LLukas22/scidocs huggingface dataset:
Anthills built to order: automating construction with artificial swarms

Social insects build large, complex structures, which emerge through the collective actions of many simple agents acting with no centralized control or preplanning. These natural systems motivate investigating the use of artificial swarms to automate construction or fabrication. The goal is to be able to take an unspecified number of simple robots and a supply of building material, give the system a high-level specification for any arbitrary structure desired, and have a guarantee that it will produce that structure without further intervention. In this thesis I describe such a distributed system for automating construction, in which autonomous mobile robots collectively build user-specified structures from square building blocks. The approach preserves many desirable features of the natural systems, such as considerable parallelism and robustness to factors like robot loss and variable order or timing of actions. Further, unlike insect colonies, it can build particular desired structures according to a high-level design provided by the user. Robots in this system act without explicit communication or cooperation, instead using the partially completed structure to coordinate their actions. This mechanism is analogous to that of stigmergy used by social insects, in which insects take actions that affect the environment, and the environmental state influences further actions. I introduce a framework of extended stigmergy in which building blocks are allowed to store, process or communicate information. Increasing the capabilities of the building material (rather than of the robots) in this way increases the availability of nonlocal structure information. Benefits include significant improvements in construction speed and in ability to take advantage of the parallelism of the swarm. This dissertation describes system design and control rules for decentralized teams of robots that provably build arbitrary solid structures in two dimensions. I present a hardware prototype, and discuss extensions to more general structures, including those built with multiple block types and in three dimensions. Thesis Supervisor: Radhika Nagpal Title: Assistant Professor of Computer Science, Harvard University Thesis Supervisor: H. Sebastian Seung Title: Professor of Computational Neuroscience
Statistical Pattern Recognition of the Iris

A method for applying pattern recognition techniques to recognize the identity of a person based on their iris is proposed. Hidden Markov Models are used to parametrically model the local frequencies of the iris. Also discussed is a transform of the iris image from two to one dimensional space and overcoming limited data with the generation of synthetic images.
Early-Onset Scoliosis: A Review of History, Current Treatment, and Future Directions.

Early-onset scoliosis (EOS) is defined as curvature of the spine in children >10° with onset before age 10 years. Young children with EOS are at risk for impaired pulmonary function because of the high risk of progressive spinal deformity and thoracic constraints during a critical time of lung development. The treatment of EOS is very challenging because the population is inhomogeneous, often medically complex, and often needs multiple surgeries. In the past, early spinal fusion was performed in children with severe progressive EOS, which corrected scoliosis but limited spine and thoracic growth and resulted in poor pulmonary outcomes. The current goal in treatment of EOS is to maximize growth of the spine and thorax by controlling the spinal deformity, with the aim of promoting normal lung development and pulmonary function. Bracing and casting may improve on the natural history of progression of spinal deformity and are often used to delay surgical intervention or in some cases obviate surgery. Recent advances in surgical implants and techniques have led to the development of growth-friendly implants, which have replaced early spine fusion as the surgical treatment of choice. Treatment with growth-friendly implants usually requires multiple surgeries and is associated with frequent complications. However, growth-friendly spine surgery has been shown to correct spinal deformity while allowing growth of the spine and subsequently lung growth.
The prevalence of comorbid depression in adults with diabetes: a meta-analysis.

OBJECTIVE
To estimate the odds and prevalence of clinically relevant depression in adults with type 1 or type 2 diabetes. Depression is associated with hyperglycemia and an increased risk for diabetic complications; relief of depression is associated with improved glycemic control. A more accurate estimate of depression prevalence than what is currently available is needed to gauge the potential impact of depression management in diabetes.


RESEARCH DESIGN AND METHODS
MEDLINE and PsycINFO databases and published references were used to identify studies that reported the prevalence of depression in diabetes. Prevalence was calculated as an aggregate mean weighted by the combined number of subjects in the included studies. We used chi(2) statistics and odds ratios (ORs) to assess the rate and likelihood of depression as a function of type of diabetes, sex, subject source, depression assessment method, and study design.


RESULTS
A total of 42 eligible studies were identified; 20 (48%) included a nondiabetic comparison group. In the controlled studies, the odds of depression in the diabetic group were twice that of the nondiabetic comparison group (OR = 2.0, 95% CI 1.8-2.2) and did not differ by sex, type of diabetes, subject source, or assessment method. The prevalence of comorbid depression was significantly higher in diabetic women (28%) than in diabetic men (18%), in uncontrolled (30%) than in controlled studies (21%), in clinical (32%) than in community (20%) samples, and when assessed by self-report questionnaires (31%) than by standardized diagnostic interviews (11%).


CONCLUSIONS
The presence of diabetes doubles the odds of comorbid depression. Prevalence estimates are affected by several clinical and methodological variables that do not affect the stability of the ORs.
Extracellular-vesicle type of volume transmission and tunnelling-nanotube type of wiring transmission add a new dimension to brain neuro-glial networks.

Two major types of intercellular communication are found in the central nervous system (CNS), namely wiring transmission (WT; point-to-point communication via private channels, e.g. synaptic transmission) and volume transmission (VT; communication in the extracellular fluid and in the cerebrospinal fluid). Volume and synaptic transmission become integrated because their chemical signals activate different types of interacting receptors in heteroreceptor complexes located synaptically and extrasynaptically in the plasma membrane. In VT, we focus on the role of the extracellular-vesicle type of VT, and in WT, on the potential role of the tunnelling-nanotube (TNT) type of WT. The so-called exosomes appear to be the major vesicular carrier for intercellular communication but the larger microvesicles also participate. Extracellular vesicles are released from cultured cortical neurons and different types of glial cells and modulate the signalling of the neuronal-glial networks of the CNS. This type of VT has pathological relevance, and epigenetic mechanisms may participate in the modulation of extracellular-vesicle-mediated VT. Gerdes and co-workers proposed the existence of a novel type of WT based on TNTs, which are straight transcellular channels leading to the formation in vitro of syncytial cellular networks found also in neuronal and glial cultures.
Mt1007. Climate Change

This first case study will examine the science behind climate change and the evidence for it. Since this, like the rest of the course, will involve mathematical and statistical models, it's a good idea to start by considering what a model is, what a mathematical model is and why we need statistical models. Science is about figuring out how nature works, so that we can predict what it will do in the future. Models play an important part in that process. Physical models of things have been used in science and engineering for a long time. For example, to understand and predict how bridges will respond to high winds, it is common practice to build model bridges and to measure how they behave in a wind tunnel. This helps understanding of airflow around the structure, and helps to predict which designs will perform well in practice and which will not. An important point about such models, is that they are not exact miniature replicas of the bridges of interest (we can't, for example, build a 1/200th scale model using 1/200th scale atoms), rather they are models designed to capture the features of a bridge that are important for air flow. Similarly, if we want to produce a model suitable for investigating and explaining the workings of a steam engine, it would be foolish to take a full size working engine and scale it down: the walls of the boiler would be too thin for one thing, but also the full engine has many detailed bits of engineering designed to maximise its efficiency and optimize its practical usefulness. A good model steam engine really doesn't need these and should be considerably simplified. Orreries are another example of physical models: these are clockwork models of the solar system, designed for showing and predicting the positions of the planets in the solar system. There is a particularly splendid orrery on display in the Physics building, just up the stairs from the PC classroom. Again, although often quite elaborate, orreries do not attempt to represent the system they describe exactly, but rather to represent some aspects of the system accurately enough to serve their purpose. So, the key feature of models is that they are simplified representations of the thing that they represent, designed to describe some aspects of it quite accurately, but omitting many less relevant or important details in the interests of …
Snakebite envenoming from a global perspective: Towards an integrated approach.

Snakebite envenoming is a neglected public health challenge of compelling importance in many regions of the world, particularly sub-Saharan Africa, Asia, Latin America and Papua-New Guinea. Addressing the problem of snakebite effectively demands an integrated multifocal approach, targeting complex problems and involving many participants. It must comprise: (a) Acquisition of reliable information on the incidence and mortality attributable to snakebite envenoming, and the number of people left with permanent sequelae. (b) Improvements in production of effective and safe antivenoms, through strategies aimed at strengthening the technological capacity of antivenom manufacturing laboratories. (c) Increasing the capacity of low-income countries to produce specific immunogens(snake venoms) locally, and to perform their own quality control of antivenoms. (d) Commitments from regional producers to manufacture antivenoms for countries where antivenom production is not currently feasible. (e) Implementation of financial initiatives guaranteeing the acquisition of adequate volumes of antivenom at affordable prices in low-income countries. (f) Performance of collaborative studies on the safety and effectiveness of antivenoms assessed preclinically and by properly designed clinical trials. (g) Development of antivenom distribution programmes tailored to the real needs and epidemiological situations of rural areas in each country. (h) Permanent training programmes for health staff, particularly in rural areas where snakebites are frequent.(i) Implementation of programmes to support those people whose snakebites resulted in chronic disabilities. (j) Preventive and educational programmes at the community level, with the active involvement of local organizations and employing modern methods of health promotion. Such an integrated approach, currently being fostered by the Global Snake Bite Initiative of the International Society on Toxinology and by the World Health Organization, will help to alleviate the enormous burden of human suffering inflicted by snakebite envenoming.
Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data

Abstract: Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%.
The role of corporate culture, market orientation and organisational commitment in organisational performance

Purpose – The study proposes a conceptual framework that explores the relationships among the following constructs: corporate/organisational culture, market orientation, organisational commitment and organisational performance in non-profit organisations. Design/methodology/approach – In line with previous studies in the field, a quantitative research design was adopted. The data collection was performed through a mail survey of a sample of Portuguese non-profit organisations operating in the area of health. Structural equation modelling was used as a means to analyse the hypothesised relationships. Findings Results have shown that organisational culture impacts on organisational performance. Concerning the market orientation consequences, the study results suggest that higher levels of market orientation result in both high levels of organisational commitment and organisational performance. Results also suggest that organisational commitment does not affect performance in a very significant way. Research limitations/implications The study was restricted to non-profit organisations, particularly those operating in the area of health and used cross-sectional data to test the research model and hypotheses. Whilst these findings remain valid, they cannot be used for universal generalisations. This study has significant theoretical and practical implications. Practical implications – Practitioners and researchers agree that organisational culture may lead to different performance results, although little attention has been given to this issue. The study is useful for understanding organisational culture, market orientation, organisational commitment and organisational performance in non-profit organisations. Originality/valueThis paper offers original findings through simultaneously examining the relationships between organisational culture, market orientation, organisational commitment and organisational performance in the context of non-profit organisations. The findings add weight to the recent emphasis on business-oriented approaches as a lever for improving performance in non-profit organisations.
Fudan-Huawei at MediaEval 2015: Detecting Violent Scenes and Affective Impact in Movies with Deep Learning

Techniques for violent scene detection and affective impact prediction in videos can be deployed in many applications. In MediaEval 2015, we explore deep learning methods to tackle this challenging problem. Our system consists of several deep learning features. First, we train a Convolutional Neural Network (CNN) model with a subset of ImageNet classes selected particularly for violence detection. Second, we adopt a specially designed two-stream CNN framework [1] to extract features on both static frames and motion optical flows. Third, Long Short Term Memory (LSTM) models are applied on top of the two-stream CNN features, which can capture the longer-term temporal dynamics. In addition, several conventional motion and audio features are also extracted as complementary information to the deep learning features. By fusing all the advanced features, we achieve a mean average precision of 0.296 in the violence detection subtask, and an accuracy of 0.418 and 0.488 for arousal and valence respectively in the induced affect detection subtask. 1. SYSTEM DESCRIPTION Figure 1 gives an overview of our system. In this short paper, we briefly describe each of the key components. For more information about the task definitions, interested readers may refer to [2].
Computer-Aided Thinking by Mapping Text-Objects Into Metric Spaces

This paper presents a system for computer-aided thinking. We propose the idea of reflecting the mental world indirectly into a metric space to support such human thinking activities as externalizing and forming new ideas. We use a method that maps text-objects into metric spaces for visualizing a user’s thought space structure. Text-objects imply fragments of a user’s idea, which have several keywords given by him/her. Spaces composed of text-objects am configured in the way “the higher the mutual relevance between a pair of text-objects is, the closer the text-objects are mapped”. The relevance values among text-objects are calculated due to cooccurrence of their keywords. Results of experiments with our implemented system, named CAT1 (computer-aided thinking, version l), show that users of the system can get effective stimuli for further thinking in creative concept formation. The paper also discusses the potential application of CAT1 to collaborative work by groups of people. @ 1997 Elsevier Science B.V.
Psychometric properties of the revised Developmental Coordination Disorder Questionnaire.

The Developmental Coordination Disorder Questionnaire (DCDQ) is a parent-completed measure designed to identify subtle motor problems in children of 8 to 14.6 years of age. The purpose of this study was to extend the lower age range to children aged 5 to 7 years, revise items to ensure clarity, develop new scoring, and evaluate validity of the revised questionnaire. Additional items with improved wording were generated by an expert panel. Analyses of internal consistency, factor loading, and qualitative/quantitative feedback from researchers, clinicians, and parents were used to select 15 items with the strongest psychometric properties. Internal consistency was high (alpha = .94). The expanded questionnaire was completed by the parents of 287 children, aged 5-15 years, who were typically developing. Logistic regression modeling was used to generate separate cutoff scores for three age groups (overall sensitivity = 85%, specificity = 71%). The revised DCDQ was then compared to other standardized measures in a sample of 232 children referred for therapy services. Differences in scores between children with and without DCD (p < .001) provide evidence of construct validity. Correlations between DCDQ scores and Movement Assessment Battery for Children (r = .55) and Test of Visual-Motor Integration (r = .42) scores support concurrent validity. The results provide evidence that the revised DCDQ is a valid clinical screening tool for DCD.
Teaching Reform of English Listening and Speaking in China Based on Mobile Micro-Learning

Mobile micro-learning is an effective form of informal learning which can meet the personalized needs of language learners. In the ultra-video era, micro-video and micro-audio with short vivid contents, consistent with the current people’s viewing habits, have become a hot focus. With the development of mobile communication technology, mobile media terminal also shows a trend of diversification, but how to combine micro-learning with micro-video and micro-audio closely, in order to learn English scientifically and effectively for learners, is a major problem. Students are at a learning stage, more eager to experiment with new things, and the mobile media tools are used widely among them. Based on the theory of mobile micro-learning, this study analyzes the advantages of the micro-video and micro-audio, and then focuses on the strategies in making or selecting micro-video and micro-audio. Through the data analysis, the author tries to get information about the actual situation of college students’ mobile learning. Based on the current situation, the author chooses to use cell phones which are widely used and easy to operate as the hardware platform. By analyzing and designing video and audio contents, some practical methods are offered. Through the study, the author hopes to help learners practice listening and speaking more effectively, and promote the teaching reform of English listening and speaking in China.
StuffNet: Using ‘Stuff’ to Improve Object Detection

We propose a Convolutional Neural Network (CNN) based algorithm – StuffNet – for object detection. In addition to the standard convolutional features trained for region proposal and object detection [33], StuffNet uses convolutional features trained for segmentation of objects and 'stuff' (amorphous categories such as ground and water). Through experiments on Pascal VOC 2010, we show the importance of features learnt from stuff segmentation for improving object detection performance. StuffNet improves performance from 18.8% mAP to 23.9% mAP for small objects. We also devise a method to train StuffNet on datasets that do not have stuff segmentation labels. Through experiments on Pascal VOC 2007 and 2012, we demonstrate the effectiveness of this method and show that StuffNet also significantly improves object detection performance on such datasets.
Detecting botnet by anomalous traffic

Botnets can cause significant security threat and huge loss to organizations, and are difficult to discover their existence. Therefore they have become one of the most severe threats on the Internet. The core component of botnets is their command and control channel. Botnets often use IRC (Internet Relay Chat) as a communication channel through which the botmaster can control the bots to launch attacks or propagate more infections. In this paper, anomaly score based botnet detection is proposed to identify the botnet activities by using the similarity measurement and the periodic characteristics of botnets. To improve the detection rate, the proposed system employs two-level correlation relating the set of hosts with same anomaly behaviors. The proposed method can differentiate the malicious network traffic generated by infected hosts (bots) from that by normal IRC clients, even in a network with only a very small number of bots. The experiment results show that, regardless the size of the botnet in a network, the proposed approach efficiently detects abnormal IRC traffic and identifies botnet activities. © 2014 Elsevier Ltd. All rights reserved.