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Abstract The backpropagation method has enabled transformative uses of neural networks. Alternatively, for energy-based models, local learning methods involving only nearby neurons offer benefits in terms of decentralized training, and allow for the possibility of learning in computationally-constrained substrates. One class of local learning methodscontraststhe desired, clamped behavior with spontaneous, free behavior. However, directly contrasting free and clamped behaviors requires explicit memory. Here, we introduce ‘Temporal Contrastive Learning’, an approach that uses integral feedback in each learning degree of freedom to provide a simple form of implicit non-equilibrium memory. During training, free and clamped behaviors are shown in a sawtooth-like protocol over time. When combined with integral feedback dynamics, these alternating temporal protocols generate an implicit memory necessary for comparing free and clamped behaviors, broadening the range of physical and biological systems capable of contrastive learning. Finally, we show that non-equilibrium dissipation improves learning quality and determine a Landauer-like energy cost of contrastive learning through physical dynamics. 

Electrification of buildings through deployment of heat pumps requires innovative design and control strategies to reduce their energy demands on the grid. Instead of the sequential approach of optimizing the design specifications and control strategies, this paper considers the benefits of the combined and simultaneous optimization of design capacities and control settings for heat pumps when specified for US residential buildings. A Genetic Algorithm optimizer is used to simultaneously adjust the main and supplementary coil capacities for the heat pump as well as the indoor temperature setpoints to minimize annual heating and cooling energy needs as well as occupant thermal discomfort levels. In comparison to design and control baselines, it is found that simultaneous optimization can achieve 21% and 7% reductions in heating and cooling annual energy consumption for the cases of variable speed and single speed heat pumps. Moreover, the analysis results indicate that these reductions are nearly double the savings obtained when design only and control only based optimizations are considered. The presented combined design and control optimization approach could potentially provide an effective paradigm shift in specifying heat pump systems for residential buildings. 

The nexus between technology and workplace inequality has been a long-standing topic of scholarly interest, now heightened by the rapid evolution of artificial intelligence (AI). Our review moves beyond dystopian or utopian views of AI by identifying four perspectives—normative, cognitive, structural, and relational—espoused by scholars examining the impact of AI on workplace inequality specifically, and the structure and organization of work more broadly. We discuss the respective strengths, limitations, and underlying assumptions of these perspectives and highlight how each perspective speaks to a particular facet of workplace inequality: either encoded, evaluative, wage, or relational inequality. Integrating these perspectives enables a deeper understanding of the mechanisms, processes, and trajectories through which AI influences workplace inequality, as well as the role that organizational managers, workers, and policymakers could play in the process. Toward this end, we introduce a framework on the “inequality cascades” of AI that traces how and when inequality emerges and amplifies cumulatively as AI systems progress through the phases of development, implementation, and use in organizations. In turn, we articulate a research agenda for management and organizational scholars to better understand AI and its multifaceted impact on workplace inequality, and we examine potential mechanisms to mitigate its adverse consequences. 

All biological systems are subject to perturbations arising from thermal fluctuations, external environments, or mutations. Yet, while biological systems consist of thousands of interacting components, recent high-throughput experiments have shown that their response to perturbations is surprisingly low dimensional: confined to only a few stereotyped changes out of the many possible. In this review, we explore a unifying dynamical systems framework—soft modes—to explain and analyze low dimensionality in biology, from molecules to ecosystems. We argue that this soft mode framework makes nontrivial predictions that generalize classic ideas from developmental biology to disparate systems, namely phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology. 

This paper expands the analysis of randomized low-rank approximation beyond the Gaussian distribution to four classes of random matrices: (1) independent sub-Gaussian entries, (2) independent sub-Gaussian columns, (3) independent bounded columns, and (4) independent columns with bounded second moment. Using a novel interpretation of the low-rank approximation error involving sample covariance matrices, we provide insight into the requirements of a good random matrix for randomized low-rank approximations. Although our bounds involve unspecified absolute constants (a consequence of underlying nonasymptotic theory of random matrices), they allow for qualitative comparisons across distributions. The analysis offers some details on the minimal number of samples (the number of columns of the random matrix ) and the error in the resulting low-rank approximation. We illustrate our analysis in the context of the randomized subspace iteration method as a representative algorithm for low-rank approximation; however, all the results are broadly applicable to other low-rank approximation techniques. We conclude our discussion with numerical examples using both synthetic and real-world test matrices.