Miracle-dz/UniFAR-FTUs huggingface dataset:
New Neighbors from 2MASS: Activity and Kinematics at the Bottom of the Main Sequence

We have combined 2MASS and POSS II data in a search for nearby ultracool (later than M6.5) dwarfs with Ks < 12. Spectroscopic follow-up observations identify 53 M7–M9.5 dwarfs and seven L dwarfs. The observed space density is 0.0045 ± 0.0008 M8–M9.5 dwarfs per cubic parsec, without accounting for biases, consistent with a mass function that is smooth across the stellar/substellar limit. We show the observed frequency of Hα emission peaks at ∼100% for M7 dwarfs and then decreases for cooler dwarfs. In absolute terms, however, as measured by the ratio of Hα to bolometric luminosity, none of the ultracool M dwarfs can be considered very active compared to earlier M dwarfs, and we show that the decrease that begins at spectral type M6 continues to the latest L dwarfs. We find that flaring is common among the coolest M dwarfs and estimate the frequency of flares at 7% or higher. We show that the kinematics of relatively active (EW > 6 Å) ultracool M dwarfs are consistent with an ordinary old disk stellar population, while the kinematics of inactive ultracool M dwarfs are more typical of a 0.5 Gyr old population. The early L dwarfs in the sample have kinematics consistent with old ages, suggesting that the hydrogen-burning limit is near spectral types L2–L4. We use the available data on M and L dwarfs to show that chromospheric activity drops with decreasing mass and temperature and that at a given (M8 or later) spectral type, the younger field (brown) dwarfs are less active than many of the older, more massive field stellar dwarfs. Thus, contrary to the well-known stellar age-activity relationship, low activity in field ultracool dwarfs can be an indication of comparative youth and substellar mass.
Efficient Speech Recognition System for Isolated Digits

In this paper, an efficient speech recognition system is proposed for speaker-independent isolated digits (0 to 9). Using the Weighted MFCC (WMFCC), low computational overhead is achieved since only 13 weighted MFCC coefficients are used. In order to capture the trends of the extracted features, the local and global features are computed using the Improved Features for Dynamic Time Warping (IFDTW) algorithm. In this work, we propose to reduce the time complexity of the recognition system by time-scale modification using a SOLA-based technique and also by using a faster implementation of IFDTW. The experiments based on TI-Digits corpus demonstrate the effectiveness of proposed system giving higher recognition accuracy of 99.16% and performing about 22 times faster than conventional techniques.
Orthonormal strongly-constrained neural learning

A class of unconventional neural optimization algorithms called orthonormal strongly-constrained (SOC) is presented. Here the general problem of the iterative search of maxima or minima of objective functions under the constraint of orthonormality is dealt with . After that general properties of the SOC algorithms are stated, examples are discussed relative to the cases of gradient-based and non-gradient-based learning rules. Finally, known algorithms found in literature are shown to belong to the SOC class.
Sparse representation for video super-resolution

Sparse representations of signals have been developed rapidly in recent years, such as in the field of super-resolution. The theory that an image is able to be decomposed into a sparse representation over an over-complete dictionary of atoms ensures the feasibility of such applications. In this paper, we produce a pair of well-trained dictionaries that has both high and low resolution patches followed by restoration using sparse representations, and then the steering kernel regression is added into the framework to restrict the whole video and reduce noise and block effects. The experiments show that the quality of recovered super-resolution video is better acceptable and competitive.
VOCALISTENER: A SINGING-TO-SINGING SYNTHESIS SYSTEM BASED ON ITERATIVE PARAMETER ESTIMATION

This paper presents a singing synthesis system,VocaListener, that automatically estimates parameters for singing synthesis from a user’s singing voice with the help of song lyrics. Although there is a method to estimate singing synthesis parameters of pitch (F 0 ) and dynamics (power) from a singing voice, it does not adapt to different singing synthesis conditions (e.g., different singing synthesis systems and their singer databases) or singing skill/style modifications. To deal with different conditions, VocaListener repeatedly updates singing synthesis parameters so that the synthesized singing can more closely mimic the user’s singing. Moreover, VocaListener has functions to help modify the user’s singing by correcting off-pitch phrases or changing vibrato. In an experimental evaluation under two different singing synthesis conditions, our system achieved synthesized singing that closely mimicked the user’s singing.
Diagnostics of speech recognition using classification phoneme diagnostic trees

More than three decades of speech recognition research resulted in a very sophisticated statistical framework. However, less attention was still devoted to diagnostics of speech recognition; most previous research report on results in terms of ever-lower WER in various intrinsic or environmental conditions. This paper presents a diagnostics of the decoding process of ASR systems. The purpose of our diagnostics is to go beyond standard evaluation in terms of WERs and confusion matrices, and to look at the recognized output in more details. During the decoding phase, some specific data are collected at the decoder as possible causes of errors, and later are statistically analyzed using classification and regression trees. Focusing on pure acoustic phone decoding without language modeling, we present and discuss the results of the diagnostics that is used for an analysis of impact of intrinsic speech variabilities on speech recognition.
Estimation of a Dynamic Auction Game

This paper proposes an estimation method for a repeated auction game under the presence of capacity contraints. The estimation strategy is computationally simple as it does not require solving for the equilibrium of the game. It uses a two stage approach. In the first stage the distribution of bids conditional on state variables is estimated using data on bids, bidder characteristics and contract characteristics. In the second stage, an expression of the expected sum of future profits based on the distribution of bids is obtained, and costs are inferred based on the first order condition of optimal bids. We apply the estimation method to repeated highway construction procurement auctions in the state of California between May 1996 and May 1999. In this market, previously won uncompleted contracts reduce the probability of winning further contracts. We quantify the effect of intertemporal constraints on bidders' costs and on bids. Due to the intertemporal effect and also to bidder asymmetry, the auction can be inefficient. Based on the estimates of costs, we quantify efficiency losses.
Neuronal Birthdate-Specific Gene Transfer with Adenoviral Vectors

The multilayered structure of the cerebral cortex has been studied in detail. Early-born neurons migrate into the inner layer and late-born neurons migrate into more superficial layers, thus establishing an inside-out gradient. The progenitor cells appear to acquire layer-specific properties at the time of neuronal birth; however, the molecular mechanisms of cell-fate acquisition are still unclear, because it has been difficult to identify a cohort of birthdate-related progenitor cells. Using replication-defective adenoviral vectors, we successfully performed “pulse gene transfer” into progenitor cells in a neuronal birthdate-specific manner. When adenoviral vectors were injected into the midbrain ventricle of mouse embryos between embryonic day 10.5 (E10.5) and E14.5, the adenoviral vectors introduced a foreign gene into a specific cohort of birthdate-related progenitor cells. The virally infected cohorts developed normally into cortical neurons and formed the canonical cortical layers in an inside-out manner. This technique allows us to distinguish a cohort of birthdate-related progenitor cells from other progenitor cells with different birthdates and to introduce a foreign gene into specific subsets of cortical layers by performing adenoviral injection at specific times. This adenovirus-meditated gene transfer technique will enable us to examine the properties of each subset of progenitor cells that share the same neuronal birthdate.
Lot-sizing problem with supplier selection considering safety stock

The Supplier selection problem is one of the most critical activities of purchasing management. Safety-stock has to be maintained to mitigate risk of stock-outs due to uncertainties in supply and demand. It is an objective to reach for organization. In this paper, we develop a new mixed integer programming model for a mono-item multi period inventory lot-sizing problem with supplier selection including safety stock. We consider a situation where the demand is known over a finite planning horizon. The buyer needs to decide what quantities to order, with which suppliers, and in which periods. The complexity of this problem is known to be NP-hard. We used a genetic algorithm (GA) to solve our problem.
The Vulnerabilities Analysis of Fuzzy Vault Using Password

Fingerprint is immutable and unchangeable. Thus, if it is disclosed, owner of fingerprint cannot use his fingerprint any longer. Fuzzy vault is a cryptographic framework that makes secure template storage to bind the template with a uniformly random key. In order to keep fuzzy vault secure, various schemes are studied using special data like password. K.Nandakumar proposed a scheme for hardening a fingerprint minutiae-based fuzzy vault using password. However, that scheme has vulnerabilities against several attacks. In this paper, we analyze vulnerabilities of K.Nandakumar¿s scheme and propose a new scheme which is secure against various attacks to fuzzy vaults.
Estimating maximum global land surface wind power extractability and associated climatic consequences

Abstract. The availability of wind power for renewable energy extraction is ultimately limited by how much kinetic energy is generated by natural processes within the Earth system and by fundamental limits of how much of the wind power can be extracted. Here we use these considerations to provide a maximum estimate of wind power availability over land. We use several different methods. First, we outline the processes associated with wind power generation and extraction with a simple power transfer hierarchy based on the assumption that available wind power will not geographically vary with increased extraction for an estimate of 68 TW. Second, we set up a simple momentum balance model to estimate maximum extractability which we then apply to reanalysis climate data, yielding an estimate of 21 TW. Third, we perform general circulation model simulations in which we extract different amounts of momentum from the atmospheric boundary layer to obtain a maximum estimate of how much power can be extracted, yielding 18–34 TW. These three methods consistently yield maximum estimates in the range of 18–68 TW and are notably less than recent estimates that claim abundant wind power availability. Furthermore, we show with the general circulation model simulations that some climatic effects at maximum wind power extraction are similar in magnitude to those associated with a doubling of atmospheric CO2. We conclude that in order to understand fundamental limits to renewable energy resources, as well as the impacts of their utilization, it is imperative to use a "top-down" thermodynamic Earth system perspective, rather than the more common "bottom-up" engineering approach.
Image segmentation by sparse representation

This paper presents a fast and efficient algorithm, named sparse representation, for solving image segmentation. Given the human motion video each frame, we can regard each frame as an image. And with the theory of sparse representation, it is reasonable to assume that the background image can be represented by a low-rank, while the foreground human motion part is spatially localized, and therefore sparse. The human motion part is obtained by recovering the low-rank and sparse matrix. This process is formulated as a convex optimization problem that minimizes a weighted combination of two ℓ1-norms, which can be efficiently solved by Augmented Lagrange Multiplier (ALM) method. The experimental results based on the HumanEva human motion dataset show that image segmentation by the proposed method achieves better results, and is more robust to noise, yet the process being much faster and efficient.
Understanding the Working Sets of Data Mining Applications

Data mining applications discover useful information or patterns in large sets of data. Because they can be highly parallelizable and computationally intensive, data mining applications have the potential to take advantage of the large numbers of processors predicted for future multi-core systems. However, the potential performance of these applications on this emerging platform is likely to be impeded by their intensive memory usage. In addition to accessing memory frequently, some of these applications exhibit exceedingly large working set sizes. Storing these large working sets on chip in their entirety may be prohibitively expensive or infeasible as these working set sizes continue to grow with problem size. Greater insight into the characteristics of these working sets is needed in order to determine alternative approaches to storing the entire working set onchip. In this paper, we examine the memory system characteristics of a set of applications from the MineBench data mining suite. We analyze these applications in an architecture independent manner in order to gain greater understanding into the composition of the data working set; in particular, we document the duration and frequency of active and idle periods for working set data. We find that working set data may be reused repeatedly throughout a program’s execution, but each use is for a short period of time. The resulting long idle periods may enable alternate techniques to be used instead of caching in order to obtain good memory performance. We show that for several of these applications, simple prefetching schemes may alleviate the need to cache the large working sets.
Thioflavine T interaction with synthetic Alzheimer's disease β‐amyloid peptides: Detection of amyloid aggregation in solution

Thioflavine T (ThT) associates rapidly with aggregated fibrils of the synthetic β/A4‐derived peptides β(1–28) and β(1–40), giving rise to a new excitation (ex) (absorption) maximum at 450 nm and enhanced emission (em) at 482 nm, as opposed to the 385 nm (ex) and 445 nm (em) of the free dye. This change is dependent on the aggregated state as monomeric or dimeric peptides do not react, and guanidine dissociation of aggregates destroys the signal. There was no effect of high salt concentrations. Binding to the β(1–40) is of lower affinity, Kd 2 μM, while it saturates with a Kd of 0.54 μM for β(1–28). Insulin fibrils converted to a β‐sheet conformation fluoresce intensely with ThT. A variety of polyhydroxy, polyanionic, or polycationic materials fail to interact or impede interaction with the amyloid peptides. This fluorometric technique should allow the kinetic elucidation of the amyloid fibril assembly process as well as the testing of agents that might modulate their assembly or disassembly.
Resonant snubbers with auxiliary switches

A resonant snubber is described for voltage-source inverters, current-source inverters, and self-commutated frequency changers. The main self-turn-off devices have shunt capacitors directly across them. The lossless resonant snubber described avoids trapping energy in a converter circuit where high dynamic stresses at both turn-on and turn-off are normally encountered. This is achieved by providing a temporary parallel path through a small ordinary thyristor (or other device operating in a similar node) to take over the high-stress turn-on duty from the main gate turn-off (GTO) or power transistor, in a manner that leaves no energy trapped after switching. <<ETX>>
Unemployment Benefits in a Period of Crisis: The Effect on Unemployment Duration

The current study shows that the disincentive effects of unemployment benefits exist even during a period of deep recession. The study uses recent data for unemployment benefit recipients in Estonia – a country where the rise in unemployment during the global financial crisis was the highest in the entire European Union. Both a higher benefit level and a longer maximum duration of benefits decrease exits from unemployment to employment. Yet, compared to the pre-crisis period, the effects of unemployment benefits are slightly milder and more homogenous. In addition, unemployed people directed to active measures tend to exhibit a lower hazard of leaving unemployment just before the period of an active measure and during the period of receiving an active measure.
Apoptosis Control in Syncytia Induced by the HIV Type 1–Envelope Glycoprotein Complex

Syncytia arising from the fusion of cells expressing a lymphotropic HIV type 1–encoded envelope glycoprotein complex (Env) with cells expressing the CD4/CXC chemokine receptor 4 complex spontaneously undergo cell death. Here we show that this process is accompanied by caspase activation and signs of mitochondrial membrane permeabilization (MMP), including the release of intermembrane proteins such as cytochrome c (Cyt-c) and apoptosis-inducing factor (AIF) from mitochondria. In Env-induced syncytia, caspase inhibition did not suppress AIF- and Cyt-c translocation, yet it prevented all signs of nuclear apoptosis. Translocation of Bax to mitochondria led to MMP, which was inhibited by microinjected Bcl-2 protein or bcl-2 transfection. Bcl-2 also prevented the subsequent nuclear chromatin condensation and DNA fragmentation. The release of AIF occurred before that of Cyt-c and before caspase activation. Microinjection of AIF into syncytia sufficed to trigger rapid, caspase-independent Cyt-c release. Neutralization of endogenous AIF by injection of an antibody prevented all signs of spontaneous apoptosis occurring in syncytia, including the Cyt-c release and nuclear apoptosis. In contrast, Cyt-c neutralization only prevented nuclear apoptosis, and did not affect AIF release. Our results establish that the following molecular sequence governs apoptosis of Env-induced syncytia: Bax-mediated/Bcl-2–inhibited MMP → AIF release → Cyt-c release → caspase activation → nuclear apoptosis.
Robustness Optimization in Subthreshold Circuits

Emerging IC applications such as wireless sensor nodes, RFIDs, and medical devices place increasing emphasis on low energy operation instead of performance. Subthreshold circuits operate with a power supply set below the transistor voltage threshold and thus offer significant power reduction, while operating at frequencies in the range of a few MHz. A number of designs have successfully been presented [1, 2, 3]. However, they rely on leakage currents that are exponentially dependent on Vt and thus subthreshold designs have dramatically increased sensitivity to process variations [3]. B Zhai, et al address the mitigation of variability in subthreshold designs by providing statistical models for energy and delay that consider the impact of process variations [4]. B. Calhoun suggests that minimumsized circuits would minimize energy per operation [5]. J. Kwong, et al suggest that design yield-based upsizing is necessary to achieve robustness at lowered supply voltages [6].
Modelling multi-period inflation uncertainty using a panel of density forecasts

This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). Based on a dynamic heterogeneous panel data model, we find that the persistence in forecast uncertainty is much less than what the aggregate time series data would suggest. In addition, the strong link between past forecast errors and current forecast uncertainty, as often noted in the ARCH literature, is largely lost in a multi-period context with varying forecast horizons. We propose a novel way of estimating 'news' and its variance using the Kullback-Leibler information, and show that the latter is an important determinant of forecast uncertainty. Our evidence suggests a strong relationship of forecast uncertainty with level of inflation, but not with forecaster discord or with the volatility of a number of other macroeconomic indicators. Copyright © 2006 John Wiley & Sons, Ltd.
Use of Fast Multipole to Accelerate Discrete Circulation-Preserving Vortex Sheets for Soap Films and Foams

We report the integration of a FMM (Fast Multipole Method) template library “FMMTL” into the discrete circulation-preserving vortex sheets method to accelerate the Biot-Savart integral. We measure the speed-up on a bubble oscillation test with varying mesh resolution. We also report a few examples with higher complexity than previously achieved.
Analyzing Object Tracking Quality under Probabilistic Coverage in Sensor Networks

The object-tracking quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Probabilistic coverage is a suitable approach to balancing the conflicting design requirements of monitoring applications. Under probabilistic coverage, we present an analytical model to analyze the object-tracking quality with respect to different network conditions and sensor scheduling schemes. Our analytical model facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Based on our model, we can provide accurate guidelines for optimal sensor deployment and power conservation, and determine the appropriate moving speed of an object that may evade sensor detection. Applying the model to real sensor networks, we further design three sensing scheduling protocols to achieve a targeted tracking quality while minimizing the power consumption. We validate the correctness of our model and the effectiveness of the proposed protocols through extensive simulation experiments.
Structure-oriented Gaussian filter for seismic detail preserving smoothing

This paper presents a structure-oriented Gaussian (SOG) filter for reducing noise in 3D reflection seismic data while preserving relevant details such as structural and stratigraphic discontinuities and lateral heterogeneity. The Gaussian kernel is anisotropically constructed based on two confidence measures, both of which take into account the regularity of the local seismic structures. So that, the filter shape is well adjusted according to different local geological features. Then, the anisotropic Gaussian is steered by local orientations of the geological features (layers) provided by the Gradient Structure Tensor. The potential of our approach is presented through a comparative experiment with seismic fault preserving diffusion (SFPD) filter on synthetic blocks and an application to real 3D seismic data.
ENTROPY OF THE THREE-DIMENSIONAL SCHWARZSCHILD de SITTER BLACK HOLE

We study the three-dimensional Schwarzschild–de Sitter (SdS3) black hole which corresponds essentially to a conical defect. We compute the mass of the SdS3 black hole from the correct definition of the mass in asymptotically de Sitter space. Then we clarify the relation between the mass, entropy and temperature for this black hole without any ambiguity. We also establish the SdS3/CFT2-correspondence for the entropy by applying the Cardy formula to a CFT with a central charge c = 3l/2G3. Finally we discuss the entropy bounds for the SdS3 black hole.