Miracle-dz/UniFAR-FTUs huggingface dataset:
A unified approach to short-time Fourier analysis and synthesis

Two distinct methods for synthesizing a signal from its short-time Fourier transform have previously been proposed. We call these methods the filter-bank summation (FBS) method and the overlap add (OLA) method. Each of these synthesis techniques has unique advantages and disadvantages in various applications due to the way in which the signal is reconstructed. In this paper we unify the ideas behind the two synthesis techniques and discuss the similarities and differences between these methods. In particular, we explicitly show the effects of modifications made to the short-time transform (both fixed and time-varying modifications are considered) on the resulting signal and discuss applications where each of the techniques would be most useful The interesting case of nonlinear modifications (possibly signal dependent) to the short-time Fourier transform is also discussed. Finally it is shown that a formal duality exists between the two synthesis methods based on the properties of the window used for obtaining the short-time Fourier transform.
An RDF-based action recognition framework with feature selection capability, considering therapy exercises utilizing depth cameras

Emerging cost-efficient depth sensor technologies reveal new possibilities to cope with difficulties in action recognition. Depth information improves the quality of skeleton detec- tion process, hence, pose estimation can be done more efficiently. Recently many studies fo- cus on temporal analyses over estimated skeleton poses to recognize actions. In this paper we have an inclusive study of the spatiotemporal kinematic features and propose an action recognition framework with feature selection capability to deal with the multitudinous of features by leveraging data mining capabilities of random decision forests. We describe human motion via a rich collection of kinematic feature time-series computed from the skel- etal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we train a support vector machine classifier on the selected features. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). To justify our method we test the framework on various datasets and compared it with state-of-the- art. On MSRC-12 dataset (25) (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset (28), collected by our group (10 physical exercise classes), the ac- curacy is 98%. On MSR Action3D dataset (9) (20 classes) we obtain 87% average accura- cy and for UTKinect-Action dataset (10) (10 classes) the accuracy is 92%. Other than regular activities, we also tried our approach to detect a falling person using the dataset which we recorded as an extension to our original dataset. We test how our method adjusts on different types of actions and we obtained promising results for this type of action. We discuss that our approach provides insights on the spatiotemporal dynamics of human ac- tions and can be used to as part of different applications especially for rehabilitation of pa- tients.
On the homotopy theory of spectral categories

Let SpΣ be the category of symmetric spectra, regarded as having the stable model structure. We prove that the category of small categories enriched over SpΣ admits a model category structure. The method of proof applies to other categories than symmetric spectra as well, as long as these categories are linked to the category of simplicial sets via a certain strong monoidal Quillen pair.
A German viseme-set for automatic transcription of input text used for audio-visual speech synthesis

In this paper, we introduce a German viseme inventory for visemically transcribing text according to phonetic transcribtion. A viseme set like the one presented in this work is essential for speech-driven audio-visual synthesis due to the fact that the selection of appropriate video segments is based on the visemically transcribed input text. For text-to-speech synthesis, a transcription of the input text into the phonemic representation is used, in order to avoid ambiguous meanings and to acquire the correct pronunciation of the underlying input text and to serve as labels in unit-selection-based synthesis systems. Likewise, the visual synthesis requires a transcription that represents - analogue to the phonemes - the visual counterpart which is called viseme in related literature and which also serves as a unit label in our data-driven video-realistic audio-visual synthesis system. We worked out an inventory of German viseme classes in a SAMPA-like labelling and trained a model for automatic visemic transcription of given input text.
Involvement of Polycomb Repressive Complex 2 in Maturation of Induced Pluripotent Stem Cells during Reprogramming of Mouse and Human Fibroblasts

Induced pluripotent stem cells (iPSCs) provide a reliable source for the study of regenerative medicine, drug discovery, and developmental biology. Despite extensive studies on the reprogramming of mouse and human fibroblasts into iPSCs, the efficiency of reprogramming is still low. Here, we used a bioinformatics and systems biology approach to study the two gene regulatory waves governing the reprogramming of mouse and human fibroblasts into iPSCs. Our results revealed that the maturation phase of reprogramming was regulated by a more complex regulatory network of transcription factors compared to the initiation phase. Interestingly, in addition to pluripotency factors, the polycomb repressive complex 2 (PRC2) members Ezh2, Eed, Jarid2, Mtf2, and Suz12 are crucially recruited during the maturation phase of reprogramming. Moreover, we found that during the maturation phase of reprogramming, pluripotency factors, via the expression and induction of PRC2 complex members, could silence the lineage-specific gene expression program and maintain a ground state of pluripotency in human and mouse naïve iPSCs. The findings obtained here provide us a better understanding of the gene regulatory network (GRN) that governs reprogramming, and the maintenance of the naïve state of iPSCs.
Design using simple modeling and experimental study on direct-drive disk-type motor with dense stator configuration

Electric propulsion system in marine application has been given high attention due to energy saving and low maintenance requirements. A transverse flux-type machine (TFM) with permanent magnets (PMs) has been developed as a direct-drive propulsion motor without gearbox called for high torque at low speed and high reliability simultaneously. This manuscript describes design procedure of the TFM using simple modeling focused on magnetic-circuit structure of the motor. The electrical design of the motor is based on electrical and magnetic theory. The minimalist design is shown to be advantageous for effective use in three-dimensional numerical analysis. The validity of the proposed method and the fulfillment of design requirements, torque density defined as torque to total volume ratio of more than 10kNm/m3 and power factor (PF) of more than 0.80, are verified through the numerical computation and experimental result on a prototype machine. High torque density and high PF contributes to compactness of whole motor-drive system including motor and power converter system.
Bayesian cramér-rao type bound for risk-unbiased estimation with deterministic nuisance parameters

In this paper, we derive a Bayesian Cramér-Rao type bound in the presence of unknown nuisance deterministic parameters. The most popular bound for parameter estimation problems which involves both deterministic and random parameters is the hybrid Cramér-Rao bound (HCRB). This bound is very useful especially, when one is interested in both the deterministic and random parameters and in the coupling between their estimation errors. The HCRB imposes locally unbiasedness for the deterministic parameters. However, in many signal processing applications, the unknown deterministic parameters are treated as nuisance, and it is unnecessary to impose unbiasedness on these parameters. In this work, we establish a new Cramér-Rao type bound on the mean square error (MSE) of Bayesian estimators with no unbiasedness condition on the nuisance parameters. Alternatively, we impose unbiasedness in the Lehmann sense for a risk that measures the distance between the estimator and the minimum MSE estimator which assumes perfect knowledge of the nuisance parameters. The proposed bound is compared to the HCRB and MSE of Bayesian estimators with maximum likelihood estimates for the nuisance parameters. Simulations show that the proposed bound provides tighter lower bound for these estimators.
Experiences from integrating algorithmic and systemic load balancing strategies

Load balancing increases the efficient use of existing resources for parallel and distributed applications. At a coarse level of granularity, advances in runtime systems for parallel programs have been proposed in order to control available resources as efficiently as possible by utilizing idle resources and using task migration. Simultaneously, at a finer granularity level, advances in algorithmic strategies for dynamically balancing computational loads by data redistribution have been proposed in order to respond to variations in processor performance during the execution of a given parallel application. Combining strategies from each level of granularity can result in a system which delivers advantages of both. The resulting integration is systemic in nature and transfers the responsibility of efficient resource utilization from the application programmer to the runtime system. This paper presents the design and implementation of a system that combines an algorithmic fine‐grained data parallel load balancing strategy with a systemic coarse‐grained task‐parallel load balancing strategy, and reports on recent experimental results of running a computationally intensive scientific application under this integrated system. The experimental results indicate that a distributed runtime environment which combines both task and data migration can provide performance advantages with little overhead. It also presents proposals for performance enhancements of the implementation, as well as future explorations for effective resource management. Copyright © 2001 John Wiley & Sons, Ltd.
Prosody beyond pitch and emotion in speech and music : evidence from right hemisphere brain damage and congenital amusia

This dissertation examines the relationship of prosodic processing in language and music from a new perspective, considering acoustic features that have not been studied before in the framework of the parallel study of language and music. These features are argued to contribute to the effect of ‘expressiveness’ which is here defined as the combination of the acoustic features (variation in duration, pitch, loudness, and articulation) that results in aesthetic appreciation of the linguistic and the musical acoustic stream and which is distinct from pitch, emotional and pragmatic prosody as well as syntactic structure. The present investigation took a neuropsychological approach, comparing the performance of a right temporo-parietal stroke patient IB; a congenitally amusic individual, BZ; and 24 control participants with and without musical training. Apart from the main focus on the perception of ‘expressiveness’, additional aspects of language and music perception were studied. A new battery was designed that consisted of 8 tasks; ‘speech prosody detection’, ‘expressive speech prosody’, ‘expressive music prosody’, ‘emotional speech prosody’, ‘emotional music prosody, ‘speech pitch’, ‘speech rate’, and ‘music tempo’. These tasks addressed both theoretical and methodological issues in this comparative cognitive framework. IB’s performance on the expressive speech prosody task revealed a severe perceptual impairment, whereas his performance on the analogous music task examining ‘expressiveness’ was unimpaired. BZ also performed successfully on the same music task despite being characterised as congenital amusic by an earlier study. Musically untrained controls also had a successful performance. The data from IB suggest that speech and music stimuli encompassing similar features are not necessarily processed by the same mechanisms. These results can have further implications for the approach to the relationship of language and music within the study of cognitive deficits.
A dynamically reconfigurable wavefront array architecture for evaluation of expressions

A reconfigurable wavefront array rDPA (reconfigurable datapath architecture) for evaluation of any arithmetic and logic expression is presented. Introducing a global I/O bus to the array simplifies the use as a coprocessor in a single bus oriented processor system. Fine grained parallelism is achieved using simple reconfigurable processing elements which are called datapath units (DPUs). The word-oriented datapath simplifies the mapping of applications onto the architecture. Pipelining is supported by the architecture. It is extendible to arbitrarily large arrays and dynamically in-circuit reconfigurable. The programming environment allows automatic mapping of the operators from high level descriptions. The corresponding scheduling techniques for I/O operations are explained. The rDPA can be used as reconfigurable ALU for bus oriented host based systems as well as for rapid prototyping of high speed datapaths. <<ETX>>
Spectral decomposition method of dialog state tracking via collective matrix factorization

The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches.
Relationship between Periodontal Diseases and Preterm Birth: Recent Epidemiological and Biological Data

For ten years, the incidence of preterm birth does not decrease in developed countries despite the promotion of public health programs. Many risk factors have been identified including ethnicity, age, tobacco, and infection. However, almost 50% of preterm birth causes remain unknown. The periodontal diseases are highly prevalent inflammatory and infectious diseases of tooth supporting tissues leading to an oral disability. They influence negatively general health worsening cardiovascular diseases and diabetes. Periodontal diseases have been also suspected to increase the rate of preterm birth, but data remain contradictory. The objective of this review is to present the principal results of epidemiological, biological, and interventional studies on the link between periodontal diseases and preterm birth. The conclusions of this work underline the importance for the physician/obstetrician to identify women at risk for preterm birth and to address these patients to dentist for periodontal examination and treatment in order to limit adverse pregnancy outcomes.
Context-Dependent Items Generation in Collaborative Filtering

Collaborative Filtering (CF) exploits users' recorded ratings for predicting ratings on items not evaluated yet. In classi- cal CF each item is modelled by a set of users' ratings not specifying in which contextual conditions the ratings were obtained (e.g., the time when the item was rated or the goal of the consumption). In some domains the context could heavily inuence the rating values. Therefore, a single rating for each user and item combination could be insucient for making accurate predictions. This paper introduces and an- alyzes a technique, item splitting, for dealing with context by generating new items. In this approach, the ratings' vectors of some items are split in two vectors containing the ratings collected in two alternative contextual conditions. Hence, each split generates two ctitious items that are used in the prediction algorithm instead of the original one. We eval- uated this approach on real world and semi-synthetic data sets using matrix-factorization and nearest neighbor CF al- gorithms. We also compared our approach to the classical reduction based context-aware CF approach. We show that item splitting can be benecial and its performance depends on the splitting criteria and on the inuence of the contex- tual variables on the item ratings. Moreover, we show that item splitting can perform better than the reduction based approach.
Supralinear Summation of Synaptic Inputs by an Invertebrate Neuron: Dendritic Gain Is Mediated by an “Inward Rectifier” K+ Current

Dendritic processing of glutamatergic synaptic inputs was investigated in the anterior pagoda cell of leech. We observed that below spike threshold, the amplitude of individual EPSPs decreased with hyperpolarization and that simultaneous stimulation of pairs of synaptic inputs leads to the supralinear summation of EPSPs. Voltage-clamp measurements revealed a hyperpolarization-activated, Ba2+-sensitive, fast, noninactivating K+ conductance that depends on the external [K+]. These features are those of an “inward rectifier,” Kir. Microsurgery experiments, in combination with electrophysiological measurements, revealed an inhomogeneous spatial distribution of the Kir conductance. Furthermore, on surgical removal of the neurites that contain the Kir conductance, the amplitude of EPSPs from the remaining synaptic inputs increased with hyperpolarization. A model cell, with the Kir conductance as the sole voltage-dependent conductance, reproduced qualitatively the observed voltage dependence of individual EPSPs as well as the supralinear summation of EPSP pairs.
International Journal of Physical Sciences the Effects of Heat Treatment on Some Mechanical Properties of Laminated Black Pine (pinus Nigra)

Black pine (Pinus nigra) has high industrial usage potential and large plantation in Turkey. Larch wood was treated with heat to determine its mechanical properties, such as bending strength and pressure and order to determine its adhesion strength. Laminating adhesives are used for the construction of polyvinyl acetate and polyurethane. Heat treatment was applied to the samples at 100-150°C for 4 h in drying oven. In the result of the experiments, heat treated samples give better results due to temperature increase.
A Distributional Analysis of an Environmental Tax Shift

I use data from the 1994 Consumer Expenditure Survey as well as other sources to measure the distributional impact of green tax reforms and consumption tax reforms using both annual income and lifetime income approaches to rank households. A modest tax reform in which environmental taxes equal to 10% of federal receipts are collected has a negligible impact on the income distribution when the funds are rebated to households through reductions in the payroll tax and personal income tax. The degree of income shifting can be adjusted with changes in how the revenues are returned to households and it is possible to increase the progressivity of the tax system with an environmental tax reform. I then compare these reforms to a reform that shifts the tax base from income to consumption. In this case, it is difficult to maintain the level of progressivity that exists under the current income tax although ways exist by which the regressivity of the reform could be blunted. Whether the long term growth gains from consumption tax reform would offset the initial increase in regressivity remains to be determined. A shift to greater reliance on environmental taxes would reduce the progressivity of the tax system. This analysis indicates that reforms can be designed to preserve the existing income distribution. In fact, it appears to be easier to maintain distributional neutrality with a Green tax reform than with a comprehensive consumption tax reform.
Privacy-preserving assessment of social network data trustworthiness

Extracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.
Narrative Visualization: A Case Study of How to Incorporate Narrative Elements in Existing Visualizations

Stories have long been used to convey information, cultural values, and experiences. Narratives not only have been the main way people make sense of the world, but also have been the easiest way humans found out to share complex information. However, today we are confronted with the problem of the amount of information available, which sometimes is hard to cope with. Combining storytelling with visualization has been pointed out as an efficient method to represent and make sense of data, at the same time allowing people to relate with the information. In this paper, we explore the benefits of adding storytelling to visualizations. Drawing on case studies from news media to visualization research websites, we identified possible strategies to introduce storytelling in visualizations such as adding short stories or narrative elements using annotations and using time to introduce the feeling of storytelling or story-flow.
Wikipedia Edit Number Prediction based on Temporal Dynamics Only

In this paper, we describe our approach to the Wikipedia Participation Challenge which aims to predict the number of edits a Wikipedia editor will make in the next 5 months. The best submission from our team, "zeditor", achieved 41.7% improvement over WMF's baseline predictive model and the final rank of 3rd place among 96 teams. An interesting characteristic of our approach is that only temporal dynamics features (i.e., how the number of edits changes in recent periods, etc.) are used in a self-supervised learning framework, which makes it easy to be generalised to other application domains.