Miracle-dz/UniFAR-FTUs huggingface dataset:
Organizational Behavior and Human Decision Processes

Most patients in the United States depend on physicians who have financial relationships with the healthcare industry. These physician-industry relationships represent a conflict of interest: a potential clash between the physicians’ professional responsibilities and their self-interest. We conducted a randomized field experiment to assess the impact of written disclosures of physicians’ conflict of interest on patients’ appointment attendance, knowledge of these conflicts of interest, and their trust in their physician and hospital. Patients (N= 1903) attending outpatient clinics at a large U.S. academic hospital from 2015 to 2016 who had appointments with physicians earning more than $20,000 from industry in the last year were randomized to receive (or not receive) disclosures of their physicians’ financial conflicts of interest (with or without explanation of the risks and/or benefits of such conflicts) in their appointment-reminder letters. There were no differences across condition in missed or cancelled appointments. For patients who attended eligible appointments with their physician and completed the post-appointment survey (N=867/1276; 68% response rate), the disclosure intervention revealed significant improvement in patients’ knowledge of their physicians’ financial relationships but no significant differences in patients’ trust in their physician or hospital. Risk and benefit framings of financial relationships did not significantly affect any outcomes. These findings highlight that although mailed financial conflict of interest disclosures are effective as an educational tool, disclosure cannot be a panacea to addressing physician-industry relationships if the intended purpose is for patients to assimilate the information into their decision-making and account for potential physician bias.
Object Recognition Using Moment Descriptors and Genetic Algorithm

In many image analysis and computer vision applications, object recognition is the ultimate goal. This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outline of the objects have been used for the whole process of the recognition. Hu’s moments and their extended counterparts have been used as features of the objects. Genetic Algorithm is applied on these moments to get optimum descriptors. These optimum descriptors are then used in process of recognition. Various similarity measures have been used and compared for recognition. The test objects are matched with the model objects in database and the object with the least similarity measure is taken as the recognized object. A detailed experimental study has been made under different conditions and circumstances including transformation, noise, and occlusion. Databases of different sizes have been used to have a look at various experimentations. Some interesting observations have been made which may be useful for research and practicing community.
Improving Wafer-Level S-parameters Measurement Accuracy and Stability with Probe-Tip Power Calibration up to 110 GHz for 5G Applications

Demands for mission critical wireless services such as autonomous driving and telehealth require higher data transfer rates and lower latencies. Such applications are now driving the use of radio frequencies (RF) at 28, 38, 60 and 73 GHz for the emerging 5G mobile communication systems. Waferlevel RF measurements of devices or circuits for 5G applications are extremely challenging due to huge system losses and calibrated state at the probe tips valid only for about 10 minutes. This paper presents a novel method of probe-tip power calibration for S-parameters calibration which is shown to greatly improve DC biasing accuracy, S-parameters measurement accuracy and post-calibration stability up to 110 GHz. The new method allows precise and low probe tip source power biasing of -20 dBm, without affecting calibration performance. It also increases system calibration stability to more than 4 hours, giving microwave engineers adequate time to test their RF devices and circuits by avoiding the need to perform frequent re-calibration, greatly enhancing test throughput.
Black Lives Matter in Wikipedia: Collaboration and Collective Memory around Online Social Movements

Social movements use social computing systems to complement offline mobilizations, but prior literature has focused almost exclusively on movement actors’ use of social media. In this paper, we analyze participation and attention to topics connected with the Black Lives Matter movement in the English language version of Wikipedia between 2014 and 2016. Our results point to the use of Wikipedia to (1) intensively document and connect historical and contemporary events, (2) collaboratively migrate activity to support coverage of new events, and (3) dynamically re-appraise preexisting knowledge in the aftermath of new events. These findings reveal patterns of behavior that complement theories of collective memory and collective action and help explain how social computing systems can encode and retrieve knowledge about social movements as they unfold.
Scribble Supervised Annotation Algorithms of Panoptic Segmentation for Autonomous Driving

Large-scale ground truth dataset is of crucial importance for deep learning based segmentation models, but annotating per-pixel masks is extremely time consuming. In this paper, we investigate semi-annotated graph based segmentation algorithms that enforce connectivity. To be more precise, we introduce a class-agnostic heuristic of a discrete Potts model, and a class-aware Integer Linear Programming (ILP) that ensures global optimum. Both algorithms are able to generate panoptic segmentation supervised by scribbles, and can take RGB, or utilize the feature maps from any DCNN, whether trained on the target dataset or not, as input. We present competitive semantic segmentation results on the PASCAL VOC dataset, as well as report panoptic segmentation result on the more challenging Cityscapes dataset. Our algorithms show superior results that makes them suitable for weakly supervised segmentation on new dataset, or interactive semi-automated ground truth generation by human annotators on existing dataset.
Modeling morphological processing in human magnetoencephalography

In this paper, we conduct a magnetoencephalography (MEG) lexical decision experiment and model morphological processing in the human brain, especially the Visual Word Form Area (VWFA) in the ventral visual stream. Five computational models of morphological processing are constructed and evaluated against human brain activities: Letter Markov Model and Syllable Markov Model as “amorphous” models without linguistically defined morphemes, and Morpheme Markov Model, Hidden Markov Model (HMM), and Probabilistic ContextFree Grammar (PCFG) as “morphous” models with different amounts of morphological supervision. The present experiment demonstrates that “amorphous” models underperformed relative to “morphous” models, PCFG was more neurologically accurate than sequential models, and PCFG better explained nested words with non-local dependencies between prefixes and suffixes. These results strongly suggest that morphemes are represented in the VWFA and parsed into hierarchical morphological structures.
Belgeo, 2 | 2016

This paper explores the urban-geographical potential of simulation approaches combining spatial and topological processes. Drawing on Vértes et al.'s (2012) economical clustering model, we propose a generative network model integrating factors captured in traditional spatial models (e.g., gravity models) and more recently developed topological models (e.g., actororiented stochastic models) into a single framework. In our urban network-implementation of the generative network model, it is assumed that the emergence of inter-city linkages can be approximated through probabilistic processes that speak to a series of contradictory forces. Our exploratory study focuses on the outline of the infrastructure networks connecting prefecturelevel cities in the highly urbanized Yangtze River Delta (China). Possible hampering factors in the emergence of these networks include distance and administrative boundaries, while stimulating factors include a measure of city size (population, gross domestic product) and a topological rule stating that the formation of connections between cities sharing nearest neighbors is more likely (i.e., a transitive effect). Based on our results, two wider implications of our research are discussed: (1) it confirms the potential of the proposed method in urban network simulation in that the inclusion of a topological factor alongside geographical factors generates an urban network that better approximates the observed network; (2) it allows exploring the differential extent to which driving forces influence the structure of different urban networks. For instance, in the Yangtze River Delta, transitivity plays a less important role in the Internet-network formation; GDP and boundaries more strongly affect the rail network; and distance decay effects play a more prominent role in the road network. Dit artikel analyseert het potentieel binnen stadsgeografisch onderzoek van simulatiebenaderingen die zowel ruimtelijke als topologische processen in rekening brengen. Op basis van Vértes et al.'s (2012) economisch clustering-model stellen we een generatief netwerkmodel voor dat factoren samenbrengt die doorgaans apart worden geanalyseerd in meer traditionele ruimtelijke modellen (bvb. zwaartekrachtmodellen) en in meer recente topologische Simulating infrastructure networks in the Yangtze River Delta (China) using g.
Finding Execution Faults in Dynamic Web Application

Pin down is a framework for root cause analysis on dynamic web application. Fault localization in dynamic web application is the problem of decisive where in the source code modifies has to be completed in order to fix the perceived failures. The cause of the failure is called as execution bug that also called as fault. The dynamic execution nature of the web application isolate the source cause of execution bug by various fault localization techniques. To identify execution bugs proficiently in web applications, some algorithms can be improved by using a comprehensive domain for conditional and function-call statements and using a source mapping but did not focus on server side vulnerability. This study use randomized input generation technique for dynamic web application to check whether the web page has vulnerable to SQL injection. An automated random input generation is constructed for each executable statement and to determine the execution failure such as a missing included file, an incorrect SQL query, or by an uncaught exception of the corresponding statement. In addition, determine HTML failures involve situations in which the generated HTML page is not syntactically correct according to an HTML validator to find HTML failures through checking appropriate tags with closing by parsing DOM tree and Less serious execution failures, like those caused by the employment of deprecated language constructs (like include & require function), produce obtrusive error messages but do not halt execution. In this study the result shows that code coverage improved from 90% to 100%. However the result indicates 100% coverage is a reliable indicator of the effectiveness of a test set.
IMPROVING THE CUBIC ZnS NANOCRYSTALS QUALITY BY SELF-ASSEMBLING INTO A MESOPOROUS STRUCTURE

Nanocrystals of cubic ZnS (cZnS) doped with 0.2 % mol Mn, self-assembled into a mesoporous structure, have been prepared at room temperature by a surfactant-assisted liquid-liquid reaction. The X-ray diffraction measurements confirm the formation of a sponge-like mesoporous structure built from ZnS nanocrystals with cubic (sphalerite) structure and pores of similar diameter of (1.8 ± 0.2) nm. The Transmission Electron Microscopy (TEM) images show that the mesoporous structure consists of nanocrystals of cZnS with a tight size distribution centered around the average diameter value (2.1 ± 0.3) nm. The analysis of the observed Electron Paramagnetic Resonance (EPR) spectrum demonstrates the presence of the Mn activating ions at isolated sites in the mesoporous material, resulting in three types of paramagnetic centers called Mn(I), Mn(II) and Mn(III). The EPR spectrum of the Mn(I) center, attributed to substitutional Mn ions at Zn cation sites in the ZnS nanocrystals, exhibits the smallest linewidth values reported so far, reflecting an increased lattice ordering. The high quality of the nanocrystals forming the mesoporous cZnS:Mn, as reflected in a tight nanocrystallites size distribution and reduced crystallites lattice disorder, is attributed to the restraining effect of the self-assembling.
Early Prediction of Sepsis: Using State-of-the-art Machine Learning Techniques on Vital Sign Inputs

Electronic Health Records (EHRs) give a lot of information regarding a patient's progress in health, who is admitted to an Intensive Care Unit (ICU). Sepsis is a critical condition suffered by a patient who, if not treated in a timely manner can cause casualties. Machine learning algorithms have evolved to utilize EHRs to help doctors detect the onset of sepsis. In this work, we present a random forest-based ensemble machine learning technique to work on patient data, also called vital sign input, from ICU. The data we used is published as a part of the Physionet Challenge 2019 [11]. The proposed technique performs well on data that contain a major chunk as missing values due to the sparsity of measurement taken in an ICU. We used a combined classifier and an early predictor approach to accomplish the task. The classifier does the job of classification when the early prediction is not possible due to a lack of data. While early predictor predicts the onset of sepsis based on the patient's information it received from previous recordings of vital sign inputs. A utility metric score is used to evaluate the early predictor. The score increases with early predictions and decreases with late predictions as well as false alarms. Our team named ‘Tricog' finished 58th in the challenge with a utility score of 0.149 in the official phase on the full test set data.
Title Probabilistic historical biogeography : new models for founder-event speciation , imperfect detection , and fossils allow improved accuracy and model-testing Permalink

Historical biogeography has been characterized by a large diversity of methods and unresolved debates about which processes, such as dispersal or vicariance, are most important for explaining distri‐ butions. A new R package, BioGeoBEARS, implements many models in a common likelihood framework, so that standard statistical model selection procedures can be applied to let the data choose the best model. Available models include a likelihood version of DIVA (“DIVALIKE”), LAGRANGE’s DEC model, and BAYAREA, as well as “+J” versions of these models which include founder‐event speciation, an important process left out of most inference methods. I use BioGeoBEARS on a large sample of island and non‐ island clades (including two fossil clades) to show that founder‐event speciation is a crucial process in almost every clade, and that most published datasets reject the non‐J models currently in widespread use. BioGeoBEARS is open‐source and freely available for installation at the Comprehensive R Archive Network at http://CRAN.R‐project.org/package=BioGeoBEARS. A step‐by‐step tutorial is available at http://phylo.wikidot. com/biogeobears.
Antecedents and consequences of luxury brand engagement in social media Antecedentes y consecuencias del compromiso con la marca de lujo en las redes sociales

Purpose – The purpose of this study is to analyse the antecedent (Consumer Involvement) and the consequences (Brand Connection and Brand Usage Intent [BUI]) of the three dimensions of Consumer Brand Engagement (CBE) (Cognitive Processing, Affection andActivation) in luxury brand engagement on Facebook. Design/methodology/approach – Data were collected through an online questionnaire completed by fans/followers of luxury brands’ Facebook pages. The empirical study was conducted using structural equation modelling. Findings – Consumer Involvement has a positive impact on the three dimensions of CBE (Cognitive Processing, Affection and Activation). This leads to the conclusion that Activation and Affection have an impact both on Self-Brand Connection and on BUI. Moreover, it was found that Cognitive Processing impacts only on BUI. Practical implications – The results identified the factors which brand managers should focus on to increase CBE on Facebook. Originality/value – This study is a pioneer, as it extends the consumer engagement model to the social media context in a hedonic and conspicuous consumption category which includes luxury brand products.
Deception Detection in Arabic Tweets and News

The project Arabic Author Proﬁling for Cyber-Security (ARAP) 1 aims at preventing cyber-threats using Machine Learning. To this end, they monitor social media to early detect threatening messages and, in such a case, to proﬁle the authors behind. Proﬁling potential terrorists from messages shared in social media may allow detecting communities whose aim is to undermine the security of others. One of this framework’s main challenges is recognizing false positives, such as potential threatening messages that are actually deceptive, ironic or humorous. This paper focuses on the goal of detecting deceptive messages, which are intentionally written trying to sound authentic. This task is performed on two diﬀerent genres of Arabic texts: Twitter messages and news headlines.
Complexity competes with transferability in machine-learning force fields: The example of Gold-Iron interactions

Machine-learning force fields have been increasingly employed in order to extend the possibility of current first-principle calculations. While the main approach consists in using a force field with the most universal and most complete analytical formulation, here we propose an alternative where we purposely design the simplest potential possible. In particular, we use a linear combination of traditional force fields and optimize the linear coefficients using a penalizing regression scheme that reduces the complexity of the resulting potential. After validating the method in the case of gold-iron structures, we study the competition between the complexity and the transferability of the potential.
Anisotropic Mesh Filtering by Homogeneous MLS Fitting

In this paper we present a novel geometric filter, a homogeneous moving least squares fitting-based filter (H-MLS filter), for anisotropic mesh filtering. Instead of fitting the noisy data by a moving parametric surface and projecting the noisy data onto the surface, we compute new positions of mesh vertices as the solutions to homogeneous least squares fitting of moving constants to local neighboring vertices and tangent planes that pass through the vertices. The normals for defining the tangent planes need not be filtered beforehand but the parameters for balancing the influences between neighboring vertices and neighboring tangent planes are computed robustly from the original data under the assumption of quadratic precision in each tangent direction. The weights for respective neighboring points for the least squares fitting are computed adaptively for anisotropic filtering. The filter is easy to implement and has distinctive features for mesh filtering. (1) The filter is locally implemented and has circular precision, spheres and cylinders can be recovered exactly by the filter. (2) The filtered mesh has a high fidelity to the original data without any position constraint and salient or sharp features can be preserved well. (3) The filter can be used to filter meshes with various kinds of noise as well as meshes with highly irregular triangulation.
Reduced Forms of Linear Differential Systems and the Intrinsic Galois-Lie Algebra of Katz

Generalizing the main result of (Aparicio, Compoint, Weil 2013), we prove that a system is in reduced form in the sense of Kochin and Kovacic if and only if any differential module in an algebraic construction admits a constant basis. Then we derive an explicit version of this statement, which was implicit in (Aparicio, Compoint, Weil 2013) and is a crucial ingredient of (Barkatou, Cluzeau, Di Vizio, Weil 2016). We finally deduce some properties of the Lie algebra of Katz's intrinsic Galois group, which is actually another fundamental ingredient of the algorithm in (Barkatou, Cluzeau, Di Vizio, Weil 2016).
Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning

Model-based reinforcement learning algorithms make decisions by building and utilizing a model of the environment. However, none of the existing algorithms attempts to infer the dynamics of any state-action pair from known state-action pairs before meeting it for sufficient times. We propose a new model-based method called Greedy Inference Model (GIM) that infers the unknown dynamics from known dynamics based on the internal spectral properties of the environment. In other words, GIM can "learn by analogy". We further introduce a new exploration strategy which ensures that the agent rapidly and evenly visits unknown state-action pairs. GIM is much more computationally efficient than state-of-the-art model-based algorithms, as the number of dynamic programming operations is independent of the environment size. Lower sample complexity could also be achieved under mild conditions compared against methods without inferring. Experimental results demonstrate the effectiveness and efficiency of GIM in a variety of real-world tasks.
Learning to Generate Dense Point Clouds with Textures on Multiple Categories

3D reconstruction from images is a core problem in computer vision. With recent advances in deep learning, it has become possible to recover plausible 3D shapes even from single RGB images. However, obtaining detailed geometry and texture for objects with arbitrary topology remains challenging. In this paper, we propose a novel approach for reconstructing point clouds from RGB images. Unlike other methods, we can recover dense point clouds with hundreds of thousands of points, and we also include RGB textures. In addition, we train our model on multiple categories, which leads to superior generalization to unseen categories com-pared to previous techniques. We achieve this using a two-stage approach, where we first infer an object coordinate map from the input RGB image, and then obtain the final point cloud using a reprojection and completion step. We show results on standard benchmarks that demonstrate the advantages of our technique.
Learning Singing From Speech

We propose an algorithm that is capable of synthesizing high quality target speaker's singing voice given only their normal speech samples. The proposed algorithm first integrate speech and singing synthesis into a unified framework, and learns universal speaker embeddings that are shareable between speech and singing synthesis tasks. Specifically, the speaker embeddings learned from normal speech via the speech synthesis objective are shared with those learned from singing samples via the singing synthesis objective in the unified training framework. This makes the learned speaker embedding a transferable representation for both speaking and singing. We evaluate the proposed algorithm on singing voice conversion task where the content of original singing is covered with the timbre of another speaker's voice learned purely from their normal speech samples. Our experiments indicate that the proposed algorithm generates high-quality singing voices that sound highly similar to target speaker's voice given only his or her normal speech samples. We believe that proposed algorithm will open up new opportunities for singing synthesis and conversion for broader users and applications.