Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Topic Models and Latent Factors for Recommendation

The research of personalized recommendation techniques today has mostly parted into two mainstream directions, i.e., the factorization-based approaches and topic models. Practically, they aim to benefit from the numerical ratings and textual reviews, correspondingly, which compose two major information sources in various real-world systems. However, although the two approaches are supposed to be correlated for their same goal of accurate recommendation, there still lacks a clear theoretical understanding of how their objective functions can be mathematically bridged to leverage the numerical ratings and textual reviews collectively, and why such a bridge is intuitively reasonable to match up their learning procedures for the rating prediction and top-N recommendation tasks, respectively. In this work, we exposit with mathematical analysis that, the vector-level randomization functions to coordinate the optimization objectives of factorizational and topic models unfortunately do not exist at all, although they are usually pre-assumed and intuitively designed in the literature. Fortunately, we also point out that one can avoid the seeking of such a randomization function by optimizing a Joint Factorizational Topic (JFT) model directly. We apply our JFT model to restaurant recommendation, and study its performance in both normal and cross-city recommendation scenarios, where the latter is an extremely difficult task for its inherent cold-start nature. Experimental results on real-world datasets verified the appealing performance of our approach against previous methods, on both rating prediction and top-N recommendation tasks.
Thermoregulatory effects of melatonin in relation to sleepiness

Thermoregulatory processes have long been implicated in the initiation of human sleep. In this paper, we review our own studies conducted over the last decade showing a crucial role for melatonin as a mediator between the thermoregulatory and arousal system in humans. Distal heat loss, via increased skin temperature, seems to be intimately coupled with increased sleepiness and sleep induction. Exogenous melatonin administration during the day when melatonin is essentially absent mimics the endogenous thermophysiological processes occurring in the evening and induces sleepiness. Using a cold thermic challenge test, it was shown that melatonin‐induced sleepiness occurs in parallel with reduction in the thermoregulatory set‐point (threshold); thus, melatonin may act as a circadian modulator of the thermoregulatory set‐point. In addition, an orthostatic challenge can partially block the melatonin‐induced effects, suggesting an important role of the sympathetic nervous system as a link between the thermoregulatory and arousal systems. A topographical analysis of finger skin temperature with infrared thermometry revealed that the most distal parts of the fingers, i.e., fingertips, represent the important skin regions for heat loss regulation, most probably via opening the arteriovenous anastomoses, and this is clearly potentiated by melatonin. Taken together, melatonin is involved in the fine‐tuning of vascular tone in selective vascular beds, as circulating melatonin levels rise and fall throughout the night. Besides the role of melatonin as “nature's soporific”, it can also serve as nature's nocturnal vascular modulator.
Rethinking Relational Autonomy

John Christman has argued that constitutively relational accounts of autonomy, as defended by some feminist theorists, are problematically perfectionist about the human good. I argue that autonomy is constitutively relational, but not in a way that implies perfectionism: autonomy depends on a dialogical disposition to hold oneself answerable to external, critical perspectives on one's action-guiding commitments. This type of relationality carries no substantive value commitments, yet it does answer to core feminist concerns about autonomy.
On the Nature and Change of an Inclusive Elementary School

Qualitative research methods were used to conduct an in-depth study of an inclusive elementary school with the intent of characterizing its context and practices. It was reasoned that such detail would provide valuable insights for those involved in systems change efforts. Data were gathered over a period of 30 months and included observational fieldnotes, interviews, shared anecdotes from teaching staff, and an analysis of school publications and products. Multiple methods of triangulation were employed to validate the data collection process and our findings. Descriptive themes emerged from the data that characterize the nature and evolution of this school's reform toward a more inclusive context. These themes are discussed and implications for future research are presented.
Re-Ranking Models Based-on Small Training Data for Spoken Language Understanding

The design of practical language applications by means of statistical approaches requires annotated data, which is one of the most critical constraint. This is particularly true for Spoken Dialog Systems since considerably domain-specific conceptual annotation is needed to obtain accurate Language Understanding models. Since data annotation is usually costly, methods to reduce the amount of data are needed. In this paper, we show that better feature representations serve the above purpose and that structure kernels provide the needed improved representation. Given the relatively high computational cost of kernel methods, we apply them to just re-rank the list of hypotheses provided by a fast generative model. Experiments with Support Vector Machines and different kernels on two different dialog corpora show that our re-ranking models can achieve better results than state-of-the-art approaches when small data is available.
Implications of adopting the WHO 2006 Child Growth Standard in the UK: two prospective cohort studies

Background: The WHO 2006 Child Growth Standard is based on data from international optimally nourished breastfed infants from birth to age 5 years. Objective: To assess the potential effect of its use on weight and growth monitoring of UK children. Participants: Full-term members of two population-based UK birth cohorts: the Children in Focus sub-cohort of the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 1335) and the Gateshead Millennium Baby Study (GMS; n = 923). Design: Growth data from birth to 5 years were converted into z-scores relative to the WHO 2006 standard. Results: Compared with the WHO standard, both UK cohorts had higher birth weights (mean z-scores: GMS, 0.17; ALSPAC, 0.34) and ALSPAC had higher birth lengths. After birth, length showed a good fit at all ages. By 2–4 months, both cohorts were similar in weight to the WHO median (mean WHO weight z-score at 4 months: GMS, 0.01; ALSPAC, −0.07), but thereafter the UK cohorts were heavier (mean WHO weight z-score at 12 months: GMS, 0.57; ALSPAC, 0.65). At age 12 months, the risk of being classified as underweight (weight <2nd centile) was considerably lower according to the WHO standard than by the UK 1990 Growth Reference (RR = 0.15, 95% CI = 0.07 to 0.32), and the risk of being classified as obese at 4–5 years (body mass index >98th centile) was slightly increased (RR = 1.35, 95% CI = 1.02 to 1.78). Conclusions: Adoption of the WHO 2006 Growth Charts would set a markedly lower standard of weight gain beyond the age of 4 months for UK infants and could support efforts to avoid future childhood obesity. However, the WHO standard is not representative of size at birth in the UK.
Report on the Dagstuhl Seminar

Over the past few years, techniques for managing, querying, and integrating data on the Web have significantly matured. Well-founded and practical approaches to assess or even guarantee a required degree of quality of the data in these frameworks, however, are still missing. This can be contributed to the lack of welldefined data quality metrics and assessment techniques, and the difficulty of handling information about data quality during data integration and query processing. Data quality problems arise in many settings, such as the integration of business data, in Web mining, data dissemination, and in querying the Web using search engines. Data quality (DQ) addresses various forms of data, including structured and semistructured data, text documents, multimedia, and streaming data. Different forms of metadata describing the quality of data is becoming increasingly important since they provide applications and users with information about the value and reliability of (integrated) data on the Web. The Dagstuhl Seminar “Data Quality on the Web”, organized by Michael Gertz, Tamer Ozsu, Gunter Saake, and Kai-Uwe Sattler, took place between August 31st and September 5th 2003 at Schloss Dagstuhl, Germany. The objective of the seminar was to (1) foster collaboration among researchers that deal with DQ in different areas, (2) assess existing results in managing the quality of data, and (3) establish a framework for future research in the area of DQ. The application contexts considered during the seminar included in particular (Web-based) data integration and information retrieval scenarios, scientific databases, and application domains in the computational sciences and Bioinformatics. In all these areas, data quality plays a crucial role and therefore different, tailored solutions have been developed. Sharing and exchanging this knowledge could result in significant synergy effects.
On the Complexity of Reconfiguration of Clique, Cluster Vertex Deletion, and Dominating Set

A graph problem P is a vertex-subset problem if feasible solutions for P consist of subsets of the vertices of a graph G. The st-connectivity problem for a vertex-subset problem P takes as input two feasible solutions Ss and St, and determines if there is a sequence of reconfiguration steps that can be applied to transform Ss into St, such that each step results in a feasible solution of P of size bounded by k and each step is a vertex addition or deletion. For most NP-complete problems, this problem has been shown to be PSPACE-complete, while for some problems in P, this problem could be either in P or PSPACE-complete. However, knowing the complexity of a decision problem does not directly imply the complexity of its st-connectivity problem. Therefore, it is natural to ask whether we can find a connection between the complexity of a decision problem and its st-connectivity problem when restricted to graph classes. This question motivated us to study the st-connectivity problems Clique Reconfiguration and Dominating Set Reconfiguration, whose decision problems’ complexity for restricted graph classes is extensively studied, to get a better understanding of the boundary between polynomialtime solvable and intractable instances of these reconfiguration problems. Furthermore, we study the Cluster Vertex Deletion Reconfiguration problem, a problem whose decision problem is related to the Clique problem, to find whether there is a connection between the complexity of this problem and the Clique Reconfiguration problem. Following are the main contributions of this thesis. First, we show that the Clique Reconfiguration problem is linear-time solvable for paths, trees, bipartite graphs, chordal graphs, and cographs. Then, we prove that the Cluster Vertex Deletion Reconfiguration problem is linear-time solvable for paths and trees, and that it is NP-hard on bipartite graphs, and PSPACE-complete in general. Finally, we determine that the Dominating Set Reconfiguration problem is linear-time solvable for paths, cographs, trees, and interval graphs. Furthermore, we show that the problem is PSPACE-complete for general graphs, bipartite graphs, and split graphs.
A Study of OFDMA Access Method in Multi-path Fading Channel

In this paper, OFDMA (Orthogonal Frequency Division Multiple Access) access methods based on PF (Proportional Fair) algorithm considering the fairness among MS (Mobile Stations) and the total throughput were studied. Concretely, four types of scheduling methods were proposed. Computer simulations were carried out with four types of proposed methods in order to clarify the performance. As the result of computer simulations, the following things have been clarified: monitoring channel quality with 2-level indicator achieved efficient throughput performance, an MS sequential selection method based on the amount of channel allocation requests was the best performance for the throughput, a sub-carrier sequential selection method based on the instantaneous SINR (Signal to Interference Noise Ratio) was the best performance for the fairness among four types of proposed scheduling methods, and the proposed scheduling method with 2-level channel quality monitoring was efficient in case where fading interval was 10 times or more longer than the average delay-time which is the time interval between a request of sub-channel allocation by an MS and establishment of a UL (Up Link) data transmission.
A surface acoustic wave sensor for the study of membrane-protein/ligand interactions using whole cells

Acoustic biosensors are sensitive not only to mass coupling but also to visco-elastic changes occurring close to the sensor surface. In this work, a surface acoustic wave (SAW) device, operating at 110 MHz and with a penetration depth of ~100 nm, has been applied to monitor the binding between membrane-bound proteins (HLA-A2) and surface-immobilized antibodies (anti-HLA-A2). The sensitivity of amplitude change correlated to the number of HLA-A2/antibody complexes formed on the device surface. This allowed for the calculation of the two-dimensional (2D) kinetics and affinity constant of the HLA-A2/antibody interaction based on real-time acoustic data.
Detecting DNS Tunnels Using Character Frequency Analysis

High-bandwidth covert channels pose significant risks to sensitive and proprietary information inside company networks. Domain Name System (DNS) tunnels provide a means to covertly infiltrate and exfiltrate large amounts of information passed network boundaries. This paper explores the possibility of detecting DNS tunnels by analyzing the unigram, bigram, and trigram character frequencies of domains in DNS queries and responses. It is empirically shown how domains follow Zipf's law in a similar pattern to natural languages, whereas tunneled traffic has more evenly distributed character frequencies. This approach allows tunnels to be detected across multiple domains, whereas previous methods typically concentrate on monitoring point to point systems. Anomalies are quickly discovered when tunneled traffic is compared to the character frequency fingerprint of legitimate domain traffic.
Reconstruction of an all-in-focus image by region-adaptive fusion of limited depth-of-field images

This paper proposes a method to reconstruct an all-in-focus (AIF) image using region-adaptive fusion from a series of limited depth-of-field images, called a focal stack. Based on the analysis of the focus profile, pixels are classified into three regions depending on the number of peaks. Different fusion rules are adaptively used according to the type of the region to construct the fusion decision map. Experimental results show that the proposed method reconstructs the AIF image with less artifacts than existing methods.
Boolean representation based data-adaptive correlation analysis over time series streams

Correlation analysis is a basic problem in the field of data stream mining. Typical approaches add sliding window to data streams to get the recent results, but the window length defined by users is always fixed which is not suitable for the changing stream environment. We propose a Boolean representation based data-adaptive method for correlation analysis among a large number of time series streams. The periodical trends of each stream series to are monitored to choose the most suitable window size and group the series with the same trends together. Instead of adopting complex pair-wise calculation, we can also quickly get the correlation pairs of series at the optimal window sizes. All the processing is realized by simple Boolean operations. Both the theory analysis and the experimental evaluations show that our method has good computation efficiency with high accuracy.
Analysis of a Maximum Likelihood Phase Estimation Method for Airborne Multibaseline SAR Interferometry

It has been shown using simulated data that phase estimation of cross-track multibaseline synthetic aperture radar (SAR) interferometric data was most efficiently achieved through a maximum likelihood (ML) method. In this paper, we apply and assess the ML approach on real data, acquired with an experimental Ka-band multibaseline system. Compared to simulated data, dealing with real data implies that several calibration steps be carried out to ensure that the data fit the model. A processing chain was, therefore, designed, including steps responsible for compensating for imperfections observed in the data, such as beam elevation angle dependent phase errors or phase errors caused by imperfect motion compensation. The performance of the ML phase estimation was evaluated by comparing it to results based on a coarse-to-fine (C2F) algorithm, where information from the shorter baselines was used only to unwrap the phase from the longest available baseline. For this purpose, flat areas with high coherence and homogeneous texture were selected in the acquired data. The results show that with only four looks, the noise level was marginally better with the C2F approach and contained fewer outliers. However, with more looks, the ML method consistently delivered better results: noise variance with the C2F approach was slightly but steadily larger than the variance obtained with ML method.
Layout and Interconnect Optimization for Low-Power and High-Sensitivity Operation of $E$ -Band SiGe HBT Frequency Dividers

A layout and interconnect optimization techniques for low-power and high-sensitivity performance of static frequency dividers in <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-band is reported. The layout optimization provides optimal transistor placement and identifies critical interconnects, which allows to reduce their parasitics. These measures provide high operation frequency and good sensitivity of the divider without a need of investing additional dc current.
Anaesthetists' Non-Technical Skills (ANTS): evaluation of a behavioural marker system.

BACKGROUND Non-technical skills are critical for good anaesthetic practice but are not addressed explicitly in normal training. Realization of the need to train and assess these skills is growing, but these activities must be based on properly developed skills frameworks and validated measurement tools. A prototype behavioural marker system was developed using human factors research techniques. The aim of this study was to conduct an experimental evaluation to establish its basic psychometric properties and usability. METHOD The Anaesthetists' Non-Technical Skills (ANTS) system prototype comprises four skill categories (task management, team working, situation awareness, and decision making) divided into 15 elements, each with example behaviours. To investigate its experimental validity, reliably and usability, 50 consultant anaesthetists were trained to use the ANTS system. They were asked to rate the behaviour of a target anaesthetist using the prototype system in eight videos of simulated anaesthetic scenarios. Data were collected from the ratings forms and an evaluation questionnaire. RESULTS The results showed that the system is complete, and that the skills are observable and can be rated with acceptable levels of agreement and accuracy. The internal consistency of the system appeared sound, and responses regarding usability were very positive. CONCLUSIONS The findings of the evaluation indicated that the ANTS system has a satisfactory level of validity, reliability and usability in an experimental setting, provided users receive adequate training. It is now ready to be tested in real training environments, so that full guidelines can be developed for its integration into the anaesthetic curriculum.
Lack of Association between the GPR3 Gene and the Risk for Alzheimer's Disease

Alzheimer's disease is the most frequent form of dementia and its incidence is rapidly increasing. Genetic factors are important determinants of the individual susceptibility to the disease and many efforts have been made to identify loci and markers involved. Recent finding describes the GPR3 gene as a modulator of β-amyloid production, suggesting that perturbation of its activity and function may contribute to the pathogenesis of AD. Furthermore, the gene is located at chromosome 1, in a region proposed as a susceptibility locus for the disease. We searched for nucleotide variations in the coding sequence and in the region 5 prime of it by dHPLC and analysed their distribution in a group of 104 AD patients and 109 age-matched controls. We identified 5 types of variation, two in the putative promoter region (g.27718954A>G and g.27719102A>T) and the others in exon 2 (c.51C>A, c.80C>G, and c.771C>T). All of them were equally represented in the two cohorts of the study, thus suggesting the absence of an association between GPR3 gene and AD in our population.
Opportunities and challenges of static code analysis of IEC 61131-3 programs

Static code analysis techniques analyze programs by examining the source code without actually executing them. The main benefits lie in improving software quality by detecting potential defects and problematic code constructs in early development stages. Today, static code analysis is widely used and numerous tools are available for established programming languages like C/C++, Java, C# and others. However, in the domain of PLC programming, static code analysis tools are still rare. In this paper we present an approach and tool support for static code analysis of PLC programs. The paper discusses opportunities static code analysis can offer for PLC programming, it reviews techniques for static analysis, and it describes our tool that implements a rule-based analysis approach for IEC 61131-3 programs.
Physical activity among Canadian children on school days and nonschool days.

BACKGROUND Schools are frequently cited as a favorable venue to promote physical activity (PA), however little data exist describing times when students are least active. Our objective was to overcome this limitation and describe time periods when students are least active. METHODS We used a cross-sectional design to assess patterns of PA in 923 grade 5 students [mean age: 10.9 (± 0.4) years] from 30 schools in Alberta, Canada. Students wore time-stamped pedometers for 9 consecutive days, providing 7 full days of data. We compared step counts adjusted for nonwear time between school days and nonschool days as well as during school hours and after school hours. RESULTS 689 (75%) students provided complete data. The average daily step count was higher on school days (boys 13,476 ± 4123 step/day; girls 11,436 ± 3158 steps/day) than nonschool days (boys 11,009 ± 5542 steps/day; girls 10,256 ± 5206 steps/day). More steps were also taken during school hours than nonschool hours (boys +206 ± 420 steps/hour, P < .001; girls 210 ± 347, P < .001 steps/hour). CONCLUSIONS PA levels of children are below Canadian recommended levels for optimal growth and health. Health promotion should emphasize PA particularly among girls, outside school hours, and weekends.