Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalability of Continuous Active Learning for Reliable High-Recall Text Classification

For finite document collections, continuous active learning ('CAL') has been observed to achieve high recall with high probability, at a labeling cost asymptotically proportional to the number of relevant documents. As the size of the collection increases, the number of relevant documents typically increases as well, thereby limiting the applicability of CAL to low-prevalence high-stakes classes, such as evidence in legal proceedings, or security threats, where human effort proportional to the number of relevant documents is justified. We present a scalable version of CAL ('S-CAL') that requires O(log N) labeling effort and O(N log N) computational effort---where N is the number of unlabeled training examples---to construct a classifier whose effectiveness for a given labeling cost compares favorably with previously reported methods. At the same time, S-CAL offers calibrated estimates of class prevalence, recall, and precision, facilitating both threshold setting and determination of the adequacy of the classifier.
The impact of water quality in Narragansett Bay on housing prices

We examine the impact of water quality in Narragansett Bay on housing prices in coastal towns and cities using a hedonic housing‐price model. Unlike other hedonic studies of water quality, we test whether housing market responds to average water quality or more to extreme events. We also test the spatial and temporal extent of effects of water quality on housing prices. We find that poor coastal water quality, measured in terms of the concentration of chlorophyll, has a negative impact on housing prices that diminishes with distance from the shoreline. Furthermore, our finding suggests that housing prices are most influenced by the extreme environmental conditions, which may be accompanied by unpleasant odors, discoloration, and even fish kills. We further predict potential increases in home values associated under water quality improvement scenarios and find an increase in the values of homes in coastal communities along Narragansett Bay of about $18 million up to $136 million.
Early Dynamic 18F-FDG PET to Detect Hyperperfusion in Hepatocellular Carcinoma Liver Lesions

In addition to angiographic data on vascularity and vascular access, demonstration of hepatocellular carcinoma (HCC) liver nodule hypervascularization is a prerequisite for certain intrahepatic antitumor therapies. Early dynamic (ED) 18F-FDG PET/CT could serve this purpose when the current standard method, contrast-enhanced (CE) CT, or other CE morphologic imaging modalities are unsuitable. A recent study showed ED 18F-FDG PET/CT efficacy in this setting but applied a larger-than-standard 18F-FDG activity and an elaborate protocol likely to hinder routine use. We developed a simplified protocol using standard activities and easily generated visual and descriptive or quantitative endpoints. This pilot study assessed the ability of these endpoints to detect HCC hyperperfusion and, thereby, evaluated the suitability in of the protocol everyday practice. Methods: Twenty-seven patients with 34 HCCs (diameter ≥ 1.5 cm) with hypervascularization on 3-phase CE CT underwent liver ED 18F-FDG PET for 240 s, starting with 18F-FDG (250-MBq bolus injection). Four frames at 15-s intervals, followed by 3 frames at 60-s intervals were reconstructed. Endpoints included focal tracer accumulation in the first 4 frames (60 s), subsequent focal washout, and visual and quantitative differences between tumor and liver regions of interest in maximum and mean ED standardized uptake value (ED SUVmax and ED SUVmean, respectively) 240-s time–activity curves. Results: All 34 lesions were identified by early focal 18F-FDG accumulation and faster time-to-peak ED SUVmax or ED SUVmean than in nontumor tissue. Tumor peak ED SUVmax and ED SUVmean exceeded liver levels in 85% and 53%, respectively, of lesions. Nadir tumor signal showed no consistent pattern relative to nontumor signal. HCC had a significantly shorter time to peak and significantly faster rate to peak for both ED SUVmax and ED SUVmean curves and a significantly higher peak ED SUVmax but not peak ED SUVmean than the liver. Conclusion: This pilot study provided proof of principle that our simplified ED 18F-FDG PET/CT protocol includes endpoints that effectively detect HCC hypervascularization; this finding suggests that the protocol can be used routinely.
Nonlinear State-Space Predictive Control With On-Line Linearisation And State Estimation

This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.
Prevalence of GBV-C/HGV-RNA, virus genotypes, and anti-E2 antibodies in autoimmune hepatitis

Objective:We investigated the prevalence of hepatitis G-RNA (GBV-C/HGV-RNA), a recently cloned new flavivirus, and of antibodies to the envelope 2 antigen (anti-E2), a marker of past infection, in patients with autoimmune hepatitis, and compared it with the prevalence in patients with chronic viral hepatitis and healthy control individuals.Methods:Sera of 63 patients with autoimmune hepatitis were studied for the presence of GBV-C/HGV-RNA by reverse-transcription polymerase chain reaction and for anti-E2 by enzyme-linked immunosorbent assay. GBV-C/HGV genotypes were determined by genome sequencing.Results:Patients with autoimmune hepatitis had a similar high prevalence of GBV-C/HGV-RNA and anti-E2 antibodies as patients with chronic viral hepatitis B or C. GBV-C/HGV-RNA was found significantly more often in patients with autoimmune hepatitis (11%, p= 0.045), hepatitis B (16%, p= 0.004), or hepatitis C (21%, p= 0.001) than in healthy controls (2%). The prevalence of anti-E2 antibodies in patients with autoimmune hepatitis was not different from healthy controls (17%vs 13%, NS). The various subtypes of autoimmune hepatitis had similar prevalence rates of GBV-C/HGV-RNA as patients with liver-kidney microsomal antibody-positive hepatitis C. All of our anti-E2+ (GBV-C/HGV-RNA−) patients were positive for anti–smooth-muscle antibody, whereas only 29% of GBV-C/HGV-RNA+ (anti-E2−) patients were positive (p= 0.025). All seven of the GBV-C/HGV-RNA+ patients with autoimmune hepatitis had genotype 2a, which is also the most prevalent genotype in our region.Conclusion:The prevalence of GBV-C/HGV-RNA is significantly increased in patients with autoimmune hepatitis, compared with healthy controls, and is similar to the increased prevalence seen in chronic hepatitis B or C patients. Anti-E2 positivity was associated with antibodies against smooth-muscle antigen in all cases. All GBV-C/HGV+ autoimmune hepatitis patients were infected with genotype 2a.
Evolutionary Body Building: Adaptive Physical Designs for Robots

Creating artificial life forms through evolutionary robotics faces a chicken and egg problem: Learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of coevolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has usually resulted in virtual entities that are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a step in addressing the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components that stick together. Evolution takes place in a simulator that computes forces and stresses and predicts stability of three-dimensional brick structures. The final printout of our program is a schematic assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.
Unsupervised Image-to-Image Translation with Generative Adversarial Networks

It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.), while keeping the original structure or semantics. We define this requirement as the "image-to-image translation" problem, and propose a general approach to achieve it, based on deep convolutional and conditional generative adversarial networks (GANs), which has gained a phenomenal success to learn mapping images from noise input since 2014. In this work, we develop a two step (unsupervised) learning method to translate images between different domains by using unlabeled images without specifying any correspondence between them, so that to avoid the cost of acquiring labeled data. Compared with prior works, we demonstrated the capacity of generality in our model, by which variance of translations can be conduct by a single type of model. Such capability is desirable in applications like bidirectional translation
The Processing of Referring Expressions within a Semantic Network

Frege (1892) is credited with emphasizing the distinction between sense and reference. His famous example involved the mornin$ star and the evenin$ star. Despite the fact that they both refer to the same object (i.e., Venus), they have different senses as witnessed by the fact that sentence (i) is not synonymous with sentence (2): (i) The morning star is the morning star. (2) The morning star is the evening star. This philosophical issue has similarities to an issue that is of importance to understanding natural language processing: How do subjects process referring expressions to extract internal representations (a) of their meaning and (b) of their referents in the external world. The example sentence that we will be returning to in this paper is: (3) The first president of the United States was a bad husband. It is clear that in understanding this sentence we both process the subject as a description, and identify this as referring to George Washington. This paper will try to explain how this comes about. As I believe that all interesting questions about representation come down to questions about memory, I will approach this question from a human memory perspective.
Dobrava-Belgrade Virus Spillover Infections, Germany

We present the molecular identification of Apodemus agrarius (striped field mouse) as reservoir host of the Dobrava-Belgrade virus (DOBV) lineage DOBV-Aa in 3 federal states of Germany. Phylogenetic analyses provided evidence for multiple spillover of DOBV-Aa to A. flavicollis, a crucial prerequisite for host switch and genetic reassortment.
A LOGIC OF ARGUMENTATION FOR REASONING UNDER UNCERTAINTY

We present the syntax and proof theory of a logic of argumentation, LA. We also outline the development of a category theoretic semantics for LA. LA is the core of a proof theoretic model for reasoning under uncertainty. In this logic, propositions are labeled with a representation of the arguments which support their validity. Arguments may then be aggregated to collect more information about the potential validity of the propositions of interest. We make the notion of aggregation primitive to the logic, and then define strength mappings from sets of arguments to one of a number of possible dictionaries. This provides a uniform framework which incorporates a number of numerical and symbolic techniques for assigning subjective confidences to propositions on the basis of their supporting arguments. These aggregation techniques are also described with examples.
Efficient Blind System Identification of Non-Gaussian Autoregressive Models With HMM Modeling of the Excitation

We have previously proposed a blind system identification method that exploits the underlying dynamics of non-Gaussian signals in [Li and Andersen, "Blind identification of Non-Gaussian Autoregressive Models for Efficient Analysis of Speech Signals," Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2006, vol. 1, pp. I-1205-I-1208]. The signal model being identified is an autoregressive (AR) model driven by a discrete-state hidden Markov process. An exact expectation-maximization (EM) algorithm was derived for the joint estimation of the AR parameters and the hidden Markov model (HMM) parameters. In this paper, we extend the system model by introducing an additive measurement noise. The identification of the extended system model becomes much more complicated since the system output is now hidden. We propose an exact EM algorithm that incorporates a novel switching Kalman smoother, which obtains nonlinear minimum mean-square error (MMSE) estimates of the system output based on the state information given by the HMM filter. The exact EM algorithms for both models are obtainable only by appropriate constraints in the model design and have better convergence properties than algorithms employing generalized EM algorithm or empirical iterative schemes. The proposed methods also enjoy good data efficiency since only second-order statistics are involved in the computation. The signal models are general and suitable to numerous signals, such as speech and baseband communication signals. This paper describes the two system identification algorithms in an integrated form and provides supplementary results to the noise-free model and new results to the extended model with applications in speech analysis and channel equalization
Microzooplankton in the surface waters of the Strait of Juan de Fuca

Microzooplankton organisms were enumerated from surface seawater samples obtained at three stations in the Strait of Juan de Fuca during 13 cruises from 1976 to 1977 (tabulated data appear in Appendix). Ciliates were the most abundant group; maximum concentrations exceeded 10,000 liter-I. The ciliate community was composed almost exclusively of oligo~ trichs, tintinnids, and the gymnostome species, Mesodinium rubrum. These groups made up an average of 60 %, 10%, and 30 %, respectively, of the total ciliate numbers at each station. Twenty-six tintinnid species and 15 oligotrich species were identified during the 2-year study. The population peaks of most of these organisms coincided with periods of high biological activity during spring and summer. Certain species, however, such as the tintinnid Stenosemella ventricosa, were most common during winter months. The ecological role of oligotrichs and tintinnids as particle grazers is distinguished from that of M. rubrum, a ciliate deriving its nutrition from photosynthetic endosymbionts.
Text Classification by Literary Period Using PPM-C Data Compression

Methods and techniques for data compression have been used for pattern recognition, including automatic text classification. The performance of the Prediction by Partial Matching (PPM) as a text classifier has already been proofed by many works, including authorship attribution for Portuguese texts. Classes involved in classification process may not be restricted by only one author. By including two or more authors in one class, one can create a literature style. This work presents a literature style classifier for texts from Brazilian literature by using the PPM-C statistical model.
I See What You Mean: How Attentional Selection Is Shaped by Ascribing Intentions to Others

The ability to understand and predict others’ behavior is essential for successful interactions. When making predictions about what other humans will do, we treat them as intentional systems and adopt the intentional stance, i.e., refer to their mental states such as desires and intentions. In the present experiments, we investigated whether the mere belief that the observed agent is an intentional system influences basic social attention mechanisms. We presented pictures of a human and a robot face in a gaze cuing paradigm and manipulated the likelihood of adopting the intentional stance by instruction: in some conditions, participants were told that they were observing a human or a robot, in others, that they were observing a human-like mannequin or a robot whose eyes were controlled by a human. In conditions in which participants were made to believe they were observing human behavior (intentional stance likely) gaze cuing effects were significantly larger as compared to conditions when adopting the intentional stance was less likely. This effect was independent of whether a human or a robot face was presented. Therefore, we conclude that adopting the intentional stance when observing others’ behavior fundamentally influences basic mechanisms of social attention. The present results provide striking evidence that high-level cognitive processes, such as beliefs, modulate bottom-up mechanisms of attentional selection in a top-down manner.
Simulation of the Effect of Land Cover Change on Water Balance and Sediment Yield of Wadi El Raml in the North Western Coast, Egypt

Rainfed farming in the North Western Coast of Egypt represents the major source of food for the majority of the local Bedouins. Population growth associated with limited resources pushed the local Bedouins to alter their lands to move from rangelands to rainfed agriculture. In this study, i.e., wadi El Raml the Sentinel 2A image launched in 2014 was used to define the main land cover types dominating the study area. The classified image showed six land cover classes i.e. water bodies (1.15%), urban land (11.43%), orchard trees (10.42%), rainfed crops (15.80%), sparsely vegetated land (34.81%) and bare soil (26.03%). The downstream impact of changing land cover in the upstream of Wadi El Raml was simulated using the semi distributed KINEROS2 model. Three assumed scenarios were tested; 1) converting the rangeland to rainfed agriculture of winter crops, 2) converting the bare soil to range land and 3) the base simulation which is the current situation. The primary results revealed that, overall the tested rain events changing the land cover type from rangelands to rainfed agriculture increased the basin average surface runoff and sediment yield by 6.39-15.37% and 16.29 36.45 %, respectively. It is recommended that a limited area should be converted from rangelands to rainfed agriculture.
Workflow discovery: the problem, a case study from e-Science and a graph-based solution

Much has been written on the promise of Web service discovery and (semi-) automated composition. In this discussion, the value to practitioners of discovering and reusing existing service compositions, captured in workflows, is mostly ignored. This paper presents one solution to workflow discovery. Through a survey with 21 scientists and developers from the myGrid workflow environment, workflow discovery requirements are elicited. Through a user experiment with 13 scientists, an attempt is made to build a gold standard for workflow ranking. Through the design and implementation of a workflow discovery tool, a mechanism for ranking workflow fragments is provided based on graph sub-isomorphism matching. The tool evaluation, drawing on a corpus of 89 public workflows from bioinformatics and the results of the user experiment, finds that the average human ranking can largely be reproduced
A review of backtesting and backtesting procedures

This paper reviews a variety of backtests that examine the adequacy of Value-at-Risk (VaR) measures. These backtesting procedures are reviewed from both a statistical and risk management perspective. The properties of unconditional coverage and independence are defined and their relation to backtesting procedures is discussed. Backtests are then classified by whether they examine the unconditional coverage property, independence property, or both properties of a VaR measure. Backtests that examine the accuracy of a VaR model at several quantiles, rather than a single quantile, are also outlined and discussed. The statistical power properties of these tests are examined in a simulation experiment. Finally, backtests that are specified in terms of a pre-specified loss function are reviewed and their use in VaR validation is discussed.
Relationship violence among female and male college undergraduate students.

OBJECTIVES To assess prevalence of victimization and perpetration of relationship violence before and during college, to explore variations by gender, and to examine differences by relationship type. DESIGN Anonymously surveyed students in 67 randomly chosen classes. SETTING Three urban college campuses. PARTICIPANTS Nine hundred ten undergraduate college students aged 17 to 22 years. MAIN OUTCOME MEASURES Self-reported victimization and perpetration of physical, emotional, and sexual violence; relationship to the victim or perpetrator. RESULTS Most (57.1%) students were female, and 58.7% were white, 16.4% black, and 15.1% Asian. Of 910 participants, 407 (44.7%) experienced partner or nonpartner violence: 383 (42.1%) reported victimization and 156 (17.1%) reported perpetration. All victimization and perpetration rates were highest before college. Emotional violence was most common before college (21.1%); during college, sexual and emotional violence were equally common (12.0% and 11.8%, respectively). Women reported more victimization than men, but male victimization was considerable (27.2%). More men perpetrated sexual violence; more women perpetrated physical violence. More than half (130 of 227) of the violence experienced during college was partner related. Students experiencing partner violence during college were more likely to experience physical and emotional violence and were less likely to experience sexual violence. CONCLUSIONS Relationship violence is prevalent among college students and frequently occurs before college. Emotional violence was most frequent before college; sexual and emotional violence were equally common during college. Women reported more victimization than men, but male victimization was common. Men perpetrated more sexual violence; women perpetrated more physical violence. Physical violence and emotional violence were most often committed by partners, while sexual violence was less likely to be partner related.
HVDC macrogrid modeling for power-flow and transient stability studies in north american continental-level interconnections

High voltage direct current (HVDC) transmission lines are being constructed throughout the world, aided by advancements in power electronics and the potential value to transfer power between distant areas and off-shore locations. Multiple HVDC lines within and across large AC interconnections could bring about economic benefits such as interregional capacity exchange and transfer of low-cost, distant electric energy directly to load centers. In addition, network configuration of HVDC lines could result in additional benefits that have not been deeply studied. This paper describes the modeling process for continentallevel power system interconnections with the addition of multiple HVDC lines configured as a macrogrid. The models used for study are based on industry-accepted power-flow and dynamic system models for the North American Eastern and Western Interconnections. The model provides insight on feasibility and initial steady-state and stability tests of the HVDC macrogrid and its interactions with the existing electricity infrastructure, opening the door to analysis of the technical value of such a macrogrid.