Miracle-dz/UniFAR-FTUs huggingface dataset:
A User-centric Taxonomy for Multidimensional Data Projection Tasks

When investigating multidimensional data sets with very large numbers of objects and/or a very large number of dimensions, a variety of visualization methods can be employed in order to represent the data effectively and to enable the user to explore the data at different levels of detail. A common strategy for encoding multidimensional data for visual analysis is to use dimensionality reduction techniques that project data from higher dimensions onto a lower-dimensional space. In this paper, we focus on projection techniques that output 2D or 3D scatterplots which can then be used for a range of data analysis tasks. Existing taxonomies for multidimensional data projections focus primarily on tasks in order to evaluate the human perception of class or cluster separation and/or preservation. However, real-world data analysis of complex data sets often includes other tasks besides cluster separation, such as: cluster identification, similarity seeking, cluster ranking, comparisons, counting objects, etc. A contribution of this paper is the identification of subtasks grouped into four main categories of data analysis tasks. We believe that this user-centric task categorization can be used to guide the organization of multidimensional data projection layouts. Moreover, this taxonomy can be used as a guideline for visualization designers when faced with complex data sets requiring dimensionality reduction. Our taxonomy aims to help designers evaluate the effectiveness of a visualization system by providing an expanded range of relevant tasks. These tasks are gathered from an extensive study of visual analytics projects across real-world application domains, all of which involve multidimensional projection. In addition to our survey of tasks and the creation of the task taxonomy, we also explore in more detail specific examples of how to represent data sets effectively for particular tasks. These case studies, while not exhaustive, provide a framework for how specifically to reason about tasks and to decide on visualization methods. That is, we believe that this taxonomy will help visualization designers to determine which visualization methods are appropriate for specific multidimensional data projection tasks.
MRIM at ImageCLEF2012. From Words to Concepts: A New Counting Approach

MRIM research group has participated in two tasks (ad-hoc image-based retrieval and case-based retrieval) of the ImageCLEF2012 Medical Retrieval track. In our contribution, we study the frequency shift problem that happens when using concepts instead of words as indexing terms. The main goal of our experiments is to check the validity of our new counting strategy of concepts (Relative Count), which is proposed as a solution to the frequency shift problem. In order to validate our new counting strategy, we compare the retrieval performance (represented by MAP) of some classical IR models using the classical counting strategy (count each concept as 1) with their performance using the new strategy. The results are promising, and using the new counting strategy shows a considerable gain in performance. We use in our experiments two sup- plementary resources: MetaMap as a text-to-concepts mapping tool, and UMLS as an external resource containing concepts.
Ontology, Types and Semantics

In this paper we argue that many problems in the semantics of natural language are due to a large gap between semantics (which is an attempt at understanding what we say in language about the world) and the way the world is. This seemingly monumental effort can be grossly simplified if one assumes, as Hobbs (1985) correctly observed some time ago, a theory of the world that reflects the way we talk about it. We demonstrate here that assuming such a strongly-typed ontology of commonsense knowledge reduces certain problems to near triviality.
Implicit policies for deformable object manipulation with arbitrary start and end states: A novel evolutionary approach

We present a novel method for training (evolving) fully convolutional neural networks (CNNs) for deformable object manipulation. Instead of using a weight update rule, we evolve an ensemble of compositional pattern generating networks (CPPNs) by means of a genetic algorithm (GA). These ensembles generate the convolutional kernels that comprise the CNN. This allows the GA to search for fit kernels in the space of spatial 2D patterns, which allows for evolution of larger networks than is feasible with direct evolution of the connection weight vector. We apply this method to a thread manipulation task, in an attempt to let the CNN grasp the deformation dynamics of the thread. We report results both on single-manipulation tasks, and on tasks requiring a sequence of two manipulations, establishing the viability of this approach.
SELF-DUAL YANG–MILLS: SYMMETRIES AND MODULI SPACE

Geometry of the solution space of the self-dual Yang–Mills (SDYM) equations in Euclidean four-dimensional space is studied. Combining the twistor and group-theoretic approaches, we describe the full infinite-dimensional symmetry group of the SDYM equations and its action on the space of local solutions to the field equations. It is argued that owing to the relation to a holomorphic analogue of the Chern–Simons theory, the SDYM theory may be as solvable as 2D rational conformal field theories, and successful nonperturbative quantization may be developed. An algebra acting on the space of self-dual conformal structures on a 4-space (an analogue of the Virasoro algebra) and an algebra acting on the space of self-dual connections (an analogue of affine Lie algebras) are described. Relations to problems of topological and N=2 strings are briefly discussed.
Direct and Resolved Pomeron in Rapidity Gap Cross Sections

We investigate the effect of a direct pomeron coupling to quarks on inclusive jet production in DIS and photoproduction. The direct pomeron coupling generates a point-like contribution to the diffractive part of the structure function $F_2$, which is analysed on the basis of the latest H1 and ZEUS data. Our model assumptions for the pomeron structure are consistent with the measured data.
Closed queueing network models of interacting long-lived TCP flows

This paper presents a new analytical model for the estimation of the performance of TCP connections. The model is based on the description of the behavior of TCP in terms of a closed queueing network. The model is very accurate, deriving directly from the finite state machine description of the protocol. The assessment of the accuracy of the analytical model is based on comparisons against detailed simulation experiments developed with the ns-2 package. The protocol model interacts with an IP network model that can take into account meshed topologies with several bottlenecks. Numerical results indicate that the proposed closed queueing network model provides accurate performance estimates in all situations. A novel and interesting property of the model is the possibility of deriving ensemble distributions of relevant parameters, such as, for instance, the transmission window size or the timeout probability, which provide useful insight into the protocol behavior and properties.
A Graph-Theoretic Approach to Brain Networks Associated with Swallowing

The functional connectivity between brain regions during swallowing is still not well understood. Understanding these complex interactions is of a great interest from scientific and clinical perspectives. In this study, we utilize functional magnetic resonance imaging (fMRI) to investigate brain functional networks during voluntary saliva swallowing in twenty two adult healthy subjects (all females, 23.1 ± 1.52 years of age). To construct these functional connections, we compute mean partial correlation matrices over ninety brain regions for each participant. Two regions are considered functionally connected if they showed statistically significant correlations. These correlation matrices are then analyzed using graph-theoretical approaches. In particular, we consider several network measures for the whole brain and swallowing-related brain regions. The results have shown that significant pairwise functional connections are mostly either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we have shown that the human brain functional network had robust small-world properties, which support efficient parallel information transfer at a relatively low cost. Swallowing related brain regions also had higher values for some of the network measures in comparison when these measures were calculated for the whole brain. Our results have demonstrated the basic network properties of the human brain compatible with previous functional network studies, but also showed unique connections in some regions during swallowing. This leads us to believe that graph-theoretical approaches are a valid tool for the analysis of the swallowing functional connectivity.
Sequential Inference for Factorial Changepoint Models

Conditional Gaussian changepoint models are an interesting subclass of jump-Markov dynamic linear systems, in which, unlike the majority of such intractable hybrid models, exact inference is achievable in polynomial time. However, many applications of interest involve several simultaneously unfolding processes with occasional regime switches and shared observations. In such scenarios, a factorial model, where each process is modelled by a changepoint model is more natural. In this paper, we derive a sequential Monte Carlo algorithm, reminiscent to the Mixture Kalman filter (MKF) [1]. However, unlike MKF, the factorial structure of our model prohibits the computation of the posterior filtering density (the optimal proposal distribution). Even evaluating the likelihood conditioned on a few switch configurations can be time consuming. Therefore, we derive a propagation algorithm (upward-downward) that exploits the factorial structure of the model and facilitates computing Kalman filtering recursions in information form without the need for inverting large matrices. To motivate the utility of the model, we illustrate our approach on a large model for polyphonic pitch tracking.
The social networks of refugees : a sociological investigation of the processes of relationship building in ESOL in the further education context

This thesis argues that social relationships, developed between refugee and migrant students in the ESOLIFE college environment, can provide individual refugees with resources that can positively impact on refugees' integration into British society. The rationale of this study is based on the recognition of refugees' increased need for communication and friendship building and the lack of consideration, so far, of their relations with ESOL students from different ethnicities, as they develop through the acquisition of the English language. The theoretical framework of this study has been informed by social networks and social capital theory, within which social capital is perceived as a resource that derives from social relationships. The study has been developed and presented through the experiences and perspectives of refugees. Its methodology is based on case study, mixed methods design. Data were collected through semi-structured interviews, a survey and participant observation in ESOL classrooms of two London FE colleges. The findings highlight that refugee ESOL students do build friendships with other ESOL students from their group and to a lesser extent with students from other groups at their college. Factors that have been identified to influence the process of relationship building include gender, age, and marital status, as well as refugees' presence of their nuclear family in London and their association with the ethnic community. Although with the passing of time refugees' English language skills improve, time does not strongly affect the formation of new friendships, but has a positive impact on existing relationships. Finally, the benefits deriving from the ESOL network that may further refugees' socio-economic integration have been identified as knowledge, information and qualifications, which constitute the social capital developed in the ESOL network. However, the refugee experience, changes in the family structure and participation in the ethnic community may impact on the recognition and utilisation of these gains as potential resources that can further the socio-economic integration of refugees into British society.
Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis. Deep convolution neural networks (CNNs), as one of the most important branch of the deep learning family, have been widely investigated for various computer-aided diagnosis tasks including long-term problems and continuously emerging new problems. Image contour detection is a fundamental but challenging task that has been studied for more than four decades. Recently, we have witnessed the significantly improved performance of contour detection thanks to the development of CNNs. Beyond purusing performance in existing natural image benchmarks, contour detection plays a particularly important role in medical image analysis. Segmenting various objects from radiology images or pathology images requires accurate detection of contours. However, some problems, such as discontinuity and shape constraints, are insufficiently studied in CNNs. It is necessary to clarify the challenges to encourage further exploration. The performance of CNN based contour detection relies on the state-of-the-art CNN architectures. Careful investigation of their design principles and motivations is critical and beneficial to contour detection. In this paper, we first review recent development of medical image contour detection and point out the current confronting challenges and problems. We discuss the development of general CNNs and their applications in image contours (or edges) detection. We compare those methods in detail, clarify their strengthens and weaknesses. Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis. We expect the paper to cover comprehensive technical ingredients of advanced CNNs to enrich the study in the medical image domain.
Rutgers Information Interaction Lab at TREC 2005: Trying HARD

Within the structure of the TREC 2005 HARD track guidelines, we investigated the following hypotheses: H1: Query expansion using a “clarity”-based approach will increase effectiveness over baseline queries and baseline queries plus pseudo-relevance feedback; H2: Query expansion based on the Web will increase effectiveness over baseline queries and baseline queries plus pseudo-relevance feedback; H3: Query expansion using terms selected by the searcher from those suggested by clarity modeling and/or the web will increase effectiveness over baseline queries, baseline queries plus pseudo-relevance feedback, and queries expanded by all suggested terms; H4: Query expansion using “problem statements” elicited from the searcher will increase effectiveness over baseline queries and baseline queries plus pseudo-relevance feedback; H5: The effectiveness of query expansion using problem statements will be negatively correlated with query “clarity”. H1 and H2 were tested without user intervention; H3 and H4 were tested using two different “clarification forms”; H5 was tested using the results of the H4 clarification form. Baseline queries were generated from the topic titles and descriptions; query expansion was accomplished by adding terms to the baseline queries, with a variety of weights given to the expansion terms, relative to the baseline terms. Preliminary results indicate that H1, H2, H3 and H4 are in part weakly supported, in that performance is increased over baseline, but it is not increased over pseudo-relevance feedback. H5 was not supported. Combining some degree of user interaction (H3) with pseudo-relevance feedback appears to lead to increased performance.
Adding robustness to language models for spontaneous speech recognition

Compared to dictation systems, recognition systems for spontaneous speech still perform rather poorly. An important weakness in these systems is the statistical language model, mainly due to the lack of large amounts of stylistically matching training data and to the occurrence of disﬂuencies in the recognition input. In this paper we investigate a method for improving the robustness of a spontaneous language model by ﬂexible manipulation of the prediction context when disﬂuencies occur. In the case of repetitions, we obtained signiﬁcantly better recognition results on a benchmark Switchboard test set.
Physiology of free will

Free will is a perception that people have that they choose to make their movements. This perception includes a sense of willing the movement and self‐agency that they are responsible for the movement. If there is a “free will force” that plays a role in movement selection, it should precede movement. There is no evidence for a driving force, and the perception of willing is not fully processed until after the movement. The perceptions of free will likely arise from an interaction between frontal and parietal areas. Free will might be considered to exist if a person's brain is functioning normally without coercion. Ann Neurol 2016;80:5–12
Designing super selectivity in multivalent nano-particle binding

A key challenge in nano-science is to design ligand-coated nano-particles that can bind selectively to surfaces that display the cognate receptors above a threshold (surface) concentration. Nano-particles that bind monovalently to a target surface do not discriminate sharply between surfaces with high and low receptor coverage. In contrast, “multivalent” nano-particles that can bind to a larger number of ligands simultaneously, display regimes of “super selectivity” where the fraction of bound particles varies sharply with the receptor concentration. We present numerical simulations that show that multivalent nano-particles can be designed such that they approach the “on-off” binding behavior ideal for receptor-concentration selective targeting. We propose a simple analytical model that accounts for the super selective behavior of multivalent nano-particles. The model shows that the super selectivity is due to the fact that the number of distinct ligand-receptor binding arrangements increases in a highly nonlinear way with receptor coverage. Somewhat counterintuitively, our study shows that selectivity can be improved by making the individual ligand-receptor bonds weaker. We propose a simple rule of thumb to predict the conditions under which super selectivity can be achieved. We validate our model predictions against the Monte Carlo simulations.
Solutions of linear ordinary differential equations in terms of special functions

We describe a new algorithm for computing special function solutions of the form <i>y</i>(<i>x</i>) = <i>m</i>(<i>x</i>)<i>F</i>(ξ(<i>x</i>)) of second order linear ordinary differential equations, where <i>m</i>(<i>x</i>) is an arbitrary Liouvillian function, ξ(<i>x</i>) is an arbitrary rational function, and <i>F</i> satisfies a given second order linear ordinary differential equation. Our algorithm, which is based on finding an appropriate point transformation between the equation defining <i>F</i> and the one to solve, is able to find all rational transformations for a large class of functions <i>F,</i> in particular (but not only) the <inf>0</inf><i>F</i><inf>1</inf> and <inf>1</inf><i>F</i><inf>1</inf> special functions of mathematical physics, such as Airy, Bessel, Kummer and Whittaker functions. It is also able to identify the values of the parameters entering those special functions, and can be generalized to equations of higher order.
Correlation test for high dimensional data with application to signal detection in sensor networks

The problem of correlation detection of multivariate Gaussian observations is considered. The problem is formulated as a binary hypothesis test, where the null hypothesis corresponds to a diagonal correlation matrix with possibly different diagonal entries, whereas the alternative would be associated to any other form of positive covariance. Using tools from random matrix theory, we study the asymptotic behavior of the Generalized Likelihood Ratio Test (GLRT) under both hypothesis, assuming that both the sample size and the observation dimension tend to infinity at the same rate. It is shown that the GLRT statistic always converges to a Gaussian distribution, although the asymptotic mean and variance will strongly depend the actual hypothesis. Numerical simulations demonstrate the superiority of the proposed asymptotic description in situations where the sample size is not much larger than the observation dimension.
EFFECTS OF CLOPIDOGREL IN ADDITION TO ASPIRIN IN PATIENTS WITH ACUTE CORONARY SYNDROMES WITHOUT ST-SEGMENT ELEVATION

A BSTRACT Background Despite current treatments, patients who have acute coronary syndromes without ST-segment elevation have high rates of major vascular events. We evaluated the efficacy and safety of the antiplatelet agent clopidogrel when given with aspirin in such patients. Methods We randomly assigned 12,562 patients who had presented within 24 hours after the onset of symptoms to receive clopidogrel (300 mg immediately, followed by 75 mg once daily) (6259 patients) or placebo (6303 patients) in addition to aspirin for 3 to 12 months. Results The first primary outcome — a composite of death from cardiovascular causes, nonfatal myocardial infarction, or stroke — occurred in 9.3 percent of the patients in the clopidogrel group and 11.4 percent of the patients in the placebo group (relative risk with clopidogrel as compared with placebo, 0.80; 95 percent confidence interval, 0.72 to 0.90; P<0.001). The second primary outcome — the first primary outcome or refractory ischemia — occurred in 16.5 percent of the patients in the clopidogrel group and 18.8 percent of the patients in the placebo group (relative risk, 0.86, P<0.001). The percentages of patients with in-hospital refractory or severe ischemia, heart failure, and revascularization procedures were also significantly lower with clopidogrel. There were significantly more patients with major bleeding in the clopidogrel group than in the placebo group (3.7 percent vs. 2.7 percent; relative risk, 1.38; P=0.001), but there were not significantly more patients with episodes of life-threatening bleeding (2.1 percent vs. 1.8 percent, P=0.13) or hemorrhagic strokes. Conclusions The antiplatelet agent clopidogrel has beneficial effects in patients with acute coronary syndromes without ST-segment elevation. However, the risk of major bleeding is increased among patients treated with clopidogrel. (N Engl J Med 2001;345: 494-502.)
Resource allocation in a middleware for streaming data

Increasingly, a number of applications rely on, or can potentially benefit from, analysis and monitoring of <i>data streams. </i> To support processing of streaming data in a grid environment, we have been developing a middleware system called GATES (Grid-based AdapTive Execution on Streams). Our target applications are those involving high volume data streams and requiring distributed processing of data arising from a distributed set of sources. This paper addresses the problem of resource allocation in the GATES system. Though resource discovery and resource allocation have been active topics in grid community, the pipelined processing and real-time constraint required by distributed streaming applications pose new challenges. We present a resource allocation algorithm that is based on minimal spanning trees. We evaluate the algorithm experimentally and demonstrate that it results in configurations that are very close to optimal, and significantly better than most other possible configurations.