Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-type Preferential Attachment Tree

A multi-type preferential attachment tree is introduced, and studied using general multi-type branching processes. For the $p$-type case we derive a framework for studying the tree where a type $i$ vertex generates new type $j$ vertices with rate $w_{ij}(n_1,n_2,\ldots, n_p)$ where $n_k$ is the number of type $k$ vertices previously generated by the type $i$ vertex, and $w_{ij}$ is a non-negative function from $\mathbb{N}^p$ to $\mathbb{R}$. The framework is then used to derive results for trees with more specific attachment rates. In the case with linear preferential attachment---where type $i$ vertices generate new type $j$ vertices with rate $w_{ij}(n_1,n_2,\ldots, n_p)=\gamma_{ij}(n_1+n_2+\dots +n_p)+\beta_{ij}$, where $\gamma_{ij}$ and $\beta_{ij}$ are positive constants---we show that under mild regularity conditions on the parameters $\{\gamma_{ij}\}, \{\beta_{ij}\}$ the asymptotic degree distribution of a vertex is a power law distribution. The asymptotic composition of the vertex population is also studied.
A team approach to the introduction of safe early mobilisation in an adult critical care unit

Delirium and intensive care unit acquired weakness are common in patients requiring critical care and associated with higher mortality and poor long-term outcomes. Early mobilisation has been shown to reduce the duration of both conditions and is recommended as part of a strategy of rehabilitation of critically ill patients starting during their stay in intensive care. Our aim was to achieve 95% reliability with a standardised mobilisation process. Multidisciplinary involvement through the use of regular focus groups lead to the development of a standardised process of sitting a ventilated or non-ventilated patient at the side of the bed for a set period of time, which was called the daily dangle. Team learning from Plan, Do, Study, Act (PDSA)cycles, as well as feedback from both staff and patients, allowed us to develop the process and achieve a median 87% reliability. Delirium rates fell from 54.1% to 28.8%. There was no change in average length of stay, and no adverse events. Ownership by the staff, development of the process by staff, iterative testing and learning, and designs for reliability were the factors behind the successful adoption of a new and challenging process. Particular changes which drove reliability were standardisation of the criteria for a dangle, standardisation of the dangle itself and a reminder included on the daily goals checklist.
Natural Language Processing in Business Process Identification and Modeling: A Systematic Literature Review

Business Process Management (BPM) has been receiving increasing attention in recent years. Many organizations have been adapting their business to a process-centered view since they started noticing its potential to reduce costs, improve productivity and achieve higher levels of quality. However, implementing BPM in organizations requires time, making the automation of process identification and discovery highly desirable. To achieve this expectation, the application of Natural Language Processing (NLP) techniques and tools has emerged to generate process models from unstructured text. In this paper, we provide the results of a systematic literature review conducted in preparation and processing of natural language text aiming the extraction of business processes and process quality assurance. The study presents techniques applied to the BPM life-cycle phases of process identification, process discovery and process analysis as well as tools to support process discovery. This review covered papers from 2009 up to 2016 and identifies 518 articles of which 33 were selected as relevant to our work. The results of the present study may be valuable to support research in extraction of business process models from natural language text.
The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation

Determining the position and orientation of a calibrated camera from a single image with respect to a 3D model is an essential task for many applications. When 2D-3D correspondences can be obtained reliably, perspective-n-point solvers can be used to recover the camera pose. However, without the pose it is non-trivial to find cross-modality correspondences between 2D images and 3D models, particularly when the latter only contains geometric information. Consequently, the problem becomes one of estimating pose and correspondences jointly. Since outliers and local optima are so prevalent, robust objective functions and global search strategies are desirable. Hence, we cast the problem as a 2D-3D mixture model alignment task and propose the first globally-optimal solution to this formulation under the robust L2 distance between mixture distributions. We derive novel bounds on this objective function and employ branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose estimate. To accelerate convergence, we integrate local optimization, implement GPU bound computations, and provide an intuitive way to incorporate side information such as semantic labels. The algorithm is evaluated on challenging synthetic and real datasets, outperforming existing approaches and reliably converging to the global optimum.
Hypospermia Improvement in Dogs Fed on a Nutraceutical Diet

Male dog infertility may represent a serious concern in the canine breeding market. The aim of this clinical evaluation was to test the efficacy of a commercially available nutraceutical diet, enriched with Lepidium meyenii, Tribulus terrestris, L-carnitine, zinc, omega-3 (N-3) fatty acids, beta-carotene, vitamin E, and folic acid, in 28 male dogs suffering from infertility associated with hypospermia. All dogs received the diet over a period of 100 days. At the end of the evaluation period, no adverse effects, including head and tail anomalies percentage onset, were reported. Interestingly, motility percentage, semen volume and concentration, and total number of sperms per ejaculation significantly increased. Further investigations on a wider cohort of dogs might be useful to better correlate the presence of oxytetracycline in pet's diet and the onset of infertility and clearly assess the action mechanism of an oxytetracycline-free nutraceutical diet.
Brain and Cognitive Mechanisms of Top–Down Attentional Control in a Multisensory World: Benefits of Electrical Neuroimaging

Abstract In real-world environments, information is typically multisensory, and objects are a primary unit of information processing. Object recognition and action necessitate attentional selection of task-relevant from among task-irrelevant objects. However, the brain and cognitive mechanisms governing these processes remain not well understood. Here, we demonstrate that attentional selection of visual objects is controlled by integrated top–down audiovisual object representations (“attentional templates”) while revealing a new brain mechanism through which they can operate. In multistimulus (visual) arrays, attentional selection of objects in humans and animal models is traditionally quantified via “the N2pc component”: spatially selective enhancements of neural processing of objects within ventral visual cortices at approximately 150–300 msec poststimulus. In our adaptation of Folk et al.'s [Folk, C. L. , Remington, R. W. , & Johnston, J. C. Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception and Performance, 18, 1030–1044, 1992] spatial cueing paradigm, visual cues elicited weaker behavioral attention capture and an attenuated N2pc during audiovisual versus visual search. To provide direct evidence for the brain, and so, cognitive, mechanisms underlying top–down control in multisensory search, we analyzed global features of the electrical field at the scalp across our N2pcs. In the N2pc time window (170–270 msec), color cues elicited brain responses differing in strength and their topography. This latter finding is indicative of changes in active brain sources. Thus, in multisensory environments, attentional selection is controlled via integrated top–down object representations, and so not only by separate sensory-specific top–down feature templates (as suggested by traditional N2pc analyses). We discuss how the electrical neuroimaging approach can aid research on top–down attentional control in naturalistic, multisensory settings and on other neurocognitive functions in the growing area of real-world neuroscience.
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language

Sign language recognition is a challenging and often underestimated problem comprising multi-modal articulators (handshape, orientation, movement, upper body and face) that integrate asynchronously on multiple streams. Learning powerful statistical models in such a scenario requires much data, particularly to apply recent advances of the field. However, labeled data is a scarce resource for sign language due to the enormous cost of transcribing these unwritten languages. We propose the first real-life large-scale sign language data set comprising over 25,000 annotated videos, which we thoroughly evaluate with state-of-the-art methods from sign and related action recognition. Unlike the current state-of-the-art, the data set allows to investigate the generalization to unseen individuals (signer-independent test) in a realistic setting with over 200 signers. Previous work mostly deals with limited vocabulary tasks, while here, we cover a large class count of 1000 signs in challenging and unconstrained real-life recording conditions. We further propose I3D, known from video classifications, as a powerful and suitable architecture for sign language recognition, outperforming the current state-of-the-art by a large margin. The data set is publicly available to the community.
A Tight Upper Bound on Mutual Information

We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and maximum a posteriori decodes (most probable signals given channel output). As part of our derivation, we describe the key properties of the distribution of signals, channel outputs and decodes, that minimizes equivocation and maximizes mutual information. This work addresses a problem in data analysis, where mutual information between signals and decodes is sometimes used to lower bound the mutual information between signals and channel outputs. Our result provides a corresponding upper bound.
A Comparative Study on the Performance of GSCA and CSA in Parameter Recovery for Structural Equation Models With Ordinal Observed Variables

A simulation based comparative study was designed to compare two alternative approaches to structural equation modeling—generalized structured component analysis (GSCA) with the alternating least squares (ALS) estimator vs. covariance structure analysis (CSA) with the maximum likelihood (ML) estimator or the weighted least squares mean and variance adjusted (WLSMV) estimator—in terms of parameter recovery with ordinal observed variables. The simulated conditions included the number of response categories in observed variables, distribution of ordinal observed variables, sample size, and model misspecification. The simulation outcomes focused on average root mean square error (RMSE) and average relative bias (RB) in parameter estimates. The results indicated that, by and large, GSCA-ALS recovered structural path coefficients more accurately than CSA-ML and CSA-WLSMV in either a correctly or incorrectly specified model, regardless of the number of response categories, observed variable distribution, and sample size. In terms of loadings, CSA-WLSMV outperformed GSCA-ALS and CSA-ML in almost all conditions. Implications and limitations of the current findings are discussed, as well as suggestions for future research.
Viral-Induced Enhanced Disease Illness

Understanding immune responses to viral infections is crucial to progress in the quest for effective infection prevention and control. The host immunity involves various mechanisms to combat viral infections. Under certain circumstances, a viral infection or vaccination may result in a subverted immune system, which may lead to an exacerbated illness. Clinical evidence of enhanced illness by preexisting antibodies from vaccination, infection or maternal passive immunity is available for several viruses and is presumptively proposed for other viruses. Multiple mechanisms have been proposed to explain this phenomenon. It has been confirmed that certain infection- and/or vaccine-induced immunity could exacerbate viral infectivity in Fc receptor- or complement bearing cells- mediated mechanisms. Considering that antibody dependent enhancement (ADE) is a major obstacle in vaccine development, there are continues efforts to understand the underlying mechanisms through identification of the epitopes and antibodies responsible for disease enhancement or protection. This review discusses the recent findings on virally induced ADE, and highlights the potential mechanisms leading to this condition.
Diagnostic Validation for Participants in the Washington State Parkinson Disease Registry

Background The Washington State Parkinson Disease Registry (WPDR) was created to facilitate recruitment for Parkinson's disease (PD) research studies conducted in the Pacific Northwest. The success of registries that rely on self-report is dependent on the accuracy of the information provided by participants, particularly diagnosis. Objective and Methods Our goal was to assess diagnostic accuracy within the WPDR cohort. We randomly selected and attempted to contact 168 of the 1,278 actively enrolled WPDR participants. Those who responded were invited to undergo an interview and neurological examination performed by a PD specialist. If an in-person assessment was not possible, we sought information collected during participation in prior research studies or from review of medical records. A diagnosis was considered “validated” if the individual met UK Parkinson's Disease Society Brain Bank (UKBB) clinical diagnostic criteria for PD. Results Data were ascertained for 106 participants; 77 underwent an in-person assessment, 21 had data available from a prior research study, and 8 provided access to medical records. Diagnostic accuracy within the overall sample was 93.4% (95% confidence interval (86.4%, 97.1%)). Seven patients did not fulfill UKBB criteria for the following reasons: early severe autonomic involvement (n=3), history of neuroleptic treatment (n=1), presence of the Babinski sign (n=1), or insufficient supportive criteria (n=2). Conclusions Our results indicate that studies which use the WPDR for recruitment will rarely encounter patients who are misdiagnosed. This further supports the utility of the WPDR as an effective recruitment tool for PD research in the Pacific Northwest.
Pairwise Crowd Judgments: Preference, Absolute, and Ratio

Relevance judgments are conventionally formed by small numbers of experts using ordinal relevance scales defined by two or more relevance categories. Such judgments often contain many ties: documents in the same category that cannot be separated by relevance. Here we explore the use of crowd-sourcing and combined three-way relevance assessments using pairwise preference, absolute relevance, and relevance ratio, with forced choice testing and embedded quality control processes, seeking to reduce assessment ties, and to increase judgment consistency. In particular, the crowd-sourced judgments from these three approaches were normalized into numeric relevance scores, and compared against judgments arising via three previous techniques: NIST binary; Sormunen; and magnitude estimation. The relationship between generated judgment reliability and number of document pairs assessed was also explored, as was the effect that factors such as document length, topic difficulty, number of documents judged, and assessment time, have on assessment reliability. Lastly, we investigate the extent to which the methodology used to collect judgments affects the ability of an experiment to discriminate between IR systems.
Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning

Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution can only be ensured with the use of problem-specific sampling distributions. Due to the large variety of driving situations within the context of automated driving, it is very challenging to manually design such distributions. This letter introduces, therefore, a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently toward the optimal solution. A benchmark highlights that the CNN predicts future vehicle poses with a higher accuracy compared to uniform sampling and a state-of-the-art A*-based approach. Combining this CNN-guided sampling with the motion planner bidirectional RRT* reduces the computation time by up to an order of magnitude and yields a faster convergence to a lower cost as well as a success rate of 100% in the tested scenarios.
Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research

The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. It can be challenging to distill a myriad of similar papers into a set of useful principles, to determine which new methodologies to use for a particular application, and to be confident that one has compared against all relevant related work when developing new ideas. However, such a rapidly growing body of research literature is a problem that other fields have already faced - in particular, medicine and epidemiology. In those fields, systematic reviews and meta-analyses have been used exactly for dealing with these issues and it is not uncommon for entire journals to be dedicated to such analyses. Here, we suggest the field of machine learning might similarly benefit from meta-analysis and systematic review, and we encourage further discussion and development along this direction.
Interplay between transcription regulators RUNX1 and FUBP1 activates an enhancer of the oncogene c-KIT and amplifies cell proliferation

Abstract Runt-related transcription factor 1 (RUNX1) is a well-known master regulator of hematopoietic lineages but its mechanisms of action are still not fully understood. Here, we found that RUNX1 localizes on active chromatin together with Far Upstream Binding Protein 1 (FUBP1) in human B-cell precursor lymphoblasts, and that both factors interact in the same transcriptional regulatory complex. RUNX1 and FUBP1 chromatin localization identified c-KIT as a common target gene. We characterized two regulatory regions, at +700 bp and +30 kb within the first intron of c-KIT, bound by both RUNX1 and FUBP1, and that present active histone marks. Based on these regions, we proposed a novel FUBP1 FUSE-like DNA-binding sequence on the +30 kb enhancer. We demonstrated that FUBP1 and RUNX1 cooperate for the regulation of the expression of the oncogene c-KIT. Notably, upregulation of c-KIT expression by FUBP1 and RUNX1 promotes cell proliferation and renders cells more resistant to the c-KIT inhibitor imatinib mesylate, a common therapeutic drug. These results reveal a new mechanism of action of RUNX1 that implicates FUBP1, as a facilitator, to trigger transcriptional regulation of c-KIT and to regulate cell proliferation. Deregulation of this regulatory mechanism may explain some oncogenic function of RUNX1 and FUBP1.
Serum Albumin Is Associated With Higher Inflammation and Carotid Atherosclerosis in Treated Human Immunodeficiency Virus Infection

Abstract Background This study was conducted to explore the associations between serum albumin and markers of inflammation and cardiovascular disease in treated human immunodeficiency virus (HIV)-infected adults. Methods We conducted a nested study within in the SATURN-HIV trial in which 147 HIV+ adults on stable antiretroviral therapy were (1) virally suppressed, (2) had a low-density lipoprotein (LDL)-cholesterol level <130 mg/dL, and (3) were randomized to 10 mg daily rosuvastatin or placebo. Measures of serum albumin, carotid intima media thickness ([cIMT] surrogate marker of atherosclerosis), inflammation, T cells, monocyte activation, and gut integrity were assessed at baseline, 48 and 96 weeks later. Spearman correlations and linear mixed-effect models were used to assess associations with serum albumin. Results Mean age was 45 years, 80% of participants were male, and 69% were African American. Mean serum albumin was similar between the groups at all time points (4.01–4.09 g/dL in statin arm vs 4.02–4.11 g/dL in placebo arm; P = .08–0.35). Lower baseline serum albumin significantly predicted larger changes in cIMT, interleukin 6, D-dimer, tumor necrosis factor α receptor 1, fibrinogen, and high-sensitivity C-reactive protein (P ≤ .03) over 96 weeks independently of statin therapy. After adjusting for age, gender, smoking, body mass index, creatinine clearance, and LDL cholesterol, every 1 g/dL decrease in serum albumin at baseline remained associated with a 0.05-mm increase in cIMT over 96 weeks (P = .05). Conclusions Lower serum albumin in controlled HIV is associated with higher markers of chronic inflammation and hypercoagulation, which could explain the prior observation that serum albumin predicts nonacquired immune deficiency syndrome events in HIV. Serum albumin may predict progression of carotid atherosclerosis independent of traditional risk factors.
Mechanical Thrombectomy by a Direct Aspiration First Pass Technique (ADAPT) in Ischemic Stroke: Results of Monocentric Study Based on Multimodal CT Patient Selection

Introduction Mechanical thrombectomy with ADAP-technique of ischemic stroke has been reported as fast and effective. Aim of this study is to evaluate imaging criteria as possible predictors of stroke severity, therapeutic success, and outcome. Materials and Methods Patients (30) presenting from October 2015 to April 2017 with Emergent Large Vessel Occlusion of the anterior circulation were treated with ADAP-technique. 22 received also IV tPA; 8 underwent endovascular treatment only. Every patient was evaluated with noncontrast CT, multiphase angiography-CT, and perfusion CT. Clinical and radiological characteristics were measured. Good clinical outcome was an improvement of 8 points on NIHSS at discharge or a modified Rankin Scale ≤2 at discharge and at 90 days. Results Successful revascularization was obtained in 57% of patients, no procedural complications were witnessed, and only two hemorrhages were reported. Good outcome at discharge was obtained in 11 patients (37%) and predicted by NCCT ASPECT and TICI; outcome at 90 days was predicted by NCCT ASPECT, clot length, and premorbid mRS. Mortality was 23% at discharge and 30% at 90 days. Conclusion ADAPT is an effective endovascular method of stroke treatment with fast procedural times. Multimodal CT evaluation is effective in assessing stroke severity, providing important prognostic information, which is able to select patients for the appropriate treatment.