Miracle-dz/UniFAR-FTUs huggingface dataset:
Missing Rings in Pinus halepensis – The Missing Link to Relate the Tree-Ring Record to Extreme Climatic Events

Climate predictions for the Mediterranean Basin include increased temperatures, decreased precipitation, and increased frequency of extreme climatic events (ECE). These conditions are associated with decreased tree growth and increased vulnerability to pests and diseases. The anatomy of tree rings responds to these environmental conditions. Quantitatively, the width of a tree ring is largely determined by the rate and duration of cell division by the vascular cambium. In the Mediterranean climate, this division may occur throughout almost the entire year. Alternatively, cell division may cease during relatively cool and dry winters, only to resume in the same calendar year with milder temperatures and increased availability of water. Under particularly adverse conditions, no xylem may be produced in parts of the stem, resulting in a missing ring (MR). A dendrochronological network of Pinus halepensis was used to determine the relationship of MR to ECE. The network consisted of 113 sites, 1,509 trees, 2,593 cores, and 225,428 tree rings throughout the distribution range of the species. A total of 4,150 MR were identified. Binomial logistic regression analysis determined that MR frequency increased with increased cambial age. Spatial analysis indicated that the geographic areas of south-eastern Spain and northern Algeria contained the greatest frequency of MR. Dendroclimatic regression analysis indicated a non-linear relationship of MR to total monthly precipitation and mean temperature. MR are strongly associated with the combination of monthly mean temperature from previous October till current February and total precipitation from previous September till current May. They are likely to occur with total precipitation lower than 50 mm and temperatures higher than 5°C. This conclusion is global and can be applied to every site across the distribution area. Rather than simply being a complication for dendrochronology, MR formation is a fundamental response of trees to adverse environmental conditions. The demonstrated relationship of MR formation to ECE across this dendrochronological network in the Mediterranean basin shows the potential of MR analysis to reconstruct the history of past climatic extremes and to predict future forest dynamics in a changing climate.
SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION

: Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K-Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.
AN ITERATIVE LEARNING ALGORITHM BASED ON LEAST SQUARES SUPPORT VECTOR REGRESSION MACHINES

Aiming at the problem of the large training data set leading to amounts of calculation a sparse approximation algorithm of least squares support vector machine are proposed. Firstly using the thought of matrix block, convert the optimization problem of a Least Squares support vector machine into low order symmetric positive definite linear systems. Furthermore, use conjugate gradient algorithm which has the number of smaller conditions ,reduce the number of iterations about least squares support vector machine learning process At the same time, we also proved theoretically that the new algorithm is of faster convergence rate. Dramatically improve the speed of the algorithm for learning and prediction. Finally, experimental results show that the algorithm has a very good performance in terms of the accuracy of forecast and speed of training.
Forest biomass retrieval from lidar and radar

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass are being considered for future Earth Observation satellite missions. Combined use of lidar sampling data and complete global coverage of L-band SAR data for vegetation 3D structure mapping requires some new data processing and fusion technologies. In this study, the potential information on biomass from a lidar waveform and the required lidar samples for reliable biomass estimation were investigated using both model and real data. First, the Laser Vegetation Imaging Sensor (LVIS) data was used to generate an above-ground biomass map of the study site. The map was considered to represent the true biomass of the area. Then random samples were taken from the biomass image and the correlation between biomass and co-located SAR signature was studied. The proper model was used to extend the biomass from lidar samples into all forested areas in the study area. The new biomass map was compared with the original biomass map derived from LVIS data. The results showed the potential of the combined use of lidar samples and radar imagery for forest biomass mapping.
Critical percolation on random regular graphs

The behavior of the random graph G(n,p) around the critical probability pc = $ {1 \over n} $ is well understood. When p = (1 + O(n1/3))pc the components are roughly of size n2/3 and converge, when scaled by n−2/3, to excursion lengths of a Brownian motion with parabolic drift. In particular, in this regime, they are not concentrated. When p = (1 ‐ ϵ(n))pc with ϵ(n)n1/3 →∞ (the subcritical regime) the largest component is concentrated around 2ϵ−2 log(ϵ3n). When p = (1 + ϵ(n))pc with ϵ(n)n1/3 →∞ (the supercritical regime), the largest component is concentrated around 2ϵn and a duality principle holds: other component sizes are distributed as in the subcritical regime.
A quantitative production analysis of picture description

Background: The Quantitative Production Analysis (QPA) (Berndt et al., 2000; Saffran et al., 1989) was developed to provide an objective means of characterising the grammaticality of spontaneous speech in aphasia. However, there is still no consensus as to the profile of performance, on this or any other measure, that identifies agrammatism. I would like to express my gratitude to my tireless research assistants—Kristin Lilly, Victoria Tumanova, and Kari Norman—for their hard work; to Rita Berndt for her patience with endless questions; and to the participants and staff at the North York Aphasia Centre for their generous cooperation. I am also grateful to the American Speech‐Language‐Hearing Foundation and to Division 2 (Neurophysiology and Neurogenic Speech and Language Disorders) of the American Speech‐Language‐Hearing Association for funding to support this research. Aim: The aim of this study is to advance the effort to characterise agrammatic speech production, by examining the influence of aphasia severity and fluency on patterns of QPA scores obtained from a broad range of individuals with aphasia. The goal of this process is to identify the quantitative variables most predictive of agrammatism. Methods & Procedures: Spontaneous speech samples were elicited from non‐brain‐damaged (NBD) older subjects and a group of participants with aphasia unselected by diagnosis, and analysed using the QPA. Data from the NBD and aphasia groups were statistically compared and correlated with ratings of severity. Fluent and non‐fluent subjects within the group with aphasia were then compared. Last, the non‐fluent group was explored for evidence of agrammatism. Outcomes & Results: Many of the QPA's measures were found to reflect the severity of aphasia and/or the fluency of speech production, suggesting that results on these measures may not be indicative of agrammatism per se. In particular, measures of output productivity (the amount and efficiency of production) were largely determined by aphasia severity, and measures of lexical selection were related to the fluency of production. Profiles of the non‐fluent subjects also varied widely, reinforcing the dissociability of different grammatical functions. Conclusions: None of the QPA's measures could be interpreted to reflect agrammatism alone; in fact, many appeared to be reflective of aphasia severity or fluency. Diagnoses of agrammatism still depend on clinical judgement; however, this judgement is aided greatly by the array of objective quantitative information provided by the QPA.
Heterogeneous network deployments in LTE – the soft-cell approach

The advantages of this technique are: enhanced uplink data rates – by at least partially taking uplink path loss into account when associating terminals with a low-power node; increased capacity – receiving downlink traffic from the low-power node even if the received signal strength from the macro is higher allows for the reuse of transmission resources across low-power nodes; and improved robustness – enlarging the coverage area of a low-power node can reduce its sensitivity to ideal placement in a traffic hotspot.
Effect of a 12-Month Exercise Intervention on the Apoptotic Regulating Proteins Bax and Bcl-2 in Colon Crypts: A Randomized Controlled Trial

Background: Cellular proliferation and apoptosis (cell death) are highly regulated in the colon as insufficient apoptosis may lead to polyps and cancer. Physical activity decreases risk of colon cancer in observational studies, but the biological basis is not well defined. The objective of this study is to examine the effects of a 12-month aerobic exercise program on expression of proteins that promote (Bax) or inhibit (Bcl-2) apoptosis in colon crypts. Methods: Two hundred two sedentary participants, 40 to 75 years, were randomly assigned to moderate-to-vigorous intensity exercise for 60 min per day, 6 days per week for 12 months, or usual lifestyle. Colon crypt samples were obtained at baseline and 12 months. Bcl-2 and Bax expression was measured by immunohistochemistry. Results: Bax density at the bottom of crypts increased in male exercisers versus controls (+0.87 versus −0.18; P = 0.05), whereas the ratio of Bcl-2 to Bax at the bottom and middle of crypts decreased as aerobic fitness (VO2max) increased (P trend = 0.02 and 0.05, respectively). In female exercisers, Bax density in the middle of crypts decreased (−0.36 versus +0.69; P = 0.03) and Bcl-2 to Bax ratio at the top of crypts increased versus controls (+0.46 versus −0.85; P = 0.03). Bax density in the middle of crypts also decreased as minutes per week of exercise increased (P trend = 0.03). Conclusions: A 12-month exercise intervention resulted in greater expression of proteins that promote apoptosis at the bottom of colon crypts in men and decreased expression of proteins that promote apoptosis at the middle and top of colon crypts in women. The difference in effect by gender and location of observed changes warrants further study. (Cancer Epidemiol Biomarkers Prev 2007;16(9):1767–74)
A Hybrid Model for Part-of-Speech Tagging and its Application to Bengali

This paper describes our work on Bengali Part of Speech (POS) tagging using a corpus-based approach. There are several approaches for part of speech tagging. This paper deals with a model that uses a combination of supervised and unsupervised learning using a Hidden Markov Model (HMM). We make use of small tagged corpus and a large untagged corpus. We also make use of Morphological Analyzer. Bengali is a highly ambiguous and relatively free word order language. We have obtained an overall accuracy of 95%.
Reeder's MIM as a Special Case of Confluence Theory

Reeder’s (this issue) multiple inference model (MIM) provides a nice account of the attribution and mind reading literatures. It is a parsimonious conception that strikes just the right balance between being sufficiently removed from the data to be new and to provide heuristic value and yet remaining sufficiently close to the data to avoid unwarranted statements. Consequently, rather than criticize this admirable work, it is more interesting to explore the possibility that Reeder’s model can be considered to be a special case of a more general theory, at the admitted risk of making statements that cannot be backed up fully by empirical evidence. After a brief review of Reeder’s model, this exploration takes place, and some possible tests are proposed.
Working Paper # 0088 Licensing interim R & D knowledge

This paper considers three firms that engage in an R&D contest to develop a new profitable technology. For a broad range of parameters, the firm that leads the contest (i.e., has the highest probability of success) is better-off licensing or selling its superior interim knowledge to one of the two lagging firms or to both rather than holding on to its lead. Although transferring interim R&D knowledge to the lagging firms erodes the technological lead of the leading firm, it allows it to extract rents from its rivals and can possibly create value by increasing the chance that the licensee(s) will develop the new technology when the leading firm fails. JEL Classification: D45, O32
A Bag-of-Feature Model for Video Semantic Annotation

Multimedia data of huge amount gets involved into people's daily life, bringing us a very important issue of efficiently managing video collections. Semantic content based on video retrieval is most effective for finding information and actual application. Of the researches of video retrieval, Bag-of-features (BoF) deriving from local key points has recently appeared promising for visual classification. This paper presents a method of video semantic annotation based on BoF. First, video clips are segmented into shots and shot key frames are extracted. Then it constructs a visual vocabulary to describe BoF through the clustering of key point features. Finally, the key frame is described as a feature vector according to the presence or count of each visual word. The feature vector forms the classifier under Support Vector Machines (SVM) for semantic annotation. We test performance of BoF on movie video and TRECVID-2007 datasets. Our experiment generates competitive performance compared to the state-of-the-art techniques.
Literature-based knowledge discovery: the state of the art

Literature-based knowledge discovery method was introduced by Dr. Swanson in 1986. He hypothesized a connection between Raynaud's phenomenon and dietary fish oil, the field of literature-based discovery (LBD) was born from then on. During the subsequent two decades, LBD's research attracts some scientists including information science, computer science, and biomedical science, etc. It has been a part of knowledge discovery and text mining. This paper summarizes the development of recent years about LBD and presents two parts, methodology research and applied research. Lastly, some problems are pointed as future research directions.
Development and trial of an educational tool to support the accessibility evaluation process

This paper describes the design and development of a web accessibility knowledge management tool, known as the Accessibility Evaluation Assistant (AEA) designed to assist novice auditors in the process of an accessibility evaluation. The software incorporates a structured walkthrough approach to guide the auditor through a series of checks for established accessibility principles with the goal of conducting a comprehensive accessibility evaluation. The tool also offers the functionality to tailor the evaluation and prioritise checks based on the needs of two different user groups, or the specific content features of the website. The tool has recently been trialled with a group of 38 undergraduate computing students studying an Accessibility and Adaptive Technologies module with the aim of assessing its reliability and accuracy to validate the method. Some initial conclusions about the reliability and validity of the method and the pedagogical implications of the tool are presented. The results will help highlight the checks that can easily be verified by novices, and those that require a more detailed understanding of accessibility; require informed judgement; or are open to individual interpretation.
A math-aware search engine for math question answering system

We propose a math-aware search engine that is capable of handling both textual keywords as well as mathematical expressions. Our math feature extraction and representation framework captures the semantics of math expressions via a Finite State Machine model. We adapt the passive aggressive online learning binary classifier as the ranking model. We benchmarked our approach against three classical information retrieval (IR) strategies on math documents crawled from Math Overflow, a well-known online math question answering system. Experimental results show that our proposed approach can perform better than other methods by more than 9%.
Haptic Voice Recognition: Augmenting speech modality with touch events for efficient speech recognition

This paper proposes the Haptic Voice Recognition (HVR), a multi-modal interface that combines speech and touch sensory inputs to perform voice recognition. These touch inputs form a series of haptic events that provide cues or ‘landmarks’ for word boundaries. These word boundary cues greatly reduce the search space for speech recognition, thereby making the decoding process more efficient and suitable for portable devices with limited compute and memory resources. Furthermore, having the knowledge of word boundaries also suppresses insertion and deletion errors. This is particularly helpful when recognition is performed in noisy environment. In this paper, a series of experiments were conducted to study the feasibility of augmenting touch events to automatic speech recognition and explore its potential benefits. Experiments were conducted with syntactically simulated haptic events on the Wall Street Journal database as well as realistic haptic events acquired using a prototype HVR interface implemented on a touchscreen device.
Surveillance Camera Calibration from Observations of a Pedestrian

Calibrated cameras are an extremely useful resource forcomputer vision scenarios. Typically, cameras are calibratedthrough calibration targets, measurements of the observedscene, or self-calibrated through features matchedbetween cameras with overlapping fields of view. This paperconsiders an approach to camera calibration based onobservations of a pedestrian and compares the resultingcalibration to a commonly used approach requiring thatmeasurements be made of the scene.
Rapid Antigen Testing for Trichomoniasis in an Emergency Department

BACKGROUND AND OBJECTIVES: Trichomoniasis is a prevalent cause of vaginitis among adolescents that increases the risk of acquiring other sexually transmitted diseases and of negative pregnancy outcomes. Treatment of trichomoniasis is therefore essential for improving sexual and reproductive health outcomes. A timely, sensitive diagnostic test for T vaginalis may increase the accuracy of clinician’s treatment decisions, resulting in more infected women receiving treatment and fewer uninfected women receiving treatment. METHODS: This study was a retrospective observational assessment of electronic medical records before and after point-of-care (POC) implementation of the rapid antigen test. Records were collected from women aged 14 to 20 years who received a T vaginalis test in the emergency department during either study period. The main outcome measures were rates of accurate treatment, inaccurate treatment, and missed treatment of trichomoniasis in each study period. RESULTS: Overall rates of accurate treatment increased from 78.7% pre-POC to 87.7% post-POC (P = .02). Specifically, rates of not treating uninfected women increased from 61.4% pre-POC to 70.4% post-POC (P = .06), and rates of treating infected women were the same pre-POC (17.3%) and post-POC (17.3%; P = .99). Rates of inaccurate treatment decreased from 23.1% pre-POC to 13.1% post-POC (P = .02). Changes in missed treatment rates (14.0% pre-POC; 8.8% post-POC; P = .73) were not statistically significant. CONCLUSIONS: POC testing can improve clinical care by decreasing the use of antibiotics in uninfected women. The results of this study support the use of a T vaginalis rapid antigen POC test for adolescents presenting to the emergency department.
Algorithms for Exponentiation in Finite Fields

Gauss periods yield (self-dual) normal bases in finite fields, and these normal bases can be used to implement arithmetic efficiently. It is shown that for a small prime power q and infinitely many integersn , multiplication in a normal basis of Fqn over Fq can be computed with O(n logn loglog n), division with O(n log2n loglog n) operations in Fq, and exponentiation of an arbitrary element in Fqn withO (n2loglog n) operations in Fq. We also prove that using a polynomial basis exponentiation in F 2 n can be done with the same number of operations in F 2 for all n. The previous best estimates were O(n2) for multiplication in a normal basis, andO (n2log n loglog n) for exponentiation in a polynomial basis.
Can beyond-CMOS devices illuminate dark silicon?

Throughout the last decade, the microprocessor industry has been struggling to preserve the benefits of Moore's Law scaling. The persistent scaling of CMOS technology no longer yields exponential performance gains due in part to the growth of dark silicon. With each subsequent technology node generation, power constraints resulting from factors such as sub-threshold leakage currents are projected to further limit the number of transistors that can be simultaneously powered. To overcome the limits of CMOS devices, researchers are working to develop “beyond-CMOS” device technologies. To determine the most promising beyond-CMOS devices, it is necessary to benchmark them against CMOS. In this paper, we present the design and validation of an analytical benchmarking model that evaluates CMOS and beyond-CMOS devices at the architectural-level. Our model is built from the device to the architectural/application-level. Our target architecture is a symmetric multi-core processor executing highly parallel applications (i.e., PARSEC). As a case study, we select one class of promising beyond-CMOS devices, tunneling field-effect transistors, to evaluate against CMOS.