Miracle-dz/UniFAR-FTUs huggingface dataset:
A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study

Background A major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. Objective We sought to (1) assess smokers’ compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. Methods An explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. Results Out of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app’s identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. Conclusions User-initiated self-report is feasible for training a cessation app about an individual’s smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.
Tracking objects using a laser scanner in driving situation based on modeling target shape

In this paper, we present a new approach to solve the problem of tracking partially hidden objects by a single layer laser scanner. Taking into consideration the shape of the detected object, we have optimized the clustering and the estimation of results. We propose to adapt the detection according to the predicted pose and the occlusions. We have developed an algorithm to detect the security barriers of the road in some conditions. Finally, we have projected our interpretation in the image of a vision system to provide the area of interest to be analyzed and fused with our results. This method is experimented using a single layer laser scanner of type SICK mounted on a vehicle and validated by the means of video data.
Exploring Gender Dimensions of Treatment Programmes for Neglected Tropical Diseases in Uganda

Background Gender remains a recognized but relatively unexamined aspect of the potential challenges for treatment programmes for Neglected Tropical Diseases (NTDs). We sought to explore the role of gender in access to treatment in the Uganda National Neglected Tropical Disease Control Programme. Methodology/Principal Findings Quantitative and qualitative data was collected in eight villages in Buyende and Kamuli districts, Eastern Uganda. Quantitative data on the number of persons treated by age and gender was identified from treatment registers in each village. Qualitative data was collected through semi-structured interviews with sub-county supervisors, participant observation and from focus group discussions with community leaders, community medicine distributors (CMDs), men, women who were pregnant or breastfeeding at the time of mass-treatment, and adolescent males and females. Findings include the following: (i) treatment registers are often incomplete making it difficult to obtain accurate estimates of the number of persons treated; (ii) males face more barriers to accessing treatment than women due to occupational roles which keep them away from households or villages for long periods, and males may be more distrustful of treatment; (iii) CMDs may be unaware of which medicines are safe for pregnant and breastfeeding women, resulting in women missing beneficial treatments. Conclusions/Significance Findings highlight the need to improve community-level training in drug distribution which should include gender-specific issues and guidelines for treating pregnant and breastfeeding women. Accurate age and sex disaggregated measures of the number of community members who swallow the medicines are also needed to ensure proper monitoring and evaluation of treatment programmes.
Post-deployment tuning of UMTS cellular networks through dual-homing of RNCs

In conventional UMTS cellular networks, during deployment usually a set of NodeBs is assigned to one Radio Network Controller (RNC), and a set of RNCs to one Serving GPRS Support Node (SGSN) for data services, as well as to one Mobile Switching Centre (MSC) for voice services. Operators thus far have considered single-homing of RNCs to MSCs/SGSNs (i.e., many-to-one mapping) with an objective to reduce the total cost over a fixed period of time. However, a single-homing network does not remain cost-effective any more when subscribers later on begin to show specific inter-MSC/SGSN mobility patterns (say, diurnality of office goers) over time. This necessitates post-deployment topological extension of the network in terms of dual-homing of RNCs, in which some specific RNCs are connected to two MSCs/SGSNs via direct links resulting in a more complex many-to-two mapping structure in parts of the network. The partial dual-homing attempts to increase link cost minimally and reduce handoff cost maximally, thereby significantly reducing the total cost in a post-deployment optimal extension. In this paper, we formulate the scenario as a combinatorial optimization problem and solve the NP-Complete problem using two meta-heuristic techniques, namely Simulated Annealing (SA) and Tabu search (TS). We then compare these techniques with a novel optimal heuristic search method that we propose typically to solve the problem. The comparative results reveal that, though all of them perform equally well for small networks, for larger networks, the search-based method is more efficient than meta-heuristic techniques in finding optimal solutions quickly.
NF-TCP: Network Friendly TCP

Delay-insensitive applications such as P2P file sharing, data center backups and software updates generate substantial amounts of traffic. This traffic, transported potentially over multiple TCP connections, competes with traffic from other possibly interactive applications. Today, with TCP, they compete on an equal level for each individual connection. In this paper, we propose a new TCP variant for such delay-insensitive applications, which we call Network Friendly TCP (NF-TCP). NF-TCP is responsive to available bandwidth, seeking to quickly and efficiently utilize the same in a congestion-free situation, while backing-off more aggressively than standard TCP on encountering competing traffic in a congested network. NF-TCP uses a novel combination of utilizing measurement of available bandwidth and ECN-based congestion avoidance techniques to ensure that it is truly friendly to existing TCP connections. We evaluate the performance of NF-TCP through ns-2 simulations and present the initial results enlightening the friendly nature of NF-TCP compared to standard TCP.
Estimation of NBA players' offense/defense ratings through shrinkage estimation

The standard plus/minus model for rating NBA players combines the offensive and defensive capabilities of each player into a single metric. While this is convenient for the sake of summary, it makes it difficult to isolate the particular contributions that a player makes to either effort. Although adjusted plus/minus and other methods are proposed to address this, given a relatively large number of players against the number of events observed in one season, estimates are subjected to high variance. To correct for this, we construct a penalized regression model that identifies the specific offensive and defensive contributions of each player on each possession, and tune the model using L2-regularization method to optimize its predictive power. It overcomes the limitations of simple and adjusted plus/minus by incorporating offensive and defensive effects separately, and the shrinkage term controls for high variance of the estimates. Furthermore, our model captures net home court advantage on offense, and estimate players’ contributions in offense and defense. Finally, we demonstrate application of our method by simulating unseen matches to correctly predict their outcomes.
CFN-based Semantic Role Labeling of Chinese Prepositional Phrase ⋆

Prepositional Phrases are often among the most frequent expressions in Chinese, but they have been ignored on the grounds of being syntactically promiscuous and semantically vacuous, and relegated to the ignominious rank of “stop word”. The Chinese FrameNet (CFN) is a lexical resource project developed by Shanxi University, Taiyuan, based on the principles of Frame Semantics and supported by corpus evidence. The CFN database contains more than 32,000 sentences annotated with semantic roles. So this paper uses CFN as the semantic role resources and researches on the Chinese PPs’ FEs’ identification, combining statistic and rule-based methods. Finally it gets a satisfied result in the experiment.
Comparative Analyses by Sequencing of Transcriptomes during Skeletal Muscle Development between Pig Breeds Differing in Muscle Growth Rate and Fatness

Understanding the dynamics of muscle transcriptome during development and between breeds differing in muscle growth is necessary to uncover the complex mechanism underlying muscle development. Herein, we present the first transcriptome-wide longissimus dorsi muscle development research concerning Lantang (LT, obese) and Landrace (LR, lean) pig breeds during 10 time-points from 35 days-post-coitus (dpc) to 180 days-post-natum (dpn) using Solexa/Illumina's Genome Analyzer. The data demonstrated that myogenesis was almost completed before 77 dpc, but the muscle phenotypes were still changed from 77 dpc to 28 dpn. Comparative analysis of the two breeds suggested that myogenesis started earlier but progressed more slowly in LT than in LR, the stages ranging from 49 dpc to 77 dpc are critical for formation of different muscle phenotypes. 595 differentially expressed myogenesis genes were identified, and their roles in myogenesis were discussed. Furthermore, GSK3B, IKBKB, ACVR1, ITGA and STMN1 might contribute to later myogenesis and more muscle fibers in LR than LT. Some myogenesis inhibitors (ID1, ID2, CABIN1, MSTN, SMAD4, CTNNA1, NOTCH2, GPC3 and HMOX1) were higher expressed in LT than in LR, which might contribute to more slow muscle differentiation in LT than in LR. We also identified several genes which might contribute to intramuscular adipose differentiation. Most important, we further proposed a novel model in which MyoD and MEF2A controls the balance between intramuscular adipogenesis and myogenesis by regulating CEBP family; Myf5 and MEF2C are essential during the whole myogenesis process while MEF2D affects muscle growth and maturation. The MRFs and MEF2 families are also critical for the phenotypic differences between the two pig breeds. Overall, this study contributes to elucidating the mechanism underlying muscle development, which could provide valuable information for pig meat quality improvement. The raw data have been submitted to Gene Expression Omnibus (GEO) under series GSE25406.
CONSTRAINTS ON THE FAINT END OF THE QUASAR LUMINOSITY FUNCTION AT z ∼ 5 IN THE COSMOS FIELD

We present the result of our low-luminosity quasar survey in the redshift range of 4.5 ≲ z ≲ 5.5 in the COSMOS field. Using the COSMOS photometric catalog, we selected 15 quasar candidates with 22 < i′ < 24 at z ∼ 5 that are ∼3 mag fainter than the Sloan Digital Sky Survey quasars in the same redshift range. We obtained optical spectra for 14 of the 15 candidates using FOCAS on the Subaru Telescope and did not identify any low-luminosity type-1 quasars at z ∼ 5, while a low-luminosity type-2 quasar at z ∼ 5.07 was discovered. In order to constrain the faint end of the quasar luminosity function at z ∼ 5, we calculated the 1σ confidence upper limits of the space density of type-1 quasars. As a result, the 1σ confidence upper limits on the quasar space density are Φ < 1.33 × 10−7 Mpc−3 mag−1 for −24.52 < M1450 < −23.52 and Φ < 2.88 × 10−7 Mpc−3 mag−1 for −23.52 < M1450 < −22.52. The inferred 1σ confidence upper limits of the space density are then used to provide constraints on the faint-end slope and the break absolute magnitude of the quasar luminosity function at z ∼ 5. We find that the quasar space density decreases gradually as a function of redshift at low luminosity (M1450 ∼ −23), being similar to the trend found for quasars with high luminosity (M1450 < −26). This result is consistent with the so-called downsizing evolution of quasars seen at lower redshifts.
Efficacy and Pharmacologic Data of Second-Generation Tyrosine Kinase Inhibitor Nilotinib in BCR-ABL-Positive Leukemia Patients with Central Nervous System Relapse after Allogeneic Stem Cell Transplantation

Central nervous system (CNS) involvement is a severe complication of BCR-ABL-positive leukemia after allogenic stem cell transplantation (alloSCT) associated with fatal outcome. Although second-generation tyrosine-kinase inhibitors (TKI) such as nilotinib have shown activity in systemic BCR-ABL+ disease, little data exists on their penetration and efficacy within the CNS. Four patients (3 male, 1 female; age 15–49) with meningeal relapse after alloSCT and subsequent treatment with nilotinib were identified. A total of 17 cerebrospinal fluid (csf) and serum samples were assessed for nilotinib concentration and patient outcome was recorded. Nilotinib concentrations showed a low median csf/plasma ratio of 0.53% (range 0.23–1.5%), yet pronounced clinical efficacy was observed with long-lasting responses (>1 year) in three patients. Comparison with historical data showed a trend towards superior efficacy of nilotinib versus imatinib. Despite poor csf penetration, nilotinib showed significant clinical activity in CNS relapse of BCR-ABL+ leukemias. As nilotinib has a high protein-binding affinity, the low-protein concentration in csf could translate into a relatively higher amount of free and therefore active nilotinib in csf as compared to blood, possibly explaining the observed efficacy. Thus, treatment with a 2nd generation TKI warrants further investigation and should be considered in cases of CNS relapse of BCR-ABL-positive leukemia after alloSCT.
PROBABILISTIC APPROACHES : SCENARIO ANALYSIS , DECISION TREES AND SIMULATIONS

In the last chapter, we examined ways in which we can adjust the value of a risky asset for its risk. Notwithstanding their popularity, all of the approaches share a common theme. The riskiness of an asset is encapsulated in one number – a higher discount rate, lower cash flows or a discount to the value – and the computation almost always requires us to make assumptions (often unrealistic) about the nature of risk. In this chapter, we consider a different and potentially more informative way of assessing and presenting the risk in an investment. Rather than compute an expected value for an asset that that tries to reflect the different possible outcomes, we could provide information on what the value of the asset will be under each outcome or at least a subset of outcomes. We will begin this section by looking at the simplest version which is an analysis of an asset's value under three scenarios – a best case, most likely case and worse case – and then extend the discussion to look at scenario analysis more generally. We will move on to examine the use of decision trees, a more complete approach to dealing with discrete risk. We will close the chapter by evaluating Monte Carlo simulations, the most complete approach of assessing risk across the spectrum. Scenario Analysis The expected cash flows that we use to value risky assets can be estimated in one or two ways. They can represent a probability-weighted average of cash flows under all possible scenarios or they can be the cash flows under the most likely scenario. While the former is the more precise measure, it is seldom used simply because it requires far more information to compile. In both cases, there are other scenarios where the cash flows will be different from expectations; higher than expected in some and lower than expected in others. In scenario analysis, we estimate expected cash flows and asset value under various scenarios, with the intent of getting a better sense of the effect of risk on value. In this section, we first consider an extreme version of scenario analysis where we consider the value in the best and the worst case scenarios, and then a more generalized version of scenario analysis.
Experimental Applications of Hierarchical Mapping Services in Wireless Sensor Networks

The construction of accurate and efficient visualisations of sense data gathered from wireless sensor networks is an open problem. This paper presents a novel solution in which groups of network nodes cooperate to produce local maps which are cached and merged at a sink node, producing a low- resolution map of the global network. The sink node receives periodic map updates from each cluster-head used to refine an up-to-date global map. The global map gives a low cost interface used to target queries for generating detailed maps from a subset of the sensors in the network. Different map resolutions are adequate for different management applications to perform at a desired level. In an energy constrained network, it is preferable to allow the application to control the level of detail and topology coverage of a generated map. This work gives a complete system solution that solves global network data mapping problems through localized and distributed computation. This is achieved by abstracting and simplifying the routing and interpolation services of the network and turning them into services of the network rather than being the result of coordinating the services of individual nodes. This system changes the mechanism of use of certain capabilities in wireless sensor network; the interpolation service is used by the sensing nodes to deal with mapping data and a cluster-based routing algorithm in which the cluster heads are selected as the caches for local maps. This technique performs favourably against similar techniques which do not exploit such services of the network to generate maps.
Event Detection via Communication Pattern Analysis

Social media applications such as Twitter provide a powerful medium through which users can communicate their observations with friends and with the world at large. We have witnessed live reporting of many events, from soccer games in Johannesburg to revolutions in Cairo and Tunis, and these reports have in many ways rivaled the content provided by the official media. Tapping into this valuable resource is a challenge, due to the heterogeneity and noise inherent in realtime text, diversity of languages, and fast-evolving linguistic norms. In this paper we seek to analyze a tweet stream to automatically discover points in time when an important event happens, and to classify such events based on the type of the sentiments they evoke, using only non-textual features of the tweeting pattern. This results not only in a robust way of analyzing tweet streams independent of the languages used; it also provides insights about how users behave on social media websites. For example, we observe that users often react to an exciting external event by decreasing the volume of communication with other users. We explain this effect through a model of how users switch between producing information or sentiments and sharing others’ news or sentiments. We develop and evaluate our models and algorithms using several Twitter data sets, focusing in particular on the tweets sent during the soccer World Cup of 2010. This data set has the feature that the underlying ground truth is welldefined and known whereby goals serve as events.
Getting the Agenda Right: Measuring Media Agenda using Topic Models

Agenda setting is the theory of how issue salience is transferred from the media to media audience. An agenda-setting study requires one to define a set of issues and to measure their salience. We propose a semi-supervised approach based on topic modeling for exploring a news corpus and measuring the media agenda by tagging news articles with issues. The approach relies on an off-the-shelf Latent Dirichlet Allocation topic model, manual labeling of topics, and topic model customization. In preliminary evaluation, the tagger achieves a micro F1-score of 0.85 and outperforms the supervised baselines, suggesting that it could be successfully used for agenda-setting studies.
Nonlinear PI controller for interior permanent magnet synchronous motor drive

This paper develops a high performance PI based controller for interior permanent magnet synchronous motor (IPMSM) drive. The performance indices which have been utilized as objective function are zero steady-state error, minimum speed deviation, and minimum settling time of the IPMSM drive. In this paper, a new method is used for tuning the PI controller. The optimal behavior of drive can be achieved by considering two control strategies: maximum torque per ampere (MTPA) control strategy in constant torque region and flux-weakening (FW) control strategy in constant power region. The nonlinear behavior of the IPMSM drives makes it difficult to tune the coefficients of the controller. This paper deals with tuning of the coefficients of the controller. In this paper a proportional-integral controller is utilized which is very simple applicable with high performance. Whereas, using the proposed tuning method, the problems that arise from conventional PI-controller, especially in nonlinear system, have been omitted.
A Bayesian network based monitoring system for sow management

Techniques for detecting deviation from expected production level has long been known and applied outside agricultural production Even though the need for mon itoring within agriculture seem obvious only few examples exists With recent methodological improvements this may be changed As an example the present pa per describes a prototype of a system for monitoring pregnancy rate in sow herds The system is based on the Bayesian network methodology This approach leads to a monitoring systems that utilises information from matings heat detections preg nancy tests and farrowings to present updated estimates of pregnancy rates in the herd as well as probability of a change point in the level In addition the system can be used to study the e ect of number of matings on utilisation of farrowing de partment Finally when information from heat detections and pregnancy rates are included the e ect of cullings on production level can be studied The underlying model is described in detail and a simulated scenario is used for illustrating the potential of the system