Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal design of wireless networks for broadband access with minimum power consumption

The continuous rise in wireless data traffic brings forth an increase in power consumption and static users constitute a large fraction of these traffic demands. This work focuses on designing cellular networks to deliver a given data rate per area and user, while minimizing the power consumption. In particular we are interested in optimizing the transmission power, density of access points (APs), number of AP antennas and number of users served in each cell. To this end, we consider a network model based on stochastic geometry and a detailed power consumption model to derive closed form expressions and obtain insights on the interplay of the aforementioned design parameters. The results show that, in contrast with previous works on optimal network design for energy efficiency, having exceedingly high AP density does not bring the most benefits in terms of power savings. Instead the AP density should be chosen according to the area data rate that we want to deliver. In addition numerical results show that the minimum power consumption is obtained in the Massive MIMO regime with many antennas and users per AP.
Location based routing protocol exploiting heterogeneous primary users in cognitive radio networks

In cognitive radio networks (CRNs), knowledge of the primary users (PUs) position can be used to avoid harmful interference to the primary network, while at the same time be exploited to improve CR performance. In this paper, a localization algorithm is developed to calculate PUs position and a novel location based CR (LCR) routing protocol is proposed that has the following properties: (i) it considers the existence of heterogeneous PUs, (ii) exploits PUs location information, (iii) jointly selects spectrum and route, (iv) protects PUs from interference. Clusters of CRs are defined according to the spectral characteristics in a given location area, and the LCR routing protocol acts in two steps: intra-cluster and inter-cluster. Simulations are conducted in terms of CR end-to-end performance and PUs collision risk. Results reveal the importance of formulating routing protocol in terms of PU protection, which is a unique features in CR networks.
Depth without distress

Upgrading a linear classifier to a nonlinear one leads to statistical or computational compromise. For example, lifting its dimension, as in kernel methods, may lead to overfitting. Nonconvex optimization algorithms, used for (deep) neural networks or decision trees, may not obtain high-quality solutions or even converge. Similarly, boosting can get stuck on increasingly difficult subproblems. We avoid concerns about overfitting and convergence with new nonlinear classifiers — smooth lists of halfspaces — and a new learning algorithm — the sequence of averages. Smooth lists have unbounded depth, yet do not need more data, in the worst case, than single linear classifiers. Theory suggests the algorithm never gets prematurely stuck and monotonically improves the classifier. In experiments inspired by challenging problems in computational learning theory, it performs well when only a moderate amount of data and time are available, and there is non-trivial noise or inconsistency. At a high level, we isolate a kind of deep learning which is easier to reason about but retains some of the advantages of depth.
Effect of Cure Cycle Heat Transfer Rates on the Physical Properties of an Epoxy Matrix Composite

Although the autoclave technique produces composite parts of high quality, the process is time consuming and has intrinsically high capital and operating costs. QuickstepTM is a novel polymer composite manufacturing technique designed for the out-of-autoclave processing of highquality, low-cost components with a reduction in cure-cycle times. This paper assesses the use of the Quickstep method for the processing of an epoxy-carbon fibre aerospace composite material. The Quickstep process is compared both to a thermal vacuum-bag only process and the manufacturer’s specifications for autoclave cured panels. Higher process ramp rates, achievable by using Quickstep, have been shown to reduce resin viscosity and facilitate void removal. Through manipulation of the Quickstep cure cycle while the resin is at low viscosity, significant effects on the mechanical properties of the product are demonstrated. Using Quickstep curing it has been found that better interlaminar properties than the manufacturers autoclave data could be obtained while the flexural strength was a little lower. The work identifies key parameters associated with the Quickstep process giving an insight into how it can be optimised further in an attempt to produce panel properties that rival those produced by autoclave methods.
Visible-light and Infrared Face Recognition

This study examines issues involved in the comparison and combination of face recognition using visible and infra-red images. This is the only study that we know of to focus on experiments involving time lapse between gallery and probe image acquisitions. Most practical applications of face recognition would seem to involve time-lapse scenarios. We find that in a time lapse scenario, (1) PCA-based recognition using visible images may outperform PCA-based recognition using infra-red images, (2) the combination of PCA-based recognition using visible and infra-red imagery substantially outperforms either one individually, and (3) the combination of PCA-based recognition using visible and infra-red also outperforms a current commercial state-of-the-art algorithm operating on visible images. For example, in one particular experiment, PCA on visible images gave 75% rank-one recognition, PCA on IR gave 74%, FaceIt on visible gave 86%, and combined PCA IR and visible gave 91%.
Relation between Average Alcohol Consumption and Disease: An Overview

Objective: To conduct an overview of alcohol-related health consequences and to estimate relative risk for chronic consequences and attributable fractions for acute consequences. Methods: Identification of alcohol-related consequences was performed by means of reviewing and evaluating large-scale epidemiological studies and reviews on alcohol and health, including epidemiological contributions to major social cost studies. Relative risks and alcohol-attributable fractions were drawn from the international literature and risk estimates were updated, whenever possible, by means of meta-analytical techniques. Results: More than 60 health consequences were identified for which a causal link between alcohol consumption and outcome can be assumed. Conclusions: Future research on alcohol-related health consequences should focus on standardization of exposure measures and take into consideration both average volume of consumption and patterns of drinking.
Differential role of mGlu1 and mGlu5 receptors in rat hippocampal slice models of ischemic tolerance

Activation of glutamate receptors has been proposed as a key factor in the induction of ischemic tolerance. We used organotypic rat hippocampal slices exposed to 30 min oxygen–glucose deprivation (OGD) to evaluate postischemic pyramidal cell death in the CA1 subregion. In this model, 10 min exposure to OGD 24 h before the exposure to toxic OGD was not lethal and reduced the subsequent OGD neurotoxicity by ∼ 53% (ischemic preconditioning). Similarly, a 30 min exposure to the group I mGlu receptor agonist DHPG (10 µM) significantly reduced OGD neurotoxicity 24 h later (pharmacological preconditioning). Ischemic tolerance did not develop when either the selective mGlu1 antagonists LY367385 and 3‐MATIDA or the AMPA/KA antagonist CNQX were present in the incubation medium during exposure to sublethal OGD. Neither the NMDA antagonist MK801 nor the mGlu5 antagonist MPEP affected the preconditioning process. On the other hand, pharmacological preconditioning was prevented not only by LY367385 or CNQX, but also by MPEP. In preconditioned slices, the toxic responses to AMPA or NMDA were reduced. The neurotoxicty of 100 µM DHPG in slices simultaneously exposed to a mild (20 min) OGD was differentially altered in the two preconditioning paradigms. After ischemic preconditioning, DHPG neurotoxicity was reduced in a manner that was sensitive to LY367385 but not to MPEP, whereas after pharmacological preconditioning it was enhanced in a manner that was sensitive to MPEP but not to LY367385. Our results show that mGlu1 and mGlu5 receptors are differentially involved in the induction and expression of ischemic tolerance following two diverse preconditioning stimuli.
History of the Discovery of the Thymosins

Abstract: From a historical perspective, the studies that led to the isolation and characterization of the thymosins began in earnest in the early 1960s in the laboratory of Abraham White at the Albert Einstein College of Medicine in New York. In a 1966 paper in the Proceedings of the National Academy of Sciences, U.S., we first named these thymic‐derived factors “Thymosins. ” By 1972, the thymosin team had moved to the University of Texas Medical Branch in Galveston (UTMB) where an extremely talented group of young scientists and students succeeded over the next 6 years in preparing and testing a highly active partially purified calf thymus preparation, termed thymosin fraction‐5 (TF5), which was amenable for scale‐up and suitable for clinical use. In 1974, we received the first IND for a thymic hormone preparation from the FDA to begin a phase‐I study with TF5 in children with primary immunodeficiency diseases at the University of California Medical Center in San Francisco. The immunorestorative and potentially life‐saving properties of TF5 in clinical medicine were first documented in a landmark paper in 1975 by Drs. Arthur Ammann and Diane Wara in the New England Journal of Medicine. TF5 consists of a family of at least 40 mostly small acidic polypeptides, with molecular weights ranging from 1000 to 15,000 Da. This article will identify the key scientists and the milestones involved in the initial studies with TF5 that have led to the chemical characterization of the thymosins and to translational studies from the lab bench to the clinic.
Query unnesting in object-oriented databases

There is already a sizable body of proposals on OODB query optimization. One of the most challenging problems in this area is query unnesting, where the embedded query can take any form, including aggregation and universal quantification. Although there is already a number of proposed techniques for query unnesting, most of these techniques are applicable to only few cases. We believe that the lack of a general and simple solution to the query unnesting problem is due to the lack of a uniform algebra that treats all operations (including aggregation and quantification) in the same way. This paper presents a new query unnesting algorithm that generalizes many unnesting techniques proposed recently in the literature. Our system is capable of removing any form of query nesting using a very simple and efficient algorithm. The simplicity of the system is due to the use of the monoid comprehension calculus as an intermediate form for OODB queries. The monoid comphrehension calculus treats operations over multiple collection types, aggregates, and quantifiers in a similar way, resulting in a uniform way of unnesting queries, regardless of their type of nesting.
The need for a faceted classification as the basis of all methods of information retrieval

Purpose – The aim of this article is to estimate the impact of faceted classification and the faceted analytical method on the development of various information retrieval tools over the latter part of the twentieth and early twenty‐first centuries.Design/methodology/approach – The article presents an examination of various subject access tools intended for retrieval of both print and digital materials to determine whether they exhibit features of faceted systems. Some attention is paid to use of the faceted approach as a means of structuring information on commercial web sites. The secondary and research literature is also surveyed for commentary on and evaluation of facet analysis as a basis for the building of vocabulary and conceptual tools.Findings – The study finds that faceted systems are now very common, with a major increase in their use over the last 15 years. Most LIS subject indexing tools (classifications, subject heading lists and thesauri) now demonstrate features of facet analysis to a gre.
Weighted Domain Translation for Online News Comments Emotion Tagging

This paper studies an emotion classification problem, which aims to classify online news comments to one of fine-grained emotion categories, e.g. happy, sad, and angry, etc. Neural networks have been widely used and achieved great success in sentiment classification. However, there must be sufficient labeled comments available for training neural networks, which usually requires labor-intensive and time-consuming manual labeling. One of the most effective solutions is to apply transfer learning, which uses abundant labeled comments from a source news domain to help the classification for another target domain with limited amount of labeled data. Still, the comments from different domains can have very different word distributions, which makes it difficult to transfer knowledge from one domain to another. In this paper, we accomplish cross-domain emotion tagging based on an advanced neural network BLSTM (bidirectional long short-term memory) with "domain translation'', which can overcome the difference between domains. A weighted linear transformation is utilized to "translate'' knowledge from source to target domain. An extensive set of experimental results on four datasets from popular online news services demonstrates the effectiveness of our proposed models.
Semantic-Based Interaction Detection in Aspect-Oriented Scenarios

Interactions between dependent or conflicting aspects are a well-known problem with aspect-oriented development (and related paradigms). These interactions are potentially dangerous and can lead to unexpected or incorrect results when aspects are composed. To date, most aspect interaction detection methods have been based either on purely syntactic comparisons or have relied on heavyweight formal methods. We present a new approach that is based instead on lightweight semantic annotations of aspects. Each aspect is annotated with domain-specific markers and a separate influence model describes how semantic markers from different domains influence each other. Automated analysis can then be used both to highlight semantic aspect conflicts and to trade-off aspects. We apply this technique to early aspects, namely, aspect scenarios, because it is desirable to detect aspect interactions as early in the software lifecycle as possible. We evaluate the technique using an industrial case study and show that the technique detects interactions that cannot be discovered using syntactic techniques.
Insider attack detection using weak indicators over network flow data

Insider attack detection in an enterprise network environment is a critical problem that currently has no promising solution. It represents a significant challenge since host availability and performance requirements cannot be ignored. A network based approach allows these requirements to be met but is limited by the granularity of data available and the near impossibility of defining exact signatures for known attack types. Anomaly detection approaches suffer from the well known problem of false positives making them hard to apply in enterprise environments where even a moderate false positive rate is not acceptable. Sophisticated attacks and complex network topologies make it hard to apply simplistic approaches to anomaly detection. This paper presents an approach that applies the unsupervised learning techniques of bi-clustering and one-class SVM to so-called weak indicators of network attacks. This approach is well suited for network flow data that is coarse grained and not amenable to simplistic anomaly detection or signature-based techniques. Further, our approach allows a security analyst to determine the cause of the anomaly, a capability that is typically not supportable by simplistic applications of unsupervised learning.
Detecting and Recognizing Centerlines as Parabolic Sections of the Steerable Filter Response

—This paper is concerned with detection and recognition of road surface markings in video acquired from the driver's perspective. In particular, we focus on centerlines which separate the two road lanes with opposed traffic directions, since they are often the only markings in many urban and suburban roads. The proposed technique is based on detecting parabolic sections of the thresholded steerable filter response in inverse perspective images. The technique has been experimentally evaluated on production videos acquired from moving service vehicles. The obtained results are provided and discussed.
Intension, Attitude, and Tense Annotation in a High-Fidelity Semantic Representation

This paper describes current efforts in developing an annotation schema and guidelines for sentences in Episodic Logic (EL). We focus on important distinctions for representing modality, attitudes, and tense and present an annotation schema that makes these distinctions. EL has proved competitive with other logical formulations in speed and inference-enablement, while expressing a wider array of natural language phenomena including intensional modification of predicates and sentences, propositional attitudes, and tense and aspect.
Towards a feature-rich data set for personalized access to long-tail content

Personalized data access has become one of the core challenges for intelligent information access, especially for nonmainstream long-tail content, as can be found in digital libraries. One of the main reasons that personalization remains a difficult task is the lack of standardized test corpora. In this paper we provide a comprehensive analysis of feature requirements for personalization together with a data collection tool for generating user models and collecting data for personalization of search and recommender system optimization in the long-tail. Based on the feature analysis, we provide a feature-rich publicly available data set, covering web content consumption and creation tasks. Our data set contains user models for eight users, including performed tasks, relevant topics for each task, relevance ratings, and relations between focus text and search queries. Altogether, the data set consists of 217 tasks, 4562 queries and over 15.000 ratings. On this data we perform automatic query prediction from web page content, achieving an accuracy of 89% using term identity, capitalization and part-of-speech tags as features. The results of the feature analysis can serve as guideline for feature collection for long-tail content personalization, and the provided data set as a gold standard for learning and evaluation of user models as well as for optimizing recommender or search engines for long-tail domains.
Fuzzy assisted grey Taguchi approach for optimisation of multiple weld quality properties in friction stir welding process

Friction stir welding (FSW) is gaining its popularity for a wide range of high strength/weight ratio materials such as aluminium, magnesium, composites and plastics. With the projected prevalent application of FSW, it is necessary to identify the optimal welding condition with a small number of experiments for effective product quality and productivity. In this study, a fuzzy assisted grey Taguchi method is proposed for optimising the FSW process parameters with multiple weld quality characteristics. The fuzzy inference system was adapted to convert the multiresponse optimisation problem into an equivalent single objective optimisation problem, which was solved by the Taguchi approach. Eight input process parameters have been varied to observe their effects on output responses, namely, ultimate tensile strength, yield strength, % elongation, weld bead thickness and nugget zone hardness. Confirmation test showed that the proposed approach can efficiently be used for the optimisation of multiple weld quality properties in FSW process.
Serum leptin, adiponectin and endometrial cancer risk in Chinese women

Objective To investigate the relationship between serum concentrations of leptin or adiponectin, and endometrial carcinoma in Chinese women. Methods We conducted a case-control study of a total of 516 Chinese women to detect the relationships between serum concentrations of leptin or adiponectin, and endometrial carcinoma in Chinese women. The study subject constituted 206 cases of endometrial cancer and 310 normal controls. Results Patients with endometrial carcinoma had higher serum leptin concentrations than controls (28.8±2.2 ug/L vs. 19.8±1.4 ug/L; p<0.001). The adiponectin levels in patients were lower than in controls with borderline statistical significance (2,330.7±180.5 ug/L vs. 2,583.9±147.2 ug/L; p=0.078). Logistic regression analysis confirmed the associations between leptin or adiponectin, and endometrial carcinoma after adjustment for age, body mass index, fasting insulin, serum glucose, cholesterol, triglycerides, and high-density lipoprotein cholesterol (odds ratio for the top tertile vs. the bottom tertile: leptin 2.05; 95% confidence interval [CI], 1.28 to 3.29; p<0.001; adiponectin 0.52; 95% CI, 0.32 to 0.83; p<0.001). Conclusion Increased leptin or decreased adiponectin levels are associated with endometrial carcinoma.
Mapping Human Whole-Brain Structural Networks with Diffusion MRI

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.
Adaptive Group Communication

Group communication denotes a set of membership, communication, and coordination services that support the development of distributed applications based on process groups. These services are typically provided by a protocol stack, which includes layers such as failure detectors, reliable multicast, view-synchrony, total order, among others. The performance of these protocols is highly dependent on the operational envelope, including network latency, link error rates, load profile, etc. Therefore, multiple implementations of each layer have been proposed, each excelling in a different scenario. This work addresses the architectures and mechanisms required to build adaptive protocol composition frameworks, namely those supporting group communication, such that they can cope with a wide range of varying requirements.