Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Ear Segmentation and Classification Through Indexing

Current growth trends in different biometrics applications present challenges to researchers. To address these challenges, we need new data storage and retrieval techniques to make the recognition process time efficient. This paper presents a system for time efficient 3D ear biometrics from a large biometrics database. The proposed system has two components that are primarily responsible for: 1) automatic 3D ear segmentation and 2) hierarchical categorization of the 3D ear database using the shape information and surface depth information, respectively. We use an active contour algorithm along with a tree-structured graph to segment the ear region from the 3D profile images. The segmented 3D ear database is then categorized based on the geometrical feature values, computed from the ear shape, into oval, round, rectangular, and triangular categories. For the categorization based on the depth information, the feature space is partitioned using tree-based indexing techniques. We used indexing techniques with balanced split (k-dimensional (KD) tree) and unbalanced split (pyramid tree) data structures to categorize the database separately and then compared their retrieval efficiency. Experiments are conducted to compare the average computation time per query when performing recognition through hierarchical categorization with the average computation time when recognition is based on sequential search. Experimental results without indexing conducted on the University of Notre Dame Collection J2 data set yielded a rank-one recognition rate of 98.5%. We applied the indexing technique to compute the rank-one recognition accuracy with a 10%-50% search space reduction using a 10% step size. With 10%, 20%, 30%, 40%, and 50% search space reduction the rank-one recognition accuracy gracefully degrades to 96.87%, 96.14%, 95.18%, 94.21%, and 93.49%, respectively, while performing nearly 3, 3.3, 4, 4.2, and 5 times faster than the state-of-the-art technique that uses sequential search.
Olfactory Neuroblastomas: An Experience of 24 Years

Objective. The aim of this study was to evaluate clinicopathological findings and the efficacy of the treatment modalities used in patients with olfactory neuroblastomas. Study Design. Retrospective record review. Setting. Istanbul University, Cerrahpasa Medical Faculty, medical oncology outpatient clinic. Subjects and Methods. There were 3 stage A tumors, 5 stage B and 11 stage C according to the Kadish staging system. There were 5 grade I/II and 12 grade III/IV according to the Hyams' histopathologic system. Involvement to orbita was detected in eight patients at the time of diagnosis. Results. The median follow-up period was 23.7 months. The 5-year survival rate for the whole group was 26%. The stage A/B groups exhibited a better survival rate than the C group with 2-year survival rates being 25 versus 71% respectively (P = .008). The grade I/II groups exhibited a better survival rate than the grade III/IV groups with 2-year survival rates being 50 versus 16% respectively (P = .001). The group who had orbital involvement exhibited a poor survival rate than the group of patients who had no involvement of the orbital. Conclusion. In our study, tumor stage, histopathologic grading, involvement of the orbita, brain and bone marow metastases were the statistically significant prognostic factors.
PUBLIC HEALTH GOALS FOR CHEMICALS IN DRINKING WATER LEAD

This Public Health Goal (PHG) technical support document provides information on health effects from contaminants in drinking water. PHGs are developed for chemical contaminants based on the best available toxicological data in the scientific literature. These documents and the analyses contained in them provide estimates of the levels of contaminants in drinking water that would pose no significant health risk to individuals consuming the water on a daily basis over a lifetime.
Apoptosis of Glutamatergic Neurons Fails to Trigger a Neurogenic Response in the Adult Neocortex

Adult neurogenesis is actively studied in part because of the potential to manipulate endogenous neural stem and progenitor cells for tissue repair. Although constitutive generation of neurons in the adult rodent olfactory bulb and hippocampal dentate gyrus is widely accepted and stroke-induced generation of striatal inhibitory neurons consistently observed, evidence supporting the generation of neurons in the neocortex after neuronal loss remains slim. Nevertheless, a few studies suggested that targeted apoptosis of neocortical glutamatergic neurons could trigger the generation of new ones in the adult brain. In light of such studies, we tested whether apoptosis of glutamatergic cortical neurons using two novel transgenic approaches in mice, an inducible Caspase-8 protein and an inducible diphtheria toxin gene, results in new neurons. After a thorough analysis, no new neurons were detected in the neocortex. Interestingly, an increase in the expression of the neuroblast marker DCX was observed in both models, in some cases in cells with morphologies previously associated with poststroke neuroblasts, but DCX+ cells coexpressed the oligodendrocyte precursor marker Olig2, suggesting caution when using DCX as a marker for neuroblasts after injury. Given that the adult neocortex lacks an innate potential to regenerate lost glutamatergic neurons, future strategies should concentrate on manipulating the differentiation potential of endogenous or exogenous precursor cells.
Interactions Between Markers Can Be Caused by the Dominance Effect of Quantitative Trait Loci

F2 populations are commonly used in genetic studies of animals and plants. For simplicity, most quantitative trait locus or loci (QTL) mapping methods have been developed on the basis of populations having two distinct genotypes at each polymorphic marker or gene locus. In this study, we demonstrate that dominance can cause the interactions between markers and propose an inclusive linear model that includes marker variables and marker interactions so as to completely control both additive and dominance effects of QTL. The proposed linear model is the theoretical basis for inclusive composite-interval QTL mapping (ICIM) for F2 populations, which consists of two steps: first, the best regression model is selected by stepwise regression, which approximately identifies markers and marker interactions explaining both additive and dominance variations; second, the interval mapping approach is applied to the phenotypic values adjusted by the regression model selected in the first step. Due to the limited mapping population size, the large number of variables, and multicollinearity between variables, coefficients in the inclusive linear model cannot be accurately determined in the first step. Interval mapping is necessary in the second step to fine tune the QTL to their true positions. The efficiency of including marker interactions in mapping additive and dominance QTL was demonstrated by extensive simulations using three QTL distribution models with two population sizes and an actual rice F2 population.
Training Neural Networks with Stochastic Hessian-Free Optimization

Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property and study stochastic HF with gradient and curvature mini-batches independent of the dataset size. We modify Martens' HF for these settings and integrate dropout, a method for preventing co-adaptation of feature detectors, to guard against overfitting. Stochastic Hessian-free optimization gives an intermediary between SGD and HF that achieves competitive performance on both classification and deep autoencoder experiments.
Cardiorenal end points in a trial of aliskiren for type 2 diabetes.

BACKGROUND This study was undertaken to determine whether use of the direct renin inhibitor aliskiren would reduce cardiovascular and renal events in patients with type 2 diabetes and chronic kidney disease, cardiovascular disease, or both. METHODS In a double-blind fashion, we randomly assigned 8561 patients to aliskiren (300 mg daily) or placebo as an adjunct to an angiotensin-converting-enzyme inhibitor or an angiotensin-receptor blocker. The primary end point was a composite of the time to cardiovascular death or a first occurrence of cardiac arrest with resuscitation; nonfatal myocardial infarction; nonfatal stroke; unplanned hospitalization for heart failure; end-stage renal disease, death attributable to kidney failure, or the need for renal-replacement therapy with no dialysis or transplantation available or initiated; or doubling of the baseline serum creatinine level. RESULTS The trial was stopped prematurely after the second interim efficacy analysis. After a median follow-up of 32.9 months, the primary end point had occurred in 783 patients (18.3%) assigned to aliskiren as compared with 732 (17.1%) assigned to placebo (hazard ratio, 1.08; 95% confidence interval [CI], 0.98 to 1.20; P=0.12). Effects on secondary renal end points were similar. Systolic and diastolic blood pressures were lower with aliskiren (between-group differences, 1.3 and 0.6 mm Hg, respectively) and the mean reduction in the urinary albumin-to-creatinine ratio was greater (between-group difference, 14 percentage points; 95% CI, 11 to 17). The proportion of patients with hyperkalemia (serum potassium level, ≥6 mmol per liter) was significantly higher in the aliskiren group than in the placebo group (11.2% vs. 7.2%), as was the proportion with reported hypotension (12.1% vs. 8.3%) (P<0.001 for both comparisons). CONCLUSIONS The addition of aliskiren to standard therapy with renin-angiotensin system blockade in patients with type 2 diabetes who are at high risk for cardiovascular and renal events is not supported by these data and may even be harmful. (Funded by Novartis; ALTITUDE ClinicalTrials.gov number, NCT00549757.).
Podality-Based Time-Optimal Computations on Enhanced Meshes

The main contribution of this paper is to present simple and elegant podality-based algorithms for a variety of computational tasks motivated by, and finding applications to, pattern recognition, computer graphics, computational morphology, image processing, robotics, computer vision, and VLSI design. The problems that we address involve computing the convex hull, the diameter, the width, and the smallest area enclosing rectangle of a set of points in the plane, as well as the problems of finding the maximum Euclidian distance between two planar sets of points, and of constructing the Minkowski sum of two convex polygons. Specifically, we show that once we fix a positive constant /spl epsiv/, all instances of size m, (n/sup 1/2 +/spl epsiv///spl les/m/spl les/n) of the problems above, stored in the first [m//spl radic/n] columns of a mesh with multiple broadcasting of size /spl radic/n/spl times//spl radic/n can be solved time-optimally in /spl Theta/(m//spl radic/n) time.
Schedulability Analysis Towards Arbitrarily Activated Tasks in Mixed-Criticality Systems

The integration of mixed-critical tasks into a platform is an increasingly important trend in the design of real-time systems due to its efficient resource usage. With a growing variety of activation patterns considered in real-time systems, some of them capture arbitrary activation patterns. As a consequence, the existing scheduling approaches in mixed-criticality systems (MCs), which assume the sporadic tasks with implicit deadlines, have sometimes become inapplicable or are ineffective. In this paper, we extend the sporadically activated task model to the arbitrarily activated task model in MCs with the preemptive fixed-task-priority schedule. By using the event arrival curve to model task activations, we present the necessary and sufficient schedulability tests that are based on the well-established results from Real-Time Calculus. We propose to use the busy-window analysis to do the sufficient test because it has been shown to be tighter than the sufficient test of using Real-Time Calculus. According.
EXACT STRING BACKGROUNDS FROM WZW MODELS BASED ON NON-SEMISIMPLE GROUPS

We formulate WZW models based on a centrally extended version of the Euclidean group in d dimensions. We obtain string backgrounds corresponding to conformal σ models in D=d2 space-time dimensions with exact central charge c=d2 and d(d−1)/2 null Killing vectors. By identifying the corresponding conformal field theory we show that the one-loop results coincide with the exact ones up to a shifting of a parameter.
Endogenous and Exogenous Stem/Progenitor Cells in the Lung and Their Role in the Pathogenesis and Treatment of Pediatric Lung Disease

The lung is a complex organ with a vast surface area whose main function is to release cellular waste to be exhaled and to replenish the supply of oxygen to the tissues of the body. The conduction of air from the external environment is not without risks, and the lung contains many specialized epithelial cell subtypes that are protecting the lung from foreign material and injury. Specialized cell subtypes are produced during lung development in the fetus as well as postnatally and injury to them due to genetic disease, premature birth, or postnatal environmental injury may lead to devastating disease. Chronic diseases, such as bronchopulmonary dysplasia, cystic fibrosis, and pulmonary arterial hypertension, contribute significantly to morbidity and mortality worldwide, yet successful interventions are often limited. Stem/progenitor cells have emerged as a potentially new preventative or therapeutic option. They are generally defined by the ability to undergo self-renewal and give rise to more differentiated cells. They are important in the early development of embryonic structures and organ differentiation in utero. Postnatally, they function in continued growth, maintenance, and regeneration. Clinically, the immunomodulatory properties of some classes of stem/progenitor cells avoid the major obstacle of immunological rejection seen in organ transplantation and other cell therapies. This review highlights some known human progenitor/stem cells and the most recent advances in stem cell therapies both in vivo and in vitro to prevent and treat pediatric lung disease.
Neuromagnetic Vistas into Typical and Atypical Development of Frontal Lobe Functions

The frontal lobes are involved in many higher-order cognitive functions such as social cognition executive functions and language and speech. These functions are complex and follow a prolonged developmental course from childhood through to early adulthood. Magnetoencephalography (MEG) is ideal for the study of development of these functions, due to its combination of temporal and spatial resolution which allows the determination of age-related changes in both neural timing and location. There are several challenges for MEG developmental studies: to design tasks appropriate to capture the neurodevelopmental trajectory of these cognitive functions, and to develop appropriate analysis strategies to capture various aspects of neuromagnetic frontal lobe activity. Here, we review our MEG research on social and executive functions, and speech in typically developing children and in two clinical groups – children with autism spectrum disorder and children born very preterm. The studies include facial emotional processing, inhibition, visual short-term memory, speech production, and resting-state networks. We present data from event-related analyses as well as on oscillations and connectivity analyses and review their contributions to understanding frontal lobe cognitive development. We also discuss the challenges of testing young children in the MEG and the development of age-appropriate technologies and paradigms.
Python I, II, and III Cosmic Microwave Background Anisotropy Measurement Constraints on Open and Flat-Λ Cold Dark Matter Cosmogonies

We use Python I, II, and III cosmic microwave background anisotropy data to constrain cosmogonies. We account for the Python beamwidth and calibration uncertainties. We consider open and spatially flat-Λ cold dark matter cosmogonies, with nonrelativistic mass density parameter Ω0 in the range 0.1-1, baryonic mass density parameter ΩB in the range (0.005-0.029) h-2, and age of the universe t0 in the range (10-20) Gyr. Marginalizing over all parameters but Ω0, the combined Python data favor an open (spatially flat-Λ) model with Ω0 ≃ 0.2 (0.1). At the 2 σ confidence level, model normalizations deduced from the combined Python data are mostly consistent with those drawn from the DMR, UCSB South Pole 1994, ARGO, MAX 4 and 5, White Dish, and SuZIE data sets.
Identity Tensions During an NGO Name Change: How Managers Experience an International Organization’s Rebranding Effort

ABSTRACT This study examined emergent identity tensions among managers of a non-governmental organization (NGO) during a major rebranding initiative. With respect to organizational identification, NGO managers expressed pride in their organization, frustration regarding public ignorance about their organization, and broader connection with NGO identity. Resulting identity tensions of certainty/uncertainty, transparency/opacity, and participation/restriction highlight that NGO managers identify more closely with their organization when it adheres to mission objectives and encourages stakeholder inclusivity, and less closely when their organization loses identity in the renaming process, or when there is less perceived transparency and inclusion. Findings suggest that NGO managers and organizational researchers may benefit from further investigation into renaming campaigns in light of NGO mission, as well as the resulting stakeholder identity implications.
Research on a Household Dual Heat Source Heat Pump Water Heater with Preheater Based on ASPEN PLUS

This article proposes a dual heat source heat pump bathroom unit with preheater which is feasible for a single family. The system effectively integrates the air source heat pump (ASHP) and wastewater source heat pump (WSHP) technologies, and incorporates a preheater to recover shower wastewater heat and thus improve the total coefficient of performance (COP) of the system, and it has no electric auxiliary heating device, which is favorable to improve the security of the system operation. The process simulation software ASPEN PLUS, widely used in the design and optimization of thermodynamic systems, was used to simulate various cases of system use and to analyze the impact of the preheater on the system. The average COP value of a system with preheater is 6.588 and without preheater it is 4.677. Based on the optimization and analysis, under the standard conditions of air at 25 °C, relative humidity of 70%, wastewater at 35 °C, wastewater flow rate of 0.07 kg/s, tap water at 15 °C, and condenser outlet water temperature at 50 °C, the theoretical COP of the system can reach 9.784 at an evaporating temperature of 14.96 °C, condensing temperature of 48.74 °C, and preheated water temperature of 27.19 °C.
The Evolution of Musical Diversity: The Key Role of Vertical Transmission

Music, like languages, is one of the key components of our culture, yet musical evolution is still poorly known. Numerous studies using computational methods derived from evolutionary biology have been successfully applied to varied subset of linguistic data. One of the major drawback regarding musical studies is the lack of suitable coded musical data that can be analysed using such evolutionary tools. Here we present for the first time an original set of musical data coded in a way that enables construction of trees classically used in evolutionary approaches. Using phylogenetic methods, we test two competing theories on musical evolution: vertical versus horizontal transmission. We show that, contrary to what is currently believed, vertical transmission plays a key role in shaping musical diversity. The signal of vertical transmission is particularly strong for intrinsic musical characters such as metrics, rhythm, and melody. Our findings reveal some of the evolutionary mechanisms at play for explaining musical diversity and open a new field of investigation in musical evolution.
Metabolic Monitoring for Patients Treated with Antipsychotic Medications

Objectives: Metabolic side effects of antipsychotic treatment include weight gain, dyslipidemia and increased susceptibility to diabetes. Patients with schizophrenia have increased coronary heart disease mortality and reduced life expectancy. There is an urgent clinical need to monitor antipsychotic-treated patients for metabolic disturbance. Our objectives were to review published international monitoring guidelines, establish goals for metabolic monitoring, and make recommendations for practice. Method: We reviewed the major published consensus guidelines for metabolic monitoring of patients treated with antipsychotic medications and selectively reviewed practice guidelines for the management of diabetes, dyslipidemia, and hypertension. Results: Patients with serious mental illness have markedly elevated rates of metabolic disturbance and limited access to general medical care. Monitoring, but not necessarily medical treatment of metabolic disorder, falls within the scope of psychiatric practice and should include screening for metabolic disturbance as well as tracking the effects of antipsychotic treatment. In addition, psychiatrists and psychiatric services should work toward facilitating patients' access to medical care. There is considerable consensus in the published guidelines. Areas of dissent include which patients to monitor, the utility of glucose tolerance testing, and the point at which to consider switching antipsychotics. Conclusion: We encourage clinicians to adopt a structured system for conducting and recording metabolic monitoring and to develop collaborations with family physicians, diabetes specialists, dieticians, and recreation therapists to facilitate appropriate medical care for antipsychotic-treated patients.
Advancing Public Trust Relationships in Electronic Government: The Singapore E-Filing Journey

E-governments have become an increasingly integral part of the virtual economic landscape. However, e-government systems have been plagued by an unsatisfactory, or even a decreasing, level of trust among citizen users. The political exclusivity and longstanding bureaucracy of governmental institutions have amplified the level of difficulty in gaining citizens' acceptance of e-government systems. Through the synthesis of trust-building processes with trust relational forms, we construct a multidimensional, integrated analytical framework to guide our investigation of how e-government systems can be structured to restore trust in citizen-government relationships. Specifically, the analytical framework identifies trust-building strategies (calculative-based, prediction-based, intentionality-based, capability-based, and transference-based trust) to be enacted for restoring public trust via e-government systems. Applying the analytical framework to the case of Singapore's Electronic Tax-Filing (E-Filing) system, we advance an e-government developmental model that yields both developmental prescriptions and technological specifications for the realization of these trust-building strategies. Further, we highlight the impact of sociopolitical climates on the speed of e-government maturity.