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The set of methods discussed in this collection has emerged from the convergence of two scientific fields-genetics and causal inference. In this introduction, we discuss relevant aspects of each field and show how their convergence arises from the natural experiments that genetics offer. We present introductory concepts useful to readers unfamiliar with genetically informed methods for causal inference. We conclude that existing applications and foreseeable developments should ensure that we rapidly reap the rewards of this relatively new field, not only in terms of our understanding of human disease and development, but also in terms of tangible translational applications.

Recent years have witnessed a tremendous increasing of first-person videos captured by wearable devices. Such videos record information from different perspectives than the traditional third-person view, and thus show a wide range of potential usages. However, techniques for analyzing videos from different views can be fundamentally different, not to mention co-analyzing on both views to explore the shared information. In this paper, we take the challenge of cross-view video co-analysis and deliver a novel learning-based method. At the core of our method is the notion of "joint attention", indicating the shared attention regions that link the corresponding views, and eventually guide the shared representation learning across views. To this end, we propose a multi-branch deep network, which extracts cross-view joint attention and shared representation from static frames with spatial constraints, in a self-supervised and simultaneous manner. In addition, by incorporating the temporal transition model of the joint attention, we obtain spatial-temporal joint attention that can robustly capture the essential information extending through time. Our method outperforms the state-of-the-art on the standard cross-view video matching tasks on public datasets. Furthermore, we demonstrate how the learnt joint information can benefit various applications through a set of qualitative and quantitative experiments.

The literature argues that a real digital transformation of firms requires holistic changes of the business model. Despite knowledge about this ambitious goal, understanding of how digital business model transformation can be achieved is still very limited. In this article, we explore how firms achieve digital business model transformation. We apply a case study design to investigate how incumbents have changed their respective business model dimensions during digital transformation. Our findings center on interview data and complementary archival records from 15 cases. We present a framework for digital business model transformation along the dimensions of value proposition, value creation, and value capture. Our results emphasize the importance of a preparatory phase in which the strategic course is set. Furthermore, our findings demonstrate that transforming a company's business model is most effective when a single person, namely the Chief Digital Officer, is responsible. Our findings contribute to the business model literature, by providing a more holistic view on how business model innovation can be utilized during digital transformation.

This paper studies the effects of peers on the adoption of a Youth Employment Subsidy in Chile since its inception. We examine the effects that former classmates' and coworkers' adoption have on one's adoption. Identification comes from discontinuities in the assignment rule that allow us to construct valid instrumental variables for peers' adoption. Using a comprehensive set of administrative records, we find that classmates and especially coworkers play significant roles in the adoption of the subsidy. Peer effects are determined during the early stages of the program's implementation and vary by network characteristics and the strength of network ties.

As a huge variety of information floods pouring over us each day, identifying the authenticity of massive events becomes a necessary task to maintain the stability of Mobile Social Networks (MSNs). With this regard, this paper proposes a diffusion model that characterizes the simultaneous diffusion of both truth and rumor in realistic MSNs, and makes the first attempt to figure out their respective sources. The problem of interest can be stated as: Given an outcome of cascade of both truth and rumor in MSNs, i.e., a set of nodes that might be the ignorant, the spreader of truth or rumor, or simply the silent receiver, how can we infer both truth sources and rumor sources Different from previous sources detection works considering single type of nodes, the interplay between truth diffusions and rumor diffusions makes the conventional methods not work. To answer this question, we aim to maximize the similarity index, i.e., the number of nodes possessing the same states between the resulting network triggered by our estimated sources with the proposed diffusion model and the given observation network.i much harder to find two kinds of sets at the same time, including truth sources and rumor sources

While mastering technology and industry convergence are essential for firms across a growing number of industries, convergence is often rapid and abrupt, challenging firms to develop appropriate strategic responses. Focusing on the historical convergence between information technology and communication technology, we examine the microlevel behaviors of scientists initiating and driving convergence. Analyzing a bibliometric dataset of 257 641 scientific articles, we demonstrate how industry convergence manifests in a microlevel scientific convergence, preceding industry convergence by several decades. Our article contributes to the literature on convergence by developing new bibliometric measures for scientific convergence, and by contrasting microlevel behaviors that underpin convergence. Based on our findings, we offer a set of methods and strategies to assist managers in technology-based businesses with anticipating and responding to convergence in a timely manner., IEEE Transactions on Engineering Management, 70 (4), ISSN:0018-9391, ISSN:1558-0040

In the absence of strong incentive schemes, public service delivery crucially depends on bureaucrat selection. Despite being widely adopted by governments to screen candidates, it is unclear whether civil service examinations can predict job performance. This paper investigates this question by focusing on a highly prestigious and influential set of bureaucrats in Brazil: state judges. We first explore data on judges' monthly output and cross-court movement to separately identify what share of observed performance is explained by judges and courts. We estimate that judges account for at least 23% of the observed variation in the number of cases disposed. Using a novel data set on examinations, we then show that, within cohorts of candidates taking the same exam, those with higher grades perform better than their lower-ranked peers. Our results suggest that competitive examinations can be an effective way to screen candidates, even among highly qualified contenders.

Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Psychologists have shown increasing interest in such models, comparing their output to psychological judgments such as similarity, association, priming, and comprehension, raising the question of whether the models could serve as psychological theories. In this article, we compare how humans and machines represent the meaning of words. We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects. Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people express through words. Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not. We discuss more promising approaches to grounding NLP systems and argue that they will be more successful with a more human-like, conceptual basis for word meaning., Comment: In press at Psychological Review

This study investigates the relevance of behavioural finance to decision making. Studies suggest that human decision making is not always rational. This paper examines three behavioural financial theories: expected utility, optimal expectation, and cumulative prospect theory, to test financial decision-making when facing skewed distribution in Vietnam. A survey was conducted using lottery tickets, with six questions divided into three pairs. Each pair was aligned with one set of theories, and the questions selected allowed conclusions to be drawn to explain participants' behaviour. The experiment tested 321 people from a variety of age ranges, genders and occupations. Findings show that gender and ages do not significantly impact the decision-making process. However, they explain the preference of participants who appear to be behaving irrationally but do show some rationality when facing the skewed distribution of return. Decision-makers look for all the possible probability payoffs and choose the best outcome with the low-frequency distribution. They follow optimal expectation and cumulative prospect theory ranking the increasing order payoff valued by the parameter and support the cumulative prospect theory set out by Tversky and Kahneman (1992) , showing "cognitive biases" and demonstrating that individuals routinely make decisions that contradict reasonable logic The behavioural finance theory is again proven to be crucial. It strongly complements the standard financial theory. Individuals show heuristic behaviour when decision making in random situations especially when facing skewed distribution.

The risk of fossil fuels escalating the discussion of renewable-energy sources (RES) for a cleaner environment coupled with campaigns to promote it. Therefore, the focus of this article is twofold. First, in this article, we propose a model to investigate the mediating role of awareness in the intention to accept RES. The study consists of a unified theory of acceptance and the use of technology and a set of four hypotheses. Second, an integrated MCDM method is applied based on the entropy TOPSIS to select the optimum RES alternative. Furthermore, a sensitivity analysis of the weight shows the robustness of the proposed integrated model and the public perception of RES is analyzed using sentiment analysis. As a part of a sufficient survey, data were collected from 300 plus people in an urban area of Malaysia and online global reports. The results indicate that awareness plays a mediating role leading to public opinions and performance expectancy for the acceptance of RES. Besides, the results reveal that biomass is the most appropriate clean energy source, having the highest score value assessed. The research in the field is lacking; however, utilizing public feedback and sentiments, the findings of this article will help effective policy and decision making.

Objective: To develop a core outcome set (COS), an agreed minimum set of outcomes to measure and report in all studies evaluating the introduction and evaluation of novel surgical techniques.Summary Background Data: Agreement on the key outcomes to measure and report for safe and efficient surgical innovation is lacking, hindering transparency and risking patient harm.Methods: (I) Generation of a list of outcome domains from published innovation-specific literature, policy/regulatory body documents, and surgeon interviews; (II) Prioritization of identified outcome domains using an international, multi-stakeholder Delphi survey; (III) Consensus meeting to agree the final COS. Participants were international stakeholders, including patients/public, surgeons, device manufacturers, regulators, trialists, methodologists and journal editors.Results: 7,972 verbatim outcomes were identified, categorized into 32 domains, and formatted into survey items/questions. 410 international participants (220 professionals, 190 patients/public) completed at least one round 1 survey item, of which 153 (69.5%) professionals and 116 (61.1%) patients completed at least one round 2 item. 12 outcomes were scored ‘consensus in’ (‘very important’ by ≥70% of patients and professionals) and 20 ‘no consensus’. A consensus meeting, involving 19 professionals and 10 patient/public representatives, led to agreement on a final 8-domain COS. Six domains are specific to a surgical innovation context: modifications, unexpected disadvantages, device problems, technical procedure success, whether the overall desired effect was achieved, surgeons’/operators’ experience. Two domains relate to intended benefits and expected disadvantages.Conclusions: The COS is recommended for use in all studies prior to definitive RCT evaluation to promote safe, transparent, and efficient surgical innovation.

This paper aims to solve two important issues that frequently occur in existing automatic personality analysis systems: 1. Attempting to use very short video segments or even single frames to infer personality traits; 2. Lack of methods to encode person-specific facial dynamics for personality recognition. Hence, we proposes a novel Rank Loss which utilizes the natural temporal evolution of facial actions, rather than personality labels, for self-supervised learning of facial dynamics. Our approach first trains a generic U-net model that can infer general facial dynamics learned from unlabelled face videos. Then, the generic model is frozen, and a set of intermediate filters are incorporated into this architecture. The self-supervised learning is then resumed with only person-specific videos. This way, the learned filters' weights are person-specific, making them a valuable source for modeling person-specific facial dynamics. We then concatenate the weights of the learned filters as a person-specific representation, which can be directly used to predict the personality traits without needing other parts of the network. We evaluate the proposed approach on both self-reported personality and apparent personality datasets. Besides achieving promising results in personality trait estimation from videos, we show that fusion of tasks reaches highest accuracy, and that multi-scale dynamics are more informative than single-scale dynamics.

Although difficulties processing both symbolic and nonsymbolic proportion compared with absolute number are well established, the mechanisms involved remain unclear. We investigate four potential explanations to account for better number processing in adulthood: (a) number is more salient than proportion, (b) number is encoded more automatically than proportion, (c) proportion is more effortfully processed than number, and (d) number competes with proportion during decision making. Across three experiments, we used a delayed match-to-sample paradigm in which adults were asked which of two alternatives matched a sample set of red and blue dots. We systematically manipulated which dimension of the sample participants matched (number of red dots, total number of dots, proportion of red dots), the presence/absence of the competing quantity in the choice alternatives, and when they were told which quantitative dimension to encode (before vs. after the sample presentation, or not at all). Overall, data reveal that proportion was less salient than the numerical subset. Additionally, the number of items within the subset, but not the total number of items in the superset, interfered with proportion-based responding. Last, even in the absence of response competition and costly task demands, proportion matching took longer than number matching, highlighting basic processing differences. Together, results reveal pervasive difficulties in representing proportion compared with number, even when task demands are unambiguous. However, this varied depending on the numerical set involved and across encoding, processing, and decision processes. We discuss the implications of these findings for theories of ratio processing and of quantity more generally. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The research aimed to know the effect of parental leadership represented by (benevolent leadership, moral leadership, and authoritarian leadership) found in the research sample, in the organizational familiarity (employee morale, empowerment, and objective merit), the research relied on the questionnaire as a key instrument to collect the necessary data to meet its goal. As (60) forms were distributed to find the level of availability of parental leadership and organizational harmony, while (56) forms were retrieved. A set of statistical methods were used, represented by normal distribution, stability factor (Alpha Kronbach), reliability, arithmetic mean, standard deviation, and coefficient Simple correlation Pearson, multiple regression coefficient. The results showed that there is a positive correlation and effect relationship with statistically significant between parental leadership with its dimensions (benevolent leadership, moral leadership, authoritarian leadership) and organizational affiliation with its dimensions (employee morale, empowerment, and merit's Objectivity), and the research showed a direct impact relationship between parental leadership and the organizational affiliation of the studied sample. Accordingly, the research concluded that the study sample should pay attention to the nature and type of empowering workers in order to give them freedom and independence in making decisions regarding the tasks assigned to them.

The promise of automated-driving cars cause the automotive and consumer electronics (CE) sector to rethink what it means to drive, but the relationship between the car and the consumer. Recent trend in Internet of Vehicle Things (IoVT) promotes robust interactions in between humans and vehicles which altimetry points to enhance human abilities such as hearing or emotion awareness as a part of safety concern. The voice-based interactions (speech recognition, stress monitoring) will improve in-time awareness of the vehicle status. Unfortunately, the existing modulation domain speech enhancement techniques achieve low satisfactory performance in detecting humans stress emotions where the environmental noise is inevitable and varies with every passing location of vehicle. In this direction, we propose frontend processing framework, in particular to stress emotion detection cases in different non-stationary noisy environments. This study encompasses three Inter-related issues: (i) analysis, modification, and synthesis of noisy speech emotion in modulation domain in realtime background noise, (ii) extracting set of Mel-frequency cepstral coefficients (MFCC) features from noisy speech stimuli for speech emotion recognition, and (iii) evaluation of overall system performance by means of objective parameters, and confusion matrix in adverse environments using speech emotion database Interactive Emotional Dyadic Motion Capture (IEMOCAP)

To promote quality education and esteem engineered services and to provide quality engineering education by including innovating teaching-learning practices are presented in this paper. Conveys our teaching values, goals and beliefs to wider learners and provides a set of criteria and standards to evaluate the quality of our teaching. To help my students acquire skills, I engage them in learning by explaining the context of the subjects, their importance and their implementation in the real world.

Cyber risk affects all organizations. Cyber risk management has generally been based on the heuristics and the availability of protective tools, such as firewalls and passwords. Only recently have there been quantitative analyses of these tools’ costs and benefits. This article presents a probabilistic method based on the existing data in an organization and on their extension to assess the probabilities of new attack scenarios. The objective is to set priorities among risk management measures and to optimize the allocation of limited resources. The model is illustrated first by a statistical analysis of 60 000 incidents, such as lost or stolen laptops, over six years in a specific organization. This analysis is then expanded to the probabilistic domain to cover threats that have not occurred yet. This requires a systematic construction of new attack scenarios and an assessment both of their probability of success and of subsequent losses. The conjunction of statistics and probabilities of more extreme scenarios yields full risk curves. These curves represent the overall cyber risk for the organization and its insurers and unable assessment of the benefits of a spectrum of protective options.

Speech Emotion Recognition (SER) aims to endow machines with the intelligence in perceiving latent affective components from speech. However, the existing works on deep-learning-based SER make it difficult to jointly consider time-frequency and sequential information in speech due to their structures, which may lead to deficiencies in exploring reasonable local emotional representations. In this regard, we propose a Convolutional-Recurrent Neural Network with Multiple Attention mechanisms (CRNN-MA) for SER in this paper, including paralleled Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) modules, using extracted Mel-spectrums and frame-level features respectively, in order to acquire time-frequency and sequential information simultaneously. Further, we set three strategies for the proposed CRNN-MA: A multiple self-attention layer in the CNN module on frame-level weights, a multi-dimensional attention layer as the input features of the LSTM, and a fusion layer summarizing the features of the two modules. Experimental results on three conventional SER corpora demonstrate the effectiveness of the proposed approach through using the convolutional-recurrent and multiple-attention modules, compared with other related models and existing state-of-the-art approaches.

Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hierarchy of policies that uses them to reach the goals. Recently introduced end-to-end HRL methods accomplish this by using the higher-level policy in the hierarchy to directly search the useful subgoals in a continuous subgoal space. However, learning such a policy may be challenging when the subgoal space is large. We propose integrated discovery of salient subgoals (LIDOSS), an end-to-end HRL method with an integrated subgoal discovery heuristic that reduces the search space of the higher-level policy, by explicitly focusing on the subgoals that have a greater probability of occurrence on various state-transition trajectories leading to the goal. We evaluate LIDOSS on a set of continuous control tasks in the MuJoCo domain against hierarchical actor critic (HAC), a state-of-the-art end-to-end HRL method. The results show that LIDOSS attains better goal achievement rates than HAC in most of the tasks.

When multiple retailers hold inventory to satisfy random demand, retailer inventory-sharing strategies can potentially reduce the supply-demand mismatch and increase overall supply chain performance. In this paper, we experimentally investigate alternative inventory-sharing strategies in a two-tier supply chain with an upstream manufacturer and two downstream retailers. In one setting, retailers act as if they are centralized and use a single quantity to fulfill joint demand. In the other, retailers are decentralized and face separate demands, but they can transfer inventory after demands are realized. In this latter decentralized scenario, we also consider whether the manufacturer or retailers have decision authority over the inventory transfer price. One key result is that when the retailers are decentralized and the manufacturer sets the transfer price, both retailers and the manufacturer earn higher profits than in the centralized retailer strategy, which runs counter to theory. We also find that when retailers are decentralized and set their own transfer price, the most equitable distribution of profits is achieved. In an effort to account for these results, we find that a model of fairness captures decisions well. Overall, by investigating how different inventory-sharing strategies affect the distribution of profits in a two-tier supply chain, our results provide guidance to firms considering how, if at all, they should enter such arrangements. This paper was accepted by Jay Swaminathan, operations management. Funding: The authors acknowledge financial support from Cornell University and the University of Virginia. Supplemental Material: The data files and electronic companion are available at https://doi.org/10.1287/mnsc.2022.4323 .

Prior research has demonstrated that angry participants exhibit biased threat detection whereby they are more likely to misidentify neutral objects as guns. Yet, it is unclear whether independent components of anger, such as conceptual knowledge about anger or the affective features of an anger instance, could lead to altered bias alone. Consistent with constructionist theories of emotion, the present set of two experiments demonstrates that threat-detection bias only differs significantly between participants in an emergent-anger condition, who had engaged both components of anger (i.e., conceptual knowledge of anger and negative, high arousal affect), and participants in a control condition, who had engaged neither. Study 2 demonstrates that this pattern of findings also extends to another threat-relevant emotional state (i.e., fear). Implications for studying anger and fear, and emotions more generally, as constructed mental experiences are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).