Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal and Land Use Effects on Amphibian Abundance and Species Richness in the Sakaerat Biosphere Reserve, Thailand

Habitat destruction and degradation in the tropics has led to a dramatic increase in altered habitats. Understanding the impacts of these disturbed areas on biodiversity will be critical to future conservation efforts. Despite heavy deforestation, Southeast Asia is underrepresented in studies investigating faunal communities in human-modified landscapes. This project assessed the herpetofaunal community in dry dipterocarp forest, secondary disturbed forest, and Eucalyptus plantations in the Sakaerat Biosphere Reserve. In May, June, and September of 2015, we surveyed using 10 passive trapping arrays. Both the Eucalyptus plantations and secondary disturbed forest habitats (224 and 141 individuals, respectively) had higher amphibian abundance than the dry dipterocarp forest (57 individuals), but we observed significant seasonal variation in amphibian abundance. During the wetter month of September, we recorded higher numbers of amphibian individuals and species. In particular, we noted that distance to a streambed influenced amphibian abundance during the rainy season. The three most abundant species in May and June were Microhyla fissipes, Fejervarya limnocharis, and Microhyla pulchra. In September, the three most abundant species were Microhyla fissipes, Glyphoglossus molossus, and Kaloula mediolineata. Our findings suggest that seasonal resources should be considered when conducting monitoring programs and making conservation decisions for amphibians.
Adaptive Visualization of Dynamic Unstructured Meshes

The amount of data available from simulation and measurement is growing at an incredible rate. A major challenge for the visualization community is to develop methods that allow users to explore these data interactively. For three-dimensional scalar fields, direct volume rendering has become an important technique in research and commercial settings. Interactive volume rendering requires the efficient use of available computational resources to keep pace with the disparity, resolution, and complexity of the volumes that are commonly produced from simulations (e.g., computational fluid dynamics or structural mechanics) and measurements (e.g., environmental observation and forecasting systems). For structured grids, direct volume rendering is well-studied and sufficiently straightforward with modern graphics hardware. This is not the case with unstructured volumes, because the elements that compose the mesh do not so easily map to current hardware. These datasets may be extremely large and contain more than a single static instance. Therefore, advanced solutions are required to achieve interactive visualization of this type of data. The goal of this dissertation is to provide several new techniques to facilitate the visualization of disparate unstructured meshes. Two new methods are proposed to accelerate volume rendering for the case of static data, one of which operates in object-space and the other in image-space. Acceleration methods may not always be enough, however, to allow interactive visualization for data that are too large to fit in the main memory of a computer. Therefore, a new progressive rendering approach is proposed that adaptively refines a visualization using remote resources. These new techniques are of great assistance for a large class of static imagery. However, dynamic volumes that change over time create unique challenges because of the amount of data that needs to be transmitted at each step. To address this issue, a new method for efficiently handling time-varying unstructured volumes is also presented in this dissertation. Together, these methods for interactive visualization provide a powerful framework for analyzing large amounts of unstructured data. To demonstrate this, a final application for transfer function design that combines many of these approaches is presented. This application includes an evaluation performed by a group of expert users to elaborate on the importance of these proposed techniques for interactive visualization.
Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study

In many important settings, subjects can show significant heterogeneity in response to a stimulus or treatment. For instance, a treatment that works for the overall population might be highly ine ective, or even harmful, for a subgroup of subjects with specific characteristics. Similarly, a new treatment may not be better than an existing treatment in the overall population, but there is likely a subgroup of subjects who would bene t from it. The notion that one size may not fit all is becoming increasingly recognized in a wide variety of fields, ranging from economics to medicine. This has drawn signi cant attention to personalize the choice of treatment, so it is optimal for each individual. An optimal personalized treatment is the one that maximizes the probability of a desirable outcome. We call the task of learning the optimal personalized treatment "personalized treatment learning". From the statistical learning perspective, this problem imposes some challenges, primarily because the optimal treatment is unknown on a given training set. A number of statistical methods have been proposed recently to tackle this problem. However, to the best of our knowledge, there has been no attempt so far to provide a comprehensive view of these methods and to benchmark their performance. The purpose of this paper is twofold: i) to describe seven recently proposed methods for personalized treatment learning and compare their performance on an extensive numerical study, and ii) to propose a novel method labeled causal conditional inference trees and its natural extension to causal conditional inference forests. The results show that our new proposed method often outperforms the alternatives on the numerical settings described in this article. We also illustrate an application of the proposed method using data from a large Canadian insurer for the purpose of selecting the best targets for cross-selling an insurance product.
Overcomplete Radon bases for target property management in sensor networks

This paper presents a scalable algorithm for managing property information about moving objects tracked by a sensor network. Property information is obtained via distributed sensor observations, but will be corrupted when objects mix up with each other. The association between properties and objects then becomes ambiguous. We build a novel representation framework, exploiting an overcomplete Radon basis dictionary to model property uncertainty in such circumstances. By making use of the combinatorial structure of the basis design and sparse representations we can efficiently approximate the underlying probability distribution of the association between target properties and tracks, overcoming the exponential space that would otherwise be required. We conduct comparative simulations and the results validate the effectiveness of our approach.
XML Element Retrieval@1CLICK-2

In this paper, we try to apply an approach of XML element retrieval to 1CLICK-2. The aim of XML element retrieval is to identify key points in structured documents, and propose them to system users. We believe that the aim of XML element retrieval is largely the same as that of 1CLICK-2; enabling direct and immediate Information Access (DIIA). We report potentiality of usefulness of XML element retrieval to DIIA, and some diculties to apply XML element retrieval to Web documents.
Adaptive Multiscale Boosting Dictionary Learning for Bearing Fault Diagnosis

Extracting the fault impulses caused by localized faults is crucial for detecting bearing faults. However, the extraction task is challenging, since the impulse signal is easily submerged by strong noise and complex harmonic interference. In this article, an adaptive multiscale boosting dictionary learning (AMBDL) method is proposed, which can effectively extract weak fault signals even in early fault stage, so that the bearing fault can be diagnosed timely. Specifically, a multiscale boosting dictionary learning (MBDL) model is first constructed, which integrates the strengthen-operate-subtract (SOS) boosting strategy and dictionary learning into multiscale transform to iteratively boost the learning ability of multiscale dictionary. Second, a robust fault-sensitive index, adaptive periodic modulation intensity (APMI), is designed for subband screening to remove subbands that mainly contain interference, enabling MBDL to effectively focus on enhancing and learning fault features in the optimal subbands. Third, a threshold estimation method is constructed to adaptively set the threshold parameters in sparse coding stage of the MBDL, which is suitable for real-time fault diagnosis. The analysis and comparison results of simulation and bearing failure experiments show that AMBDL is superior to some advanced methods in fault impulse extraction, while requiring lower computational costs and achieving adaptivity.
Improved lower and upper bound algorithms for pricing American options by simulation

This paper introduces new variance reduction techniques and computational improvements to Monte Carlo methods for pricing American-style options. For simulation algorithms that compute lower bounds of American option values, we apply martingale control variates and introduce the local policy enhancement, which adopts a local simulation to improve the exercise policy. For duality-based upper bound methods, specifically the primal–dual simulation algorithm, we have developed two improvements. One is sub-optimality checking, which saves unnecessary computation when it is sub-optimal to exercise the option along the sample path; the second is boundary distance grouping, which reduces computational time by skipping computation on selected sample paths based on the distance to the exercise boundary. Numerical results are given for single asset Bermudan options, moving window Asian options and Bermudan max options. In some examples the computational time is reduced by a factor of several hundred, while the confidence interval of the true option value is considerably tighter than before the improvements.
Comparison of Pressure Sensor in Flow Analyzer Design for Peep Measurement on Ventilators

The PEEP parameter is one of the parameters that need to be considered in mechanical ventilation because inappropriate parameter values can increase the risk of death for patients. Monitoring of these parameters can be done using a flow analyzer. The purpose of this study is to find out which pressure sensor is stable and has high accuracy. This study compares MPX2010DP and MPX5010DP pressure sensors where the readings of the two sensors will be compared with the pressure read by DPM (Digital Pressure Meter). The pressure read is the pressure obtained from the lung test with 11 pressure reading points and carried out as many as 5 experiments. The results of this study found that the smallest percentage of error was found in the MPX2010DP sensor at a pressure of 5 cmH2O rising pressure and 20 cmH2O rising pressure with an error percentage of 0.00%, while the largest percentage of error was found in the MPX2010DP sensor at a pressure of 5 cmH2O down pressure with an error percentage of 5.16%. The largest standard deviation value is 0.52 found on the MPX5010DP sensor at a pressure of 20 cmH2O rising pressure. The greatest uncertainty value is 0.23 found in the MPX5010DP sensor at a pressure of 20 cmH2O rising pressure. While the largest correction value is 0.54 on the MPX5010DP sensor at a pressure of 25 cmH2O rising pressure. From the data obtained, it can be said that the sensor MPX2010DP more accurate and more stable.
\True" Stochastic Volatility and a Generalized Class

True" Stochastic Volatility and a Generalized Class of AÆne Models We identify a class of term structure models possessing a generalized aÆne-structure that signi cantly extends the class studied by DuÆe, Pan, and Singleton (2000). For this class of models, which includes both in nite-state-variable (ie HJM-type) and in nite-factor (random eld) models, closed-form solutions for bond-option prices are obtained. Two special cases are investigated. First, a parsimonious model of `true' stochastic volatility is proposed, where innovations in derivative securities cannot be hedged by innovations in bond prices. Empirical support for this type of model is presented. Second, a two-factor arbitrage-free model of a longrate, similar in spirit to that proposed by Brennan and Schwartz (1979, 1982), is introduced.
[Polymorphic analysis of two Y chromosome short tandem repeat loci in Chinese Chengdu Han population].

OBJECTIVE To reveal the distribution of genetic polymorphism of two novel Y chromosome short tandem repeat (Y-STR) loci (DYS508, DYS516) in Chinese Chengdu Han population. METHODS Two Y-STR loci were amplified with PCR. The products of PCR were analyzed with polyacrylamide gel electrophoresis. Each allele of these 2 Y-STR loci was sequenced and the allele ladders were constructed. Alleles of these Y-STR loci were nominated according to recommendations of the International Society of Forensic Genetics (ISFG). RESULTS Six and 6 alleles were observed in loci DYS508 and DYS516 respectively. The gene diversity of DYS508 and DYS516 was 0.7242 and 0.7931 respectively. The diversity of haplotype for these Y-STR loci was 0.9397. The value of discrimination power and the exclusion chance of paternity for these Y-STR loci were same as the value of haplotype diversity. CONCLUSION Both Y-STR loci are high genetic polymorphic. They are beneficial to the test of male-female mixtures and paternity identification in forensic science.
Resource allocation under uncertainty using the maximum entropy principle

In this paper, we formulate and solve a problem of resource allocation over a given time horizon with uncertain demands and uncertain capacities of the available resources. In particular, we consider a number of data sources with uncertain bit rates, sharing a set of parallel channels with time-varying and possibly uncertain transmission capacities. We present a method for allocating the channels so as to maximize the expected system throughput. The framework encompasses quality-of-service (QoS) requirements, e.g., minimum-rate constraints, as well as priorities represented by a user-specific cost per transmitted bit. We assume only limited statistical knowledge of the source rates and channel capacities. Optimal solutions are found by using the maximum entropy principle and elementary probability theory. The suggested framework explains how to make use of multiuser diversity in various settings, a field of recently growing interest in communication theory. It admits scheduling over multiple base stations and includes transmission buffers to obtain a method for optimal resource allocation in rather general multiuser communication systems.
Surface‐exosphere coupling due to thermal tides

Using densities measured by accelerometers on the CHAMP and GRACE satellites, and taking advantage of the local time precession characteristics of these near‐polar orbiting satellites, exosphere temperatures are derived as a function of local time, longitude and latitude. Significant longitude variability (e.g., ±25K maximum to minimum over the equator) in geomagnetically‐quiet exosphere temperatures is shown to exist, and is attributed to a spectrum of diurnal and semidiurnal thermal tides that are excited in the troposphere and strongly influenced by the global land‐sea distribution. Since exosphere temperatures are independent of height, this discovery constitutes evidence that exosphere variability is linked to surface variability. Recent evidence suggests that analogous effects exist at Mars.
Risks to the public

Edited by Peter G. Neumann (Risks Forum Moderator and Chairman of the ACM Committee on Computers and Public Policy), plus personal contributions by others, as indicated. Opinions expressed are individual rather than organizational, and all of the usual disclaimers apply. We address problems relating to software, hardware, people, and other circumstances relating to computer systems. To economize on space, we include pointers to items in the online Risks Forum: (R i j) denotes RISKS vol i number j. Cited RISKS items generally identify contributors and sources, together with URLs. Official RISKS archives are available at www.risks. org (which redirects to Newcastle and gets you nice html formatting and a search engine courtesy of Lindsay Marshall; http://catless.ncl.ac.uk/Risks/i.j.html gets you (R i j)) and at ftp://www.sri. com/risks.
Long-term effects of gestational diabetes on offspring health are more pronounced in skeletal growth than body composition and glucose tolerance

Infants of diabetic mothers may have low arachidonic acid (AA) and develop obesity and insulin resistance in adulthood. The present study tested the effect of maternal diabetes and AA supplementation on offspring body composition, bone mass and glucose tolerance from 4 to 12 weeks. Rat dams were randomised into six groups using a 3 × 2 design. The rat dams were treated using the following treatments: saline-placebo, streptozotocin-induced diabetes (STZ) with glucose controlled at < 13 mmol/l (STZ/GC) or poorly controlled at 13–20 mmol/l (STZ/PC) using insulin, and fed either a control or an AA (0·5 % of fat) diet throughout reproduction. Weaned offspring were fed regular chow. Measurements included offspring body composition, bone and oral glucose tolerance testing (OGTT) plus liver fatty acids of dam and offspring. Comparable to saline-placebo offspring, the STZ/GC offspring had greater (P < 0·03) whole body and regional bone area than STZ/PC offspring. Maternal glucose negatively correlated (P < 0·05) with offspring whole body bone area and mineral content at 4 weeks in all offspring, and with tibia area in males at 12 weeks. Maternal liver DHA negatively (P < 0·03) correlated with femur and tibia mineral content and tibia mineral density of female offspring at 12 weeks. Offspring from AA-supplemented dams had higher (P = 0·004) liver AA at 4 weeks. Liver AA at 4 weeks positively (P = 0·05) correlated with lumbar spine mineral density in males. OGTT was not affected by maternal treatment or diet. These results suggest that maternal glucose control has long-term consequences to bone health of adult offspring. Skeletal growth appears more sensitive to maternal hyperglycaemia than glucose tolerance.
Detecting gravitational waves from precessing binaries of spinning compact objects: Adiabatic limit

Black-hole (BH) binaries with single-BH masses m=(5-20)M, moving on quasicircular orbits, are among the most promising sources for first-generation ground-based gravitational-wave (GW) detectors. Until now, the development of data-analysis techniques to detect GWs from these sources has been focused mostly on nonspinning BHs. The data-analysis problem for the spinning case is complicated by the necessity to model the precession-induced modulations of the GW signal, and by the large number of parameters needed to characterize the system, including the initial directions of the spins, and the position and orientation of the binary with respect to the GW detector. In this paper we consider binaries of maximally spinning BHs, and we work in the adiabatic-inspiral regime to build families of modulated detection templates that (i) are functions of very few physical and phenomenological parameters, (ii) model remarkably well the dynamical and precessional effects on the GW signal, with fitting factors on average greater than or similar to0.97, (iii) but, however, might require increasing the detection thresholds, offsetting at least partially the gains in the fitting factors. Our detection-template families are quite promising also for the case of neutron-star-black-hole binaries, with fitting factors on average approximate to0.93. For these binaries we also suggest (but do not test) a further template family, which would produce essentially exact waveforms written directly in terms of the physical spin parameters.
Relationship Between the Development of Pulmonary Fibrosis and Lung Tumors in Syrian Golden Hamsters Induced by N-methyl-N-nitrosourethane

To cast light on the relationship between the development of pulmonary fibrosis and lung cancer, female Syrian golden hamsters were given 5 sc injections of 0.6 mg/animal of N-methyl- N-nitrosourethane (MNUR) at 2-wk intervals and then maintained without any treatment for 26 wk. Bronchiolo-alveolar cell tumors and hyperplasias, which were recognized from week 4 after termination of treatment, were each subdivided morphologically into 3 types. Bronchiolo-alveolar cell tumors included papillary tumors consisting of basophilic cells, papillary tumors consisting of clear cells, and papillary/solid tumors consisting of pleomorphic cells, with papillary tumors consisting of basophilic cells predominating. Bronchiolo-alveolar cell hyperplasias encountered were glandular metaplasia, simple hyperplasia, and papillary hyperplasia. Glandular metaplasia was the most common of the hyperplastic lesions. To some extent, all the proliferative lesions were associated with inflammatory cell infiltration, but this varied greatly. The rate for papillary tumors consisting of basophilic cells with connective tissue proliferation was 35%. Among the hyperplastic lesions, the cell prolif erative activity of papillary hyperplasia (12.5%) was significantly higher than in other hyperplastic lesions, suggesting that this lesion might be a preneoplastic change. None of the lung proliferative lesions showed any unequivocal immunoreactivity for the p53 protein. The present study suggests that most lung tumors may develop from pulmonary inflammatory lesions induced by MNUR, but the possibility that DNA injuries to the respiratory epithelial cells by MNUR may cause lung tumors cannot be precluded.
Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique

Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG–CARS–RF and SG–CARS–SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.