Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech enhancement based on noise compensated magnitude spectrum

In this paper, a speech enhancement method based on noise compensation performed on short time magnitude spectra is presented following the geometric approach. Here the noise estimate to be subtracted from the noisy speech spectrum is proposed to be determined by exploiting the low frequency regions of noisy speech of the current frame rather than depending only on the initial silence frames. We argue that this approach of noise estimation offers the capability of tracking the time variation of the non-stationary noise. A noise estimation confidence parameter is also introduced to prevent the subtraction of the overestimated noise. A procedure is thus formulated to compensate for the distortion in the magnitude spectrum of the noisy speech signal. The noise compensated magnitude spectrum is then recombined with the noisy unchanged phase spectrum to produce a modified complex spectrum prior to synthesize an enhanced frame. Extensive simulations are carried out using NOIZEUS database in order to evaluate the performance of proposed method. It is shown in terms of objective measures, spectrogram analysis and informal subjective listening test that the proposed method consistently outperforms one of the state-of-the-art methods of speech enhancement from noisy speech corrupted by babble or car noise at high as well as very low levels of SNR.
Is there an optimum number needed to retrieve to justify inclusion of a database in a systematic review search?

OBJECTIVE To determine whether calculation of a 'Number Needed to Retrieve' (NNTR) is possible and desirable as a means of evaluating the utility of a database for systematic review. METHODS To determine an overall NNTR, eight systematic reviews were tracked to determine how many abstracts were retrieved compared to the number of articles meeting the inclusion criteria. An NNTR was calculated for each database searched to measure the utility of including it in systematic review searches. RESULTS Across eight systematic reviews, 17 378 abstracts were reviewed. Of these, 122 met the inclusion criteria for their reviews resulting in an overall NNTR of 142. Individual reviews had an NNTR range of 28-310. Three databases delivered unique results (medline, cinahl and globalhealth). The majority of the included studies appeared in multiple databases. Only five articles were found in a single database. CONCLUSIONS This research offers a proof of concept of 'NNTR'. While the eight review NNTRs varied widely, all were consistent with the range initially reported by Booth. Included articles consistently appeared in multiple databases, suggesting that duplicate abstracts should be screened first as these are likely to include highly relevant, high-quality results.
Monte Carlo Sampling Methods Using Markov Chains and Their Applications

SUMMARY A generalization of the sampling method introduced by Metropolis et al. (1953) is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates. Examples of the methods, including the generation of random orthogonal matrices and potential applications of the methods to numerical problems arising in statistics, are discussed. For numerical problems in a large number of dimensions, Monte Carlo methods are often more efficient than conventional numerical methods. However, implementation of the Monte Carlo methods requires sampling from high dimensional probability distributions and this may be very difficult and expensive in analysis and computer time. General methods for sampling from, or estimating expectations with respect to, such distributions are as follows. (i) If possible, factorize the distribution into the product of one-dimensional conditional distributions from which samples may be obtained. (ii) Use importance sampling, which may also be used for variance reduction. That is, in order to evaluate the integral J = X) p(x)dx = Ev(f), where p(x) is a probability density function, instead of obtaining independent samples XI, . , Xv from p(x) and using the estimate J, = Zf(xi)/N, we instead obtain the sample from a distribution with density q(x) and use the estimate J2 = Y{f(xj)p(x1)}/{q(xj)N}. This may be advantageous if it is easier to sample from q(x) thanp(x), but it is a difficult method to use in a large number of dimensions, since the values of the weights w(xi) = p(x1)/q(xj) for reasonable values of N may all be extremely small, or a few may be extremely large. In estimating the probability of an event A, however, these difficulties may not be as serious since the only values of w(x) which are important are those for which x -A. Since the methods proposed by Trotter & Tukey (1956) for the estimation of conditional expectations require the use of importance sampling, the same difficulties may be encountered in their use. (iii) Use a simulation technique; that is, if it is difficult to sample directly from p(x) or if p(x) is unknown, sample from some distribution q(y) and obtain the sample x values as some function of the corresponding y values. If we want samples from the conditional dis
Expression of the Baculovirus p35 Gene Inhibits Mammalian Neural Cell Death

Expression of the apoptosis suppressor genep35, derived from the baculovirus Autographa californica nuclear polyhedrosis virus, markedly inhibited the cell death of stably transfected mammalian neural cells whether the cell death was induced by glucose withdrawal, calcium ionophore, or serum withdrawal. The p35 protein, which is required to block virus‐induced apoptosis of cultured insect cells, is only the second gene product shown to block mammalian neural cell death, with Bcl‐2 being the first. Because there is no apparent homology between p35 and Bcl‐2, the existence of a cellular death program that may be modulated at multiple points is suggested/Furthermore, these findings demonstrate that the putative cellular death program is conserved across species and cell types.
3D IC architecture for high density memories

3D ICs for high-density memories have significant benefits compared to conventional memories. The first one is high productivity. An area for memory arrays is not required on the semiconductor substrate and the area for the memory control logic can be further reduced for an optimized logic process. Hence, about 4 times more die-per-wafer can be expected for DRAMs with a cell efficiency of about 50%, leading to reduced fab tool investment with increased productivity. Furthermore, the process is optimized for both logic and memory cells because they are processed sequentially. It is well known that processes for logic and memory, especially DRAM and flash, are not compatible. Using 3D ICs, process incompatibility problems are easily solved and SoC (System-on-a-Chip) can be implemented with various embedded memories. An additional advantage is the small form factor. As die sizes shrink for 3D ICs, the yield will increase rapidly, especially important for FPGAs with distributed memories and high-performance CPUs with large cache memories. 3D ICs for high-density memories will extend the lifespan of low-cost CMOS memories.
Round window vibroplasty in chronic ear surgery: comparison with conventional hearing rehabilitation

Abstract Conclusion: Functional hearing results with round window vibroplasty in chronically disabled middle ears were comparable and, at high frequencies, superior to the results achieved with previously used conventional hearing aids even after extended surgery. Soft tissue transfer appears to be more important than floating mass transducer (FMT) alignment with the round window membrane (RWM) for efficient coupling or sonoinversion. Objectives: To evaluate the functional hearing results of an active middle ear implant (AMEI) to the round window niche (RWN). The results were compared with previously used conventional hearing aids. The position of the FMT was determined by cone-beam computed tomography (CBCT). Methods: This was a prospective cohort study carried out in a tertiary referral center. Seven patients with severe middle ear disease were implanted with an AMEI with round window application. The postoperative hearing outcome was compared with preoperative hearing using unaided and conventionally aided conditions. The results were correlated with the physical/geometric relation of the FMT to the RWM as determined with CBCT. Results: Dislocation of the FMT was not observed. One patient was re-implanted due to accidental damage to the electrode. In all patients, the pertinent functional hearing results were achieved and were comparable to previous rehabilitation results.
Analisis sentimen film pada Twitter berbahasa Indonesia menggunakanEnsemble Features dan Naïve Bayes

Analisis sentimen atau opinion mining merupakan salah satu topik penelitian terkini di bidang pengolahan informasi yang bertujuan untuk mengetahui apakah polaritas sebuah data yang berbentuk teks (dokumen, kalimat, paragraf) akan mengarah pada bersifat positif, negatif, ataupun netral. Dokumen teks yang digunakan dalam penelitian berasal dari Twitter tentang opini film berbahasa Indonesia. Twitter memiliki banyak fitur yang mana salah satunya dapat dimanfaatkan sebagai sarana untuk mengetahui opini film dari seluruh pengguna secara umum tidak terbatas oleh pecinta film saja. Dalam hal ini analisis sentimen mempunyai peranan penting untuk menunjang sarana tersebut. Metode yang digunakan adalah Naive Bayes dengan menggunakan Ensemble Features sebagai pembaharuan fitur selain Bag of Words Features. Jenis-jenis Ensemble Features antara lain Twitter specific features, textual features, part of speech features, dan lexicon based features. Data yang digunakan pada penelitian ini sebanyak 500 data yang dibagi menjadi dua jenis data dengan perbandingan 70% untuk data latih dan 30% untuk data uji. Hasil akurasi sistem diperoleh dari analisis sentimen dengan metode Naive Bayes dan Ensemble Features tanpa bag of words features sebesar 61,33%, precision sebesar 0,6308, recall sebesar 0,5467 dan f-measure sebesar 0,5857. Sedangkan hasil akurasi sistem menggunakan Ensemble Features dan Bag of Words Features sebesar 88,67 %, precision sebesar 0,9143, recall sebesar 0,8583, dan f-measure sebesar 0,8828.
Semi-supervised Component Analysis

Object re-identification techniques are essential to improve the identification performance in video surveillance tasks. The re-identification problem is equal to a multi-view problem that an unknown individual is identified across spatially disjoint data. For the re-identification techniques, several multi-view feature transformation methods have been proposed. These methods are formulated by the supervised learning framework and show the better performances in multi-view classification tasks in which the training data are observed by the different sensors. However, in the reidentification tasks, these methods may not be required because the simple feature transformation method such as linear discriminant analysis (LDA) shows the reasonable identification rates. In this paper, we propose a novel semi supervised feature transformation method, which is formulated as a natural coupling with PCA and LDA modeled by the graph embedding framework. Our method showed best re-identification performances compared with other feature transformation methods.
Diffusion-Weighted Imaging in Fetuses with Unilateral Cortical Malformations and Callosal Agenesis

SUMMARY: DWI was performed in fetuses with callosal agenesis and unilateral cortical malformations. ADC values were retrospectively measured in the developing white matter underlying the cortical malformation and compared with the corresponding contralateral white matter. In all 3 patients, ADC values were lower under the areas of cortical malformation compared with the normal contralateral side. Our findings suggest that there are structural differences in the developing white matter underlying areas of cortical malformation.
Interpolation of sparse graph signals by sequential Adaptive Thresholds

This paper considers the problem of interpolating signals defined on graphs. A major presumption considered by many previous approaches to this problem has been low-pass/band-limitedness of the underlying graph signal. However, inspired by the findings on sparse signal reconstruction, we consider the graph signal to be rather sparse/compressible in the Graph Fourier Transform (GFT) domain and propose the Iterative Method with Adaptive Thresholding for Graph Interpolation (IMATGI) algorithm for sparsity promoting interpolation of the underlying graph signal. We analytically prove convergence of the proposed algorithm. We also demonstrate efficient performance of the proposed IMATGI algorithm in reconstructing randomly generated sparse graph signals. Finally, we consider the widely desirable application of recommendation systems and show by simulations that IMATGI outperforms state-of-the-art algorithms on the benchmark datasets in this application.
Using Deep Neural Network Approximate Bayesian Network

We present a new method to approximate posterior probabilities of Bayesian Network using Deep Neural Network. Experiment results on several public Bayesian Network datasets shows that Deep Neural Network is capable of learning joint probability distri- bution of Bayesian Network by learning from a few observation and posterior probability distribution pairs with high accuracy. Compared with traditional approximate method likelihood weighting sampling algorithm, our method is much faster and gains higher accuracy in medium sized Bayesian Network. Another advantage of our method is that our method can be parallelled much easier in GPU without extra effort. We also ex- plored the connection between the accuracy of our model and the number of training examples. The result shows that our model saturate as the number of training examples grow and we don't need many training examples to get reasonably good result. Another contribution of our work is that we have shown discriminative model like Deep Neural Network can approximate generative model like Bayesian Network.
The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience*

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online.
Systematic Risk, CDS Spread and Market Integration: An Empirical Investigation

This study is the first to analyse the key drivers of changes in the CDS spreads of Australian companies. Our findings indicate that firm-specific measures lead changes in spreads in less than 16 per cent of cases. These results suggest that much of the movement in spreads can be explained by international developments, with the S and P 500 and CBOE VIX indices being dominant factors. This also provides strong evidence of a high degree of integration in the markets for credit risk, which was widely felt during the global financial crisis.
Carriage of Methicillin-Resistant Staphylococci and Their SCCmec Types in a Long-Term-Care Facility

ABSTRACT Following an outbreak caused by staphylococcal cassette chromosome mec (SCCmec) type V methicillin (meticillin)-resistant Staphylococcus aureus (MRSA), a point-prevalence survey of the nasal carriage of staphylococci was conducted in a long-term-care facility in northern Finland in 2004. The focus was directed at methicillin-resistant coagulase-negative staphylococci (MR-CNS) and their SCCmec elements. A nasal swab was taken from 76 of the 80 residents 6 months after the onset of the outbreak. Staphylococcal isolates were identified by conventional methods and the GenoType Staphylococcus test, and their SCCmec elements were analyzed. Of the 76 individuals, 24 (32%) carried S. aureus and 67 (88%) CNS in their nostrils. Of the CNS carriers, 41 (61%) had at least one mecA-positive MR-CNS, and two individuals (3%) had both MRSA and methicillin-resistant Staphylococcus epidermidis (MRSE). Among the 61 MR-CNS isolates identified, 49 (80%) were MRSE. The distribution of the SCCmec types was diverse: 20 (33%) were of type IV, 11 (18%) of type V, 4 (6%) of type I or IA, 3 (4%) of type II, and 23 (38%) of new types (with six different combinations of ccr and other mec genes or only mecA). Both of the individuals with MRSA and MRSE shared SCCmec type V among their isolates. Nasal MR-CNS carriage was common among the residents of this long-term-care facility. A variety of SCCmec types, including many new types, were identified among the MR-CNS strains. The horizontal transfer of SCCmec elements is speculated based on the sharing of SCCmec type V between MRSA and MRSE.
Forecast Performance of the Taiwan Weighted Stock Index

This research introduces the following to establish a Taiwan Weighted stock index (TAIEX) prediction model: intervention analysis integrated into the ARIMA–GARCH model, error correction model (ECM), intervention analysis integrated into the transfer function model, the simple average combination forecasting model, and the minimum error combination forecasting model. The results show that intervention analysis integrated into the transfer function model yields a more accurate prediction model than ECM and intervention analysis integrated into the ARIMA–GARCH model. The minimum error combination forecasting model can improve prediction accuracy much better than non-combination models and also maintain robustness. Intervention analysis integrated into the transfer function model shows that the TAIEX is affected by external factors, the INDU, the exchange rate, and the consumer price index; therefore, facing the different issues of the TAIEX, the government could pursue some macroeconomic policies to reach the goals of policies.
Indirect inference under stochastic restrictions

This paper investigates inference methods to introduce prior information in econometric modelling. The set up includes the nonlinear least squares and indirect inference estimators. The goal is to show that stochastic restrictions method estimates can be asymptotically more efficient than estimates ignoring prior information and can achieve efficiency of the restricted estimate if prior information grows faster than the sample information in the asymptotics. Finally, the proposed approach is applied to a macroeconomics model where high efficiency gains are shown.
Structure of Alba: an archaeal chromatin protein modulated by acetylation

Eukaryotic DNA is packaged into nucleosomes that regulate the accessibility of the genome to replication, transcription and repair factors. Chromatin accessibility is controlled by histone modifications including acetylation and methylation. Archaea possess eukary otic‐like machineries for DNA replication, transcription and information processing. The conserved archaeal DNA binding protein Alba (formerly Sso10b) interacts with the silencing protein Sir2, which regulates Alba's DNA binding affinity by deacetylation of a lysine residue. We present the crystal structure of Alba from Sulfolobus solfataricus at 2.6 Å resolution (PDB code 1h0x). The fold is reminiscent of the N‐terminal DNA binding domain of DNase I and the C‐terminal domain of initiation factor IF3. The Alba dimer has two extended β‐hairpins flanking a central body containing the acetylated lysine, Lys16, suggesting three main points of contact with the DNA. Fluorescence, calorimetry and electrophoresis data suggest a final binding stoichiometry of ∼5 bp DNA per Alba dimer. We present a model for the Alba–DNA interaction consistent with the available structural, biophysical and electron microscopy data.
Autonomous Mobile Sensor Placement in Complex Environments

In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it be predicted. We propose a new algorithm for autonomous sensor movements and positioning, called DOMINO (DeplOyment of MobIle Networks with Obstacles). Unlike traditional approaches, DOMINO explicitly addresses these issues by realizing a grid-based deployment throughout the Area of Interest (AoI) and subsequently refining it to cover the target area more precisely in the regions where devices experience reduced sensing. We demonstrate the capability of DOMINO to entirely cover the AoI in a finite time. We also give bounds on the number of sensors necessary to cover an AoI with asperities. Simulations show that DOMINO provides a fast deployment with precise movements and no oscillations, with moderate energy consumption. Furthermore, DOMINO provides better performance than previous solutions in all the operative settings.
Testing the Profitability of Contrarian Trading Strategies Based on the Overreaction Hypothesis

We develop 200 contrarian trading strategies based on significant market variations to test whether it is possible to benefit from the well-known psychological bias of overreaction that plagues investors. We conduct the most recent and appropriate statistical tests to ensure that none of these active strategies beats the buy-and-hold strategy due to pure luck only. Each of these strategies are tested on 13 different underlying assets, including exchange rates and stock indexes. When both transaction and borrowing costs are taken into account, our empirical results suggest that the use of significant market variations as daily reversal signals does not lead to any abnormal profit.