Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation in stereoacuity: normative description, fixation disparity, and the roles of aging and gender.

PURPOSE Variation in stereoacuity was examined in a large group of observers with Snellen acuity of 20/30 or less. METHODS Threshold retinal disparity for 2.78 degrees x 2.28 degrees rectangular test stimuli was determined as a function of the retinal disparity (varied from 55 arcmin uncrossed to 55 arcmin crossed) of a 5.57 degrees x 4.8 degrees rectangular pedestal stimulus in 160 observers 15 to 79 years of age. In most cases, data were collected during viewing of random dot stereograms (RDSs) presented for 100-ms, which prevents involvement of vergence or monocular depth cues. RESULTS When plotted logarithmically, 100-ms thresholds in 106 observers less than 60 years of age approximated a normal distribution (mean, 1.57 +/- 0.227 [SD] log arcsec [37 linear arcsec]). Among these, one observer was supernormal, 88% were within the normal range (+/-2 SD of the log mean), 2% had elevated thresholds, and 8% failed testing with 100-ms stimuli but had residual binocular depth discrimination; 1 observer was stereoblind. In contrast, only 37% of the observers aged 60 to 69 and 25% of the observers aged 70 to 79 had stereoacuity within the normal range. Moreover, the extent of the stereo deficiencies became more pronounced with age. Fixation disparity was operationally defined as optimal stereoscopic threshold with a nonzero retinal disparity pedestal. Of the 151 normal observers tested, 89% were maximally sensitive to disparities within 11 arcmin of fixation: all males were maximally sensitive to pedestals within 22 arcmin of fixation, whereas 8% of females had fixation disparities of more than 22 arcmin. Males were more likely to be sensitive with uncrossed-disparity pedestals, whereas females were more likely to be sensitive with crossed disparity. CONCLUSIONS Age-related deterioration in stereoacuity is reflected not only by a linear correlation between age and threshold but also by a catastrophic factor that produces more marked deterioration after age 60. Both factors are probably cerebral and not specifically related to stereopsis. The prevalence of fixation disparity in the normal population is probably more common than previously reported.
Rapamycin Protects against Neuron Death in In Vitro andIn Vivo Models of Parkinson's Disease

We report that rapamycin, an allosteric inhibitor of certain but not all actions of the key cellular kinase mammalian target of rapamycin (mTOR), protects neurons from death in both cellular and animal toxin models of Parkinson's disease (PD). This protective action appears to be attributable to blocked translation of RTP801/REDD1/Ddit4, a protein that is induced in cell and animal models of PD and in affected neurons of PD patients and that causes neuron death by leading to dephosphorylation of the survival kinase Akt. In support of this mechanism, in PD models, rapamycin spares phosphorylation of Akt at a site critical for maintenance of its survival-promoting activity. The capacity of rapamycin to provide neuroprotection in PD models appears to arise from its selective suppression of some but not all actions of mTOR, as indicated by the contrasting finding that Torin1, a full catalytic mTOR inhibitor, is not protective and induces Akt dephosphorylation and neuron death.
Indian Ocean Tsunami: Disaster, Generosity and Recovery

This paper is an exploratory investigation into the nature and effectiveness of international humanitarian aid effort after the Indian Ocean tsunami disaster. Relief assistance poured in quickly and copiously, and helped avert ‘second mortality’ from exposure and starvation in the tsunami-affected countries. Foreign aid also provided vital leeway in the reconstruction phase, but limited the aid absorptive capacity of the recipient countries and excessive competition among aid organizations (mostly non-government organizations) hindered effective aid utilization. The findings of the present study make a strong case for designing policies and programs for dealing with disasters as an integral part of national development strategies and highlight the need for combining international aid commitments with solutions to the limited aid absorptive capacity of disaster-affected countries.
FREE PRODUCT FORMULAE FOR QUANTUM PERMUTATION GROUPS

Associated to a finite graph $X$ is its quantum automorphism group $G(X)$. We prove a formula of type $G(X*Y)=G(X)*_{\mathrm{w}}G(Y)$, where $*_{\mathrm{w}}$ is a free wreath product. Then we discuss representation theory of free wreath products, with the conjectural formula $\mu(G*_{\mathrm{w}}H)=\mu(G)\boxtimes\mu(H)$, where $\mu$ is the associated spectral measure. This is verified in two situations: one using free probability techniques, the other one using planar algebras.
Do Mega Sporting Events Promote International Trade?

Large sporting events, such as the Olympic Games, usually end up imposing large costs on their hosts that are not nearly compensated by either the revenues earned during the event or the legacy of large facilities that are left behind. The implausibility of recovering such a sizable investment explains why economists are usually skeptical that hosting these types of mega sporting events can be profitable. However, the desire to host mega sporting events such as the World Cup or the Olympic Games is widely held by both the masses and political elites. These events may allow countries to signal that they intend to intensify their participation on the world stage, and that there may be tangible benefits of doing so.
Non Parametric Learning of Sensori-Motor Maps. Application to the Control of Multi Joint Systems

At the light of control and learning theories, this paper addresses the question of controlling multi-joint system using sensory feedback. A generic Sensory-Motor Control Model (SMCM) is firstly presented that solves the inverse kinematics difficulty at a theoretical level. Computational implementations of SMCM requires the knowledge of sensory motor transforms that are directly dependent to the multi-joint structure that is to be controlled. To avoid the dependency of SMCM to the analytical knowledge of these transforms, a non parametric learning approach is developed to identify non linear mappings between sensory signals and motor commands involved in SMCM. The resulting adaptive SMCM (ASMCM) is intensively tested within the scope of hand-arm reaching movements. ASMCM shows to be very effective and robust at least for this task. Its generic properties and effectiveness allow to foresee wide area of application.
Sparse Gaussian graphical models for speech recognition

We address the problem of learning the structure of Gaussian graphical models for use in automatic speech recognition, a means of controlling the form of the inverse covariance matrices of such systems. With particular focus on data sparsity issues, we implement a method for imposing graphical model structure on a Gaussian mixture system, using a convex optimisation technique to maximise a penalised likelihood expression. The results of initial experiments on a phone recognition task show a performance improvement over an equivalent full-covariance system.
Internal controllability of the Korteweg-de Vries equation on a bounded domain

This paper is concerned with the control properties of the Korteweg-de Vries (KdV) equation posed on a bounded interval with a distributed control. When the control region is an arbitrary open subdomain, we prove the null controllability of the KdV equation by means of a new Carleman inequality. As a consequence, we obtain a regional controllability result, the state function being controlled on the left part of the complement of the control region. Finally, when the control region is a neighborhood of the right endpoint, an exact controllability result in a weighted L2 space is also established.
Comparison of Clustering Approaches for Gene Expression Data

Clustering algorithms have been used to divide genes into groups ac- cording to the degree of their expression similarity. Such a grouping may suggest that the respective genes are correlated and/or co-regulated, and subsequently in- dicates that the genes could possibly share a common biological role. In this pa- per, four clustering algorithms are investigated: k-means, cut-clustering, spectral and expectation-maximization. The algorithms are benchmarked against each other. The performance of the four clustering algorithms is studied on time series expres- sion data using Dynamic Time Warping distance in order to measure similarity be- tween gene expression profiles. Four different cluster validation measures are used to evaluate the clustering algorithms: Connectivity and Silhouette Index for esti- mating the quality of clusters, Jaccard Inde xf or evaluating the stability of ac luster method and Rand Index for assessing the accuracy. The obtained results are ana- lyzed by Friedman's test and the Nemenyi post-hoc test. K-means is demonstrated to be significantly better than the spectral clustering algorithm under the Silhouette and Rand validation indices. Keywords. gene expression data, graph-based clustering algorithm, minimum cut clustering, partitioning algorithm, dynamic time warping
A S patial Domain Image Steganography Technique Based on Plane Bit Substitution Method

Abst ract - Steg anography is the art and science of hiding information by embedding data into cover media. In this paper we propose a new method of information hiding in digital image in spatial domain. In this method we use Plane Bit Substitution Method (PBSM) technique in which message bits are embedded into the pixel value(s) of an image. We first, proposed a Steganography transformation machine (STM) for solving Binary operation for manipulation of original image with help to least significant bit (LSB) operator based matching. Second, we use pixel encryption and decryption techniques under theoretical and experimental evolution. Our experimental, techniques are sufficient to discriminate analysis of stego and cover image as each pixel based PBSM, and operand with LSB. Ke ywords : spatial domain, pbsm, stm, lsb.
Ginsenoside Rd Attenuates Myocardial Ischemia/Reperfusion Injury via Akt/GSK-3β Signaling and Inhibition of the Mitochondria-Dependent Apoptotic Pathway

Evidence suggests Ginsenoside Rd (GSRd), a biologically active extract from the medical plant Panax Ginseng, exerts antioxidant effect, decreasing reactive oxygen species (ROS) formation. Current study determined the effect of GSRd on myocardial ischemia/reperfusion (MI/R) injury (a pathological condition where ROS production is significantly increased) and investigated the underlying mechanisms. The current study utilized an in vivo rat model of MI/R injury and an in vitro neonatal rat cardiomyocyte (NRC) model of simulated ischemia/reperfusion (SI/R) injury. Infarct size was measured by Evans blue/TTC double staining. NRC injury was determined by MTT and lactate dehydrogenase (LDH) leakage assay. ROS accumulation and apoptosis were assessed by flow cytometry. Mitochondrial membrane potential (MMP) was determined by 5, 5′, 6, 6′-tetrachloro-1, 1′, 3, 3′-tetrathylbenzimidazol carbocyanine iodide (JC-1). Cytosolic translocation of mitochondrial cytochrome c and expression of caspase-9, caspase-3, Bcl-2 family proteins, and phosphorylated Akt and GSK-3β were determined by western blot. Pretreatment with GSRd (50 mg/kg) significantly augmented rat cardiac function, as evidenced by increased left ventricular ejection fraction (LVEF) and ±dP/dt. GSRd reduced myocardial infarct size, apoptotic cell death, and blood creatine kinase/lactate dehydrogenase levels after MI/R. In NRCs, GSRd (10 µM) inhibited SI/R-induced ROS generation (P<0.01), decreased cellular apoptosis, stabilized the mitochondrial membrane potential (MMP), and attenuated cytosolic translocation of mitochondrial cytochrome c. GSRd inhibited activation of caspase-9 and caspase-3, increased the phosphorylated Akt and GSK-3β, and increased the Bcl-2/Bax ratio. Together, these data demonstrate GSRd mediated cardioprotective effect against MI/R–induced apoptosis via a mitochondrial-dependent apoptotic pathway.
Lexicon construction: A topic model approach

Sentiment Analysis has been an interesting task of web content mining these years due to rapid growth of user generating content. As the annotated data are expensive to get, the unsupervised approaches are preferred. Usually, a lexicon is required when apply the unsupervised approaches. In the paper, based on Latent Dirichlet Allocation (LDA), we propose a model to construct a lexicon for sentiment analysis task, which is domain independent. Through experiments, we compare our generated lexicon with some widely used lexicons and with trivial lexicon construction algorithm. The experiments show our approach is competitive and flexible.
Biomechanical analysis of whiplash injuries; women are not scaled down men

Whiplash is the most common soft tissue injury sustained in car accidents. The term is commonly associated with hyperextension of the neck as the head rotates backwards in rear end collisions but the exact injury mechanism is not fully understood because the neck is an anatomically and mechanically complex structure. Experimental studies of the mechanism of injury are limited by several ethical and practical factors, so biomechanical computational simulation, based upon experimental research and mathematical modelling, appears to be the most appropriate method of investigation.
Schedulability Aware WCET-Optimization of Periodic Preemptive Hard Real-Time Multitasking Systems

In hard real-time multitasking systems, applying WCET-oriented code optimizations to individual tasks may not lead to optimal results with regard to the system's schedulability. We propose an approach based on Integer-Linear Programming which is able to perform schedulability aware code optimizations for periodic task sets with fixed priorities. We evaluate our approach by using a static instruction SPM optimization for the Infineon TriCore microcontroller.
A hybrid Artificial Bee Colony algorithm with bacterial foraging optimization

The Artificial Bee Colony (ABC) algorithm is a new swarm optimization algorithm with good numerical functions optimization results. In order to enhance the performance ability of ABC algorithm, a hybrid ABC (HAB) algorithm is presented where swarming behavior of bacterial foraging optimization algorithm is introduced into the ABC algorithm to do local search. The performance of the proposed method is examined on well-known six numerical benchmark functions and the obtained results are compared with basic ABC algorithm and BFO algorithm. The experimental results show that the proposed approach is very effective method for solving numeric benchmark functions and successful in terms of solution quality and convergence to the global optimum, especially on the multimodal functions.
Frontiers in Insider Threats and Data Leakage Prevention

Organizations continue to be plagued by information leaks caused by insiders with legitimate access to critical or proprietary information. Such unauthorized leaks may result in significant damage to competitiveness, reputation and finances, and organizations should consider proactive approaches to preventing, detecting, and responding to this threat. In this special issue, we have selected eight papers describing recent work on insider threat and data leakage prevention. These include four papers [1][2][3][4] derived from the third International Workshop on Managing Insider Security Threats (MIST 2011)1 in conjunction with the third IEEE International Conference on Intelligent Networking and Collaborative Systems (IEEE INCoS 2011). In the first paper, titled “From Insider Threats to Business Processes that are Secure-by-Design” [1], the author suggests that insider threat is a placeholder term indicating the transition from securing IT infrastructures to securing the socio-technical systems. While observing that the concept of an insider is not helpful in today’s dynamic heterogeneous organizations, he adopts “business processes that are secure-by-design (sustainable business processes)” as a new paradigm where those processes remain viable even when attacks are launched with insider knowledge. Finally, the author presents two research challenges for the sustainable business processes, modelling socio-technical systems and exploring the foundations of judgement-based risk analysis methods. The second paper, titled “Combining Baiting and User Search Profiling Techniques for Masquerade Detection” proposes an integrated masquerade detection to combine user behavior profiling with a baiting technique [5]. The proposed approach reduces false positives when compared to user behavior profiling alone. In addition, it is shown that this approach can harden a masquerade attack detector against mimicry attacks. In the third paper, titled “A Certificateless Ordered Sequential Aggregate Signature Scheme Secure against Super Adversaries” [2], the authors propose an ordered sequential aggregate signature in certificateless setting. Further, they discuss its security against super adversaries who can obtain signature of a target signer but without providing a secret value for a challenger. The fourth paper titled “Security Analysis of Offline E-cash Systems with Malicious Insider” analyses security of offline anonymous electronic cash systems. This includes recent systems based on the
Representing Items as Word-Embedding Vectors and Generating Recommendations by Measuring their Linear Independence

In order to generate effective results, it is essential for a recommender system to model the information about the user interests in a profile. Even though word embeddings (i.e., vector representations of textual descriptions) have proven to be effective in many contexts, a content-based recommendation approach that employs them is still less effective than collaborative strategies (e.g., SVD). In order to overcome this issue, we introduce a novel criterion to evaluate the word-embedding representation of the items a user rated. The proposed approach defines a vector space in which the similarity between an unevaluated item and those in a user profile is measured in terms of linear independence. Experiments show its effectiveness to perform a better ranking of the items, w.r. t. collaborative filtering, both when compared to a latent-factor-based approach (SVD) and to a classic neighborhood user-based system.
Hierarchical statistical models for the fusion of multiresolution image data

This paper presents a class of nonlinear hierarchical algorithms for the fusion of multiresolution image data in low-level vision. The approach combines nonlinear causal Markov models defined on hierarchical graph structures, with standard bayesian estimation theory. Two random processes defined on simple hierarchical graphs (quadtrees or "ternary graphs") are introduced to represent the multiresolution observations at hand and the hidden labels to be estimated. An optimal algorithm (inspired from the Viterbi algorithm) is developed to compute the bayesian estimates on the hierarchical graph structures. Estimates are obtained within two passes on the graph structure. This algorithm is non-iterative and yields a per pixel computational complexity which is independent of image size. This approach is compared to the multiscale algorithm proposed by (Bouman et al., 1994) for single-resolution image segmentation (that we have extended for multiresolution data fusion). <<ETX>>
Automated Classiication of Dna Structure from Sequence Information

We introduce an algorithm, lllama, which combines simple pattern recognizers into a general method for estimating the entropy of a sequence. Each pattern recognizer exploits a partial match between subsequences to build a model of the sequence. Since the primary features of interest in biological sequence domains are subsequences with small variations in exact composition, lllama is particularly suited to such domains. We describe two methods, lllama-length and lllama-alone, which use this entropy estimate to perform maximum a posteriori classiication. We apply these methods to several problems in three-dimensional structure classiication of short DNA sequences. The results include a 1 surprisingly low 3.6% error rate in predicting helical conformation of oligonucleotides. We compare our results to those obtained using more traditional methods for automated generation of classiiers.
Partitioning signal processing applications to different granularity reconfigurable logic

In this paper, we propose a methodology for partitioning DSP applications between the fine and coarse-grain reconfigurable hardware for improving performance. The fine-grain logic is implemented by an embedded FPGA unit, while for the coarse-grain reconfigurable hardware, a 2-Dimensional array of Processing Elements is considered. These different granularity reconfigurable functional units are embedded in a hybrid platform. The proposed methodology mainly consists of three steps, the analysis, the mapping onto the coarse-grain reconfigurable array, and the mapping onto the fine-grain reconfigurable hardware. The experiments for five real-world applications show that the speedup, relative to an all-FPGA solution, ranges from 1.4 to 3.1 for the considered applications.
XSS-FP: Browser Fingerprinting using HTML Parser Quirks

There are many scenarios in which inferring the type of a client browser is desirable, for instance to fight against session stealing. This is known as browser fingerprinting. This paper presents and evaluates a novel fingerprinting technique to determine the exact nature (browser type and version, eg Firefox 15) of a web-browser, exploiting HTML parser quirks exercised through XSS. Our experiments show that the exact version of a web browser can be determined with 71\% of accuracy, and that only 6 tests are sufficient to quickly determine the exact family a web browser belongs to.
Determination of willingness to pay for entrance fee to national park: An empirical investigation

This paper examine the determinants of willingness to pay (WTP) of Bhitarakanika National Park (BNP), Odisha in India. Primary data has been collected from the visitors through survey method. We used multiple regression model to determine the WTP for entrance fee at BNP. The estimated results concluded that, income, age, day spent, marital status and travel cost have positively significant relationship with WTP whereas, gender is negatively affecting to WTP. From the policy perspective, we are suggesting to raise the entrance fee from Rs.20 to Rs. 40 which can raise the park revenue of BNP.
Semantic Preferential Subsumption

We present a general preferential semantic framework for plausible subsumption in description logics, analogous to the KLM preferential semantics for propositional entailment. We introduce the notion of ordered interpretations for description logics, and use it to define two mutually dual non-deductive subsumption relations ***⊑ and ***⊑*. We outline their properties and explain how they may be used for inductive and abductive reasoning respectively. We show that the preferential semantics for subsumption can be reduced to standard semantics of a sufficiently expressive description logic. This has the advantage that standard DL algorithms can be extended to reason about our notions of plausible subsumption.