Miracle-dz/UniFAR-FTUs huggingface dataset:
The Positive Impact of Exposure to Nature versus Urban-Environment Images on Cognitive Processing: Working Memory and Problem Solving

The benefit of natural environments, compared to urban environments, to cognitive resources such as working memory have been documented and replicated (e.g., Berman et al., 2012; Berman et al., 2008; Berto, 2005). However, existing data do not extend beyond lower-order cognitive resources to higher-order cognitive processes such as problem solving. The goal of this thesis was to address this gap. The experiment tested both the beneficial effect of nature on executive function and problem solving. Using a backward digit-span task to measure working memory, I hypothesized that participants would repeat a higher number of sequences correctly after versus before viewing nature images, but not urban-environment images. Using a grid-pattern task (Adams et al., 2021) to assess problem solving, I hypothesized that participants would be more likely to make subtractive changes after versus before viewing nature compared to urban-environment images. Results did not support the hypotheses. Participants performed significantly better on the grid task after image viewing, regardless of image type. Participants also liked nature images better than urban-environment images, but liking was not correlated with working-memory or problem-solving performance. I finish with a discussion in which I argue that the lack of significant effects for image type suggests future research should only be conducted outside of the lab, and with the speculation that the restorative effect of nature might be attributed more to affective rather than cognitive mechanisms.
VM VM VM VM VMVM Customers Derivative Cloud Native IaaS Cloud Request Servers Lease Servers Resell Servers VM Server Pool Server Pool Server Pool VMVM

Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users. SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotCheck’s design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazon’s EC2 and show that it i) provides nested VMs to users that are 99.9989% available, ii) achieves nearly 5× cost savings compared to using equivalent types of ondemand VMs, and iii) eliminates any risk of losing VM state.
On continuous, discontinuous, mixed, and primal hybrid finite element methods for second‐order elliptic problems

Finite element formulations for second‐order elliptic problems, including the classic H1‐conforming Galerkin method, dual mixed methods, a discontinuous Galerkin method, and two primal hybrid methods, are implemented and numerically compared on accuracy and computational performance. Excepting the discontinuous Galerkin formulation, all the other formulations allow static condensation at the element level, aiming at reducing the size of the global system of equations. For a three‐dimensional test problem with smooth solution, the simulations are performed with h‐refinement, for hexahedral and tetrahedral meshes, and uniform polynomial degree distribution up to four. For a singular two‐dimensional problem, the results are for approximation spaces based on given sets of hp‐refined quadrilateral and triangular meshes adapted to an internal layer. The different formulations are compared in terms of L2‐convergence rates of the approximation errors for the solution and its gradient, number of degrees of freedom, both with and without static condensation. Some insights into the required computational effort for each simulation are also given.
Evaluating Deep Learning Approaches for Character Identification in Multiparty Dialogues Final Paper : CS 585 Sree

This paper describes our submission for the SemEval 2018 Task 4 : “Character Identification on Multiparty Dialogues ”. Our approach for solving this problem has been to model this task as co-reference resolution followed by entity linking for assigning character labels to clusters of named entity mentions. We found that the state-of-the-art co-reference resolution systems performed poorly on the given dataset. However, using an agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings vastly improved the cluster purity scores for coreference resolution. The mention embeddings learned were used to create cluster embeddings which were used as inputs to a heuristic entity linker. We were able to achieve a character identification accuracy of 85.28% and an F1-score of 84.41% (on the main 6 characters) on the held-out episodes of the Friends dataset.
Light field camera self-calibration and registration

The multi-view light fields (MVLF) provide new solutions to the existing problems in monocular light field, such as the limited field of view. However as key steps in MVLF, the calibration and registration have been limited studied. In this paper, we propose a method to calibrate the camera and register different LFs without the checkboard at the same time, which we call the self-calibrating method. We model the LF structure as a 5-parameter two-parallel-plane (2PP) model, then represent the associations between rays and reconstructed points as a 3D projective transformation. With the constraints of ray-ray correspondences in different LFs, the parameters can be solved with a linear initialization and a nonlinear refinement. The result in real scene and 3D point clouds registration error of MVLF in simulated data verify the high performance of the proposed model.
The Monetary Approach to Exchange Rates in the Ceecs

A panel data set for six Central and Eastern European countries (the Czech Republic, Hungary, Poland, Romania, Slovakia and Slovenia) is used to estimate the monetary exchange rate model with panel cointegration methods, including the Pooled Mean Group estimator, the Fully Modified Least Square estimator and the Dynamic Least Square estimator. The monetary model is able to convincingly explain the long-run dynamics of exchange rates in CEECs, particularly when this is supplemented by a Balassa-Samuelson effect. We then use our long-run monetary estimates to compute equilibrium exchange rates. Finally, we discuss the implications for the accession of selected countries to the European Economic and Monetary Union.
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100

Human-induced climate change is increasing the incidence of ﬁre events and associated impacts on livelihood, biodiversity, and nature across the world. Understanding current and projected ﬁre activity together with its impacts on ecosystems is crucial for evaluating future risks and taking actions to prevent such devastating events. Here we focus on ﬁre weather as a key driver of ﬁre activity. Fire weather products that have a global homogenous distribution in time and space provide many advantages to advance ﬁre science and evaluate future risks. Therefore, in this study we calculate and provide for the ﬁrst time the Canadian Fire Weather Index (FWI) with all available simulations of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Furthermore, we expand its regional applicability by combining improvements to the original algorithm for the FWI from several packages. A sensitivity analysis of the default version versus our improved version shows signiﬁcant differences in the ﬁnal FWI. With the improved version, we calculate the FWI using average relative humidity in one
Behavioral Use Licensing for Responsible AI

Scientific research and development relies on the sharing of ideas and artifacts. With the growing reliance on artificial intelligence (AI) for many different applications, the sharing of code, data, and models is important to ensure the ability to replicate methods and the democratization of scientific knowledge. Many high-profile journals and conferences expect code to be submitted and released with papers. Furthermore, developers often want to release code and models to encourage development of technology that leverages their frameworks and services. However, AI algorithms are becoming increasingly powerful and generalized. Ultimately, the context in which an algorithm is applied can be far removed from that which the developers had intended. A number of organizations have expressed concerns about inappropriate or irresponsible use of AI and have proposed AI ethical guidelines and responsible AI initiatives. While such guidelines are useful and help shape policy, they are not easily enforceable. Governments have taken note of the risks associated with certain types of AI applications and have passed legislation. While these are enforceable, they require prolonged scientific and political deliberation. In this paper we advocate the use of licensing to enable legally enforceable behavioral use conditions on software and data. We argue that licenses serve as a useful tool for enforcement in situations where it is difficult or time-consuming to legislate AI usage. Furthermore, by using such licenses, AI developers provide a signal to the AI community, as well as governmental bodies, that they are taking responsibility for their technologies and are encouraging responsible use by downstream users.
Geodesic‐based probability propagation for efficient optical flow

Optical flow estimation has bottlenecks such as large displacement and motion blur. In this Letter, the authors propose a geodesic-based probability propagation (GeoFlow) method combining the global geodesic with local spatial similarity to build a non-local superpixel graph. To achieve efficient belief propagation, a probabilistic framework for optimising the Markov random field (MRF) objective is proposed. In this way, the limitation of local propagation can be tackled in the global image level, and the probabilistic framework reduces computational complexity in the optimisation. In experiments, their method showed promising performance by improving the results on two public large displacement benchmark datasets.
On the Treatment of Hypersingularity for Solving Volume Integral Equations

In the meshless scheme for solving volume integral equations (VIEs), one needs to evaluate hypersingular integrals over a small cylindrical domain excluded from the whole domain for the sake of regularizing integrands. Those integrals can be evaluated semi-numerically but the implementation may not be convenient. In this work, we present some new formulas which are purely analytical for those integrals so that their evaluation can be greatly simplifled. A numerical example for electromagnetic (EM) scattering is presented to demonstrate the new formulas.
University of Huddersfield Repository Oxidative damage control in a human (mini-) organ: Nrf2 activation protects against oxidative stress-induced hair growth inhibition in a activation protects against

The in situ control of redox insult in human organs is of major clinical relevance, yet remains incompletely understood. Activation of Nrf2, the “master regulator” of genes controlling cellular redox homeostasis, is advocated as a therapeutic strategy for diseases with severely impaired redox balance. It remains to be shown whether this strategy is effective in human organs, rather than isolated human cell types. We have therefore explored the role of Nrf2 in a uniquely accessible human (mini-) organ, human scalp hair follicles (HFs). Microarray and qPCR analysis of human HFs following Nrf2 activation using sulforaphane identified the modulation of phase II metabolism, ROS clearance, the pentose phosphate pathway and glutathione homeostasis. Nrf2 knockdown (siRNA) in cultured human HFs confirmed the regulation of key Nrf2 target genes (i.e. HO-1, NQO1, GSR, GCLC, ABCC1, PRDX1). Importantly, Nrf2 activation significantly reduced ROS levels and associated lipid peroxidation. Nrf2 pre-activation reduced oxidative stress-stimulated (H 2 O 2 or menadione) premature catagen and hair growth inhibition, significantly ameliorated the H 2 O 2 -dependent increase in matrix keratinocyte apoptosis and reversed the ROS-induced reduction in proliferation. This study thus provides direct evidence for the crucial role of Nrf2 in protecting human organ function (i.e. scalp HFs) against redox insult. represent a promising therapeutic strategy for reducing the damage from excessive accumulation of both endogenous and exogenous ROS. This study demonstrates that utilising ex vivo organ cultured human HFs as a model for Activation of Nrf2 (i.e. by SFN) will induce the expression of a host of downstream targets genes encompassing pathways responsible for the reduction of reactive quinones, direct ROS clearance, GSH homeostasis, cytoprotection and NADPH production. The net effect is to reduce or limit ROS production and associated damaging effects, effectively reducing apoptosis and catagen induction.
COST292 experimental framework for TRECVID 2006

In this paper we give an overview of the four TRECVID tasks submitted by COST292, European network of institutions in the area of semantic multimodal analysis and retrieval of digital video media. Initially, we present shot boundary evaluation method based on results merged using a confidence measure. The two SB detectors user here are presented, one of the Technical University of Delft and one of the LaBRI, University of Bordeaux 1, followed by the description of the merging algorithm. The high-level feature extraction task comprises three separate systems. The first system, developed by the National Technical University of Athens (NTUA) utilises a set of MPEG-7 low-level descriptors and Latent Semantic Analysis to detect the features. The second system, developed by Bilkent University, uses a Bayesian classifier trained with a "bag of subregions" for each keyframe. The third system by the Middle East Technical University (METU) exploits textual information in the video using character recognition methodology. The system submitted to the search task is an interactive retrieval application developed by Queen Mary, University of London, University of Zilina and ITI from Thessaloniki, combining basic retrieval functionalities in various modalities (i.e. visual, audio, textual) with a user interface supporting the submission of queries using any combination of the available retrieval tools and the accumulation of relevant retrieval results over all queries submitted by a single user during a specified time interval. Finally, the rushes task submission comprises a video summarisation and browsing system specifically designed to intuitively and efficiently presents rushes material in video production environment. This system is a result of joint work of University of Bristol, Technical University of Delft and LaBRI, University of Bordeaux 1.
Deriving a preference-based utility measure for cancer patients from the european Organisation for the Research and Treatment of cancer ’ s Quality of life Questionnaire c 30 : a confirmatory versus exploratory approach

License. The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. Permissions beyond the scope of the License are administered by Dove Medical Press Limited. Information on how to request permission may be found at: http://www.dovepress. com/permissions.php Patient Related Outcome Measures 2014:5 119–129 Patient Related Outcome Measures Dovepress
Interpretable Text Classification Via Prototype Trajectories

We propose a novel interpretable deep neural network for text classification, called ProtoryNet, based on a new concept of prototype trajectories. Motivated by the prototype theory in modern linguistics, ProtoryNet makes a prediction by finding the most similar prototype for each sentence in a text sequence and feeding an RNN backbone with the proximity of each sentence to the corresponding active prototype. The RNN backbone then captures the temporal pattern of the prototypes, which we refer to as prototype trajectories. Prototype trajectories enable intuitive and fine-grained interpretation of the reasoning process of the RNN model, in resemblance to how humans analyze texts. We also design a prototype pruning procedure to reduce the total number of prototypes used by the model for better interpretability. Experiments on multiple public data sets show that ProtoryNet is more accurate than the baseline prototype-based deep neural net and reduces the performance gap compared to state-of-the-art black-box models. In addition, after prototype pruning, the resulting ProtoryNet models only need less than or around 20 prototypes for all datasets, which significantly benefits interpretability. Furthermore, we report a survey result indicating that human users find ProtoryNet more intuitive and easier to understand than other prototype-based methods.
Delay-Dependent Impulsive Distributed Synchronization of Stochastic Complex Dynamical Networks With Time-Varying Delays

This paper studies the problem of synchronization for a class of stochastic complex dynamical networks. It designs for the first time a distributed impulsive protocol based on pinning control that involves a constant signal transmission delay to tackle synchronization issues of such networks. Novel criteria on network synchronization are established by employing a time-dependent Lyapunov functional and a mathematical induction approach, where information on the state variables themselves and their neighbors is sufficiently utilized. Moreover, it is shown that the frequency of impulsive occurrence, impulsive input delays, stochastic perturbations, and the feedback control strength can significantly affect the synchronization performance. Numerical simulations are given to illustrate the effectiveness of the derived theoretical results.
An Artiﬁcial Intelligence Approach to Network Fault Management ‡

Traditionally, network management activities, such as fault management, have been performed with direct human involvement. However, these activities are becoming more demanding and data intensive, due to the heterogeneous nature and increasing size of networks today. For these reasons, it is becoming necessary to automate network management activities. Artiﬁcial intelligence technologies can play an important role in the problem solving and reasoning techniques that are employed in fault management. Expert systems have been successfully applied to some types of fault management. However, these systems are not ﬂexible enough for today’s evolving network needs. We propose a hybrid AI solution that employs both neural networks and case-based reasoning techniques for the fault management of heterogeneous distributed information networks.
A FFT-based formulation for efficient mechanical fields computation in isotropic and anisotropic periodic discrete dislocation dynamics

In this paper, we propose a novel full-field approach based on the fast Fourier transform (FFT) technique to compute mechanical fields in periodic discrete dislocation dynamics (DDD) simulations for anisotropic materials: the DDD-FFT approach. By coupling the FFT-based approach to the discrete continuous model, the present approach benefits from the high computational efficiency of the FFT algorithm, while allowing for a discrete representation of dislocation lines. It is demonstrated that the computational time associated with the new DDD-FFT approach is significantly lower than that of current DDD approaches when large number of dislocation segments are involved for isotropic and anisotropic elasticity, respectively. Furthermore, for fine Fourier grids, the treatment of anisotropic elasticity comes at a similar computational cost to that of isotropic simulation. Thus, the proposed approach paves the way towards achieving scale transition from DDD to mesoscale plasticity, especially due to the method’s ability to incorporate inhomogeneous elasticity.
Studies of the Effect of Formative Assessment on Student Achievement: So Much More Is Needed.

Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms.
PRIMARY AMENORRHEA IN KABUKI SYNDROME: A CASE REPORT

Address for Correspondence: Dr Sarah Ramamurthy, Assistant Professor, Department of Anatomy, Pondicherry Institute of Medical Sciences, Kalapet, Pondicherry, India. Phone:9962267590 E-Mail: drsarahsenthilkumar@gmail.com Kabuki syndrome also called as Niikawa Kuroki syndrome is a paediatric congenital disorder characterised by distinctive facial features, skeletal anomalies, short stature, dermatoglyphic abnormalities and mental retardation. This syndrome usually manifests with precocious puberty. We are presenting a case of Kabuki syndrome from the South-Indian population with primary amenorrhea for the first time. Further, the clinical features had considerable overlap with Turner syndrome, and chromosomal analysis revealed the presence of ring (X) chromosome with 45,X karyotype.
Lunar Low-Titanium Magmatism during Ancient Expansion inferred from Ejecta originating from Linear Gravity Anomalies

Linear gravity anomalies (LGAs) on the Moon have been interpreted as ancient magmatic intrusions formed during the lunar expansion. The composition of such ancient subsurface intrusions may oﬀer a hint for the lunar thermodynamic state in the initial stage of lunar history. To pose a ﬁrst compositional constraint on magmatism related to lunar expansion, this study analyzed the spectrum and gravity around craters on LGAs, such as Rowland, Roche
Scientific Report

Current models envision replicative senescence to be under dual control by the p53 and retinoblastoma (RB) tumour-suppressor pathways. The role of the p16 INK4a –RB pathway is controversial, and the function of RB in human cells has not been tested directly. We used targeted homologous recombina-tion to knock out one copy of RB in presenescent human fibro-blasts. During entry into senescence, RB +/– cells underwent spontaneous loss of heterozygosity and the resultant RB –/– clones bypassed senescence. The extended lifespan phase was eventually terminated by a crisis-like state. The same phenotype was documented for p21 CIP1/WAF1 and p53 heterozygous cells, indicating that loss of function of all three genes results in failure to establish senescence. By contrast, the abolition of p16 function by the expression of a p16-insensitive cyclin-dependent kinase 4 protein or siRNA-mediated knockdown provided only minimal lifespan extension that was terminated by senescence. We propose that p53, p21 and RB act in a linear genetic pathway to regulate cell entry into replicative senescence.