Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Help Help ? A Bayes Net Approach to Modeling Tutor Interventions

This paper describes an effort to measure the effectiveness of tutor help in an intelligent tutoring system. Although conventional preand posttest experiments can determine whether tutor help is effective, they are expensive to conduct. Furthermore, preand posttest experiments often do not model student knowledge explicitly and thus are ignoring a source of information: students often request help about words they do not know. Therefore, we construct a dynamic Bayes net (which we call the Help model) that models tutor help and student knowledge in one coherent framework. The Help model distinguishes two different effects of help: scaffolding immediate performance vs. teaching persistent knowledge that improves long term performance. We train the Help model to fit student performance data gathered from usage of the Reading Tutor (Mostow & Aist, 2001). The parameters of the trained model suggest that students benefit from both the scaffolding and teaching effects of help. That is, students are more likely to perform correctly on the current attempt and learn persistent knowledge if tutor help is provided. Thus, our framework is able to distinguish two types of influence that tutor help has on the student, and can determine whether help helps learning without an explicit controlled study.
Dynamic Asset Pricing Theory with Uncertain Time-Horizon

An investment horizon is in practice not frequently known with certainty at the initial investment date. This paper addresses the problem of pricing and hedging a random cash-flow received at a random date in a general stochastic environment. We first argue that specific timing risk is induced by the presence of an uncertain time-horizon if and only if the random time under consideration is not a stopping time of the filtration generated by prices of traded assets. In that context, we provide an explicit characterization of the set of equivalent martingale measures, as well as a necessary and sufficient condition for a convenient separation between adjustment for market risk and timing risk. These results allow us to clarify the definition of the market price for timing risk, and lead to general pricing formulae and explicit hedging strategies for random cash-flows in the presence of timing risk. Potential applications are the valuation of employee stock options, real options, catastrophe insurance contracts, credit derivatives, callable and convertible bonds, mortgage-backed securities, as well as any other asset featuring an embedded prepayment option.
Transparent user models for personalization

Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it. In this work, we propose a framework for addressing this problem by developing a new user-interpretable feature set upon which to base personalized recommendations. These features, which we call badges, represent fundamental traits of users (e.g., "vegetarian" or "Apple fanboy") inferred by modeling the interplay between a user's behavior and self-reported identity. Specifically, we consider the microblogging site Twitter, where users provide short descriptions of themselves in their profiles, as well as perform actions such as tweeting and retweeting. Our approach is based on the insight that we can define badges using high precision, low recall rules (e.g., "Twitter profile contains the phrase 'Apple fanboy'"), and with enough data, generalize to other users by observing shared behavior. We develop a fully Bayesian, generative model that describes this interaction, while allowing us to avoid the pitfalls associated with having positive-only data. Experiments on real Twitter data demonstrate the effectiveness of our model at capturing rich and interpretable user traits that can be used to provide transparency for personalization.
The Autonomous City Explorer (ACE) project — mobile robot navigation in highly populated urban environments

One of the greatest challenges nowadays in robotics is the advancement of robots from industrial tools to companions and helpers of humans, operating in natural, populated environments. In this respect, the Autonomous City Explorer (ACE) project aims to combine the research fields of autonomous mobile robot navigation and human robot interaction. A robot has been created that is capable of navigating in an unknown, highly populated, urban environment, based only on information extracted through interaction with passers-by and its local perception capabilities. This paper describes the algorithms and architecture that make up the navigation subsystem of ACE. More specifically, the algorithms used for Simultaneous Localization and Mapping (SLAM), path planning in dynamic environments and behavior selection are presented, as well as the system architecture that integrates them to a complete working system. Results from an extended field experiment, where the robot navigated autonomously through the downtown city area of Munich, are analyzed and show that the robot is capable of long-term, safe navigation in real-world settings.
Repetitive Diving in Trained Rats Still Increases Fos Production in Brainstem Neurons after Bilateral Sectioning of the Anterior Ethmoidal Nerve

This research was designed to investigate the role of the anterior ethmoidal nerve (AEN) during repetitive trained diving in rats, with specific attention to activation of afferent and efferent brainstem nuclei that are part of this reflexive response. The AEN innervates the nose and nasal passages and is thought to be an important component of the afferent limb of the diving response. Male Sprague-Dawley rats (N = 24) were trained to swim and dive through a 5 m underwater maze. Some rats (N = 12) had bilateral sectioning of the AEN, others a Sham surgery (N = 12). Twelve rats (6 AEN cut and 6 Sham) had 24 post-surgical dive trials over 2 h to activate brainstem neurons to produce Fos, a neuronal activation marker. Remaining rats were non-diving controls. Diving animals had significantly more Fos-positive neurons than non-diving animals in the caudal pressor area, ventral medullary dorsal horn, ventral paratrigeminal nucleus, nucleus tractus solitarius, rostral ventrolateral medulla, Raphe nuclei, A5, Locus Coeruleus, and Kölliker-Fuse area. There were no significant differences in brainstem Fos labeling in rats diving with and without intact AENs. Thus, the AENs are not required for initiation of the diving response. Other nerve(s) that innervate the nose and nasal passages, and/or suprabulbar activation of brainstem neurons, may be responsible for the pattern of neuronal activation observed during repetitive trained diving in rats. These results help define the central neuronal circuitry of the mammalian diving response.
Target Tracking based on the Dynamic Cluster Method in the Acoustic Sensor Network

When finding the moving target in the acoustic sensor network, a dynamic cluster is formed to triangulate the target's position. While the target is moving, the old cluster is revoked and a new cluster is formed to follow the target. An election strategy based on the probability is used to select the cluster head and thus improve the cluster formation probabilities. Given the random function distribution, the paper also presents how to design the random function. The time window which is associated with the data association process is a very important parameter. The paper also presents the domain of the time window. The simulation shows that choosing right random function and right value of the time window is very important to the tracking precision
The impact of training problem-solving skills on self-esteem and behavioral adjustment in teenage girls who have irresponsible parents or no parents

Proper psychological interventions are of great importance because they help enhancing psychological and public health in adolescents with irresponsible parents or no parents. The current research aimed to examine the impact of training problem-solving experiment on self-esteem and behavioral adjustment in teenage girls with irresponsible parents or no parents. Methodology: The approach of the present research was a semi-test via a post-test-pre-test model and a check team. Hence, in Tehran, 40 girls with irresponsible parents or no parents were chosen by using the Convenience modeling, and they were classified into 2 teams: control and experiment. Both groups were pre-tested by using a demography questionnaire, Rosenberg’s self-esteem scale, and a behavioral adjustment questionnaire. Afterwards, both groups were post-tested, and the obtained data were examined by using inferential and descriptive methods through SPSS 21. Findings: Findings indicated that the training problem-solving skills significantly increased the self-esteem and the behavioral adjustment in teenage girls with irresponsible parents or no parents (P < 0/ 001). Conclusion: The conclusion of this research was that training problem-solving methods greatly helps endangered people such as teenage girls with irresponsible parents or no parents, because these methods are highly efficient especially when they are performed in groups, as they are cheap and accepted by different people.
TUNING ACCURACY INVESTIGATION OF VARIABLE IIR DIGITAL FILTERS REALIZED AS A CASCADE OF IDENTICAL SUB-FILTERS

The accuracy and range of tuning, the sensitivities and the overall performance of variable IIR filters, designed as a cascade of several identical sub-filters, have been studied in this work and compared with these of the corresponding classically designed variable filters. It was firmly shown that, at the price of a slight increase of the filter order, the cascaded sub-filter reali- zations are achieving much better performance. All results are verified experimentally.
Sequential Change Detection on Data Streams

Model-based declarative queries are becoming an attractive paradigm for interacting with many data stream applications. This has led to the development of techniques to accurately answer the queries using distributional models rather than raw values. The quintessential problem with this is that of detecting when there is a change in the input stream, which makes models stale and inaccurate. We adopt the sound statistical method of sequential hypothesis testing to study this problem on streams, without independence assumption. It yields algorithms that are fast, space-efficient, and oblivious to data's underlying distributions. Our experiments demonstrate the effectiveness of our methods to not only determine the existence of a change, but also the point where the change is initiated, relative to the ground truth we obtain. Our methods work seamlessly without window limitations inherent in prior work, thus have clearly shorter delays compared to alternative window-based solutions.
Automated Semantic Tagging of Textual Content

Motivated by a continually increasing demand for applications that depend on machine comprehension of text-based content, researchers in both academia and industry have developed innovative solutions for automated information extraction from text. In this article, the authors focus on a subset of such tools--semantic taggers--that not only extract and disambiguate entities mentioned in the text but also identify topics that unambiguously describe the text's main themes. The authors offer insight into the process of semantic tagging, the capabilities and specificities of today's semantic taggers, and also indicate some of the criteria to be considered when choosing a tagger.
The Molecular Basis for Attractive Salt-Taste Coding in Drosophila

Too Much or Too Little An important task of the nervous system is to distribute information appropriately throughout the brain. The olfactory and gustatory systems of Drosophila provide good models for understanding these processes and the underlying mechanisms (see the Perspective by Su and Carlson). Lin et al. (p. 1338) mapped out the circuit that detects carbon dioxide (CO2), an important environmental and communication signal for fruit flies. Two distinct classes of projection neurons mediate avoidance of high and low concentrations of CO2, while a third class, comprising inhibitory neurons, shuts down the low-concentration pathway at high concentrations. In contrast to other basic taste qualities, salt is innately attractive at low concentrations, but aversive at high concentrations. The mechanisms underlying salt detection are poorly understood in any species mainly because of a lack of specific molecular tools. Zhang et al. (p. 1334) discovered that Drosophila uses two types of gustatory receptor neurons to distinguish between high and low concentrations of salt. One type is activated maximally by low salt and induces attractive feeding behavior. The other class of receptors is activated primarily by high salt and leads to avoidance behavior. Low or high concentrations of sodium chloride activate distinct receptor pathways and, hence, elicit attractive or aversive responses. [Also see Perspective by Su and Carlson] Below a certain level, table salt (NaCl) is beneficial for animals, whereas excessive salt is harmful. However, it remains unclear how low- and high-salt taste perceptions are differentially encoded. We identified a salt-taste coding mechanism in Drosophila melanogaster. Flies use distinct types of gustatory receptor neurons (GRNs) to respond to different concentrations of salt. We demonstrated that a member of the newly discovered ionotropic glutamate receptor (IR) family, IR76b, functioned in the detection of low salt and was a Na+ channel. The loss of IR76b selectively impaired the attractive pathway, leaving salt-aversive GRNs unaffected. Consequently, low salt became aversive. Our work demonstrated that the opposing behavioral responses to low and high salt were determined largely by an elegant bimodal switch system operating in GRNs.
A Method of 3D Surface Correspondence for Automated Landmark Generation

A method for corresponding the triangulated mesh surface representations of two shapes is presented. The algorithm produces a matching pair of sparse polyhedral approximations, one for each shape surface, using a global Euclidean measure of similarity. A method of surface patch parameterisation is presented and its use in the interpolation of surfaces for the construction of a merged mean shape with a densely triangulated surface is described. Results are presented for the production of a binary tree of merged biological shapes which may be used as a basis for automated landmarking.
Developing guidelines for in-the-field control of a team of robots

In this work we explore the development of guidelines for creating “in-the-field” interfaces for enabling a single user to remotely control multiple robots. The problem of controlling a remote team of robots is complex, requiring a user to monitor and interpret robotic state and sensor information in real time, and to simultaneously communicate direction commands to the robots. The result is that a robot controller is often seated at a console; for many relevant applications such as search and rescue or firefighting this removes the user from the field of action, rendering them unable to directly participate in a task at hand. Therefore, one challenge in HRI is to develop efficient interfaces that will enable a user to effectively control and monitor a team of robots in the field. In our project we explore various interface designs in terms of supporting this goal, taking the approach of involving a panel of professionals in the design process to direct exploration and development.
Gliotransmitters Travel in Time and Space

Alfonso Araque,1,2 Giorgio Carmignoto,3,* Philip G. Haydon,4 Stéphane H.R. Oliet,5,6 Richard Robitaille,7,8 and Andrea Volterra9 1Instituto Cajal, Consejo Superior de Investigaciones Cientı́ficas, 28002 Madrid, Spain 2Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA 3Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche and Dipartimento Scienze Biomediche, Università di Padova, 35121 Padova, Italy 4Department of Neuroscience, Tufts University School of Medicine, Boston, MA 02111, USA 5Inserm U862, Neurocentre Magendie, 33077 Bordeaux, France 6Université de Bordeaux, 33077 Bordeaux, France 7Département de Neurosciences, Université de Montréal, Montréal, QC H3C 3J7, Canada 8Groupe de Recherche sur le Système Nerveux Central, Université de Montréal, Montréal, QC H3C 3J7, Canada 9Département de Neurosciences Fondamentales (DNF), Faculté de Biologie et de Médecine, Université de Lausanne, 1005 Lausanne, Switzerland *Correspondence: giorgio.carmignoto@bio.unipd. it http://dx.doi. org/10.1016/j.neuron. 2014.02. 007
Neuromodulation of Word Meaning Selection

Processes of word meaning generation, word as­ sociation and understanding are known to be impaired in schizophrenia and related diseases. Word meaning selection requires the involve­ ment of prefrontal cortex and processes of work­ ing memory and selective attention. Under the dopaminergic hypothesis of schizophrenia, the normal neuromodulatory activation of pre­ frontal cortex for the performance of working memory-related tasks is disturbed. We present a model of selective attention and its modula­ tion by dopamine and show how abnormal levels of dopamine availability may lead to some of the observed impairments in word meaning selection, namely (a) failure to construct contextually ap­ propriate meanings and (b) intrusions of phono­ logical and episodic associative links within se­ mantic processing.
Visualizing Missing Data : Classification and Empirical Study

Most visualization tools fail to provide support for missing data. We identify sources of missing, and categorize data visualization techniques based on the impact missing data have on the display: region dependent, attribute dependent, and neighbor dependent. We then report on a user study with 30 participants that compared three design variants. A between-subject graph interpretation study provides strong evidence for the need of indicating the presence of missing information, and some direction for addressing the problem.
MAXIMUM MASS–RADIUS RATIO FOR COMPACT GENERAL RELATIVISTIC OBJECTS IN SCHWARZSCHILD–DE SITTER GEOMETRY

Upper limits for the mass–radius ratio are derived for arbitrary general relativistic matter distributions in the presence of a cosmological constant. General restrictions for the redshift and total energy (including the gravitational contribution) for compact objects in the Schwarzschild–de Sitter geometry are also obtained in terms of the cosmological constant and of the mean density of the star.
Quantitative 3-Dimensional Echocardiography for Accurate and Rapid Cardiac Phenotype Characterization in Mice

Background—Insufficient techniques exist for rapid and reliable phenotype characterization of genetically manipulated mouse models of cardiac dysfunction. We developed a new, robust, 3-dimensional echocardiography (3D-echo) technique and hypothesized that this 3D-echo technique is as accurate as magnetic resonance imaging (MRI) and histology for assessment of left ventricular (LV) volume, ejection fraction, mass, and infarct size in normal and chronically infarcted mice. Methods and Results—Using a high-frequency, 7/15-MHz, linear-array ultrasound transducer, we acquired ECG and respiratory-gated, 500-&mgr;m consecutive short-axis slices of the murine heart within 4 minutes. The short-axis movies were reassembled off-line in a 3D matrix by using the measured platform locations to position each slice in 3D. Epicardial and endocardial heart contours were manually traced, and a B-spline surface was fitted to the delineated image curves to reconstruct the heart volumes. Excellent correlations were obtained between 3D-echo and MRI for LV end-systolic volumes (r=0.99, P<0.0001), LV end-diastolic volumes (r=0.99, P<0.0001), ejection fraction (r=0.99, P<0.0001), LV mass (r=0.94, P<0.0019), and infarct size (r=0.98, P<0.0001). Also, excellent correlations were found between the 3D-echo–derived LV mass and necropsy LV mass in normal mice (r=0.99, P<0.0001), as well as for 3D-echo–derived infarct size and histologically determined infarct size (r=0.99, P<0.0001) in mice with chronic heart failure. Bland-Altman analysis showed excellent limits of agreement between techniques for all measured parameters. Conclusion—This new, fast, and highly reproducible 3D-echo technique should be of widespread applicability for high-throughput murine cardiac phenotyping studies.
Dynamical condition of neutral hydrogen envelopes of dwarf galaxies and their possible morphological evolution

We investigate the star-formation history of gas-rich dwarf galaxies, taking account of the dynamical evolution of their neutral hydrogen (H{\sc i}) envelope. Gas-rich dwarfs are classified into blue compact dwarfs (BCDs) and dwarf irregulars (dIrrs). In this paper, their H{\sc i} envelope is clearly shown not to be blown away by their stellar feedback. This is concluded since the observed star-formation rate (SFR) of gas-rich dwarfs is generally smaller than a critical SFR, $\psi_{\rm crit}$, at which stellar feedback accelerates the H{\sc i} envelope to the escape velocity. From this standpoint and the chemical property of sample BCDs, we suggest two possibilities; (1) The H{\sc i} gas in the envelope of BCDs is consumed to fuel their star-formation; and (2) BCDs have a similar star-formation history. We also discuss morphological evolution among dwarf galaxies. As long as gas-rich dwarfs are isolated, it is difficult for them to evolve into dwarf ellipticals (dEs). When the H{\sc i} envelope in gas-rich dwarfs is consumed in subsequent star-formation, a morphological exchange between BCDs and dIrrs is still expected, consistent with previous studies. If the SFR of gas-rich dwarfs was much higher than $\psi_{\rm crit}$ in the past, interestingly, an evolutionary scenario from dEs to gas-rich dwarfs is possible.
Context Representation for Context-Aware Mobile Multimedia Content Recommendation

Very few of the current solutions for content recommendation take into consideration the context of usage when analyzing the preferences of the user and issuing recommendations. Nonetheless, context can be extremely useful to help identify appropriate content for the specific situation or activity the user is in, while consuming the content. In this paper, we present a solution to allow content-based recommendation systems to take full potential of contextual data, by defining a standards-based representation model which accounts for possible relationships among low-level contexts. The MPEG-7 and MPEG-21 standards are used for content description and low-level context representation. OWL/RDF ontologies are used to capture contextual concepts and, together with SWRL to establish relationships and perform reasoning to derive high-level concepts the way humans do. This knowledge is then used to drive the recommendation and content adaptation processes. As a side achievement, an extension to the MPEG-21 specification was developed to accommodate the description of user activities, which we believe have a great impact on the type of content to be recommended.