Miracle-dz/UniFAR-FTUs huggingface dataset:
A combinatorial allocation mechanism with penalties for banner advertising

Most current banner advertising is sold through negotiation thereby incurring large transaction costs and possibly suboptimal allocations. We propose a new automated system for selling banner advertising. In this system, each advertiser specifies a collection of host webpages which are relevant to his product, a desired total quantity of impressions on these pages, and a maximum per-impression price. The system selects a subset of advertisers as 'winners' and maps each winner to a set of impressions on pages within his desired collection. The distinguishing feature of our system as opposed to current combinatorial allocation mechanisms is that, mimicking the current negotiation system, we guarantee that winners receive at least as many advertising opportunities as they requested or else receive ample compensation in the form of a monetary payment by the host. Such guarantees are essential in markets like banner advertising where a major goal of the advertising campaign is developing brand recognition. As we show, the problem of selecting a feasible subset of advertisers with maximum total value is inapproximable. We thus present two greedy heuristics and discuss theoretical techniques to measure their performances. Our first algorithm iteratively selects advertisers and corresponding sets of impressions which contribute maximum marginal per-impression profit to the current solution. We prove a bi-criteria approximation for this algorithm, showing that it generates approximately as much value as the optimum algorithm on a slightly harder problem. However, this algorithm might perform poorly on instances in which the value of the optimum solution is quite large, a clearly undesirable failure mode. Hence, we present an adaptive greedy algorithm which again iteratively selects advertisers with maximum marginal per-impression profit, but additionally reassigns impressions at each iteration. For this algorithm, we prove a structural approximation result, a newly defined framework for evaluating heuristics [10]. We thereby prove that this algorithm has a better performance guarantee than the simple greedy algorithm.
Cardiovascular Risk in Type 2 Diabetes Is Associated With Variation at the PPARG Locus: A Go-DARTS Study

Objective—The Pro12Ala polymorphism of PPARG modulates risk of developing type 2 diabetes. The Ala allele has also been associated with a reduced risk of cardiovascular events. We have shown previously that the linked T allele of the C1431T polymorphism influences Ala12-associated diabetes risk and that the 2 polymorphisms have opposing associations with body weight. We therefore investigated the association of these 2 variants with cardiovascular events in people with type 2 diabetes. Methods and Results—We performed a cohort study of 2016 individuals and used Cox proportional hazards to analyze risk of myocardial infarction or death by PPARG Pro12Ala and C1431T genotypes, adjusting for age, sex, and smoking status. In individuals enrolled <70 years of age, the hazard for a first nonfatal event associated with the Ala12 allele was 0.21 (CI, 0.06 to 0.69; P=0.01) and the T1431 allele 9.9 (CI, 1.90 to 51.29; P=0.007). These opposing associations remained significant after correction for other conventional risk factors. The T1431 allele was also associated with all-cause mortality. Conclusions—This study confirms the association of the Ala12 allele with reduced risk of myocardial infarction in a type 2 diabetic population and demonstrates that the T allele independently associates with an increased risk.
Generating and exploring S-box multivariate quadratic equation systems with SageMath

A new method to derive Multivariate Quadratic equation systems (MQ) for the input and output bit variables of a cryptographic S-box from its algebraic expressions with the aid of the computer mathematics software system SageMath is presented. We consolidate the deficiency of previously presented MQ metrics, supposed to quantify the resistance of S-boxes against algebraic attacks.
BeWell+: multi-dimensional wellbeing monitoring with community-guided user feedback and energy optimization

Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing applications capable of automatically monitoring multiple aspects of physical and mental health. In this paper, we present BeWell+ the next generation of the BeWell smartphone health app, which continuously monitors user behavior along three distinct health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone's wallpaper. With BeWell+, we introduce new wellbeing mechanisms to address challenges identified during the initial deployment of the BeWell app; specifically, (i) community adaptive wellbeing feedback, which automatically generalize to diverse user communities (e.g., elderly, young adults, children) by balancing the need to promote better behavior yet remains realistic to the user's goals; and, (ii) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions of wellbeing (e.g., social interaction) where the user needs most help. We evaluate the performance of these mechanisms as part of an initial deployment and user study that includes 27 people using BeWell+ over a 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer-grade smartphones, but users understand feedback and respond by taking positive steps towards leading healthier lifestyles.
Automatic Embedded Multicore Generation and Evaluation Methodology: a Case Study of a NOC Based 2400-cores on Very Large Scale Emulator

Future generation embedded multicore will be based on hundreds of processors connected through Network on Chip (NOC) . Design productivity of embedded multicore is a major challenge for the semiconductor industry. In this paper, an automatic very large scale NoC design methodology based on FPGA IP is proposed to accelerate the embedded multicore design productivity using very large scale multi-FPGA platform emulation. The large scale multiprocessor is divided into pieces of FPGA IP. Local NoC is designed for each FPGA IP. Then a top level NoC connects all the local NoCs together to form the large scale multiprocessor. In this paper we extend our previous work to reach 2400-cores NoC based multiprocessor which is generated using this methodology within 3 days.
Find You from Who You Mentioned @: Home Location Inference Based on Social Ties in Social Media

Location of user plays an increasingly critical role in applications in social media. However, its utility is limited by missing of location information for the privacy and security concerns. This paper presents a novel approach to infer user’s home location based on social ties. We observe that users interact with friends closer and hypothesis that social interactions is competent to infer locations. We define similarity between users, leverage a Gaussian mixture model based on users’ social ties. The experimental results on a large scale dataset in Tencent microblog demonstrate that the social-tie-based-algorithm outperforms the state-of-the-art approaches in accuracy.
Recognizing activities of daily living from patterns and extraction of web knowledge

The ability to infer and anticipate the activities of elderly individuals with cognitive impairment has made it possible to provide timely assistance and support, which in turn allows them to lead an independent life. Traditional non-intrusive activity recognition approaches are dependent on the use of various machine learning techniques to infer activities given the collected object usage data. Current activity recognition approaches are also based on knowledge driven techniques that require extensive modelling of the activities that needs to be inferred. These models can be seen as too restrictive, prescriptive and static as they are based on a finite set of activities. In this paper, we propose a novel "top down" approach to recognising activities based on object usage data, which detects patterns associated with the activity-object relationship and utilizes web knowledge in order to build dynamic activity models based on the objects used to perform the activity. Experimental results using the Kasteren dataset shows it is comparable to existing approaches.
Programming Language Trends in Open Source Development: An Evaluation Using Data from All Production Phase SourceForge Projects

In this work, we analyze data collected from the CVS repositories of 9,997 Open Source projects hosted on SourceForge in an eﬀort to understand trends in programming language usage in the Open Source community between 2000 and 2005. The trends we consider include: 1) the relative popularity of the ten most popular programming languages over time, 2) the use of multiple programming languages by individual programmers and by individual projects, and 3) the programming languages most often used in combination
High Throughput Genetic Analysis of Congenital Myasthenic Syndromes Using Resequencing Microarrays

Background The use of resequencing microarrays for screening multiple, candidate disease loci is a promising alternative to conventional capillary sequencing. We describe the performance of a custom resequencing microarray for mutational analysis of Congenital Myasthenic Syndromes (CMSs), a group of disorders in which the normal process of neuromuscular transmission is impaired. Methodology/Principal Findings Our microarray was designed to assay the exons and flanking intronic regions of 8 genes linked to CMSs. A total of 31 microarrays were hybridized with genomic DNA from either individuals with known CMS mutations or from healthy controls. We estimated an overall microarray call rate of 93.61%, and we found the percentage agreement between the microarray and capillary sequencing techniques to be 99.95%. In addition, our microarray exhibited 100% specificity and 99.99% reproducibility. Finally, the microarray detected 22 out of the 23 known missense mutations, but it failed to detect all 7 known insertion and deletion (indels) mutations, indicating an overall sensitivity of 73.33% and a sensitivity with respect to missense mutations of 95.65%. Conclusions/Significance Overall, our microarray prototype exhibited strong performance and proved highly efficient for screening genes associated with CMSs. Until indels can be efficiently assayed with this technology, however, we recommend using resequencing microarrays for screening CMS mutations after common indels have been first assayed by capillary sequencing.
Learning from Worked-Out-Examples: A Study on Individual Differences

The goal of this study was to Investigate individual differences in learning from worked-out examples with respect to the quality of self-explanations. Restrictions of former studies (e.g., lacking control of time-on-task) were avoided and additional research questions (e.g., reliability and dimensionality of self-explanation characteristics) were addressed. An investigation with 36 university freshmen students of education working in individual sessions was conducted. The domain was probability calculation. Prior knowledge and the quality of self-explanations (protocols of the individuals' thinking aloud) were assessed as predictors of learning. A post-test was employed to measure the learning gains as the dependent variable. The following main results were obtained. Most self-explanation characteristics could be regarded as relatively stable person characteristics. The individual differences in the quality of self-explanations were, however, found to be multidimensional. Most Important, even when controlling for time-on-task (quantitative aspect), learning gains could be substantially predicted by qualitative differences of self-explanation characteristics. In particular, successful learners tended to employ more prlnciplebased explanations, more explication of operator-goal combinations, and more anticipative reasoning. In addition, there were two types of effective learners, labeled anticipative reasoners and principle-based explainers.
Physiological Study of Lactobacillus delbrueckii subsp. bulgaricus Strains in a Novel Chemically Defined Medium

ABSTRACT We developed a chemically defined medium called milieu proche du lait (MPL), in which 22 Lactobacillus delbrueckii subsp.bulgaricus (L. bulgaricus) strains exhibited growth rates ranging from 0.55 to 1 h−1. MPL can also be used for cultivation of other lactobacilli and Streptococcus thermophilus. The growth characteristics of L. bulgaricus in MPL containing different carbon sources were determined, including an initial characterization of the phosphotransferase system transporters involved. For the 22 tested strains, growth on lactose was faster than on glucose, mannose, and fructose. Lactose concentrations below 0.4% were limiting for growth. We isolated 2-deoxyglucose-resistant mutants from strains CNRZ397 and ATCC 11842. CNRZ397-derived mutants were all deficient for glucose, fructose, and mannose utilization, indicating that these three sugars are probably transported via a unique mannose-specific-enzyme-II-like transporter. In contrast, mutants of ATCC 11842 exhibited diverse phenotypes, suggesting that multiple transporters may exist in that strain. We also developed a protein labeling method and verified that exopolysaccharide production and phage infection can occur in MPL. The MPL medium should thus be useful in conducting physiological studies ofL. bulgaricus and other lactic acid bacteria under well controlled nutritional conditions.
Killing Them with Kindness? In-Hive Medications May Inhibit Xenobiotic Efflux Transporters and Endanger Honey Bees

Background Honey bees (Apis mellifera) have recently experienced higher than normal overwintering colony losses. Many factors have been evoked to explain the losses, among which are the presence of residues of pesticides and veterinary products in hives. Multiple residues are present at the same time, though most often in low concentrations so that no single product has yet been associated with losses. Involvement of a combination of residues to losses may however not be excluded. To understand the impact of an exposure to combined residues on honey bees, we propose a mechanism-based strategy, focusing here on Multi-Drug Resistance (MDR) transporters as mediators of those interactions. Methodology/Principal Findings Using whole-animal bioassays, we demonstrate through inhibition by verapamil that the widely used organophosphate and pyrethroid acaricides coumaphos and τ-fluvalinate, and three neonicotinoid insecticides: imidacloprid, acetamiprid and thiacloprid are substrates of one or more MDR transporters. Among the candidate inhibitors of honey bee MDR transporters is the in-hive antibiotic oxytetracycline. Bees prefed oxytetracycline were significantly sensitized to the acaricides coumaphos and τ-fluvalinate, suggesting that the antibiotic may interfere with the normal excretion or metabolism of these pesticides. Conclusions/Significance Many bee hives receive regular treatments of oxytetracycline and acaricides for prevention and treatment of disease and parasites. Our results suggest that seasonal co-application of these medicines to bee hives could increase the adverse effects of these and perhaps other pesticides. Our results also demonstrate the utility of a mechanism-based strategy. By identifying pesticides and apicultural medicines that are substrates and inhibitors of xenobiotic transporters we prioritize the testing of those chemical combinations most likely to result in adverse interactions.
Consumer Targeting in Residential Demand Response Programmes

Demand response refers to a family of techniques that are available to electricity suppliers to aid with balancing supply and demand, typically by calling on consumers of electricity to reduce consumption during periods of high demand. In this paper we propose a novel approach to residential demand response, in which incentives are targeted at the subset of consumers who are both relevant (likely to use shiftable appliances, such as washing machines and dishwashers during peak hours) and willing to reduce (likely to react positively to a reduction request from their electricity supplier). To this end, we present a mixed integer programming solution that finds the optimal subset of consumers to target with incentives. We show that our solution is capable of significantly reducing supplier costs and smoothing peaks in electricity demand by targeting only a subset of the consumer pool.
Mayotte coral reveals hydrological changes in the western Indian Ocean between 1881 and 1994

We reconstruct the hydrologic history of the tropical western Indian Ocean by calculating the δ18Oseawater from coupled coral Sr/Ca and δ18O measurements in a massive Porites coral from Mayotte (Comoros) between 1881 and 1994. We found that the precipitation‐evaporation balance varies naturally on time scales of 5–6 years and 18–25 years. High (low) SSTs are associated with positive (negative) δ18Oseawater implying that atmospheric variability is linked with remote climate modes in the Indian Ocean and the tropical/extratropical Pacific Ocean. Warm El Niño‐Southern Oscillation events are associated with a negative freshwater balance at Mayotte. This case study demonstrates that a much denser network of δ18Oseawater reconstructions is crucial for understanding the spatial patterns of hydrological conditions.
Thermal-aware dynamic voltage frequency scaling for many-core processors under process variations

Process variations yield the asymmetry on core performance in many-core processors. Adaptive voltage scaling can hide the variations, but that results in the different thermal characteristics of cores. By using the thermal characteristics, the efficiency of energy optimization and temperature management can be improved. Experiments showed that the proposed dynamic voltage frequency scaling consumes up to 25.2% less energy than the existing thermal management technique while remaining the ratio of peak temperature violations under 1%.
Criticality of Domains in Ferromagnets described by the Landau Functional

The distribution of magnetic moments in finite ferromagnetic bodies was first investigated by Landau an Lifshitz in a famous paper, where they obtained the domain structure of a ferromagnetic crystal at low temperatures, in the regime of saturated magnetization. We propose a generalization of the above approach, using from the start the Landau theory of phase transitions, where the free energy of the system is written as a functional with the magnetic permeability as the order-parameter density. We obtain more general field solutions, allowing nonhomogeneous regimes, which are used to describe the ferromagnetic phase transition at Tc. We find that domain sizes and walls are enlarged when the temperature is increased in direction of Tc. Very close to Tc however, the walls tend to suppress the layers, leading to a continuous vanishing of the domain structure. We discuss the validity of the model in the critical region.
Condom utilization and sexual behavior of female sex workers in Northwest Ethiopia: A cross-sectional study

Introduction Sexually transmitted infections are among the most important public health problems in the world. People who indulge in unsafe sex, such as female sex workers are the most at risk population groups due to multiple sexual partners and inconsistent condom use. The aim of this study was to assess condom utilization and sexual behavior of female sex workers in Gondar town, Northwest Ethiopia. Methods A quantitative cross-sectional study triangulated with qualitative method was conducted from March 20 - April 10, 2014 in Gondar town. The quantitative data were collected through interviewing 488 female sex workers while in-depth interview was administered to collect qualitative data from 10 female sex workers. The collected data were entered into EPI-INFO version 3.5. 3 and exported to SPSS version 20.0 software for analysis. Logistic regression analysis was done to determine the association between condom utilization and independent variables. Results This study revealed that less than half (47.7%) of the respondents utilized condom with any type of client. Secondary education or above, perceiving themselves at risk of HIV/AIDS infection, having awareness that sexually transmitted infections could increase HIV infection, being tested for HIV/AIDS in the last 12 months, and having lower number of clients in a month were positively associated with condom utilization. Conclusion This finding depicted that condom utilization was low among female sex workers. Thus, developing and implementing target oriented behavioral change and communication strategies are needed to prevent the risk of acquiring HIV/AIDS and other sexually transmitted infections in female sex workers.
Learning in the Presence of Prior Knowledge : A Case Study Using Model

Computational models of natural systems often contain free parameters that must be set to optimize the predictive accuracy of the models. This process|called calibration|can be viewed as a form of supervised learning in the presence of prior knowledge. In this view, the xed aspects of the model constitute the prior knowledge, and the goal is to learn correct values for the free parameters. We report on a series of attempts to learn parameter values for a global vegetation model called MAPSS (Mapped Atmosphere-Plant-Soil System) developed by our collaborator, Ron Neilson. Unfortunately, attempts to apply standard machine learning methods|speciically global error functions and gradient descent search|do not work with MAPSS, because the constraints introduced by the structure of the model (the prior knowledge) create a very diicult non-linear optimization problem. Successful calibration of MAPSS required taking a divide-and-conquer approach in which subsets of the parameters were calibrated while others were held constant. This approach was made possible by carefully selecting training sets that exercised only portions of the model and by designing error functions for each part that had desirable properties. The automated calibration tool that we have developed is currently being applied to calibrate MAPSS against a global climate data set.
A 0.24-nJ/b Wireless Body-Area-Network Transceiver With Scalable Double-FSK Modulation

An energy-efficient wireless body-area-network (WBAN) transceiver is implemented in 0.18-μm CMOS technology with 1-V supply voltage. For the low energy consumption, the body channel communication (BCC) PHY is utilized with the theoretical results of Maxwell's equation analysis behind the BCC. Based on the channel analysis, the resonance matching (RM) and contact impedance sensing (CIS) techniques are proposed to enhance the quality of the body channel. A double-FSK modulation scheme is adopted with high scalability to fulfill the IEEE 802.15. 6 Task Group specifications. In addition, a low-power double-FSK transceiver is implemented by five circuit techniques: 1) a reconfigurable LNA with CIS; 2) a current-reuse wideband demodulator; 3) a divider-based local oscillator (LO) generation with duty-cycle correction in the receiver; 4) a reconfigurable driver with RM; and 5) a divider-based digital double-FSK modulator in the transmitter. As a result, fully WBAN compatible receiver and transmitter consume 2.4 and 2 mW, respectively, at a data rate of 10 Mb/s, corresponding to energy consumption of 0.24 nJ per received bit and 0.2 nJ per transmitted bit.