Miracle-dz/UniFAR-FTUs huggingface dataset:
Static and dynamic modulation spectrum for speech recognition

We present a feature extraction technique based on static and dynamic modulation spectrum derived from long-term envelopes in sub-bands. Estimation of the sub-band temporal envelopes is done using Frequency Domain Linear Prediction (FDLP). These sub-band envelopes are compressed with a static (logarithmic) and dynamic (adaptive loops) compression. The compressed sub-band envelopes are transformed into modulation spectral components which are used as features for speech recognition. Experiments are performed on a phoneme recognition task using a hybrid HMM-ANN phoneme recognition system and an ASR task using the TANDEM speech recognition system. The proposed features provide a relative improvements of 3.8 % and 11.5 % in phoneme recognition accuracies for TIMIT and conversation telephone speech (CTS) respectively. Further, these improvements are found to be consistent for ASR tasks on OGI-Digits database (relative improvement of 13.5 %).
User Evaluation of God-like Interaction Techniques

God-like interaction is a metaphor for improved communication of situational and navigational information between outdoor users, equipped with mobile augmented reality systems, and indoor users, equipped with tabletop projector display systems. This paper presents the results of a user evaluation that explores user experience to a subset of the god-like interaction metaphor. The tasks performed by the participants were designed around known problems in the AR community. The results of the evaluation are intended to help define the boundaries in which the god-like interaction metaphor is practical for communication of navigational and situational information. This paper reports the findings of the evaluation as well as recommendations for further development of the interaction metaphor.
Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1

Brief periods of sleep loss have long-lasting consequences such as impaired memory consolidation. Structural changes in synaptic connectivity have been proposed as a substrate of memory storage. Here, we examine the impact of brief periods of sleep deprivation on dendritic structure. In mice, we find that five hours of sleep deprivation decreases dendritic spine numbers selectively in hippocampal area CA1 and increased activity of the filamentous actin severing protein cofilin. Recovery sleep normalizes these structural alterations. Suppression of cofilin function prevents spine loss, deficits in hippocampal synaptic plasticity, and impairments in long-term memory caused by sleep deprivation. The elevated cofilin activity is caused by cAMP-degrading phosphodiesterase-4A5 (PDE4A5), which hampers cAMP-PKA-LIMK signaling. Attenuating PDE4A5 function prevents changes in cAMP-PKA-LIMK-cofilin signaling and cognitive deficits associated with sleep deprivation. Our work demonstrates the necessity of an intact cAMP-PDE4-PKA-LIMK-cofilin activation-signaling pathway for sleep deprivation-induced memory disruption and reduction in hippocampal spine density. DOI: http://dx.doi. org/10.7554/eLife.13424. 001
Performance test of a refrigerator cooled magnet fabricated using Bi-2212 multilayer superconducting tapes

A stacked pancake coil was fabricated by Ag-alloy sheathed Bi-2212 multilayer tapes and tested using a refrigerator. The magnet consisted of ten double pancakes wound with 100 m class tapes. The I/sub c/ values of the tapes varied from 80 A to 150 A at 4.2 K and 10 T because of the scattering of the thickness in the superconducting layers. The magnet carried a critical current of 57 A at 12 K and generated B/sub max/ of 1.4 T in a 60 mm bore. At 12 K, the magnet could operate with an operating current of 45 A which corresponded to about 80% of I/sub c/. Thermal runaway was not observed even with a ramp rate of 10 T/min.
Towards a Ryll‐Nardzewski‐type theorem for weakly oligomorphic structures

A structure is called weakly oligomorphic if its endomorphism monoid has only finitely many invariant relations of every arity. The goal of this paper is to show that the notions of homomorphism‐homogeneity, and weak oligomorphy are not only completely analogous to the classical notions of homogeneity and oligomorphy, but are actually closely related. We first prove a Fraïssé‐type theorem for homomorphism‐homogeneous relational structures. We then show that the countable models of the theories of countable weakly oligomorphic structures are mutually homomorphism‐equivalent (we call first order theories with this property weakly ω‐categorical). Furthermore we show that every weakly oligomorphic homomorphism‐homogeneous structure contains (up to isomorphism) a unique homogeneous, homomorphism‐homogeneous core, to which it is homomorphism‐equivalent. As a consequence we obtain that every countable weakly oligomorphic structure is homomorphism‐equivalent to a finite or ω‐categorical structure. As a corollary we obtain a characterization of positive existential theories of weakly oligomorphic structures as the positive existential parts of ω‐categorical theories.
Random Forest Classification for Detecting Android Malware

Internet connected smartphone devices play a crucial role in the application domain of Internet of Things. These devices are being widely used for day-to-day activities such as remotely controlling lighting and heating at homes, paying for parking, and recently for paying for goods using saved credit card information using Near Field Communication (NFC). Android is the most popular smartphone platform today. It is also the choice of malware authors to obtain secure and private data. In this paper we exclusively apply the machine learning ensemble learning algorithm Random Forest supervised classifier on an Android feature dataset of 48919 points of 42 features each. Our goal was to measure the accuracy of Random Forest in classifying Android application behavior to classify applications as malicious or benign. Moreover, we wanted to focus on detection accuracy as the free parameters of the Random Forest algorithm such as the number of trees, depth of each tree and number of random features selected are varied. Our experimental results based on 5-fold cross validation of our dataset shows that Random Forest performs very well with an accuracy of over 99 percent in general, an optimal Out-Of-Bag (OOB) error rate [3] of 0.0002 for forests with 40 trees or more, and a root mean squared error of 0.0171 for 160 trees.
Spectral Enhancement of Whispered Speech Based on Probability Mass Function

Whispered speech can be effectively used for quiet and private communications over mobile phones and is also the communication means for ENT patients under a regime of voice rest. The reconstruction of natural sounding speech from such whispers can be useful for several types of application across different scientific fields ranging from communications to biomedical engineering. Despite the useful applications for a such technology, the reconstruction of natural speech from whispers has received relatively little research effort to date. This paper presents novel methods for spectral enhancement and formant smoothing with the aim of attaining more natural sounding speech within the reconstruction process. The proposed approach uses a probability mass-density function to identify a reliable formant trajectory through whispers and apply vocal modifications accordingly. Subjective evaluation experiments were performed, and are reported, to assess the performance of the techniques. A method for the near real-time conversion of whispers to normal phonated speech through a modified CELP codec has been discussed in our previously published work which, the proposed formant modification approach in this paper builds upon.
On-line identification of language measure parameters for discrete event supervisory control

The concept and construction of quantitative measures of regular languages have been recently reported in literature. Major applications of the language measure include quantitative analysis and synthesis of discrete-event supervisory control algorithms. This paper formulates and experimentally validates an on-line parameter identification procedure for the language measure based on a deterministic finite state automation model of the physical plant. The recursive algorithm of this identification procedure relies on observed simulation and/or experimental data. Efficacy of the language parameter identification procedure is demonstrated for real-time supervisory control by experiments on a mobile robotic system.
Popular music access: The Sony music browser

The intentionally ambiguous expression "Popular Music Browser" reflects the two main goals of this project, which started in 1998, at Sony Computer Science Laboratories. First, we are interested in human-centered issues related to browsing "Popular Music. " Popular here means that the music accessed is distributed widely and known to many listeners. Second, we consider "popular browsing" of music, i.e., making music accessible to nonspecialists (music lovers) and allowing sharing of musical tastes and information within communities, departing from the usual, single-user view of digital libraries. This research project covers all areas of the music-to-listener chain, from music description, descriptor extraction from the music signal, or data mining techniques, similarity-based access, and novel music retrieval methods such as automatic sequence generation, and to user interface issues. This article describes the scientific and technical issues at stake and the results obtained, and is illustrated by prototypes developed within the European IST project Cuidado.
Fraud detection in telecommunication industry using Gaussian mixed model

The prevalence of fraud activities in telecommunication industry has reached a critical point so that efficient algorithms to identify such activities are greatly needed. In this article, we propose a new fraud detection algorithm using Gaussian mixed model (GMM), a probabilistic model successfully used in speech recognition problem. The expectation maximization algorithm is used to estimate the parameter of the model such that the initial values of the algorithm is determined using the kernel method. Using data obtained from one of the leading telecommunication companies in Malaysia, we show that the proposed algorithm has successfully not only detected fraud calls as suspected by the company, but also to identify suspicious calls which can be candidates of fraud call. The proposed algorithm is easy to implement with a great potential to be extended to detect (billed/outgoing) fraud calls and hence reduces the lost incurred by the telecommunication companies.
Novel approaches to ultrafast fiber laser design for ablation-cooled material removal

NOVEL APPROACHES TO ULTRAFAST FIBER LASER DESIGN FOR ABLATION-COOLED MATERIAL REMOVAL Saniye Sinem YILMAZ Ph.D. in Physics Advisor: Fatih Ömer İlday September 2016 The past few decades in particular have witnessed the tremendous beneficial impact of innovations in laser technology ranging from biomedical to industrial applications in response to enhancing community’s quality of life. From the beginning, laser technology, especially ultrafast lasers have provided a very convenient platform for producing need-based laser signals, which are addressed to a wide variety of scientific and technological problems. However, there is a scarcity of utilization of ultrafast lasers as well-developed tools for various applications, mostly in industry and research laboratories due to their complexity, low reliability and high cost as a result of the dominance of solid-state lasers. Fiber lasers, on the other hand, are inherently inexpensive, compact in size and robust in their operation under harsh conditions. Applications of ultrafast laser material processing have become extremely diverse, yet ultrafast material processing is still extremely complex, costly and quite slow in terms of material removal, which is particularly taxing for biological tissue removal, rendering ultrafast lasers uncompetitive compared to mechanical techniques. This thesis represents a series of work about developing fiber laser systems which address this technological problem. The motivation of this thesis is to develop fiber laser systems for applying the ablation cooled laser material removal idea which has recently proposed by our group [1] for tissue and material processing. Ablation cooling becomes significant above a certain repetition rate, which depends on the thermal diffusivity of the target material. Besides, the speed with which the laser beam can be repositioned over a target is limited. As a remedy, burst-mode operation, also proposed by our group [2] has been implemented, where the laser produces groups of high repetition rate pulses, which are, in turn, repeated with a lower frequency. Consequently, the burst-mode fiber laser system operating at 1 μm was demonstrated with an all-fiber architecture and we scaled it to 100 MHz intra-burst repetition rate and 1 MHz burst repetition rate
A Study on Cutting Force of Machining In Situ TiB2 Particle-Reinforced 7050Al Alloy Matrix Composites

In situ TiB2 particle-reinforced 7050Al alloy matrix composites are a new category of particulate metal matrix composites with improved mechanical and physical properties. At present, the study of machining in situ TiB2/Al composite is limited and no specific study has been presented on cutting force. Based on previous work, experimental investigation of cutting in situ TiB2/Al composite was carried out in this study to investigate the cutting force, shear angle, mean friction angle, and shear stress. The results indicated that the feed rate, instead of cutting speed, has a significant influence on the shear angle, mean friction angle, shear stress, and forces, which is different from cutting ex situ SiC/Al composites. Meanwhile, based on Merchant’s theory, a force model, which consists of chip formation and ploughing force, was established to have a better understanding of force generation. A comparison of the results show an acceptable agreement between the force model and experiments. Additionally, at varying feed rates, the linear relationship between the shear angle and mean friction angle is still suitable for cutting in situ TiB2/7050Al alloy composites.
MERLIN -- A Tool for Multi-party Privacy-Preserving Record Linkage

Many organizations, including businesses, government agencies and research organizations, are collecting vast amounts of data, which are stored, processed and analyzed to mine interesting patterns and knowledge to support efficient and quality decision making. In order to improve data quality and to facilitate further analysis, many application domains require information from multiple sources to be integrated and combined. The process of matching and aggregating records that relate to the same entities from different data sources without compromising their privacy is known as 'privacy-preserving record linkage' (PPRL), 'blind data linkage' or 'private record linkage'. In this paper we present MERLIN, an online tool that demonstrates various PPRL methods in a multi-party context. In this demonstration we show different private multi-party blocking and matching techniques, and illustrate the usability of MERLIN by presenting quality and performance measures of various PPRL methods. We believe MERLIN will help practitioners and researchers to better understand the pipeline of the PPRL process, to compare different multi-party PPRL techniques, and to determine the best technique to use for their needs.
Lane marking detection based on Convolution Neural Network from point clouds

Lane marking detection, as a key technique for the Highly Automated Driving (HAD) Map, has drawn much attention recently. Traditional methods mainly focused on processing color images, where the performance was affected by illumination variation, over-exposure and occlusion severely. Confronted with above problems, this paper proposes a lane detection algorithm based on Convolution Neural Network (CNN) from point clouds. Our contributions are twofold. On one hand, a CNN framework via gradual up-sampling is introduced, where robust and accurate detection results are achieved. Before applying the CNN model, we also design pre-processing steps, including point clouds registration, the road surface segmentation and orthogonal projection. On the other hand, we propose to analyze the layout of lanes by utilizing the global information and domain knowledge. Hence, false detections caused by ground arrows and texts could be eliminated. Visual and quantitative experiments demonstrate the effectiveness of our algorithm.
Adrenomedullin Inhibits Adipogenesis Under Transcriptional Control of Insulin

We generated preadipocyte cell lines impaired in adrenomedullin production through integration of an adrenomedullin small interfering RNA expression vector. The reduction of adrenomedullin synthesis strongly accelerated adipose differentiation. These results were bolstered when overexpression of active adrenomedullin peptide led to delayed differentiation. Therefore, we propose that adrenomedullin is an antiadipogenic factor. Moreover, we checked whether insulin, a proadipogenic factor, regulates expression of adrenomedullin. We observed that insulin had an inhibitory effect on adrenomedullin expression in isolated human adipocyte cells. This response was dose dependent and was reversed by resistin, a new anti-insulin agent. We quantified circulating adrenomedullin in healthy obese patients and observed a threefold increase of adrenomedullin compared with lean patients. Furthermore, adrenomedullin plasma levels are negatively correlated to plasma insulin levels in these obese patients. The insulin inhibitory response was also observed in vivo in Sprague-Dawley rats but not in the insulin-resistant Zucker rat, suggesting that adrenomedullin expression is upregulated in insulin-resistant adipose cells. Using adrenomedullin promoter-luciferase reporter gene constructs, we have shown that the adrenomedullin response to insulin is mediated by insulin-responsive elements. These findings provide new insight into fat mass development and the relationship between obesity and elevated circulating adrenomedullin levels in diabetic patients.
Metapopulation Dynamics Enable Persistence of Influenza A, Including A/H5N1, in Poultry

Highly pathogenic influenza A/H5N1 has persistently but sporadically caused human illness and death since 1997. Yet it is still unclear how this pathogen is able to persist globally. While wild birds seem to be a genetic reservoir for influenza A, they do not seem to be the main source of human illness. Here, we highlight the role that domestic poultry may play in maintaining A/H5N1 globally, using theoretical models of spatial population structure in poultry populations. We find that a metapopulation of moderately sized poultry flocks can sustain the pathogen in a finite poultry population for over two years. Our results suggest that it is possible that moderately intensive backyard farms could sustain the pathogen indefinitely in real systems. This fits a pattern that has been observed from many empirical systems. Rather than just employing standard culling procedures to control the disease, our model suggests ways that poultry production systems may be modified.
Emeralds: a small-memory real-time microkernel

EMERALDS (Extensible Microkernel for Embedded, ReAL-time, Distributed Systems) is a real-time microkernel designed for small-memory embedded applications. These applications must run on slow (15-25MHz) processors with just 32-128 kbytes of memory, either to keep production costs down in mass-produced systems or to keep weight and power consumption low. To be feasible for such applications, the OS must not only be small in size (less than 20 kbytes), but also have low-overhead kernel services. Unlike commercial embedded OSs which rely on carefully-crafted code to achieve efficiency, EMERALDS takes the approach of re-designing the basic OS services of task scheduling, synchronization, communication, and system call mechanism by using characteristics found in small-memory embedded systems, such as small code size and a priori knowledge of task execution and communication patterns. With these new schemes, the overheads of various OS services are reduced 20-40% without compromising any OS functionality.
A performance benchmark for NetFlow data analysis on distributed stream processing systems

Modern distributed stream processing systems can potentially be applied to real time network flow processing. However, differences in performance make some systems more suitable than others for being applied to this domain. We propose a novel performance benchmark, which is based on common security analysis algorithms of NetFlow data to determine the suitability of distributed stream processing systems. Three of the most used distributed stream processing systems are bench-marked and the results are compared with NetFlow data processing challenges and requirements. The benchmark results show that each system reached a sufficient data processing speed using a basic deployment scenario with little to no configuration tuning. Our benchmark, unlike any other, enables the performance of small structured messages to be processed on any stream processing system.
Responsive Hydrogels for Label-Free Signal Transduction within Biosensors

Hydrogels have found wide application in biosensors due to their versatile nature. This family of materials is applied in biosensing either to increase the loading capacity compared to two-dimensional surfaces, or to support biospecific hydrogel swelling occurring subsequent to specific recognition of an analyte. This review focuses on various principles underpinning the design of biospecific hydrogels acting through various molecular mechanisms in transducing the recognition event of label-free analytes. Towards this end, we describe several promising hydrogel systems that when combined with the appropriate readout platform and quantitative approach could lead to future real-life applications.
Long-term outcome of patients with acquired primary idiopathic pure red cell aplasia receiving cyclosporine A. A nationwide cohort study in Japan for the PRCA Collaborative Study Group.

BACKGROUND AND OBJECTIVES Cyclosporine A (CsA) has become one of the leading agents for the treatment of pure red cell aplasia (PRCA). However, further studies are necessary to determine the relapse-free survival (RFS) and overall survival (OS) of patients treated with this drug, the minimum duration of therapy for induction of remission, and whether or not there is need for maintenance treatment. DESIGN AND METHODS We conducted a nationwide survey in Japan. From a total of 185 patients (with 73 primary idiopathic PRCA and 112 with secondary PRCA), we evaluated 62 patients with primary idiopathic PRCA for this report. RESULTS The remission induction therapy for these patients included CsA (n=31), corticosteroids (CS) (n=20) or other drugs (n=11). CsA and CS produced remissions in 23 (74%) and 12 (60%) patients, respectively. The salvage treatment produced remissions in 58 patients (94%). Forty-one and 15 patients were maintained on CsA+/-CS (CsA-containing group) or CS alone (CS group), respectively. The median RFS in the CsA-containing group was 103 months, longer than that seen in the CS group (33 months) (p<0.01). Of 14 patients whose CsA was discontinued, 12 patients (86%) relapsed after a median of 3 months (range 1.5 to 40 months), while only 3 of 27 patients (11%) relapsed during CsA-containing maintenance therapy. Thus, the discontinuance of maintenance therapy was strongly correlated with relapse (p<0.001). Four patients in the CsA-containing group died; however, the OS of this group was not significantly different from that of the CS-groups (p=0.104). INTERPRETATION AND CONCLUSIONS CsA-containing regimens sustain prolonged RFS more effectively than CS in primary idiopathic PRCA and seem to be important to prevent relapse.