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Register a new userFriend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem
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Added by
Kenneth Joseph
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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We introduce the identity labeling problem - given an individual in a social situation, can we predict what identity(ies) they will be labeled with by someone else? This problem remains a theoretical gap and methodological challenge, evidenced by the fact that models of social-cognition often sidestep the issue by treating identities as already known. We build on insights from existing models to develop a new framework, entitled Latent Cognitive Social Spaces, that can incorporate multiple social cues including sentiment information, socio-demographic characteristics, and institutional associations to estimate the most culturally expected identity. We apply our model to data collected in two vignette experiments, finding that it predicts identity labeling choices of participants with a mean absolute error of 10.9%, a 100% improvement over previous models based on parallel constraint satisfaction and affect control theory.
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Solid state electrolytes are widely considered as the enabler of lithium metal anodes for safe, durable and high energy density rechargeable lithium ion batteries. Despite the promise, failure mechanisms associated with solid state batteries are not well-established, largely due to limited understanding of the chemomechanical factors governing them. We focus on the recent developments in understanding the solid state aspects including the effects of mechanical stresses, constitutive relations, fracture, void formation and outline the gaps in literature. We also provide an overview of the manufacturing and processing of solid state batteries in relation to chemomechanics. The gaps identified provides concrete directions towards the rational design and development of failure resistant solid state batteries.
This paper studies the potential of harvesting energy from the self-interference of a full-duplex base station. The base station is equipped with a self-interference cancellation switch, which is turned-off for a fraction of the transmission period for harvesting the energy from the self-interference that arises due to the downlink transmission. For the remaining transmission period, the switch is on such that the uplink transmission takes place simultaneously with the downlink transmission. A novel energy-efficiency maximization problem is formulated for the joint design of downlink beamformers, uplink power allocations and transmission time-splitting factor. The optimization problem is nonconvex, and hence, a rapidly converging iterative algorithm is proposed by employing the successive convex approximation approach. Numerical simulation results show significant improvement in the energy-efficiency by allowing self-energy recycling.
Inter-carrier interference (ICI) poses a significant challenge for OFDM joint radar-communications (JRC) systems in high-mobility scenarios. In this paper, we propose a novel ICI-aware sensing algorithm for MIMO-OFDM JRC systems to detect the presence of multiple targets and estimate their delay-Doppler-angle parameters. First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. To account for the presence of multiple targets at a given angle, we devise an iterative interference cancellation based orthogonal matching pursuit (OMP) procedure, where at each iteration the generalized likelihood ratio test (GLRT) detector is employed to form decision statistics, providing as by-products the maximum likelihood estimates (MLEs) of radar channels and CFOs. In the final step, target detection is performed in delay-Doppler domain using target-specific, ICI-decontaminated channel estimates over time and frequency, where CFO estimates are utilized to resolve Doppler ambiguities, thereby turning ICI from foe to friend. The proposed algorithm can further exploit the ICI effect to introduce an additional dimension (namely, CFO) for target resolvability, which enables resolving targets located at the same delay-Doppler-angle cell. Simulation results illustrate the ICI exploitation capability of the proposed approach and showcase its superior detection and estimation performance in high-mobility scenarios over conventional methods.
Cell type (e.g. pluripotent cell, fibroblast) is the end result of many complex processes that unfold due to evolutionary, developmental, and transformational stimuli. A cells phenotype and the discrete, a priori states that define various cell subtypes (e.g. skin fibroblast, embryonic stem cell) are ultimately part of a continuum that may predict changes and systematic variation in cell subtypes. These features can be both observable in existing cellular states and hypothetical (e.g. unobserved). In this paper, a series of approaches will be used to approximate the continuous diversity of gene expression across a series of pluripotent, totipotent, and fibroblast cellular subtypes. We will use a series of previously-collected datasets and analyze them using three complementary approaches: the computation of distances based on the subsampling of diversity, assessing the separability of individual genes for a specific cell line both within and between cell types, and a hierarchical soft classification technique that will assign a membership value for specific genes in specific cell types given a number of different criteria. These approaches will allow us to assess the observed gene-expression diversity in these datasets, as well as assess how well a priori cell types characterize their constituent populations. In conclusion, the application of these findings to a broader biological context will be discussed.
The impact of online social media on societal events and institutions is profound; and with the rapid increases in user uptake, we are just starting to understand its ramifications. Social scientists and practitioners who model online discourse as a proxy for real-world behavior, often curate large social media datasets. A lack of available tooling aimed at non-data science experts frequently leaves this data (and the insights it holds) underutilized. Here, we propose birdspotter -- a tool to analyze and label Twitter users --, and birdspotter.ml -- an exploratory visualizer for the computed metrics. birdspotter provides an end-to-end analysis pipeline, from the processing of pre-collected Twitter data, to general-purpose labeling of users, and estimating their social influence, within a few lines of code. The package features tutorials and detailed documentation. We also illustrate how to train birdspotter into a fully-fledged bot detector that achieves better than state-of-the-art performances without making any Twitter API online calls, and we showcase its usage in an exploratory analysis of a topical COVID-19 dataset.
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