Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate and development finance institutions: linking climate finance, development finance and the transition to low-carbon, climate-resilient economic models

Development finance institutions (DFIs) are in a position to be key actors in aligning development and the 2° challenge. One of the principal challenges t oday is to scale-up the financial flows to the trillions of dollars per year necessary to achieve the 2°C lo ng-term objectives. Achieving this transition to a low-carbon, climate resilient (LCCR) economic model requires the integration or ‘mainstreaming’ of climate issues as a prism through which all investment decisions should be made. This paper presents an overview of the opportunities and challenges of linking a LCCR transition with the objectives of development finance. It first presents the two-fold challenge of climate change and development for countries around the world. Second, the paper explores the role of development finance institutions and their support for the transition to a low-carbon, climate-resilient economic model. Finally, it examines a necessary paradigm shift to integrate climate and development objectives to establish a ‘LCCR development model’ able to simultaneously tackling development priorities and needs for resilient, low-carbon growth. This will necessitate a move from focusing on a ‘siloed’ vision of climate finance to a means of aligning activities across the economy with the LCCR objectives to ensure that the majority of investments are coherent with this long-term transition. 1 The research project under which this report was elaborated received financial support from Caisse des Dépots and Agence Française de Développement. 2 Claire Eschalier was a Research Fellow at I4CE during the elaboration of this report 3 Ian Cochran is Program Director on Finance, Investment, Climate at I4CE – ian.cochran@i4ce.org 4 Mariana Deheza is Project Manager on Climate and Development at I4CE – mariana.deheza@i4ce.org Mainstreaming Low-Carbon Climate-Resilient growth pathways into Development Finance Institutions’ activities A research project on the standards, tools and metrics to support transition to the low-carbon climate-resilient development models 1
The Political Consequences of Racial/Ethnic Tension: Theory and Evidence

By counting the number of articles published in major US newspapers containing carefully selected keywords, we construct a time varying measure of racial/ethnic tension. Then, we empirically test the predictions of a theoretical model by using the aforementioned measure, and investigate how ethnic/racial tension affects presidential approval ratings by different racial groups. Our results show that while ethnic/racial tension decreases the approval by white voters, the opposite is true for the approval by African American voters. Further scrutiny reveals that this maybe explained by the fact that government transfers to African Americans increase as a result of higher ethnic tension.
Electronic Health Record Implementation Strategies

Adoption of electronic health records (EHR) systems in nonfederal acute care hospitals has increased, with adoption rates across the United States reaching as high as 94%. Of the 330 plus acute care hospital EHR implementations in Texas, only 31% have completed attestation to Stage 2 of the meaningful use (MU) criteria. The purpose of this multiple case study was to explore strategies that hospital chief information officers (CIOs) used for the successful implementation of EHR. The target population consists of 3 hospitals CIOs from a multi-county region in North Central Texas who successfully implemented EHRs meeting Stage 2 MU criteria. The conceptual framework, for this research, was the technology acceptance model theory. The data were collected through semistructured interviews, member checking, review of the literature on the topic, and publicly available documents on the respective hospital websites. Using methodological triangulation of the data, 4 themes emerged from data analysis: EHR implementation strategies, overcoming resistance to technology acceptance, strategic alignment, and patient wellbeing. Participants identified implementation teams and informatics teams as a primary strategy for obtaining user engagement, ownership, and establishing a culture of acceptance to the technological changes. The application of the findings may contribute to social change by identifying the strategies hospital CIOs used for successful implementation of EHRs. Successful EHR implementation might provide positive social change by improving the quality of patient care, patient safety, security of personal health information, lowering health care cost, and improvements in the overall health of the general population.
Influence of Patient-Specific Head Modeling on EEG Source Imaging

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject's head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.
ENHANCEMENT OF MECHANICAL PROPERTY OF ALUMINUM ALLOY 2024 : An OVERVIEW

1 M-Tech student, Department of Mechanical Engineering, ITM University, Gwalior. MP, India. 2 Assistant professor, Department of Mechanical Engineering, ITM University, Gwalior, MP, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Jobs are being done in the field of mechanical engineering to achieve better results either by alloying, changing grain structure or enhancing the mechanical properties of materials. In the following paper we are trying to enhance the mechanical property and the samples have been tested on tensile test machine, hardness testing machine, XRD analysis, and wear test. Fretting wear test is used to determine the enhanced wear properties. Material selected is aluminum alloy 2024. For hardness test, Vickers hardness test is applied. For change in grain structure optical microscopy is used. After experiments have been done samples came out with high durability and high ductility.
IMPROVING ENERGY EFFICIENCY BY BAYESIAN CHANNEL ESTIMATION AND JOINT MULTIFLOW BEAMFORMING IN MASSIVE MIMO SYSTEM

:To improve the cellular energy efficiency, without sacrificing quality-of-service (QoS) at the users, the network topology must be densified to enable higher spatial reuse. In this work a combination of two densification approaches, namely “massive” multiple-input multiple-output (MIMO) base stations and multiflowbeamforming technique are incorporated to improve the performance of the System. The fundamental limit on the performance of Massive -MIMO antenna systems is due to failure in accurate channel estimation. To address this problem, an estimation of channel parameters of the desired links in a target cell is proposed. It is shown that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. The signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators; the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination. In addition to channel estimation, efficient energy power control technique for massive MIMO is adopted i.e., the total power consumption is minimized (both dynamic emitted power and static hardware power) while satisfying QoS constraints. This problem is proved to have a hidden convexity that enables efficient solution algorithms. Interestingly, the optimal solution promotes exclusive assignment of users to transmitters. Furthermore, promising simulation results showing how the total power consumption can be greatly improved by combining massive MIMO and joint multiflowbeamforming are provided.
Archaeology and GIS: Prehistoric Habitat Reconstruction

Archaeologically, site-catchment analysis can provide valuable information on ancient survival strategies and social organization. From a cultural resource management perspective, developing models of prehistoric site selection and settlement patterns can improve analysis methodology. Digital access to archaeological data allows the incorporation of cultural resource information into the overall planning process and GIS provides a means to integrate environmental and archaeological information into an effective tool. GIS technology can be used to create environmental archaeology maps to facilitate cultural resource management, planning, mitigation, preservation, excavation, and analyses. This GIS research project establishes site-catchment areas and identifies environmental variables associated with those areas. The sites are then statistically evaluated to determine if the distribution of artifacts is due to chance or related to specific environmental variables. The results are interpreted with ecological, anthropological and geographical theories to provide insight on prehistoric socioeconomic behavior as reflected by the corresponding settlement patterns.
The Solar-wind with Hydrogen Ion Exchange and Large-scale Dynamics (SHIELD) model: A Self-Consistent Kinetic-MHD Model of the Outer Heliosphere

Neutral hydrogen has been shown to greatly impact the plasma flow in the heliopshere and the location of the heliospheric boundaries. We present the results of the Solar-wind with Hydrogen Ion Exchange and Large-scale Dynamics (SHIELD) model, a new, self-consistent, kinetic-MHD model of the outer heliosphere within the Space Weather Modeling Framework. The charge-exchange mean free path is on order of the size of the heliosphere; therefore, the neutral atoms cannot be described as a fluid. The SHIELD model couples the MHD solution for a single plasma fluid to the kinetic solution from for neutral hydrogen atoms streaming through the system. The kinetic code is based on the Adaptive Mesh Particle Simulator (AMPS), a Monte Carlo method for solving the Boltzmann equation. The SHIELD model accurately predicts the increased filtration of interstellar neutrals into the heliosphere. In order to verify the correct implementation within the model, we compare the results of the SHIELD model to other, well-established kinetic-MHD models. The SHIELD model matches the neutral hydrogen solution of these studies as well as the shift in all heliospheric boundaries closer to the Sun in comparison the the multi-fluid treatment of the neutral hydrogen atoms. Overall the SHIELD model shows excellent agreement to these models and is a significant improvement to the fluid treatment of interstellar hydrogen.
Performance Evaluation of SWIPT-enabled Cellular Networks with Adaptive Modulation - A Stochastic Geometry Approach L’évaluation des performances des réseaux cellulaires compatibles SWIPT avec modulation adaptative - Une approche de la géométrie stochastique

: The performance of simultaneous wireless information and power transfer (SWIPT)-enabled cellular networks by utilizing adaptive modulation are investigated. In particular, the coverage probability (Pcov), the occurrence probabilities of diﬀerent mode of modulations and the achievable spectral eﬃciency (ASE) are provided by leveraging tools from stochastic geometry. It should be noted that diﬀerent from works in the literature which are ignored the spatial-correlation between base stations (BSs) as well as the correlation between information decoding and harvested energy at the end-devices (EDs), we take these into considerations by yielding the recent deﬁnition of the coverage probability. Finally, our numerical results show that employing adaptive modulation in SWIPT-enabled ED is a promising way to improve both spectral eﬃciency (SE) and energy eﬃciency (EE) of cellular networks. Résumé :
Generalized entanglement temperature and entanglement thermodynamics

In this paper, we observe that a Smarr like thermodynamic relation is satisfied by the HEE of a gravity background dual to a nonconformal field theory. Such a relation emerges naturally by demanding that the generalized entanglement temperature ($T_g$) produces the exact Hawking temperature as the leading term in the IR limit ($l\rightarrow \infty$). In the UV limit ($l\rightarrow 0$) the leading term of the generalized entanglement temperature ($T_g$) gives the entanglement temperature which is inversely proportional to the subsystem size $l$. The results reduces to the existing results in the limiting case of the nonconformal parameter going to zero.
The Effectiveness of Checklists versus Bar-codes towards Detecting Medication Planning and Execution Errors

The primary objective of this research was to evaluate the effectiveness of a checklist, compared to a smart pump and bar-code verification system, at detecting different categories of errors in intravenous medication administration. To address this objective, a medication administration safety checklist was first developed in an iterative user-centered design process. The resulting checklist design was then used in a high-fidelity simulation experiment comparing the effectiveness of interventions towards two classifications of error: execution and planning errors. Results showed the checklist provided no additional benefit for error detection over the control condition of current nursing practice. Relative to the checklist group, the smart pump and barcoding intervention demonstrated increased effectiveness at detecting planning errors. Results of this work will this work will help guide the selection, implementation and design of appropriate interventions for error mitigation in medication administration.
Revisiting the linearity in cross-lingual embedding mappings: from a perspective of word analogies

Most cross-lingual embedding mapping algorithms assume the optimised transformation functions to be linear. Recent studies showed that on some occasions, learning a linear mapping does not work, indicating that the commonly-used assumption may fail. However, it still remains unclear under which conditions the linearity of cross-lingual embedding mappings holds. In this paper, we rigorously explain that the linearity assumption relies on the consistency of analogical relations encoded by multilingual embeddings. We did extensive experiments to validate this claim. Empirical results based on the analogy completion benchmark and the BLI task demonstrate a strong correlation between whether mappings capture analogical information and are linear.
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition

Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embed-dings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)

Deep neural networks (DNN) have shown remarkable success in a variety of machine learning applications. The capacity of these models (i.e., number of parameters), endows them with expressive power and allows them to reach the desired performance. In recent years, there is an increasing interest in deploying DNNs to resource- constrained devices (i.e., mobile devices) with limited energy, memory, and computational budget. To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e.g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e.g., MAC operations are reduced to a few accumulations plus two multiplications). This approach consists of two steps. First, a super-network is created by scaling the dimensions of a pre-trained model (i.e., its width and depth). Subsequently, this super-network is simultaneously pruned (using an entropy constraint) and quantized (that is, ternary values are assigned layer-wise) in a training process, resulting in a sparse and ternary network representation. We validate the proposed approach in CIFAR-10, CIFAR-100, and ImageNet datasets, showing its effectiveness in image classification tasks.
A Survey on Conversational Recommender Systems

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this article, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.
The maximum number of induced C5 's in a planar graph

Finding the maximum number of induced cycles of length k in a graph on n vertices has been one of the most intriguing open problems of Extremal Graph Theory. Recently Balogh, Hu, Lidický and Pfender answered the question in the case k=5 . In this paper we determine precisely, for all sufficiently large n , the maximum number of induced 5‐cycles that an n ‐vertex planar graph can contain.
Curved-field optical coherence tomography: large-field imaging of human corneal cells and nerves

High-resolution optical imaging methods, such as confocal microscopy and full-field optical coherence tomography, capture flat optical sections of the sample. If the sample is curved, the optical field sections through several sample layers and the view of each layer is reduced. Here we present curved-field optical coherence tomography, capable of capturing optical sections of arbitrary curvature. We test the device on a challenging task of imaging the human cornea in vivo and achieve 10x larger viewing area comparing to the clinical state-of-the-art. This enables more precise cell and nerve counts, opening a path to improved diagnosis of corneal and general health conditions (e.g. diabetes). The method is non-contact, compact and works in a single-shot, making it readily available for use in optical research and clinical practice.
Quantifying the Effect of Field Variance on the Hα Luminosity Function with the New Numerical Galaxy Catalog (ν2GC)

We construct a model of Hα emitters (HAEs) based on a semianalytic galaxy formation model, the New Numerical Galaxy Catalog (ν2GC). In this paper, we report our estimate for the field variance of the HAE distribution. By calculating the Hα luminosity from the star formation rate of galaxies, our model well reproduces the observed Hα luminosity function (LF) at z = 0.4. The large volume of the ν2GC makes it possible to examine the spatial distribution of HAEs over a region of (411.8 Mpc)3 in the comoving scale. The surface number density of z = 0.4 HAEs with erg s−1 is 308.9 deg−2. We have confirmed that the HAE is a useful tracer for the large-scale structure of the universe because of their significant overdensity (>5σ) at clusters and the filamentary structures. The Hα LFs within a survey area of ∼2 deg2 (typical for previous observational studies) show a significant field variance up to ∼1 dex. Based on our model, one can estimate the variance on the Hα LFs within given survey areas.
Effects of Atwood and Reynolds numbers on the evolution of buoyancy-driven homogeneous variable-density turbulence

The evolution of buoyancy-driven homogeneous variable-density turbulence (HVDT) at Atwood numbers up to 0.75 and large Reynolds numbers is studied by using high-resolution direct numerical simulations. To help understand the highly non-equilibrium nature of buoyancy-driven HVDT, the flow evolution is divided into four different regimes based on the behaviour of turbulent kinetic energy derivatives. The results show that each regime has a unique type of dependence on both Atwood and Reynolds numbers. It is found that the local statistics of the flow based on the flow composition are more sensitive to Atwood and Reynolds numbers compared to those based on the entire flow. It is also observed that, at higher Atwood numbers, different flow features reach their asymptotic Reynolds-number behaviour at different times. The energy spectrum defined based on the Favre fluctuations momentum has less large-scale contamination from viscous effects for variable-density flows with constant properties, compared to other forms used previously. The evolution of the energy spectrum highlights distinct dynamical features of the four flow regimes. Thus, the slope of the energy spectrum at intermediate to large scales evolves from $-7/3$ to $-1$, as a function of the production-to-dissipation ratio. The classical Kolmogorov spectrum emerges at intermediate to high scales at the highest Reynolds numbers examined, after the turbulence starts to decay. Finally, the similarities and differences between buoyancy-driven HVDT and the more conventional stationary turbulence are discussed and new strategies and tools for analysis are proposed.