Miracle-dz/UniFAR-FTUs huggingface dataset:
Incremental Text Indexing for Fast Disk-Based Search

Real-time search requires to incrementally ingest content updates and almost immediately make them searchable while serving search queries at low latency. This is currently feasible for datasets of moderate size by fully maintaining the index in the main memory of multiple machines. Instead, disk-based methods for incremental index maintenance substantially increase search latency with the index fragmented across multiple disk locations. For the support of fast search over disk-based storage, we take a fresh look at incremental text indexing in the context of current architectural features. We introduce a greedy method called Selective Range Flush (SRF) to contiguously organize the index over disk blocks and dynamically update it at low cost. We show that SRF requires substantial experimental effort to tune specific parameters for performance efficiency. Subsequently, we propose the Unified Range Flush (URF) method, which is conceptually simpler than SRF, achieves similar or better performance with fewer parameters and less tuning, and is amenable to I/O complexity analysis. We implement interesting variations of the two methods in the Proteus prototype search engine that we developed and do extensive experiments with three different Web datasets of size up to 1TB. Across different systems, we show that our methods offer search latency that matches or reduces up to half the lowest achieved by existing disk-based methods. In comparison to an existing method of comparable search latency on the same system, our methods reduce by a factor of 2.0--2.4 the I/O part of build time and by 21--24% the total build time.
Fast Image-based Rendering using Hierarchical Image-based Priors

Novel view synthesis using image-based priors has recently been shown to provide high quality renderings of complex 3D scenes. However, current methods are extremely slow, requiring of the order of hours to render a single frame. In this paper we show how a coarse-to-fine method can be used to reduce this time significantly. In contrast to traditional multiple-view stereo methods, devising a coarse-to-fine strategy for this problem is complicated by the fact that image-based priors are strongly tied to the scale at which rendering is performed. We show how a hierarchical decomposition of the texture patch database both allows multiple-scale analysis and speeds up the imposition of the priors. Examples are shown on a number of challenging sequences, and illustrate that the new method yields comparable results to the previous method, with significant gains in speed.
Exact Gravitational Shockwaves and Planckian Scattering on Branes

We obtain a solution describing a gravitational shockwave propagating along a Randall-Sundrum brane. The interest of such a solution is twofold: on the one hand, it is the rst exact solution for a localized source on a Randall-Sundrum three-brane. On the other hand, one can use it to study forward scattering at Planckian energies, including the eects of the continuum of Kaluza-Klein modes. We map out the dierent regimes for the scattering obtained by varying the center-of-mass energy and the impact parameter. We also discuss exact shockwaves in ADD scenarios with compact extra dimensions.
The Research on Long-Term Financial Early-Warning System of Listed Companies

The article takes listed companies as research samples. Firstly, it selects 36 ST or *ST companies listed in Shanghai and Shenzhen Stock Exchange Market, who received special treatment during 2007 to 2009 for the first time and it also chooses another 36 normal companies as paired ones. Then, after using Factor analysis for identifying indexes, the paper go on with utilizing logistic to structure a financial long-term warning model. To verify the effectiveness of the model, the paper selects another 12 financial crisis companies and 12 financial fit companies to test. The results come out to show that establishing an effective long-term financial early-warning system helps enterprises to avoid financial crisis.
Source based morphometry using structural MRI phase images to identify sources of gray matter and white matter relative differences in schizophrenia versus controls

We present a novel multivariate approach called source based morphometry (SBM) to study the novel structural MRI phase images and get sources of relative gray matter and white matter differences between patients and healthy controls. SBM considers the information cross brain voxels and provides spatially maximal independent sources about localization of changes. The structural MRI phase images efficiently summarize the relationship that exists between the gray and white matter without having to increase the dimensionality of the problem. SBM was then applied to the phase images. Results identified patient versus control differences in gray matter and white matter for visual-motor cortex as well as other areas. These interesting findings show that SBM is a useful multivariate approach for studying the brain. Moreover, the use of structural MRI phase images to joint gray and white matter together provides a significant advantage.
Marker Detection and Tracking for Augmented Reality Applications

This paper explores a simple method for detecting and tracking “fiducial” markers in a webcam video stream. The system first uses SIFT feature matching to detect when a marker is present in a frame of the video stream. Then, the detected keypoints are given to a KLT optical flow tracker, which tracks the keypoints frame-by-frame as they move through the video. In this paper, we will describe the method in detail, and present our results and analysis.
Increasing the Gap between Descriptional Complexity and Algorithmic Probability

The coding theorem is a fundamental result of algorithmic information theory. A well known theorem of Gács shows that the analog of the coding theorem fails for continuous sample spaces. This means that descriptional monotonic complexity does not coincide within an additive constant with the negative logarithm of algorithmic probability. Gács's proof provided a lower bound on the difference between these values. He showed that for infinitely many finite binary strings, this difference was greater than a version of the inverse Ackermann function applied to string length. This paper establishes that this lower bound can be substantially improved. The inverse Ackermann function can be replaced with a function O(log(log(x))). This shows that in continuous sample spaces, descriptional monotonic complexity and algorithmic probability are very different. While this proof builds on the original work by Gács, it does have a number of new features, in particular, the algorithm at the heart of the proof works on sets of strings as opposed to individual strings.
Behavioral and Biochemical Evidences for Antidepressant-Like Activity of Celastrus Paniculatus Seed Oil in Mice

Introduction: Celastrus paniculatus seed oil, commonly known as Malkangni or Jyotishmati, was in use from time immemorial to treat brain related disorders. Celastrus paniculatus seed oil has significant antidepressant-like activity in chronic unpredictable stressed mice. The present study was undertaken to evaluate the antidepressant-like effect of Celastrus paniculatus seed oil in unstressed mice and to explore its mechanism of action. Methods: The seed oil (50, 100, and 200 mg/kg, PO) and fluoxetine per se were administered for 14 successive days to Swiss young albino mice. On the 14th day, 60 min after drug administration, animals were subjected to Tail Suspension Test (TST) and Forced Swim Test (FST). The mechanism of action was also studied. Results: The oil significantly decreased immobility period of mice in both tail suspension test and forced swim test, indicating its significant antidepressant-like activity. The efficacy was found to be comparable to fluoxetine (P<0.0001). ED50 value of celastrus seed oil using FST and TST were 17.38 and 31.62 mg/kg, respectively. The oil did not show any significant effect on locomotor activity. It significantly inhibited brain MAO–A activity and decreased plasma corticosterone levels. Sulpiride (selective D2-receptor antagonist), p-CPA (tryptophan hydroxylase inhibitor), and baclofen (GABAB agonist) significantly attenuated the oil-induced antidepressant-like effect, when assessed during TST. Discussion: Celastrus paniculatus seed oil produced significant antidepressant-like effect in mice possibly through interaction with dopamine D2, serotonergic, and GABAB receptors; as well as inhibition of MAO–A activity and decrease in plasma corticosterone levels.
The interdomain interface in bifunctional enzyme protein 3/4A (NS3/4A) regulates protease and helicase activities

Hepatitis C (HCV) protein 3/4A (NS3/4A) is a bifunctional enzyme comprising two separate domains with protease and helicase activities, which are essential for viral propagation. Both domains are stable and have enzymatic activity separately, and the relevance and implications of having protease and helicase together as a single protein remains to be explored. Altered in vitro activities of isolated domains compared with the full‐length NS3/4A protein suggest the existence of interdomain communication. The molecular mechanism and extent of this communication was investigated by probing the domain–domain interface observed in HCV NS3/4A crystal structures. We found in molecular dynamics simulations that the two domains of NS3/4A are dynamically coupled through the interface. Interestingly, mutations designed to disrupt this interface did not hinder the catalytic activities of either domain. In contrast, substrate cleavage and DNA unwinding by these mutants were mostly enhanced compared with the wild‐type protein. Disrupting the interface did not significantly alter RNA unwinding activity; however, the full‐length protein was more efficient in RNA unwinding than the isolated protease domain, suggesting a more direct role in RNA processing independent of the interface. Our findings suggest that HCV NS3/4A adopts an “extended” catalytically active conformation, and interface formation acts as a switch to regulate activity. We propose a unifying model connecting HCV NS3/4A conformational states and protease and helicase function, where interface formation and the dynamic interplay between the two enzymatic domains of HCV NS3/4A potentially modulate the protease and helicase activities in vivo.
Factors influencing the robustness of P-value measurements in CT texture prognosis studies

Several studies have recently reported on the value of CT texture analysis in predicting survival, although the topic remains controversial, with further validation needed in order to consolidate the evidence base. The aim of this study was to investigate the effect of varying the input parameters in the Kaplan–Meier analysis, to determine whether the resulting P-value can be considered to be a robust indicator of the parameter’s prognostic potential. A retrospective analysis of the CT-based normalised entropy of 51 patients with lung cancer was performed and overall survival data for these patients were collected. A normalised entropy cut-off was chosen to split the patient cohort into two groups and log-rank testing was performed to assess the survival difference of the two groups. This was repeated for varying normalised entropy cut-offs and varying follow-up periods. Our findings were also compared with previously published results to assess robustness of this parameter in a multi-centre patient cohort. The P-value was found to be highly sensitive to the choice of cut-off value, with small changes in cut-off producing substantial changes in P. The P-value was also sensitive to follow-up period, with particularly noisy results at short follow-up periods. Using matched conditions to previously published results, a P-value of 0.162 was obtained. Survival analysis results can be highly sensitive to the choice in texture cut-off value in dichotomising patients, which should be taken into account when performing such studies to avoid reporting false positive results. Short follow-up periods also produce unstable results and should therefore be avoided to ensure the results produced are reproducible. Previously published findings that indicated the prognostic value of normalised entropy were not replicated here, but further studies with larger patient numbers would be required to determine the cause of the different outcomes.
Utilization of Data-Mining Techniques for Evaluation of Patterns of Asthma Drugs Use by Ambulatory Patients in a Large Health Maintenance Organization

A major problem of drugs utilization is to identify outlier patients who are using large quantities of drugs over extended periods of time. Today, healthcare and health insurance systems have to deal with an increased number of patients suffering from chronic diseases, such as asthma, who are continuously using a combination of several medications. This has caused a substantial increase in the cost of providing healthcare for such patients. In Israel, 11% of the national health care budget is spent on medications. However, healthcare management operations do not have the information that can assist in determining whether extensive multi-year drug utilization by a chronic patient is an outlier or misuse of resources. In this work, we construct a prediction model for asthma drug utilization by applying novel methods of knowledge discovery in time-series databases to a multi-year asthma drug utilization data set. Methods of mining utilization patterns combine clustering algorithms, clustering validity measures, and decision-tree classification algorithms. This methodology is applied to a regional patients' database maintained in 'Clalit Health Services' HMO, Beer-Sheva, Israel between January 2000 and November 2002. The clustering results reveal that 274 asthma patients who received 9,319 prescriptions during that period can be partitioned into three groups of utilization patterns, where ten patients (3.6%) who used 1,333 prescriptions (14.3%) are classified as outliers. The classification results show that the use of corticosteroids medications (oral or by inhalation) and the age of a patient can be considered as the main predictive factors in the induced models.
Isolated-Word Recognition with Penalized Logistic Regression Machines

We propose a new approach to isolated-word speech recognition based on penalized logistic regression machines (PLRMs). With this approach we combine the hidden Markov model (HMM) with multiclass logistic regression resulting in a powerful speech recognizer which provides us with the posterior probability for each word. Experiments on the English E-set show significant improvements compared to conventional HMM-based speech recognition
Replication of Small Effect Quantitative Trait Loci for Behavioral Traits Facilitated by Estimation of Effect Size from Independent Cohorts

Quantitative trait locus (QTL) mapping is often done in a single segregating population, such as a backcross or an intercross. Both QTL location and effect size are then estimated from the same dataset. This approach results in an over‐estimate of effect size for two reasons: (1) LOD scores, which are maximized over numerous point‐wise tests, are correlated with estimated effect size and (2) small effect QTLs are often undetected in underpowered experiments, yielding inflated effect sizes for detected QTLs (the Beavis effect). When it is impractical to maintain or generate large population sizes, an alternative is to use two populations, one for initial detection and localization and a second for a locus‐matched estimate of effect size, not conditioned on significance. Recombinant inbred (RI) panels are eminently suitable for this approach, as each strain genotype can be sampled repeatedly. We present mapping results from the LXS RI panel for two behavioral phenotypes relating to ethanol response: low‐dose ethanol activation and loss of righting following high‐dose injection. Both the phenotypes were measured in two or three independent cohorts, which were then used to re‐estimate effect size. Many small‐effect QTLs replicated using this approach, but in all cases, effect size, in the replicate cohorts, was reduced from the initial estimate, often substantially. Such a reduction will have important consequences for power analyses in which sample sizes are determined for subsequent confirmation studies.
Comparing neural substrates of emotional vs. non-emotional conflict modulation by global control context

The efficiency with which the brain resolves conflict in information processing is determined by contextual factors that modulate internal control states, such as the recent (local) and longer-term (global) occurrence of conflict. Local “control context” effects can be observed in trial-by-trial adjustments to conflict (congruency sequence effects: less interference following incongruent trials), whereas global control context effects are reflected in adjustments to the frequency of conflict encountered over longer sequences of trials (“proportion congruent effects”: less interference when incongruent trials are frequent). Previous neuroimaging and lesion studies suggest that the modulation of conflict-control processes by local control context relies on partly dissociable neural circuits for cognitive (non-emotional) vs. emotional conflicts. By contrast, emotional and non-emotional conflict-control processes have not been contrasted with respect to their modulation by global control context. We addressed this aim in a functional magnetic resonance imaging (fMRI) study that varied the proportion of congruent trials in emotional vs. non-emotional conflict tasks across blocks. We observed domain-general conflict-related signals in the dorsal anterior cingulate cortex (dACC) and pre-supplementary motor area and, more importantly, task-domain also interacted with global control context effects: specifically, the dorsal striatum and anterior insula tracked control-modulated conflict effects exclusively in the emotional domain. These results suggest that, similar to the neural mechanisms of local control context effects, there are both overlapping as well as distinct neural substrates involved in the modulation of emotional and non-emotional conflict-control by global control context.
Using contextual information to improve retrieval performance

In this paper, we propose a contextual retrieval framework which incorporates the user and global contextual information into the probabilistic retrieval model. We investigate different techniques of using contextual information to improve information retrieval performance in details. In particular, (1) we use the related text contextual information for query expansion; (2) we use the granularity information to construct the document level index and paragraph level index; (3) we use the geographic information for filtering. In addition, a new term weighting function BM5O is proposed based on the global context information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets.
Shape Recovery From Medical Image Data Using Extended Superquadrics

Rapid and representative reconstruction of geometric shape models from surface measurements has applications in diverse arenas ranging from industrial product design to biomedical organ/tissue modeling. However, despite the large body of work, most shape models have had limited success in bridging the gap between reconstruction, recognition, and analysis due to conflicting requirements. On one hand, large numbers of shape parameters are necessary to obtain meaningful information from noisy sensor data. On the other hand, search and recognition techniques require shape parameterizations/abstractions employing few robust shape descriptors. The extension of such shape models to encompass various analysis modalities (in the form of kinematics, dynamics and FEA) now necessitates the inclusion of the appropriate physics (preferably in parametric form) to support the simulation based refinement process. Thus, in this paper we discuss development of a class of parametric shape abstraction models termed as extended superquadrics. The underlying geometric and computational data structure intimately ties together implicit-, explicit- and parametric- surface representation together with a volumetric solid representation that makes them well suited for shape representation. Furthermore, such models are well suited for transitioning to analysis, as for example, in model-based non rigid structure and motion recovery or for mesh generation and simplified volumetric-FEA applications. However, the development of the concomitant methods and benchmarking is necessary prior to widespread acceptance. We will explore some of these aspects further in this paper supported with case studies of shape abstraction from image data in the biomedical/life-sciences arena whose diversity and irregularities pose difficulties for more traditional models. © 2005 ASME
Obesity and Renal Transplant Outcome: A Report of the North American Pediatric Renal Transplant Cooperative Study

Objective. Obesity is increasing in the end-stage renal disease population. Studies that have evaluated the effect of obesity on transplant outcomes in adults have yielded varying results. This issue has received little attention in the pediatric population. Methods. We performed a retrospective study of the effect of obesity on pediatric renal transplant outcomes using the North American Pediatric Renal Transplant Cooperative Study database. Registry data from 1987 through 2002 on 6658 children aged 2 to 17 years were analyzed. Obesity was defined by a BMI >95th percentile for age. Results. Overall, 9.7% were obese with an increase noted in recent years (12.4% after 1995 vs 8% before 1995). Obese children were significantly younger and shorter and had been on dialysis for a longer time than nonobese children. There was no significant difference in the overall patient and allograft survival between the 2 groups. However, obese children aged 6 to 12 years had higher risk for death than nonobese patients (adjusted relative risk: 3.65 for living donor; adjusted relative risk: 2.94 for cadaver), and death was more likely as a result of cardiopulmonary disease (27% in obese vs 17% in nonobese). Overall, graft loss as a result of thrombosis was more common in obese as compared with nonobese (19% vs 10%). Conclusions. Obesity is an increasing problem in children who present for transplantation and may have an adverse effect on allograft and patient survival.
A Pilot Experiment on Exploiting Translations for Literary Studies on Kafka’s “Verwandlung”

We present a manually annotated word alignment of Franz Kafka’s “Verwandlung” and use this as a controlled test case to assess the principled usefulness of word alignment as an additional information source for the (monolingually motivated) identification of literary characters, focusing on the technically wellexplored task of co-reference resolution. This pilot set-up allows us to illustrate a number of methodological components interacting in a modular architecture. In general, co-reference resolution is a relatively hard task, but the availability of word-aligned translations can provide additional indications, as there is a tendency for translations to explicate underspecified or vague passages.