哈佛大学

High renewable penetration increases the frequency and magnitude of net-load ramps, stressing real-time flexibility. Two commonly deployed remedies are look-ahead economic dispatch (LAED) and ramp products (RPs), yet their operational equivalence under the industry-standard rolling-window dispatch implementation is not well understood. This paper develops linear optimization models for multi-interval LAED and RP-based co-optimization, and proves that an enhanced RP formulation can match LAED's dispatch feasible region at a single time step when additional intertemporal deliverability constraints are enforced. We then show that this equivalence does not generally persist under rolling-window operation because LAED and RP formulations optimize different intertemporal objectives, leading to divergent end-of-window states. Using different test systems under stressed ramping conditions and multiple load levels, we show LAED achieves similar or lower load shedding than RP implementations with the same look-ahead horizon, with the most pronounced differences under high-load, ramp-limited conditions. The study highlights the limitations of current ramp product implementations and suggests enhancements, such as introducing more mid-duration RPs.

Voice phishing (vishing) attacks have traditionally been limited by the need for human operators. The rapid emergence of high-quality AI voice synthesis and large language models (LLMs) reduces this bottleneck and enables scalable, automated scams. In this paper, we conduct a large-scale survey experiment (N=4100) and qualitative interviews (N=12) to assess U.S. adults’ susceptibility to AI-powered voice phishing attacks. Participants were exposed to audio recordings or transcripts of scam scenarios generated using leading voice models such as Llama Full Duplex (Llama FD), Sesame, Gemini, OAI AVM, Play.AI, and ElevenLabs and the corresponding human baselines. The results show high compliance rates. Up to 36% of participants would or might comply with phishing requests in the “relative-in-distress” category. Overall compliance rate across all five scam categories was 16.5%, a striking figure given the low cost and high scalability of AI-automated voice phishing. Caller persuasiveness was the strongest predictor of compliance and certain models (most notably Sesame) achieved ratings comparable to human voices, or sometimes even slightly surpassing them. Our economic analysis suggests that while human-operated vishing is unprofitable at US wages, AI-powered vishing appears to be economically viable for several models. The primary risk of present-day AI-enabled vishing thus lies in the economics of automation rather than novel or “superhuman” persuasive techniques, though these cannot be ruled out for future systems. This raises significant concerns for the design of AI systems, consumer protection, and model release policies.

Kirkland and Šmigoc constructed a family of stochastic matrices realising the Type III boundary polynomials of the Karpelevič region and conjectured that, conversely, every stochastic realisation of such a polynomial must come from their construction. We prove this conjecture for the full nonzero parameter range 0<α≤1, for the reduced Ito polynomialsfα(x)=xy(xq−(1−α))d−αd,n=qd+y, associated with Type III Farey-neighbour arcs of order n. For 0<α<1, the boundary theorem of Dmitriev and Dynkin restricts the possible nonzero entries of a realising matrix. After relabelling the vertices, its directed graph consists of the edges i→i+n1 and a selection of edges i→i+n(n+1−q), with indices taken modulo n. Coates' coefficient formula and a weighted form of Turán's theorem then show that the directed cycles of length q fall into d classes. Any two cycles in the same class share a vertex, while cycles in different classes have no vertices in common. Each class has the same sum of cycle weights, where the weight of a cycle is the product of its edge weights. The positions of these cycles along the n-cycle then yield the product condition required by Kirkland and Šmigoc. The case α=1 is treated separately, and an explanation is provided for the failure when α=0.

Neuronal activity-regulated gene expression has been suggested to be an important mediator of long-lasting, experience-dependent changes in the nervous system, but the activity-dependent component of gene transcription has never been selectively isolated and tested for its functional significance. Here, we demonstrate that introduction of a subtle knockin mutation into the mouse Bdnf gene that blocks the ability of the activity-regulated factor CREB to bind Bdnf promoter IV results in an animal in which the sensory experience-dependent induction of Bdnf expression is disrupted in the cortex. Neurons from these animals form fewer inhibitory synapses, have fewer spontaneous inhibitory quantal events, and exhibit reduced expression of inhibitory presynaptic markers in the cortex. These results indicate a specific requirement for activity-dependent Bdnf expression in the development of inhibition in the cortex and demonstrate that the activation of gene expression in response to experience-driven neuronal activity has important biological consequences in the nervous system.

We construct a publicly available atlas of children's outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children's earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children's outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $4,200 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child's own Census tract, characteristics of tracts that are one mile away have little predictive power for a child's outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods' causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers' outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children's long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.