Miracle-dz/UniFAR-FTUs huggingface dataset:
A MULTI-SPECTROGRAM DEEP NEURAL NETWORK FOR ACOUSTIC SCENE CLASSIFICATION Technical Report

This work targets the task 1A and 1B of DCASE2019 challenge that are Acoustic Scene Classification (ASC) over ten different classes recorded by a same device (task 1A) and mismatched devices (task 1B). For the front-end feature extraction, this work proposes a combination of three types of spectrograms: Gammatone (GAM), logMel and Constant Q Transform (CQT). The back-end classification shows two training processes, namely pre-trained CNN and posttrained DNN, and the result of post-trained DNN is reported. Our experiments over the development dataset of DCASE2019 1A and 1B show significant improvement, increasing 14% and 17.4 % compared to DCASE2019 baseline of 62.5% and 41.4%, respectively. The Kaggle report also confirms the classification accuracy of 79% and 69.2% for task 1A and 1B.
A Growth Model of the Data Economy

The rise of information technology and big data analytics has given rise to "the new economy. " But are its economics new? This article constructs a growth model where firms accumulate data, instead of capital. We incorporate three key features of data: 1) Data is a by-product of economic activity; 2) data is information used for prediction, and 3) uncertainty reduction enhances firm profitability. The model can explain why data-intensive goods or services, like apps, are given away for free, why many new entrants are unprofitable and why some of the biggest firms in the economy profit primarily from selling data. While our transition dynamics differ from those of traditional growth models, the long run still features diminishing returns. Just like accumulating capital, accumulating predictive data, by itself, cannot sustain long-run growth.
VARIETAL SUITABILITY ASSESSMENT OF SUGARCANE UNDER RAINFED CONDITION IN HIGH BARIND TRACT WITH CHICKPEA AS INTERCROP

An experiment was conducted under farmer’s condition during 2007-2008 and 2008-2009 cropping seasons to study the performance of six newly released sugarcane varieties viz. Isd 32, Isd 33, Isd 35, Isd 36, Isd 37 and Isd 38 at High Barind Tract of Rajshahi area under rainfed condition with chick pea as intercrop. The results revealed that Isd 37 showed the highest cane yield of 58.74 t ha followed by Isd 38 (51.09 t ha ), Isd 35 (46.87 t ha ), Isd 36 (44.94 t ha ), Isd 32 (39.11 t ha ) and Isd 33 (38.59 t ha ) among the newly released varieties in 2007-2008 cropping season. Incase of cropping season 2008-2009 variety Isd 37 also produced the highest cane yield of 78.63 t ha -1 followed by Isd 32 (70.45 t ha ), Isd 38 (69.01 t ha ), Isd 33 (65.37 t ha ), Isd 36 (61.90 t ha ), and Isd 35 (57.43 t ha ). Highest adjusted cane yield of 78.74 t ha -1 and 99.03 t ha -1 was recorded from treatment T5 (Isd 37 + Chick pea) in cropping season 2007-2008 and 20082009 respectively. So there is a great scope to increase productivity of sugarcane by using newly released varieties in farmer’s fields in High Barind Tract of Rajshahi area under rainfed condition.
A water flow-like algorithm for capacitated vehicle routing problem

The goal of the capacitated vehicle routing problem (CVRP) is finding a useful vehicle route which is a fundamental issue of logistic management. CVRP consists of finding the optimal routes for distributing various items between customers and depot by a fleet of vehicles at a minimum traveling distance without exceeding the capacity of each vehicle. Among many proposed schemes, meta-heuristic algorithm is a well-known optimization method for solving non-deterministic polynomial-time problems. The populationbased meta-heuristics has shown the ability to obtain an excellent solution in many domains but consumes time. This is due to the nature of the algorithms that have fixed a number of the solutions, so they suffer from high computation times to reach the solution. In this paper a new approximation algorithm named Water Flow-like Algorithm (WFA) is proposed to tackle CVRP. It is inspired by the natural behavior of water flowing from a higher to a lower level, which is self-adaptive and dynamic based on population sizes and parameter settings. The performance of the proposed algorithm is evaluated using 14 benchmark datasets. The result shown performance of the proposed algorithm is comparable with other recent enhanced algorithms. Therefore, it indicates that WFA is a potential alternative to solve the CVRP using other enhancement on the algorithm.
Accurate visualization of distributed system execution

The concurrent execution of processes in a distributed system makes the interactions between them difficult to understand. A clear image of the execution sequence that occurs as well as possible alternative scenarios is paramount to providing the software engineer the understanding needed to create reliable software. In this thesis, we examine several representation methods of presenting a pictorial view of the execution of a distributed system and evaluate each in terms of three criteria. First, only those relationships that are created during the execution can and must be presented. Second, the presentation must be such that a reasonable amount of information can be extracted from the picture. Third, the technique must be scalable to an arbitrary large system. We begin by examining an informative and scalable technique. We expose a simple graph that, if present in the system's execution, prevents accuracy from being achieved in this technique. We prove that the absence of this graph is sufficient to ensure that an accurate representation is possible. Our own technique is developed next. We first prove that the technique is accurate in that a representation of an interprocess relationship is presented if and only if it actually exists in the execution being displayed. While being theoretically scalable to any number of processes, the practical use of the technique is limited to a small distributed system that is relatively short-lived. In addition, the display is of only moderate use since it quickly becomes too complex for information to be extracted by the software engineer. We conclude by presenting our second technique that is accurate, scalable, and informative. The technique does not attempt to display the complete set of relationships in a single picture. Instead, an accurate subset is presented and the engineer is allowed to easily alter the selected subset. We show that the presentation is clear and concise. An implemented prototype demonstrates the utility of the technique in providing the software engineer with a tool that can be used to develop software that is less likely to suffer from concurrency related problems.
Efficient Featurized Image Pyramid Network for Single Shot Detector

Single-stage object detectors have recently gained popularity due to their combined advantage of high detection accuracy and real-time speed. However, while promising results have been achieved by these detectors on standard-sized objects, their performance on small objects is far from satisfactory. To detect very small/large objects, classical pyramid representation can be exploited, where an image pyramid is used to build a feature pyramid (featurized image pyramid), enabling detection across a range of scales. Existing single-stage detectors avoid such a featurized image pyramid representation due to its memory and time complexity. In this paper, we introduce a light-weight architecture to efficiently produce featurized image pyramid in a single-stage detection framework. The resulting multi-scale features are then injected into the prediction layers of the detector using an attention module. The performance of our detector is validated on two benchmarks: PASCAL VOC and MS COCO. For a 300$\times$300 input, our detector operates at 111 frames per second (FPS) on a Titan X GPU, providing state-of-the-art detection accuracy on PASCAL VOC 2007 testset. On the MS COCO testset, our detector achieves state-of-the-art results surpassing all existing single-stage methods in the case of single-scale inference.
Leveraging the Present to Anticipate the Future in Videos

Anticipating actions before they are executed is crucial for a wide range of practical applications including autonomous driving and the moderation of live video streaming. While most prior work in this area requires partial observation of executed actions, in the paper we focus on anticipating actions seconds before they start. Our proposed approach is the fusion of a purely anticipatory model with a complementary model constrained to reason about the present. In particular, the latter predicts present action and scene attributes, and reasons about how they evolve over time. By doing so, we aim at modeling action anticipation at a more conceptual level than directly predicting future actions. Our model outperforms previously reported methods on the EPIC-KITCHENS and Breakfast datasets.
CALIBRATING NEURAL NETWORKS FOR SECONDARY RECORDING DEVICES Technical Report

This report describes the solution to Task 1B of the DCASE 2019 challenge proposed by Samsung R&D Institute Poland. Primary focus of the system for task 1B was a novel technique designed to address issues with learning from microphones with different frequency responses in settings with limited examples for the targeted secondary devices. This technique is independent from the architecture of the predictive model and requires just a few examples to become effective.
Effects of the 1986-1987 El Nino on Variations of the Vertical and the Tilt

We attempt to present an explanation for both deflection of the vertical westward 1 ms at Mizusawa and the E-up tilt 50 mas at the Esashi Earth Tides Station during the 1986-1987 El Nino by taking into account bottom pressure which uplifts the northeast Japan plate . The Indo-extratropic Pacific coupling and increase of length of day (LOD) transfer the atmospheric angular momentum (AAM) into the solid Earth in the form of bottom pressure . Ocean loading due to bottom pressure is so small that we can not explain variations of tilt during the El Nino event with the use of ocean tidal loading models . Both the northeast Japan plate and the Pacific plate are assumed to be a thin elastic plate and a moderately thick elastic plate . The Pacific plate bends by uniform pressure over the surface . The ocean plate extends horizontally near the surface relative to the bottom side. The ocean plate induces the bending mo68 Chuichi Kakuta, Tsuneya Tsubokawa and Kenzaburo Iwadate ment at the edge, which contacts with the island plate. We assume that the bending moment and the modified combined shearing forces are continuous at the boundary between the Pacific plate and the island plate. The island plate bends by the buoyancy forces when the plate is depressed into the lower high density layer, and the compressive stress. With numerical values of uniform bottom pressure 25 Pa over 500 km from the center of the loaded ocean plate, the horizontal length of the island plate taken to be 400 km and 300 km with the elastic thickness to be 30 km, we have deflection of the vertical westwards to be 18 is and 25 ,us at Mizusawa and the E-up tilt to be 30 mas and 40 mas at Esashi respectively. A thin elastic plate shows large values of bending contrary to the observational results.
The Great Micro Moderation∗

This paper documents that individual income volatility in the United States has declined in an almost secular fashion since 1980—a phenomenon that we call the “Great Micro Moderation. ” This finding contrasts with the conventional wisdom, based on studies using survey data, that income volatility—a simple measure of uncertainty—has increased substantially during the same period. The finding of declining volatility is consistent with a handful of recent papers that use administrative data. We substantially extend the existing empirical findings of declining volatility using data from both administrative and survey-based data sets. A key contribution of our paper is to link patterns of income volatility on the worker side to outcomes (and volatility) on the firm/employer side. With the information revealed by these linkages, we investigate several potential drivers of this trend to understand if declining volatility represents a broadly positive development—declining income risk and uncertainty—or a negative one, i.e., declining business dynamism.
Mares Susceptible or Resistant to Endometritis Have Similar Endometrial Echographic and Inflammatory Cell Reactions at 96 Hours After Infusion with Frozen Semen and Extender

Mares susceptible or resistant to endometritis have a similar mild endometrial inflammatory cell reaction after breeding with frozen semen or extender. Uterine ultrasonographic features posttreatment with frozen semen or extender were similar in resistant and susceptible mares, but fluid at 24 and 96 h was a risk factor for susceptibility after bacterial infection. After artificial insemination with frozen semen, mares that develop significant post-breeding inflammation are likely to have bacterial endometritis. Author’s address: Mount Vernon Veterinary Service, Bradshaw Rd, Mount Vernon, WA 98273; Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine (Otto, Ball, Card); Department of Obstetrics and Gynecology, Royal University Hospital, (Pierson), University of Saskatchewan, Saskatoon, Saskatchewan, S7N 5B4, Canada © 2002 AAEP.
Centrifugal Incentives In Multicandidate Elections

This paper analyzes factors that affect candidates’ position-taking incentives in multicandidate and multiparty elections. Following Cox (1990), we define centrifugal incentives as those that motivate vote-seeking candidates to take more extreme positions relative to the center of the voter distribution. For a multivariate vote model that includes a Left-Right policy component, a party identification component and an unmeasured term that renders the vote choice probabilistic, we present theoretical and computer simulation results that quantify candidates’ incentives to shift their policies away from the center in the direction of their partisan constituencies’ mean policy preferences. Centrifugal incentives are found to increase with (1) the salience of policies and party identification, (2) the size of the candidate field, (3) the size of a candidate’s partisan constituency, and (4) more extreme constituency policy preferences. Thus, ceteris paribus, candidates who represent large constituencies are motivated to present more extreme policies than are candidates who represent small ones.
Incorporating vegetation time series to improve rainfall-runoff model predictions in gauged and ungauged catchments

Conceptual lumped rainfall-runoff models are widely used for surface runoff predictions in gauged and ungauged catchments because they are simple, easy to calibrate, and give relatively accurate runoff predictions. Lumped rainfall-runoff model inputs are generally daily precipitation and potential evapotranspiration (or air temperature). Vegetation processes are seldom considered in these models but they can play an important role in controlling runoff production in mid-latitude catchments. The use of surface vegetation information in rainfall-runoff modeling may allow better estimation of water balance components, evapotranspiration and water storage change resulting in more accurate estimates of runoff. Most studies calibrate the rainfall-runoff models against gauged streamflow data and use regionalisation methods to specify parameter values to model runoff in the ungauged catchments. This study investigates the potential to improve runoff and soil moisture prediction by incorporating vegetation time series data into lumped rainfall-runoff modelling. The modelling experiments are carried out using a daily conceptual lumped rainfall-runoff model SIMHYD. Daily rainfall, meteorological and streamflow data, NOAA-AVHRR monthly remotely sensed leaf area and TRMM-TMI daily microwave surface soil moisture for 470 unregulated catchments (50-5,000 km) across Australia over the period of 1981 to 2006 are used. The SIMHYD model is adapted to incorporate leaf area index series and land cover types by modifying the evapotranspiration sub-model (called SIMHYD-ET), with an additional one parameter for SIMHYD (total of 10 parameters). The original and modified versions of the SIMHYD models are then calibrated against daily streamflow in each of the 470 catchments. The model’s ability to predict runoff and soil moisture in ‘ungauged’ catchments is then assessed by using parameter values from the geographically closest gauged catchment. The calibration and prediction results of the SIMHYD and SIMHYD-ET models are then evaluated using the Nash-Sutcliffe Efficiency (NSE) of daily runoff, Water Balance Errors (WBE) percentage and correlation coefficient between modeled daily soil moisture and TRMM-TMI soil moisture. The modelling results indicate that the daily runoff series and total runoff volume modelled by the SIMHYDET model are similar to (or only very marginally better than) those simulated by the original SIMHYD model. The SIMHYD-ET model, however, performs noticeably better than the SIMHYD model in soil moisture predictions for both gauged and ungauged catchments. It is possible that better prediction skills can be achieved by modifying the lumped SIMHYD-ET model into a gridded model to take advantage of gridded/spatial rainfall and remote sensing data (leaf area index and land cover types) inputs.
1 0 O ct 2 01 6 Homogenization for dislocation based gradient visco-plasticity

In this work we study the homogenization for infinitesimal dislocation based gradient viscoplasticity with linear kinematic hardening and general non-associative monotone plastic flows. The constitutive equations in the models we study are assumed to be only of monotone type. Based on the generalized version of Korn’s inequality for incompatible tensor fields (the non-symmetric plastic distortion) due to Neff/Pauly/Witsch, we derive uniform estimates for the solutions of quasistatic initial-boundary value problems under consideration and then using a modified unfolding operator technique and a monotone operator method we obtain the homogenized system of equations. A new unfolding result for the Curl Curloperator is presented in this work as well. The proof of the last result is based on the Helmholtz-Weyl decomposition for vector fields in general L -spaces.
Postprint A new spatio-spectral morphological segmentation for multispectral remote sensing images

A general framework of spatio-spectral segmentation for multispectral images is introduced in this paper. The method is based on classification-driven stochastic watershed by Monte Carlo simulations, and it gives more regular and reliable contours than standard watershed. The present approach is decomposed into several sequential steps. First, a dimensionality reduction stage is performed using Factor Correspondence Analysis method. In this context, a new way to select the factor axes (eigenvectors) according to their spatial information is introduced. Then a spectral classification produces a spectral pre-segmentation of the image. Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic watershed approach driven by the spectral classification. The pdf of contours is finally segmented by a watershed controlled by markers coming from a regularization of the initial classification.
Estimating regional model parameters using spatial land cover information – implications for predictions in ungauged basins

There have been numerous regionalization studies on runoff prediction in ungauged catchments. Most studies calibrate the rainfall-runoff (RR) models against gauged streamflow data and use regionalisation methods to specify parameter values to model runoff in the ungauged catchments. This study evaluates the relative benefits of different calibration (local and regional) and regionalisation methods (nearest neighbour and regional forest and non-forest parameters). The modelling experiments are carried out using two RR models (Sacramento and SIMHYD) and daily climate and streamflow data for 10 unregulated gauged catchments from southeast Australia. Three sets of model calibrations are carried out. In the first set, the rainfall-runoff models are calibrated to individual catchments. In the second set, spatial land cover information is used to calibrate two sets of rainfall-runoff model parameters (one set for forest and another for non-forest, a total of two calibrated parameter sets for all 10 catchments). In the third set, a cross-verification analysis is carried out where one catchment is dropped out in turn and the models are calibrated to the remaining nine catchments together using forest and non-forest land cover labels for individual grid cells within each catchment (one set for forest and another for non-forest, a total of two calibrated parameter sets for all nine catchments). The calibrated models ability to predict runoff in ‘ungauged’ catchments is then assessed by: (i) using parameter values from the geographically closest gauged catchment (nearest neighbour regionalisation); (ii) using forest and non-forest parameter sets calibrated to the remaining nine catchments (forest and non-forest regionalisation). The calibration results show that the median of Nash-Sutcliffe efficiency (NSE) for the 10 catchments for individual catchment calibration is slightly higher than that for the regional forest and non-forest calibration. This is to be expected as we are calibrating only two sets of parameter values in the latter case instead of 10 sets. But the regionalisation results show that incorporating spatial land cover information in the gridded calibration of RR model improves runoff prediction in ungauged catchments with the median of regionalisation NSE for the forest and non-forest regionalisation significantly higher than that for the nearest neighbour method. The results and conclusions here are based on analysis undertaken using only 10 catchments and all these catchments are in close proximity with similar climate and physical characteristics. The results may be different when using catchments spread across a larger region. But the results here suggest that this approach can provide better estimates of runoff within a river basin and it has the potential to be used to estimate the impacts of land cover change. We are currently exploring the applicability and suitability of the approach over a larger region using data from 100 catchments in southeast Australia.
The yield curve and its relation to economic activity in Mexico

In this paper are analyzed the main components of the interest rate variation of debt instruments, issued by the Mexican Government, in the period 1978-2017. By means of an autoregressive vector, there is M. B. Mota Aragón y L. Mata Mata / Contaduría y Administración 63 (4), 2018, 1-24 http://dx.doi. org/10.22201/fca.24488410e. 2018.1204 2