Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-step learning and underlying structure in statistical models

In multi-step learning, where a final learning task is accomplished via a sequence of intermediate learning tasks, the intuition is that successive steps or levels transform the initial data into representations more and more ``suited" to the final learning task. A related principle arises in transfer-learning where Baxter (2000) proposed a theoretical framework to study how learning multiple tasks transforms the inductive bias of a learner. The most widespread multi-step learning approach is semi-supervised learning with two steps: unsupervised, then supervised. Several authors (Castelli-Cover, 1996; Balcan-Blum, 2005; Niyogi, 2008; Ben-David et al, 2008; Urner et al, 2011) have analyzed SSL, with Balcan-Blum (2005) proposing a version of the PAC learning framework augmented by a ``compatibility function" to link concept class and unlabeled data distribution. We propose to analyze SSL and other multi-step learning approaches, much in the spirit of Baxter's framework, by defining a learning problem generatively as a joint statistical model on $X \times Y$. This determines in a natural way the class of conditional distributions that are possible with each marginal, and amounts to an abstract form of compatibility function. It also allows to analyze both discrete and non-discrete settings. As tool for our analysis, we define a notion of $\gamma$-uniform shattering for statistical models. We use this to give conditions on the marginal and conditional models which imply an advantage for multi-step learning approaches. In particular, we recover a more general version of a result of Poggio et al (2012): under mild hypotheses a multi-step approach which learns features invariant under successive factors of a finite group of invariances has sample complexity requirements that are additive rather than multiplicative in the size of the subgroups.
Seizure Prediction Using Undulated Global and Local Features

In this study, a seizure prediction method is proposed based on a patient-specific approach by extracting undulated global and local features of preictal/ictal and interictal periods of EEG signals. The proposed method consists of feature extraction, classification, and regularization. The undulated global feature is extracted using phase correlation between two consecutive epochs of EEG signals and an undulated local feature is extracted using the fluctuation and deviation of EEG signals within the epoch. These features are further used for classification of preictal/ictal and interictal EEG signals. A regularization technique is applied on the classified outputs for the reduction of false alarms and improvement of the overall prediction accuracy (PA). The experimental results confirm that the proposed method provides high PA (i.e., 95.4%) with low false positive per hour using intracranial EEG signals in different brain locations of 21 patients from a benchmark dataset. Combining global and local features enables the transition point to be determined between different types of signals with greater accuracy, resulting successful versus unsuccessful prediction of seizure. The theoretical contribution of this study may provide an opportunity for the development of a clinical device to predict forthcoming seizure in real time.
The triple model: Combining cutting-edge web technologies with a cognitive model in an ECA

This paper introduces a new model which is intended to combine the power of a connectionist engine based on fast matrix calculation, RDF based memory and inference mechanisms, and affective computing in a hybrid cognitive model. The model is called triple and has the following three parts: a reasoning module making advantage of RDF based long-term memory by performing fast inferences, a fast connectionist mapping engine, which can establish relevance and similarities, including analogies, and an emotional module which modulates the functioning of the model. The reasoning engine synchronizes the connectionist and the emotional modules which run in parallel, and controls the communication with the user, retrieval from memory, transfer of knowledge, and action execution. The most important cognitive aspects of the model are context sensitivity, specific experiential episodic knowledge and learning. At the same time, the model provides mechanisms of selective attention and action based on anticipation by analogy. The inference and the connectionist modules can be optimized for high performance and thus ensure the real-time usage of the model in agent platforms supporting embodied conversational agents.
Sex and mortality of hospitalized adults after admission to an intensive care unit.

BACKGROUND After admission to intensive care, women have higher mortality rates than do men. The reasons for the greater mortality in women are not fully understood. OBJECTIVE To determine if increased mortality in women was due to delays in the recognition of critical illness or to delays in timely admission to intensive care. METHODS A total of 241 consecutive admissions to intensive care from medical and surgical units during a 12-month period were analyzed retrospectively. Patients' demographics, illness severity, and delay between the time the patients would have fulfilled criteria for calling a medical emergency team and consultation with and admission to intensive care were analyzed. RESULTS Delay from fulfillment of criteria for calling a medical emergency team and consultation with intensive care and from consultation to admission to intensive care did not differ between sexes. Despite similar delays in admission to intensive care, women had a higher 30-day mortality than did men (44.9% vs 30.5%; P = .02). The increased mortality was more pronounced in the medical patients (53% vs 34%; P = .02). Multivariate analysis of mortality data yielded a mortality odds ratio of 0.35 (95% CI, 0.16-0.74) for men, significantly different from values for women (P = .006). CONCLUSION After admission to intensive care from medical or surgical units, women had higher mortality rates than did men, and the difference was more pronounced in medical patients. The difference in mortality between sexes was not explained by delayed recognition of critical illness or delayed admission to intensive care.
Optimization of interconnect architectures through coding: A review

Interconnects are becoming a major bottleneck in the design of modern Systems-on-Chip. Power consumption, propagation delay, statistical variability, and reliability are some of the key challenges that must be addressed to fully optimize the interconnect architecture. This work provides a thorough review and analysis of the advantages and possibilities of coding for addressing the aforementioned challenges during the optimization of the communication architecture of a System-on-Chip. An outlook into future research directions is also presented.
Beyond admissibility: accepting cycles in argumentation with game protocols for cogency criteria

Fil: Bodanza, Gustavo Adrian. Universidad Nacional del Sur. Departamento de Humanidades; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Bahia Blanca. Instituto de Investigaciones Economicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economia. Instituto de Investigaciones Economicas y Sociales del Sur; Argentina
The Role of Spirituality in Sustaining the Psychological Well-Being of HIV-Positive Black Women

ABSTRACT Historically, spirituality has been an instrumental component to the survival of Black women. In an era when the HIV epidemic disproportionately compromises their health, it is imperative to explore spirituality's role in sustaining the psychological health of Black women living with HIV. This study examined the relationship between spirituality and self-reported depression among Black women living with HIV. A sample of 308 HIV-positive Black women were recruited from HIV/ AIDS clinics in the Southeastern United States. Participants completed an interview assessing demographics, quality of life, depression, coping, and spirituality. A hierarchical multiple regression was used to determine the association between spirituality and depression. The results suggest that in our sample, spirituality accounted for a small, yet significant proportion of variance in reducing depressive symptoms, above and beyond variance accounted for by demographic variables and other theoretically important psychosocial factors. In light of these findings, future studies with HIV-positive Black women should assess spirituality as a salient factor affecting psychological health. Developing interventions that address spirituality may serve to enhance women's psychological adjustment to living with HIV.
Geometric modeling and numerical simulation of the blood flow in human arterial system

Simulating the blood flow in human arterial system is an important task as it can help us to better understand the human arterial system and hence, cure and/or prevent heart and arterial diseases. From a physical point of view, the blood flow can be considered as a fluid dynamics problem (blood flow) coupled with a structural deflection problem (deflection of the blood vessel). This paper first presents a method of constructing 3D geometrical model for the human arterial system, which provides detailed physical parameters for the arterial system, including the distribution, the length, the diameter and the wall thickness. Then, an algorithm of solving the fluid-structure interaction problem based on 1D governing equation is introduced. A numerical simulation has been conducted to obtain the detailed blood flow information.
A Data Structure for Content-based Routing

In this paper, we formally define a useful data structure for content-based routing and event delivery. The poset (partially ordered set)-derived forest data structure is induced by the partial order created by the covering relation between event filters. Two extensions of the basic data structure are discussed: colored forests and the weakly merging forest. We give performance figures for the data structure using a workload generator and compare it with the filters poset structure. The experimental results indicate that the poset-derived forest performs considerably better than directed acyclic graph-based structures. The presented algorithms and mechanisms generalize to any covering relations and are thus applicable for different filtering languages.
Pleiotropy between Genetic Markers of Obesity and Risk of Prostate Cancer

Background: To address inconsistent findings of obesity and prostate cancer risk, we analyzed the association between prostate cancer and genetic markers of obesity and metabolism. Methods: Analyses included 176,520 single-nucleotide polymorphisms (SNP) associated with 23 metabolic traits. We examined the association between SNPs and prostate cancer in 871 cases and 906 controls, including 427 high-grade cases with Gleason ≥ 7. Genetic risk scores (GRS) for body mass index (BMI) and waist-to-hip ratio (WHR) were also created by summing alleles associated with increasing BMI or WHR. Results: Prostate cancer was associated with five loci, including cyclin M2, with P values less than 1 × 10−4. In addition, the WHR GRS was associated with high-grade prostate cancer versus controls [OR, 1.05; 95% confidence interval (CI), 1.00–1.11; P = 0.048] and high-grade prostate cancer versus low-grade prostate cancer (OR, 1.07; 95% CI, 1.01–1.13; P = 0.03). None of these findings exceeds the threshold for significance after correction for multiple testing. Conclusions: Variants in genes known to be associated with metabolism and obesity may be associated with prostate cancer. We show evidence for pleiotropy between WHR GRS and prostate cancer grade. This finding is consistent with the function of several WHR genes and previously described relationships with cancer traits. Impact: Limitations in standard obesity measures suggest alternative characterizations of obesity may be needed to understand the role of metabolic dysregulation in prostate cancer. The underlying genetics of WHR or other Metabochip SNPs, while not statistically significant beyond multiple testing thresholds within our sample size, support the metabolic hypothesis of prostate carcinogenesis and warrant further investigation in independent samples. Cancer Epidemiol Biomarkers Prev; 22(9); 1538–46. ©2013 AACR.
Different focuses image fusion with directional support value transform

To recover an everywhere-in-focus image, the multi-scale analysis image fusion is a classical method. Within these multi-resolution decompositions, the salient feature components image sequences with the largest magnitude are selected at each pixel location and finally, the fused image can be recovered from the decomposed components image sequences. Under the LS-SVM framework, salient features underlying image are represented by support values, and support value transform (SVT) has been developed for image fusion. To represent edges more efficiently, we analyze image under the weighted mapping LS-SVM framework, and deduce the directional support value filters and develop directional SVT to separate edges with different orientations in each image. The parameters of the weighted mapping LS-SVM for directional support value filter is optimized for the different focuses image fusion. Experimental results demonstrate that the proposed method can give superior results in the fused images comparing to the standard SVT and the discrete wavelet transform methods.
A track-before-detect algorithm for the detection of a Markov target in the presence of missing observations

In this paper we address the problem of multi-frame detection (MFD) of a Markov target observed through noisy measurements. To limit the system complexity, we gate the detector with a pre-processing stage which discards unreliable observations in each frame. A novel dynamic programming algorithm is introduced, which applies a track-before-detect (TBD) logic to declare the presence of the target and jointly estimate its position. The closed-form complexity analysis and the numerical examples show that advantageous complexity-performance tradeoffs can be obtained with respect to the single-frame detector and with respect to other strategies already present in the literature.
Process Improvement of Pap Smear Tracking in a Women's Medicine Center Clinic in Residency Training

Abstract: Application of Six‐Sigma methodology and Change Acceleration Process (CAP)/Work Out (WO) tools to track pap smear results in an outpatient clinic in a hospital‐based residency‐training program. Observational study of impact of changes obtained through application of Six‐Sigma principles in clinic process with particular attention to prevention of sentinel events. Using cohort analysis and applying Six‐Sigma principles to an interactive electronic medical record Soarian workflow engine, we designed a system of timely accession and reporting of pap smear and pathology results. We compared manual processes from January 1, 2007 to February 28, 2008 to automated processes from March 1, 2008 to December 31, 2009. Using the Six‐Sigma principles, CAP/WO tools, including “voice of the customer” and team focused approach, no outlier events went untracked. Applying the Soarian workflow engine to track prescribed 7 day turnaround time for completion, we identified 148 pap results in 3,936, 3 nongynecological results in 15, and 41 surgical results in 246. We applied Six‐Sigma principles to an outpatient clinic facilitating an interdisciplinary team approach to improve the clinic's reporting system. Through focused problem assessment, verification of process, and validation of outcomes, we improved patient care for pap smears and critical pathology.
“Get a Life!”: On Interviewing Law Clerks

Law clerks are a relatively underutilized yet potentially valuable resource for researchers. Their firsthand knowledge and practical experience provide much information about judges' decision making. Drawing on our experiences in studying clerks at both the U.S. Supreme Court and U.S. Courts of Appeals, we focus on written surveys and in-person interviewing. We discuss both theoretical and practical aspects of interviewing clerks, including how to approach potential respondents, the types of questions to ask, and issues about clerk-judge confidentiality.
Electron detection in the intermediate chamber of the variable pressure SEM

Two concepts of the secondary electron detection inside the intermediate chamber of the variable pressure scanning electron microscopy have been discussed. One of them, the two‐stage secondary electron detector has been the subject of previous publications. The other one, the intermediate secondary electron detector is an improved solution in respect of its complexity, vacuum demands and dimensions. Both detector systems apply some kinds of scintillators, so they present advantages specific for scintillator detectors but extended to a wide range of gas pressures, from high vacuum to pressures exceeding 10 mbar. The detector functioning is illustrated with computer simulations of electron and ion flow in the input region of the detectors made with use of the MC–SIMION program. This software combines a commercially available program SIMION 3D v.7. 0, destined for computations of charged particles trajectories in electric and magnetic fields, and the Monte Carlo one written in the SIMION internal language.
MicroRNA-204-5p inhibits invasion and metastasis of laryngeal squamous cell carcinoma by suppressing forkhead box C1

Background and aim: Understanding the molecular biological mechanisms underlying laryngeal squamous cell carcinoma (LSCC) invasion and metastasis is crucial for diagnosis, treatment, and prognosis. We aimed to examine the expression of the tumor suppressor microRNA-204-5p (miR-204-5p) and its target gene, forkhead box C1 (FOXC1), in human LSCC and explore their roles in the malignant behaviors of LSCC Hep-2 and TU-177 cells. Methods: The regulatory effects of miR-204-5p on the 3′ untranslated region of FOXC1 predicted by bioinformatics were tested by dual-luciferase reporter assay. Quantitative RT-PCR was used to detect mRNA expression in 43 fresh samples of LSCC and corresponding adjacent normal mucosa (ANM). FOXC1 protein expression was examined by immunohistochemistry. miR-204-5p mimics and FOXC1 siRNA were transfected into LSCC cell lines Hep-2 and TU-177 to observe malignant behavior. miR-204-5p mimics were injected into Hep-2 or TU-177 xenograft tumors in nude mice to examine tumor growth. Results: The miR-204-5p mRNA level was lower in all 43 LSCC samples than in the ANM samples, but the FOXC1 level was higher in the LSCC samples than in the ANM samples. The miR-204-5p level was lower for stage III and IV cancer and lymph node N+ status samples than for stage I and II cancer and N0 status samples. FOXC1 mRNA and protein levels were higher for N+ than for N0 LSCC. The miR-204-5p mRNA levels were lower in Hep-2 and TU-177 cells than in ANM tissues, but FOXC1 mRNA levels were higher in Hep-2 and TU-177 cells than in ANM tissues. Dual-luciferase reporter assays demonstrated the targeted regulatory effects of miR-204-5p on the FOXC1 3′ UTR. Cell proliferation and colony formation was facilitated with miR-204-5p mimics and FOXC1 siRNA, with weaker cell migration and invasion than the controls. Moreover, miR-204-5p overexpression or FOXC1 knockdown inhibited the EMT process in LSCC cells. In vivo experiments demonstrated that injection of miR-204-5p into Hep-2 and TU-177 xenograft tumors in nude mice significantly inhibited tumor growth. Conclusions: miR-204-5p is involved in the invasion and metastasis of LSCC. It has a targeted regulatory effect on FOXC1 expression; malignant LSCC behaviors, including cell proliferation, invasion, and metastasis, are suppressed, and tumor growth in vivo is inhibited. This suggests that miR-204-5p may be a target for molecular therapy of LSCC in the future.
Two Projection Streams from Macaque V1 to the Pale Cytochrome Oxidase Stripes of V2

In the primate visual cortex, areas V1 and V2 distribute information they receive from the retina to virtually all extrastriate cortex, parsing this information into dorsal and ventral streams. Therefore, understanding the connectivity between V1 and V2 is crucial to understand visual cortical processing. Cytochrome oxidase staining in V2 reveals a repeating pattern of pale–thick–pale–thin stripes. V1 sends parallel output pathways to distinct V2 stripes. Previous models proposed either three or two parallel V1-to-V2 pathways in macaque, but both models viewed the two pale stripes within a single stripe cycle as a single compartment. However, recent studies have suggested that the two pale stripes may be functionally distinct, and in marmosets they also differ anatomically in the laminar origin of projections they receive from V1. Here we have asked whether the two pale stripes are also anatomically distinct in macaque. We made small retrograde tracer injections in different pale stripe types. We found that while both pale stripes receive a predominant V1 input from layers 2/3, only one set of pale stripes (pale lateral) receives significant projections from layer 4B, while the other set (pale medial) receives few or no layer 4B projections. Moreover, different tracer injections in nearby pale stripe types revealed that 97–99% of layer 2/3 cells only project to a single pale stripe type. These results demonstrate that in macaque, the two pale stripes are anatomically distinct compartments, and support the notion of two distinct projection streams from V1 to the two pale stripes of V2.
MCMC Approach to Classical Estimation with Overidentifying Restrictions

This paper extends the Laplace estimators proposed by Chernozhukov and Hong (2003) to incorporate the statistic that tests the overidentifying restrictions in the GMM. This information was previously ignored during parameter estimation in econometrics with Bayesian methods. The parameters and the test statistic are estimated simultaneously using information in the entire domain of the estimation equations, not at the global minimum only. We avoid the curse of dimensionality by using MCMC, following Chernozhukov and Hong (2003). Multivariate kernel density estimation gives a smooth distribution of the parameter values that are a solution to the optimization in Laplace estimation. The transformed estimators perform better in a simulation exercise than those version that do not use the information in the OR during parameter estimation. Furthermore, the kernel density also allows for the calculation of alternative estimators that condition the estimation on the OR being satisfied. In the presence of multiple solutions of the GMM objective function, conditioning on the OR brings economic theory as a criteria for estimate selection. As a consequence, our estimators perform better than their unconditional counterparts in a simulation exercise. We simulate a model in Hall and Horowitz (1996) that frequently presents multiple local solutions.