Miracle-dz/UniFAR-FTUs huggingface dataset:
Merging R into the web: DNMAD & Tnasas, web-based resources for microarray data analysis.

DNMAD and Tnasas are two web-based resources for microarray data analysis (normalization and class prediction), that implement R code into a web server conflguration. These tools are included in GEPAS, a suite for gene expression data analysis [4]. The flrst tool we present is called DNMAD that stands for \Diagnosis and Normalization for MicroArray Data". DNMAD is essentially a web-based interface for the Bioconductor package limma [7]. DNMAD has been designed to provide a simple and at the same time robust and fast way for normalization of microarray data. We provide global and print-tip loess normalization [9]. We also ofier options for the use of quality ∞ags, background adjustment, input of compressed flles with many of arrays, and slide-scale normalization. Finally, to enable a fully featured pipeline of analysis, direct links to the central hub of GEPAS, the Preprocessor [5] are provided. The tool, help flles, and tutorials are available at http://dnmad.bioinfo. cnio.es. The second tool we present is called Tnasas for \This is not a substitute for a statistician". Tnasas performs class prediction (using either SVM, KNN, DLDA, or Random Forest) together with gene selection, including cross-validation of the gene selection process to account for \selection bias" [1], and cross-validation of the process of selecting the number of genes that yields the smallest error. This tool has mainly an exploratory and pedagogical purpose: to make users aware of the widespread problem of selection bias and to provide a benchmark against some (overly) optimistic claims that occasionally are attached to new methods and algorithms; Tnasas can also be used as a \quick and dirty" way of building a predictor. Tnasas uses packages e1071 [2], randomForest [6], and class (part of the VR bundle [8]). The tool, help flles, and tutorials are available at http://tnasas.bioinfo. cnio.es. Both tools use a Perl-CGI script that checks all the data input by the user and, once checked, sends it to an R script that performs the actions requested by the user. To merge the Perl-CGI script with the script in R we use the R package CGIwithR [3]. Source code for both applications will be released under the GNU GPL license.
Imaging Individual Differences in the Response of the Human Suprachiasmatic Area to Light

Circadian disruption is associated with poor health outcomes, including sleep and mood disorders. The suprachiasmatic nucleus (SCN) of the anterior hypothalamus acts as the master biological clock in mammals, regulating circadian rhythms throughout the body. The clock is synchronized to the day/night cycle via retinal light exposure. The BOLD-fMRI response of the human suprachiasmatic area to light has been shown to be greater in the night than in the day, consistent with the known sensitivity of the clock to light at night. Whether the BOLD-fMRI response of the human suprachiasmatic area to light is related to a functional outcome has not been demonstrated. In a pilot study (n = 10), we investigated suprachiasmatic area activation in response to light in a 30 s block-paradigm of lights on (100 lux) and lights off (< 1 lux) using the BOLD-fMRI response, compared to each participant's melatonin suppression response to moderate indoor light (100 lux). We found a significant correlation between activation in the suprachiasmatic area in response to light in the scanner and melatonin suppression, with increased melatonin suppression being associated with increased suprachiasmatic area activation in response to the same light level. These preliminary findings are a first step toward using imaging techniques to measure individual differences in circadian light sensitivity, a measure that may have clinical relevance in understanding vulnerability in disorders that are influenced by circadian disruption.
Language Transfer in Learning Japanese and Interlanguage Development

This paper explains language transfer in second/foreign language learning by exploring the nature and sources of learner errors from some psycholinguistic perspectives. It claims that language transfer occurs at several abstract levels during second/foreign language production processes, rather than at the surface level of configurations. Three such abstract levels are identified in relation to potential sources of learner errors: the conceptual level, the functional level, and the positional level. Each level plays its own distinctive role, and the three levels are related in a sequential order of speech production process. It is assumed that language transfer may occur at any abstract level in second/foreign language production. Thus, language transfer and learner errors are viewed as part of learners’ active mental process, rather than mere surface carryover of linguistic items from one language into another. For the study, the language transfer data, including both speaking and writing instances, were collected from adult learners of Japanese as a foreign language with English as their native language.
New models and patterns for traceability

In this paper we describe two closely related subjects 1) Traceability Profile 2) Traceability Patterns. We introduce a profile that allows traceability to be represented in the UML. The patterns are a novel intuitive component that emerges from traceability activities. We describe the different types of patterns and provide a template for their creation. We provide examples of patterns that emerged during a recent survey. The purpose of our approach is to improve the understandability, reusability and communication of traceability practices.
Combined anatomic reconstruction of the anterior cruciate and anterolateral ligaments

DOI: 10.32098/mltj.01. 2019.18 SUMMARY Despite the improvement of surgical techniques, up to 25% of all patients who undergo ACL reconstruction may experience residual rotational instability. The anterolateral ligament (ALL) of the knee is as a potential contributor to symptomatic residual anterolateral rotatory laxity in ACL-deficient patients. Indeed, combined ACL and ALL reconstruction significantly reduce the risk of ACL graft rupture, and prevents damage to the menisci and future knee osteoarthritis of the knee. Furthermore, complications are similar to those encountered when ACL reconstruction is performed alone. We report the procedure developed by the senior author to reconstruct the ACL and the ALL in a minimally invasive anatomical fashion.
Automatically extracting important sentences from story based on connection patterns of propositions in propositional network

In recent years, the world is filled with a large amount of information through the internet and so on. Such a situation increasingly enhances the worth of the automatic text summarization which can support a quick grasp of the text content. The automatic text summarization has so far been accomplished by extracting some important sentences from a text based on various surface cues. To be compared, we tried to devise a new method to extract the important sentences from the story according to the way in which the people comprehend it. In designing this new method, we took account of the text comprehension model, that is, how people comprehend the story text. Then we devised the procedure for transforming from a set of the propositions to the propositional network. In Experiment 1, the participants were asked to select the sentences regarded as important from five stories. Then we examined how the propositions drawn from each important sentence were connected in the propositional network of each story. As a result, we identified three distinctive connection patterns. In Experiment 2, it was examined whether those connection patterns are valid as the rules to extract the important sentences from five new stories. From the sentences extracted our system and the important sentences selected by the participants, we calculated the aggregation accuracy measures. As a result, it was found that they were clearly higher than the baselines. Moreover they were equal to or higher than ones obtained in the previous researches. This finding was replicated to the stories used in Experiment 1.
A hybrid, combinatorial-Markov method of solving performance and reliability models

This thesis presents a hybrid, hierarchical technique for solving performance and reliability models. The building blocks for the models are of two types: combinatorial and Markov. The Markov model is an acyclic Markov or semi-Markov chain or a cyclic Markov chain that is either irreducible or whose state space can be partitioned into transient and absorbing states. The combinatorial model takes the form of a series-parallel graph whose nodes represent events that may occur serially, probabilistically, or concurrently. When a set of events occurs concurrently, the condition for the set of events to complete is that some specified number of the events must complete. The combinatorial model can be viewed as a generalized, series-parallel stochastic PERT chart. As special cases, we have series-parallel reliability block diagrams and fault trees, without repeated nodes. At the lowest level, basic components represent events that have duration. In all but the Markov chain model, an event may have any time-to-occurrence distribution that is exponential polynomial in form, possibly with a mass at zero and at infinity, and possibly containing complex numbers. The combinatorial submodel is solved for the cumulative distribution function of the time needed for all events to finish. A Markov submodel with absorbing states is solved for the distribution of the time needed to reach absorption. If the chain is acyclic, the probability of visiting each state is computed. An irreducible Markov submodel is solved for steady state probabilities. Each CDF is symbolic in the time variable t; the CDF can thus be used as a parameter to other submodels. Scalar values such as a probability or the mean, variance, or value of a CDF at a value of t can also be used as parameters to other submodels. Examples are presented that show how combinations of submodels can be used to evaluate the reliability/availability or performance of large systems or programs. Some of the examples show how the technique can be used to do decomposition/aggregation and to model systems whose structure is not series-parallel.
Quality in dementia care : evaluating staff attitudes and behaviour.

Research suggests that for many older people with dementia living in residential care, communication with staff often provides the most significant element of their day. It seems likely that the quality of the interactions provided by staff during their care of people with dementia will be an essential factor in affecting the person's quality of life. Research also suggests that the attitudes of nursing and care staff towards people with dementia is a central component in the quality of care they deliver, however, to date, there has been no empirically established evidence that staff attitudes have a direct effect on the quality of life of the people cared for. This study aims to examine the impact of staff attitudes on both the quality of care delivered and on the quality of life of residents and to examine whether there are any changes in any of these areas as a result of training and development interventions. The research includes a review of existing attitude measurements and the development of a new attitude scale for use with dementia care professionals, offering evidence for its reliability and validity. In order to evaluate quality of care, a new observational technique is developed, which draws on previous observational methodologies, with a focus on the behaviour of staff during their care of people with dementia. Results suggest that staff with more 'hopeful' attitudes about people with dementia are more likely to engage in social interactions and activities with residents and use more quality indicators (such as giving choice and information) during physical care tasks. The study also showed improvements in staff attitudes, the quality of care provided and in resident well-being following a number of training and development inputs. The implications of the results are discussed in relation to the limitations of the research.
Attendance Maximization for Successful Social Event Planning

Social event planning has received a great deal of attention in recent years where various entities, such as event planners and marketing companies, organizations, venues, or users in Event-based Social Networks, organize numerous social events (e.g., festivals, conferences, promotion parties). Recent studies show that "attendance" is the most common metric used to capture the success of social events, since the number of attendees has great impact on the event's expected gains (e.g., revenue, artist/brand publicity). In this work, we study the Social Event Scheduling (SES) problem which aims at identifying and assigning social events to appropriate time slots, so that the number of events attendees is maximized. We show that, even in highly restricted instances, the SES problem is NP-hard to be approximated over a factor. To solve the SES problem, we design three efficient and scalable algorithms. These algorithms exploit several novel schemes that we design. We conduct extensive experiments using several real and synthetic datasets, and demonstrate that the proposed algorithms perform on average half the computations compared to the existing solution and, in several cases, are 3-5 times faster.
Scalable and practical probability density estimators for scientific anomaly detection

Originally, astronomers dealt with stars. Later, with galaxies. Today, large scale cosmological structures are so complex, they must be first reduced into more succinct representations. For example, a universe simulation containing millions of objects is characterized by its halo occupation distribution. This progression is typical of many disciplines of science, and even resonates in our daily lives. The easier it is for us to collect new data, store it and manage it, the harder it becomes to keep up with what it all means. For that we need to develop tools capable of mining big data sets. This new generation of data analysis tools must meet the following requirements. They have to be fast and scale well to big data. Their output has to be straightforward to understand and easy to visualize. They need to only ask for the minimum of user input—ideally they would run completely autonomously once given the data. I focus on clustering. Its main advantage is its generality. Separating data into groups of similar objects reduces the perception problem significantly. In this context, I propose new algorithms and tools to meet the challenges: an extremely fast spatial clustering algorithm, which can also estimate the number of clusters; a novel and highly comprehensible mixture model; a sub-linear learner for dependency trees; and an active learning framework to minimize the burden on a human expert hunting for rare anomalies. I implemented the algorithms and used them with very large data sets in a wide variety of applications, including astrophysics.
REPRESENTING AND RECOGNIZING POINT OF VIEW

A representation of ideological point of view is articulated and a method for detecting the point(s) of view expressed in a news story is described. A version of the method, actor-role analysis, is encoded in a computer program, SpinDoetor, which can automatically detect the point(s) of view represented in some news stories. SpinDoctor is a computer program designed to detect ideological point of viewlin news stories. To detect point of view, SpinDoctor implements a critical reading strategy called actor-role analysis (Sack, 1994a; Sack, 1994b). Actor-role analysis was developed around the following observation: one means of detecting point of view is to examine how certain people, who appear again and again in the news (i.e., news actors), are described or portrayed (i.e., are assigned roles). Thus, for example, if one is given a news story which mentions Oliver North -- (in)famous for his role in the Iran-Contra affair and recent senatorial campaign -- and the story assigns North the role of patriot (via the use of certain adjectives and verbs), one can quite certain that the point of view expressed in the story is significantly to the right (in the spectrum of US politics) than that expressed by another news story which assigns North the role of villain or criminal. Two aspects of actor-role analysis, as it is implemented in the SpinDoctor system, might be of especial interest to researchers concerned with textual analysis: (1) A representation for ideological point of view: Although practically all, contemporary, AI systems for NLP are capable of finding actors and roles in texts to fill in scripts, frames, or templates (e.g., Jacobs and Ran, 1993), none of these systems assign any political significance to the pairing of certain actors with certain roles (e.g., North as patriot versus North as criminal). contrast, I maintain that sets of actor-role pairs are an interesting and implementable representation for differing ideological points of view. The proposed actor-role representation of ideological point of view accords with some recent work by Lakoff (1991) and generalizes and improves upon previous AI work on representation of ideology (e.g., Abelson and Carroll, 1966; Carbonell, 1978). (2) An algorithm for anaphoric resolution: Actor-role analysis incorporates a new anaphoric resolution algorithm. It is shown how careful attention to actor-role pairings assists in the resolution of anaphoric reference. By noting, for example, that an instance of the pronoun "he" is cast in the role of victim and that, earlier in the same story, "Lieut. Rodriguez" is also cast in the role of
TTRTS: A Tool for Testing Real Time Systems

Real-time systems (RTS for short) are simply those systems whose behavior is time dependent. We encounter many of such systems in our daily life (e.g., airplanes traffic control systems, patient monitoring systems, etc.). The correctness of RTS is very critical because a functional misbehavior may have harmful consequences on both human lives and environment. Testing is one of the formal techniques that can be used to ensure the correctness of RTS. In this paper, we present a tool that generates timed test cases for RTS modeled as Timed Input Output Automaton (TIOA). The proposed tool is based on state identification technique and the test cases generated by the tool have full fault coverage. We applied our tool on many examples with different sizes. Such examples and their corresponding results are presented in this paper.
Building interactive eyegaze menus for surgery

A real-time hands-free eyegaze menu selection interface was implemented using a commercially available eyetracking system, with selections activated by eyegaze fixations and glances on menu widgets. A pilot study tested three different spatial layouts of the menu widgets and employed a highly accurate two-stage selection mechanism. Improvements based on the pilot results were incorporated into a second revision of the interface with a more streamlined selection mechanism which allowed us to test users’ selection accuracy. Another study was conducted on the revised interface and received a positive response from our participants in addition to a faster selection while maintaining high selection accuracy. A software framework was created for building eyetracking applications which provides basic eyetracking and interaction features in a modular format. The framework is also expandable to include more features by adding customized modules. Two new applications built using the framework were evaluated to demonstrate its flexibility.
On social interaction metrics : social network crawling based on interestingness

With the high use of online social networks we are entering the era of big data. With limited resources it is important to evaluate and prioritize interesting data. This thesis addresses the following aspects of social network analysis: efficient data collection, social interaction evaluation and user privacy concerns. It is possible to collect data from online social networks via their open APIs. However, a systematic and efficient collection of online social networks data is still challenging. To improve the quality of the data collection process, prioritizing methods are statistically evaluated. Results suggest that the collection time can be reduced by up to 48% by prioritizing the collection of posts. Evaluation of social interactions also require data that covers all the interactions in a given domain. This has previously been hard to do, but the proposed crawler is capable of extracting all social interactions from a given page. With the extracted data it is for instance possible to illustrate indirect interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have been developed. These methods are evaluated with the too Linguistic Inquiry and Word Count. The privacy of the content produced; and the users’ private information provided on social networks is important to protect. Users must be aware of the consequence of posting in online social networks in terms of privacy. Methods to protect user privacy are presented. The proposed crawler in this thesis has, over the period of 20 months, collected over 38 million posts from public pages on Facebook covering: 4 billion likes and 340 million comments from over 280 million users. The performed data collection yielded one of the largest research dataset of social interactions on Facebook today, enabling qualitative research in form of social network analysis.
Orbit Determination of Meteors Using the MU Radar

Meteor storms and showers are now considered as potential hazard in the space environment. Radar observations of meteors has an advantage of a much higher sensitivity over optical observations. The MU radar of Kyoto University, Japan has a unique capability of very fast beam steerability as well as a high sensitivity to the echoes from ionization around the meteors. We developed a special observation scheme which enables us to determine the orbit of individual meteors. The direction of the target is determined by comparing the echo intensity at three adjacent beams. The Doppler pulse compression technique is applied to improve the signal-tonoise ratio of the echoes from the very fast target, and also to determine the range accurately. The developed scheme was applied to the observation made during the Leonid meteor storm on November 18, 1998 (JST). Estimated orbital distribution seems to suggest that the very weak meteors detected by the MU radar are dominated by sporadic meteors rather than the stream meteors associated with the Leonids storm. key words: meteor observation, MU radar, orbit determination, Doppler pulse compression, space environment
Making garbage collection independent of the amount of garbage

(Appendix to SICS Research Report R86009) This appendix shows in detail how to make the time for the garbage collection algorithm presented in [AHS 86] become proportional to n log n , where n is the number of non-garbage cells. It is assumed that the reader is familiar with [AHS 86] since no further presentation of the notation used is made here. The compaction phase of that algorithm is proportional to the amount of memory, i.e. proportional to the sum of garbage and non-garbage. This is unfortunate since for instance a garbage collection using copying is just proportional to the amount of non-garbage. If a program generates much more garbage than non-garbage, it might be a severe drawback of a garbage collection algorithm to depend on the amount of garbage. This paper shows how to make the garbage collection in [AHS] independent of the amount of garbage.