Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlled but Independent: Effects of Mental Rotation and Developmental Dyslexia in Dual-Task Settings

In two experiments, we compared the performance of normal-reading (n = 26) and dyslexic children (n = 22) in discriminating letters from their mirrored images. In experiment 1, they were always presented in the upright orientation; in experiment 2, they were presented in different angular orientations (0°, 90°, 180°). In order to determine whether task and dyslexia affect reaction times in early or late stages of processing, a dual-task paradigm was adopted in which the primary task was tone discrimination. Stimulus onset asynchrony (SOA) between first and second task was systematically varied (50 ms versus 400 ms). In both experiments dyslexics were slower overall than controls. No effects of mirror-image letters were found. In experiment 2, mental-rotation effects were additive with SOA. In accordance with earlier findings we concluded that the mental-rotation effect involves central processing capacity. Mental-rotation effects were the same for normal-reading and dyslexic children; mental rotation is not impaired in dyslexia. Remarkably, SOA effects were larger in normal readers than in dyslexics. This result was explained by observing that dyslexics experience decision difficulties already on the first task. As a result, they do not benefit optimally from increased latencies between first and second tasks.
Estimation of Prestress Force Distribution in Multi-Strand System of Prestressed Concrete Structures Using Field Data Measured by Electromagnetic Sensor

The recently developed smart strand can be used to measure the prestress force in the prestressed concrete (PSC) structure from the construction stage to the in-service stage. The higher cost of the smart strand compared to the conventional strand renders it unaffordable to replace all the strands by smart strands, and results in the application of only a limited number of smart strands in the PSC structure. However, the prestress forces developed in the strands of the multi-strand system frequently adopted in PSC structures differ from each other, which means that the prestress force in the multi-strand system cannot be obtained by simple proportional scaling using the measurement of the smart strand. Therefore, this study examines the prestress force distribution in the multi-strand system to find the correlation between the prestress force measured by the smart strand and the prestress force distribution in the multi-strand system. To that goal, the prestress force distribution was measured using electromagnetic sensors for various factors of the multi-strand system adopted on site in the fabrication of actual PSC girders. The results verified the possibility to assume normal distribution for the prestress force distribution per anchor head, and a method computing the mean and standard deviation defining the normal distribution is proposed. This paper presents a meaningful finding by proposing an estimation method of the prestress force based upon field-measured data of the prestress force distribution in the multi-strand system of actual PSC structures.
Consensus-based framework for illuminant chromaticity estimation

Several algorithms were proposed in the literature to re- cover the illuminant chromaticity of the original scene. These algo- rithms work well only when prior assumptions are satisfied, and the best and the worst algorithms may be different for different scenes. We investigate the idea of not relying on a single method but instead consider a consensus decision that takes into account the re- sponses of several algorithms and adaptively chooses the algo- rithms to be combined. We investigate different combining strate- gies of state-of-the-art algorithms to improve the results in the illuminant chromaticity estimation. Single algorithms and combined ones are evaluated for both synthetic and real image databases using the angular error between the RGB triplets of the measured illuminant and the estimated one. Being interested in comparing the performance of the methods over large data sets, experimental re- sults are also evaluated using the Wilcoxon signed rank test. Our experiments confirm that the best and the worst algorithms do not exist at all among the state-of-the-art ones and show that simple combining strategies improve the illuminant estimation. © 2008 SPIE
Context-sensitive query auto-completion

Query auto completion is known to provide poor predictions of the user's query when her input prefix is very short (e.g., one or two characters). In this paper we show that context, such as the user's recent queries, can be used to improve the prediction quality considerably even for such short prefixes. We propose a context-sensitive query auto completion algorithm, NearestCompletion, which outputs the completions of the user's input that are most similar to the context queries. To measure similarity, we represent queries and contexts as high-dimensional term-weighted vectors and resort to cosine similarity. The mapping from queries to vectors is done through a new query expansion technique that we introduce, which expands a query by traversing the query recommendation tree rooted at the query. In order to evaluate our approach, we performed extensive experimentation over the public AOL query log. We demonstrate that when the recent user's queries are relevant to the current query she is typing, then after typing a single character, NearestCompletion's MRR is 48% higher relative to the MRR of the standard MostPopularCompletion algorithm on average. When the context is irrelevant, however, NearestCompletion's MRR is essentially zero. To mitigate this problem, we propose HybridCompletion, which is a hybrid of NearestCompletion with MostPopularCompletion. HybridCompletion is shown to dominate both NearestCompletion and MostPopularCompletion, achieving a total improvement of 31.5% in MRR relative to MostPopularCompletion on average.
User Modeling via Stereotypes

This paper addresses the problems that must be considered if computers are going to treat their users as individuals with distinct personalities, goals, and so forth. It first outlines the issues, and then proposes stereotypes as a useful mechanism for building models of individual users on the basis of a small amount of information about them. In order to build user models quickly, a large amount of uncertain knowledge must be incorporated into the models. The issue of how to resolve the conflicts that will arise among such inferences is discussed. A system, Grundy, is described that builds models of its users, with the aid of stereotypes, and then exploits those models to guide it in its task, suggesting novels that people may find interesting. If stereotypes are to be useful to Grundy, they must accurately characterize the users of the system. Some techniques to modify stereotypes on the basis of experience are discussed. An analysis of Grundy's performance shows that its user models are effective in guiding its performance.
Mitochondria and other calcium buffers of squid axon studied in situ

Continuous nondestructive monitoring of intracellular ionized calcium in isolated squid axons by differential absorption spectroscopy (using arsenazo III and antipyrylazo III) was used to study uptake of calcium by carbonyl cyanide, p-trifluoromethoxy-phenylhydrazone (FCCP)- and (or) cyanide (CN)-sensitive and insensitive constituents of axoplasm. Known calcium loads imposed on the axon by stimulation produced proportional increments of free axoplasmic calcium. Measurement of increments in ionized calcium as a function of load confirmed earlier reports of buffering in normal and FCCP- and (or) CN-poisoned axons. Measurement of rates of calcium uptake by presumed mitochondria showed little uptake at ambient Ca below 200--400 nM, with sigmoidal rise to about 20--30 mumol/kg axoplasm per min (calculated to be about 200 mmol/kg mitochondrial protein per min) at 50 micrometer, indicating a functional threshold for presumed mitochondrial uptake well above physiological ionized calcium concentration. Treatment of stimulated axons with cyanide, to release calcium from presumed mitochondria, showed that the sensitivity to cyanide decreased progressively with time after stimulation (t 1/2 = 3--10 min) implying transfer of sequestered calcium into a less metabolically labile form.
Volatile oil composition of Carthamus Tinctorius L. flowers grown in Kazakhstan.

INTRODUCTION Carthamus tinctorius L. is commonly known as Safflower. C. tinctorius extracts and oil are important in drug development with numerous pharmacological activities in the world. This plant is cultivated mainly for its seed which is used as edible oil. For a long time, C. tinctorius has been used in traditional medicines as a purgative, analgesic, antipyretic and an antidote to poisoning. It is a useful plant in painful menstrual problems, post-partum haemorrhage and osteoporosis. OBJECTIVE The subject of this study is the seeds of Kazakhstan species of 'Akmai' safflower, collected in the flowering stage in Southern Kazakhstan. Volatile oil was carry out to study the component composition of Kazakhstan 'AkMai' safflower flowers. MATERIALS AND METHOD Pale yellow oily extracts were obtain by varying the process parameters. The volatile oil obtained by hydrodistillation of the petals Carthamus tinctorius L. was analyzed by gas chromatography/mass spectrometry (GC/MS). The yield of the oil was 0.175 % (v/w). 20 compounds representing 99.81% of the oil were characterized. The volatile oil was found to be rich in undecanoic acid, octane, 2-nonen -1-ol, hexadecanal, dodecanal, dec-2-en-1-ol, nonanoic acid, tetradecanoic acid, 2 pentadecanone, 6,10,14-trimethyl, 1,2-benzenedicarboxylic acid, isobutyl-beta-phenylpropionate, 1.3-cyclohexadiene, myrtenoic acid, octadecanoic acid, heneicosanoic acid, 2(3H)-furanone, 4,4-dipropylheptane, hexcosane,1-eicosanol, as well as heptocosane. RESULTS Volatile oil from the flowers of the Kazakhstan safflower species 'Ak-Mai' were investigated by GC/MS which allowed the detection of 20 compounds. Biologically active complex of the flower of the Kazakhstan safflower species 'Ak-Mai' was released for the first time by using this oil.
Comparative Analysis of Thoracotomy and Sternotomy Approaches in Cardiac Reoperation

Background Reoperation of cardiac surgery via median sternotomy can be associated with significant complications. Thoracotomy is expected to reduce the risk of reoperation and to enhance the surgical outcomes. We retrospectively analyzed two operative approaches (thoracotomy vs. sternotomy) in cardiac reoperation. Materials and Methods From September 2007 to December 2010, 35 patients who required reoperation of the mitral valvular disease following previous median sternotomy were included. Average age of patients was 45.8±15.4 years (range, 14 to 76 years) and male-to-female was 23:12. Interval period between primary operation and reoperation was 135.8±105.6 months (range, 3.3 to 384.9 months). Results Comparative analysis was done dividing the patient group into two groups that are thoracotomy group (22 patients) and sternotomy group (13 patients). Thoracotomy group was significantly lower in operative time (415.2±90.3 vs. 497.5±148.0, p<0.05), bleeding control time (108.0±29.5 vs. 146.4±66.8, p<0.05) and chest tube drainage (287.5±211.5 mL vs. 557.3±365.5 mL, p<0.05) compared to sternotomy group. Conclusion The thoracotomy approach is superior to sternotomy in some variables, and it is considered as a valid alternative to repeat median sternotomy in patients who underwent a previous median sternotomy.
Inter-program optimizations for conserving disk energy

Previous work has shown that intra-program optimizations, i.e., optimizations performed on individual programs in isolation, can be very effective in reducing disk energy in streaming applications. This paper investigates the potential additional benefits of inter-program optimizations where sets of programs are optimized together. Experimental results on different subsets of three streaming applications show that 7-49% additional energy savings (27.3% on average) can be obtained with negligible performance penalties using two novel inter-program optimizations, namely execution context sensitive buffer size selection and inverse barrier synchronization. These figures were obtained via physical measurements on two laptop disks.
The Double-Barrelled LASSO

We present a new method which solves a double-barelled LASSO in a convex least squares approach. In the presented method we focus on t he scenario where one is interested in (or limited to) a primal (feature) repre sentation for the first view while having a dual (kernel) representation for the sec ond view. DB-LASSO minimises the number of features used in both the primal and d ual projections while minimising the error (maximising the correlation) be tw en the two views.
Finding Triangles and Computing the Girth in Disk Graphs ∗

Let S ⊂ R be a set of n point sites, where each s ∈ S has an associated radius rs > 0. The disk graph D(S) of S is the graph with vertex set S and an edge between two sites s and t if and only if |st| ≤ rs + rt, i.e., if the disks with centers s and t and radii rs and rt, respectively, intersect. Disk graphs are useful to model sensor networks. We study the problems of finding triangles and of computing the girth in disk graphs. These problems are notoriously hard for general graphs, but better solutions exist for special graph graph classes, such as planar graphs. We obtain similar results for disk graphs. In particular, we observe that the unweighted girth of a disk graph can be computed in O(n log n) worst-case time and that a shortest (Euclidean) triangle in a disk graph can be found in O(n log n) expected time.
Loss of cyclin D1 impairs cerebellar development and suppresses medulloblastoma formation

Medulloblastoma, the most common malignant brain tumor of childhood, is believed to derive from immature granule neuron precursors (GNPs) that normally proliferate in the external granule layer before exiting the cell cycle and migrating to their mature location in the inner granule layer. In this study, we examined the expression of D type cyclins in GNPs during cerebellar development and showed that GNPs in early development expressed only cyclin D1, whereas later GNPs expressed both cyclins D1 and D2. Coinciding with the period of cyclin D1-only expression, Ccnd1-/- mice showed reduced proliferation of GNPs and impaired growth of the cerebellum. Interestingly, removal of cyclin D1 was sufficient to drastically reduce the incidence of medulloblastoma in Ptch1+/- mice, despite the fact that these tumors showed upregulation of both cyclins D1 and D2. We showed that cyclin D1 has an earlier role in tumorigenesis: in the absence of cyclin D1, the incidence and overall volume of `preneoplastic' lesions were significantly decreased. We propose a model that links a role of cyclin D1 in normal GNP proliferation with its early role in tumorigenesis.
Scaling to 1024 software processes and hardware cores of the distributed simulation of a spiking neural network including up to 20G synapses

This short report describes the scaling, up to 1024 software processes and hardware cores, of a distributed simulator of plastic spiking neural networks. A previous report demonstrated good scalability of the simulator up to 128 processes. Herein we extend the speed-up measurements and strong and weak scaling analysis of the simulator to the range between 1 and 1024 software processes and hardware cores. We simulated two-dimensional grids of cortical columns including up to ~20G synapses connecting ~11M neurons. The neural network was distributed over a set of MPI processes and the simulations were run on a server platform composed of up to 64 dual-socket nodes, each socket equipped with Intel Haswell E5-2630 v3 processors (8 cores @ 2.4 GHz clock). All nodes are interconned through an InfiniBand network. The DPSNN simulator has been developed by INFN in the framework of EURETILE and CORTICONIC European FET Project and will be used by the WaveScalEW tem in the framework of the Human Brain Project (HBP), SubProject 2 - Cognitive and Systems Neuroscience. This report lays the groundwork for a more thorough comparison with the neural simulation tool NEST.
On the Capacity of Hopfield Neural Networks as EDAs for Solving Combinatorial Optimisation Problems

Multi-modal optimisation problems are characterised by the presence of either local sub-optimal points or a number of equally optimal points. These local optima can be considered as point attractors for hill climbing search algorithms. It is desirable to be able to model them either to avoid mistaking a local optimum for a global one or to allow the discovery of multiple equally optimal solutions. Hopfield neural networks are capable of modelling a number of patterns as point attractors which are learned from known patterns. This paper shows how a Hopfield network can model a number of point attractors based on non-optimal samples from an objective function. The resulting network is shown to be able to model and generate a number of local optimal solutions up to a certain capacity. This capacity, and a method for extending it is studied.
Comparison of Different Neural Network Architectures for Classification of Feature Transformed Data for Face Recognition

In this paper neural network classifier is applied on transformed shape features for face recognition. Classification by neural networks to a large extent depends on the neural network architecture. We have investigated three different neural network architectures for classification namely-Feed Forward Neural Network, Cascade Feed Forward Neural Network and Radial Basis Function Neural Network and tested their performance for three sets of feature extracted data. For feature extraction we convert the 2-D gray level face images into their respective depth maps or physical shape which are subsequently transformed by three different methods to get three separate data sets,namely-Coiflet Packet , Radon Transform and Fourier Mellin Transform to compute energy for feature extraction. After feature extraction each of the training classes are optimally separated using linear discriminant analysis. The neural network classifiers have been tested on each of the three sets of feature extracted data and a comparative analysis has been done on the results obtained. The proposed algorithms have been tested on the ORL database, widely used for face recognition experiments. General Terms Pattern Recognition, Computer Vision, Artificial Intellegence,Image Processing
Interval fecal immunochemical testing in a colonoscopic surveillance program speeds detection of colorectal neoplasia.

BACKGROUND & AIMS Rapidly progressing or missed lesions can reduce the effectiveness of colonoscopy-based colorectal cancer surveillance programs. We investigated whether giving fecal immunochemical tests (FITs) for hemoglobin between surveillance colonoscopies resulted in earlier detection of neoplasia. METHODS The study included 1736 patients with a family history or past neoplasia; they received at least 2 colonoscopy examinations and were followed for a total of 8863 years. Patients were excluded from the study if they had genetic syndromes, colorectal surgery, or inflammatory bowel disease. An FIT was offered yearly, in the interval between colonoscopies; if results were positive, the colonoscopy was performed earlier than scheduled. RESULTS Among the 1071 asymptomatic subjects (61%) who received at least 1 FIT, the test detected 12 of 14 cancers (86% sensitivity) and 60 of 96 (63%) advanced adenomas. In patients with positive results from the FIT, the diagnosis of cancer was made 25 months (median) earlier and diagnosis of advanced adenoma 24 months earlier. Patients who had repeated negative results from FIT had an almost 2-fold decrease in risk for cancer and advanced adenoma compared with patients who were not tested (5.5% vs 10.1%, respectively, P = .0004). The most advanced stages of neoplasia, observed across the continuum from nonadvanced adenoma to late-stage cancer, were associated with age (increased with age), sex (increased in males), and FIT result. The probability of most advanced neoplastic stage was lowest among those with a negative result from the FIT (odds ratio, 0.68; P < .001). CONCLUSIONS Interval examinations using the FIT detected neoplasias sooner than scheduled surveillances. Subjects with negative results from the FIT had the lowest risk for the most advanced stage of neoplasia. Interval FIT analyses can be used to detect missed or rapidly developing lesions in surveillance programs.
Developing a Social, Cultural and Economic Report Card for a Regional Industrial Harbour

Report cards are increasingly used to provide ongoing snap-shots of progress towards specific ecosystem health goals, particularly in coastal regions where planners need to balance competing demands for coastal resources from a range of industries. While most previous report cards focus on the biophysical components of the system, there is a growing interest in including the social and economic implications of ecosystem management to provide a greater social-ecological system understanding. Such a report card was requested on the Gladstone Harbour area in central Queensland, Australia. Gladstone Harbour adjoins the southern Great Barrier Reef, and is also a major industrial and shipping port. Balancing social, economic and environmental interests is therefore of great concern to the regional managers. While environmental benchmarking procedures are well established within Australia (and elsewhere), a method for assessing social and economic performance of coastal management is generally lacking. The key aim of this study was to develop and pilot a system for the development of a report card relating to appropriate cultural, social and economic objectives. The approach developed uses a range of multicriteria decision analysis methods to assess and combine different qualitative and quantitative measures, including the use of Bayesian Belief Networks to combine the different measures and provide an overall quantitative score for each of the key management objectives. The approach developed is readily transferable for purposes of similar assessments in other regions.
Probabilistic Argument Graphs for Argumentation Lotteries

Uncertainty about which arguments or attacks should appear in an argument graph means that there is uncertainty as to the structure of the argument graph. When informal arguments are presented, there may be imprecision in the language used, and so the audience may be uncertain as to the structure of the argument graph as intended by the presenter of the arguments. For a presenter of arguments, it is useful to know the audience’s argument graph, but the presenter may be uncertain as to the structure of it. To model each of these situations, we can use probabilistic argument graphs. The set of subgraphs of an argument graph is a sample space. A probability value is assigned to each subgraph such that the sum is 1, thereby reflecting the uncertainty over which is the actual subgraph. We can then determine the probability that a particular set of arguments is included or excluded from an extension according to a particular Dung semantics. We harness this to define the notion of an argument lottery, which can be used by the audience to determine the expected utility of a debate, and can be used by the presenter to decide which arguments to present by choosing those that maximize expected utility.