Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised model training for overlapping sound events based on unsupervised source separation

Sound event detection is addressed in the presence of overlapping sounds. Unsupervised sound source separation into streams is used as a preprocessing step to minimize the interference of overlapping events. This poses a problem in supervised model training, since there is no knowledge about which separated stream contains the targeted sound source. We propose two iterative approaches based on EM algorithm to select the most likely stream to contain the target sound: one by selecting always the most likely stream and another one by gradually eliminating the most unlikely streams from the training. The approaches were evaluated with a database containing recordings from various contexts, against the baseline system trained without applying stream selection. Both proposed approaches were found to give a reasonable increase of 8 percentage units in the detection accuracy.
Contextual Data Extraction and Instance-Based Integration

Generative Process SG SY SR ticker price daymax day-min volume beta . nasdaq:aapl 256.88 259.40 253.35 29,129,032 1.50 . nasdaq:goo g 485.63 493.45 483.00 2,894,755 1.10 . nyse:cat 60.76 62.42 60.05 7,709,405 1.78 . "Hidden Relation" = λG (e G(σG(π G(R0)))) = λY(e Y(σY(π Y(R0)))) =λR (e R(σR(πR(R0)))) Abstract Generative ProcessGenerative Process • Each source generated by: – π projection – σ selection – e error (e.g. approx, mistakes, formattings) – λ template encoding ticker price daymax day-min volume beta . nasdaq:aapl 256.88 259.40 253.35 29,129,032 1.50 . nasdaq:goo g 485.63 493.45 483.00 2,894,755 1.10 . nyse:cat 60.76 62.42 60.05 7,709,405 1.78 . SY = λY (e Y (σtickerlike "nasdaq:%" (πticker, price, volume(R0)))) e Y(): round(volume, 1000), price(Nσ) "Hidden Relation" Problem: Invert the Process ticker price daymax day-min volume beta . nasdaq:aapl 256.88 259.40 253.35 29,129,032 1.50 . nasdaq:goo g 485.63 493.45 483.00 2,894,755 1.10 . nyse:cat 60.76 62.42 60.05 7,709,405 1.78 . "Hidden Relation" The Flint Experience • Greedy best-effort algorithm for integration and extraction [Blanco et al. WebDb2010, WWW2011, CAiSE2010] • Promising experimental results • here we investigate the foundations of the problem and propose a principled solution
Assessment of Estrogen Receptor Expression in Epithelial Ovarian Cancer Patients Using 16α-18F-Fluoro-17β-Estradiol PET/CT

The estrogen receptor α (ERα) is expressed in approximately 70% of ovarian cancer tumors. PET of tumor ERα expression with the tracer 16α-18F-fluoro-17β-estradiol (18F-FES) may be valuable to select ovarian cancer patients for endocrine therapy. The aim of this study was to evaluate the feasibility of 18F-FES PET to determine tumor ERα expression noninvasively in epithelial ovarian cancer patients. Methods: 18F-FES PET/CT was performed shortly before cytoreductive surgery. Tumor 18F-FES uptake was quantified for all lesions 10 mm or greater on CT and expressed as maximum standardized uptake value. 18F-FES PET/CT findings were compared with histology and immunohistochemistry for ERα, ERβ, and progesterone receptor. Receptor expression was scored semiquantitatively using H-scores (percentage of positive tumor cells × staining intensity). The optimum threshold to discriminate ER-positive and -negative lesions was determined by receiver-operating-characteristic analysis. Results: In the 15 included patients with suspected ovarian cancer, 32 measurable lesions greater than 10 mm were present on CT. Tumor 18F-FES uptake could be quantified for 28 lesions (88%), and 4 lesions were visible but nonquantifiable because of high uptake in adjacent tissue. During surgery, histology was obtained of 23 of 28 quantified lesions (82%). Quantitative 18F-FES uptake correlated with the semiquantitative immunoscore for ERα (ρ = 0.65, P < 0.01) and weakly with progesterone receptor expression (ρ = 0.46, P = 0.03) and was not associated with ERβ expression (ρ = 0.21, P = 0.33). The optimum threshold to discriminate ERα-positive and ERα-negative lesions was a maximum standardized uptake value greater than 1.8, which provided a 79% sensitivity, 100% specificity, and area under the curve of 0.86 (95% confidence interval, 0.70–1.00). In 2 of 7 patients with cytology/histology available at primary diagnosis and at debulking surgery, immunohistochemical ERα expression had changed over time. 18F-FES PET was in accordance with histology at debulking surgery but not at primary diagnosis, indicating that 18F-FES PET could provide reliable information about current tumor ERα status. Conclusion: 18F-FES PET/CT can reliably assess ERα status in epithelial ovarian cancer tumors and metastases noninvasively. Evaluation of the predictive value of 18F-FES PET/CT for endocrine therapy in epithelial ovarian cancer patients is warranted.
CELIA: Cost-Time Performance of Elastic Applications on Cloud

Clouds offer great flexibility for scaling applications due to the wide spectrum of resources with different cost-performance, inherent resource elasticity and pay-peruse charging. However, determining cost-time-efficient cloud configurations to execute a given application in the large resource configuration space remains a key challenge. The growing importance of elastic applications for which the accuracy is a function of resource consumption introduces new opportunities to exploit resource elasticity on clouds. In this paper, we introduce CELIA, a measurement-driven analytical modeling approach to determine cost-time-optimal cloud resource configurations to execute a given elastic application with a time deadline and a cost budget. We evaluate CELIA with three representative elastic applications on more than ten million configurations consisting of Amazon EC2 resource types with different cost-performance. Using CELIA, we show that multiple cost-time Pareto-optimal configurations exist among feasible cloud configurations that execute an elastic application within a time deadline and cost budget. These Pareto-optimal configurations exhibit up to 30% cost savings for an elastic application representing n-body simulation. We investigate the impact of fixed-time scaling on the cost of executing elastic applications on cloud. We show that cost gradient with respect to resource demand is smaller when cloud resources with better cost-performance are used. Furthermore, we show that the relative increase in cost is always smaller compared to the relative reduction of execution time deadline. For example, tightening the execution time deadline by two-thirds incurs only 40% increase in cost for the n-body simulation application.
Mechanism of chiral proofreading during translation of the genetic code

The biological macromolecular world is homochiral and effective enforcement and perpetuation of this homochirality is essential for cell survival. In this study, we present the mechanistic basis of a configuration-specific enzyme that selectively removes D-amino acids erroneously coupled to tRNAs. The crystal structure of dimeric D-aminoacyl-tRNA deacylase (DTD) from Plasmodium falciparum in complex with a substrate-mimicking analog shows how it uses an invariant ‘cross-subunit’ Gly-cisPro dipeptide to capture the chiral centre of incoming D-aminoacyl-tRNA. While no protein residues are directly involved in catalysis, the unique side chain-independent mode of substrate recognition provides a clear explanation for DTD’s ability to act on multiple D-amino acids. The strict chiral specificity elegantly explains how the enriched cellular pool of L-aminoacyl-tRNAs escapes this proofreading step. The study thus provides insights into a fundamental enantioselection process and elucidates a chiral enforcement mechanism with a crucial role in preventing D-amino acid infiltration during the evolution of translational apparatus. DOI: http://dx.doi. org/10.7554/eLife.01519. 001
On-line Learning with Abstention

We introduce and analyze an on-line learning setting where the learner has the added option of abstaining from making a prediction at the price of a fixed cost. When the learner abstains, no feedback is provided, and she does not receive the label associated with the example. We design several algorithms and derive regret guarantees in both the adversarial and stochastic loss setting. In the process, we derive a new bound for online learning with feedback graphs that generalizes and extends existing work. We also design a new algorithm for on-line learning with sleeping experts that takes advantage of time-varying feedback graphs. We present natural extensions of existing algorithms as a baseline, and we then design more sophisticated algorithms that explicitly exploit the structure of our problem. We empirically validate the improvement of these more sophisticated algorithms on several datasets.
Fitness Liderlerinin İş ve Yaşam Doyumunun Belirlenmesinde Tutkunluğun Rolü: Ankara İli Örneği

The purpose of this study was to investigate the role of passion in predicting job and life satisfaction in fitness leaders and examine the sex and job experience differences in these variables. Two hundred (Mage=29.52+-4.38) fitness leaders participated in this study. “Passion Scale” (Vallerand et al., 2003), “Satisfaction with Life Scale” (Diener et al., 1985), and “Minnesota Job Satisfaction Questionnaire” (Weiss et al., 1967) were administered to participants. Stepwise Multiple Regression Analysis was used to determine whether fitness leaders’ levels of passion predict their job and life satisfaction. 2x2 (sex x year of job experience) Multivariate Analysis of Variance (MANOVA) was used to test the sex and year of job experience differences in passion and job satisfaction. Furthermore, 2x2 ANOVA was conducted to test sex and year of job experiences differences in life satisfaction. Results of Regression Analysis indicated that obsessive passion was a significant predictor of extrinsic job satisfaction; both harmonious and obsessive passion were a significant predictor of intrinsic job satisfaction, general job satisfaction and life satisfaction of fitness leaders (p .05). On the other hand, MANOVA and ANOVA analysis indicated that fitness leaders’ job satisfaction and life satisfaction scores didn’t differ with respect to sex and year of job experience (p>.05). It can be said that, the more fitness leaders love their profession, make more effort on it and the more they become addict on their profession, the more they satisfy with their job and life. In addition, sex and job experience has no effects on fitness leaders’ job satisfaction and life satisfaction.
Hybrid Manifold Embedding

In this brief, we present a novel supervised manifold learning framework dubbed hybrid manifold embedding (HyME). Unlike most of the existing supervised manifold learning algorithms that give linear explicit mapping functions, the HyME aims to provide a more general nonlinear explicit mapping function by performing a two-layer learning procedure. In the first layer, a new clustering strategy called geodesic clustering is proposed to divide the original data set into several subsets with minimum nonlinearity. In the second layer, a supervised dimensionality reduction scheme called locally conjugate discriminant projection is performed on each subset for maximizing the discriminant information and minimizing the dimension redundancy simultaneously in the reduced low-dimensional space. By integrating these two layers in a unified mapping function, a supervised manifold embedding framework is established to describe both global and local manifold structure as well as to preserve the discriminative ability in the learned subspace. Experiments on various data sets validate the effectiveness of the proposed method.
A Framework for Rapid Prototyping of Augmented Reality Interfaces

Augmented reality is a technique to display information superimposed on a users per- ception of reality. When developing computer systems employing augmented reality, a key consideration beyond technical parameters is the paradigm provided for the usage of the system. This thesis presents a framework to rapidly develop augmented reality applications through scripting within a specialized JavaScript runtime environment, to elaborate on and evaluate different interaction techniques and use cases. The framework provides an application developer with a platform to model virtual worlds with a condensed set of concepts from a full- featured scene graph implementa- tion, and the functionality to spatially align objects in the virtual worlds with markers recognized by a camera. Custom components can be registered to modify the virtual worlds in response to events issued in the system. The framework is subsequently used to model the application NautiNav, a mock-up of a nautical navigation software, to validate the approach and illustrate the usage of the framework.
Feature extraction and supervised learning on fMRI : from practice to theory

Until the advent of non-invasive neuroimaging modalities the knowledge of the human brain came from the study of its lesions, post-mortem analyses and invasive experimentations. Nowadays, modern imaging techniques such as fMRI are revealing several aspects of the human brain with progressively high spatio-temporal resolution. However, in order to answer increasingly complex neuroscientific questions the technical improvements in acquisition must be matched with novel data analysis methods. In this thesis we examine different applications of machine learning to the processing of fMRI data. We propose novel extensions and investigate the theoretical properties of different models. % The goal of an fMRI experiments is to answer a neuroscientific question. However, it is usually not possible to perform hypothesis testing directly on the data output by the fMRI scanner. Instead, fMRI data enters a processing pipeline in which it suffers several transformations before conclusions are drawn. Often the data acquired through the fMRI scanner follows a feature extraction step in which time-independent activation coefficients are extracted from the fMRI signal. The first contribution of this thesis is the introduction a model named Rank-1 GLM (R1-GLM) for the joint estimation of time-independent activation coefficients and the hemodynamic response function (HRF). We quantify the improvement of this approach with respect to existing procedures on different fMRI datasets. The second part of this thesis is devoted to the problem of fMRI-based decoding, i.e., the task of predicting some information about the stimuli from brain activation maps. From a statistical standpoint, this problem is challenging due to the high dimensionality of the data, often thousands of variables, while the number of images available for training is small, typically a few hundreds. We examine the case in which the target variable consist of discretely ordered values. The second contribution of this thesis is to propose the following two metrics to assess the performance of a decoding model: the absolute error and pairwise disagreement. We describe several models that optimize a convex surrogate of these loss functions and examine their performance on different fMRI datasets. Motivated by the success of some ordinal regression models for the task of fMRI-based decoding, we turn to study some theoretical properties of these methods. The property that we investigate is known as consistency or Fisher consistency and relates the minimization of a loss to the minimization of its surrogate. The third, and most theoretical, contribution of this thesis is to examine the consistency properties of a rich family of surrogate loss functions that are used in the context of ordinal regression. We give sufficient conditions for the consistency of the surrogate loss functions considered. This allows us to give theoretical reasons for some empirically observed differences in performance between surrogates.
The Conflict Trap

Several studies indicate that internal armed conflict breeds conflict. Armed conflict creates conditions that increase the chances of war breaking out again. This 'conflict trap' works through several channels: Conflicts (1) polarize populations and create deep resentments and build up the organizational capacity for future warfare, (2) undermine democratic political institutions, and (3) exacerbate the conditions that favor insurgency by increasing poverty, causing capital flight, destabilizing neighboring countries etc. This study quanti es the total e ect of the conflict trap more precisely than in earlier studies by means of a transition model and extensive use of simulations to estimate the total e ect over long time periods and across countries.
Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems

In this paper, an adaptive neural decentralized control approach is proposed for a class of multiple input and multiple output uncertain stochastic nonlinear strong interconnected systems. Radial basis function neural networks are used to approximate the packaged unknown nonlinearities, and backstepping technique is utilized to construct an adaptive neural decentralized controller. The proposed control scheme can guarantee that all signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded in the sense of fourth moment, and the tracking errors eventually converge to a small neighborhood around the origin. The main feature of this paper is that the proposed approach is capable of controlling the stochastic systems with strong interconnected nonlinearities both in the drift and diffusion terms that are the functions of all states of the overall system. Simulation results are used to illustrate the effectiveness of the suggested approach.
How linguistic structure influences and helps to predict metaphoric meaning

This paper argues that two properties of the linguistic structure of an utterance influence and partially determine whether the utterance has a metaphoric meaning that results in a stable interpretation: (i) degree of meta­ phoricity and (ii) degree of metaphoric saturation. A majority of metaphoric utter­ ances in a corpus study (66%) were unsaturated, low metaphoricity utterances that behave as expected by Max Black and the cognitive linguistics paradigm. However, a significant minority (34%) of the metaphoric utterances were satu­ rated or high metaphoricity utterances that behave partially as expected by Donald Davidson and others working in his tradition. This suggests that the di­ rect and indirect interpretation views of metaphor are not incompatible but apply to different sub­groups of metaphoric utterances. The paper then constructs a model of metaphoric meaning that makes falsifiable predictions about the inter­ pretations of metaphoric utterances in order to provide further evidence that unsaturated, low metaphoricity utterances have stable interpretations. This re­ search provides both converging evidence for the cognitive linguistic view of metaphor and also a framework for limiting its scope to most, but not all, meta­ phoric utterances.
Systematic OO Programming with Axiomatic Design

Axiomatic design offers a systematic and orderly way to proceed through the software development process. The methodology ensures that developers make the best possible design decisions by providing decision-making criteria in the form of two axioms: the independence axiom suggests that the best designs maintain the independence of the functional requirements, ensuring that the design can achieve each function without inadvertently affecting any other function; the information axiom suggests that the best designs minimize their information content. Thus, the solution with the greatest likelihood of success is the simplest solution. Software developers can determine the quality of the design based upon the degree to which it satisfies the defined functional requirements; ensure that the design satisfies each function independently and that coupling, which could cause unintended consequences, does not occur; enjoy easier job assignment and team management because they can use the system architecture diagram to assign tasks and define the interrelationships between various modules or classes; and handle software change orders quickly and easily because the system architecture identifies the modules affected by a change. Axiomatic design lets the developer document the design fully, making it easier to change or add extensions.
Process monitoring and configural display design: A neuroimaging study

In this paper an investigation is reported of neural imaging of the electrocortical activity involved when users monitored dynamic visual interfaces for process failures. For six different visual displays, the form of the display and the directness of mapping between process parameters and visual form were varied. Performance data showed best results with a configural display that mapped the process parameter most important for failure detection to a simple visual property of the display geometry. In this display condition, performance variability was lowest of all conditions and self-reports of users' monitoring strategy revealed the least variability among users. Neuroimaging results for this display condition revealed that changes in electrocortical activity were most consistent between subjects compared with other displays, while still remaining small in absolute terms. These results are interpreted in the light of previous findings in ecological psychology and control of dynamic systems and implications for their use in dynamic visual display design are outlined.
A transcription factor regulatory circuit in differentiated pancreatic cells

Mutations in the human genes encoding hepatocyte nuclear factors (HNF) 1α, 1β, 4α, and IPF1(PDX1/IDX1/STF1) result in pancreatic β cell dysfunction and diabetes mellitus. In hepatocytes, hnf4α controls the transcription of hnf1α, suggesting that this same interaction may operate in β cells and thus account for the common diabetic phenotype. We show that, in pancreatic islet and exocrine cells, hnf4α expression unexpectedly depends on hnf1α. This effect is tissue-specific and mediated through direct occupation by hnf1α of an alternate promoter located 45.6 kb from the previously characterized hnf4α promoter. Hnf1α also exerts direct control of pancreatic-specific expression of hnf4γ and hnf3γ. Hnf1α dependence of hnf4α, hnf4γ, hnf3γ, and two previously characterized distal targets (glut2 and pklr) is established only after differentiated cells arise during pancreatic embryonic development. These studies define an unexpected hierarchical regulatory relationship between two genes involved in human monogenic diabetes in the cells, which are relevant to its pathophysiology. Furthermore, they indicate that hnf1α is an essential component of a transcription factor circuit whose role may be to maintain differentiated functions of pancreatic cells.
Visualization with stylized line primitives

Line primitives are a very powerful visual attribute used for scientific visualization and in particular for 3D vector-field visualization. We extend the basic line primitives with additional visual attributes including color, line width, texture and orientation. To implement the visual attributes we represent the stylized line primitives as generalized cylinders. One important contribution of our work is an efficient rendering algorithm for stylized lines, which is hybrid in the sense that it uses both CPU and GPU based rendering. We improve the depth perception with a shadow algorithm. We present several applications for the visualization with stylized lines among which are the visualizations of 3D vector fields and molecular structures.