Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainability of Natural Populations: Lessons from Indigenous Knowledge

Management schemes for wildlife are often unsuccessful in maintaining healthy, sustainable populations, especially approaches based on Maximum Sustainable Yield, which fail to account for variation in reproductive output. North American Indigenous hunters had considerable knowledge about the dynamics of local populations and were aware that they were subject to major fluctuations. These peoples developed the concept of “Keepers of the Game,” which suggested that if fish or game animals were not treated with proper respect, these “keepers” could remove their population, making it unavailable for human exploitation. The “keepers” concept may have represented recognition of exceptional individuals that had unusual influence on local population dynamics. Existence of such individuals has been established empirically through long-term studies that show that less than 10% of a cohort may contribute 75–90% of recruits to succeeding generations. Incorporation of this concept into management schemes might lead to increased sustainability of wildlife populations.
Efficient chart parsing of speech recognition candidates

In a spoken language system that uses the N-best interface between the continuous-speech recognition component and the natural language processing component, the role of the language processing component is to filter speech recognition candidates and to determine the semantic interpretation of the input. We propose as efficient chart parsing method of finding the most plausible sentence within the N-best candidate sentences and determining the semantic interpretation of the sentence, while avoiding the re-computation of the common substrings. We use syntactic and semantic constraints described in the unification-based framework and context-sensitive conditional probability CFG preferences to reorder the N-best candidates. In preliminary tests, our parser has been successful in reducing parsing steps by sharing common substrings and in selecting correct sentences first. <<ETX>>
Short Circuit Studies on a Distribution System Integrated with Various Distributed Generations

Distributed Generations (DGs) are one of the important and prominent smart grid technologies that will lead the world to meet the objective of sustainable energy for the future. There are many technical as well as social benefits that can be achieved using DGs but at the same time DGs also offer a set of challenges which need to be addressed aswell. This paper will present the issues related to the fault level contributions in a Distribution System (DS) with and without DGs. The cases considered for the study are by analyzing the fault levels with different kind of DGs (Diesel, PV system and Wind DFIG) integrated with the DS. The effect of grounding during an asymmetrical fault considering various transformer winding configurations is presented in this paper. A comparative study has been made from the considered cases and conclusions are drawn. MiPower, a Power System software simulation tool has been used for carrying out the Short circuit studies.
Multidrug Resistance–like Genes of Arabidopsis Required for Auxin Transport and Auxin-Mediated Development Article, publication date, and citation information can be found at www.aspb.org/cgi/doi/10.1105/tpc.010350.

Arabidopsis possesses several genes related to the multidrug resistance (MDR) genes of animals, one of which, AtMDR1, was shown to be induced by the hormone auxin. Plants having mutations in AtMDR1 or its closest relative, AtPGP1, were isolated by a reverse genetic strategy. Auxin transport activity was greatly impaired in atmdr1 and atmdr1 atpgp1 double mutant plants. Epinastic cotyledons and reduced apical dominance were mutant phenotypes consistent with the disrupted basipetal flow of auxin. The auxin transport inhibitor 1-naphthylphthalamic acid was shown to bind tightly and specifically to AtMDR1 and AtPGP1 proteins. The results indicate that these two MDR-like genes of Arabidopsis encode 1-naphthylphthalamic acid binding proteins that are required for normal auxin distribution and auxin-mediated development.
Cortisol outcomes among caucasian and latina/hispanic women caring for a family member with dementia: a preliminary examination of psychosocial predictors and effects of a psychoeducational intervention

A host of interventions are now known to be helpful to alleviate subjective distress and improve well-being in dementia caregivers. However, few intervention studies have focused on measures of physical health, and none have examined cortisol as an outcome—despite the fact that cortisol is regarded as a crucial biological intermediary by which chronic stress leads to disease. In this study, we examined demographic and psychosocial factors as predictors of salivary cortisol at a baseline assessment, among a sample of 175 Latino/Hispanic and Caucasian women caring for a family member with dementia. We also examined the influence of a cognitive–behaviour-based psychoeducational intervention (Coping with Caregiving) on cortisol at a post-treatment assessment, compared with a minimal support condition. Results revealed that caregivers with high intensity caregiving situations, characterized by long hours of care and co-residence with the care recipient, tended to have less adaptive cortisol patterns. However, these ‘at-risk’ caregivers benefited most from the Coping with Caregiving intervention and had more normal cortisol patterns at post-treatment, compared with caregivers in the control condition. Copyright © 2011 John Wiley & Sons, Ltd.
Political Television Advertising in Campaign 2000

Most studies of political advertising focus on presidential television spots. However, far more commercials are broadcast for other races. This study applies the functional theory of political campaign discourse to 584 television spots from presidential, gubernatorial, senatorial, house, and local races in the 2000 election. The most common functions were acclaims (67%) followed by attacks (32%) and defenses (1%). When non-presidential ads are examined, incumbents acclaimed more and attacked less than challengers. Party-sponsored ads employed more attacks than ads sponsored by the candidates. These messages emphasized policy more than character (62% against 38%). Democratic candidates discussed policy more than Republicans. Presidential and gubernatorial ads (executive offices) discussed policy more than ads for other offices.
Negotiating Concurrently with Unknown Opponents in omplex, Real-Time Domains

We propose a novel strategy to enable autonomous agents to negotiate concurrently with multiple, unknown opponents in real-time, over complex multi-issue domains. We formalise our strategy as an optimisation problem, in which decisions are based on probabilistic information about the opponents' strategies acquired during negotiation. In doing so, we develop the first principled approach that enables the coordination of multiple, concurrent negotiation threads for practical negotiation settings. Furthermore, we validate our strategy using the agents and domains developed for the International Automated Negotiating Agents Competition (ANAC), and we benchmark our strategy against the state-of-the-art. We find that our approach significantly outperforms existing approaches, and this difference improves even further as the number of available negotiation opponents and the complexity of the negotiation domain increases.
Automated Detection of Breast Cancer Metastases in Whole Slide Images

Cancer is considered as one of the most widely spread disease in today’s world. Many people are suffering from this disease unaware of the facts about cancer. Women are more likely to suffer from breast cancer. Cancer spread in the body when the healthy cells grow out of control and form a mass of cells resulting in tumor. This paper describes an algorithm for automated detection of breast cancer metastases in Whole Slide Images. The current procedure for detecting metastases in a breast lymph node is manual and time-consuming in which an experienced pathologist specializing in detection and characterization of tumor regions spends hours to analyze histological slides. This algorithm leverages the capability of advanced Image Processing and Machine learning to improve the detection accuracy as well as overall time needed to localize tumorous regions in Whole Slide Image.
ICDAR2017 Competition on Information Extraction in Historical Handwritten Records

The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results.
Effects of irreversible olivary system lesion on the gain adaptation of optokinetic response eye movement: a model based study

The optokinetic reflex (OKR) is a behavioral oculomotor response which serves to stabilize moving images on the retina. As the cerebellum is intimately involved in the adaptive control of such compensatory eye movement, the OKR has been turned to a favorable test bed for modeling and assessing cerebellar learning function. Learning process in the cerebellum consists of two distinct phases: 1) short-term learning, which is acquired by single session of behavioral training and disappears within 24 hours, and 2) long-term learning which is induced by repeating sessions and persists for days. On the other hand, emerging evidences from experimental studies indicates high level of coordination between olivary system activity and cerebellar learning. However, it is still unclear which phase of learning will be affected by olivary system disruption. In this paper, we proposed a simple model for learning adaptation and memory formation of the cerebellum. The model is capable to reproduce the OKR gain adaptation of eye movement in both long and short term phases. The simulation results were found to strongly agree with previously reported experimental data from wild type mice. As a second step, we explore the effects of irreversible olivary system lesion on the gain adaptability of OKR by cutting off the connection of climbing fiber, which originates from the inferior olive (IO) neuron. Thereafter, comparing to the normal case, the gain of OKR undergoes a significant decline in both short and long phases of learning. This suggests that the olivary system plays a critical role in both short-and long-term adaption of OKR.
Learning User Dependencies for Recommendation

Social recommender systems exploit users’ social relationships to improve recommendation accuracy. Intuitively, a user tends to trust different people regarding with different scenarios. Therefore, one main challenge of social recommendation is to exploit the most appropriate dependencies between users for a given recommendation task. Previous social recommendation methods are usually developed based on pre-defined user dependencies. Thus, they may not be optimal for a specific recommendation task. In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which can automatically learn the dependencies between users to improve recommendation accuracy. In PRMF, users’ latent features are assumed to follow a matrix variate normal (MVN) distribution. Both positive and negative user dependencies can be modeled by the row precision matrix of the MVN distribution. Moreover, we also propose an alternating optimization algorithm to solve the optimization problem of PRMF. Extensive experiments on four real datasets have been performed to demonstrate the effectiveness of the proposed PRMF model.
Dimensions of Spline Spaces over 3D Hierarchical T-Meshes ?

A 3D T-mesh is basically a partition of a cuboid such that every part is a smaller cuboid. In this paper we deflne the spline spaces over 3D T-meshes, which would play an important role in adaptive and dynamic implicit surface reconstruction from unorganized point clouds, and present a dimension formula about the spline space over a special kind of T-mesh, i.e., 3D hierarchical T-mesh. The formula holds when the smoothness is less than half of the degree of the spline functions, and it involves only the topological quantities of the T-meshes. The construction of basis functions of the spline space is brie∞y discussed.
Scleral lens use in dry eye syndrome

Purpose of review Dry eye syndrome can be difficult to manage in severe or refractory cases. In patients in whom traditional treatments have limited efficacy, alternative treatments may be considered for dry eye syndrome, including scleral lenses. The present review summarizes the evidence regarding scleral lens use in dry eye syndrome. Recent findings Scleral lenses have become a viable option for severe dry eye syndrome, and have been shown to be efficacious and well tolerated, with most reports citing improved visual acuity and relief of symptoms. Currently, there are 18 manufacturers of scleral lenses, although published reports on scleral lenses primarily focus on the BostonSight PROSE and the Jupiter Lens. Summary Scleral lenses are efficacious and well tolerated for use in severe dry eye syndrome. Further research is needed to compare different sizes and types of lenses, and to standardize outcome measures.
CNS Metastases in Breast Cancer: Old Challenge, New Frontiers

Despite major therapeutic advances in the management of patients with breast cancer, central nervous system (CNS) metastases remain an intractable problem, particularly in patients with metastatic HER2-positive and triple-negative breast cancer. As systemic therapies to treat extracranial disease improve, some patients are surviving longer, and the frequency of CNS involvement seems to be increasing. Furthermore, in the early-stage setting, the CNS remains a potential sanctuary site for relapse. This review highlights advances in the development of biologically relevant preclinical models, including the development of brain-tropic cell lines for testing of agents to prevent and treat brain metastases, and summarizes our current understanding of the biology of CNS relapse. From a clinical perspective, a variety of therapeutic approaches are discussed, including methods to improve drug delivery, novel cytotoxic agents, and targeted therapies. Challenges in current trial design and endpoints are reviewed. Finally, we discuss promising new directions, including novel trial designs, correlative imaging techniques, and enhanced translational opportunities. Clin Cancer Res; 19(23); 6404–18. ©2013 AACR.
Resource sharing with subexponential distributions

We investigate the distribution of the waiting time V in an M/G/1 processor sharing queue with traffic intensity /spl rho/ < 1. This queue represents a baseline model for evaluating efficient and fair network resource sharing algorithms, e.g. TCP flow control. When the distribution of job size B belongs to a class of subexponential distributions with tails heavier than e/sup -/spl radic/x/, it is shown that as x/spl rarr//spl infin/ P[V > x] = P[B > (1 - /spl rho/)x](1 + o(1)) Furthermore, we demonstrate that the preceding relationship does not hold if the job distribution has a lighter tail than e/sup -/spl radic/x/. This result provides a new tool for analyzing network congestion points with moderately heavy-tailed characteristics, e.g. log normal distributions, that have been recently empirically discovered in Web traffic. The accuracy of our approximation is confirmed with simulation experiments.
Forecasting Influenza Epidemics from Multi-Stream Surveillance Data in a Subtropical City of China

Background Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China. Methods Dynamic linear models with the predictors of single or multiple surveillance data for influenza-like illness (ILI) were adopted to forecast influenza epidemics from 2006 to 2012 in Shenzhen. Temporal coherence of these surveillance data with laboratory-confirmed influenza cases was evaluated by wavelet analysis and only the coherent data streams were entered into the model. Timeliness, sensitivity and specificity of these models were also evaluated to compare their performance. Results Both influenza virology data and ILI consultation rates in Shenzhen demonstrated a significant annual seasonal cycle (p<0.05) during the entire study period, with occasional deviations observed in some data streams. The forecasting models that combined multi-stream ILI surveillance data generally outperformed the models with single-stream ILI data, by providing more timely, sensitive and specific alerts. Conclusions Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen.
Adaptive steganography by oracle (ASO)

HUGO [1] and MOD [2] are the most secure adaptive embedding algorithms of 2011. These algorithms strive to hide a secret message, while minimizing an ad hoc embedding impact. They use a detectability map, which, if properly defined, is correlated to the security. In this paper, we present a new adaptive embedding scheme: Adaptive Steganography by Oracle (ASO). It is based on an oracle used to calculate the detectability map, and this oracle use the Kodovský's ensemble classifiers [3]. Our approach preserves both cover image and sender's database distributions during the embedding process, which improves the security. In addition, it offers to the sender the opportunity to choose the most reliable image( s), during his secret communication. Experimental results show that our embedding scheme presents good security performances, since the detection error of ASO is much higher than that of HUGO.
Dual Mode Control of Grid Tied Solar PV-DSTATCOM by Utilizing Warped Digital Filter in Distribution System

For the efficient functioning of grid tied solar photovoltaic array in distribution system, the control algorithm is proposed here utilizing a warped digital filter. The solar photovoltaic (PV) array supplies maximum power to the grid/the load by maximum power point tracking (MPPT) technique namely a perturb and observe (P&O) method. The control is termed as a dual mode, as it performs satisfactorily during availability/unavailability of solar PV array power. When the solar PV array power is incident, the proposed system is capable of transferring active power from solar PV array to the grid/the load while improving power quality using a voltage source converter (VSC) working as a distribution static compensator (DSTATCOM). However, during cloudy days or at night, when solar power is not present, the system performs as DSTATCOM for reactive power compensation and elimination of harmonics. The control algorithm provides smooth transition between these modes of operation of the system. The control implemented using warped digital filters, is adaptive as its coefficients are updated depending upon system requirements. A feed-forward term for solar PV array power is incorporated, for the enhancement of dynamic response. A prototype of the system is developed in the laboratory and its performance is studied for varying loads, varying solar insolation conditions and change of modes between grid tied PV array and DSTATCOM.
Grid Integration of a Multi-Objective Single Stage PV System Using Tapped Delay Continuous Time LMS Based Control Technique

In this paper, a photovoltaic (PV) array is interfaced to a three-phase distribution network. A three-leg voltage source converter (VSC) is used for injecting power to the utility. The PV array is connected to DC bus capacitor of VSC. The maximum power is harvested from the PV array, by generating a reference DC bus voltage, making the use of an incremental conductance (INC) derived MPPT technique. A tapped delay continuous time least mean square (TDCTLMS) based control scheme, is utilized to estimate switching pulses for the VSC switches. For improvement in system response to abrupt changes in irradiance, and grid voltage variations, the control includes a PV feed-forward term. The response is evaluated on an experimental prototype, for balanced and unbalanced nonlinear loads, and dynamic conditions of irradiance variations, and abrupt load unbalancing. The PV system demonstrates the additional functions of correction of power factor, grid currents balancing, and harmonics currents mitigation to within the IEEE 519 standard.
Shifting the Goalposts: A Longitudinal Mixed-Methods Study of the Health of Long-Term Incapacity Benefit Recipients during a Period of Substantial Change to the UK Social Security System

Abstract The UK social security safety net for those who are out of work due to ill health or disability has experienced significant change, most notably the abolition of Incapacity Benefit (IB) and the introduction of Employment and Support Allowance (ESA). These changes have been underpinned by the assumption that many recipients are not sufficiently sick or disabled to ‘deserve’ welfare benefits – claims that have been made in the absence of empirical data on the health of recipients. Employing a unique longitudinal and mixed-methods approach, this paper explores the health of a cohort of 229 long-term IB recipients in the North East of England over an eighteen-month period, during a time of significant changes to the UK welfare state. In-depth interviews with twenty-five of the survey cohort are also presented to illustrate the lived experiences of recipients. Contributing to debates surrounding the conceptualisation of work-readiness for sick and disabled people, findings indicate IB recipients had significantly worse health than the general population, with little change in their health state over the eighteen-month study period. Qualitative data reinforced the constancy of ill health for IB recipients. Finally, the paper discusses the implications for social policy, noting how the changing nature of administrative definitions and redefinitions of illness and capacity to work can impact upon the lives of sick and disabled people.
Structural Geodesic-Tchebychev Transform: An Image Similarity Measure for Face Recognition

This work presents a new holistic measure for face recognition. Face recognition involves three steps: Face Detection, Feature Extraction and Matching. In the face detection process to identify the face area in face images, Viola-Jones algorithm has been used. Feature extraction is based on performing double-transformation, where discrete Tchebychev transform is performed on the geodesic distance transform of the grayscale image. Structural Similarity (SSIM) is applied to the resulting image double-transform to find matching factor with other image faces in the FEI (Brazilian) database. Performance is measured using a confidence criterion based on the similarity distance between the recognized person (best match) and the next possible ambiguity (second-best match). Simulation results showed that the proposed approach handles the face recognition efficiently as compared with SSIM.