Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-heuristic Coefficient for Indexing Geometric Objects in Hierarchical Spatial Data-Structures

Hierarchical spatial data structures are usually employed for indexing geometric objects and are characterized by recursively decomposing the space. Due to this recursion process is necessary to define a decision criteria to determine when to stop the process of spatial decomposition. In this paper, a new recursion threshold for indexing hierarchical spatial data structures that is independent of the nature of the data is introduced. The objective is to reduce the execution time of space searches that arise in various applications of modern computing systems such as mining, solid modelling, simulation and others. Results indicate that the proposed recursion threshold reduces the execution time of space searches respect to others criteria of general purpose reported in the literature, however, RAM consumption is increased considerably.
Revisiting Netrin-1: One Who Guides (Axons)

Proper patterning of the nervous system requires that developing axons find appropriate postsynaptic partners; this entails microns to meters of extension through an extracellular milieu exhibiting a wide range of mechanical and chemical properties. Thus, the elaborate networks of fiber tracts and non-fasciculated axons evident in mature organisms are formed via complex pathfinding. The macroscopic structures of axon projections are highly stereotyped across members of the same species, indicating precise mechanisms guide their formation. The developing axon exhibits directionally biased growth toward or away from external guidance cues. One of the most studied guidance cues is netrin-1, however, its presentation in vivo remains debated. Guidance cues can be secreted to form soluble or chemotactic gradients or presented bound to cells or the extracellular matrix to form haptotactic gradients. The growth cone, a highly specialized dynamic structure at the end of the extending axon, detects these guidance cues via transmembrane receptors, such as the netrin-1 receptors deleted in colorectal cancer (DCC) and UNC5. These receptors orchestrate remodeling of the cytoskeleton and cell membrane through both chemical and mechanotransductive pathways, which result in traction forces generated by the cytoskeleton against the extracellular environment and translocation of the growth cone. Through intracellular signaling responses, netrin-1 can trigger either attraction or repulsion of the axon. Here we review the mechanisms by which the classical guidance cue netrin-1 regulates intracellular effectors to respond to the extracellular environment in the context of axon guidance during development of the central nervous system and discuss recent findings that demonstrate the critical importance of mechanical forces in this process.
Update on Randomized Placebo-Controlled Trials in the Past Decade for Treatment of Major Depressive Disorder in Child and Adolescent Patients: A Systematic Review.

OBJECTIVES Antidepressant treatment for major depressive disorder (MDD) has been a topic of continued interest with a number of placebo randomized-controlled trials (RCTs) being published in the past decade. We review the updated literature since the 2007 Bridge meta-analysis, and reassess safety data looking at signals of treatment-emergent suicidality with the Columbia Suicide Severity Rating Scale (CSSR-S). METHODS PubMed literature review was performed searching for RCTs published since the 2007 article and supplemented with manual search. RESULTS Findings from seven trials (five industry sponsored, one NIMH funded, and one other) were included in this systematic review, which showed high medication and placebo response rates, with only fluoxetine and escitalopram treatment reaching statistical significance. Fluoxetine was also shown to prevent relapse of MDD with continued treatment with an odds ratio of 3.2 for prevention of relapse compared with placebo. There were no increases in treatment-emergent suicidality associated with antidepressant medication in any trial measuring suicidality systematically using the CSSR-S. CONCLUSIONS Depressed pediatric patients respond similarly in these trials to antidepressant intervention and placebo, with recent studies showing that newer agents did not clearly demonstrate benefit above placebo. The evidence continues to support fluoxetine and escitalopram as first-line treatment and demonstrated effect to prevent relapse. Use of newer rating scales reveals similar rates of treatment-emergent suicidality in patients on antidepressants as placebo, in contrast to increased suicide signal suggested by past research using adverse events data. Antidepressant treatment is generally safe and well tolerated in this age group.
An SMDP-Based Service Function Allocation Scheme for Mobile Edge Clouds

With the increasing global mobile traffic, there is a trend to deploy network services at mobile edge clouds. Benefiting from the techniques of Network Function Virtualization and Software-Defined Networking, service function chains are enabled to compose a series of required network functions dynamically. As a consequence, most of common-used and IT-based mobile network services can be deployed at MEC cloud networks under the 5G context, remarkably reducing user latency and network traffic. However, as resources in cloud networks are limited, it is challenging to promote the system utilization with guaranteed user experience. Thus, in this paper, we formulate the allocation problem of service functions in MECs as an Semi-Markov Decision Process model and present a value iteration algorithm to find the optimized solution, aiming to increase request acceptance rate. Additionally, we discuss the parameter settings of the proposed scheme under different cases to find higher rewards.
Perceptual consciousness and cognitive access from the perspective of capacity-unlimited working memory

Theories of consciousness divide over whether perceptual consciousness is rich or sparse in specific representational content and whether it requires cognitive access. These two issues are often treated in tandem because of a shared assumption that the representational capacity of cognitive access is fairly limited. Recent research on working memory challenges this shared assumption. This paper argues that abandoning the assumption undermines post-cue–based ‘overflow’ arguments, according to which perceptual consciousness is rich and does not require cognitive access. Abandoning it also dissociates the rich/sparse debate from the access question. The paper then explores attempts to reformulate overflow theses in ways that do not require the assumption of limited capacity. Finally, it discusses the problem of relating seemingly non-probabilistic perceptual consciousness to the probabilistic representations posited by the models that challenge conceptions of cognitive access as capacity-limited. This article is part of the theme issue ‘Perceptual consciousness and cognitive access'.
Prognostic Factors of Neurological Complications in Spinal Surgeries

Study Design Retrospective study. Purpose To determine prognostic factors of neurological complications (NCs) of posterior thoracolumbar surgeries. Overview of Literature There have been few reports on the prognosis of NCs according to the causes and treatment methods. Methods The subjects were 65 patients who had NCs for 19 years (1995–2013) after posterior thoracolumbar surgeries in Seoul Sacred Heart General Hospital. The degree of neurological injury was assessed using numeric scales as follows: G1, increased leg pain or sensory loss; G2, hemiparesis; G3, paraparesis; G4, cauda equine syndrome; and G5, complete paraplegia. The relative degree of neurological recovery was evaluated using four numeric scales as follows: Gr1, complete recovery; Gr2, almost complete recovery with residual sensory loss or numbness; Gr3, partial recovery with apparent neurological deficit; and Gr4, no recovery. The prognostic factors were investigated in terms of demographic and surgical variables that were available in a retrospective review. Results The causes were as follows: epidural hematoma (EH), 25 patients (38.5%); insufficient decompression and fusion, 14 patients (21.5%); mechanical injury, 11 patients (16.9%); insufficient discectomy, four patients (6.2%); and unknown, 11 patients (23.1%). The grade of neurological injury was as follows: G1, 11 patients (16.9%); G2, 34 patients (52.3%); G3, 15 patients (23.1%); G4, three patients (4.6%); and G5, two patients (3.1%). Thirteen patients received conservative treatment, and 52 underwent revision surgeries. Neurological recovery was as follows: Gr1, 21 patients (32.3%); Gr2, 17 patients (26.2%); Gr3, 20 patients (30.8%); and Gr4, seven patients (10.8%). The prognosis depended on the causes (p =0.041). The subgroup analysis of the revision group revealed a significant correlation between the degree of neurological recovery and the timing of revision, irrespective of causes (r =0.413, p =0.002). Conclusions The prognosis of NC depended on the causes. EH was the best and unknown was the worst prognostic factor. Revision should be performed as soon as possible for a better prognosis.
Effect of Parameters Tuned by a Taguchi Design L_9 3^4 in the GRASP Algorithm to Solve the Vehicle Routing Problem with Time Windows

Metaheuristic algorithms are black box procedures that analyze a subset of possible solutions to solve a problem or a set of instances. Before they are implemented, it is necessary to select an optimum parameter vector P ∗, a task known as tuning. The vector P ∗ affects the efficiency of metaheuristics in solving a given problem. In this paper, the impact of tuning parameters using the Taguchi L_9 3^4 statistical procedure is analyzed. The effect of this method is analyzed in the metaheuristic algorithm named Greedy Randomized Adaptive Search Procedure (GRASP), solving the problem of Vehicle Routes with Time Windows (VRPTW). The results offered by the algorithm in a subset of instances of 25 clients improve on average to those reported in the literature, using a P ∗ proposed by Taguchi calibration.
Evaluating Complexity and Digitizability of Regulations and Contracts for a Blockchain Application Design

Blockchain technology becomes the key solution to provide trust and security without any need for a central supervisory authority to validate the transactions. By now, it plays a key role in the digital transformation of several processes and industries with varying application use cases. To promote the wide adoption of blockchain technology we need mechanisms to identify the digitizability level of the given regulations to smart contracts and mechanisms to specify which blockchain technology is best suitable for the given regulations. In this work, we propose a modeling approach that supports the automated analysis of human-readable regulation representations by suggesting how much percentage of regulation is digitizable and the suitable blockchain environment to design the application. We identify smart contract components that correspond to real-world entities and its pertaining clauses and its digitizability property. With selected examples, we explore this capability and discuss our future research directions on smart contract generation according to the recommended environment.
Rate Splitting Based Asymmetric Uplink-Downlink Cooperative Transmission in Dynamic TDD MIMO Small Cell Networks

The new short-length single-direction frame structure for ultra-dense small cells (SCs) in the next- generation communication systems allows the dynamic time division duplex implemented by flexible transmit direction selection in each frame. Meanwhile, asymmetric uplink (UL) or downlink (DL) modes will introduce more complex and diverse interference in the adjacent SCs. Instead of treating the interference as noise as the conventional schemes do, we utilize some of the interference properties and propose a rate- splitting (RS) strategy to split each message into a common part and a private part. The two parts are decoded using successive interference cancellation (SIC). To maximize the time-averaged sum rate, we design the transmit direction and the covariances of the private and common messages jointly. The problem is NP-hard, as it subjects to mixed Boolean variables, transmit power and SIC decodability constraints. This paper achieves the suboptimal solutions numerically by difference of concave programming and convex-relaxed linear programming. The results show that the proposed strategy brings gain in time-averaged sum rate compared to the conventional schemes using symmetric UL-DL transmission without RS for different users' locations and network sizes. And the results indicate that the proposed strategy improves the performance of the users at the cell boundary and is superior for dense SC networks.
Developing a Neural Machine Translation Service for the 2017-2018 European Union Presidency

The paper describes Tilde’s work on developing a neural machine translation (NMT) tool for the 2017-2018 Presidency of the Council of the European Union. The tool was developed by combining the European Commission’s eTranslation service with a set of customized, domain-adapted NMT systems built by Tilde. The central aim of the tool is to assist staff members, translators, EU delegates, journalists, and other visitors at EU Council Presidency events in Estonia, Bulgaria, and Austria. The paper provides details on the workﬂow used to collect, ﬁlter, clean, normalize, and pre-process data for the NMT systems; and the methods applied for training and adaptation of the NMT systems for the EU Council Presidency. The paper also compares the trained NMT systems to other publicly available MT systems for Estonian and Bulgarian, showing that the custom systems achieve better results than competing systems.
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. Hauser and B\"uhlmann (2012) previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. In this paper, we extend these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them. We define and characterize the interventional Markov equivalence class that can be identified from general (not necessarily perfect) intervention experiments. We also propose the first provably consistent algorithm for learning DAGs in this setting and evaluate our algorithm on simulated and biological datasets.
Landscaper: A Modeling System for 3D Printing Scale Models of Landscapes

Landscape models of geospatial regions provide an intuitive mechanism for exploring complex geospatial information. However, the methods currently used to create these scale models require a large amount of resources, which restricts the availability of these models to a limited number of popular public places, such as museums and airports. In this paper, we have proposed a system for creating these physical models using an affordable 3D printer in order to make the creation of these models more widely accessible. Our system retrieves GIS relevant to creating a physical model of a geospatial region and then addresses the two major limitations of affordable 3D printers, namely the limited number of materials and available printing volume. This is accomplished by separating features into distinct extruded layers and splitting large models into smaller pieces, allowing us to employ different methods for the visualization of different geospatial features, like vegetation and residential areas, in a 3D printing context. We confirm the functionality of our system by printing two large physical models of relatively complex landscape regions.
Clinical Impact of Bleeding in Cancer-Associated Venous Thromboembolism: Results from the Hokusai VTE Cancer Study

Abstract In the Hokusai VTE Cancer study, edoxaban was non-inferior to dalteparin for the composite outcome of recurrent venous thromboembolism (VTE) and major bleeding in 1,050 patients with cancer-associated VTE. The absolute rate of recurrent VTE was 3.4% lower with edoxaban, whereas the absolute rate of major bleeding was 2.9% higher. The present analysis focuses on the sites, clinical presentation, course and outcome of bleeding events, and the associated tumour types. Major bleeds and their severity (categories 1–4) were blindly adjudicated by a committee using a priori defined criteria, and data were analysed in the safety population. Major bleeding occurred in 32 of 522 patients given edoxaban (median treatment duration, 211 days) and in 16 of 524 patients treated with dalteparin (median treatment duration, 184 days); no patients had more than one major bleed. There were no fatal bleeds with edoxaban, and two with dalteparin. Severe bleeding at presentation (category 3 or 4) occurred in 10 (1.9%) and 11 (2.1%) patients in the edoxaban and dalteparin groups, respectively. The excess of major bleeding with edoxaban was confined to patients with gastrointestinal cancer. However, severe major bleeding at presentation (category 3 or 4) in this sub-group occurred in 5 of 165 (3.0%) and in 3 of 140 (2.1%) patients given edoxaban or dalteparin, respectively. In conclusion, this analysis suggests that while oral edoxaban is an appropriate alternative to subcutaneous dalteparin for treatment of cancer-associated VTE, the use of edoxaban in patients with gastrointestinal cancer requires careful benefit–risk weighting.
Review and Theory Symbiosis: An Introspective Retrospective

This paper presents a polylithic framework of review and theory development (RTD) papers. Based upon a reflective analysis of review papers that I have written, read, and/or reviewed, I build a framework suggesting four types of RTD papers: organizing reviews, assessing reviews, specific-theorizing reviews, and broad-theorizing reviews. The four types vary according to the research focus and research objectives, with research focus ranging from primarily description to the identification of gaps, and research objective ranging from primarily synthesizing to primarily theorizing. The framework and accompanying discussion are intended to provide scholars a perspective of the different ways that theory development and review papers intersect. The paper proposes criteria to help evaluate the quality of RTD papers and provides suggestions to authors on how to craft RTD papers.
Linkky: Extraction de clés de liage par une adaptation de l'analyse relationnelle de concepts

Les cles de liage permettent de specifier la maniere d’engendrer des liens entre deux sources de donnees RDF. L’objet de cette demonstration est de presenter Linkky, un prototype implementant l’extraction de familles de cles de liage dependantes a l’aide de techniques d’analyse formelle de concept.
The association between hypertensive arteriopathy and cerebral amyloid angiopathy in spontaneously hypertensive stroke‐prone rats

We aimed to test the hypothesis that in spontaneously hypertensive stroke‐prone rats (SHRSP), non‐amyloid cerebral small vessel disease/hypertensive arteriopathy (HA) results in vessel wall injury that may promote cerebral amyloid angiopathy (CAA). Our study comprised 21 male SHRSP (age 17–44 weeks) and 10 age‐ and sex‐matched Wistar control rats, that underwent two‐photon (2PM) imaging of the arterioles in the parietal cortex using Methoxy‐X04, Dextran and cerebral blood flow (CBF) measurements. Our data suggest that HA in SHRSP progresses in a temporal and age‐dependent manner, starting from small vessel wall damage (stage 1A), proceeding to CBF reduction (stage 1B), non‐occlusive (stage 2), and finally, occlusive thrombi (stage 3). Wistar animals also demonstrated small vessel wall damage, but were free of any of the later HA stages. Nearly half of all SHRSP additionally displayed vascular Methoxy‐X04 positivity indicative of cortical CAA. Vascular β‐amyloid deposits were found in small vessels characterized by thrombotic occlusions (stage 2 or 3). Post‐mortem analysis of the rat brains confirmed the findings derived from intravital 2PM microscopy. Our data thus overall suggest that advanced HA may play a role in CAA development with the two small vessel disease entities might be related to the same pathological spectrum of the aging brain.
MapReduce Enabling Content Analysis Architecture for Information-Centric Networks Using CNN

Information Centric Network (ICN) is one of the promising architectures in the next generation networks. The content-based routing in ICN can satisfy the content distribution of large-scale data. For prompt content obtainment, it is important to realize the content analysis before the content reaches application layer. The novel characteristics of data naming in ICN make it possible to search and analyse content during the transmission of content, which can directly get the critical content without the process of the application layer. In this paper, we propose a MapReduce enabling content analysis architecture for ICN. MapReduce framework can realize the parallelization of content collection and analysis during the routing process. For more efficient content collection, we put forward an optimal selection for mapper nodes. Moreover, Convolutional Neural Network (CNN) is deployed in the MapReduce architecture providing further analysis for ICN content. The simulation result shows the advantages of the proposed architecture.
An Emotion-driven Approach for Aspect-based Opinion Mining

The remarkable ability to understand the opinion of a user about a specific topic of discussion allows intelligent systems to provide more specific and personalized suggestions especially when no other information is available. The strategies for opinion mining, also known as sentiment analysis, are in last years topic of in-depth studies. In this work, we present an approach of text mining for detecting the topic of discussion for textual contents and the emotion that the writer feels while writing it. Conversely to the classic strategies of sentiment analysis, we enrich the standard polarity prediction task with more fine-grained information about user’s emotion. By using this information, the final behavior of the personalized system could be designed by taking into account the view about the topic of the specific user. For performing this task, we adopted a hybrid approach which uses both lexicons and semantic representation of sentences for the operation of aspect classification. Training data for the aspects detection module have been extracted from already categorized last year world news. The emotional labeling approach is, instead, based on the posts left by users on Facebook, which have been annotated using the emoticon encountered. The evaluation has been conducted on a dataset of tweets opportunely collected using hash-tags which refer both to the topic of discussion and the