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This paper presents new weighted lower and upper bounds for the Euclidean numerical radius of pairs of operators in Hilbert spaces. We show that some of these bounds improve on recent results in the literature. We also find new inequalities for the numerical radius and the Davis–Wielandt radius. The lower and upper bounds we obtain are not symmetrical. [ABSTRACT FROM AUTHOR]

This paper presents new lower and upper bounds for the Euclidean numerical radius of operator pairs in Hilbert spaces, demonstrating improvements over recent results by other authors. Additionally, we derive new inequalities for the numerical radius and the Davis–Wielandt radius as natural consequences of our findings. [ABSTRACT FROM AUTHOR]

This paper provides a response to B. Davis' and H. Alexander's article "Israel Education: A Philosophical Analysis," published in this same issue of the Journal of Jewish Education. The authors provide a valuable conceptual map of six distinctive, sometimes intersecting and sometimes conflicting ideologies and purposes that different educators and educational institutions take in teaching Israel to Jewish learners outside of Israel. They then argue for an educational approach described as Mature Zionism. While their educational strategy appears laudable, it is rooted in a premise that claims the ethical liberalism of many American Jews is incompatible with instilling a rich conception of Jewish life. This paper challenges Davis and Alexander to begin from a more value neutral premise, rather than claiming the ethical liberalism of American Jews as weakness that needs to be corrected by offering alternative paradigms. This paper offers another approach described as "teaching towards ambivalence. While similar in some ways to the framework of value pluralism proposed by Davis and Alexander and Davis, this approach begins with accepting that a wide range of views, understandings, and relationships with Israel are possible in American Jewish life. It also recognizes that a commitment and connection to Israel in a vision for the "good life" is not a prerequisite for rich cultural vitality in liberal American Jewish life. [ABSTRACT FROM AUTHOR]

Inspired by classical feature descriptors in motion matching, this paper proposes a multimodal failure matching point collection method, which is defined as FMP. FMP is, in fact, a collection of unstable features with a low matching degree in the conventional matching task. Based on FMP, a novel model for the saliency detection of motion object is developed. Models are evaluated on the DAVIS and SegTrackv2 datasets and compared with recently advanced object detection algorithms. The comparison results demonstrate the availability and effectiveness of FMP in the detection of motion object saliency. [ABSTRACT FROM AUTHOR]

There is increasing interest in the use of artificial intelligence (AI) to improve organizational decision-making. However, research indicates that people's trust in and choice to rely on "AI decision aids" can be tenuous. In the present paper, we connect research on trust in AI with Mayer, Davis, and Schoorman's (1995) model of organizational trust to elaborate a conceptual model of trust, perceived risk, and reliance on AI decision aids at work. Drawing from the trust in technology, trust in automation, and decision support systems literatures, we redefine central concepts in Mayer et al.'s (1995) model, expand the model to include new, relevant constructs (like perceived control over an AI decision aid), and refine propositions about the relationships expected in this context. The conceptual model put forward presents a framework that can help researchers studying trust in and reliance on AI decision aids develop their research models, build systematically on each other's research, and contribute to a more cohesive understanding of the phenomenon. Our paper concludes with five next steps to take research on the topic forward. [ABSTRACT FROM AUTHOR]

This study aims to analyze the time-varying effects of global economic policy uncertainty (GEPU) shocks on macroeconomic activity in Turkey over the quarterly period of 1999q1 to 2020q4. To this end, the study uses the GEPU index developed by Davis (Working paper 22740, National Bureau of Economic Research, 2016) and employs the time-varying parameter vector autoregression (TVP-VAR) model. Empirical evidence shows that the GEPU shocks have adverse effects on the macroeconomic activity as they result in declines in share prices, investment, employment, consumption, and GDP growth. It is also evident that these effects vary over time, with the highest impact observed following the crises periods such as the 9/11 attacks, the Iraq invasion, the global financial crisis (GFC), and the Covid-19 pandemic. The time-varying impacts typically reach the maximum in the first and second lag periods, and the most severe impacts of uncertainty are observed on share prices and investment. The study also examines three substantial events (the 9/11 attacks, the GFC, and the Brexit referendum) and finds that the responses of the underlying variables to GEPU shocks vary both in magnitude and signs over the sample period. The responses of the variables are mainly more severe during the periods when geopolitical and economic concerns are high compared to periods of political crises. Overall, the findings from the analyses indicate that the Turkish economy still maintains its fragile structure and suggest that the adverse macroeconomic effects of foreign uncertainty shocks do not remain the same over time. [ABSTRACT FROM AUTHOR]

Computing equilibrium shapes of crystals (ESCs) is a challenging problem in materials science that involves minimizing an orientation-dependent (i.e., anisotropic) surface energy functional subject to a prescribed mass constraint. The highly nonlinear and singular anisotropic terms in the problem make it very challenging from both analytical and numerical aspects. Especially when the strength of anisotropy is very strong (i.e., strongly anisotropic cases), the ESCs will form some singular, sharp corners even if the surface energy function is smooth. Traditional numerical approaches, such as the H-1 gradient flow, are unable to produce true sharp corners due to the necessary addition of a high-order regularization term that penalizes sharp corners and rounds them off. In this paper, we propose a new numerical method based on the Davis--Yin splitting (DYS) optimization algorithm to predict the ESCs instead of using gradient flow approaches. We discretize the infinite-dimensional phase-field energy functional in the absence of regularization terms and transform it into a finite-dimensional constraint minimization problem. The resulting optimization problem is solved using the DYS method, which automatically guarantees the mass-conservation and bound-preserving properties. We also prove the global convergence of the proposed algorithm. These desired properties are numerically observed. In particular, the proposed method can produce real sharp corners with satisfactory accuracy. Finally, we present numerous numerical results to demonstrate that the ESCs can be well simulated under different types of anisotropic surface energies, which also confirms the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Video object segmentation (VOS) has made significant progress with matching-based methods, but most approaches still show two problems. Firstly, they apply a complicated and redundant two-extractor pipeline to use more reference frames for cues, increasing the models' parameters and complexity. Secondly, most of these methods neglect the spatial relationships (inside each frame) and do not fully model the temporal relationships (among different frames), i.e., they need adequate modeling of spatial-temporal relationships. In this paper, to address the two problems, we propose a unified transformer-based framework for VOS, a compact and unified single-extractor pipeline with strong spatial and temporal interaction ability. Specifically, to slim the common-used two-extractor pipeline while keeping the model's effectiveness and flexibility, we design a single dynamic feature extractor with an ingenious dynamic input adapter to encode two significant inputs, i.e., reference sets (historical frames with predicted masks) and query frame (current frame), respectively. Moreover, the relationships among different frames and inside every frame are crucial for this task. We introduce a vision transformer to exploit and model both the temporal and spatial relationships simultaneously. By the cascaded design of the proposed dynamic feature extractor, transformer-based relationship module, and target-enhanced segmentation, our model implements a unified and compact pipeline for VOS. Extensive experiments demonstrate the superiority of our model over state-of-the-art methods on both DAVIS and YouTube-VOS datasets. We also explore potential solutions, such as sequence organizers, to improve the model's efficiency. On DAVIS17 validation, we achieve ∼ 50% faster inference speed with only a slight 0.2% ( J & F ) drop in segmentation quality. Codes are available at https://github.com/sallymmx/TransVOS.git. [ABSTRACT FROM AUTHOR]

This paper investigates the convergence properties and applications of the three-operator splitting method, also known as the Davis-Yin splitting (DYS) method, integrated with extrapolation and plug-and-play (PnP) denoiser within a nonconvex framework. We first propose an extrapolated DYS method to effectively solve a class of structural nonconvex optimization problems that involve minimizing the sum of three possibly nonconvex functions. Our approach provides an algorithmic framework that encompasses both extrapolated forward-backward splitting and extrapolated Douglas-Rachford splitting methods. To establish the convergence of the proposed method, we rigorously analyze its behavior based on the Kurdyka-Łojasiewicz property, subject to some tight parameter conditions. Moreover, we introduce two extrapolated PnP-DYS methods with convergence guarantee, where the traditional regularization step is replaced by a gradient step-based denoiser. This denoiser is designed using a differentiable neural network and can be reformulated as the proximal operator of a specific nonconvex functional. We conduct extensive experiments on image deblurring and image superresolution problems, where our numerical results showcase the advantage of the extrapolation strategy and the superior performance of the learning-based model that incorporates the PnP denoiser in terms of achieving high-quality recovery images. [ABSTRACT FROM AUTHOR]

The simultaneous generation, dissipation and redistribution of excess pore pressures within the layers of a soil deposit, due to a seismic event, can significantly modify the seismic response of the whole deposit. The reliable estimate of the excess pore pressure induced by shaking within the soil is important to predict the behaviour of the soil at a large scale, and consequently, earthquake effects on built environment. Recently, pore pressure energy-based models are developing. Despite several advantages, their calibration is generally complex. The paper aims to provide a simple calibration procedure of the pore pressure energy-based prediction model developed by Berrill and Davis (1985), in order to make easier and more common the use to practitioners. The energy-based model of Berrill and Davis (1985) has been calibrated in this study by means of a dataset of 46 undrained cyclic triaxial and simple shear tests carried out on different sandy soils. The best fitting procedure with the envelope of the experimental curves has been adopted. The experimental evidences show that the two parameters (α and β) on which the model depends, are linked and can be related to the results of CPT or SPT in situ tests. The paper introduces two relationships to compute the calibration parameters from the well-known equivalent cone tip resistance (qc1Ncs) or the corrected SPT blow count ((N1)60cs). The applicability of the proposed procedure at a large scale has been discussed interpreting the results of cyclic simple shear tests on undisturbed sandy specimens from an energetic perspective. The validity of the calibration procedure has been finally verified performing 1D non-linear site response analyses by means of DEEPSOIL code, reproducing two centrifuge tests and three case histories. The close matching between the simulations of the excess pore pressure time histories with the experimental data of the centrifuge tests, together with the simulated excess pore pressure profiles of three case histories compared with the results achieved by using another 1D non-linear code demonstrates the effectiveness of the simplified procedure to calibrate the pore pressure energy-based prediction model of Berrill and Davis (1985). [ABSTRACT FROM AUTHOR]

This paper discusses whether Texas Germans can be viewed through the lens of transnationalism. As a group, Texas Germans exhibit a rich linguistic and cultural heritage, both of which have significantly contributed to the formation of a strong sense of "Texas‐Germanness." My goal is to investigate this particular instance of "Germanness beyond Germany" (Maxwell and Davis, German Studies Review, 39, 1–15, 2016) and to determine if Texas Germans represent a previously unidentified form of linguistic transmigrant. The study utilizes a mixed‐methods approach, consulting both quantitative (surveys) and qualitative data (open‐ended interviews; semi‐structured phone interviews). The results indicate the presence of various transnational elements and confirm that both linguistic and cultural heritage are defining elements in the construction and maintenance of Texas German identity. Language, moreover, appears as an important connector and mediator between these concepts. [ABSTRACT FROM AUTHOR]

The paper aims to illustrate female characters' efforts in subverting Pakistani nationalism that is gendered due to the patriarchal influences of religion and culture in Karachi, You're Killing Me! (2014) by Saba Imtiaz. The article uses qualitative cum descriptive approach for the textual analysis of the selected text. The concept of Islamization introduced by President Ziaul-Haq confined women to domestic spaces and silenced them in the national narratives. Similarly, the cultural model introduced by Yuval-Davis and Floya Anthias is quite applicable to the Pakistani context where women are reduced to the symbolic position of mothers of nation and cultural transmitters. The research is significant since it exposes how the cultural and religious influences contribute to the exclusion of women from the national narratives. However, the study contends that women resist the patriarchal notions of nationalism through technology in the novel Karachi, You're Killing Me! (2014) by Saba Imtiaz. The protagonist of the novel, Ayesha and her friend Zara use digital technologies and social media to define new social, political, and cultural space for women which subvert the dominant modes of gendered nationalism. Thus, the article proves that if given opportunities, women prove themselves an equal gender unlike the national narratives built by the patriarchy mentioning women as second gender. [ABSTRACT FROM AUTHOR]

This paper investigates the uncertainty in the parameters used in the calibration of an Davis Equation Of State (EOS) for the detonation products of the High Explosive PBX 9501. The procedure sought to make use of all available information about this HE to inform the best set of calibration parameters as well as the uncertainty in these parameters. The procedure made use of historical experimental data, the results from thermo‐chemical modeling as well as data on the best isentrope function fit to cylinder test experimental data. Combining all these heterogeneous data sources together in a Bayesian calibration, yielded a posterior mean and covariance. Sampling from the posterior distribution and evaluating an important Quantity Of Interest (QOI) in the EOS model, the detonation speed of a one‐inch rate stick, produced a distribution which showed variations which were in agreement with experiments. The uncertainty in the EOS was reported as eleven sets of model calibrations which spanned the range of this QOI. [ABSTRACT FROM AUTHOR]

Electromagnetic Gaussian beams may be described by using a Davis scheme of approximations. It is demonstrated that this scheme also may be used, with minor changes, to manage the description of acoustical waves. The acoustical version of the Davis scheme afterward allows one to establish an efficient and accurate localized approximation to evaluate beam shape coefficients, which encode the structures of acoustical waves, similar to the localized approximation, which has been made famous when dealing with electromagnetic waves. The present paper is restricted to the case of on-axis beams. [ABSTRACT FROM AUTHOR]

The COVID-19 Pandemic forced educators across the country into online classes and, for many, using unfamiliar online tools for the first time. This research paper focuses on business professors and their acceptance of many online tools during and after the pandemic. Using the Technology Acceptance Model (TAM) created by Davis (1989), this paper examines Perceived Usefulness and Perceived Ease of Use along with Attitude Towards Use and Intention to Use. This study will help determine not only usage of these new technological tools during the pandemic but also the adoption of these tools by business professors once they returned to the classroom again. The results of this study confirm usage of TAM, examine one-way teaching that has changed due to the pandemic and discusses further potential studies. [ABSTRACT FROM AUTHOR]

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding learning of foreground object (s), we consider background should be equally treated. Thus, we propose a Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. CFBI separates the feature embedding into the foreground object region and its corresponding background region, implicitly promoting them to be more contrastive and improving the segmentation results accordingly. Moreover, CFBI performs both pixel-level matching processes and instance-level attention mechanisms between the reference and the predicted sequence, making CFBI robust to various object scales. Based on CFBI, we introduce a multi-scale matching structure and propose an Atrous Matching strategy, resulting in a more robust and efficient framework, CFBI+. We conduct extensive experiments on two popular benchmarks, i.e., DAVIS and YouTube-VOS. Without applying any simulated data for pre-training, our CFBI+ achieves the performance ($\mathcal {J}$ J & $\mathcal {F}$ F ) of 82.9 and 82.8 percent, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI. [ABSTRACT FROM AUTHOR]

In this paper, we study the family of one-dimensional piecewise smooth dynamical systems in which two classic theorems are still permanent. One of them is Birkhoff transitivity theorem and the other one is Banks, Brooks, Cairns, Davis, and Stacey theorem. Baker-like maps with N-branches (N ≥ 2) constitute an open subset of this family. We show that under certain conditions, a full chaos always occurs in the family of Baker-like maps. [ABSTRACT FROM AUTHOR]

The greatest challenge for healthcare in drug repositioning and discovery is identifying interactions between known drugs and targets. Experimental methods can reveal some drug-target interactions (DTI) but identifying all of them is an expensive and time-consuming endeavor. Machine learning-based algorithms currently cover the DTI prediction problem as a binary classification problem. However, the performance of the DTI prediction is negatively impacted by the lack of experimentally validated negative samples due to an imbalanced class distribution. Hence recasting the DTI prediction task as a regression problem may be one way to solve this problem. This paper proposes a novel convolutional neural network with an attention-based bidirectional long short-term memory (CNN-AttBiLSTM), a new deep-learning hybrid model for predicting drug-target binding affinities. Secondly, it can be arduous and time-intensive to tune the hyperparameters of a CNN-AttBiLSTM hybrid model to augment its performance. To tackle this issue, we suggested a Memetic Particle Swarm Optimization (MPSOA) algorithm, for ascertaining the best settings for the proposed model. According to experimental results, the suggested MPSOA-based CNN- Att-BiLSTM model outperforms baseline techniques with a 0.90 concordance index and 0.228 mean square error in DAVIS dataset, and 0.97 concordance index and 0.010 mean square error in the KIBA dataset. [ABSTRACT FROM AUTHOR]

This paper provides a systematic description and analysis of the non-predictive use of the Italian future. Several authors claim that, on this use, the Italian future is an evidential (Squartini 2001, Mari 2010, Eckardt and Beltrama 2019, Frana and Menéndez-Benito 2019). Others argue that the non-predictive future does not directly contribute an evidential signal (e.g., Giannakidou and Mari 2018, Farkas and Ippolito 2022). We side with the evidential camp. From an empirical standpoint, we present the results of a battery of tests that show that the non-predictive future patterns with evidentials cross-linguistically. From a theoretical standpoint, we put forward an analysis that combines a slightly modified version of the proposal for evidentials in Davis et al. (2007) with Schlenker's (2007) view of expressive content. On this account, the Italian evidential future (i) lowers the quality threshold required to perform a successful speech act (Davis et al. 2007) and (ii) triggers an evidential presupposition relativized to the speaker's beliefs (modeled after Schlenker's analysis of expressives). Our treatment of the evidential component as an expressive presupposition opens up a new perspective on the study of evidentiality and highlights the need for further detailed empirical studies exploring the extent to which this perspective is applicable cross-linguistically. [ABSTRACT FROM AUTHOR]

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This paper examines the effect of executive board gender diversity on the relationship between economic policy uncertainty (EPU) and bank liquidity hoarding (LH). We focus on the Russian banking sector, which, relative to most of the world, has a high share of women on bank executive boards. Using the news‐based EPU index developed by Baker, Bloom, and Davis (2016) and LH measures proposed by Berger, Guedhami, Kim, and Li (2022), we exploit a unique dataset from the Russian banking sector. While higher economic policy uncertainty tends to increase liquidity hoarding, we find that this effect diminishes as the gender diversity of the board increases. We attribute this finding to the moderating influence of gender diversity on stability and overreaction in decision‐making. Additionally, we find that the channel through which board gender diversity affects the impact of economic policy uncertainty on liquidity hoarding takes place via the hoarding of liquid assets. Our findings are robust to the use of alternative measures for economic policy uncertainty and gender diversity and hold after addressing endogeneity concerns. As women are still significantly under‐represented on bank boards in most countries, these results argue for policies to promote gender diversity on bank boards as a means of limiting the detrimental effects of economic policy uncertainty. [ABSTRACT FROM AUTHOR]

Let p be an odd prime and let ρ : ℤ / p → GL n ⁡ (ℤ) be an action of ℤ / p on a lattice and let Γ := ℤ n ⋊ ρ ℤ / p be the corresponding semidirect product. The torus bundle M := T ρ n × ℤ / p S ℓ over the lens space S ℓ / ℤ / p has fundamental group Γ. When ℤ / p fixes only the origin of ℤ n , Davis and Lück (2021) compute the L-groups L m 〈 j 〉 ⁢ (ℤ ⁢ [ Γ ]) and the structure set 풮 geo , s ⁢ (M) . In this paper, we extend these computations to all actions of ℤ / p on ℤ n . In particular, we compute L m 〈 j 〉 ⁢ (ℤ ⁢ [ Γ ]) and 풮 geo , s ⁢ (M) in a case where E ¯ ⁢ Γ has a non-discrete singular set. [ABSTRACT FROM AUTHOR]

In engineering practice, it is usual to adopt pile groups as a foundation solution, whereby the block only distributes the loads from the superstructure to the piles. However, the blocks generally have contact with the ground and this portion of strength can be significant. Thus, the concept of piled footing arises. These foundations are composed of deep and shallow foundation elements and can promote technical and economic advantages over a traditional design. The efficiency of the shallow element contact depends on the type of soil. The residual soil weakly cemented existing in Brazil and other tropical countries has peculiar characteristics and is little addressed by traditional geotechnics. Thus, the paper aims to study piled footing foundations supported in this soil, in comparison with traditional pile groups. Seven full-scale load tests were carried out on different foundations executed on the experimental site. Also, simplified methods were applied (PDR—Poulos, Davis & Randolph and Mandolini et al.) to predict the piled footings behavior. The results showed that the contribution of the footing contact reduced the settlements and increased load capacity (reaching 148% of failure load improvement), providing a significant reduction in foundation cost in relation to traditional design in pile groups. It was also found that the bearing capacity for the piled footing is on average 85% of the sum of the bearing capacities of the individual elements (pile group + footing). The simplified methods were satisfactory in representing the behavior of the piled footings on residual soils, especially Mandolini et al. [ABSTRACT FROM AUTHOR]

How do British Pluralist traditions need to be re-imagined to address the issues at the heart of the Brexit vote? This paper will use qualitative research about why Britain voted for Brexit to examine this question. The paper interrogates the question that we require a more decentred local government at a community level in order for people to feel both represented, and able to participate. Firstly, it will analyse the values, attitudes and beliefs of Leave voters who participated in the study, and situate them in terms of the affective assemblages of symbolic meaning, ideas, beliefs, values and emotion through which they imagined themselves and their community. It will examine the 'deep story' (Boler and Davis, in Emotion Space Soc 27:75–85, 2018) through which participants affective responses are situated into inherited historical cultures and traditions, exploring where participants located themselves in relation to others and their particular cultures and traditions. In the final part of the paper, I consider what this means for British pluralist traditions at a local and community level in a post-Brexit polity. I find that the Leave vote signals and symbolises a turn to the traditional Nation State as the political space that can protect and care for individuals who long for control over their worlds. This is potentially at odds with their expressed desire stronger democratic engagement. [ABSTRACT FROM AUTHOR]

The Winograd Schema Challenge (WSC, proposed by Levesque, Davis & Morgenstern 2012) is considered to be the novel Turing Test to examine machine intelligence. Winograd schema questions require the resolution of anaphora with the help of world knowledge and commonsense reasoning. Anaphora resolution is itself an important and difficult issue in natural language processing, therefore, many other datasets have been created to address this issue. In this paper we look into the Winograd schemata and other Winograd-like datasets and the translations of the schemata to other languages, such as Chinese, French and Portuguese. We present the Hungarian translation of the original Winograd schemata and a parallel corpus of all the translations of the schemata currently available. We also adapted some other anaphora resolution datasets to Hungarian. We aim to discuss the challenges we faced during the translation/adaption process. [ABSTRACT FROM AUTHOR]

We reply to some comments made by Davis (2020 Eur. J. Phys. 40 018001) on our paper (2020 Eur. J. Phys. 41 035202), by arguing that Davis's assertions are unsupported in some cases and are unsatisfactory in other cases. [ABSTRACT FROM AUTHOR]

Purpose: Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM). Design/methodology/approach: Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution. Findings: It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration. Research limitations/implications: The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation. Originality/value: The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies. [ABSTRACT FROM AUTHOR]

In this paper, we look at the evolution of consumption and wage inequality from 1980 to 2016 in the US. We use data from the Consumer Expenditure Survey (CEX) and the Current Population Survey (CPS) to look at differences in consumption and wages across groups in the population defined by educational attainment of the household head and year‐of‐birth cohort. We show that the results obtained by Attanasio and Davis (1996) for non‐durable consumption still hold in more recent decades. In addition to non‐durable consumption and services, we look at inequality measured in terms of expenditure on and stock of vehicles. The advantages of looking at these measures are that information on cars is typically measured more accurately than other components of expenditure and consumers are more likely to react by adjusting their stock of vehicles on the basis of long‐term expectations about their economic prospects. [ABSTRACT FROM AUTHOR]

Disability studies, rejecting the medical model which advocates “fixing” a body, views disability as socially constructed. Whatever does not fit into the hegemonized notion of the “norm” is precluded from society. Disability studies is, however, extremely important in understanding the interrelatedness of various forms of oppression, as Davis (1995) identifies disability as “the missing term in the race, class and gender triad.” “Oppression” is a concept that is often found in sociological, historical, and literary texts, which is simply defined in terms of a dominant group subjugating a minor group. The acclaimed Brazilian theorist on oppression, Paulo Friere, in The Pedagogy of the Oppressed, discusses many themes of oppression that include all forms of “-isms”, which are based on race, ethnicity, gender, class, caste, religion, and disability. Toni Morrison’s novels almost always feature disabled characters who have some form of disability or impairment or are distinguished by a different feature. Morrison deploys disability in a unique but traditional way, which not only critiques the attitude of society towards disabled people but also analyzes the intersection between race, gender, and disability. Drawing a link between disability, race, and oppression is not new, but people have seldom acknowledged this link in literature. This paper attempts to find how the disabled characters of color are portrayed in Morrison’s novels and how these characters are oppressed on the basis of their deviance from the politicized concept of the “norm”. [ABSTRACT FROM AUTHOR]

Community-based learning is a pedagogical technique designed to bring students out of the classroom and into their communities. Students typically pair with local nonprofit organizations to complete work which ties into their scholarship. Faculty, students, and community members can all benefit from these partnerships, and university-community relations are strengthened by them. These connections deepen the educational experience and improve student success and retention, and build civic engagement skills that benefit the university community and the student's home community (Strait, Turk, and Nordyke in: JR Strait, K Nordyke (eds), Pedagogy Of Civic Engagement, High-Impact Practices, and e-Service- L Earning, Stylus Publishing, Virginian, 2015; Bednarz in Journal of Geography in Higher Education 32:87–100, 2008; Mohan in Journal of Geography in Higher Education 19:129–142, 1995). Spatial citizenship, while vital to such engagement and to effective community participation, is seldom taught in traditional pedagogy (Kanwischer, Schulze, and Gryl in: Thomas Jekel, Adrijana Car, Josef Strobl, and Gerald Griesebner (eds), Spatial citizenship—dimensions of a curriculum, Wichmann Verlag, Berlin, 2012). Connecting place to pedagogy with spatially-enabled learning helps students investigate complex global concepts at a manageable local scale. Geography is an intrinsic part of scholarship, to varying degrees, and spatial thinking can bring added dimension and value to the educational process (Vogler in: Thomas Jekel, Adrijana Car, Josef Strobl, and Gerald Griesebner (eds), Wichmann Verlag, Berlin, 2012). The intersectionalities which exist within the community, when examined with a spatial lens, are the core of community geography, a praxis-focused method of engaged scholarship (Shannon in Progress in Human Geography, 10.1177/0309132520961468, 2020). Community-based learning is not clearly defined, yet some established models exist. Place-based learning communities move cohorts of students through a curriculum that is centered on local community issues, with the community as both laboratory and lens, and building place attachment (Schweizer, Davis, and Thompson in Environmental Communication 7:42–62, 2013). Service learning, while less clearly defined, typically involves direct work with community organizations, identifying, investigating, and contributing to solutions for local issues (Strait, Turk, and Nordyke in: JR Strait, K Nordyke (eds), Pedagogy Of Civic Engagement, High-Impact Practices, and e-Service- L Earning, Stylus Publishing, Virginian, 2015; Cal Corps Public Service in Designing Community-Based Courses, 1–45, 2015). Built around the concept of place, the added dimension of improved spatial citizenship benefits both community and students. This paper will review community-based learning as practiced through upper-division service learning courses in geography at two universities, and the development of a new course, as methods of engagement with local communities through a spatial lens. [ABSTRACT FROM AUTHOR]

Background: Drug–target interaction (DTI) prediction plays a crucial role in drug discovery. Although the advanced deep learning has shown promising results in predicting DTIs, it still needs improvements in two aspects: (1) encoding method, in which the existing encoding method, character encoding, overlooks chemical textual information of atoms with multiple characters and chemical functional groups; as well as (2) the architecture of deep model, which should focus on multiple chemical patterns in drug and target representations. Results: In this paper, we propose a multi-granularity multi-scaled self-attention (SAN) model by alleviating the above problems. Specifically, in process of encoding, we investigate a segmentation method for drug and protein sequences and then label the segmented groups as the multi-granularity representations. Moreover, in order to enhance the various local patterns in these multi-granularity representations, a multi-scaled SAN is built and exploited to generate deep representations of drugs and targets. Finally, our proposed model predicts DTIs based on the fusion of these deep representations. Our proposed model is evaluated on two benchmark datasets, KIBA and Davis. The experimental results reveal that our proposed model yields better prediction accuracy than strong baseline models. Conclusion: Our proposed multi-granularity encoding method and multi-scaled SAN model improve DTI prediction by encoding the chemical textual information of drugs and targets and extracting their various local patterns, respectively. [ABSTRACT FROM AUTHOR]

Event cameras such as DAVIS can simultaneously output high temporal resolution events and low frame-rate intensity images, which own great potential in capturing scene motion, such as optical flow estimation. Most of the existing optical flow estimation methods are based on two consecutive image frames and can only estimate discrete flow at a fixed time interval. Previous work has shown that continuous flow estimation can be achieved by changing the quantities or time intervals of events. However, they are difficult to estimate reliable dense flow, especially in the regions without any triggered events. In this paper, we propose a novel deep learning-based dense and continuous optical flow estimation framework from a single image with event streams, which facilitates the accurate perception of high-speed motion. Specifically, we first propose an event-image fusion and correlation module to effectively exploit the internal motion from two different modalities of data. Then we propose an iterative update network structure with bidirectional training for optical flow prediction. Therefore, our model can estimate reliable dense flow as two-frame-based methods, as well as estimate temporal continuous flow as event-based methods. Extensive experimental results on both synthetic and real captured datasets demonstrate that our model outperforms existing event-based state-of-the-art methods and our designed baselines for accurate dense and continuous optical flow estimation. [ABSTRACT FROM AUTHOR]

The video super-resolution (SR) task refers to the use of corresponding low-resolution (LR) frames and multiple neighboring frames to generate high-resolution (HR) frames. Existing deep learning-based approaches usually utilize LR optical flow for video SR tasks. However, the accuracy of LR optical flow is not enough to recover the fine detail part. In this paper, we propose a video SR network that uses optical flow SR and optical flow enhancement algorithms to provide accurate temporal dependency. And extract the detail component of LR adjacent frames as supplementary information for accurate feature extraction. Firstly, the network infers HR optical flow from LR optical flow, and uses the optical flow enhancement algorithm to enhance HR optical flow. Then the processed HR optical flows are used as the input of the motion compensation network. Secondly, we extract detail component to reduce the error caused by motion compensation based on optical flow. Finally, the SR results are generated through the SR network. We perform comprehensive comparative experiments on two datasets: Vid4 and DAVIS. The results show that, compared with other state-of-the-art methods, the proposed video SR method achieves the better performance. [ABSTRACT FROM AUTHOR]

Video inpainting is a task of synthesizing spatio-temporal coherent content in missing regions of the given video sequence, which has recently drawn increasing attention. To utilize the temporal information across frames, most recent deep learning-based methods align reference frames to target frame firstly with explicit or implicit motion estimation and then integrate the information from the aligned frames. However, their performance relies heavily on the accuracy of frame-to-frame alignment. To alleviate the above problem, in this paper, a novel Temporal Group Fusion Network (TGF-Net) is proposed to effectively integrate temporal information through a two-stage fusion strategy. Specifically, the input frames are reorganized into different groups, where each group is followed by an intra-group fusion module to integrate information within the group. Different groups provide complementary information for the missing region. A temporal attention model is further designed to adaptively integrate the information across groups. Such a temporal information fusion way gets rid of the dependence on alignment operations, greatly improving the visual quality and temporal consistency of the inpainted results. In addition, a coarse alignment model is introduced at the beginning of the network to handle videos with large motion. Extensive experiments on DAVIS and Youtube-VOS datasets demonstrate the superiority of our proposed method in terms of PSNR/SSIM values, visual quality and temporal consistency, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the extent to which economic policy uncertainty shocks in major economies affect real economic activity in small open economies. We use Hong Kong as a case study. Following Baker, Bloom and Davis (2016), we construct a newspaper‐based economic policy uncertainty index for Hong Kong for the period 1998 to 2016. We estimate international spillovers of uncertainty and find large spillovers of uncertainty from major economies to Hong Kong. Furthermore, using a structural vector autoregressive approach, we show that a rise in domestic economic policy uncertainty leads to tight financial conditions, and lower investment and vacancy posting, dampening domestic output growth. [ABSTRACT FROM AUTHOR]

An equiangular tight frame (ETF) is a type of optimal packing of lines in a real or complex Hilbert space. In the complex case, the existence of an ETF of a given size remains an open problem in many cases. In this paper, we observe that many of the known constructions of ETFs are of one of two types. We further provide a new method for combining a given ETF of one of these two types with an appropriate group divisible design (GDD) in order to produce a larger ETF of the same type. By applying this method to known families of ETFs and GDDs, we obtain several new infinite families of ETFs. The real instances of these ETFs correspond to several new infinite families of strongly regular graphs. Our approach was inspired by a seminal paper of Davis and Jedwab which both unified and generalized McFarland and Spence difference sets. Our main result is a combinatorial analog of their algebraic results. [ABSTRACT FROM AUTHOR]

This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWD SVM) and ANN (ZWD ANN) is compared with the ZWD obtained from the GPS (ZWD GPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWV SVM , PWV ANN) is compared with the radiosonde PWV (PWV radiosonde) and voxel-based PWV (PWV VBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations. [ABSTRACT FROM AUTHOR]

Davis's (1971) article "That's interesting! Towards a phenomenology of sociology and a sociology of phenomenology" is regarded by many management researchers as a classic work and a basis for guiding management studies; in the wake of its publication, an interesting research advocacy gradually emerged. However, from the perspective of scientific research, Davis's core argument that great theories have to be interesting is seriously flawed. Interestingness is not regarded as a virtue of a good scientific theory and thus has little value in science. Moreover, obsession with interestingness can lead to at least five detrimental outcomes, namely promoting an improper way of doing science, encouraging post hoc hypothesis development, discouraging replication studies, ignoring the proper duties of a researcher, and undermining doctoral education. [ABSTRACT FROM AUTHOR]

The purpose of video inpainting is to get a reasonable content from the video to fill in the missing region. Video is a continuous four-dimensional sequence in the temporal dimension. It's difficult to ensure the temporal continuity of video by inpaint video frames respectively along the time dimension. Video inpainting has gone from the traditional inpainting algorithm to the advanced learning based inpainting method. It has been able to inpainting for a variety of scenes. However, there are still unresolved questions in video inpainting, and video inpainting is still a challenging task. Existing works focused on fixing the problem of object removal in the video, and neglected the importance of inpainting the occlusion scene in the middle region. For the occlusion problem in the middle region, we propose a local and nonlocal optical flow video inpainting framework. First, according to the forward and backward directions of the reference frame and the sampling window, we divide the video into local and nonlocal frames, extract the local and nonlocal optical flow and feed them to the residual network for rough inpainting. Next, our approach extracts and completes the edges of the predicted flow. Finally, the composed optical flow field guides the propagation of pixels to inpaint the video content. Experimental results on DAVIS and YouTube-VOS datasets show that our method has significantly improved in terms of the image quality and optical flow quality compared with the state of the art. Codes are available at {https://github.com/lengfengio/LNFVI.git.} [ABSTRACT FROM AUTHOR]

Drug-Target Binding Affinity (DTBA) prediction is one class of Drug-Target Interaction problem (DTI), where the focus is to predict the binding strength of a drug-target pair. Several machine learning approaches have been developed for this purpose. However, almost all rely on the use of increasingly sophisticated inputs to improve the obtained results besides that they don't allow any analysis or interpretation due to their black-box characteristic. This work is an attempt to address these limitations by leveraging the use of attention mechanisms with convolution-deconvolution architecture. In this paper, we define two multilevel attention-based models for DTBA prediction. Our two approaches attempt to get advantage of the attention concept, by probing different abstraction levels of drug-target feature maps. We evaluate the performance of our methods on two benchmark datasets, KIBA and Davis. The results show that both approaches are very effective. Compared to other well-known methods, they achieved excellent results regarding the considered performance metrics, while using merely sequences as inputs and providing a potential way of results interpretation. [ABSTRACT FROM AUTHOR]

Ectogestation involves the gestation of a fetus in an ex utero environment. The possibility of this technology raises a significant question for the abortion debate: Does a woman's right to end her pregnancy entail that she has a right to the death of the fetus when ectogestation is possible? Some have argued that it does not Mathison & Davis (Bioeth 31:313–320, 2017). Others claim that, while a woman alone does not possess an individual right to the death of the fetus, the genetic parents have a collective right to its death Räsänen (Bioeth 31:697–702, 2017). In this paper, I argue that the possibility of ectogestation will radically transform the problem of abortion. The argument that I defend purports to show that, even if it is not a person, there is no right to the death of a fetus that could be safely removed from a human womb and gestated in an artificial womb, because there are competent people who are willing to care for and raise the fetus as it grows into a person. Thus, given the possibility of ectogestation, the moral status of the fetus plays no substantial role in determining whether there is a right to its death. [ABSTRACT FROM AUTHOR]

This paper shall concern itself with two variants of instrumental realism that have developed independently of each other and have made a mark on contemporary philosophies of science as well as of technology in their own respective ways. One is that of Don Ihde, the progenitor of the postphenomenological approach to technoscience, and the other that of Davis Baird, who emphasizes the epistemic centrality of instruments as bearers of knowledge in themselves. I shall juxtapose Ihde's instrumental realism with the instrumental realism of Baird, both of whom emphasize the importance of experimentation and instrumentation to any comprehensive philosophy of science. Whereas Ihde wants to extend hermeneutics to science praxis, Baird wants to maintain an epistemological commitment to what he calls 'thing knowledge.' In comparing and contrasting these two variants of instrumental realism, I shall discern the implicit ontological and epistemological claims that underlie the two realisms in the background of scientific realism and critically evaluate their contributions to a more comprehensive understanding of science, technology, and the relation between the two. [ABSTRACT FROM AUTHOR]

The paper presents a concise history of enactivism in education, especially in mathematics education. Cases described by Davis's, Proulx and Simmt's work showcase the idea that enactivism is a viable alternative to constructivism or to classical views both in terms of practical teaching and theoretical models related to the process of learning. The idea that the student should solve a fixed problem, discover the universally correct solution, and eventually store that correct solution to find many other universally correct solutions to other fixed problems reduces the student to a very simple mechanism aimed at informational efficiency. This problem is met by the enactivistic tradition that began with Varela and Maturana's work, now updated to the aforementioned researchers. Contra the classical perspective, enactivism proposes the idea that the student collaboratively produces the problem, being able to see multiple solutions, and eventually becoming a performer of knowledge. The article takes these ideas developed in mathematics education and finds their use in philosophical education. The article especially focuses on the student's problem of being unable to link a new philosophical text discussed in class with their intuition. The last part of the article offers a lesson design example. The philosophical design focuses on making the students explore their own thinking regarding the topic about to be discussed by using a philosophy text before introducing the text. [ABSTRACT FROM AUTHOR]

The fall armyworm (Spodoptera frugiperda, Lepidoptera: Noctuidae), which is native to the Americas, has recently invaded Africa and Asia. There, it has become a major pest of maize (Zea mays). The variety of methods used to assess feeding damage caused by its caterpillars makes it difficult to compare studies. In this paper, we aim at determining which leaf damage rating scales for fall armyworm are most consistently used for which purposes, might provide most possibilities for statistical analyses, and would be an acceptable compromise between detail and workload. We first conducted a literature review and then validated the most common scales under field and laboratory conditions. Common leaf damage scales are the nominal "yes-no damage scale" that only assesses damage incidence, as well as difficult-to-analyse ordinal scales which combine incidence and severity information such as the "Simple 1 to 5 whole plant damage scale", "Davis' 0 to 9 whorl & furl damage scale", or "Williams' 0 to 9 whole plant damage scale". These scales have been adapted many times, are sometimes used incorrectly, or were wrongly cited. We therefore propose simplifications of some of these scales as well as a novel "0.0 to 4.0 fall armyworm leaf damage index" which improves precision and possibilities for parametric data analyses. We argue that the choice of a scale to use should depend on the desired level of detail, type of data analyses envisioned, and manageable time investment. [ABSTRACT FROM AUTHOR]

A semi-supervised, one-dimensional recurrent neural network (RNN) approach called RVS has been proposed in this paper for video salient object detection. The proposed RVS approach involves the processing of each frame independently without explicitly considering temporal information. The RNN is trained using one-dimensional superpixel features to classify the salient object regions into salient foreground and non-salient background superpixels. Deep learning algorithms generally exhibit heavy dependence on training data size and often take extremely long time for training. On the contrary, the proposed RVS approach involves the training of an RNN using a small data which results in significant reduction in training time. The RVS approach has been extensively evaluated and its results are compared with those of several state-of-the-art methods using the public-domain VideoSeg, SegTrack v1 and SegTrack v2 benchmark video datasets. Further, the RVS approach has been tested using the authors' own video dataset and the complex DAVIS and video object segmentation datasets to evaluate the impact of motion and blur on its performance. The RVS approach delivers results superior to those of several approaches that strongly rely upon spatio-temporal features in detecting the salient objects from the video sequences. [ABSTRACT FROM AUTHOR]

In his recent paper (2020 Eur. J. Phys. 41 045202), Davis makes the claim that potentials and fields are ill-defined in the conventional treatment of electromagnetism. He argues that 'the usual treatment is ambiguous, with that ambiguity being reflected in the gauge transformation equations'. He then proposes an approach based on two operational versions of Helmholtz's theorem and claims that his approach does not exhibit gauge freedom and allows a rigourous definition of electromagnetic potentials. Here I argue that Davis's approach does not provide a more rigours definition of potentials than that provided by the standard approach. Apparently, Davis does not realize that when applying an operational version of Helmholtz's theorem to Maxwell's equations, he is not avoiding gauge invariance but tacitly applying it by choosing the particular gauge-condition related to this version of the theorem. The application of the instantaneous Helmholtz's theorem to Maxwell's equations is equivalent to the choice of the Coulomb-gauge condition, and the application of the retarded Helmholtz's theorem to these equations is equivalent to the choice of the Lorentz-gauge condition. [ABSTRACT FROM AUTHOR]

In 1977, Colville, Davis, and Keimel [Positive derivations on f-rings, J. Aust. Math. Soc. Ser. A 23 1977, 3, 371–375] proved that a positive derivation on an Archimedean f-algebra A has its range in the set of nilpotent elements of A. The main objective of this paper is to obtain a generalization of the above Colville, Davis and Keimel result to general derivations. Moreover, we give a new version of the Singer–Wermer conjecture for the class of second-order derivations acting on uniformly complete almost f-algebras. [ABSTRACT FROM AUTHOR]