Core Building Block Research Articles

CoHA-NTT: A Configurable Hardware Accelerator for NTT-based Polynomial Multiplication

In this paper, we introduce a configurable hardware architecture that can be used to generate unified and parametric NTT-based polynomial multipliers that support a wide range of parameters of lattice-based cryptographic schemes proposed for post-quantum cryptography. Both NTT and inverse NTT operations can be performed using the unified butterfly unit of our architecture, which constitutes the core building block in NTT operations. The multitude of this unit plays an essential role in achieving the performance goals of a specific application area or platform. To this end, the architecture takes the size of butterfly units as input and generates an efficient NTT-based polynomial multiplier hardware to achieve the desired throughput and area requirements. More specifically, the proposed hardware architecture provides run-time configurability for the scheme parameters and compile-time configurability for throughput and area requirements. This work presents the first architecture with both run-time and compile-time configurability for NTT-based polynomial multiplication operations to the best of our knowledge. The implementation results indicate that the advanced configurability has a negligible impact on the time and area of the proposed architecture and that its performance is on par with the state-of-the-art implementations in the literature, if not better. The proposed architecture comprises various sub-blocks such as modular multiplier and butterfly units, each of which can be of interest on its own for accelerating lattice-based cryptography. Thus, we provide the design rationale of each sub-block and compare it with those in the literature, including our earlier works in terms of configurability and performance.

DO-Conv: Depthwise Over-Parameterized Convolutional Layer

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel. The composition of the two convolutions constitutes an over-parameterization, since it adds learnable parameters, while the resulting linear operation can be expressed by a single convolution layer. We refer to this depthwise over-parameterized convolutional layer as DO-Conv, which is a novel way of over-parameterization. We show with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs on many classical vision tasks, such as image classification, detection, and segmentation. Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization. As DO-Conv introduces performance gains without incurring any computational complexity increase for inference, we advocate it as an alternative to the conventional convolutional layer. We open sourced an implementation of DO-Conv in Tensorflow, PyTorch and GluonCV at https://github.com/yangyanli/DO-Conv.

Benchmark construction and experimental evaluations for incoherent ontologies

As the core building blocks of the Semantic Web, ontologies provide shared vocabularies and conceptual knowledge for specific application fields. At the same time, ontologies can restrict their individuals and relationships through their semantic schema. However, logical conflicts of an ontology are often inevitably unavoidable in actual application scenarios when the ontology contains any form of disjointness or negation. Generally, logical conflicts can be divided into incoherence and inconsistency. Reasoning with incoherent ontologies may obtain many redundant relationships, and incoherence is a potential cause of inconsistency which seriously affects the correctness of semantic reasoning Therefore, handling incoherence is imperative, which mainly involves the fields of conflicts detection, ontology repair and justification computation. Various incoherent ontologies are indispensable to evaluate the proposed methods for handling incoherence. We provide a survey of relevant research works to study the proposed construction methods of incoherent ontologies. It is observed that incoherent ontologies in existing works still have the following limitations: (1) Lots of web pages used to download ontologies were temporarily constructed are now inaccessible; (2) Most of the existing incoherent ontologies were constructed in a simple way such as randomly adding disjointness axioms or merging ontologies through their alignments without considering all possible incoherence cases. To address the limitations, we propose a general framework to construct incoherent ontologies and design a hybrid algorithm to instantiate this framework. With the implemented construction methods, a comprehensive benchmark containing 116 ontologies is constructed. In our evaluations, incoherent ontologies in the benchmark are measured with 11 classic metrics. We then compare 5 representative ontology debugging systems and 3 repair methods. The evaluation results reveal that these ontologies could reflect different characteristics of each ontology system and repair method. All observations could guide researchers to select incoherent ontologies. The availability of our benchmark makes a contribution to the community of ontology debugging and repair fields.

VILS: A Verifiable Image Licensing System

Image licensing regulates the scope, type, and limitations of using an image through an agreement. However, it is challenging to verify whether an agreement has been fulfilled honestly. Existing techniques, such as watermarking and perceptual hashing, help check image originality and editing operations specified in the agreement, but fail to achieve editor designation. In this paper, we propose a verifiable image licensing system (VILS) which provides an effective solution to verify if a received image is used legally according to its licensing agreement. The core building block of our design is a new kind of cryptographic primitive, called accumulator with a designated entity. The new accumulator helps achieve not only editing restriction, but also editor designation in image authentication. Our VILS has the following two appealing features: (1) Authorization: Only an authorized licensee who edits an image with operations declared in a licensing agreement can produce valid images; (2) Efficiency: The verification of VILS is efficient and independent of the number of operations or image size. Compared with the most relevant schemes from the state-of-the-art, the new design enriches the functionality of image authentication but reduces the verification time by 40%.

AOP Key Event Relationship report: Linking decreased retinoic acid levels with disrupted meiosis in developing oocytes

The Adverse Outcome Pathway (AOP) concept is an emerging tool in regulatory toxicology that uses simplified descriptions to show cause-effect relationships between stressors and toxicity outcomes in intact organisms. The AOP structure is a modular framework, with Key Event Relationships (KERs) representing the unit of causal relationship based on existing knowledge, describing the connection between two Key Events. Because KERs are the only unit to support inference it has been argued recently that KERs should be recognized as the core building blocks of knowledge assembly within the AOP-Knowledge Base. Herein, we present a first case to support this proposal and provide a full description of a KER linking decreased all-trans retinoic acid (atRA) levels in developing ovaries with disrupted meiotic entry of oogonia. We outline the evidence to support a role for atRA in inducing meiosis in oogonia across mammals; this is important because elements of the RA synthesis/degradation pathway are recognized targets for numerous environmental chemicals. The KER we describe will be used to support an intended AOP linking inhibition of the atRA producing ALDH1A enzymes with reduced fertility in women.

Gender-sensitive word embeddings for healthcare.

To analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its performance. We analyze gender bias in clinical trials described by 16772 PubMed abstracts (2008-2018). We present a method to augment word embeddings, the core building block of NLP-centric representations, by weighting abstracts by the number of women participants in the trial. We evaluate the resulting gender-sensitive embeddings performance on several clinical prediction tasks: comorbidity classification, hospital length of stay prediction, and intensive care unit (ICU) readmission prediction. For female patients, the gender-sensitive model area under the receiver-operator characteristic (AUROC) is 0.86 versus the baseline of 0.81 for comorbidity classification, mean absolute error 4.59 versus the baseline of 4.66 for length of stay prediction, and AUROC 0.69 versus 0.67 for ICU readmission. All results are statistically significant. Women have been underrepresented in clinical trials. Thus, using the broad clinical trials literature as training data for statistical language models could result in biased models, with deficits in knowledge about women. The method presented enables gender-sensitive use of publications as training data for word embeddings. In experiments, the gender-sensitive embeddings show better performance than baseline embeddings for the clinical tasks studied. The results highlight opportunities for recognizing and addressing gender and other representational biases in the clinical trials literature. Addressing representational biases in data for training NLP embeddings can lead to better results on downstream tasks for underrepresented populations.

Open banking in Europe: The effect of the Revised Payment Services Directive on Solarisbank and Insha

The concept of open banking is gaining global recognition for helping to integrate innovative financial service providers into a sustainable financial ecosystem. This paper discusses open banking, including its core building blocks, prospects and challenges. Given the European Union’s pioneering role in adopting open banking regulations, the paper also reviews the revised Payment Services Directive (PSD2) and its role in advancing fast payment systems in Europe. The paper goes on to consider the practical implications of the PSD2 in Germany through case studies of the banking-asa-service platform, Solarisbank, and the Islamic digital bank, Insha. Finally, the paper sheds light on the benefits of open banking for ecosystem stakeholders, including fast payment system providers, and how they can benefit from the introduction of open banking regulations.

Facial mimicry in the congenitally blind

Imitation is one of the core building blocks of human social cognition, supporting capacities as diverse as empathy, social learning, and knowledge acquisition1. Newborns' ability to match others' motor acts, while quite limited initially, drastically improves during the first months of development2. Of notable importance to human sociality is our tendency to rapidly mimic facial expressions of emotion. Facial mimicry develops around six months of age3, but because of its late emergence, the factors supporting its development are relatively unknown. One possibility is that the development of facial mimicry depends on seeing emotional imitative behavior in others4. Alternatively, the drive to imitate facial expressions of emotion may be independent of visual learning and be supported by modality-general processes. Here we report evidence for the latter, by showing that congenitally blind participants facially imitate smiles heard in speech, despite having never seen a facial expression.

To Grasp the World at a Glance: The Role of Attention in Visual and Semantic Associative Processing.

Associative relations among words, concepts and percepts are the core building blocks of high-level cognition. When viewing the world ‘at a glance’, the associative relations between objects in a scene, or between an object and its visual background, are extracted rapidly. The extent to which such relational processing requires attentional capacity, however, has been heavily disputed over the years. In the present manuscript, I review studies investigating scene–object and object–object associative processing. I then present a series of studies in which I assessed the necessity of spatial attention to various types of visual–semantic relations within a scene. Importantly, in all studies, the spatial and temporal aspects of visual attention were tightly controlled in an attempt to minimize unintentional attention shifts from ‘attended’ to ‘unattended’ regions. Pairs of stimuli—either objects, scenes or a scene and an object—were briefly presented on each trial, while participants were asked to detect a pre-defined target category (e.g., an animal, a nonsense shape). Response times (RTs) to the target detection task were registered when visual attention spanned both stimuli in a pair vs. when attention was focused on only one of two stimuli. Among non-prioritized stimuli that were not defined as to-be-detected targets, findings consistently demonstrated rapid associative processing when stimuli were fully attended, i.e., shorter RTs to associated than unassociated pairs. Focusing attention on a single stimulus only, however, largely impaired this relational processing. Notably, prioritized targets continued to affect performance even when positioned at an unattended location, and their associative relations with the attended items were well processed and analyzed. Our findings portray an important dissociation between unattended task-irrelevant and task-relevant items: while the former require spatial attentional resources in order to be linked to stimuli positioned inside the attentional focus, the latter may influence high-level recognition and associative processes via feature-based attentional mechanisms that are largely independent of spatial attention.

A Pragmatic Approach to Adverse Outcome Pathway Development and Evaluation.

The adverse outcome pathway (AOP) framework provides a practical means for organizing scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. It has reached wide acceptance as a tool to aid chemical safety assessment and regulatory toxicology by supporting a systematic way of predicting adverse health outcomes based on accumulated mechanistic knowledge. A major challenge for broader application of the AOP concept in regulatory toxicology, however, has been developing robust AOPs to a level where they are peer reviewed and accepted. This is because the amount of work required to substantiate the modular units of a complete AOP is considerable, to the point where it can take years from start to finish. To help alleviate this bottleneck, we propose a more pragmatic approach to AOP development whereby the focus becomes on smaller blocks. First, we argue that the key event relationship (KER) should be formally recognized as the core building block of knowledge assembly within the AOP knowledge base (AOP-KB), albeit framing them within full AOPs to ensure regulatory utility. Second, we argue that KERs should be developed using systematic review approaches, but only in cases where the underlying concept does not build on what is considered canonical knowledge. In cases where knowledge is considered canonical, rigorous systematic review approaches should not be required. It is our hope that these approaches will contribute to increasing the pace at which the AOP-KB is populated with AOPs with utility for chemical safety assessors and regulators.

Steps towards a European Fiscal Union: Has the revised Stability and Growth Pact delivered so far?

Since 2008-09, the European Union (EU) experienced two major economic crises revealing all the flaws of the existing model of economic governance. By leaving the majority of the countries with high levels of deficit and public debt, the two crises have shown that the Economic and Monetary Union (EMU) is indeed an unfinished project where the monetary union alone is not sufficient to safeguard the entire EU economy. To strengthen the EMU and to mitigate future risks that could possibly lead to the collapse of the euro-area, many called for a deeper fiscal integration by creating a central fiscal capacity for the EMU or, in other words, a fiscal union. Due to the present political unfeasibility of such an endeavour, however, concrete steps towards a European Fiscal Union (EFU) have been modest and the revised Stability and Growth Pact (SGP) remains its core building block. As the Pact defines supranational and shapes the creation of national fiscal rules, maintaining its credibility continues to be vital. This article analyses the effects of the fiscal rules on the public finances of the member states. It is assumed that by adhering to the supranational and adopting quality domestic fiscal rules, the member states are better equipped in remaining fiscally prudent, thus also affirming the revamped SGP as a solid base for the furthering of the EFU. The two-track evaluation approach defines dynamic panels for the EFU as a whole and for the selected country groups. It finds certain benevolent effects on budgetary performance at the EFU level, as well as for the countries with higher quality of the fiscal rules.

The Building Blocks of Implementation Frameworks and Models in Primary Care: A Narrative Review.

Background: Our aim is to identify the core building blocks of existing implementation frameworks and models, which can be used as a basis to further develop a framework for the implementation of complex interventions within primary care practices. Within the field of implementation science, various frameworks, and models exist to support the uptake of research findings and evidence-based practices. However, these frameworks and models often are not sufficiently actionable or targeted for use by intervention designers. The objective of this research is to map the similarities and differences of various frameworks and models, in order to find key constructs that form the foundation of an implementation framework or model that is to be developed.Methods: A narrative review was conducted, searching for papers that describe a framework or model for implementation by means of various search terms, and a snowball approach. The core phases, components, or other elements of each framework or model are extracted and listed. We analyze the similarities and differences between the frameworks and models and elaborate on their core building blocks. These core building blocks form the basis of an overarching model that we will develop based upon this review and put into practice.Results: A total of 28 implementation frameworks and models are included in our analysis. Throughout 15 process models, a total of 67 phases, steps or requirements are extracted and throughout 17 determinant frameworks a total of 90 components, constructs, or elements are extracted and listed into an Excel file. They are bundled and categorized using NVivo 12© and synthesized into three core phases and three core components of an implementation process as common elements of most implementation frameworks or models. The core phases are a development phase, a translation phase, and a sustainment phase. The core components are the intended change, the context, and implementation strategies.Discussion: We have identified the core building blocks of an implementation framework or model, which can be synthesized in three core phases and three core components. These will be the foundation for further research that aims to develop a new model that will guide and support intervention designers to develop and implement complex interventions, while taking account contextual factors.

AMBITION – comet nucleus cryogenic sample return

We describe the AMBITION project, a mission to return the first-ever cryogenically-stored sample of a cometary nucleus, that has been proposed for the ESA Science Programme Voyage 2050. Comets are the leftover building blocks of giant planet cores and other planetary bodies, and fingerprints of Solar System’s formation processes. We summarise some of the most important questions still open in cometary science and Solar System formation after the successful Rosetta mission. We show that many of these scientific questions require sample analysis using techniques that are only possible in laboratories on Earth. We summarize measurements, instrumentation and mission scenarios that can address these questions. We emphasize the need for returning a sample collected at depth or, still more challenging, at cryogenic temperatures while preserving the stratigraphy of the comet nucleus surface layers. We provide requirements for the next generation of landers, for cryogenic sample acquisition and storage during the return to Earth. Rendezvous missions to the main belt comets and Centaurs, expanding our knowledge by exploring new classes of comets, are also discussed. The AMBITION project is discussed in the international context of comet and asteroid space exploration.

Balanced Charge Carrier Transport Mediated by Quantum Dot Film Post-organization for Light-Emitting Diode Applications.

In light-emitting diodes (LEDs), balanced electron and hole transport is of particular importance to achieve high rates of radiative recombination. Most quantum dot (QD)-based LEDs, however, employ infinitesimal core-shell QDs which inherently have different electron and hole mobilities. As QDs are the core building blocks of QD-LEDs, the inherent mobility difference in the core-shell QDs causes significantly unbalanced charge carrier transport, resulting in detrimental effects on performances of QD-LEDs. Herein, we introduce a post-chemical treatment to reconstruct the QD films through the solvent-mediated self-organization process. The treatment using various poly-alkyl alcohol groups enables QD ensembles to transform from disordered solid dispersion into an ordered superlattice and effectively modulate electron and hole mobilities, which leads to the balanced charge carrier transport. In particular, ethanol-treated QD films exhibit enhanced charge carrier lifetime and reduced hysteresis due to the balanced charge carrier transport, which is attributed to the preferential-facet-oriented QD post-organization. As a result, 63, 78, and 54% enhancements in the external quantum efficiency were observed in red, green, and blue QD-LEDs, respectively. These results are of fundamental importance to understand both solvent-mediated QD film reconstruction and the effect of balanced electron and hole transport in QD-LEDs.

P120 catenin potentiates constitutive E-cadherin dimerization at the plasma membrane and regulates trans binding

Cadherins are essential adhesion proteins that regulate tissue cohesion and paracellular permeability by assembling dense adhesion plaques at cell-to-cell contacts. Adherens junctions are central to a wide range of tissue functions; identifying protein interactions that potentiate their assembly and regulation has been the focus of research for over 2 decades. Here, we present evidence for a new, unexpected mechanism of cadherin oligomerization on cells. Fully quantified spectral imaging fluorescence resonance energy transfer (FSI-FRET) and fluorescence intensity fluctuation (FIF) measurements directly demonstrate that E-cadherin forms constitutive lateral (cis) dimers at the plasma membrane. Results further show that binding of the cytosolic protein p120ctn binding to the intracellular region is required for constitutive E-cadherin dimerization. This finding differs from a model that attributes lateral (cis) cadherin oligomerization solely to extracellular domain interactions. The present, novel findings are further supported by studies of E-cadherin mutants that uncouple p120ctn binding or with cells in which p120ctn was knocked out using CRISPR-Cas9. Quantitative affinity measurements further demonstrate that uncoupling p120ctn binding reduces the cadherin trans binding affinity and cell adhesion. These findings transform the current model of cadherin assembly at cell surfaces and identify the core building blocks of cadherin-mediated intercellular adhesions. They also identify a new role for p120ctn and reconcile findings that implicate both the extracellular and intracellular cadherin domains in cadherin clustering and intercellular cohesion.

A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion

Driven by deep neural networks (DNN), the recent development of computer vision makes vision sensors such as stereo cameras and Lidars ubiquitous in autonomous cars, robotics and traffic monitoring. However, a traditional DNN-based data fusion pipeline like object tracking has to hard-wire an engineered set of DNN models to a fixed processing logic, which makes it difficult to infuse new models to that pipeline. To overcome this, we propose a novel neural-symbolic stream reasoning approach realised by semantic stream reasoning programs which specify DNN-based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. The reasoning task over this program is governed by a novel incremental reasoning algorithm, which lends itself also as a core building block for a scalable and parallel algorithm to learn the weights for such program. Extensive experiments with our first prototype on multi-object tracking benchmarks for autonomous driving and traffic monitoring show that our flexible approach can considerably improve both accuracy and processing throughput compared to the DNN-based counterparts.

EvoSets: Tracking the Sensitivity of Dimensionality Reduction Results Across Subspaces

Dimensionality reduction is commonly used for identifying and analyzing patterns in the visual analysis of multi-dimensional datasets. The selection of subspaces is a core building block in projecting high-dimensional data to low-dimensional space, which is usually illustrated as a scatterplot for analysts to easily understand and explore. This process involves human prior knowledge and domain-specific requirements. Thus, quantifying and tracking the changes of dimensionality reduction results across subspaces remain challenging. Existing methods can neither quantify the subsets-based changes of dimensionality reduction results when switching subspaces, nor automatically and comprehensively display the overall and subtle differences among dimensionality reduction results. To address this, we developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EvoSets</i> , a novel visual analytics system designed to help users understand how subspaces affect dimensionality reduction results. The effects are quantified based on the distribution of subsets within projections to tracking the sensitivity of dimensionality reduction results across subspaces. In addition, the system supports the exploration of the overall evolution of the dimensionality reduction results for helping users track the convergence and divergence behavior changes of subsets based on an extended <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bubble Sets</i> visualization. Similarities are intuitively illustrated, and dissimilarities are highlighted among the generated dimensionality reduction results across subspaces based on different layout constraints. The usefulness and effectiveness of the system are further evaluated with a user study and two case studies on multi-dimensional datasets.

A roadmap for research in medical physics via academic medical centers: The DIVERT Model.

The field of medical physics has struggled with the role of research in recent years, as professional interests have dominated its growth toward clinical service. This article focuses on the subset of medical physics programs within academic medical centers and how a refocused academic mission within these centers should drive and support Discovery and Invention with Ventures and Engineering for Research Translation (DIVERT). A roadmap to a DIVERT-based scholarly research program is discussed here around the core building blocks of: (a) creativity in research and team building, (b) improved quality metrics to assess activity, (c) strategic partnerships and spinoff directions that extend capabilities, and (d) future directions driven by faculty-led initiatives. Within academia, it is the unique discoveries and inventions of faculty that lead to their recognition as scholars, and leads to financial support for their research programs and reconition of their intellectual contributions. Innovation must also be coupled to translation to demonstrate outcome successes. These ingredients are critical for research funding, and the two-decade growth in biomedical engineering research funding is an illustration of this, where technology invention has been the goal. This record can be contrasted with flat funding within radiation oncology and radiology, where a growing fraction of research is more procedure-based. However, some centers are leading the change of the definition of medical physics, by the inclusion or assimilation of researchers in fields such as biomedical engineering, machine learning, or data science, thereby widening the scope for new discoveries and inventions. New approaches to the assessment of research quality can help realize this model, revisiting the measures of success and impact. While research partnerships with large industry are productive, newer efforts that foster enterprise startups are changing how institutions see the benefits of the connection between academic innovation and affiliated startup company formation. This innovation-to-enterprise focus can help to cultivate a broader bandwidth of donor-to-investor networks. There are many predictions on future directions in medical physics, yet the actual inventive and discovery steps come from individual research faculty creativity. All success through a DIVERT model requires that faculty-led initiatives span the gap from invention to translation, with support from institutional leadership at all steps in the process. Institutional investment in faculty through endowments or clinical revenues will likely need to increase in the coming years due to the relative decreasing size of grants. Yet, radiology and radiation oncology are both high-revenue, translational fields, with the capacity to synergistically support clinical and research operations through large infrastructures that are mutually beneficial. These roadmap principles can provide a pathway for committed academic medical physics programs in scholarly leadership that will preserve medical physics as an active part of university academics.

Exploring periodic mesoporous organosilicas for ethane–ethylene adsorption–separation

Ethylene is a core building block in the chemical industry and its separation from ethane is very challenging due to high energy requirements. Adsorption-based processes can be an alternative to current processes based on cryogenic distillation. In this work, we explore for the first time the application of periodic mesoporous organosilica (PMO) materials, whose surface properties can be tuned with different functional groups. With the aim of correlating the PMO structure with ethane and ethylene adsorption, eight different PMO materials have been prepared, viz. the phenylene-bridged PMO, PMO aminated at the organic bridges, and PMO functionalized by silylation of free silanol (inorganic) moieties. High pressure adsorption isotherms were measured, and the separation selectivity and phase diagrams of a binary mixture of ethane and ethylene were estimated. Results have shown that, overall, the PMO studied tend to be more selective towards ethylene than ethane probably due to the interactions between the quadrupole moment of ethylene and the free silanols in the samples. After silylation, the novel materials presented surfaces with higher affinity towards ethane than those of the pristine material. For the aminated samples, functionalization with primary amines originated materials displaying better selectivity towards ethylene than those functionalized with secondary or tertiary amines.

Employee engagement constructs: “CARE” model of engagement – need to look beyond the obvious

PurposeThe purpose of this article is to identify new variables that form the core building blocks of employee engagement model which can be leveraged by human resources' (HR) practitioners to develop HR strategies to drive employee engagement initiatives in their companies.Design/methodology/approachThe authors have undertaken extensive literature review to identify the variables that enhances engagement. These variables are yet to be leveraged fully in engagement models that have been developed but have the potential to impact the motivation levels of the employees. Exploratory survey to validate the relevance of these variables was conducted and subsequently these variables were grouped into a Context, Altruistic, Resonance and Enable (“CARE”) framework that can be leveraged by HR practitioners.FindingsThe changing nature of workforce, increasing diversity and tenure of jobs has resulted in changing aspirations of employees as well. The building blocks hitherto adopted by companies to craft their employee engagement strategies have to undergo a perceptible change as well. This paper offers an alternative perspective of relooking the engagement levers currently adopted by firms (work environment, autonomy, appreciation, organization support, etc.) and proposes 15 new building blocks (resonant connections, value congruence, socialization practices, micro-moments of connections, fit amongst others) which are logically grouped under CARE model over which employee engagement strategies can be built by companies.Originality/valueThis paper provides insights into this important concept of employee engagement using a set of newer constructs that can form the foundation of newer models that will drive future HR strategies to enhance engagement.