Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing the assessment of population stability as a protection goal in ecotoxicology

Intent. The intent of Learned Discourses is to provide a forum for open discussion. These articles reflect the professional opinions of the authors regarding scientific issues. They do not represent SETAC positions or policies. And, although they are subject to editorial review for clarity, consistency, and brevity, these articles are not peer reviewed. The Learned Discourses date from 1996 in the North America SETAC News and, when that publication was replaced by the SETAC Globe, continued there through 2005. The continued success of Learned Discourses depends on our contributors. We encourage timely submissions that inform and stimulate discussion. We expect that many of the articles will address controversial topics and promise to give dissenting opinions a chance to be heard. This section is dedicated to the memory of Dr. Peter M. Chapman, who founded Learned Discourses and served as Editor until he passed away in 2017. Rules.All submissionsmust be succinct: no longer than 1000 words, nomore than6 references, andatmost 1 table or figure. Reference format must follow the journal requirement found at http://www.setacjournals. org. Topics must fall within IEAM’s sphere of interest. Submissions.All manuscripts should be sent via email as Word attachments to the IEAM Editorial Office (learned_ discourses@setac.org).
Parameterization of Atmospheric Convection ( COSTES 0905 book ) in

It may seem a little bit surprising to initiate and put forward this kind of research, because one may consider that all the basic moist-air properties are already described in the available textbooks dealing with atmospheric thermodynamics (de Groot and Mazur, 1962; Glansdorff and Prigogine, 1971; Iribarne and Godson, 1973; Dufour and Van Mieghem, 1975; Bejan, 1988; Emanuel, 1994; Bohren and Albrecht, 1998; Zdunkowski and Bott, 2004; Ambaum, 2010). It is however explained in this chapter that there are some aspects which are not easily understood and some problems which are not easy to solve cleanly in order to compute locally the moist-air enthalpy and entropy. It is shown in next sections that the computations of these local state functions require the knowledge of reference values of enthalpies and entropies for all species forming the moist-air (basically N2, O2 and H2O), and that the Third Law of thermodynamics can be used to improve the present formulas for moist-air enthalpy and entropy.
Building Capacity for Trauma-Informed Care in the Child Welfare System

Exposure to childhood trauma is a major public health concern and is especially prevalent among children in the child welfare system (CWS). State and tribal CWSs are increasingly focusing efforts on identifying and serving children exposed to trauma through the creation of trauma-informed systems. This evaluation of a statewide initiative in Connecticut describes the strategies used to create a trauma-informed CWS, including workforce development, trauma screening, policy change, and improved access to evidence-based trauma-focused treatments during the initial 2-year implementation period. Changes in system readiness and capacity to deliver trauma-informed care were evaluated using stratified random samples of child welfare staff who completed a comprehensive assessment prior to (N = 223) and 2 years following implementation (N = 231). Results indicated significant improvements in trauma-informed knowledge, practice, and collaboration across nearly all child welfare domains assessed, suggesting system-wide improvements in readiness and capacity to provide trauma-informed care. Variability across domains was observed, and frontline staff reported greater improvements than supervisors/managers in some domains. Lessons learned and recommendations for implementation and evaluation of trauma-informed care in child welfare and other child-serving systems are discussed.
Formation Mechanism of “Drag Depressions” and Irregular Boundaries in Intraplate Deformation

Almost all intraplate caprocks experienced strong deformation during the convergence of microplates, and then disintegrated into many secondary geologic units with the special characters, such as irregular boundaries and particular structural assemblages. In order to understand the formation mechanism of these special phenomena, a rheological experiment on the structural scenery of the Tongling area is carried out. The result shows that the primary regular and uniform boundaries of the Tongling area becomes irregular because of the enclosing and confinement of surrounding geological units in the process of "compression-shearing-rotation-drag"; simultaneously, two specific "drag depressions" developed at two opposite comers of the block. The former and the later phenomena can be regarded as a typical regional-scale rheological effect and necessary outcome of intraplate deformation respectively.
El Niño – related precipitation variability in Perú

Abstract. The relationship between monthly mean sea surface temperature (SST) anomalies in the commonly used El Nino regions and precipitation for 44 stations in Peru is documented for 1950–2002. Linear lag correlation analysis is employed to establish the potential for statistical precipitation forecasts from SSTs. Useful monthly mean precipitation anomaly forecasts are possible for several locations and calendar months if SST anomalies in El Nino 1+2, Nino 3.4, and Nino 4 regions are available. Prediction of SST anomalies in El Nino regions is routinely available from Climate Prediction Center, NOAA, with reasonable skill in the El Nino 3.4 region, but the prediction in El Nino 1+2 region is less reliable. The feasibility of using predicted SST anomalies in the El Nino 3.4 region to predict SST anomalies in El Nino 1+2 region is discussed.
Repurposing Entailment for Multi-Hop Question Answering Tasks

Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which require reasoning with multiple sentences, it remains unclear how best to utilize entailment models pre-trained on large scale datasets such as SNLI, which are based on sentence pairs. We introduce Multee, a general architecture that can effectively use entailment models for multi-hop QA tasks. Multee uses (i) a local module that helps locate important sentences, thereby avoiding distracting information, and (ii) a global module that aggregates information by effectively incorporating importance weights. Importantly, we show that both modules can use entailment functions pre-trained on a large scale NLI datasets. We evaluate performance on MultiRC and OpenBookQA, two multihop QA datasets. When using an entailment function pre-trained on NLI datasets, Multee outperforms QA models trained only on the target QA datasets and the OpenAI transformer models.
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization

Recent work has shown that stochastically perturbed gradient methods can efficiently escape strict saddle points of smooth functions. We extend this body of work to nonsmooth optimization, by analyzing an inexact analogue of a stochastically perturbed gradient method applied to the Moreau envelope. The main conclusion is that a variety of algorithms for nonsmooth optimization can escape strict saddle points of the Moreau envelope at a controlled rate. The main technical insight is that typical algorithms applied to the proximal subproblem yield directions that approximate the gradient of the Moreau envelope in relative terms.
Augmented MPM for phase-change and varied materials

In this paper, we introduce a novel material point method for heat transport, melting and solidifying materials. This brings a wider range of material behaviors into reach of the already versatile material point method. This is in contrast to best-of-breed fluid, solid or rigid body solvers that are difficult to adapt to a wide range of materials. Extending the material point method requires several contributions. We introduce a dilational/deviatoric splitting of the constitutive model and show that an implicit treatment of the Eulerian evolution of the dilational part can be used to simulate arbitrarily incompressible materials. Furthermore, we show that this treatment reduces to a parabolic equation for moderate compressibility and an elliptic, Chorin-style projection at the incompressible limit. Since projections are naturally done on marker and cell (MAC) grids, we devise a staggered grid MPM method. Lastly, to generate varying material parameters, we adapt a heat-equation solver to a material point framework.
Forebrain deletion of the dystonia protein torsinA causes dystonic-like movements and loss of striatal cholinergic neurons

Striatal dysfunction plays an important role in dystonia, but the striatal cell types that contribute to abnormal movements are poorly defined. We demonstrate that conditional deletion of the DYT1 dystonia protein torsinA in embryonic progenitors of forebrain cholinergic and GABAergic neurons causes dystonic-like twisting movements that emerge during juvenile CNS maturation. The onset of these movements coincides with selective degeneration of dorsal striatal large cholinergic interneurons (LCI), and surviving LCI exhibit morphological, electrophysiological, and connectivity abnormalities. Consistent with the importance of this LCI pathology, murine dystonic-like movements are reduced significantly with an antimuscarinic agent used clinically, and we identify cholinergic abnormalities in postmortem striatal tissue from DYT1 dystonia patients. These findings demonstrate that dorsal LCI have a unique requirement for torsinA function during striatal maturation, and link abnormalities of these cells to dystonic-like movements in an overtly symptomatic animal model. DOI: http://dx.doi. org/10.7554/eLife.08352. 001
Code package for calculating modified cosmic ionization and thermal histories with dark matter and other exotic energy injections

We present a new public Python package, DarkHistory, for computing the effects of dark matter annihilation and decay on the temperature and ionization history of the early universe. DarkHistory simultaneously solves for the evolution of the free electron fraction and gas temperature, and for the cooling of annihilation/decay products and the secondary particles produced in the process. Consequently, we can self-consistently include the effects of both astrophysical and exotic sources of heating and ionization, and automatically take into account backreaction, where modifications to the ionization/temperature history in turn modify the energy-loss processes for injected particles. We present a number of worked examples, demonstrating how to use the code in a range of different configurations, in particular for arbitrary dark matter masses and annihilation/decay final states. Possible applications of DarkHistory include mapping out the effects of dark matter annihilation/decay on the global 21cm signal and the epoch of reionization, as well as the effects of exotic energy injections other than dark matter annihilation/decay. The code is available at this https URL with documentation at this https URL . Data files required to run the code can be downloaded at this https URL .
The Endoplasmic Reticulum—Gateway of the Secretory Pathway

In the mid to late 1960s, the discovery that eukaryotic secreted proteins are first segregated in the lumen of the endoplasmic reticulum (ER) before traveling within membranous structures to reach the cell surface placed the ER at the start point of a newly recognized metabolic pathway now known as
Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories With GANs

This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene. As GANs are very susceptible to mode collapsing and dropping, we show that the recently proposed Info-GAN allows dramatic improvements in multi-modal pedestrian trajectory prediction to avoid these issues. We also left out L2-loss in training the generator, unlike some previous works, because it causes serious mode collapsing though faster convergence. We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution. In particular, to prove this claim, we have designed a toy example dataset of trajectories that can be used to assess the performance of different methods in preserving the predictive distribution modes.
High-Order Vertex-Centered U-MUSCL Schemes for Turbulent Flows

Many production and commercial unstructured CFD codes provide no better than 2nd-order spatial accuracy. Unlike structured grid procedures where there is an implied structured connectivity between neighboring grid points, for unstructured grids, it is more difficult to compute higher derivatives due to a lack of explicit connectivity beyond the first neighboring cells. Our goal is to develop a modular highorder scheme with low dissipation flux difference splitting that can be integrated into existing CFD codes for use in improving the solution accuracy and to enable better prediction of complex physics and noise mechanisms and propagation. In a previous study, a 3rd-order U-MUSCL scheme using a successive differentiation method was derived and implemented in FUN3D. Verification studies of the acoustic benchmark problems showed that the new scheme can achieve up to 4th-order accuracy. Application of the high-order scheme to acoustic transport and transition-to-turbulence problems demonstrated that with just 10% overhead, the solution accuracy can be dramatically improved by as much as a factor of eight. This paper examines the accuracy of the high-order scheme for turbulent flow over single and tandem cylinders. Considerably better agreement with experimental data is observed when using the new 3rd-order U-MUSCL scheme. AMS subject classifications: 76M12
Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market

Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.
Exoplanet Reflected-light Spectroscopy with PICASO

Here we present the first open-source radiative transfer model for computing the reflected light of exoplanets at any phase geometry, called PICASO: the planetary intensity code for atmospheric scattering observations. This code, written in Python, has heritage from a decades-old, well-known Fortran model used for several studies of planetary objects within the solar system and beyond. We have adopted it to include several methodologies for computing both direct and diffuse-scattering phase functions, and have added several updates including the ability to compute Raman scattering spectral features. Here we benchmark PICASO against two independent codes and discuss the degree to which the model is sensitive to a user’s specification for various phase functions. Then, we conduct a full information-content study of the model across a wide parameter space in temperature, cloud profile, signal-to-noise ratio, and resolving power.
Semantic segmentation of remote sensing ship image via a convolutional neural networks model

Semantic segmentation of remote sensing ship targets is one of the most challenging works in image processing, especially for small and multi-scale ship target detection. To solve these problems, an efficient method based on convolutional neural networks (CNN) to detect ship targets is proposed. This method introduces the attention model to the network to enhance the characteristics of small targets and combines atrous convolution with traditional CNN to increase the receptive field. To preserve the information lost by pooling, the proposed method uses the passthrough layer method to retain more features and concatenate the high- and low-resolution features. To verify the effectiveness of the method proposed in this study, the performance was evaluated by using precision, recall, F1-Score, mean intersection-over-union (IoU), and pixel accuracy measurements. These performances are all higher than the traditional semantic segmentation network SegNet. Mean IoU increases to 0.783 and pixel accuracy increases to 0.935. This method can conclusively identify ship targets in remote sensing images and has a certain reference value for remote sensing target detection.
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In this paper, we propose a novel meshfree Generalized Finite Difference Method (GFDM) approach to discretize PDEs defined on manifolds. Derivative approximations for the same are done directly on the tangential space, in a manner that mimics the procedure followed in volume-based meshfree GFDMs. As a result, the proposed method not only does not require a mesh, it also does not require an explicit reconstruction of the manifold. In contrast to existing methods, it avoids the complexities of dealing with a manifold metric, while also avoiding the need to solve a PDE in the embedding space. A major advantage of this method is that all developments in usual volume-based numerical methods can be directly ported over to surfaces using this framework. We propose discretizations of the surface gradient operator, the surface Laplacian and surface Diffusion operators. Possibilities to deal with anisotropic and discontinous surface properties (with large jumps) are also introduced, and a few practical applications are presented.
A Three-Dimensional Polar Ice-Sheet Model

Abstract A three-dimensional model of the temperature and velocity distribution within any arbitrary-shaped ice mass is described. There is a mutual interaction in the model between the flow of the ice and its thermodynamics, since the flow law used in the model is temperature-dependent. Ice growth in three dimensions is governed by mass accumulation through precipitation, by mass depletion through loss of ice over the ocean, and by continuity requirements. Phase changes at the base of the ice are accounted for. The model has been applied in art exploratory manner to the Greenland ice sheet. Changes in the ice shape and temperature are presented and discussed. The basic shortcoming of the model as here presented appears primarily due to the coarse finite-difference mesh used, and to an unsophisticated approach to modelling the boundary ice.
Precision of working memory for speech sounds

Memory for speech sounds is a key component of models of verbal working memory (WM). But how good is verbal WM? Most investigations assess this using binary report measures to derive a fixed number of items that can be stored. However, recent findings in visual WM have challenged such “quantized” views by employing measures of recall precision with an analogue response scale. WM for speech sounds might rely on both continuous and categorical storage mechanisms. Using a novel speech matching paradigm, we measured WM recall precision for phonemes. Vowel qualities were sampled from a formant space continuum. A probe vowel had to be adjusted to match the vowel quality of a target on a continuous, analogue response scale. Crucially, this provided an index of the variability of a memory representation around its true value and thus allowed us to estimate how memories were distorted from the original sounds. Memory load affected the quality of speech sound recall in two ways. First, there was a gradual decline in recall precision with increasing number of items, consistent with the view that WM representations of speech sounds become noisier with an increase in the number of items held in memory, just as for vision. Based on multidimensional scaling (MDS), the level of noise appeared to be reflected in distortions of the formant space. Second, as memory load increased, there was evidence of greater clustering of participants’ responses around particular vowels. A mixture model captured both continuous and categorical responses, demonstrating a shift from continuous to categorical memory with increasing WM load. This suggests that direct acoustic storage can be used for single items, but when more items must be stored, categorical representations must be used.
Corporate Governance In The Nigerian Banking Sector: An Ethical Analysis Of The 2009 Regulator Intervention And Operators’ Behaviors

This paper conducts an ethical analysis of the 2009 Central Bank of Nigeria intervention: the firing of the CEOs of eight banks in Nigeria and their boards of directors, their replacement with CBN-appointed officers and the injection of government funds into the affected banks; as well as an analysis of the behaviors of the fired executives as leaders of operator banks in the Nigerian Banking system. Within the ethical frameworks of the Stakeholder theory, the Systems theory of Ethics and the Caux Round Table Business Principles, the results of the analysis justify the actions of the CBN as ethical and label the behaviors of the bank operator executives as unethical. The basis of this analysis stems from the selected frameworks that view the Nigerian banking system as an inter play of relationships amongst stakeholders with different but unique responsibilities and which must operate within the boundaries of ethics, for to operate outside these boundaries is to act inimically to the essence and purposes for which these institutions were initially set up and to the detriment of the banking system. The above position underscores the underlying tenet that business and ethical actions cannot be separated. As such this paper recommends the investment in ethics programs and ethics investigation models by the stakeholders in the Nigerian banking system. Disciplines Organizational Behavior and Theory Comments Submitted to the Program of Organizational Dynamics in the Graduate Division of the School of Arts and Sciences in Partial Fulfillment of the Requirements for the Degree of Master of Science in Organizational Dynamics at the University of Pennsylvania Advisor: John H. Fielder This thesis or dissertation is available at ScholarlyCommons: http://repository.upenn. edu/od_theses_msod/29 CORPORATE GOVERNANCE IN THE NIGERIAN BANKING SECTOR: AN ETHICAL ANALYSIS OF THE 2009 REGULATOR INTERVENTION AND OPERATORS’ BEHAVIORS