Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking Anonymity for Social Networks

—This paper explains why existing anonymity models such as k-anonymity cannot be applied to the most common form of private data release on the internet, social network APIs. An alternative anonymity model, PP-anonymity, is presented, which measures the posterior probability of an attacker logically deducing previously unknown private information using a social network API. Finally, the feasibility of such an approach is evaluated suggesting that a social network site such as Facebook could practically implement an anonymous API using PP-anonymity, providing its users with an anonymous option to the current application model.
Rethinking Anonymity for Social Networks ; CU-CS-1065-10

This paper explains why existing anonymity models such as k-anonymity cannot be applied to the most common form of private data release on the internet, social network APIs. An alternative anonymity model, PP-anonymity, is presented, which measures the posterior probability of an attacker logically deducing previously unknown private information using a social network API. Finally, the feasibility of such an approach is evaluated suggesting that a social network site such as Facebook could practically implement an anonymous API using PP-anonymity, providing its users with an anonymous option to the current application model.
Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China.

Abstract: Paddy rice is one of the most important crops in the world. Accurate estimation and monitoring of paddy rice phenology is necessary for management and yield prediction. Remotely sensed time-series data are essential for estimation of crop phenology stages across large areas. Here, the paddy rice phenological stages (i.e., transplanting, tillering, heading, and harvesting) were detected in Jiangxi Province, China. A comparison study was conducted using ground observation data from 10 agricultural meteorological stations, collected between 2006 and 2008. The phenological stages were detected using Moderate Resolution Imaging Spectroradiometer (MODIS) time-series enhanced vegetation index (EVI) data. Savitzky–Golay filter and wavelet transform were used to reduce the noise in the time-series EVI data and reconstruct the smoothed EVI time-series profile. Key phenological stages of double-cropping rice were detected using the characteristics of the smoothed EVI profile. The root mean square errors (RMSEs) for each stage were ±10 days around the ground observation data. The results suggest that Savitzky–Golay filter and wavelet transform are promising approaches for reconstructing high-quality EVI time-series data. Moreover, the phenological stages of double-cropping rice could be detected using time-series MODIS EVI data smoothed by Savitzky–Golay filter and wavelet transform. Keywords: remote sensing, phenology, paddy rice, time series MODIS EVI, growth monitoring, Savitzky-Golay filter, wavelet transform DOI: 10.3965/j.ijabe. 20140706.005 Citation: Li S H, Xiao J T, Ni P, Zhang J, Wang H S, Wang J X. Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China. Int J Agric & Biol Eng, 2014; 7(6): 28－36.
The surface rupture zone distribution of the Wenchuan earthquake (Ms8.0) happened on May 12th, 2008

On May 12, 2:28 pm, an earthquake (Ms 8.0) struck the Longmenshan area (centred in Wenchuan County of Sichuan Province), the eastern segment of the Tibetan Plateau. According to the field survey, the earthquake occurs on the NE striking Longmenshan fault belt. The belt has been with the thrusting rate smaller than 1 mm/a and the shortening rate smaller than 3 mm/a according to GPS measurement. The causative mechanism is mainly the thrust (from NW to SE), accompanied with dextral strike-slip. The earthquake produced several co-seismic surface rupture zones that were 275 km long and 15 km wide in general. The Yingxiu-Beichuan rupture zone is 275 km long and dominative;The Hanwang rupture zone takes the second place, which is 70 km long and occurs along the south part of the Guanxian-Anxian fault, the frontal fault of the Longmenshan fault belt. There are another two minor rupture zones between two zones mentioned above, one is the Shenxigou rupture zone, is in NE direction and about 10 km long, close to the southern segment of the main rupture zone, whose deformation characteristics are similar to and can be involved into the Yingxiu-Beichuan rupture zone; the other is named as Xiaoyudong rupture zone, is in NW direction and 6 km long, produced by the thrust from SW to NE and associated lateral slip, which is the link between Yingxiu-Beichuan and Hanwang rupture zone and characterized by the lateral ramp. Besides, in the Sichuan Basin that eastern to the Guanxian-Anxian fault, a sand liquefaction belt with NE direction strike developed from Juyuan town of Dujiangyan to Jiangyou, which might be the result of the activity of a blind fault deep in the western Sichuan Basin. According to the feature of surface coseismic rupture zones, active tectonics in the Longmenshan fault belt have intense thrusting accompanied with significant dextral strike-slip. The fault is developing toward the Sichuan Basin. The recurrence intervals of great earthquake such as the Wenchuan earthquake can be 3000-6000 years.
Quantifying posterior eﬀect size distribution of susceptibility loci by common summary statistics

Testing millions of SNPs in genetic association studies has become standard routine for disease gene discovery, followed by prioritization of the strongest signals based on the set of the smallest P-values. In light of recent re-evaluation of statistical practice, it has been suggested that P-values are unﬁt as summaries of statistical evidence. Despite this criticism, P-values are commonly used and are unlikely to be abandoned by practitioners. Moreover, P-values contain information that can be utilized to address the concerns about their ﬂaws and misuse. We present a new method for utilizing evidence summarized by P-values for estimating odds ratio (OR) based on its approximate posterior distribution. In our method, only P-value, sample size, and standard deviation for log(OR) are needed as summaries of data, accompanied by a suitable prior distribution for log(OR) that can assume any shape. The parameter of interest, log(OR), is the only parameter with a speciﬁed prior distribution, hence our model is a mix of classical and Bayesian approaches. We show that our “Mix Bayes” (MB) method retains the main advantages of the Bayesian approach: it yields direct probability statements about hypotheses for OR and is resistant to biases caused by selection of top-scoring SNPs. MB enjoys greater ﬂexibility than similarly inspired methods in the assumed distribution for the summary statistic and in the form of the prior for the parameter of interest. We illustrate our method by presenting interval estimates of eﬀect size for reported genetic associations with lung cancer. Although we focus on OR, our method is not limited to this particular measure of eﬀect size and can be used broadly for assessing reliability of ﬁndings in studies testing multiple predictors.
The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component of the GFDL global coupled model CM3

The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a 1 coupled general circulation model (CM3) for atmosphere, oceans, land, and 2 sea ice. The goal of CM3 is to address emerging issues in climate change, 3 including aerosol-cloud interactions, chemistry-climate interactions, and cou4 pling between the troposphere and stratosphere. The model is also designed 5 to serve as the physical-system component of earth-system models and mod6 els for decadal prediction in the near-term future, for example, through im7 proved simulations in tropical land precipitation relative to earlier-generation 8 GFDL models. This paper describes the dynamical core, physical parameter9 izations, and basic simulation characteristics of the atmospheric component 10 (AM3) of this model. 11 Relative to GFDL AM2, AM3 includes new treatments of deep and shal12 low cumulus convection, cloud-droplet activation by aerosols, sub-grid vari13 ability of stratiform vertical velocities for droplet activation, and atmospheric 14 chemistry driven by emissions with advective, convective, and turbulent 15 transport. AM3 employs a cubed-sphere implementation of a finite-volume 16 dynamical core and is coupled to LM3, a new land model with eco-system 17 dynamics and hydrology. 18 Most basic circulation features in AM3 are simulated as realistically, or 19 more so, than in AM2. In particular, dry biases have been reduced over South 20 America. In coupled mode, the simulation of Arctic sea ice concentration 21 has improved. AM3 aerosol optical depths, scattering properties, and surface 22
Behavioral and Brain Functions

Background: Low serotonergic (5-HT) activity correlates with increased impulsive-aggressive behavior, while the opposite association may apply to cognitive impulsiveness. Both types of impulsivity are associated with attention-deficit/hyperactivity disorder (ADHD), and genes of functional significance for the 5-HT system are implicated in this disorder. Here we demonstrate the separation of aggressive and cognitive components of impulsivity from symptom ratings and test Published: 20 October 2008 Behavioral and Brain Functions 2008, 4:48 doi:10.1186/1744-9081-4-48 Received: 2 June 2008 Accepted: 20 October 2008 This article is available from: http://www.behavioralandbrainfunctions. com/content/4/1/48 © 2008 Oades et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Diversity and phenology of orchidaceae in an upper montane forest, department of Cusco, Peru

DIVERSITY AND PHENOLOGY OF ORCHIDACEAE IN AN UPPER MONTANE FOREST, DEPARTMENT OF CUSCO, PERU by Rebecca Elaine Repasky, M.S., 2008 Department of Biology Texas Christian University Thesis Advisor: John Janovec, Botanical Research I nstitute of Texas Thesis Co-Advisor: John Horner, Professor of Biolo gy A study of orchids was carried out in a cloud fores t in southeastern Peru, in the department of Cusco, between September 2005 and August 2006. A s ystematic collection found 239 different morphospecies of orchids which was compar ed to those found at two similar areas, Manu National Park and Machu Picchu Historical Soci ety. A set of 47 plots were set up and monitored for orchid diversity, abundance, and phen ology of the area. Based on habit (epiphytic or terrestrial), the number of species v aried among habitats. Orchids were shown to be flowering at all times of the year with some species being rarer. Flowering times of the orchids were maximized in times of high precipitati on levels (February). Recommendations are made for future research, including study lengt h and design. A potential protocol for orchid conservation is also described, with attenti on o size of a reserve to include multiple variables.
Investigating the impact of climate variability and climate change on tick

The impact of climate variability and climate change on tickborne diseases in Chiredzi District was investigated. Ticks are responsible for several diseases (tickborne diseases) that significantly affect livestock, especially cattle. The treatment of tickborne diseases and the resulting secondary infections result in very high economic losses in cattle production. Ticks attach themselves on the cattle and this causes irritation and this affects the feeding of cattle resulting in reduced milk production and poor meat quality. Several methods have been developed in tick control, however a more climate based method is required especially where climate variability and change are significant. A climate based method if combined with the already present methods like the use of acaricides and vaccines allows better resource use and more efficient disease management. The research used historic climatic data provided by the Zimbabwe Meteorological Services Department (ZMSD). The data included total monthly precipitation, maximum and minimum temperature values. The data was used to construct time series graphs which showed that generally rainfall is on the decrease while temperature has increased for the periods 1965/66 â€“2007/08 and 1979-2007 respectively. Data on tickborne disease incidence was obtained from the Department of Veterinary Services Zimbabwe (DVSZ) as collected by local farmers at household level. Downscaled climate data from Global climate models were used to make future climatic prediction for the period 2046-2065 using the Climate Change Explorer Tool. Regression analyses as well as RMSE analysis were done to assess model efficiency. The models performed better in predicting minimum and maximum temperatures with R2 values of about 0.80 and 0.6 respectively. However the models were very poor in predicting rainfall values. An epidemic risk model was developed relating temperature and rainfall to disease occurrence incidence. An equation y=0.451x+2.013 was developed where y is the disease incidence and x is percentage epidemic risk which is derived from the equation The equation relates temperature (T) and rainfall (R) data to percentage epidemic risk. From the data given the epidemic risk model was validated for its ability to predict disease occurrence using previously recorded data. The R2 value for observed disease occurrence against the disease occurrence values predicted by the model was R2= 0.589.
Corporate Social Responsibility and its Impact on Financial Performance: Investigation of U.S. Commercial Banks

This paper analyzes corporate social responsibility (CSR) for banks and its impact on bank financial performance in a context of the recent financial crisis. The largest banks consistently have higher CSR strengths and CSR concerns during the sample period. However, this group sees a steep increase in CSR strengths and a steep drop in CSR concerns after 2009. Banks that are profitable, have higher capital ratios, charge lower fees to deposits, and with more female and minority directors have significantly higher CSR strengths scores. For banks with low involvement in low income communities, it is the smallest banks that show many significant relations between corporate social responsibility and bank characteristics. Yet, for banks with high involvement in low income communities, it is the largest banks that show many significant relations. Finally, we find that the largest banks appear to be rewarded for their social responsibility, as both industry adjusted ROA and ROE are positively and significantly related to CSR scores.
Rowan Digital Works Rowan Digital Works

The Escherichia coli microcin C (McC) and related compounds are potent Trojan horse peptide-nucleotide antibiotics. The peptide part facilitates transport into sensitive cells. Inside the cell, the peptide part is degraded by nonspeciﬁc peptidases releasing an aspartamide-adenylate containing a phos-phoramide bond. This nonhydrolyzable compound inhibits aspartyl-tRNA synthetase. In addition to the efﬁcient export of McC outside the producing cells, special mechanisms have evolved to avoid self-toxicity caused by the degradation of the peptide part inside the producers. Here, we report that histidine-triad (HIT) hydrolases encoded in biosynthetic clusters of some McC homologs or by stand-alone genes confer resistance to McC-like compounds by hydrolyzing the phos-phoramide bond in toxic aspartamide-adenosine, rendering them inactive.
Multivariate Estimation of Rock Mass Characteristics Respect to Depth Using ANFIS Based Subtractive Clustering- Khorramabad-Polezal Freeway Tunnels

Received: 26 Aug 2013 Revised: 30 Dec 2014 Abstract Combination of Adoptive Network based Fuzzy Inference System (ANFIS) and subtractive clustering (SC) has been used for estimation of deformation modulus (Em) and rock mass strength (UCSm) considering depth of measurement. To do this, learning of the ANFIS based subtractive clustering (ANFISBSC) was performed firstly on 125 measurements of 9 variables such as rock mass strength (UCSm), deformation modulus (Em), depth, spacing, persistence, aperture, intact rock strength (UCSi), geomechanical rating (RMR) and elastic modulus (Ei). Then, at second phase, testing the trained ANFISBSC structure has been perfomed on 40 data measurements. Therefore, predictive rock mass models have been developed for 2-6 variables
Railroads, Technology Adoption, and Modern Economic Development: Evidence from Japan

Railroad access can accelerate the technological progress in the industrial sector and therefore induce structural change and urbanization, the two common features of modern economic growth. I examine this particular mechanism in the context of Japanese railroad network expansion and modern economic growth in the late 19th century and early 20th centuries. By digitizing a novel data set that measures the use of steam engines at the factory level, allowing me to directly observe the diffusion of steam power, I analyze the effect of railroad access on the adoption of steam power. To overcome the endogeneity prob- lem, I determine the cost-minimizing path between destinations, and use this to construct an instrument for railroad access. I find that railroad access led to an increased adoption of steam power by factories, which in turn reallocated labor from the agricultural to the industrial sector, thereby inducing structural change. Railroad network also broke mean reversion in population growth, eventually leading to urban- ization. My results support the view that railroad network construction was key to the modern economic growth in pre-First World War Japan.
A ﬂ exible copper sul ﬁ de composite membrane with tunable plasmonic resonance absorption for near-infrared light-driven seawater desalination †

Near-infrared light driven devices for water evaporation are strictly limited by their in ﬂ exibility, high cost, complicated fabrication processes, and low energy-conversion e ﬃ ciency. Here, a ﬂ exible copper sul ﬁ de composite membrane with tunable plasmonic resonance absorption for an e ﬃ cient near-infrared light photothermal conversion is proposed. Both the uniformity of the morphology and the proportion of Cu + in the ﬂ ower-like copper sul ﬁ de (CuS) superstructure are easily controlled by adjusting the amount of polyvinylpyrrolidone (PVP), which e ﬀ ectively improves the absorption of the CuS superstructure in the near-infrared region. Furthermore, the ﬂ exible CuS/Matrimid composite membrane constructed by combining CuS and polyimide membranes exhibits highly ﬂ exible properties, strong NIR absorption, fast heating (10 s), and good thermal stability. A highly e ﬃ cient photothermal conversion is achieved by near-infrared light-driven water evaporation. Under 808 nm light irradiation, the water evaporation conversion e ﬃ ciency is ca. 80% and has excellent evaporation stability. The ﬂ exible CuS/Matrimid composite membrane developed in this study could have promising practical applications in near-infrared light-driven devices for seawater desalination.
The Impact of Climate Change on Danish Agriculture

New and unique climate prediction data show the regional effect of climate change for weather outcomes in Denmark. The present study utilizes this data to forecast and discuss the impacts of climate change on Danish agriculture, namely for the distribution of wheat yields. Using a large data set with farm-level information combined with data on local meteorological observations, a fixed effect panel data model for wheat yields is estimated, including a prior tobit model to correct for the selection bias from wheat production. Climate change is found to a have strong negative impact on average yields under the current technological constraints on agriculture. This fall in yields is transformed into a net increase in mean yields once the impact of technological progress on yields is taken into account. In a regionally differentiated analysis, the peninsular Jutland in the west experiences a larger decrease in mean yields than the eastern parts of the country. In this context I point to the need for further studies on the specific cause of the regional differences of the impact as well as the informational needs on climate change, in order to improve the understanding of potential adaptation strategies.
OncoBERT: Building an Interpretable Transfer Learning Bidirectional Encoder Representations from Transformers Framework for Longitudinal Survival Prediction of Cancer Patients

Deep learning transformer models have exhibited exceptional performance in various clinical tasks, including cancer outcome prediction, when applied to electronic health records (EHR). Inspired by the success of bidirectional encoder representations from transformers (BERT) in natural language processing, we present OncoBERT, a deep transformer transfer learning framework tailored for cancer outcome prediction using unstructured clinical notes from diverse cancer sites including Glioma, Prostate, and Breast. OncoBERT adapts the BERT framework for unstructured EHR notes processing and employs a logistic regression model for binary survival prediction. Our framework analyzes aggregated EHR notes for each patient from the time of diagnosis to the initial outcome evaluation timepoint, progressively incorporating new notes for up to one year, which allows us to capture the evolving nature of cancer prognostication. In comparison to stage-based outcome models and a conventional NLP method, OncoBERT outperforms them in predicting cancer outcomes across three distinct cancer types. Furthermore, we introduce a novel interpretability pipeline that allows us to extract clinical note segments identified by the OncoBERT model as the most relevant indicators of prognosis. This pipeline enhances the interpretability of the predictive outcome, surpassing the limitations of attention weights. By providing clearer insights into the model's decision-making process, our approach improves the transparency and comprehensibility of the results, ultimately empowering clinicians and patients to make more informed decisions.
Estimation of evaporative fractions by the use of vegetation and soil component temperature determined by means of dual-looking remote sensing

Knowledge of evaporation on local scale is a prerequisite for the prediction of drought. The Surface Energy Balance System (SEBS) provides the means to do this. Input data of SEBS are satellite data and a limited set of ground measurements. By using the dual-looking viewing capabilities of the ATSR sensor on board the ERS-II it is possible to determine vegetation and soil temperature separately. These two temperatures can be used in SEBS - the dual source SEBS - in order to gain more insight into theevaporation process and to improve the physical basis of the algorithm. Testing and validation of single and dual source SEBS has been carried out with the use of existing data sets of the Netherlands and Spain. The algorithm sensitivity towards input variables is established. Air and surface temperatures and wind speed have a large impact on the results. Two input variables proof to be very hard or impossible to measure, i.e. the roughness length for momentum and the roughness length for heat transfer.The existing methods and models do not give satisfactory results. Validation is carried out with ground fluxes, measured by eddy devices and scintillometers.