Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-Columbian Floristic Legacies in Modern Homegardens of Central Amazonia

Historical ecologists have demonstrated legacy effects in apparently wild landscapes in Europe, North America, Mesoamerica, Amazonia, Africa and Oceania. People live and farm in archaeological sites today in many parts of the world, but nobody has looked for the legacies of past human occupations in the most dynamic areas in these sites: homegardens. Here we show that the useful flora of modern homegardens is partially a legacy of pre-Columbian occupations in Central Amazonia: the more complex the archaeological context, the more variable the floristic composition of useful native plants in homegardens cultivated there today. Species diversity was 10% higher in homegardens situated in multi-occupational archaeological contexts compared with homegardens situated in single-occupational ones. Species heterogeneity (β-diversity) among archaeological contexts was similar for the whole set of species, but markedly different when only native Amazonian species were included, suggesting the influence of pre-conquest indigenous occupations on current homegarden species composition. Our findings show that the legacy of pre-Columbian occupations is visible in the most dynamic of all agroecosystems, adding another dimension to the human footprint in the Amazonian landscape.
The 6 D 5 L description system for health state valuation

When we ask a person to value time spent in a health state without any information about the key domains of health, (s)he must guess a description of the health state. The description visualized by the valuer would usually be implicit in the valuation task. This will inevitably introduce measurement error and a potential for bias in the results. Another consideration is how to convey relevant information about a hypothetical health state to the individual undertaking the valuation, who might not have personally encountered the state. Disease labels are short and parsimonious, but do not convey adequate information about functional status. Moreover, disease labels are vulnerable to different interpretations based on cultural and personal settings. Here we report the 6D5L description system developed and used by the Andhra Pradesh Health State Valuation Study 1999 (APHSV99). Results from this study are presented in chapter 9.4. More details of design, methods, data collection and analysis have been reported (Mahapatra et al. 2000). We first review current literature on the theoretical question of how to describe health states for valuation. We then describe components of the 6D5L system developed to describe health states for valuation. In the third and final section of this chapter we present some results about the usage of the 6D5L system by valuers in the APHSV99 Study.
Ontology and Lexicon Evolution by Text Understanding

We introduce a methodology for automating the maintenance and extension of domain taxonomies, combined with the acquisition of lexical knowledge on the basis of a natural language understanding system. By defining the strength of various forms of evidence, conceptual as well as lexical hypotheses are ranked according to qualitive plausibility criteria and the most reasonable ones are selected for assimilation into the already given domain ontology and lexical class hierarchy.
AMC and HARQ: Effective capacity analysis

Modern wireless systems deal with the adverse and unpredictable channel conditions using two main transmission schemes considered as complimentary: adaptive modulation and coding (AMC) and hybrid ARQ (HARQ). In this work we use the effective capacity as the performance measure to evaluate different design options. We thus show how to calculate this performance measure under independent, identically distributed (i.i.d.) block-fading channel model considering AMC, HARQ and more importantly, a combination of thereof. Numerical results indicate that AMC alone outperforms the other schemes in the region of high average SNR; we provide an explanation for this observation and discuss possible paths for improvement.
Automatic Extraction of Concealed Relations from Email Logs (Extended Abstract)

People interact with each other for various reasons. Based on the purpose of the relationship, these interactions exhibit certain characteristics. One such important characteristic is that of concealment. Concealed relations can often be a source of interest especially in the domain of counterterrorism where relations fostering malicious activities tend to be secretive or concealed from the general public. In this paper we propose a technique for extracting concealed relations from social network data. The technique analyzes actors’ perceptions regarding other actors’ social interactions and requires that they can be constructed from the social network data. One popular communication medium for which this can be done eciently is electronic mail. The proposed technique uses the popular and robust tf-idf measure from the information retrieval literature to quantify the concept of concealment. We present experimental results from the Enron email corpus.
A Structural Vector Error Correction Model with Short-run and Long-run Restrictions

We consider structural vector error correction models (VECMs) in which permanent shocks are partially identified with a set of long-run restrictions, and fully identified with an additional set of short-run restrictions. An identification method with a combination of short-run and long-run restrictions has been studied in the vector autoregressive models literature, but not thoroughly applied to the VECM framework. There exists a separation in the literature; permanent shocks are identified with long-run restrictions while transitory shocks are identified with short-run restrictions. This paper's innovation is the identification of permanent shocks using both horizontal restrictions.
Interest/intention classification for the Fish-Bird new media artwork

This paper investigates the appropriateness of various features and classifiers to an interest classification task. The data for the task is obtained from various exhibitions of the Fish-Bird artwork. A number of features from the dataset are calculated and described, based on psychology literature on human motion. These features are used in both a Dynamic Bayesian Network classifier, and a Conditional Random Field classifier, representative types of both Discriminative and Generative classifiers. These two trained classifiers are both discussed along with a Heuristic-based approach. The Classification Accuracy for the two trained classifiers is compared using both real-world data and simulated data.
Distributed Database Checkpointing Extended Abstract

Data checkpointing is an important problem of distributed database systems. Actually, transactions establish dependence relations on data checkpoints taken by data object managers. So, given an arbitrary set of data checkpoints (including at least a single data checkpoint from a data manager, and at most a data checkpoint from each data manager), an important question is the following one: \Can these data checkpoints be members of a same consistent global checkpoint? ". This paper answers this question and proposes a non-intrusive data check-pointing protocol.
Do cortical maps adapt to optimize information density?

Topographic maps are found in many biological and artificial neural systems. In biological systems, some parts of these can form a significantly expanded representation of their sensory input, such as the representation of the fovea in the visual cortex. We propose that a cortical feature map should be organized to optimize the efficiency of information transmission through it. This leads to a principle of uniform cortical information density across the map as the desired optimum. An expanded representation in the cortex for a particular sensory area (i.e. a high magnification factor) means that a greater information density is concentrated in that sensory area, leading to finer discrimination thresholds. Improvement may ultimately be limited by the construction of the sensors themselves. This approach gives a good fit to threshold versus cortical area data of Recanzone et al on owl monkeys trained on a tactile frequency-discrimination task.
Sexed Semen Economics

The goal with sexed semen is to produce a calf of a specific sex. Sexed semen is widely available now and many dairy producers are using it to obtain more (and better) heifer calves. Because of its higher cost per dose of semen, combined with a reduced conception risk, sexed semen is primarily recommended for use in virgin heifers. The use of sexed semen varies widely among dairy producers. Some producers do not use it at all while others use it on heifers only, and some use it on both heifers and cows (Anonymous, 2008). With heifers (and cows), sexed semen is usually used for first and perhaps second breedings, but typically not for later breedings. The economic benefits of the use of sexed semen are different for every dairy farm. This paper summarizes some of the recent literature on sexed semen in dairy heifers and cows. Further, the economics of the use of sexed semen in heifers and cows are evaluated.
scat: Learning from a single execution of a binary

Retrieving information from a binary code is required in several application domains such as system integration or security analysis. Providing tools to help engineers in this task is therefore an important need. We present in this paper scat, an open-source toolbox, relying on lightweight runtime instrumentation to infer source-level and behavioral information from a binary code, like function prototypes or data-flow relations. We explain the functioning principle of this toolbox, and we give some results obtained on real examples to show its effectiveness.
Differences in stakeholder expectations in the outcome of physiotherapy management of acute low back pain.

OBJECTIVE To compare stakeholder expectations of outcome of physiotherapy management of acute low back pain. DESIGN Observational design using interviews and questionnaires. SETTING Practice/workplace. STUDY PARTICIPANTS The study sample was from South Australia. It comprised 74 physiotherapists randomly selected from professional association listings (49.3% response rate), 121 physiotherapy patients (recruited by participating physiotherapists when attending their first physiotherapy treatment for acute low back pain), 21 general practitioners randomly selected from medical practitioner listings in the metropolitan telephone book (36.2% response rate) and 13 third party payers of a total of 16 available insurers in the metropolitan area (82% response rate). MAIN OUTCOME MEASUREMENTS Stakeholders reported expectations of outcome at the end of the first treatment session and at the completion of the episode of care. RESULTS There were differences in expectations between stakeholders, as well as between naive and experienced patients. Overall, patients expected symptom relief at the end of the first treatment. Naive patients decided to return for further treatment based on the relationship established with the therapist, whereas experienced patients also expected some advice on their condition during the first contact. Physiotherapists and referrers expected symptom relief and then long-term management strategies to be provided, and third party payers expected cost-efficient management of the condition and patient satisfaction. CONCLUSION Physiotherapists need to address potential imbalance of consumer knowledge and foster a quality partnership with their patients on the first visit to physiotherapy. Patients who are in pain may not derive full value from information provided in an untimely manner.
Mapping of science by combined co-citation and word analysis, I. Structural aspects

The claim that co-citation analysis is a useful tool to map subject-matter specialties of scientific research in a given period, is examined. A method has been developed using quantitative analysis of content-words related to publications in order to: (1) study coherence of research topics within sets of publications citing clusters, i.e., (part of) the “current work” of a specialty; (2) to study differences in research topics between sets of publications citing different clusters; and (3) to evaluate recall of “current work” publications concerning the specialties identified by co-citation analysis. Empirical support is found for the claim that co-citation analysis identifies indeed subject-matter specialties. However, different clusters may identify the same specialty, and results are far from complete concerning the identified “current work. ” These results are in accordance with the opinion of some experts in the fields. Low recall of co-citation analysis concerning the “current work” of specialties is shown to be related to the way in which researchers build their work on earlier publications: the “missed” publications equally build on very recent earlier work, but are less “consensual” and/or less “attentive” in their referencing practice. Evaluation of national research performance using co-citation analysis appears to be biased by this “incompleteness. ”
GOLIAT: an Optimizing Linker for the IA-64 ArchiTecture

Modern compilers are extremely sophisticated and complex. This enables them to produce highly optimized code. However, due to the limited scope of compilers, the optimization opportunities that arise from analyzing and optimizing across module boundaries remain largely unexplored. We examined the viability of optimizing statically linked programs for the IA-64 architecture. As a result, the code size was reduced on average with 20% and the execu-
A Review on Driver Drowsiness Detection Techniques

Number of accidents during driving is increasing day by day and drowsy driving has been implicated as a causal factor in many accidents. Goal of driver drowsiness detection systems is to reduce these accidents. It has been seen that most of the accidents occur due to driver’s fatigue and a small due to inattention factor, therefore this paper reviews driver’s fatigue monitoring techniques in detail with a little overview of others also. Keywords-Driver drowsiness; Driver monitoring; Review
Tau Pathology Distribution in Alzheimer's disease Corresponds Differentially to Cognition-Relevant Functional Brain Networks

Neuropathological studies have shown that the typical neurofibrillary pathology of hyperphosphorylated tau protein in Alzheimer's disease (AD) preferentially affects specific brain regions whereas others remain relatively spared. It has been suggested that the distinct regional distribution profile of tau pathology in AD may be a consequence of the intrinsic network structure of the human brain. The spatially distributed brain regions that are most affected by the spread of tau pathology may hence reflect an interconnected neuronal system. Here, we characterized the brain-wide regional distribution profile of tau pathology in AD using 18F-AV 1451 tau-sensitive positron emission tomography (PET) imaging, and studied this pattern in relation to the functional network organization of the human brain. Specifically, we quantified the spatial correspondence of the regional distribution pattern of PET-evidenced tau pathology in AD with functional brain networks characterized by large-scale resting state functional magnetic resonance imaging (rs-fMRI) data in healthy subjects. Regional distribution patterns of increased PET-evidenced tau pathology in AD compared to controls were characterized in two independent samples of prodromal and manifest AD cases (the Swedish BioFINDER study, n = 44; the ADNI study, n = 35). In the BioFINDER study we found that the typical AD tau pattern involved predominantly inferior, medial, and lateral temporal cortical areas, as well as the precuneus/posterior cingulate, and lateral parts of the parietal and occipital cortex. This pattern overlapped primarily with the dorsal attention, and to some extent with higher visual, limbic and parts of the default-mode network. PET-evidenced tau pathology in the ADNI replication sample, which represented a more prodromal group of AD cases, was less pronounced but showed a highly similar spatial distribution profile, suggesting an earlier-stage snapshot of a consistently progressing regional pattern. In conclusion, the present study indicates that the regional deposition of tau aggregates in AD predominantly affects higher-order cognitive over primary sensory-motor networks, but does not appear to be specific for the default-mode or related limbic networks.
Data Mining on Patient Data

In this paper, we use machine learning schemes IR, FOIL, InductH and C5.0 to generate decision trees and rules from the examples in the medical dataset. The aim of our study is to infer the patterns that can help doctors to identify, recognize and predict the effect of the risk factors on the long term subjective cure rates of patients who undergo colposuspension. High test classification was sometimes achieved. Our best results came when one learning method suggested the preprocessing steps to be used for another method
A novel method for recognizing fruits with plastic packing

According to the need of automatic weighing and pricing system for fruits in the supermarket and the limitation of current methods, this paper proposes a new segmentation method for fruits with plastic packing and a modified minimum distance classifier. Firstly, we segment images and obtain the region of interest (ROI) in the HSV color space. Secondly, we extract color features and texture features which can be derived from gray level co-occurrence matrix (GLCM), and saving these features into the classifier. Then, we apply modified classifier to recognize and categorize. The experiment results show that our segmentation method can separate ROI from the background with plastic packaging effectively. Furthermore, this novel method can also achieve a better recognition performance with overall recognition rate above 91%.
Iterative Reweighted <formula formulatype="inline"> <tex Notation="TeX">$\ell_1$</tex></formula> and <formula formulatype="inline"> <tex Notation="TeX">$\ell_2$</tex></formula> Methods for Finding Sparse Solutions

A variety of practical methods have recently been introduced for finding maximally sparse representations from overcomplete dictionaries, a central computational task in compressive sensing applications as well as numerous others. Many of the underlying algorithms rely on iterative reweighting schemes that produce more focal estimates as optimization progresses. Two such variants are iterative reweighted l1 and l2 minimization; however, some properties related to convergence and sparse estimation, as well as possible generalizations, are still not clearly understood or fully exploited. In this paper, we make the distinction between separable and non-separable iterative reweighting algorithms. The vast majority of existing methods are separable, meaning the weighting of a given coefficient at each iteration is only a function of that individual coefficient from the previous iteration (as opposed to dependency on all coefficients). We examine two such separable reweighting schemes: an l2 method from Chartrand and Yin (2008) and an l1 approach from Cande's (2008), elaborating on convergence results and explicit connections between them. We then explore an interesting non-separable alternative that can be implemented via either l2 or l1 reweighting and maintains several desirable properties relevant to sparse recovery despite a highly non-convex underlying cost function. For example, in the context of canonical sparse estimation problems, we prove uniform superiority of this method over the minimum l1 solution in that, 1) it can never do worse when implemented with reweighted l1, and 2) for any dictionary and sparsity profile, there will always exist cases where it does better. These results challenge the prevailing reliance on strictly convex (and separable) penalty functions for finding sparse solutions. We then derive a new non-separable variant with similar properties that exhibits further performance improvements in empirical tests. Finally, we address natural extensions to group sparsity problems and non-negative sparse coding.
Vitamin D and Pain: Vitamin D and Its Role in the Aetiology and Maintenance of Chronic Pain States and Associated Comorbidities

The emergence of new data suggests that the benefits of Vitamin D extend beyond healthy bones. This paper looks at Vitamin D and its role in the aetiology and maintenance of chronic pain states and associated comorbidities. The interfaces between pain and Vitamin D and the mechanisms of action of Vitamin D on pain processes are explored. Finally the association between Vitamin D and pain comorbidities such as sleep and depression is investigated. The paper shows that Vitamin D exerts anatomic, hormonal, neurological, and immunological influences on pain manifestation, thereby playing a role in the aetiology and maintenance of chronic pain states and associated comorbidities. More research is necessary to determine whether Vitamin D is useful in the treatment of various pain conditions and whether or not the effect is limited to patients who are deficient in Vitamin D.
Thruster modeling for a Hovering Autonomous Underwater Vehicle considering thruster-thruster and thruster-hull interaction

This paper presents the thruster modeling for a Hovering Autonomous Underwater Vehicle developed by Underwater, Control, and Robotics Group. Bollard-pull experiments are done to find the relationship between axial forces generated by thrusters and input signal given to Arduino Uno board in degree, taking into account the effect of thruster-thruster and thruster-hull interaction. Experiments results show that a thruster will lose efficiency due to the interaction with the vehicle's body. Besides, experiment results also show that the effect of thruster-thruster interaction is not obvious for far separating thrusters.
Utilization of Mathematica environment for designing the forecast model of heat demand BRONISLAV CHRAMCOV

Time series analysis and subsequent forecasting can be an important part of a process control system. By monitoring key process variables and using them to predict the future behavior of the process, it may be possible to determine the optimal time and extent of control action. We can find applications of this prediction also in the control of the Centralized Heat Supply System (CHSS), especially for the control of hot water piping heat output. Knowledge of heat demand is the base for input data for the operation preparation of CHSS. The course of heat demand can be demonstrated by means of the Daily Diagram of Heat Supply (DDHS) which demonstrates the course of requisite heat output during the day. This diagram is of essential importance for technical and economic considerations. The aim of paper is to give some background about analysis of time series of heat demand and identification of forecast model using Time series package which is integrated with the Wolfram Mathematica environment. The paper illustrates using this environment for designing the forecast model of heat demand in specific locality. Key-Words: Prediction, District Heating Control, Box-Jenkins, Control algorithms, Time series analysis, modelling