Miracle-dz/UniFAR-FTUs huggingface dataset:
Discharge patterns and functional organization of mammalian retina.

THE DISCHARGES carried in the optic nerve fibers contain all the information which the central nervous system receives from the retina. A correct interpretation of discharge patterns therefore constitutes an important step in the analysis of visual events. Further, investigations of nervous activity arising in the eye reveal many aspects of the functional organization of the neural elements within the retina itself. Following studies of discharges in the optic nerve of the eel’s eye by Adrian and Matthews (2,3), Hartline and his colleagues described the discharge pattern in the eye of the Limulus in a series of important and lucid papers (for a summary see 20). In the Limulus the relationship between the stimulus to the primary receptor cell and the nerve discharges proved relatively simple, apparently because the connection between sense cell and nerve fiber was a direct one. Thus, when stimulation is confined to one receptor the discharge in a single Limulus nerve fiber will provide a good indication of excitatory events which take place as a result of photochemical processes. Discharges last for the duration of illumination and their frequency is a measure of stimulus strength. Lately, however, it was shown by Hartline et al. (22) that inhibitory interactions may be revealed when several receptors are excited. On the whole, the Limulus preparation shows many features which are similar to other simple sense organs, for instance, stretch receptors. In the latter, however, instead of photochemical events, stretch-deformation acts as the adequate stimulus on sensory terminals and is translated into a characteristic discharge pattern. The discharge from the cold-blooded vertebrate retina (mainly frogs) proved much more complex. Hartline found three main types when recording from single optic nerve fibers: (i) “on” discharges, similar to those in the Limulus, firing for the duration of the light stimulus, (ii) “off” discharges appearing when a light stimulus was withdrawn, and (iii) ‘con-off” discharges, a combination of the former two, with activity confined mainly to onset and cessation of illumination. The mammalian discharge patterns were studied in a number of species by Granit and his co-workers in the course of their extensive work on the physiology of the visual system (summaries in 13, 15). On the whole, they did not observe any fundamental differences between frog and mammalian discharge types (see later).
Identifying active subgroups in online communities

As online communities proliferate, methods are needed to explore and capture patterns of activity within them. This paper focuses on the problem of identifying active subgroups within online communities. k-plex analysis and hierarchical clustering are used to identify and contrast subgroups, and the methodology is demonstrated in a case study involving the TorCamp Google group community. We assessed the validity of the subgroups obtained in the case study by comparing them with the members' experienced sense of community, and their self-reported acquaintanceships. Results suggest that active subgroups of people not only interact with each other at a higher rate, but also have a greater experienced sense of community. It is concluded that detection of active subgroups in online communities can be implemented widely using automated tools for analyzing the social networks implied by online interactions.
Evidence and decision making

Evidence and Decision Making illuminates the crucial roles that teachers, administrators, and other education professionals play in constructing and using evidence to make decisions that support learning. Authors analyze different practices of constructing and using evidence in classrooms, teacher communities, schools, and school districts; consider the roles that district, state, and federal education agencies can play in supporting sound practice; and provide historical contexts, theoretical resources for studying evidence use, and epistemological resources for warranting different types of decisions. The volume provides a realistic and complex vision of what counts as evidence and how evidence is or might be used to inform professional learning and decision making across levels of the educational system.
Design and Analysis of Parametric Query Optimization Algorithms

Query optimizers normally compile queries into one optimal plan by assuming complete knowledge of all cost parameters such as selectivity and resource availability. The execution of such plans could be sub-optimal when cost parameters are either unknown at compile time or change significantly between compile time and runtime [Loh89, GrW89]. Parametric query optimization [INS+92, CG94, GK94] optimizes a query into a number of candidate plans, each optimal for some region of the parameter space. In this paper, we present parametric query optimization algorithms. Our approach is based on the property that for linear cost functions, each parametric optimal plan is optimal in a convex polyhedral region of the parameter space. This property is used to optimize linear and non-linear cost functions. We also analyze the expected sizes of the parametric optimal set of plans and the number of plans produced by the Cole and Graefe algorithm [CG94].
Two stage genetic approach for bio-chemical named entity recognition

Determining different mentions of chemical names from texts has a wide-spread application in real life. Chemical names are complex in nature and there exist several representations and nomenclatures (like SMILES, InChI, IUPAC) which create a big challenge to their automatic identification and classification. In this paper we present a feature selection approach for appropriate feature subset selection from a well-known supervised machine learning approach namely conditional random field based classifier (CRF). Several features are identified and extracted without using any domain specific knowledge and/or resources for determining mentions of IUPAC and IUPAC-like names from scientific text using some supervised classification technique. The appropriate set of features for a particular supervised classification technique is extracted from this huge collection of features using some single objective genetic algorithm based feature selection technique. Experiments are carried out on the benchmark patent dataset. Evaluation shows encouraging performance with the overall F-measure values of 70.01% by single objective optimization based approach on patent 2008 test data set.
First description of Yersinia ruckeri serotype O2 in Spain

Yersinia ruckeri was isolated from diseased unvaccinated rainbow trout obtained from a farm in which vaccination against enteric redmouth disease (ERM) has been systematically performed for more than 10 years. Affected fish presented typical signs of yersiniosis including exophthalmia and eye haemorrhages. Serological characterization of the isolate demonstrated that it belongs to the serotype O2b of this bacterial pathogen, which was never before described in Spain.
Mutations in the Drosophila melanogaster Gene Encoding S-adenosylmethionine Suppress Position-Effect Variegation

In Drosophila melanogaster, the study of trans-acting modifier mutations of position-effect variegation and Polycomb group (Pc-G) genes have been useful tools to investigate genes involved in chromatin structure. We have cloned a modifier gene, Suppressor of zeste 5 (Su(z)5), which encodes S-adenosylmethionine synthetase, and we present here molecular results and data concerning its expression in mutants and genetic interactions. The mutant alleles Su(z)5, l(2)R23 and l(2)M6 show suppression of wm4 and also of two white mutants induced by roo element insertions in the regulatory region i.e., wis (in combination with z1) and wsp1. Two of the Su(z)5 alleles, as well as a deletion of the gene, also act as enhancers of Polycomb by increasing the size of sex combs on midleg. The results suggest that Su(z)5 is connected with regulation of chromatin structure. The enzyme S-adenosylmethionine synthetase is involved in the synthesis of S-adenosylmethionine, a methyl group donor and also, after decarboxylation, a propylamino group donor in the bio-synthesis of polyamines. Our results from HPLC analysis show that in ovaries from heterozygous Su(z)5 mutants the content of spermine is significantly reduced. Results presented here suggest that polyamines are an important molecule class in the regulation of chromatin structure.
A Theory of Accounting Relativity: Double-Entry Bookkeeping as a Transformation of Coordinates

The dividend discount model is a special case of the residual income model and the fundamental value of the firm is a scalar invariant under any accounting system with the clean-surplus. Under double-entry bookkeeping, any accounting system is relative concerning valuation in the sense that the fundamental value is a scalar invariant. A dividends stream or any accounting sequence has no a priori conceptual as well as empirical grounds to claim its primacy over others in valuation.
An Improved Retinex Image Enhancement Technique for Dim Target Extraction in Infrared Images

Image enhancement based dim target extraction techniques are proposed. Firstly, the dim targets are enhanced through an improved retinex (MLIP_MSR) method which combines the multi-scale retinex (MSR) algorithm and the modified logarithmic image processing (LIP) model. Secondly, the targets are roughly obtained by applying the relaxation iterative segmentation algorithm (RIS), while part background is also retained, then the background filtering is used to remove the remained background. Finally the canny operator is applied to get the contour of the final dim targets and add it to the original image. Because of the MLIP_MSR enhancement, RIS can efficiently segment the dim infrared image. Experimental results verified the effective performance of the proposed method.
Visual Semantic Role Labeling

In this paper we introduce the problem of Visual Semantic Role Labeling: given an image we want to detect people doing actions and localize the objects of interaction. Classical approaches to action recognition either study the task of action classification at the image or video clip level or at best produce a bounding box around the person doing the action. We believe such an output is inadequate and a complete understanding can only come when we are able to associate objects in the scene to the different semantic roles of the action. To enable progress towards this goal, we annotate a dataset of 16K people instances in 10K images with actions they are doing and associate objects in the scene with different semantic roles for each action. Finally, we provide a set of baseline algorithms for this task and analyze error modes providing directions for future work.
Automatic Correction of Arabic Text: a Cascaded Approach

This paper describes the error correction model that we used for the Automatic Correction of Arabic Text shared task. We employed two correction models, namely a character-level model and a casespecific model, and two punctuation recovery models, namely a simple statistical model and a CRF model. Our results on the development set suggest that using a cascaded correction model yields the best results.
A Fast Inpainting Algorithm Using Half-Point Gradient

Image inpainting is an old technique to reconstitute the damaged artwork. Given a user-defined region, Digital image inpainting approaches can restore the damaged region automatically on digital images. In the inpainting algorithm this paper proposed, gradients of half-points in the neighborhood of a damaged point and its matching point are used to estimate the gray value of the damaged point, instead of replacing the gray value with that of the matching point directly. The inpainting order is from outside to inside of the damaged region, following the rule "the more known neighbors, the sooner to be inpainted". It can repair the damaged image effectively as the experimental results show.
A ROTATIONALLY INVARIANT BLOCK MATCHING STRATEGY IMPROVING IMAGE DENOISING WITH NON-LOCAL MEANS

We propose a rotationally invariant similarity measure as a modiﬁcation of the well-known block matching algo-rithm for ﬁnding similar regions in an image or an image sequence. This algorithm can ﬁnd similar patches even if they appear in several rotated or even mirrored instances. We demonstrate the application of this approach to enhance the quality of the non-local means algorithm for image denoising. For this ﬁltering method, we also introduce a locally adaptive way of choosing the parameters. Numerical examples show that both modiﬁcations lead to a visible and measurable qualitative improvement of the denoising results.
Optimizing for Space : Measurements and Possibilities for Improvement

GCC’s optimization for space seems to have been often neglected, in favor of performance tuning. With this work we aim at determining the weakpoints of GCC concerning its optimization capability for space. We compare (1) GCC with two nonfree ARM cross-compiler toolchains, (2) how GCC evolved from release 3.2. 2 to version 3.3, and (3) two runtime libraries for the Linux kernel. All tests were performed using the C front end and for the ARM target both as standalone and as Linux executables. The test suite is comprised of applications from wellknown benchmark suites such as SPEC and Mediabench. An optimal combination of compiler (and linker) options with respect to minimal code size is elaborated as well. We conclude that GCC 3.3 steadily improves with respect to version 3.2. 2 and that it is only about 11% behind a high-performance non-free compiler. At the same time, we were able to document a number of issues that deserve further investigation in order to improve code generation for space.
Secure network coding based on homomorpuic signature against pollution attacks

The network coding systems are vulnerable to pollution attacks, in which the adversary forwarders can inject polluted or forged messages into networks. In this paper, we propose a homomorphic signature scheme to provide security protection against pollution attacks, which mainly uses the homomorphic property of signature based on Paillier cryptosystem. On one hand, it guarantees the forwarders to verify the received messages effectively. If the verification is failed, the message is assumed to be polluted and should be discarded. On the other hand, the proposed scheme can generate the valid signatures for its output messages from the signatures of input ones without knowing the source's private key such that extra secure channels are not needed. We also make the security analysis and prove that our signature scheme is secure enough to defend against pollution attacks.
Design, kinematic modeling, and implementation of autonomous fish robot for underwater sensing

Mechatronics design of fish robot and kinematic modeling for its movements including straight, turn, and up-down movements have been successfully implemented. The fish robot is designed with two joints to adopt the movement of carangiform fish which creates motion and controls its movements by using 1/3 of its body. Two methods for producing up-down motion are investigated, namely the pectoral fin and weight moving method and it is found that for this particular robot pectoral fin method is more effective. In addition to kinematic modeling for its movement, the fish robot is also equipped with navigation sensor such that infrared and Inertial Navigation System (INS) so that it can move autonomously. Main navigation parameters such as yaw, pitch, roll, and heading have been successfully monitored for evaluation of positions and movements. The fish robot is also equipped with sensor to obtain data related to water quality and wifi for data transmission. Thus the robot is highly potential for effective and efficient underwater sensing tasks.
Anterior cingulate cortex activity and impaired self-monitoring of performance in patients with schizophrenia: an event-related fMRI study.

OBJECTIVE The authors examined brain activity associated with the internal monitoring of performance to test the hypothesis that error-related activity in the anterior cingulate cortex is impaired in patients with schizophrenia. METHOD Seventeen patients with schizophrenia and 16 healthy comparison subjects underwent event-related functional magnetic resonance imaging during a continuous performance task; stimulus degradation was used to increase error rates. RESULTS Comparison subjects, but not schizophrenic patients, showed error-related activity in the anterior cingulate cortex, and this difference in brain activity was significantly different across the two groups. Patients also showed less slowing of reaction time after error commission. CONCLUSIONS Lower error-related activity in the anterior cingulate cortex and less performance adjustment after error commission are consistent with the hypothesis that disturbances in anterior cingulate cortex function are related to a specific alteration in an evaluative component of executive functioning-the internal monitoring of performance.
Rotator Cuff Repair in Patients over 75 Years of Age: Clinical Outcome and Repair Integrity

Background Some studies have shown significant functional improvement after rotator cuff (RC) repair in elderly patients. However, few studies have reported the healing potential of RC tears in elderly patients. Methods Twenty-five patients aged ≥ 75 years who underwent RC repair were enrolled. The mean age at the time of surgery was 78.3 years (range, 75 to 88 years) while the mean follow-up was 36.3 months (range, 18 to 114 months). We evaluated clinical and structural outcomes after RC repair in the retear and healed groups. Results Of 25 patients, 16 (64%) had healed RC lesions and 9 (36%) had retorn cuff lesions. The retear rate increased significantly with increasing initial tear size (small to medium, 13%; large, 60%; massive, 80%; p = 0.024) but not with increasing age (p = 0.072). The mean visual analog scale (VAS), University of California Los Angeles (UCLA), and Constant scores significantly improved from 5.2, 15.8, and 49.3 preoperatively to 1.4, 31.1, and 71.9 in the healed group and from 6.0, 14.4, and 39.5 preoperatively to 2.4, 28.3, and 63.6 in the retear group at the final follow-up (p < 0.05, respectively). There were no significant differences in clinical outcomes between the 2 groups at the final follow-up. Retear was significantly correlated with initial tear size (p = 0.001; odds ratio [OR], 2.771; 95% confidence interval [CI], 1.394 to 5.509 for large to massive tears) (p = 0.001; OR, 0.183; 95% CI, 0.048 to 0.692 for small to medium tears). Conclusions There were significant improvements in clinical outcomes after RC repair in patients ≥ 75 years. Structural integrity after cuff repair did not affect the final clinical outcome. Even in elderly patients aged ≥ 75 years, healing of repaired RC can be expected in cases of small to medium tears. Although the retear rate was relatively high for large to massive tears, clinical outcomes still showed significant improvement.
Gene signature selection for cancer prediction using an integrated approach of genetic algorithm and support vector machine

Classification of tumor types based on genomic information is essential for improving future cancer diagnosis and drug development. Since DNA microarray studies produce a large amount of data, effective analytical methods have to be developed to sort out whether specific cancer samples have distinctive features of gene expression over normal samples or other types of cancer samples. In this paper, an integrated approach of support vector machine (SVM) and genetic algorithm (GA) is proposed for this purpose. The proposed approach can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied in searching the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one of fourteen patient samples suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma.
Cross-situational observation and the semantic bootstrapping hypothesis

How do children learn their first words? Everyone who has thought about the problem long enough to consider one has come up with essentially the same solution. Initially learners must: pair each word with the extralinguistic contexts in which it occurs, collect several such pairs, and then identify the common element in the scenes. We may disagree on the parents’ role in structuring the input or the range of hypotheses that the child entertains but we all agree on the data source. After all, what other information does the novice language learner have? But reasonable people do disagree about how much children can or must learn in this manner. Both empiricist philosophers (Locke, 1690; Hume, 1758) and nativist linguists (Wexler & Cullicover, 1980; Pinker, 1984) have suggested that children use extralinguistic contexts to learn the meanings of a large and diverse set of words. In contrast, the proponents of syntactic bootstrapping claim that this word-to-world mapping procedure can only provide the child with a small starter lexicon, consisting mostly of concrete nouns. This initial vocabulary is a wedge that the child uses to begin constructing representations of the sentences in which novel words are used. These representations constrain her interpretation of extralinguistic context, allowing her to acquire verbs, adjectives and abstract nouns
Defining Human Behaviors Using Big Data Analytics in Social Internet of Things

As we delve into the Internet of Things (IoT), we are witnessing the intensive interaction and heterogeneous communication among different devices over the Internet. Consequently, these devices generate a massive volume of Big Data. The potential of these data has been analyzed by the complex network theory, describing a specialized branch, known as 'Human Dynamics. ' The potential of these data has been analyzed by the complex network theory, describing a specialized branch, known as 'Human Dynamics. ' In this extension, the goal is to describe human behavior in the social area at real-time. These objectives are starting to be practicable through the quantity of data provided by smartphones, social network, and smart cities. These make the environment more intelligent and offer an intelligent space to sense our activities or actions, and the evolution of the ecosystem. To address the aforementioned needs, this paper presents the concept of 'defining human behavior' using Big Data in SIoT by proposing system architecture that processes and analyzes big data in real-time. The proposed architecture consists of three operational domains, i.e., object, SIoT server, application domain. Data from object domain is aggregated at SIoT server domain, where the data is efficiently store and process and intelligently respond to the outer stimuli. The proposed system architecture focuses on the analysis the ecosystem provided by Smart Cities, wearable devices (e.g., body area network) and Big Data to determine the human behaviors as well as human dynamics. Furthermore, the feasibility and efficiency of the proposed system are implemented on Hadoop single node setup on UBUNTU 14.04 LTS coreTMi5 machine with 3.2 GHz processor and 4 GB memory.