Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity‐Stability Relationships: Statistical Inevitability or Ecological Consequence?

Department of Ecology, Evolution, and Behavior, 1987 Upper logical interactions, and for previous data (in Tilman Buford Circle, University of Minnesota, St. Paul, Minnesota 1996), the relationship between variance in species abun55108 dances and mean species abundances is such that there is little or no net effect of diversity on stability via statistical Submitted June 16, 1997; Accepted October 7, 1997 averaging. However, interspecific interactions, such as competition, that cause compensatory changes in the abundances of species can stabilize community biomass in these cases.
Highly Informative Priors

SUMMARY After discussing the role of prior information in statistical inference, historically and in current problems, we analyze the problem of seasonal adjustment i n economics. Litterman 1980 has shown how informative priors for autoregressive coeecients can improve economic forecasts. We nd that in seasonal adjustment informative priors can have a m uch greater eeect on our conclusions. In our model, even the dimensionality of the joint posterior distribution of the irregulars depends on prior information about the seasonal component; and some functions of the ir-regulars can be determined more accurately than in sampling theory.
BIRA: Improved Predictive Exchange Word Clustering

Word clusters are useful for many NLP tasks including training neural network language models, but current increases in datasets are outpacing the ability of word clusterers to handle them. Little attention has been paid thus far on inducing high-quality word clusters at a large scale. The predictive exchange algo-rithm is quite scalable, but sometimes does not provide as good perplexity as other slower clustering algorithms. We introduce the bidirectional, interpolated, reﬁning, and alternating (BIRA) predictive exchange algorithm. It improves upon the predictive exchange algorithm’s perplexity by up to 18%, giving it perplexities comparable to the slower two-sided exchange algorithm, and better perplexities than the slower Brown clustering algorithm. Our B IRA implementation is fast, clustering a 2.5 billion token English News Crawl corpus in 3 hours. It also reduces machine translation training time while preserving translation quality. Our implementation is portable and freely available.
Hydrogen and Oxygen Stable Isotope Fractionation in Body Fluid Compartments of Dairy Cattle According to Season, Farm, Breed, and Reproductive Stage

Environmental temperature affects water turnover and isotope fractionation by causing water evaporation from the body in mammals. This may lead to rearrangement of the water stable isotope equilibrium in body fluids. We propose an approach to detect possible variations in the isotope ratio in different body fluids on the basis of different homoeothermic adaptations in varying reproductive stages. Three different reproductive stages (pregnant heifer, primiparous lactating cow, and pluriparous lactating cow) of two dairy cattle breeds (Italian Friesian and Modenese) were studied in winter and summer. Blood plasma, urine, faecal water, and milk were sampled and the isotope ratios of H (2H/1H) and O (18O/16O) were determined. Deuterium excess and isotope-fractionation factors were calculated for each passage from plasma to faeces, urine and milk. The effects of the season, reproductive stages and breed on δ 2H and δ 18O were significant in all the fluids, with few exceptions. Deuterium excess was affected by season in all the analysed fluids. The correlations between water isotope measurements in bovine body fluids ranged between 0.6936 (urine-milk) and 0.7848 (urine-plasma) for δ 2H, and between 0.8705 (urine-milk) and 0.9602 (plasma-milk) for δ 18O. The increase in both isotopic δ values in all body fluids during summer is representative of a condition in which fractionation took place as a consequence of a different ratio between ingested and excreted water, which leads to an increased presence of the heavy isotopes. The different body water turnover between adult lactating cattle and non-lactating heifers was confirmed by the higher isotopic δ for the latter, with a shift in the isotopic equilibrium towards values more distant from those of drinking water.
Self-Control in Learning for Healthy Living Among Students in a Nigerian College of Education

The rapid increase in health information requires that learners exert some degree of self-control in learning to make an appropriate selection of what to learn about healthy living. The objective of this study was to determine the students’ level of self-control in learning for healthy living. A questionnaire adapted from the self-control subscale of the Self-Directed Learning Readiness (SDLR) Scale developed by Fisher, King, and Tague was used in determining the levels of self-control in learning for healthy living among students in Enugu State College of Education (Technical), Enugu, Nigeria. The study revealed that the students’ level of self-control was a little less than the recommended level. A significant difference was found in the male and female students’ level of self-control in learning for healthy living with the male students possessing a higher level of self-control. Significant differences also occurred in the students’ level of self-control in learning for healthy living when the students were classified based on their courses of study. The students’ level of self-control in learning for healthy living at the College of Education (Technical), Enugu, needs to be improved. Educators at the college should employ self-control-enhancing strategies to improve the level of self-control in learning among the students.
Convergence Analysis and BER Performance of Finite-Length Turbo Codes

We propose the signal-to-noise ratio (SNR) transfer characteristic band (TCB) chart of extrinsic information (EI) as a basic tool for the probabilistic convergence analysis of iterative decoding. The proposed SNR TCB chart is a general version of the widely used SNR transfer chart, and it can model the behavior of EI in iterative decoding with any blocklength. We propose an algorithm using the SNR TCB chart to obtain the information, which is both quantitative and qualitative, on the probabilistic convergence behavior of iterative decoding. We also propose the bit-error rate (BER) estimate, in the form of a lower bound, of turbo codes over an additive white Gaussian noise channel. The proposed estimate is especially useful in estimating the BER of finite-length turbo codes in a waterfall region, while it is meaningful for all blocklengths and all Eb/N0
Business, politics and ideology: Neoliberalism and capitalist class formation in Argentina and Chile (1990 – 2014)

This paper explores the connection between neoliberalism and the capitalist classes in Argentina and Chile. In particular, it investigates the legacies of neoliberal reform for capitalist class formation, asking why capitalists were able to achieve a hegemonic class position through reform in Chile though not in Argentina. Albeit many historical commonalities, market reforms ended up producing different outcomes and reactions: a tempered neoliberalism in Chile that have only recently been contested, and a post-neoliberal backlash in Argentina in the wake of the 2001 crisis. These divergent paths are related with the outcome of market reforms and various other factors, including the capacity of businessmen to restore political and class power. The ideological cohesion, political influence, and symbolic power of the Argentinean and Chilean business classes are contrasted. I conclude by discussing the relationships between capitalist classes’ power and their capacity to resist collectivist attempts to undo marketization.
Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE)

The objective of this study was to develop and validate an automated acquisition system to assess quality of care (QC) measures for cardiovascular diseases. This system combining searching and retrieval algorithms was designed to extract QC measures from electronic discharge notes and to estimate the attainment rates to the current standards of care. It was developed on the patients with ST-segment elevation myocardial infarction and tested on the patients with unstable angina/non-ST-segment elevation myocardial infarction, both diseases sharing almost the same QC measures. The system was able to reach a reasonable agreement (kappa value) with medical experts from 0.65 (early reperfusion rate) to 0.97 (beta-blockers and lipid-lowering agents before discharge) for different QC measures in the test set, and then applied to evaluate QC in the patients who underwent coronary artery bypass grafting surgery. The result has validated a new tool to reliably extract QC measures for cardiovascular diseases.
High performance sensorless control of IPMSM at low speed

In this paper, a simpler but more effective sensorless control scheme is proposed for an interior permanent-magnet synchronous motor drives at very low speed. Compared to the commonly method using three filter to extract rotor position signal, the new method uses only one low pass filter, leads to a lower implementation processing time, the simulations have proved the correctness and validity of this method.
Oxidative-Nitrosative Stress and Myocardial Dysfunctions in Sepsis: Evidence from the Literature and Postmortem Observations

Background. Myocardial depression in sepsis is common, and it is associated with higher mortality. In recent years, the hypothesis that the myocardial dysfunction during sepsis could be mediated by ischemia related to decreased coronary blood flow waned and a complex mechanism was invoked to explain cardiac dysfunction in sepsis. Oxidative stress unbalance is thought to play a critical role in the pathogenesis of cardiac impairment in septic patients. Aim. In this paper, we review the current literature regarding the pathophysiology of cardiac dysfunction in sepsis, focusing on the possible role of oxidative-nitrosative stress unbalance and mitochondria dysfunction. We discuss these mechanisms within the broad scenario of cardiac involvement in sepsis. Conclusions. Findings from the current literature broaden our understanding of the role of oxidative and nitrosative stress unbalance in the pathophysiology of cardiac dysfunction in sepsis, thus contributing to the establishment of a relationship between these settings and the occurrence of oxidative stress. The complex pathogenesis of septic cardiac failure may explain why, despite the therapeutic strategies, sepsis remains a big clinical challenge for effectively managing the disease to minimize mortality, leading to consideration of the potential therapeutic effects of antioxidant agents.
Cohort Differences in the Association of Cardiovascular Risk and Cognitive Aging

Aim: To investigate birth cohort differences in associations between cardiovascular risk and fluid cognition between the age of 70 and 79. Method: Data were drawn from representative population-based cohort samples (H70), born 1901–1902, 1906–1907, and 1930, measured at ages 70, 75, and 79 on fluid cognitive measures (spatial ability and logical reasoning). The Framingham Risk Score (FRS), derived from office-based nonlaboratory predictors (age, sex, systolic blood pressure, BMI, smoking, diabetes status), was used to measure cardiovascular risk. Multiple-group latent growth curve models were fitted to the data. Findings: Estimates revealed small associations between the FRS and fluid cognition. These associations were slightly reduced in the 1930 cohort. Conclusion: Findings suggest diminishing adverse effects of cardiovascular risk on cognitive aging in cohorts born later.
Reverse ranking query over imprecise spatial data

The <i>reverse rank</i> of a (data) object <i>o</i> with respect to a given query object <i>q</i> (that measures the relative nearness of <i>q</i> to <i>o</i>) is said to be <i>k</i> when <i>q</i> is the <i>k</i>-th nearest neighbor of <i>o</i> in a geographical space. Based on the notion of reverse ranks, a <i><u>R</u>everse <u>R</u>anking</i> (RR) query determines <i>t</i> objects with the smallest <i>k</i>'s with respect to a given query object <i>q</i>. In many situations that locations of objects and a query object can be imprecise, objects would receive multiple possible <i>k</i>'s. In this paper, we propose a notion of <i>expected reverse ranks</i> and evaluation of RR queries over imprecise data based on expected reverse ranks. For any object <i>o</i>, an expected reverse rank <i>k</i>k is a weighted average of possible reverse ranks for individual instances of <i>o</i> with respect to different instances of a given query object <i>q</i> by taking their probabilities into account. We devise and present incremental <i>k</i>k computation and two <i>k</i>k-Estimating algorithms to efficiently evaluate RR queries over imprecise data. The efficiency of our approach is demonstrated through experiments.
Awakening Leviathan: The effect of democracy on state capacity

Recent debates over the relative importance of democracy and state capacity for human development have led to the prevailing view that a strong state must be built before the introduction of democracy. Our research challenges this “sequencing approach” in international development. Using a global panel of countries over 50 years, we document that democracy has a substantial, positive causal effect on state capacity with identification strategies that adjust for pre-treatment dynamics. The state-enhancing effect of democracy is robust to alternative measures of key variables, a large set of time-varying confounders and an instrumental variable design that leverages variation in regional democratic diffusions. Subsequent analysis suggests contestation, rather than participation, as a potential causal mechanism. Our findings contribute to the burgeoning literature on sources of state capacity in the developing world and yield practical implications for democracy assistance.
Rule-based Reordering Space in Statistical Machine Translation

In Statistical Machine Translation (SMT), the constraints on word reorderings have a great impact on the set of potential translations that are explored. Notwithstanding computationnal issues, the reordering space of a SMT system needs to be designed with great care: if a larger search space is likely to yield better translations, it may also lead to more decoding errors, because of the added ambiguity and the interaction with the pruning strategy. In this paper, we study this trade-off using a state-of-the art translation system, where all reorderings are represented in a word lattice prior to decoding. This allows us to directly explore and compare different reordering spaces. We study in detail a rule-based preordering system, varying the length or number of rules, the tagset used, as well as contrasting with oracle settings and purely combinatorial subsets of permutations. We focus on two language pairs: English-French, a close language pair and English-German, known to be a more challenging reordering pair.
Interactive comment on “ Retrieval of cloud liquid water distributions from a single scanning microwave radiometer aboard a moving platform – Part 1 : Field trial results from the Wakasa Bay experiment ” by D

Tomographic methods offer great potential for retrieving three-dimensional spatial distributions of cloud liquid water from radiometric observations by passive microwave sensors. Fixed tomographic systems require multiple radiometers, while mobile systems can use just a single radiometer. Part 1 (this paper) examines the results from a limited cloud tomography trial with a single-radiometer airborne system carried out as part of the 2003 AMSR-E validation campaign over Wakasa Bay of the Sea of Japan. During this trial, the Polarimetric Scanning Radiometer (PSR) and Microwave Imaging Radiometer (MIR) aboard the NASA P3 research aircraft provided a useful dataset for testing the cloud tomography method over a system of low-level clouds. We do tomographic retrievals with a constrained inversion algorithm using three configurations: PSR, MIR, and combined PSR and MIR data. The liquid water paths from the PSR retrieval are consistent with those from the MIR retrieval. The retrieved cloud field based on the combined data appears to be physically plausible and consistent with the cloud image obtained by a cloud radar. We find that some vertically-uniform clouds appear at high altitudes in the retrieved field where the radar shows clear sky. This is likely due to the sub-optimal data collection strategy. This sets the stage for Part 2 of this study that aims to define optimal data collection strategies using observation system simulation experiments. Correspondence to: D. Huang (dhuang@bnl.gov)
One of us? How leaders can use subtle identity performances to build trust among ingroups and outgroups

Leaders often face the dilemma of earning trust among different subgroups. How can leaders “reach across the aisle” to build trust among outgroup members without alienating their own ingroup in the process? We suggest one solution is for leaders to couple their efforts to appeal to the outgroup with subtle identity performances, behavioral cues that ingroup members understand as prototypical, but that are effectively invisible to outgroup members who are not familiar with ingroup symbols and norms. We conducted an experiment in which we manipulated the leader’s group membership (ingroup or outgroup) and favoritism (ingroup favoritism, outgroup favoritism, or outgroup favoritism with a subtle identity performance). Results show that leaders can maximize their perceived prototypicality, fairness, and trust among the ingroup and outgroup if outgroup favoritism is coupled with a subtle identity performance for the ingroup.
Resolving Speculation: MaxEnt Cue Classification and Dependency-Based Scope Rules

This paper describes a hybrid, two-level approach for resolving hedge cues, the problem of the CoNLL-2010 shared task. First, a maximum entropy classifier is applied to identify cue words, using both syntactic- and surface-oriented features. Second, a set of manually crafted rules, operating on dependency representations and the output of the classifier, is applied to resolve the scope of the hedge cues within the sentence.
Enhanced performance of integrated silicon nanophotonic devices engineered by sub-wavelength grating structures

Sub-wavelength gratings, segmented resonant-less structures with geometries featuring scales considerably smaller than the wavelength of light, have enabled an attractive technological concept to locally control light guiding properties in planar silicon chip architectures. This concept has allowed for additional degrees of freedom to tailor effective mode index, modal confinement, waveguide dispersion, as well as anisotropy, thereby providing a vital route towards high performing devices with engineered optical properties. Sub-wavelength integrated nanophotonics has opened up new horizons for realization of key building components that afford outstanding device performances, typically beyond those achieved by conventional design strategies, yet favorably benefiting from the sub-100-nm pattern resolution of established semiconductor manufacturing tools in nanophotonic foundries. The distinctive features of sub-wavelength grating structures are considered essential for future generation of chip-scale applications in optical communications and interconnects, biomedicine, as well as quantum-based technologies. In this work, we report recent advances in the development of high-performance on-chip nanophotonic waveguides and devices engineered with the sub-wavelength grating metamaterial structures. In particular, we discuss recent achievements of low-loss waveguides with controlled chromatic dispersion, high-efficiency fiber-to-chip surface grating couplers, micro-ring resonators, and grating-assisted waveguide filters, implemented on the mature silicon-on-insulator technology.
AntiLoiter: A Loitering Discovery System for Longtime Videos across Multiple Surveillance Cameras

Since loitering is a suspicious behavior that often leads to abnormal situations, such as pickpocketing and terrorist attacks, its analysis attracts increasing research attention. In this paper, we present AntiLoiter, a practical loitering discovery system for surveillance. AntiLoiter can efficiently discover loitering candidates from longtime videos across multiple cameras. Most of existing systems mainly focus on how to detect or identify loiterers by behavior tracking techniques. However, the difficulties of tracking-based methods are known as that their analysis results are heavily influenced by occlusions, overlaps, and shadows. Moreover, tracking-based methods need to track the human appearance continuously. Therefore, they are not readily applied to longtime videos across multiple cameras due to the appearance discontinuity of criminal loitering. For instance, a suspect would probably come to the same place several times on different dates for preliminary inspections before a terrorist attack happens, where the loitering behavior is discontinuous. To solve this problem, we abandon the tracking method, instead, design a novel approach to efficiently discover loiterers based on their frequent appearance patterns for longtime videos across multiple cameras. Our approach simply yet effectively adopts existing face recognition techniques for loitering discovery. We conducted extensive experiments on both synthetic and real surveillance videos to evaluate the efficiency and efficacy of our approach. The experimental results show that our system can find out loitering candidates correctly and outperforms existing method by 100 times in terms of runtime.
Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters

Power-aware scheduling problem has been a recent issue in cluster systems not only for operational cost due to electricity cost, but also for system reliability. As recent commodity processors support multiple operating points under various supply voltage levels, Dynamic Voltage Scaling (DVS) scheduling algorithms can reduce power consumption by controlling appropriate voltage levels. In this paper, we provide power-aware scheduling algorithms for bag-of-tasks applications with deadline constraints on DVS-enabled cluster systems in order to minimize power consumption as well as to meet the deadlines specified by application users. A bag-of-tasks application should finish all the sub-tasks before the deadline, so that the DVS scheduling scheme should consider the deadline as well. We provide the DVS scheduling algorithms for both time-shared and space-shared resource sharing policies. The simulation results show that the proposed algorithms reduce much power consumption compared to static voltage schemes.