Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing Projections: Interaction Techniques for Interpreting Arrangements and Errors of Dimensionality Reductions

We introduce a set of integrated interaction techniques to interpret and interrogate dimensionality-reduced data. Projection techniques generally aim to make a high-dimensional information space visible in form of a planar layout. However, the meaning of the resulting data projections can be hard to grasp. It is seldom clear why elements are placed far apart or close together and the inevitable approximation errors of any projection technique are not exposed to the viewer. Previous research on dimensionality reduction focuses on the efficient generation of data projections, interactive customisation of the model, and comparison of different projection techniques. There has been only little research on how the visualization resulting from data projection is interacted with. We contribute the concept of probing as an integrated approach to interpreting the meaning and quality of visualizations and propose a set of interactive methods to examine dimensionality-reduced data as well as the projection itself. The methods let viewers see approximation errors, question the positioning of elements, compare them to each other, and visualize the influence of data dimensions on the projection space. We created a web-based system implementing these methods, and report on findings from an evaluation with data analysts using the prototype to examine multidimensional datasets.
High-Resolution Genomic Profiling Reveals Association of Chromosomal Aberrations on 1q and 16p with Histologic and Genetic Subgroups of Invasive Breast Cancer

Purpose: Invasive ductal carcinoma and invasive lobular carcinoma (ILC) represent the major histologic subtypes of invasive breast cancer. They differ with regard to presentation, metastatic spread, and epidemiologic features. To elucidate the genetic basis of these differences, we analyzed copy number imbalances that differentiate the histologic subtypes. Experimental Design: High-resolution genomic profiling of 40 invasive breast cancers using matrix-comparative genomic hybridization with an average resolution of 0.5 Mb was conducted on bacterial artificial chromosome microarrays. The data were subjected to classification and unsupervised hierarchical cluster analyses. Expression of candidate genes was analyzed in tumor samples. Results: The highest discriminating power was achieved when combining the aberration patterns of chromosome arms 1q and 16p, which were significantly more often gained in ILC. These regions were further narrowed down to subregions 1q24.2-25.1, 1q25.3-q31.3, and 16p11.2. Located within the candidate gains on 1q are two genes, FMO2 and PTGS2, known to be overexpressed in ILC relative to invasive ductal carcinoma. Assessment of four candidate genes on 16p11.2 by real-time quantitative PCR revealed significant overexpression of FUS and ITGAX in ILC with 16p copy number gain. Unsupervised hierarchical cluster analysis identified three molecular subgroups that are characterized by different aberration patterns, in particular concerning gain of MYC (8q24) and the identified candidate regions on 1q24.2-25.1, 1q25.3-q31.3, and 16p11.2. These genetic subgroups differed with regard to histology, tumor grading, frequency of alterations, and estrogen receptor expression. Conclusions: Molecular profiling using bacterial artificial chromosome arrays identified DNA copy number imbalances on 1q and 16p as significant classifiers of histologic and molecular subgroups.
Advising in a Multidisciplinary Master's Program: An Evaluation

Multidisciplinary programs have begun to complement traditional models of graduate and professional education. The development of these programs has begun to reflect the change in graduate student advisement. Multidisciplinary programs necessitate the need for quality advisement approaches. This study assessed faculty satisfaction and commitment to advising graduate students in the Multidisciplinary Studies Individualized (MDSI) program at a metropolitan college in New York State. The intent was to examine faculty level of satisfaction, level of commitment and identify barriers to advising MDSI students. This quantitative study employed a paired samples t – test to compare faculty advising groups. The findings revealed MDSI graduate faculty experience lower levels of satisfaction and commitment compared to single disciplinary graduate faculty. Quotes obtained from faculty advising groups revealed academic advising and MDSI program structure as key barriers to advising MDSI students. Recommendations offer strategies for enhancing advising practices to benefit MDSI graduate faculty, students and the college.
PREDICTABILITY-BASED ADAPTIVE MOUSE INTERACTION AND ZOOMING FOR VISUAL FLOW EXPLORATION

Flow fields are often investigated by adopting a Lagrangian view, for example, by particle tracing of integral curves such as streamlines and path lines or by computing delocalized quantities. For visual exploration, mouse interaction is predominantly used to define starting points for time-dependent Lagrangian methods. This paper focuses on the uncertainty of mouse input and its impact on the visualization process. In typical cases, the interaction is achieved by mouse motion, exhibiting uncertainty in the range of a screen pixel. From the perspective of dynamical systems theory, an integral curve represents an initial value problem, the uncertainty a perturbation of its initial condition, and the uncertainty of the visualization procedure a predictability problem. Predictability analysis is concerned with the growth of perturbations under the action of flow. In our case, it is not unusual that the perturbations grow from single pixels to substantial deviations. We therefore present an interaction scheme based on the largest finite-time Lyapunov exponent and the flow map gradient, providing accurate, smooth, and easy-to-use flow exploration. This scheme employs data-driven adaptation of mouse speed and direction as well as optional augmentation by an adaptive zoom lens with consistent magnification. We compare our approach to non-adaptive mouse interaction and demonstrate it for several examples of datasets. Furthermore, we present results from a user study with nine domain experts.
Training-induced recovery of low-level vision followed by mid-level perceptual improvements in developmental object and face agnosia

Long-term deprivation of normal visual inputs can cause perceptual impairments at various levels of visual function, from basic visual acuity deficits, through mid-level deficits such as contour integration and motion coherence, to high-level face and object agnosia. Yet it is unclear whether training during adulthood, at a post-developmental stage of the adult visual system, can overcome such developmental impairments. Here, we visually trained LG, a developmental object and face agnosic individual. Prior to training, at the age of 20, LG's basic and mid-level visual functions such as visual acuity, crowding effects, and contour integration were underdeveloped relative to normal adult vision, corresponding to or poorer than those of 5–6 year olds (Gilaie-Dotan, Perry, Bonneh, Malach & Bentin, 2009). Intensive visual training, based on lateral interactions, was applied for a period of 9 months. LG's directly trained but also untrained visual functions such as visual acuity, crowding, binocular stereopsis and also mid-level contour integration improved significantly and reached near-age-level performance, with long-term (over 4 years) persistence. Moreover, mid-level functions that were tested post-training were found to be normal in LG. Some possible subtle improvement was observed in LG's higher-order visual functions such as object recognition and part integration, while LG's face perception skills have not improved thus far. These results suggest that corrective training at a post-developmental stage, even in the adult visual system, can prove effective, and its enduring effects are the basis for a revival of a developmental cascade that can lead to reduced perceptual impairments.
Scalable parallel OPTICS data clustering using graph algorithmic techniques

OPTICS is a hierarchical density-based data clustering algorithm that discovers arbitrary-shaped clusters and eliminates noise using adjustable reachability distance thresholds. Parallelizing OPTICS is considered challenging as the algorithm exhibits a strongly sequential data access order. We present a scalable parallel OPTICS algorithm (POPTICS) designed using graph algorithmic concepts. To break the data access sequentiality, POPTICS exploits the similarities between the OPTICS algorithm and PRIM's Minimum Spanning Tree algorithm. Additionally, we use the disjoint-set data structure to achieve a high parallelism for distributed cluster extraction. Using high dimensional datasets containing up to a billion floating point numbers, we show scalable speedups of up to 27.5 for our OpenMP implementation on a 40-core shared-memory machine, and up to 3,008 for our MPI implementation on a 4,096-core distributed-memory machine. We also show that the quality of the results given by POPTICS is comparable to those given by the classical OPTICS algorithm.
The relationship between sport tourists’ perceived value and destination loyalty: an experience-use history segmentation approach

ABSTRACT Understanding active sport tourists’ profiles and nurturing their destination loyalty are important considerations in the tourism market. Loyal tourists are valuable because they tend to revisit a focal destination and elicit positive word-of-mouth. Focusing on a snow-based active sport tourism context, this study was conducted to examine (1) the usefulness of experience-use history (EUH) as a segmentation tool and (2) the relationship between perceived value factors (i.e. quality, emotional response, monetary price, behavioral price, and destination reputation) and destination loyalty in each EUH group. Data were collected from the Niseko ski resort in Japan (N = 328). Based on the EUH variables (i.e. length and frequency of snow-based sport participation), participants were categorized into four segments (i.e. novice, short-active, long-inactive, and expert). The results showed significant differences among EUH groups in terms of their socio-demographics (e.g. gender, age, and income). A series of regression analyses also showed that each segment’s destination loyalty was uniquely enhanced by perceived value factors. Specifically, perceived quality was a significant antecedent of destination loyalty for sport tourists in the novice group. For those in the short-active, long-inactive, and expert groups, perceived emotional response significantly predicted destination loyalty. Perceived reputation of the destination was also important to enhance destination loyalty for the short-active group whereas perceived monetary price was positively associated with the long-inactive group’s destination loyalty. Marketing managers in snow-based sport tourism destinations can utilize the findings to increase the loyalty of active sport tourists, which will improve their competitiveness in the marketplace.
A NOVEL HIGH CAPACITY 3D STEGANOGRAPHIC ALGORITHM

This paper presents a powerful high capacity 3D steganographic algorithm which can embed more than 9.6 million bits of a secret message using a 3D polygon model with only 14,004 vertices. To the best of our knowledge, this is the largest capacity ever reported in the literature. We accomplish this novel algorithm by using two levels of message encoding. In the first level, we subdivide the secret message into a number of payload fragments. We then encode each payload fragment by utilizing a corresponding random sample generated on a unit sphere. In the second level, we encode these random samples to a number of feature points which convey the payload fragment and are skillfully located on the surface of an existing 3D polygon model. The procedure produces a point cloud stego model that can be delivered in a public channel for steganographic purposes. We produce dummy points by uniform sampling over the surface area of the 3D polygon model which increases the rendering quality of the point cloud stego model and also prevents suspicion of the messages by eavesdroppers. Our scheme has five features. First, it can embed a significantly large number of payloads using 3D polygonal models with small complexity. Theoretical analysis indicates that our algorithm has a significantly high capacity with the magnitude of 50 bits per feature point, outperforming that offered by the current state-of-the-art 3D steganographic algorithms. Second, the scheme is flexible, allowing it to convey various capacities by adjusting two region partition parameters. Third, it belongs to a distortion-free manner, and encounters no model variation because of the hidden message. Fourth, the algorithm belongs to a blind extraction, enabling it to extract the secret message without referring to the original cover model. Finally, the scheme provides independency for the secret payload embedding and supports progressive message extraction.
Spreading Activation Approach for Social Recommendations: The Case of Microblogging Services

Social media and social networks have received increasing attention by the scientific community due to their large amounts of user generated and enriched multimedia contents. They encourage interaction between users and their generated content, introduce new types of information for recommender systems. Meanwhile, social media bring heterogeneity to the networks. Traditionally entities of social networks are treated homogeneously, e.g. Messaging network, friendship network. However, real world social networks are intrinsically multidimensional and heterogeneous with different types of entities and different types of relationships. We suggest that different types of entities and relationships of social networks can be naturally and accurately modeled by heterogeneous networks, and propose a spreading activation approach for social recommendations. We employ the data of the microblogging service Twitter using our approach to recommend interesting people and topics to follow. The experimental results demonstrate the potential of our spreading activation approach.
Viewpoint Independent Human Motion Analysis in Man-made Environments

This work addresses the problem of human motion analysis in video sequences of a scene observed by a single fixed camera with high perspective effect. The goal of this work is to make a 2D-Model (made of Shape and Stick figure) viewpoint-insensitive and preprocess the input image for removing the perspective effect. We focus our methodology on using the 3D principal directions of man-made environments and also the direction of motion to transform both 2D-Model and input images to a common frontal view (parallel or orthogonal to the direction of motion) before the fitting process. The inverse transformation is then performed on the resulting human features obtaining a segmented silhouette and a pose estimation in the original input image. Preliminary results are very promising since the proposed algorithm is able to locate head and feet with a better precision than previous one.
Underwater image restoration based on contrast enhancement

Low contrast and color distortion are two major problems suffered by underwater images. In this work, a novel and systematic enhancement method based on contrast enhancement is proposed to restore the degraded underwater images. The proposed method mainly contains three steps. First, the quadtree subdivision strategy is adopted to estimate the waterlight. Then we formulate a cost function and minimize it so as to find the optimal transmission map which is able to maximize the image contrast. Once the waterlight and transmission map are obtained, based on the underwater image degradation model, the backscattering effect and color distortion caused by the light attenuation along the underwater object-camera path can be corrected. Finally, to further compensate for the color loss along the surface-object propagation path, a simple but effective color correction method is applied. Experimental results demonstrate that our proposed method can achieve comparable or even better results than some state-of-the-art approaches.
Interference rejection with a small antenna array at the mobile scattering environment

In this paper we study the effect of the scattering environment around the mobile on the performance of a two antenna array receiver that employs an adaptive interference rejection combiner (IRC). Two different scattering scenarios are considered. Firstly, scatterers are assumed to be independent for the desired and interfering signals and, secondly, scatterers are assumed to be the same for the desired and interfering signals. All the simulations are done with the GSM system.
Seeing Like the Fed: Culture, Cognition, and Framing in the Failure to Anticipate the Financial Crisis of 2008

One of the puzzles about the financial crisis of 2008 is why regulators, particularly the Federal Open Market Committee (FOMC), were so slow to recognize the impending collapse of the financial system and its broader consequences for the economy. We use theory from the literature on culture, cognition, and framing to explain this puzzle. Consistent with recent work on “positive asymmetry,” we show how the FOMC generally interpreted discomforting facts in a positive light, marginalizing and normalizing anomalous information. We argue that all frames limit what can be understood, but the content of frames matters for how facts are identified and explained. We provide evidence that the Federal Reserve’s primary frame for making sense of the economy was macroeconomic theory. The content of macroeconomics made it difficult for the FOMC to connect events into a narrative reflecting the links between foreclosures in the housing market, the financial instruments used to package the mortgages into securities, and the threats to the larger economy. We conclude with implications for the sociological literatures on framing and cognition and for decision-making in future crises.
mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k. a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries.
The READY Model: Patterns of Dynamic Behavior in REA-Based Accounting Applications

Abstract The Resource-Event-Agent (REA) model has gained considerable attention in accounting literature. While REA denotes a data model, which represents only the static aspect of a system, the dynamic aspect has now been introduced as the scenario concept in a recently proposed REA ontology. Using the Unified Modeling Language (UML) sequence diagram—a popular method of showing interactions among objects—and building on the REA framework and the scenario notion, the paper presents the READY model to illustrate patterns of dynamic behavior in accounting scenarios.
Associations of Rs3744841 and Rs3744843 Polymorphisms in Endothelial Lipase Gene with Risk of Coronary Artery Disease and Lipid Levels in a Chinese Population

Objective The aim of the present study was to assess the association between the 2037T/C and 2237G/A polymorphisms in the EL gene and the risk of CAD and lipid levels in a Chinese population. Methods A case-control study including 706 patients with CAD and 315 controls was performed. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used to identify the genotypes. Results The EL 2037 T/C polymorphism was associated with CAD risk and HDL-C levels. No significant differences were found between the EL 2237 G/A genotypes and CAD risk and lipid levels in the whole population. However, carriers of the 2237 A allele had higher Apo A1 levels than those with the 2237 GG genotype and in the CAD subgroup (P = 0.044). The CAD cases have a significantly lower frequency of the C-G haplotypes than the controls, and the T-A haplotype was significantly more common in the CAD patients than in the controls. Conclusions Our study concluded that the EL 2037 T/C polymorphism was associated with CAD risk and HDL-C levels, and that the C allele might be a protective factor against CAD in the Chinese Han population. In addition, the EL 2237 A allele might be associated with an increased Apo A1 level in CAD subjects.
Anti-Hypotensive Treatment and Endothelin Blockade Synergistically Antagonize Exercise Fatigue in Rats under Simulated High Altitude

Rapid ascent to high altitude causes illness and fatigue, and there is a demand for effective acute treatments to alleviate such effects. We hypothesized that increased oxygen delivery to the tissue using a combination of a hypertensive agent and an endothelin receptor A antagonist drugs would limit exercise-induced fatigue at simulated high altitude. Our data showed that the combination of 0.1 mg/kg ambrisentan with either 20 mg/kg ephedrine or 10 mg/kg methylphenidate significantly improved exercise duration in rats at simulated altitude of 4,267 m, whereas the individual compounds did not. In normoxic, anesthetized rats, ephedrine alone and in combination with ambrisentan increased heart rate, peripheral blood flow, carotid and pulmonary arterial pressures, breathing rate, and vastus lateralis muscle oxygenation, but under inspired hypoxia, only the combination treatment significantly enhanced muscle oxygenation. Our results suggest that sympathomimetic agents combined with endothelin-A receptor blockers offset altitude-induced fatigue in rats by synergistically increasing the delivery rate of oxygen to hypoxic muscle by concomitantly augmenting perfusion pressure and improving capillary conductance in the skeletal muscle. Our findings might therefore serve as a basis to develop an effective treatment to prevent high-altitude illness and fatigue in humans.
Smart TV-Smartphone Multiscreen Interactive Middleware for Public Displays

A new generation of public displays demands high interactive and multiscreen features to enrich people's experience in new pervasive environments. Traditionally, research on public display interaction has involved mobile devices as the main characters during the use of personal area network technologies such as Bluetooth or NFC. However, the emergent Smart TV model arises as an interesting alternative for the implementation of a new generation of public displays. This is due to its intrinsic connection capabilities with surrounding devices like smartphones or tablets. Nonetheless, the different approaches proposed by the most important vendors are still underdeveloped to support multiscreen and interaction capabilities for modern public displays, because most of them are intended for domestic environments. This research proposes multiscreen interactive middleware for public displays, which was developed from the principles of a loosely coupled interaction model, simplicity, stability, concurrency, low latency, and the usage of open standards and technologies. Moreover, a validation prototype is proposed in one of the most interesting public display scenarios: the advertising.