading causesof cancer death worldwide[Wor06]. Colonoscopyis the accepted gold standard for detecting colon cancer or its precursorlesions, colorectalpolyps. The removal of adenomatous polyps has been shown to prevent most cancer.Although colonoscopy hasbecome the preferred method of screening colon cancer, severalstudies showed that it contains an inherent miss rate of up to 25% for the detection ofpolyps and cancer [Lie05, BPV
Efficient Multipath Routing Protocol Against Path Failures in Wireless Sensor Networks

In wireless sensor networks, emergency applications such as fire alert or intruder detection are mostly needed to accomplish reliable and real-time data delivery on a path from a source to a sink. However, WSNs might have frequent path failures due to node or link failures. Thus, addressing these path failures effectively is one of very important issues. Especially, the multipath routing approach of using both the primary path and the alternative path is considered as an attractive technique to deal with path failures. However, the approach still has two issues to be addressed for enhancing network performances. The first issue is that the approach has no handling scheme for packets on the failed primary path, which reduces reliability of data packet delivery, The second issue is that the approach globally constructs a new alternative path made instead of the failure primary path from a source to a sink, which consume high energy of sensor nodes. Thus, this paper proposes a new multipath routing protocol based on a virtual line to efficiently address these two issues. Our protocol can enhance delivery reliability and reduce energy consumption by using a multipath routing structure based on a virtual line. Simulation results verified that the proposed protocol is superior to the existing protocols.
Stimulant Use Patterns and HIV Transmission Risk Among HIV-Serodiscordant Male Couples

Background:Substance use is strongly linked to HIV risk, and members of couples can have a powerful influence on each other's health behaviors. We examined whether couple-level patterns of stimulant use were differentially associated with engaging in condomless anal intercourse with primary partners and outside partners. Methods:Members of HIV serodiscordant male couples (N = 117 couples, 232 men) completed surveys, and HIV-positive men had blood drawn for viral load. Results:Results revealed that stimulant use by only one partner in the couple was associated with a decrease in the odds of engaging in condomless anal sex with one's primary partner (AOR = 0.09, 95% CI: 0.01, 0.89). When both partners reported stimulant use, the HIV-negative partner had an increase in the odds of condomless sex with outside partners (AOR = 6.68, 95% CI: 1.09, 8.01). Conclusions:Understanding the role of couples' stimulant use patterns in HIV transmission risk is an important area for future research and intervention.
How to build a good practice software project portfolio?

What can we learn from historic data that is collected in three software companies that on a daily basis had to cope with highly complex project portfolios? In this paper we analyze a large dataset, containing 352 finalized software engineering projects, with the goal to discover what factors affect software project performance, and what actions can be taken to increase project performance when building a software project portfolio. The software projects were classified in four quadrants of a Cost/Duration matrix: analysis was performed on factors that were strongly related to two of those quadrants, Good Practices and Bad Practices. A ranking was performed on the factors based on statistical significance. The paper results in an inventory of ‘what factors should be embraced when building a project portfolio? ’ (Success Factors), and ‘what factors should be avoided when doing so? ’ (Failure Factors). The major contribution of this paper is that it analyzes characteristics of best performers and worst performers in the dataset of software projects, resulting in 7 Success Factors (a.o. steady heartbeat, a fixed, experienced team, agile (Scrum), and release-based), and 9 Failure Factors (a.o. once-only project, dependencies with other systems, technology driven, and rules- and regulations driven).
Personalized Re-ranking for Improving Diversity in Live Recommender Systems

Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the users. However, in practice, list-wise recommendation is implemented as top-N recommendation. Top-N recommendation selects the first N items from candidates to display. The list is generated by a ranking function, which is learned from labeled data to optimize accuracy.However, top-N recommendation may lead to suboptimal, as it focuses on accuracy of each individual item independently and overlooks mutual influence between items. Therefore, we propose a personalized re-ranking model for improving diversity of the recommendation list in real recommender systems. The proposed re-ranking model can be easily deployed as a follow-up component after any existing ranking function. The re-ranking model improves the diversity by employing personalized Determinental Point Process (DPP). DPP has been applied in some recommender systems to improve the diversity and increase the user engagement.However, DPP does not take into account the fact that users may have individual propensities to the diversity. To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user. We implement and deploy the personalized DPP model on alarge scale industrial recommender system. Experimental results on both offline and online demonstrate the efficiency of our proposed re-ranking model.
The biology of camel-spiders (Arachnida, Solifugae)

Preface. 1. An Introduction to the Solifugae. 2. Functional Anatomy and Physiology. 3. Neurobiology. 4. Life History Parameters. 5. Ecology. 6. Behavior. 7. Phylogeny, Biogeography, and Taxonomy. 8. Field Techniques and Laboratory Rearing Methods. References. Index.
Hb DELFZICHT [α9(A7)Asn→Lys (α1)]: A NEW, CLINICALLY SILENT HEMOGLOBIN VARIANT OBSERVED IN A DUTCH PATIENT

a Gene point mutations inducing a-thalassemia (thal) or abnormal hemoglobins (Hbs) are frequently observed at the Hemoglobinopathies Laboratory (Leiden, The Netherlands). Mutations causing structural changes are usually detectable at the electrophoretic or chromatographic level. Some of these, if not associated with clinical symptoms, remain undetected or are observed by chance in elderly patients being monitored for diabetes mellitus by Hb A1c analysis. A blood sample from a 69-year-old Dutch female, collected in Na2 EDTA, was sent to our laboratory after an abnormal Hb pattern was observed during Hb A1c analysis at the Department of Clinical Chemistry of the Delfzicht Hospital, Delfzijl, The Netherlands. The complete hemoglobinopathy analysis was done as previously described. Electrophoretic analyses of the Hb fractions included starch gel electrophoresis (pH 8.6), isoelectrofocusing (IEF) on polyacrylamide gel, cellulose acetate electrophoresis at alkaline pH, and citrate agar gel electrophoresis. Globin chain electrophoreses were done on polyacrylamide gel in 6 M urea at pH 6.0 and 9.0 and in presence of Triton X-100 (UT). The percentage of the abnormal components was determined by cation exchange high performance liquid chromatography (HPLC) on the VARIANT System (Bio-