Miracle-dz/UniFAR-FTUs huggingface dataset:
A NEW MODEL-BASED UNDERDETERMINED SPEECH SEPARATIO

In this paper we present a new model-based blind sp eech separation for underdetermined case. Under sparsity assumption, separation is achieved by applying soft time frequency masks to observations. The masks are deri ve by estimating the parameters of an ad-hoc distribution of the Interchannel Level/Phase Difference (ILD/IPD). Thes e parameters are estimated using an expectation-maxim izat on (EM) procedure. The performance of the algorithm is evaluated on real-world convolved mixtures using th e database of the first audio source evaluation campa ign [5]. Results show that the proposed algorithm outperform s the algorithm presented in the campaign in terms of art i acts and distortion noise.
A Statistical Analysis Method for Gene Expression Based on EM Algorithm

1 Department of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan 2 CREST, JST (Japan Science and Technology Corporation) 3 Department of Biological Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan 4 Research and Education Center for Genetic Information, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan
Mitigating effect of immigration on the relation between income inequality and mortality: a prospective study of 2 million Canadians

Background The relation between income inequality and mortality in Canada is unclear, and modifying effects of characteristics such as immigration have not been examined. Methods Using a cohort of 2 million Canadians followed for mortality from 1991–2001, we calculated HRs and 95% CIs for income inequality of 140 urban areas (Gini coefficient, Atkinson index, coefficient of variation; expressed as continuous variables) and working age (25–64 y) or post-working age (≥65 y) mortality in men and women according to immigration status, accounting for individual and neighbourhood income, and sociodemographic characteristics. Major causes of mortality were examined. Results Relative to low income inequality, high inequality was associated with greater working age mortality in male (HRGini 1.08, 95% CI 1.04 to 1.13) and female (HRGini 1.12, 95% CI 1.06 to 1.18) non-immigrants for all income inequality indictors. Results were similar for female post-working age mortality. There was no relation between income inequality and mortality in immigrants. Among Canadian-born individuals, associations were greater for alcohol-related mortality (both sexes) and smoking-related causes/transport injuries (women). Conclusion Income inequality is associated with mortality in Canadian-born individuals but not immigrants.
Advanced Encryption Algorithm Using Fuzzy Logic

Sri Lanka Institute of Information Technology, Sri Lanka Abstract. Sending information from one point to another is called data communication. Today the security is the main issue in data communication. Encryption can provide a fine solution for it. The encryption algorithm is the mathematical procedure for performing encryption on data. A key is used to cipher a message and to decipher it back to the original message. The implementation of these algorithms can be very intricate. After conducting a research on currently using encryption algorithms, we have identified that all these algorithms only concern about security. But consuming a less processing power is also equally important as the security for connections with low bandwidths. The proposed algorithm supports for user desired security level and processing level. It is a block cipher which is a derivation on the fiestle network architecture. The algorithm provides security levels and their corresponding processing levels by using various keys for the encryption/decryption process. This facility is achieved by using fuzzy logic. The results of the proposed encryption algorithm will be analyzed by comparing with other existing encryption algorithms. Finally the aim of the research is to come up with an encryption algorithm which can provide either low processing or high security according to user’s requirement which will be more advanced than the existing encryption algorithms.
FED CATTLE PRICE DISCOVERY: DIRECTED ACYCLIC GRAPH AND TIME SERIES MODELING

Locating fed cattle price discovery center is revisited using the framework that combines recent progress in causal research with time series analysis. The Bernanke factorization in innovation accounting is obtained by a graphical method called directed acyclic graph which uses data to identify the contemporaneous causal structure among the innovations. This may represent an improvement over the conventional methods which use human judgment and/or theory to supply such information. Results indicate that Kansas market is a dominant price leader where new information is discovered. Contrary to the previous studies, Nebraska market does not appear to be a price discovery location.
Chandra High-Resolution Spectrum of the Anomalous X-Ray Pulsar 4U 0142+61

We report on a 25 ks observation of the 8.7 s anomalous X-ray pulsar 4U 0142+61 with the High Energy Transmission Grating Spectrometer on the Chandra X-Ray Observatory. The continuum spectrum is consistent with previous measurements and is well fitted by an absorbed power law+blackbody with a photon index Γ = 3.3 ± 0.4 and kT = 0.418 ± 0.013 keV. No evidence was found for emission or absorption lines, with an upper limit of ≈50 eV on the equivalent width of broad features in the 2.5-13 Å (0.95-5.0 keV) range and an upper limit of ≈10 eV on the equivalent width of narrow features in the 4.1-17.7 Å (0.7-3.0 keV) range. If the source is a magnetar, then the absence of a proton cyclotron line strongly constrains magnetar atmosphere models and hence the magnetic field strength of the neutron star. We find no strong features that are indicative of cyclotron absorption for magnetic field strengths of (1.9-9.8) × 1014 G. This is still consistent with the dipole field strength of B = 1.3 × 1014 G (at the polar cap) estimated from the pulsar's spin-down.
The grand macroeconomics circular tour: from 'the classics' to Keynes, and back again

Having noted the fundamental difference between the pre-Keynesian (classical) and Keynesian conceptions of the working of the macro system - i.e. from the one (classical) perspective that aggregate demand naturally accommodates itself to supply, and from the other, that planned aggregate demand is the key determinant of economic activity - this paper goes on to draw attention to the fact that the pre-Keynesian vision has re-emerged as the conventional wisdom as expounded in contemporary mainstream textbooks, a development intimately bound up with the adoption of the highly problematic ADAS model as the expository framework of choice.
Some integrals and integral transformations of lightning pulse functions

Some integrals and integral transformations of a lightning pulse function useful for lightning discharge modeling are presented in this paper. The integral of the new channel-base current function (NCBC), the integral of the square of this function, its Laplace transform and Fourier transform are calculated analytically. Some results are compared to the results from literature for the Heidler's lightning channel-base function. These integrals of pulse functions are used in analytical calculations of lightning electromagnetic field (LEMF) and the results are presented in the paper. It makes the NCBC function suitable for computations of lightning induced effects.
Hedging Break-Even Biodiesel Production Costs Using Soybean Oil Futures

The effectiveness of hedging volatile input prices for biodiesel producers is examined over one- to eight-week time horizons. Results reveal that hedging break-even soybean costs with soybean oil futures offers significant reductions in input price risk. The degree of risk reduction is dependent upon type of hedge, naive or risk-minimizing, and upon time horizon. In contrast, cross-hedging break-even poultry fat costs with soybean oil futures failed to reduce input price risk.
Nonlinear regulation in constrained input discrete-time linear systems

The use of composite linear and non-linear feedback laws for the control of constrained input discrete-time linear systems is re-examined. By making use of the delta operator formulation of a discrete-time system, an apparent restriction on the magnitude of the non-linear control law is removed, and the similarities between the continuous and discrete-time solutions to the problem are elucidated. In order to develop the results, unconstrained systems are treated initially, but it is shown that, locally, the sufficient conditions for the stabilization of such systems are actually equivalent to those for the stabilization of the corresponding constrained systems.
The Demand for Cigarettes and Restrictions on Smoking in the Workplace

The purpose of this paper is to empirically test the effect that restrictive clean air laws have on the level of smoking. Restrictive clean air laws refers to the laws which prohibit smoking in private workplaces as well as in public places. The data employed in this study consist of a time series of cross sections of the fifty states of the U.S., and Washington D.C., over the time period from 1975 through 1985, Since states where sentiment is strongly against cigarettes are more likely to pass a clean air law, endogeneity between cigarette demand and the clean air law is a problem. A two step estimation model is used to control for endogeneity. Both a single equation and a two equation model of cigarette demand were estimated. The single equation results indicate that a clean air law has a significant negative effect on cigarette demand. However, the two equation model indicates that cigarette demand has a significant negative effect on the probability of passing a clean air law. The results indicate that when endogeneity is controlled for the clean air law dose not have a significant effect on cigarette demand. This does not imply that the enactment of a clean air law would not reduce the level of smoking if such a law were imposed in all states, but rather that only states with low levels of smoking are able to pass restrictive clean sir laws.
Segmental conditional random fields with deep neural networks as acoustic models for first-pass word recognition

Discriminative segmental models, such as segmental conditional random fields (SCRFs), have been successfully applied to speech recognition recently in lattice rescoring to integrate detectors across different levels of units, such as phones and words. However, the lattice generation has been constrained by a baseline decoder, typically a frame-based hybrid HMMDNN system, which still suffers from the well-known frame independent assumption. In this paper, we propose to use SCRFs with DNNs directly as the acoustic model, a one-pass unified framework that can utilize local phone classifiers, phone transitions and long-span features, in direct word decoding to model phones or sub-phonetic segments with variable length. We describe a WFST-based approach to utilize the proposed acoustic model efficiently with the language model in first-pass word recognition. Our evaluation on the WSJ corpus shows our SCRF-DNN system outperforms a hybrid HMM-DNN system and a frame-level CRF-DNN system using the same label space.
Attention for Fine-Grained Categorization

This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the attention RNN. Most work in attention models to date focuses on tasks with toy or more constrained visual environments, whereas we present results for fine-grained categorization better than the state-of-the-art GoogLeNet classification model. We show that our model learns to direct high resolution attention to the most discriminative regions without any spatial supervision such as bounding boxes, and it is able to discriminate fine-grained dog breeds moderately well even when given only an initial low-resolution context image and narrow, inexpensive glimpses at faces and fur patterns. This and similar attention models have the major advantage of being trained end-to-end, as opposed to other current detection and recognition pipelines with hand-engineered components where information is lost. While our model is state-of-the-art, further work is needed to fully leverage the sequential input.
Design of an extremely high performance counter mode AES reconfigurable processor

In this paper, we presented our implementation of a counter mode AES processor based on the Xilinx Virtex2 FPGA platform. We have studied different techniques to implement the AES rijndael algorithm in reconfigurable hardware and choose the proper method to further optimize the structure of the cipher. This result in a clock frequency of 212.5 mHz and translate to throughput of 27.1 Gb/s, the highest throughput that have ever reported. We also, in this paper, compared the operation modes of AES, their security and efficiency.
Comparing Hierarchical Models for Spatio-temporallyMisaligned Data using the DIC

Bayes and empirical Bayes methods have proven eeective in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining overall geographic trends and patterns. Recent work extends these methods to the analysis of areal data which are spatially misaligned, i.e., involving variables (typically counts or rates) which are ag-gregated over diiering sets of regional boundaries. The addition of a temporal aspect complicates matters further, since now the misalignment can arise either within a given time point, or across time points (as when the regional boundaries themselves evolve over time). Hierarchical Bayesian methods (implemented via modern Markov chain Monte Carlo computing methods) enable the tting of such models, but a formal comparison of their t is hampered by their large size and often improper prior speciications. In this paper, we accomplish this comparison using the De-viance Information Criterion (DIC), a recently proposed generalization of the Akaike Information Criterion (AIC) designed for complex hierarchical model settings like ours. We investigate the use of the delta method for obtaining an approximate variance estimate for DIC, in order to attach signiicance to apparent diierences between models. We illustrate our approach using a spatially misaligned dataset relating a measure of traac density to pediatric asthma hospitalizations in San Diego County, California.
Inexpensive robot for experimental validation of planning and control algorithms

This paper describes the structure of a mobile robot suitable for laboratory experiments. The robot is developed with the ideas of minimizing the production costs and of executing motion commands received from an external computer that runs the (possible computational intensive) planning algorithms. The proposed system is a differential wheeled robot built based on an Arduino platform, it has a small physical size and it is capable of wireless communication. These characteristics make it useful for validating planning algorithms for mobile robots in computer-guided laboratory experiments, while lowering the expenses required by such a setup. Moreover, in order to compensate the communication delay between the central computer and the robot, an estimation algorithm is implemented on the robot for obtaining a smoother motion.
The need for robust validation for MDRD-based glomerular filtration rate estimation in various CKD populations.

BACKGROUND Currently, estimated glomerular filtration rate (eGFR) equations have been validated only in Caucasians and African-Americans and is not applicable to people of other races/ethnicities as shown in studies conducted in two Asian populations: Chinese and Japanese. Because of this, it is important that eGFR equations are validated in its prospective population before applying it in the clinical setting and in epidemiologic studies. Therefore, we examined all eGFR equations available: reexpressed isotope dilution mass spectroscopy (IDMS)-traceable Modification of Diet in Renal Disease (MDRD) equation, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, Chinese equation and Japanese equation. METHODS A total of 350 adult Thai CKD patients were studied. The (99m)Tc-DTPA plasma clearance was used as a reference for glomerular filtration rate (GFR). The serum creatinine was determined by IDMS reference enzymatic methods (Cr(Enz)) and Jaffe's kinetic assay (Cr(Jaffe)) as indicated in each equation. RESULTS The disagreement between the reference GFR and eGFR (reference GFR minus eGFR) was 9.6 mL/min/1.73 m(2) for the reexpressed IDMS-traceable MDRD equation, 8.0 mL/min/1.73 m(2) for CKD-EPI equation, 1.9 mL/min/1.73 m(2) for eGFR equation from the Chinese study and 20.9 mL/min/1.73 m(2) for the eGFR equation from the Japanese study. The Thai coefficient for the reexpressed MDRD was 1.129. The reexpressed MDRD equation for Thais is as follows: 175 × Cr(Enz) ((-1.154)) × Age ((-0.203)) × 0.742 (if female) × 1.129 (if Thai). When stepwise multiple regression analysis was used, the Thai eGFR formula is: 375.5 × Cr(Enz) ((-0.848)) × Age ((-0.364)) × 0.712 (if female). CONCLUSIONS Differences in race/ethnicity can significantly affect the results obtained from MDRD-based eGFR equation. It is highly recommended that each population should validate eGFR equations before applying the equation in epidemiologic studies or clinical use.
Power Efficient Energy-Aware Routing Protocol for Wireless Sensor Networks

: - The most important issue that must be solved in designing a data transmission algorithm for wireless sensor networks (WSNs) is how to save sensor node energy while meeting the needs of applications/users as the sensor nodes are battery limited. While satisfying the energy saving requirement, it is also necessary to achieve the quality of service. In case of emergency work, it is necessary to deliver the data on time. Achieving quality of service in WSNs is also important. In order to achieve this requirement, Power-efficient Energy-Aware routing protocol for wireless sensor networks is proposed that saves the energy by efficiently selecting the energy efficient path in the routing process. When source finds route to destination, it calculates α for each route. The value α is based on largest minimum residual energy of the path and hop count of the path. If a route has higher α, then that path is selected for routing the data. The value of α will be higher, if the largest of minimum residual energy of the path is higher and the number of hop count is lower. Once the path is selected, data is transferred along the path. In order to increase the energy efficiency further transmission power of the nodes is also adjusted based on the location of their neighbor. If the neighbors of a node are closely located to that node, then transmission range of the node is decreased. Therefore it is enough for the node to have the transmission power to reach the neighbor within that range. As a result transmission power of the node is reduce which subsequently reduces the energy consumption of the node. Our proposed work is simulated through Network Simulator (NS-2). Existing AODV and Man-Min energy routing protocol also simulated through NS-2 for performance comparison. Packet Delivery Ratio, Energy Consumption and end-to-end delay (Quality of Service metric) are chosen as performance metrics. Performance of the proposed protocol is compared with the existing AODV and Max-Min energy routing protocol by based on the metrics. Our proposed protocol provides lower energy consumption than AODV and lower delay than the Max-Min energy routing protocol. Thus, our proposed routing protocol saves the energy and also achieves the good quality of service by providing lower delay for data transfer.
Heuristics for dimensioning large-scale MPLS networks

MultiProtocol Label Switching (MPLS) technology allows the support of multiple services with different Quality of Service (QoS) requirements in classical IP networks. In an MPLS domain, packet flows belonging to a particular class are classified in the same Forward Equivalence Class (FEC). Based on different FECs, each service can be set up in the network through logical networks. Each logical network is a set of Label Switched Paths (LSPs), one for each service traffic trunk. The network-dimensioning problem is formulated as the determination of routes for all LSPs to achieve the least cost physical network. To solve this problem some widely known heuristics are used and two enhancement algorithms are proposed that allow for significant gains when compared with the basic heuristics. The heuristics tested include a genetic algorithm, a greedy based heuristic and a lagrangean relaxation based heuristic. The enhancements are proposed for application to the greedy based heuristic and to the lagrangean heuristic. The results show that the enhanced lagrangean heuristic is the best overall technique for the case studies presented. This technique yields significant average gains when compared to the basic lagrangean heuristic.
Research of the tracking accuracy and stabilization accuracy test on airborne platform

A measurement method of tracking accuracy and stabilization test on the airborne platform which based on the optical reflection is proposed in this paper. The method detected the position of the reflected light that on the autocollimator in the condition of the simulation of aircraft vibration and target moving then analysed the data and got the tracking accuracy. In addition, the paper investigated the detection method of stabilization accuracy which is one of the most important factors that influenced the tracking accuracy. We designed different detection systems on tracking accuracy and stabilization accuracy. This paper introduced the composition of the detection system as well, and designed the crucial parameters and its detecting principles. The result indicated that this method had advantages such as high frequency of sampling, high accuracy of measurement and true simulation of working. So it satisfied the requirement of airborne platform tracking accuracy and stabilization accuracy test.
Global routing congestion reduction with cost allocation look-ahead

This work presents two techniques to define and treat areas that have high interconnection demand in the design of VLSI circuits, during global routing. These techniques are applied in two steps over all global routing flow. The first technique is executed in the pre-routing phase, where are identified regions with a high interconnection density, (there is a large number of sources or destinations reducing its ability to allocate interconnections); the second technique is applied in the iterative routing phase, identifying and protecting those regions from having high congestion, by cost pre-allocation techniques. Three cost pre-allocation parameters were identified and their values are defined on-the-fly, by functions, described in this paper. These techniques were included in an existing global routing flow, called GR-WL, to validate the impact of its implementation, through the extraction of three global routing metrics: wirelength, total value of maximum overflow (TOF) and maximum obtained overflow (MOF). By running experiments using these techniques, total congestion reduction was up to 16%. The results are more relevant when using benchmark circuits for which there is still no valid solutions in the literature. Furthermore, the running times achieved were up to 30% faster when compared to the reference implementation, with a maximum impact of 1.39% in the total wirelength.