Miracle-dz/UniFAR-FTUs huggingface dataset:
Central Adaptation to Odorants Depends on PI3K Levels in Local Interneurons of the Antennal Lobe

We have previously shown that driving PI3K levels up or down leads to increases or reductions in the number of synapses, respectively. Using these tools to assay their behavioral effects in Drosophila melanogaster, we showed that a loss of synapses in two sets of local interneurons, GH298 and krasavietz, leads to olfaction changes toward attraction or repulsion, while the simultaneous manipulation of both sets of neurons restored normal olfactory indexes. We show here that olfactory central adaptation also requires the equilibrated changes in both sets of local interneurons. The same genetic manipulations directed to projection (GH146) or mushroom body (201Y, MB247) neurons did not affect adaptation. Also, we show that the equilibrium is a requirement for the glomerulus-specific size changes which are a morphological signature of central adaptation. Since the two sets of local neurons are mostly, although not exclusively, inhibitory (GH298) and excitatory (krasavietz), we interpret that the normal phenomena of sensory perception, measured as an olfactory index, and central adaptation rely on an inhibition/excitation ratio.
Stable prediction of mood and anxiety disorders based on behavioral and emotional problems in childhood: a 14-year follow-up during childhood, adolescence, and young adulthood.

OBJECTIVE The goal of this study was to predict the onset of mood and anxiety disorders from parent-reported emotional and behavioral problems in childhood across a 14-year period from childhood into young adulthood. METHOD In 1983, parent reports of behavioral and emotional problems were obtained with the Child Behavior Checklist for children and adolescents 4-16 years of age from the Dutch general population. At follow-up 14 years later, lifetime mood and anxiety diagnoses were obtained by a standardized DSM-IV interview for 1,580 subjects. Cox proportional hazards models were used to predict the incidence of mood and anxiety disorders from childhood problems and demographic covariates. RESULTS Mood disorders were significantly predicted by high scores on the anxious/depressed scale and on the internalizing composite (withdrawn, somatic complaints, and anxious/depressed). Anxiety disorders were significantly predicted by the social problems scale and the externalizing composite (delinquent behavior and aggressive behavior). Anxiety disorders predominantly started in childhood and early adolescence, whereas the incidence of mood disorders increased sharply in adolescence and young adulthood. CONCLUSIONS These results suggest different developmental pathways for mood and anxiety disorders. The predictions based on problem behavior remained stable during the 14-year period across adolescence and young adulthood. The results therefore underline the importance of early intervention and prevention of behavioral and emotional problems in childhood.
Unsupervised learning of categories with local feature sets of image

By an increase in the volume of image data in the age of information, a need for image categorization systems is greatly felt. Recent activities in this area has shown that the image description by local features, often has very strong similarities among local partials of an image, but these methods are also challenging, because the use of a set of vectors for each image does not permit the direct use of most of the common learning methods and distance functions. On the other hand, measuring the created similarities of the collection of unordered features is also problematic, because most of the proposed methods have rather high time complexities computationally. In this article, an unsupervised learning method of categorization of objects from a collection of unlabeled images is introduced. Each image is described by a set of unordered local features and clustering is performed on the basis of partial similarities existing among these sets of features. For this purpose, by the use of pyramid match algorithm, the set of the features are mapped in multi-resolution histograms and two sets of feature vectors in time-line are calculated in this new distance. These similarities are employed as a criterion distance among the patterns in hierarchical clustering; therefore, the categorization of objects by the use of common learning methods is performed with acceptable accuracy and faster than the existing algorithms.
Analytical predictions for statistics of cosmic shear: tests against simulations

Weak gravitational lensing surveys are rapidly becoming important tools to probe directly the mass density fluctuations in the universe and its background dynamics. Earlier studies have shown that it is possible to model the statistics of the convergence field on small angular scales by suitably modeling the statistics of the underlying density field in the highly non-linear regime. We extend such methods to model the complete probability distribution function, PDF, of the shear as a function of smoothing angle. Our model relies on a simple hierarchical Ansatz for the behaviour of the higher-order correlations in the density field. We compare our predictions with the results of numerical simulations and find excellent agreement for different cosmological scenarios. Our method provides a new way to study the evolution of non-Gaussianity in gravitational clustering and should help to break the degeneracies in parameter estimation based on analysis of the power spectrum alone.
Optimization and Design of Small Circular Coils in a Magnetically Coupled Wireless Power Transfer System in the Megahertz Frequency

The methods of optimizing and designing compact circular coils for a higher quality factor (Q-factor) and effective wireless power transfer (WPT) for an application of a WPT system in the megahertz frequency range to implantable medical devices are presented. For an optimal design, the ohmic resistance and inductance of the coils, including skin and proximity effects, are calculated by applying the volume filament model (VFM). In a calculation using the VFM, a circular coil is regarded as a concentric multiloop coil, and then, the resistance and inductance of the coil are derived by calculating the voltages of each loop obtained from the VFM. According to the results, the Q-factors of the coils are derived and analyzed. In addition, compact Rx and Tx coils for higher power transfer efficiency are properly designed at 6.78 MHz. For verification, theoretical calculation and analysis results are compared with the simulation and previous results. Coils properly designed are fabricated, and their resistance, inductance, Q-factor, and efficiency are measured. Measurement results have good agreement with the calculation. The fabricated system is compared with the previous results designed at a lower frequency from the viewpoint of size and efficiency.
Alignment-based reordering for SMT

We present a method for improving word alignment quality for phrase-based statistical machine translation by reordering the source text according to the target word order suggested by an initial word alignment. The reordered text is used to create a second word alignment which can be an improvement of the first alignment, since the word order is more similar. The method requires no other pre-processing such as part-of-speech tagging or parsing. We report improved Bleu scores for English-to-German and English-to-Swedish translation. We also examined the effect on word alignment quality and found that the reordering method increased recall while lowering precision, which partly can explain the improved Bleu scores. A manual evaluation of the translation output was also performed to understand what effect our reordering method has on the translation system. We found that where the system employing reordering differed from the baseline in terms of having more words, or a different word order, this generally led to an improvement in translation quality.
Content-Aware Projection for Tiny Planets

Tiny Planets visualise the world looking down at the ground, with physically unrealisable projections that curve the ground plane to look like small worlds. Whilst certain geometries, such as Stereographic, are known to give good Tiny Planet visualisations, the best projection to use depends on the image content. In this work we define a family of Tiny Planet projections that includes several commonly used projection types, but allows for datadependent adaptation to best present the image content to the viewer. We show how to select optimal content-aware projections from this set, minimising distortions from conformality whilst closing gaps and emphasising salient areas in the scene.
The planet method for designing relationships in B2B E-commerce

Business to Business (B2B) relationships in E-commerce are becoming increasingly important. However, there is little guidance available for managers about how relationships should be formulated or managed. Further, requirements analysis for information technology support of B2B inter-organisational relationships (IORs) depends on the business model of the relationship, and needs a predictive basis. This paper describes a method for planning and managing IORs based on Williamson's transaction cost theory (1981), to guide managers when negotiating B2B alliances, and procurement of IT support for them. This method covers market analysis, relationship planning, transaction cost estimation and IT requirements analysis. Lessons learned in presenting the method to managers and using it in case studies are reviewed.
Learning Dynamic Policies from Demonstration

We address the problem of learning a policy directly from expert demonstrations. Typically, this problem is solved with a supervised learning method such as regression or classification to learn a reactive policy. Unfortunately, reactive policies lack the ability to model long-range dependancies and this omission can result in suboptimal performance. So, we take a different approach. We observe that policies and dynamical systems are mathematical duals, and then use this fact to leverage the rich literature on system identification to learn dynamic policies with state directly from demonstration. Many system identification algorithms have desirable properties like the ability to model long-range dependancies, statistical consistency, and efficient off-the-shelf implementations. We show that by employing system identification algorithms to learning from demonstration problems, all of these properties can be carried over to the learning from demonstration domain. We further show that these properties can be beneficial in practice by applying state-of-the-art system identification algorithms to real-world direct learning from demonstration problems.
From pixels to objects: Enabling a spatial model for humanoid social robots

This work adds the concept of object to an existent low-level attention system of the humanoid robot iCub. The objects are defined as clusters of SIFT visual features. When the robot first encounters an unknown object, found to be within a certain (small) distance from its eyes, it stores a cluster of the features present within an interval about that distance, using depth perception. Whenever a previously stored object crosses the robot's field of view again, it is recognized, mapped into an egocentrical frame of reference, and gazed at. This mapping is persistent, in the sense that its identification and position are kept even if not visible by the robot. Features are stored and recognized in a bottom-up way. Experimental results on the humanoid robot iCub validate this approach. This work creates the foundation for a way of linking the bottom-up attention system with top-down, object-oriented information provided by humans.
Mining the multilingual terminology from the web

Multilingual linguistic resources are usually constructed from parallel corpora, but since these corpora are available only for selected text domains and language pairs, the potential of other resources is being explored as well. This article seeks to explore and exploit the idea of using multilingual Web-based encyclopaedias such as Wikipedia as comparable corpora for multilingual terminology extraction. We propose an approach to extract terms and their translations from different types of Wikipedia link information and texts. The next step will be using the linguistics information to re-rank and filter the extracted term candidates in the target language. Preliminary evaluations using the combined statistics-based and linguistic-based approaches were applied on Japanese-French-English languages. These evaluations showed a real open improvement and good quality of the extracted term candidates for building or enriching multilingual ontologies, dictionaries or feeding a cross-language information retrieval system with the related expansion terms of the source query.
Model Order Estimation for Independent Component Analysis of Epoched EEG Signals

In analysis of multi-channel event related EEG signals indepedent component analysis (ICA) has become a widely used tool to attempt to separate the data into neural activity, physiological and non-physiological artifacts. High density elctrode systems offer an opportunity to estimate a corresponding large number of independent components (ICs). However, too large a number of ICs leads to overfitting of the ICA model, which can have a major impact on the model validity. Consequently, finding the optimal number of components in the ICA model is an important problem. In this paper we present a method for model order selection, based on a probabilistic framework. The proposed method is a modification of the Molgedey Schuster (MS) algorithm to epoched, i.e. event related data. Thus, the contribution of the present paper can be summarized as follows: 1) We advocate MS as a low complexity ICA alternative for EEG. 2) We define an epoch based likelihood function for estimation of a principled unbiased ’test error’. 3) Based on the unbiased test error measure we perform model order selection for ICA of EEG. Applied to a 64 channel EEG data set we were able to determine an optimum order of the ICA model and to extract 22 ICs related to the neurophysiological stimulus responses as well as ICs related to physiologicaland non-physiological noise. Furthermore, highly relevant high frequency response information was captured by the ICA model.
Performance Modeling of Resource Failures in Grid Environments

Grid is susceptible to a number of software and hardware failures, so a deep understanding of and modeling the grid resource failures are a challenge and have significant influence on grid researching. However, due to various reasons such as commercial secret and security, it is difficult to obtain real historical logs of grids. Therefore, an accurate model of resource failures is critically useful. In the paper, through analyzing the grid log data, we detail the suitability of three potential statistical distributions for each data set: Weibull, Zipf’s law and Pareto. Then, this paper develops a grid resource failure simulator. Finally, with the different failure patterns generated by the failure simulator, the paper evaluates several common scheduling algorithms used in grid systems.
The Gauss-Seidel fast affine projection algorithm

In this paper we propose a new stable fast affine projection algorithm based on Gauss-Seidel iterations (GSFAP). We investigate its implementation using the logarithmic number system (LNS) and compare it with two other fast affine projection (FAP) algorithms. Simplified and multi-input GSFAP versions are also proposed. We show that the algorithm is only marginally more complex than NLMS and simpler than other FAP algorithms. Its application for acoustic echo cancellation is also investigated.
State Space Analysis of Dominant Structures in Dynamic Social Systems

OF THE DISSERTATION State Space Analysis of Dominant Structures in Dynamic Social Systems by Jeremy B. Sato Doctor of Philosophy in Systems Science & Mathematics Washington University in St. Louis, 2016 Professor Joseph A. O’Sullivan, Chair Many systems involving human relationships are modeled as dynamic systems, as diverse as urban population growth, diffusion of innovations, spread of viruses, and supply chain management. A fundamental assumption is that these systems contain variables which accumulate and deplete over time (people, innovation adoptions, infections, and orders), and whose dynamics are determined by societal rules and human decision making processes. These assumptions allow the system to be formally expressed by ordinary differential equations which are often nonlinear and contain multiple state variables and feedback loops. Analytical methods have been developed to identify the dominant feedback loops which primarily influence the behavior of the system. However, these dominance methods can produce conflicting results and are often performed in the time-domain under specific initial conditions. This thesis takes a state-space approach to dominance analysis and, in the process, re-examines the definition of dominance. A formal, mathematical definition of dominance is proposed and an analytical procedure is developed and applied to previously studied models. The method produces results consistent with previous analyses and is able explain inconsistencies between other methods. The procedure is then applied in the state-domain and used to identify state-space regions in xiv which certain feedback loops dominate behavior. The procedure is then used to identify the stability properties of equilibria, and a theorem is developed to provide a necessary condition for stability, based on the dominance of balancing (negative) feedback. Lastly, the method is applied to a problem in public health in which a model of the supply and demand of cancer control services is analyzed. The dominant feedback loops are identified for the purpose of revealing potential sources of health disparities between distinct population segments. The analysis revealed the existence of a tipping point condition associated with a single unstable equilibrium point which influences population health outcomes. Furthermore, trajectories near the unstable equilibrium point are dominated by reinforcing (positive) feedback loops which affect the proportion of people seeking cancer control services. These loops result in either virtuous or vicious cycles, depending on which side of the tipping point the system is operating in the state-space. The methods were then used to identify potential leverage points in the system in which small parameter changes cause significant behavior changes. Potential avenues for future dominance research are discussed as well as future transdisciplinary research in public health and implementation science.
CORRUPTION AND CHANGE: THE IMPACT OF FOREIGN DIRECT INVESTMENT

This study examines influences on the level of corruption in countries from a strategic perspective. Corruption is one of the country-level influences on market entry, investment, and other decisions fundamental to strategic management at the international level. The study examines the impact on corruption of change in levels of foreign direct investment (FDI). It uses the Corruption Perceptions Index (CPI) scores computed by Transparency International for 1999 and 2000. Results indicate that the more rapid the rate of change in FDI, the higher the level of corruption. Higher levels of perceived corruption are associated with each of two dimensions of national culture: uncertainty avoidance and masculinity. Research and managerial implications are also discussed. Copyright © 2004 John Wiley & Sons, Ltd.
The CMU Motion of Body (MoBo) Database

In March 2001 we started to collect the CMU Motion of Body (MoBo) database. To date the database contains 25 individuals walking on a treadmill in the CMU 3D room. The subjects perform four different walk patterns: slow walk, fast walk, incline walk and walking with a ball. All subjects are captured using six high resolution color cameras distributed evenly around the treadmill. In this technical report we describe the capture setup, the collection procedure and the organization of the database.
Emotional processing in Parkinson’s disease and schizophrenia: evidence for response bias deficits in PD

Deficits in facial emotion recognition in Parkinson’s disease (PD) patients has been well documented. Nevertheless, it is still not clear whether facial emotion recognition deficits are secondary to other cognitive impairments. The aim of this study was to answer the question of whether deficits in facial emotion recognition in PD result from impaired sensory processes, or from impaired decision processes. To address this question, we tested the ability to recognize a mixture of basic and complex emotions in 38 non-demented PD patients and 38 healthy controls matched on demographic characteristics. By using a task with an increased level of ambiguity, in conjunction with the signal detection theory, we were able to differentiate between sensitivity and response bias in facial emotion recognition. Sensitivity and response bias for facial emotion recognition were calculated using a d-prime value and a c index respectively. Our study is the first to employ the EIS-F scale for assessing facial emotion recognition among PD patients; to test its validity as an assessment tool, a group comprising schizophrenia patients and healthy controls were also tested. Patients with PD recognized emotions with less accuracy than healthy individuals (d-prime) and used a more liberal response criterion (c index). By contrast, patients with schizophrenia merely showed diminished sensitivity (d-prime). Our results suggest that an impaired ability to recognize facial emotions in PD patients may result from both decreased sensitivity and a significantly more liberal response criteria, whereas facial emotion recognition in schizophrenia may stem from a generalized sensory impairment only.
Image Denoise Using Auto-Adapted Empirical Mode Decomposition Algorithm

In this paper, a new image denoise method is presented. The main contribution of our approach is to apply an auto-adapted empirical mode decomposition algorithm (AAEMD) to remove the noise. By means of AAEMD, the image can be decomposed into a number of intrinsic mode function (IMF), and the histogram of IMF observes the normal distribution, whether the original image has noise or not. Here, histogram matching is applied for denoise in the first few IMF images. In other words, the histogram of the new IMF images after Appling histogram matching observes the special normal distribution. At last, the modified and unmodified IMF images are added up to get the denoise image. Experiments prove that the novel algorithm is efficient in image denosise and better than current algorithms. Keywords-AAEMD; IMF; histogram matchinge;
Code Similarity Detection in Multiple Large Source Trees using Token Hashes

The ability to find similarities between two source code bases, or within one code base, has many uses including the detection of student plagiarism, the identification of intellectual property violations and the location of repeated code in a code base amenable to refactoring. Previous structure-metric approaches have used either suffix trees or modified Longest Common Subsequence algorithms to detect code similarities. This paper introducesctcompare, a tool which uses hashes on fixed-length token sequences to find code similarities. One significant benefit of this approach is the ability to search multiple large trees of source code simultaneously for similarities . A performance analysis of ctcompare on a large body of code is also given.