Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissociation Between Speech and Emotion Effects in Short-Term Memory: A Data Reanalysis

Performance in visual serial recall tasks is often impaired by irrelevant auditory distracters. The duplex-mechanism account of auditory distraction states that if the distracters provide order cues, these interfere with the processing of the order cues in the serial recall task (interference by process). In contrast, the unitary account states that distracters capture only attention on a general level (attentional distraction) without interfering specifically with order processing. Marsh et al. (2018, Journal of Experimental Psychology-Learning Memory and Cognition, 44, 882-897) reported finding a dissociation between the effects of serial recall tasks and those of a missing-item task on the disruptive effects of speech and of emotional words, as predicted by the duplex-mechanism account. Critically, the reported analyses did not test specifically for the claimed dissociation. Therefore, I reanalyzed the Marsh et al. data and conducted the appropriate analyses. I also tested the dissociation more directly and added a Bayesian hypothesis test to measure the strength of the evidence for a dissociation. Results provided strong evidence for a dissociation (i.e., crossover interaction) between effects of speech and of emotion. Because the duplex-mechanism account predicts this dissociation between speech effects (interference by process) and emotion effects (attentional diversion) whereas the unitary account does not, Marsh et al.’s data support the duplex-mechanism account. However, to show that this dissociation is robust, researchers are advised to replicate this dissociation in an adversarial registered report.
Aspects of capacity of entanglement

Many quantum information theoretic quantities are similar to and/or inspired by thermodynamic quantities, with entanglement entropy being a well-known example. In this paper, we study a less well-known example, capacity of entanglement, which is the quantum information theoretic counterpart of heat capacity. It can be defined as the second cumulant of the entanglement spectrum and can be loosely thought of as the variance in the entanglement entropy. We review the definition of capacity of entanglement and its relation to various other quantities such as fidelity susceptibility and Fisher information. We then calculate the capacity of entanglement for various quantum systems, conformal and nonconformal quantum field theories in various dimensions, and examine their holographic gravity duals. Resembling the relation between response coefficients and order parameter fluctuations in Landau-Ginzburg theories, the capacity of entanglement in field theory is related to integrated gravity fluctuations in the bulk. We address the question of measurability, in the context of proposals to measure entanglement and Renyi entropies by relating them to U(1) charges fluctuating in and out of a subregion, for systems equivalent to non-interacting fermions. From our analysis, we find universal features in conformal field theories, in particular the area dependence of the capacity of entanglement appears to track that of the entanglement entropy. This relation is seen to be modified under perturbations from conformal invariance. In quenched 1+1 dimensional CFTs, we compute the rate of growth of the capacity of entanglement. The result may be used to refine the interpretation of entanglement spreading being carried by ballistic propagation of entangled quasiparticle pairs created at the quench.
Index estimates and compactness for constant mean curvature surfaces and Steklov eigenvalues

In this work, we focus on three problems. First, we give a relationship between the number of eigenvalues of the Jacobi operator below a certain threshold and the topology of closed constant mean curvature (CMC) surfaces in three-dimensional Riemannian manifolds. We then obtain that the (weak) Morse index of CMC surfaces in an arbitrary 3-manifold is bounded below by a linear function of the genus when the constant mean curvature is greater than a certain nonnegative value. In particular, this implies that stable CMC surfaces are topological spheres. Corresponding results for CMC surfaces with free boundary in 3-manifolds with boundary are obtained as well. Second, we consider the space of embedded free boundary CMC surfaces with bounded topology, bounded area, and bounded boundary length in a 3-manifold N with boundary. We show that this space is almost compact in the sense that any sequence of surfaces in this space has a convergent subsequence that converges to a free boundary CMC surface, graphically and smoothly except on a finite set of singularities. If in addition RicN > 0 and the boundary of N is convex, then the convergence is at most 2-sheeted. In particular, it is 1sheeted if the limiting surface is not a minimal surface. Third, we consider the maximization of Steklov eigenvalues in higher dimensions. We show that for compact manifolds of dimension at least 3 with nonempty boundary, we can modify the manifold by performing surgeries of codimension 2 or higher, while keeping the Steklov spectrum nearly unchanged. This shows that certain changes in the topology of a domain do not have an effect when considering shape optimization questions for Steklov eigenvalues in dimension 3 and higher.
Three‐dimensional numerical inversion of pneumatic cross‐hole tests in unsaturated fractured tuff: 1. Methodology and borehole effects

We describe a three‐dimensional numerical inverse model for the interpretation of cross‐hole pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site (ALRS) near Superior, Arizona. The model combines a finite volume flow simulator, FEHM, an automatic mesh generator, X3D, a parallelized version of an automatic parameter estimator, PEST, and a geostatistical package, GSTAT. The tests are simulated by considering single‐phase airflow through a porous continuum, which represents primarily interconnected fractures at the site. The simulator solves the airflow equations in their original nonlinear form and accounts directly for the ability of all packed‐off borehole intervals to store and conduct air through the system. Computations are performed in parallel on a supercomputer using 32 processors. We analyze pneumatic cross‐hole test data, previously conducted by our group at ALRS, in two ways: (1) by considering pressure records from individual borehole monitoring intervals one at a time, while treating the rock as being spatially uniform, and (2) by considering pressure records from multiple tests and borehole monitoring intervals simultaneously, while treating the rock as being randomly heterogeneous. The first approach yields a series of equivalent air permeabilities and air‐filled porosities for the rock volume being tested, having length scales of the order of meters to tens of meters. The second approach yields a high‐resolution geostatistical estimate of how air permeability and air‐filled porosity, defined on grid blocks having a length scale of 1 m, vary spatially throughout the tested rock volume. It amounts to three‐dimensional pneumatic “tomography” or stochastic imaging of the rock, a concept originally proposed by one of us in 1987. The first paper of this two‐part series describes the field data, the model, and the effect of boreholes on pressure propagation through the rock. The second paper implements our approach on selected cross‐hole test data from ALRS.
Endogenously Unstable Demand Deposit Contracts: Triggering Bank Runs without Sunspots

The literature on bank runs reduces all coordination mechanisms triggering attacks on banks to exogenous realizations derived from either fundamental or sunspot variables. We present a general equilibrium version of these models where the state uncertainty faced by depositors is modeled explicitly, such that bank runs arise as optimal endogenous equilibrium outcomes corresponding to Bayesian coordination games played by rational agents before depositing. Differentials in state information sets between the bank and its depositors lead to rational self-contained equilibrium runs that do not violate the revelation principle. Several numerical simulations illustrating these results are provided.
Testing for the Fisher Hypothesis under Regime Shifts in Turkey: New Evidence from Time-Varying Parameters

This paper examines the validity of Fisher hypothesis in Turkey for the time period 1987Q1-2010Q3. For this purpose, we employ cointegration test with a structural break as well as time varying parameters approach (TVP) that takes into account the effects of regime or policy changes on the relation between interest rate and inflation rate. The empirical results show that weak form of the Fisher hypothesis holds in Turkish economy.
Assessment of Pesticides Use and its Economic Impact on the Apiculture Subsector in Selected Districts of Amhara Region, Ethiopia

Apiculture is one of the most widely spread agricultural subsectors making substantial contribution to household food security through providing income, quality foods and assets to the rural beekeepers in Ethiopia. Ethiopia is endowed with immense apicultural resources and long standing traditional beekeeping practices. Recently, there is a growing pesticides grievance on honeybee population and their products decline with considerable economic impacts on beekeepers. To ascertain the gripe, field assessment was conducted on pesticides use and its economic impacts on the apiculture subsector in three districts of Amhara region (Dangila, Guangua and Mecha) in April 2014. Random household survey was conducted on a total of 270 respondents (90 per district) of which 137 were beekeepers and 133 none beekeepers. From the total respondents, 147 uses pesticides and Dimethoate 40% EC, Ethiolathion 50%, Karate 5EC, 2,4-D were the most often used pesticides. March, June, July and September are identified as pesticides applications months. From the total pesticides users (147), 114 (78%) apply before flowering stage, 25 both before and during flowering stages and the rest applies whenever they feel pests and/ or weeds occur. Besides, more than 60% of the respondents know pesticides kill honeybees and results in dwindling and absconding. Three years trends analysis of honeybee colony number and honey yield indicated dramatic decline mainly attributed to indiscriminate applications of pesticides. This assessment revealed indiscriminate uses of pesticides caused fatalities on 22987 honeybee colonies and incurred economic loss amounting USD 819291.37. This study is appropriate and timely to develop and implement effective development and extension strategies to minimize and/or control the ill effects of accidentally using pesticides.
Corporate Governance and Deposit Money Banks’ Performance in Nigeria

this study examined the influence of corporate governance on the financial performance of banks in Nigeria. The specific objective of the study is to examine the influence of corporate governance on the financial performance of banks. Bank performance was used as a dependent variable during Board size, Board composition. Gender diversity and Number of audit meetings were used as independent variables; profit after tax was used as a proxy to measure performance. The study adopted the ex-post facto as its research design while the secondary data was used sourcing its data from the annual report of banks for fifteen years (2003-2017). The student t-test, f-test, coefficient of determination and Pearson correlation were the statistical tools used in testing the significance of the variables and the model. The findings revealed that all the independent variables have a significant influence on the dependent variable through the Pearson correlation result revealed that Audit meeting, Board composition, and Gender diversity have a positive relationship while board size has a negative relationship with banks financial performance. The study conclusively agreed that corporate governance have an existing influence of money deposit bank banks performance and therefore we recommend that discretion of rules among the directors should be specified and democratically practiced within the banking rules.
Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

: In recent years, video surveillance application played a significant role in our daily lives. Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility. The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery, object detection, target killing, and surveillance. To remove fog and enhance visibility, a number of visibility enhancement algorithms and methods have been proposed in the past. However, these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision applications. The existing techniques do not perform well when images contain heavy fog, large white region and strong atmospheric light. This research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze images. The proposed framework is based on a Conditional generative adversarial network (CGAN) with two networks; generator and discriminator, each having distinct properties. The generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free image. Experiments are conducted demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images. visibility restoration (IVR) module and Haze thickness estimation (THE) module. The IVR module recovers brightness in the fog free image. For the test, 1586 real world images were used. Three well-known performance metrics were used such as e, r − , and σ . The Experimental result shows that the method good result than advised methods.
KILIMANJARO ICE CLIFF MONITORING WITH CLOSE RANGE PHOTOGRAMMETRY

The glaciers on the summit plateau of Kibo, the main peak of the Kilimanjaro massif (3°S, 37°E, 5895 m a.s.l.) in Tanzania, are characterized by steep ice cliffs at their margins. These form-persistent cliffs continuously retreat and, consequently, govern the decrease in plateau glacier area. In order to quantify the ice cliff recession and study their morphology, close-range terrestrial photogrammetry combined with automatic stereo matching techniques was used to derive high resolution digital surface models of a south-facing “sample cliff” at four different dates. Results confirm, firstly, the annually bimodal nature of the recession being 15 cm/month during a 4.5 month sunlit phase and 2 cm/month during the remaining 7.5 month shaded phase, and, secondly, the tendency towards an “ideal cliff orientation”, which is either south- or north-facing and about 70°-75° steep. Moreover, the hypothesis of a predefined decay period for the plateau ice is supported by this study and it is shown that terrestrial photogrammetry is not only cheap and lightweight but also very suitable for ice surveys at the decameter scale.
Artificial Counselor System for Stock Investment

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor should invest in different existing stocks to have an optimum expected profit considering their level of risk tolerance. Two different methods are used for suggesting best portions, which are Markowitz portfolio theory and fuzzy investment counselor. The first approach is an optimization-based method which considers merely technical features, while the second approach is based on Fuzzy Logic taking into account both technical and fundamental features of the stock market. The experimental results on New York Stock Exchange (NYSE) show the effectiveness of the proposed system.
Image Generation From Small Datasets via Batch Statistics Adaptation

Thanks to the recent development of deep generative models, it is becoming possible to generate high-quality images with both fidelity and diversity. However, the training of such generative models requires a large dataset. To reduce the amount of data required, we propose a new method for transferring prior knowledge of the pre-trained generator, which is trained with a large dataset, to a small dataset in a different domain. Using such prior knowledge, the model can generate images leveraging some common sense that cannot be acquired from a small dataset. In this work, we propose a novel method focusing on the parameters for batch statistics, scale and shift, of the hidden layers in the generator. By training only these parameters in a supervised manner, we achieved stable training of the generator, and our method can generate higher quality images compared to previous methods without collapsing, even when the dataset is small (~100). Our results show that the diversity of the filters acquired in the pre-trained generator is important for the performance on the target domain. Our method makes it possible to add a new class or domain to a pre-trained generator without disturbing the performance on the original domain. Code is available at github.com/nogu-atsu/small-dataset-image-generation
Fire history in the Araucaria araucana forests of Argentina: human and climate influences

Little is known about drivers and trends of historic fire regimes in the Araucaria araucana forests of south-western Argentina. Fire history in these forests was reconstructed by the analysis of 246 fire-scarred partial cross-sections from this fire-resistant tree collected at 10 sites in Neuquen, northern Patagonia. Fire chronologies showed an increase in fire occurrence during the nineteenth century and a sharp decrease since the early twentieth century. The creation of Lanin National Park in 1937, the change in human activities, and the active suppression of wildfires led to a significant increase in mean fire intervals since 1930. In addition to these multidecadal to centennial scale drives of fire frequency, interannual variability in wildfire activity was associated with El Nino–Southern Oscillation. Years of widespread fire are related to negative departures of both Nino 3.4 and Pacific Decadal Oscillation indexes (i.e. La Nina conditions), as well as coincident phases of positive Southern Annular Mode and La Nina events. Temporal variations in the Araucaria fire history in Argentina clearly show the combined effect of human and climate influences on fire regimes. A comparison with previous fire history studies in the Araucaria forests of Chile reveals substantial differences related to differences in human activities on both sides of the Andes and the earlier implementation of protected areas in Argentina.
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data

In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two 2D and a 3D deep learning-based post processing methods, three iterative reconstruction methods and two recently proposed methods for dynamic cardiac MRI based on 2D and 3D cascaded networks. Our method outperforms the 2D spatially trained U-net and the 2D spatio-temporal U-net. Compared to the 3D spatio-temporal U-net, our method delivers comparable results, but requiring shorter training times and less training data. Compared to the compressed sensing-based methods kt-FOCUSS and a total variation regularized reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to the iterative reconstruction method based on adaptive regularization with dictionary learning and total variation and when compared to the methods based on cascaded networks, while only requiring a small fraction of the computational and training time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the one of the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial 2D U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required.
Molecular Detection of Staphylococcal enterotoxin C (SEC) and Staphylococcal enterotoxin D (SED) from the Cattle Nares in Aizawl, Mizoram (India)

Staphylococcus aureus is a commensal and significant opportunistic pathogen that causes a wide range of diseases in humans and animals, with a high effect on public health and the livestock sector (Rahimi et al., 2015). S. aureus develops a large variety of staphylococcal enterotoxins with demonstrated emetic activities (SEs, SEA to SEE, SEG to SEI, SER to SET) as well as staphylococcal-like (SEL) proteins not emetic in the primate model (SElL and SElQ) (Argudin et al., 2010). Staphylococcal enterotoxins (SE) can cause toxin-mediated disease, and those that function as superantigens are implicated in the pathogenesis of allergic diseases (Varshney et al., 2009).
COMPARATIVE STUDY OF SIX MAIZE (ZEA MAYS L.) CULTIVARS CONCERNING CADMIUM UPTAKE, PARTITIONING AND TOLERANCE

This experiment was conducted to determine Cd accumulation, partitioning, translocation and their ultimate consequences on growth and biochemical traits of six maize cultivars to screen out Cd tolerant one. A significant decrease in growth and biochemical traits as well as in Cd tolerance index was found, especially at higher Cd level. Moreover, the varieties and Cd treatments were found significant for the growth and biochemical traits. Uptake of Cd, Ca, K and P in the different plant tissues were significantly affected by the varieties and Cd treatments. Translocation, bioconcentration and bioaccumulation factors were found non-significant for varieties and Cd treatments. A significant positive correlation was found between growth and biochemical traits while, among plant total Cd, Ca, K and P, total plant dry; and fresh weight and plant height were significantly negative. Furthermore, the PC1 was loaded by growth traits; Ca, K and P while PC2 by the Cd in different plant tissues. Hence, a strong negative correlation was observed between the PC1 and PC2 parameters. The maximum Cd accumulation was in the roots rather than the crown and leaf tissues of maize cultivars. The EV-1098 was to be more tolerant to Cd stress than other cultivars.