Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing spectral-energy efficiency forLTE-advanced heterogeneous networks: a users social pattern perspective

The development of LTE-Advanced and beyond cellular networks is expected to offer considerably higher data rates than the existing 3G networks. Among the many potential technologies in LTE-Advanced systems, users? characteristics and social behavior have been studied to improve the networks? performance. In this article we present the concept of user social pattern (USP), which characterizes the general user behavior, pattern, and rules of a group of users in a social manner, and utilize USP as an optimization basis for network performance enhancement. From large-scale traffic traces collected from current mobile cellular networks, the USP model is evaluated and verified. Furthermore, to evaluate the potential of spectral efficiency and energy efficiency enhancement based on USP in LTE-A HetNets, we establish a complete system and link-level HetNet simulation platform according to 3GPP LTE-A standards. Then, based on the platform, simulations are performed to evaluate the impact of USP on spectral and energy efficiency in an LTE-A network, and a USP-based spectral efficiency and energy efficiency enhancement scheme is proposed for a HetNet of the LTE-A system. Simulation results validate that USP can be used as an effective concept for network performance optimization in an LTE-A system.
Proceedings of the 10th ACM Conference on Recommender Systems

It is our great pleasure to welcome you to the 10th ACM Conference on Recommender Systems (RecSys 2016), held in Boston, MA, USA, from September 15th through 19th. Since it was first held ten years ago, RecSys has grown to become the leading conference for the presentation and discussion of recommender systems research, bringing together the world's leading recommender systems researchers and e-commerce companies. The program for RecSys 2016 reflects the growth of the Recommender Systems community. For the second time in the history of RecSys we will offer two parallel tracks during the three days of the main conference with a record breaking 112 contributions including 51 technical papers, 9 Past, Present, and Future papers, 15 industry papers, four tutorials, three keynotes, and 30 demos and posters. We again offer an extensive pre-conference program with nine workshops, the RecSys Challenge and a doctoral symposium. The technical program for RecSys 2016 drew upon a record 294 total submissions. To celebrate the tenth year of the conference, the program features a new track reflecting on past, present, and future research in the field of recommender systems. Papers in this track consider a broad perspective on how the field has evolved and the challenges and directions that lie ahead. The review process for all tracks was highly selective. In the main program, 29 long papers were accepted out of 159 submissions (18.2% acceptance rate), 22 out of 110 short papers (20% acceptance rate), and 9 out of 25 Past, Present and Future paper submissions (36% acceptance rate). Prominent topics covered by these papers include human factors, social aspects, context awareness, cold start, novelty and diversity, and core algorithmic research (matrix factorization, deep learning, probabilistic approaches, etc.). Building on the tradition established by previous years, RecSys 2016 features a strong focus on significant real-world challenges facing industrial practitioners and practical solutions to those challenges. The three industry sessions feature a rich set of talks from Mendeley, Meetup, Bloomberg, Foursquare, Spotify, Netflix, Pandora, Stitch Fix, Expedia, Nara Logics, GraphSQL, Retail Rocket, Quora, Google and Pinterest. A wide range of domains are represented in these sessions including publishing, news, Q & A, events, music, movies, television, fashion, apps and games.
Shape Matching and Object Recognition Using Shape Contexts

ÐWe present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by 1) solving for correspondences between points on the two shapes, 2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.
Anaerobic digestion of organic matter. Biogas production from energy crop residues (Maize stalks)

Juri: Presidente: Doutora Elizabeth da Costa Neves Fernandes de Almeida Duarte, Professora Catedratica do Instituto Superior de Agronomia da Universidade Tecnica de Lisboa. Vogais: Doutor Santino Eugenio di Berardino, Investigador Principal do Laboratorio Nacional de Energia e Geologia, I.P; Doutora Claudia Saramago de Carvalho Marques dos Santos Cordovil, Professora Auxiliar do Instituto Superior de Agronomia da Universidade Tecnica de Lisboa; Mestre Lopo Jose Infante Câmara Lopo de Carvalho, na qualidade de especialista.
Communes of less favoured areas in landscape parks of West Pomeranian Voivodeship

Functional classification of communes with less favoured areas in landscape parks in West Pomeranian Voivodeship has shown that among 10 analyzed features the biggest differences between communes were in livestock unit per 100 ha of agricultural land, density of population, proportion of non-agricultural farms in the total number of farms, as well as proportion of agricultural land and forests in the total area. Moreover, among three groups of communes, the best conditions for agricultural production were in group C which included Barwice, Dobrzany, Polczyn-Zdroj and Wegorzyno. In the rest of the studied communes, group A and B, rural tourism, agrotourism and other non-agricultural activities are preferable.
Automatically proving the correctness of program analyses and transformations

In this dissertation, I describe a technique for automatically proving compiler optimizations sound, meaning that their transformations are always semantics-preserving. I first present a domain-specific language, called Rhodium, for implementing optimizations using local propagation and transformation rules that manipulate explicit dataflow facts. Then I describe a technique for automatically proving the soundness of Rhodium optimizations. The technique requires an automatic theorem prover to discharge a simple proof obligation for each propagation and transformation rule. I have written a variety of forward and backward intraprocedural dataflow optimizations in Rhodium, including constant propagation and folding, branch folding, full and partial redundancy elimination, full and partial dead assignment elimination, an intraprocedural version of Andersen's points-to analysis, arithmetic-invariant detection, loop-induction-variable strength reduction, and redundant array load elimination. I implemented Rhodium's soundness-checking strategy using the Simplify theorem prover, and I have used this implementation to automatically prove that the Rhodium optimizations I wrote were sound. I implemented a prototype execution engine for Rhodium, so that Rhodium optimizations can be directly executed. I also developed a way of interpreting Rhodium optimizations in both flow-sensitive and -insensitive ways, and of applying them interprocedurally given a separate context-sensitivity strategy, all while retaining soundness.
Estimating the extent of drained supraglacial lakes on the Greenland Ice Sheet

Supraglacial lakes are a common feature of the ablation zone of the Greenland Ice Sheet and have significant implications for the water budget of the area, because when they drain they can increase the speed at which ice moves to lower elevations. One valuable tool in assessing the water balance of ice sheets is to track the volume of lakes as they form, through in situ measurements or by determining lake area and depth from aircraft or spacecraft imagery. However, since supraglacial lakes drain unpredictably and rapidly, it is possible that they can form and drain without being observed. Therefore, it is valuable to create tools that can detect the previous presence of supraglacial lakes after they have drained. Three methods of distinguishing drained supraglacial lakes in Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery and hyperspectral airborne imagery were analysed: spectral signature analysis on raw data, band ratio analysis, and textural analysis. All three methods show promise that they could be used to detect former (i.e. drained) supraglacial lakes, thereby refining estimates of the water balance of the Greenland Ice Sheet and providing valuable data to climate models.
Resolving IT-Culture Conflict in Enterprise 2.0 Implementations

Social media technologies have grown from the personal recreation tools of college students to systems that can benefit business. In addition to using social networking to market, sell, and manage external stakeholder relationships, some organizations are leveraging social networking’s communication and collaboration tools to improve internal operations. However, implementing social networking systems (hereinafter SNSs) within an organization can prove challenging since the values associated with SNSs (i.e., having fun and getting to know people personally) may conflict with an organization’s culture (i.e., work, control, and authority). Therefore, the purpose of this case study is to understand how to resolve IT– culture conflict in workplace implementations of internal SNSs, often referred to as Enterprise 2.0.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics (Supplementary Material)

In the main paper, we analyzed the inherent problems of using Q-diversity metrics to select high quality ensembles. In comparison, our FQ-diversity metrics can (1) separately measure and compare the ensemble teams of equal size, (2) leverage the negative samples from the focal model to measure ensemble diversity, and (3) partition the candidate ensemble teams by using binary clustering with strategically selected initial centroids. These optimizations enable FQ-diversity metrics to more accurately capture the failure in-dependence among the member models of ensemble teams, and efﬁciently select high quality ensemble teams. Furthermore, we further improve the quality of selected ensemble teams by introducing EQ diversity metrics to combine the top performing FQ metrics. The source codes are provided at https://github.com/git-disl/DP-Ensemble . In this supplementary material, we provide additional materials and technical details. We organize the supplementary material into ﬁve sections: (1) Additional examples and visualization to illustrate the strength of FQ diversity metrics
Abstract—text Categorization Is the Problem of Classifying Text

documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.
The Use of Ozone in Hospital Wastewater Treatment

Abstract. The wastewater produced in municipal and industrial sources is a desalted water which may be used in agriculture if purified and refined well. In many countries, the treated wastewater is reused for irrigation and aquaculture. The most important issue in using municipal wastewater for irrigation is to ensure about the efficiency of process of waste water disinfection to decrease pathogenic microorganisms. Ozonation is an environment friendly and advanced technology with high efficiency in decreasing viruses and parasites and can be an appropriate alternative for chlorination of wastewater. Hospital wastewater has complex and various compounds such as pathogenic factors and medical and toxic chemicals which are mainly disposed in cesspools or enter into the city swage system that lead to water resources pollution crisis. Therefore, the aim of this study is to examine the use of ozone in refining hospital wastewater.
Caracol, Belize: Evidence of Ancient Maya Agricultural Terraces

AbstractRecent studies of ancient Maya settlement and demography indicate population densities exceeding those achievable using traditional swidden farming techniques, and suggest that more intensive farming methodologies such as terracing were employed. This paper describes recent archaeological investigations at the major Maya ceremonial center of Caracol, Cayo District, Belize, a poorly known region of the southern Maya lowlands. Research concentrated upon a six-hectare zone of terraced hillslope and included detailed mapping and excavations. An examination of the terraces and associated residential units is made, with discussion of the antiquity of these structures, their distribution, terrace size, form, and probable function. The first radiocarbon dates for Caracol, as well as data on terrace soils and pollen, are presented.
Complete genome sequences of butyrate producing Anaerostipes hadrus strains BA1 and GIF7 isolated from the terminal ileum of a healthy lean male

ABSTRACT Anaerostipes hadrus strains BA1 and GIF7 were isolated from a healthy man. The complete genomes’ sizes are 2,946,270 bp (BA1) and 2,907,308 bp (GIF7), with high average nucleotide identity (ANIb = 100%) and alignments ≥96.86% between strains. Conversely, both strains share 97.47% (ANIb) identity and ≤77.36% alignments to A. hadrus ATCC 29173T.
A New Profile of Migrants in the Aftermath of the Recent Economic Crisis

Growing international migration and diverse characteristics of migrant populations make internationally comparable high-quality data on migrants essential. Regular update of these data is crucial to capture the changes in size and composition of migrant populations. This document presents the first results of the update of the Database on Immigrants in OECD Countries (DIOC) for 2010/11. It describes immigrant and emigrant populations by socio-demographic characteristics and labour market outcomes in the OECD, and shows their evolution in the past decade. It also provides updated emigration rates and brain drain figures. L’accroissement des migrations internationales et les caracteristiques particulieres des populations immigres necessitent de produire des donnees de qualite comparables au niveau international. La mise a jour reguliere de ces donnees est essentielle pour bien saisir les changements dans la taille et la composition des populations migrantes. Ce document presente les premiers resultats de la mise a jour de la base de donnees sur les immigres dans les pays de l'OCDE (DIOC) pour 2010/11. Il decrit les populations immigrees et emigrees selon leurs caracteristiques sociodemographiques et leurs resultats sur le marche du travail des pays de l'OCDE, et montre leur evolution au cours de la derniere decennie. Il fournit egalement des taux d'emigration mis a jour et des donnees sur la fuite des cerveaux.
Bacterial processes in the intermediate and deep layers of the Ionian Sea in winter 1999: Vertical profiles and their relationship to the different water masses

[1] Dissolved and particulate organic carbon, bacterial biomass, microbial enzymatic activities (EEA: leucine aminopeptidase, β-glucosidase, and alkaline phosphatase), bacterial production, respiration rates, and bacterial growth efficiency were determined in 10 stations of the Ionian Sea (winter 1998–1999) with the aim of characterizing the recycling of biogenic carbon and phosphorus in the different water masses, previously identified on the basis of their hydrographical properties. All microbial activities decreased markedly with depth, with a sharp increase in the benthic boundary layer, where potential remineralization rates of phosphorus up to 1.03 μg P·dm−3d−1 and bacterial carbon production of 0.078 μg C·dm−3 d−1 were recorded. Those rates were close to the surface ones; the bacterial growth efficiency was also around 20%, similar to the surface value, sustaining the microbial food chain at the bottom. The daily hydrolysis of the organic carbon pool estimated by EEA varied from 0.67% (Ionian Surface Water) to 0.02% (Deep Water). Alkaline phosphatase activity was generally low in the intermediate and deep layers, in relation to the higher inorganic P content. The last facts support the hypothesis that deep waters of Ionian Sea, and in general of the entire Mediterranean basin, because of their young age, carry a larger amount of labile dissolved organic carbon, which reduces the need for a high recycling activity by bacterial community. As a matter of fact, a relatively higher activity per cell in carbon production rates was found in the deep layer where a large volume of the very recently formed Cretan Sea Outflow Water was present.
Association of C-reactive protein with future development of diabetes: a population-based 7-year cohort study among Norwegian adults aged 30 and older in the Tromsø Study 2007–2016

Objectives The extent to which observed associations between high-sensitivity C-reactive protein (hs-CRP) and incident diabetes are explained by obesity and hypertension remains unclear. This study aimed to investigate the association of hs-CRP with developing diabetes in a Norwegian general population sample. Design A cohort study using two population-based surveys of the Tromsø Study: the sixth survey Tromsø6 (2007–2008) as baseline and the seventh survey Tromsø7 (2015–2016) at follow-up. Setting Tromsø municipality of Norway, a country with increasing proportion of older adults and a high prevalence of overweight, obesity and hypertension. Participants 8067 women and men without diabetes, aged 30–87 years, at baseline Tromsø6 who subsequently also participated in Tromsø7. Outcome measures Diabetes defined by self-reported diabetes, diabetes medication use and/or HbA1c≥6.5% (≥48 mmol/mol) was modelled by logistic regression for the association with baseline hs-CRP, either stratified into three quantiles or as continuous variable, adjusted for demographic factors, behavioural and cardiovascular risk factors, lipid-lowering medication use, and hypertension. Interactions by sex, body mass index (BMI), hypertension or abdominal obesity were assessed by adding interaction terms in the fully adjusted model. Results There were 320 (4.0%) diabetes cases after 7 years. After multivariable adjustment including obesity and hypertension, individuals in the highest hs-CRP tertile 3 had 73% higher odds of developing diabetes (OR 1.73; p=0.004; 95% CI 1.20 to 2.49) when compared with the lowest tertile or 28% higher odds of incidence per one-log of hs-CRP increment (OR 1.28; p=0.003; 95% CI 1.09 to 1.50). There was no evidence for interaction between hs-CRP and sex, hypertension, BMI or abdominal obesity. Conclusions Raised hs-CRP was associated with future diabetes development in a Norwegian adult population sample. The CRP-diabetes association could not be fully explained by obesity or hypertension.
A Deep Learning Method to Translate Phonocardiogram (PCG) Signals to Electrocardiogram (ECG) Waveforms

In clinical practice, an electrocardiogram (ECG), which analyzes the electrical activity of the human heart, is a common cardiovascular diagnostic test. The ECG data can be stored and diagnosed effectively. However, precise positioning of the ECG electrodes is necessary for a reliable ECG tracing. This has an impact on how ECG monitors are used to check on patients at home. On the other hand, the Phonocardiogram (PCG) is a standard method for quantifying the properties of heart sounds in order to monitor and assess cardiac activity. With the advancement of wearable sensors, it is now simpler to record the heart's sound. We proposed a deep learning approach to translating PCG signals to ECG waveforms. This technique can be utilized to monitor cardiovascular disease in consumer wearable device development. The proposed method is evaluated on a benchmark dataset of normal and anormal cardiac data. Experimental results show that our proposed approach is reasonable and effective. The generated ECG outputs can achieve a high accuracy, and the root mean squared error (RMSE) is 0.053.
Towards a Lifelong Mapping Approach Using Lanelet 2 for Autonomous Open-Pit Mine Operations

Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine.
Chemical composition of single aerosol particles at Idaho Hill: Positive ion measurements

The chemical compositions of single aerosol particles larger than 0.3 μm optical diameter were measured at Idaho Hill using a laser ionization mass spectrometer. The mass spectrometer can analyze either positive or negative ions; this paper covers the positive ion results. Potassium, iron, and organics were among the most common positive ions in the spectra of particles at Idaho Hill. Organic ions were the largest contributor to the ion current from accumulation mode particles and were also significant in larger particles. Iron was present mostly in coarse mode particles. Potassium was bimodal, with large amounts of K in coarse mode particles and large amounts in particles near 0.3 μm in west winds but not in east winds. Boron was observed in a number of particles. A wide variety of metals were also observed including lead, tin, and vanadium. Lead was less common in polluted air. Silver, copper, zinc, nickel, molybdenum, rhenium, and thallium were also observed in a few particles. Ammonium ions and cluster ions were more abundant in upslope conditions. Amines seem to be evident in many of the spectra, especially in east winds. Elemental carbon was observed in 2 to 3% of the particles. Almost all of the particles contained some nonvolatile material.