Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological Difference of Choroidal Vasculature Between Polypoidal Choroidal Vasculopathy and Neovascular AMD on OCT: From the Perspective of Pachychoroid.

BACKGROUND AND OBJECTIVE To investigate the morphological difference of choroidal vasculature between polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) on optical coherence tomography (OCT). PATIENTS AND METHODS One hundred eighty-nine patients with macula-involved PCV (n = 107) or nAMD (n = 82) were retrospectively reviewed. The subfoveal choroidal thickness (SFCT) and thickness of the Haller's layer were determined on enhanced depth imaging optical coherence tomography (EDI-OCT). The mean diameters of subfoveal large choroidal vessels were also calculated. RESULTS Both the SFCT (257.31 μm ± 100.50 μm vs. 209.95 μm ± 97.51 μm) (P < .01) and the thickness of the Haller's layer (213.68 μm ± 82.65 μm vs. 159.67 μm ± 79.86 μm) (P < .01) were greater in PCV patients than nAMD patients. The ratio of thickness of the Haller's layer to the SFCT was higher in the PCV group (0.83 ± 0.07) than the nAMD group (0.7 5± 0.11) (P < .01). The mean diameter of subfoveal large choroidal vessels was greater in PCV patients (163.55 μm ± 62.23 μm vs. 112.81 ± 58.87 μm) (P < .01). CONCLUSION Choroidal thickening and dilation of large choroidal vessels were commonly seen in PCV patients, supporting that PCV belongs to the pachychoroid spectrum disorders and might be a different entity from nAMD. [Ophthalmic Surg Lasers Imaging Retina. 2018;49:e114-e121. ].
Handling Rare Items in Data-to-Text Generation

Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism. We investigate the relative impact of these two methods on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that rare items strongly impact performance; (ii) that combining delexicalisation and copying yields the strongest improvement; (iii) that copying underperforms for rare and unseen items and (iv) that the impact of these two mechanisms greatly varies depending on how the dataset is constructed and on how it is split into train, dev and test.
Incidence of indications for tonsillectomy and frequency of evidence-based surgery: a 12-year retrospective cohort study of primary care electronic records.

BACKGROUND Neither the incidence of indications for childhood tonsillectomy nor the proportion of tonsillectomies that are evidence-based is known. AIM To determine the incidence of indications for tonsillectomy in UK children, and the proportion of tonsillectomies meeting evidence-based criteria. DESIGN AND SETTING A retrospective cohort study of electronic medical records of children aged 0-15 years registered with 739 UK general practices contributing to a research database. METHOD Children with recorded indications for tonsillectomy were identified from electronic medical records. Evidence-based indications included documented sore throats of sufficient frequency and severity (Paradise criteria); periodic fever, aphthous stomatitis, pharyngitis, and cervical adenitis syndrome (PFAPA); or tonsillar tumour. Other indications were considered non-evidence-based. The numbers of children subsequently undergoing tonsillectomy was then identified. The numbers with evidence-based and non-evidence-based indications for surgery among children who had undergone tonsillectomy were determined. RESULTS The authors included 1 630 807 children followed up for 7 200 159 person-years between 2005 and 2016. Incidence of evidence-based indications for tonsillectomy was 4.2 per 1000 person years; 13.6% (2144/15 760) underwent tonsillectomy. Incidence of childhood tonsillectomy was 2.5 per 1000 person years; 11.7% (2144/18 281) had evidence-based indications, almost all with Paradise criteria. The proportion of evidence-based tonsillectomies was unchanged over 12 years. Most childhood tonsillectomies followed non-evidence-based indications: five to six sore throats (12.4%) in 1 year, two to four sore throats (44.6%) in 1 year, sleep disordered breathing (12.3%), or obstructive sleep apnoea (3.9%). CONCLUSION In the UK, few children with evidence-based indications undergo tonsillectomy and seven in eight of those who do (32 500 of 37 000 annually) are unlikely to benefit.
Human Action Recognition Algorithm Based on Adaptive Initialization of Deep Learning Model Parameters and Support Vector Machine

Action recognition is a trending topic and key research direction in computer vision, machine learning, artificial intelligence, and other fields. This research seeks to identify human action in image and video data. Its research results have been widely used in the fields of safety monitoring, disability monitoring, understanding multimedia content, human–computer interaction, virtual reality, and so on. However, the existing traditional human action recognition technology has many limitations in practical application, such as low accuracy and weak adaptive ability. Although the action recognition based on deep learning can self-learn and improve the action recognition accuracy, there are many difficulties in training the deep neural network model, such as gradient disappearance, gradient explosion, and overfitting. Therefore, this paper will reduce the abovementioned difficulties in deep neural network model training from the perspective of deep neural network model parameter initialization and then propose a model parameter initialization method based on the multilayer maxout network activation function to solve the difficulties in deep neural network model training. Then, on this basis, a method of learning the temporal and spatial characteristics of human action based on the deep neural network model is proposed. First, the method detects and tracks the human action and uses the restricted Boltzmann machine (RBM) to encode the temporal and spatial features of various parts of the human body. Second, the temporal and spatial feature codes of various parts of the human body are integrated into a global temporal and spatial feature representation method of the action video through a RBM neural network. Finally, the trained SVM classifiers are used to recognize human action. Experiments show that the human action recognition method proposed in this paper not only has high recognition accuracy but also has great adaptability. Thus, this method extracts temporal and spatial features from the shape feature sequences of various parts of the human body, thus opening up a new way to extract human action features and solving the problem of human action recognition in complex scenes. Its proposal provides an exploratory technical method and approach for self-adaptive recognition of human action. It also gives directional enlightenment to the development and improvement of self-adaptive human action methods.
CampusWatch

Community policing to collaboratively maintain community safety and order in conjunction with law enforcement is becoming increasingly popular and efficient with the use of mobile technologies. Beyond sharing information about local problems such as crime via online discussion forums, there has been an increased focus on the impact of mobile crowdsourcing systems on community policing. In this study, we designed a novel crowdsourced patrolling campaign in which community members schedule their own patrol times and routes, then perform bike-based patrolling with video capturing using their smartphones. We conducted a four-week field study (n=20) on a university campus to verify the campaign's feasibility and observe users' behavior. Our results show key findings about users' task scheduling, event capturing and reporting behaviors, factors affecting task selection and execution and user motivation and engagement. Finally, we discuss several practical design implications in building crowdsourcing systems for community policing.
Using particles to sample and control implicit surfaces

We present a new particle-based approach to sampling and controlling implicit surfaces. A simple constraint locks a set of particles onto a surface while the particles and the surface move. We use the constraint to make surfaces follow particles, and to make particles follow surfaces. We implement control points for direct manipulation by specifying particle motions, then solving for surface motion that maintains the constraint. For sampling and rendering, we run the constraint in the other direction, creating floater particles that roam freely over the surface. Local repulsion is used to make floaters spread evenly across the surface. By varying the radius of repulsion adaptively, and fissioning or killing particles based on the local density, we can achieve good sampling distributions very rapidly, and maintain them even in the face of rapid and extreme deformations and changes in surface topology.
Infant Temperament in the FMR1 Premutation and Fragile X Syndrome

Although temperament has been studied for decades as a predictor of psychopathology in the general population, examining temperament in neurogenetic groups has unique potential to inform the genetic and biological factors that may confer risk for psychopathology in later development. The present study examined early temperament in two heritable neurogenetic conditions associated with atypical CGG repeat expansions on the FMR1 gene: the FMR1 premutation (FXpm; 55–200 repeats) and fragile X syndrome (FXS; > 200 repeats). We focus specifically on the FXpm, as the condition is highly prevalent (1:209–291 female individuals, 1:430–855 male individuals) and has been preliminarily associated with increased risk for pediatric psychopathology, including attention problems, autism, and anxiety. In contrast, FXS is a low-incidence disorder (1:7,143 males, 1:11,111 females) often associated with intellectual disability and severe co-occurring psychosocial conditions, particularly in male individuals. Given information on infant clinical phenotypes in the FXpm and FXS is sparse, we aimed to characterize parent-reported infant temperament in infants with the FXpm (n = 22) relative to FXS (n = 24) and controls (n = 24) assessed on 1 to 3 occasions each. Temperament in infants with the FXpm largely fell between TD and FXS groups, with trends toward suppressed negative affect in younger participants, similar to lower negative affect previously reported in FXS. The FXS group consistently demonstrated lower negative affect and surgency than TD controls. These data suggest that FMR1 gene mutations are associated with atypical temperament that emerges as early as infancy, particularly among infants with FXS, warranting further study of whether temperament may index emergent clinical risks in these populations.
Neural Response Ranking for Social Conversation: A Data-Efficient Approach

The overall objective of ‘social’ dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart from raw dialogue data, user-provided ratings are the most common signal used to train such systems to produce engaging responses. In this paper we show that social dialogue systems can be trained effectively from raw unannotated data. Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting ‘good’ system responses to user utterances, i.e. responses which are likely to lead to long and engaging conversations. We show that (1) our neural ranker consistently outperforms several strong baselines when trained to optimise for user ratings; (2) when trained on larger amounts of data and only using conversation length as the objective, the ranker performs better than the one trained using ratings – ultimately reaching a Precision@1 of 0.87. This advance will make data collection for social conversational agents simpler and less expensive in the future.
Symbolic representation of neuronal dynamics

We demonstrate a GPU-based symbolic toolkit to study a whole range of dynamical behaviors occurring in neuron models. Its algorithms include periodicity detection, hashing, and Lempel-Ziv complexity to process symbolic sequences extracted from wave-form traces using voltage and time interval partitions. This aggregated partitioning scheme is well applicable to a broad spectrum of other dynamical systems across diverse disciplines. Our approach is motivated by experimental neurophysiology where voltage wave-forms are often the only observables available. This symbolic toolkit can offset and complement other computational tools for studying neuronal dynamics such as spike counting, Lyapunov exponents and parameter continuation.
Fusion of depth maps with multiple scales

Multi-view stereo systems can produce depth maps with large variations in viewing parameters, yielding vastly different sampling rates of the observed surface. We present a new method for surface reconstruction by integrating a set of registered depth maps with dramatically varying sampling rate. The method is based on the construction of a hierarchical signed distance field represented in an incomplete primal octree by incrementally adding triangulated depth maps. Due to the adaptive data structure, our algorithm is able to handle depth maps with varying scale and to consistently represent coarse, low-resolution regions as well as small details contained in high-resolution depth maps. A final surface mesh is extracted from the distance field by construction of a tetrahedral complex from the scattered signed distance values and applying the Marching Tetra-hedra algorithm on the partition. The output is an adaptive triangle mesh that seamlessly connects coarse and highly detailed regions while avoiding filling areas without suitable input data.
The role of parasitism in the energy management of a free-ranging bird

ABSTRACT Parasites often prompt sub-lethal costs to their hosts by eliciting immune responses. These costs can be hard to quantify but are crucial to our understanding of the host's ecology. Energy is a fundamental currency to quantify these costs, as energetic trade-offs often exist between key fitness-related processes. Daily energy expenditure (DEE) comprises of resting metabolic rate (RMR) and energy available for activity, which are linked via the energy management strategy of an organism. Parasitism may play a role in the balance between self-maintenance and activity, as immune costs can be expressed in elevated RMR. Therefore, understanding energy use in the presence of parasitism enables mechanistic elucidation of potential parasite costs. Using a gradient of natural parasite load and proxies for RMR and DEE in a wild population of breeding European shags (Phalacrocorax aristotelis), we tested the effect of parasitism on maintenance costs as well as the relationship between proxies for RMR and DEE. We found a positive relationship between parasite load and our RMR proxy in females but not males, and no relationship between proxies for RMR and DEE. This provides evidence for increased maintenance costs in individuals with higher parasite loads and suggests the use of an allocation energy management strategy, whereby an increase to RMR creates restrictions on energy allocation to other activities. This is likely to have fitness consequences as energy allocated to immunity is traded off against reproduction. Our findings demonstrate that understanding energy management strategies alongside fitness drivers is central to understanding the mechanisms by which these drivers influence individual fitness. Summary: Demonstration of the sub-lethal costs of endo-parasitism for a wild population of seabirds via increased maintenance metabolism.
Exploring HPC and Big Data Convergence: A Graph Processing Study on Intel Knights Landing

The question "Can big data and HPC infrastructure converge? " has important implications for many operators and clients of modern computing. However, answering it is challenging. The hardware is currently different, and fast evolving: big data uses machines with modest numbers of fat cores per socket, large caches, and much memory, whereas HPC uses machines with larger numbers of (thinner) cores, non-trivial NUMA architectures, and fast interconnects. In this work, we investigate the convergence of big data and HPC infrastructure for one of the most challenging application domains, the highly irregular graph processing. We contrast through a systematic, experimental study of over 300,000 core-hours the performance of a modern multicore, Intel Knights Landing (KNL) and of traditional big data hardware, in processing representative graph workloads using state-of-the-art graph analytics platforms. The experimental results indicate KNL is convergence-ready, performance-wise, but only after extensive and expert-level tuning of software and hardware parameters.
Efficient computation of expressions with common subexpressions

(b) if we wish to avoid redundant computation of common subexpressions then generation of optimal code becomes very difficult, even for quite simple machines [3,9]. Elaborating on (b) above, Bruno and Sethi [9] show that the problem of generating optimal code for a one register machine is NP-complete. Aho et al. [3] add that even when all common subexpressions have exactly one operation, optimal code generation is NP-complete for one register machines and also for infinite register machines. 1 Expressions will be represented by dags (directed acyclic graphs) as in Figure 1. If no common subexpressions occur, then the dag representation is a tree. Real machines have many features not found in models of machines, so any theoretical results can at best provide insight into the design of code generators. Johnson [12] eloquently describes the application of theoretical results on code generation from trees to the design of a portable code generator. See also the discussion in Wulf et al. [201.
Constructing and mining web-scale knowledge graphs: KDD 2014 tutorial

Recent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, YAGO, Google's Knowledge Graph, and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of vertices and edges. In this tutorial, we will present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, available datasets, and open research challenges. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques.
Industry-Wide Analysis of Open Source Security

Open Source Software (OSS) has become de-facto industry standard for developing software solutions and services. Either it's Telecommunication industry or Aerospace or Health Care or Media and Entertainment, OSS is widely used because of its benefits and community based support. Regardless of the benefits, OSS continues to attract security vulnerabilities due to its inherent open nature. Because of the security vulnerabilities industries need to constantly evaluate security posture of OSS projects. Unfortunately, there are no readily available studies that have analyzed the security posture of various OSS projects with respect to various industries. This is the precise goal of this research, which not only analyzes the popularity of various OSS projects among various industries but also provides insights into the security vulnerabilities and their impact on various industries that consume those OSS projects. Toward that end this paper makes the following contributions. (1) We evaluated the OSS usage trends across various categories of industries, which has never been attempted before. (2) We proposed two metrics to quantify the impact of security vulnerabilities in OSS projects that are used by various categories of industries. (3) We conducted a detailed set of analysis using real datasets to evaluate the proposed metrics and their impact on various industries. We have concluded this paper with some future
Understanding Reduced-Voltage Operation in Modern DRAM Devices

The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM supply voltage conservatively, which reduces some of the DRAM energy consumption. We would like to reduce the DRAM supply voltage more aggressively, to further reduce energy. Aggressive supply voltage reduction requires a thorough understanding of the effect voltage scaling has on DRAM access latency and DRAM reliability. In this paper, we take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the supply voltage is lowered below the nominal voltage level specified by DRAM standards. Using an FPGA-based testing platform, we perform an experimental study of 124 real DDR3L (low-voltage) DRAM chips manufactured recently by three major DRAM vendors. We find that reducing the supply voltage below a certain point introduces bit errors in the data, and we comprehensively characterize the behavior of these errors. We discover that these errors can be avoided by increasing the latency of three major DRAM operations (activation, restoration, and precharge). We perform detailed DRAM circuit simulations to validate and explain our experimental findings. We also characterize the various relationships between reduced supply voltage and error locations, stored data patterns, DRAM temperature, and data retention. Based on our observations, we propose a new DRAM energy reduction mechanism, called Voltron. The key idea of Voltron is to use a performance model to determine by how much we can reduce the supply voltage without introducing errors and without exceeding a user-specified threshold for performance loss. Our evaluations show that Voltron reduces the average DRAM and system energy consumption by 10.5% and 7.3%, respectively, while limiting the average system performance loss to only 1.8%, for a variety of memory-intensive quad-core workloads. We also show that Voltron significantly outperforms prior dynamic voltage and frequency scaling mechanisms for DRAM.