Miracle-dz/UniFAR-FTUs huggingface dataset:
Models for forest ecosystem management: a European perspective.

BACKGROUND Forest management in Europe is committed to sustainability. In the face of climate change and accompanying risks, however, planning in order to achieve this aim becomes increasingly challenging, underlining the need for new and innovative methods. Models potentially integrate a wide range of system knowledge and present scenarios of variables important for any management decision. In the past, however, model development has mainly focused on specific purposes whereas today we are increasingly aware of the need for the whole range of information that can be provided by models. It is therefore assumed helpful to review the various approaches that are available for specific tasks and to discuss how they can be used for future management strategies. SCOPE Here we develop a concept for the role of models in forest ecosystem management based on historical analyses. Five paradigms of forest management are identified: (1) multiple uses, (2) dominant use, (3) environmentally sensitive multiple uses, (4) full ecosystem approach and (5) eco-regional perspective. An overview of model approaches is given that is dedicated to this purpose and to developments of different kinds of approaches. It is discussed how these models can contribute to goal setting, decision support and development of guidelines for forestry operations. Furthermore, it is shown how scenario analysis, including stand and landscape visualization, can be used to depict alternatives, make long-term consequences of different options transparent, and ease participation of different stakeholder groups and education. CONCLUSIONS In our opinion, the current challenge of forest ecosystem management in Europe is to integrate system knowledge from different temporal and spatial scales and from various disciplines. For this purpose, using a set of models with different focus that can be selected from a kind of toolbox according to particular needs is more promising than developing one overarching model, covering ecological, production and landscape issues equally well.
THE BEHAVIOR PATHOLOGY IN ALZHEIMER'S DISEASE RATING SCALE (BEHAVE‐AD): RELIABILITY AND ANALYSIS OF SYMPTOM CATEGORY SCORES

The interrater reliability of the Behavior Pathology in Alzheimer's Disease Rating Scale (BEHAVE‐AD), a psychiatric rating scale especially designed for the evaluation of behavioral disturbances in dementia patients, was studied in both English‐speaking US patients and a French‐speaking elderly patient population from France. Additionally, the quantitative relationship between severity of Alzheimer's disease (AD) and mean scores on each of the BEHAVE‐AD categories and mean total BEHAVE‐AD score was determined in a separate study of 140 patients diagnosed as having probable AD. In both reliability studies significant correlations were obtained for all BEHAVE‐AD symptom category scores and for mean total BEHAVE‐AD scores. Analysis of BEHAVE‐AD scores as a function of disease severity demonstrated a non‐linear relationship between severity of behavioral symptoms and the global and cognitive advance of AD. Moreover, score analysis of the BEHAVE‐AD indicates that these behavioral disturbances become most severe in the moderate and moderately severe stages of the illness. The results of these studies provide support for the reliability of the BEHAVE‐AD. Additionally, the BEHAVE‐AD provides basic knowledge about the nature and severity of the behavioral symptoms in AD. The latter information may be useful for the development of appropriate and effective psychopharmacologic intervention strategies for these difficult to manage behaviors. Furthermore, these results have implications for the methodology of pharmacologic trials of putative cognitive‐enhancer compounds in AD.
Within and beyond citizenship: alternative educational initiatives in the Arab society in Israel

In recent years, Arab-Palestinian citizens in Israel are in search of ‘a new vocabulary of citizenship’, among other ways, by resorting to ‘alternative educational initiatives’. We investigate and compare three alternative schools, each challenging the contested conception of Israeli citizenship. Our findings reveal different educational strategies to become ‘claimants of rights’, yet all initiatives demonstrate the constraints Arab citizens face while trying to become ‘activist citizens’ (E.F. Isin, 2009. Citizenship in flux: the figure of the activist citizen. Subjectivity, 29 (1), 367–388. ).
Extracting multi-modal dynamics of objects using RNNPB

Dynamic features play an important role in recognizing objects that have similar static features in colors and or shapes. This paper focuses on active sensing that exploits dynamic feature of an object. An extended version of the robot, Robovie-IIs, moves an object by its arm to obtain its dynamic features. Its issue is how to extract symbols from various kinds of temporal states of the object. We use the recurrent neural network with parametric bias (RNNPB) that generates self-organized nodes in the parametric bias space. The RNNPB with 42 neurons was trained with the data of sounds, trajectories, and tactile sensors generated while the robot was moving/hitting an object with its own arm. The clusters of 20 kinds of objects were successfully self-organized. The experiments with unknown (not trained) objects demonstrated that our method configured them in the PB space appropriately, which proves its generalization capability.
Administrative Cuts Amidst Decline in American Railroads

During organizational decline, the administrative component grows in some organizations and shrinks in others. Why do such differences occur? In a study of U.S. railroads, efficient organizations were found to be most prone to make administrative cuts. Furthermore, railroads making such cuts improved their chances of long-term survival. When an organization is faced with organizational decline, the results suggest that managers must cut to survive.
Grounding the cognitive neuroscience of semantics in linguistic theory

The mission of cognitive neuroscience is to represent the interaction of cognitive science and neuroscience: cognitive models of the mind guide a neuroscientific investigation of the brain bases of mental processes. In this endeavour, a cognitive model is crucial as without it, the cognitive neuroscientist does not know what to look for in the brain, what the nature of the relevant representations might be, or how the different components of a process might interact with each other. In the cognitive neuroscience of language, the interaction of theoretical models and brain research has, however, been far from ideal, especially when it comes to the study of meaning at the sentence level. Although theoretical semantics has a long history in linguistics and thus offers detailed and comprehensive models of the nature of semantic representations, these theories have had minimal impact on the brain investigation of semantic processing. In this article, we outline what a theoretically grounded cognitive neuroscience of semantics might look like and summarise our own findings regarding the neural bases of semantic composition, the basic combinatory operation that builds the complex meanings of natural language.
ON THE ERGODIC THEORY OF DISCRETE DYNAMICAL SYSTEMS

In this paper we study a class of measures, called harmonic measures, that one can associate to a dynamical system consisting og a space X and a finitely generated group of transformations. If the group of transformations is infinite cyclic, then these measures are invariant. We show how the theory of classical dynamical systems with invariant measure can be extended to the case of harmonic measure. Other properties of harmonic measures are best understood in the light of the harmonic analysis on a free group.
Dual Career Pathways: The Occupational Attainment of Married Couples in Hungary

This paper explores career mobility in the family, between the time of marriage and the time of data collection. The upward and downward moves of husbands and of wives are analysed separately, in the light of various theoretical frameworks, such as advantage redistribution, advantage accumulation, and status similarity. Empirically, the statistical models consider cohort and age differences, work experience, the social characteristics of respondents (education and occupation), the number and age of the children in the household, and the characteristics of the spouse. Another factor expected to be crucial to the chances of a person experiencing mobility is the occupational heterogeneity or homogeneity of the marriage. The data analysed are taken from the Hungarian Social Mobility Survey for 1992. The probability of upward and downward mobility by partners during the marriage has been subjected to event-history analysis. This reveals strong evidence that the resources of the spouse have positive effects on the occupational success of both husbands and wives, which supports the process of advantage accumulation in Hungarian households.With comparative advantages, results do not support advantage redistribution but status similarity as the framework for couples' career mobility.
SAR Image Segmentation Using Voronoi Tessellation and Bayesian Inference Applied to Dark Spot Feature Extraction

This paper presents a new segmentation-based algorithm for oil spill feature extraction from Synthetic Aperture Radar (SAR) intensity images. The proposed algorithm combines a Voronoi tessellation, Bayesian inference and Markov Chain Monte Carlo (MCMC) scheme. The shape and distribution features of dark spots can be obtained by segmenting a scene covering an oil spill and/or look-alikes into two homogenous regions: dark spots and their marine surroundings. The proposed algorithm is applied simultaneously to several real SAR intensity images and simulated SAR intensity images which are used for accurate evaluation. The results show that the proposed algorithm can extract the shape and distribution parameters of dark spot areas, which are useful for recognizing oil spills in a further classification stage.
Gentrification, Social Mix, and Social Polarization: Testing the Linkages in Large Canadian Cities

Gentrification in the form of "neighborhood revitalization" is increasingly touted as one way of decreasing the social exclusion of residents of poor inner-city neighborhoods and of increasing levels of social mix and social interaction between different classes and ethnic groups. Yet the gentrification literature also suggests that the process may lead to increased social conflict, displacement of poorer residents to lower quality housing elsewhere, and, ultimately, social polarization. Much of this hinges on whether gentrifying neighborhoods can remain socially mixed, and whether neighborhood compositional changes result in more or less of a polarized class and ethnic structure. However, the impact of revitalization and gentrification on levels of social mix, income polarization, or ethnic diversity within neighborhoods remains unclear and under-explored. This study addresses this gap by examining the relationship between the timing of gentrification, changes in the income structure, and shifts in immigrant concentration and ethnic diversity, using census tract data for each decade from 1971 to 2001 in Toronto, Montreal, and Vancouver. This research demonstrates that gentrification is followed by declining, rather than improving, levels of social mix, ethnic diversity, and immigrant concentration within affected neighborhoods. At the same time, gentrification is implicated in the growth of neighborhood income polarization and inequality.
Real-time Topic Detection with Bursty N-grams

Twitter is becoming an ever more popular platform for discovering and sharing information about current events, both personal and global. The scale and diversity of messages makes the discovery and analysis of breaking news very challenging. Nonetheless, journalists and other news consumers are increasingly relying on tools to help them make sense of Twitter. Here, we describe a fully-automated system capable of detecting trends related to breaking news in real-time. It identies words or phrases that ‘burst’ with sudden increased frequencies, and groups these into topics. It identies a diverse set of recent tweets that are related to these topics, and uses these to create a suitable human-readable headline. In addition, images coming from the diverse tweets are also added to the topic. Our system was evaluated using 24 hours of tweets as part of the Social News On the Web (SNOW) 2014 data challenge.
Missed Appointment Rates in Primary Care: The Importance of Site of Care

The objective was to determine whether race, language, or gender concordance between primary care providers (PCPs) and patients is associated with lower missed appointment rates in neighborhood health centers. An additional objective was to determine whether site of care is a determinant of missed appointment rates. In analyses of 74,120 follow-up visits by 13,882 patients, odds ratios for missing an appointment for patients who had language, race or gender concordance with their PCP were 0.90 (95% confidence interval [CI], 0.81–0.99), 0.84 (95% CI, 0.79–0.90) and 1.01 (95% CI, 0.95–1.07) respectively, after adjustment for age, insurance, language, individual PCP open access, sessions per week PCP in practice, and health center. Odds ratios for missing an appointment varied nearly three-fold, depending upon the particular site of care. Race and language concordance between patients and PCPs has only a modest effect on missed appointment rates. Receipt of primary care services at specific neighborhood health centers was the strongest predictor of missed appointment rates in this sample.
Antenna Pattern Correction Technique Based on Signal-to-Interference Ratio Optimization

An antenna pattern correction algorithm that is based on optimization of the signal-to-interference ratio (SIR) is presented. In this method, the antenna pattern of the antenna under test is measured in the far field or a compact range several times at different spatial locations. The method is demonstrated with measurements and simulations in a hologram-based compact antenna test range at 310 GHz.
Chromosomes are predominantly located randomly with respect to each other in interphase human cells

To test quantitatively whether there are systematic chromosome–chromosome associations within human interphase nuclei, interchanges between all possible heterologous pairs of chromosomes were measured with 24-color whole-chromosome painting (multiplex FISH), after damage to interphase lymphocytes by sparsely ionizing radiation in vitro. An excess of interchanges for a specific chromosome pair would indicate spatial proximity between the chromosomes comprising that pair. The experimental design was such that quite small deviations from randomness (extra pairwise interchanges within a group of chromosomes) would be detectable. The only statistically significant chromosome cluster was a group of five chromosomes previously observed to be preferentially located near the center of the nucleus. However, quantitatively, the overall deviation from randomness within the whole genome was small. Thus, whereas some chromosome–chromosome associations are clearly present, at the whole-chromosomal level, the predominant overall pattern appears to be spatially random.
Applying the Formal Elements Art Therapy Scale (FEATS) to Adults in an Asian Population

Abstract Assessment is the foundation for conceptualizing effective interventions. Due to their nonverbal nature, art therapy assessments have an advantage over traditional verbal assessments in some populations and potentially across cultures. This pilot study provides preliminary reliability data to support the cross-cultural use of the Formal Elements Art Therapy Scale (FEATS) as an art therapy assessment tool. A sample of 51 Chinese residents of Hong Kong completed the Person Picking an Apple From a Tree task, and their drawings were scored by two groups of art therapists and social workers in order to examine interrater reliability and to provide normative data for an Asian population. Results of the study showed high interrater reliability on the majority of the FEATS scales. Recommendations are offered to improve the rigor of art therapy assessment research and future normative studies.
Indirect detection of clock skew induced hold-time violations on functional paths using scan shift operations

Hold-time violations in a scan circuit may occur both in the scan chain and in its combinational logic part. If a hold-time violation occurs on the scan path from one scan cell to another, it is also likely to happen on short functional paths between the two cells, which have clock skew. This paper is intended to indirectly detect hold-time violations on short functional paths using scan shift operations. A greedy approach to scan chain ordering is presented to detect as many hold-time violations as possible using scan shift operations. Extensive experiments are conducted to detect hold-time violations on short functional paths of various lengths by scan shift operations. Experimental results show that many hold-time violations on short functional paths can be detected by choosing an appropriate order of the scan cells in the scan chain.
Price Discovery and Causality in the NSE Futures Market Kailash

The study investigates the causal relationship between the spot and futures on individual securities. The objectives of the study are examined by employing Johansen’s cointegration test and vector error correction model (VECM). The daily closing data is taken from November 9, 2001 to September 29, 2005 for the analysis. The results revealed that futures leads the spot in case of 9 individual securities, spot leads the futures in case of 7 individual securities and the feedback relation takes place between two markets in case of 9 individual securities.
PREDICTING PREFERENCES Analyzing Reading Behavior and News Preferences

News reading has gradually become a significant activity in our daily life as the world is saturated with new information. We consume so much information every day that it becomes extremely difficult to filter and extract only the interesting and relevant news to us. To improve readers’ experience, we need to be able to accurately predict the new stories that are most probable for them to read. This reduces to predicting preferences which is a common problem of finding out which items are most relevant to each user and essentially rank or feed them to tailored users. In our study, we in particular look at the stories users click and read previously in a news-reading mobile application Pulse in order to predict which stories users are most likely to read in the next few days. Similar challenge has been recently tackled by researchers in data mining and natural language processing because its implications can be applied to other popular areas such as search engine recommendation system. This paper considers applying different machine learning algorithms in the realm of supervised learning and unsupervised learning in attempt to predict the news stories that each user are most likely to read from a set of all available stories, which are much too large for the average users to parse and find the most relevant ones. In particular, using users’ click and read history, we explore text categorization algorithms by comparing the accuracy of several supervised learning methods such as a simple Naive Bayes, L2-norm regularized logistic regression, and L1-norm regularized logistic regression with Lasso algorithm. We then move on to investigate common unsupervised learning technique such as k-mean clustering of users and a simple implementation of collaborative filtering.
Pre‐maturation with cAMP modulators in conjunction with EGF‐like peptides during in vitro maturation enhances mouse oocyte developmental competence

Recent studies have independently shown that cyclic adenosine 3′5′‐monophosphate (cAMP) modulation prior to in vitro maturation (IVM) and epidermal growth factor (EGF)‐like peptide supplementation during IVM improve subsequent oocyte developmental outcomes. This study investigated the effects of an IVM system that incorporates these two concepts. Cumulus–oocyte complexes (COCs) were collected from pre‐pubertal mice either 46 hr post‐equine chorionic gonadotropin (eCG) (IVM) or post‐eCG + post‐human chorionic gonadotropin (hCG) stimulation (in vivo maturation; IVV). IVM COCs were treated with the cAMP modulators forskolin and IBMX for 1, 2, or 4 hr (pre‐IVM phase) prior to IVM. COCs then underwent IVM with the EGF‐like peptides amphiregulin or epiregulin, or with the common IVM stimulants follicle‐stimulating hormone (FSH) or EGF. A pre‐IVM phase increased the size of the subsequent blastocysts' inner‐cell‐mass compared to standard IVM, regardless of IVM treatment (P < 0.05). Unlike FSH or EGF, amphiregulin or epiregulin significantly increased blastocyst quality (trophectoderm and total cell numbers) and/or yield (P < 0.01) compared to standard IVM, and were the only treatments that produced blastocysts comparable to IVV‐derived blastocysts. Forskolin acutely up‐regulated EGF‐like peptide mRNA expression after a 2‐hr pre‐IVM phase (P < 0.001), although EGF receptor and ERK1/2 activities were not significantly different than control. IVV‐like levels of EGF‐like peptide mRNA expression during IVM were maintained only by supplementing with EGF‐like peptides and EGF, since expression levels induced by FSH were significantly lower in vitro than during IVV. However, EGF receptor and ERK1/2 phosphorylation levels were not significantly different across treatment groups. In conclusion, a pre‐IVM phase in conjunction with IVM in the presence of EGF‐like peptides endows high oocyte developmental competence, as evidenced by increased embryo yield and/or quality relative to FSH and EGF. Mol. Reprod. Dev. 81: ? –? , 2014. © 2014 Wiley Periodicals, Inc.
HLS-l: High-level synthesis of high performance latch-based circuits

An inherent performance gap between custom designs and ASICs is one of the reasons why many designers still start their designs from register transfer level (RTL) description rather than from behavioral description, which can be synthesized to RTL via high-level synthesis (HLS). Sequencing overhead is one of the factors for this performance gap; the choice between latch and flip-flop is not typically taken into account during HLS, even though it affects all the steps of HLS. HLS-l is a new design framework that employs high-performance latches during scheduling, allocation, and controller synthesis. Its main feature is a new scheduler that is based on a concept of phase step (as opposed to conventional control step), which allows us scheduling in finer granularity, register allocation that resolves the conflict of latch being read and written at the same time, and controller synthesis that exploits dual-edge triggered storage elements to support phase step based scheduling. In experiments on benchmark designs implemented in 1.2 V, 65-nm CMOS technology, HLS-l reduced latency by 16.6% on average, with 9.5% less circuit area, compared to the designs produced by conventionalHLS.
Educator and Parent Views of the Effectiveness of Individualized Learning Plans for Students With Disabilities

The purpose of this study was to gather information from educators and parents on their perceptions of the effectiveness of Individualized Learning Plans (ILPs) in assisting students with disabilities in gaining the experiences needed to develop college and career readiness skills. Results from focus groups and surveys of 1,117 parents and 484 educators in three states indicated that while a majority of parents and educators felt that transition assistance was helpful for youth without disabilities, less positive perceptions emerged regarding the adequacy of preparation for youth with disabilities and, particularly among educators, regarding the readiness of youth with disabilities to transition into higher education and careers. Based on these findings, implications for strengthening ILP efforts in schools are discussed.