Search for: All records

Abstract An interior-point algorithm framework is proposed, analyzed, and tested for solving nonlinearly constrained continuous optimization problems. The main setting of interest is when the objective and inequality constraint functions may be nonlinear and/or nonconvex, and when constraint values and derivatives are tractable to compute, but objective function values and derivatives can only be estimated. The algorithm is intended primarily for a setting that is similar for stochastic-gradient methods for unconstrained optimization, i.e., the setting when stochastic-gradient estimates are available and employed in place of gradients, and when no objective function values (nor estimates of them) are employed. This is achieved by the interior-point framework having a single-loop structure rather than the nested-loop structure that is typical of contemporary interior-point methods. Convergence guarantees for the framework are provided both for deterministic and stochastic settings. Numerical experiments show that the algorithm yields good performance on a large set of test problems. 

Abstract Human mobility is becoming increasingly complex in urban environments. However, our fundamental understanding of urban population dynamics, particularly the pulsating fluctuations occurring across different locations and timescales, remains limited. Here, we use mobile device data from large cities and regions worldwide combined with a detrended fractal analysis to uncover a universal spatiotemporal scaling law that governs urban population fluctuations. This law reveals the scale invariance of these fluctuations, spanning from city centers to peripheries over both time and space. Moreover, we show that at any given location, fluctuations obey a time-based scaling law characterized by a spatially decaying exponent, which quantifies their relationship with urban structure. These interconnected discoveries culminate in a robust allometric equation that links population dynamics with urban densities, providing a powerful framework for predicting and managing the complexities of urban human activities. Collectively, this study paves the way for more effective urban planning, transportation strategies, and policies grounded in population dynamics, thereby fostering the development of resilient and sustainable cities. 

IntroductionNeuropathic pain is characterized by mechanical allodynia and thermal (heat and cold) hypersensitivity, yet the underlying neural mechanisms remain poorly understood. MethodsUsing chemogenetic excitation and inhibition, we examined the role of inhibitory interneurons in the basolateral amygdala (BLA) in modulating pain perception following nerve injury. ResultsChemogenetic excitation of parvalbumin-positive (PV⁺) interneurons significantly alleviated mechanical allodynia but had minimal effects on thermal hypersensitivity. However, inhibition of PV⁺interneurons did not produce significant changes in pain sensitivity, suggesting that reductions in perisomatic inhibition do not contribute to chronic pain states. In contrast, bidirectional modulation of somatostatin-positive (SST⁺) interneurons influenced pain perception in a modality-specific manner. Both excitation and inhibition of SST⁺interneurons alleviated mechanical allodynia, indicating a potential compensatory role in nociceptive processing. Additionally, SST⁺neuron excitation reduced cold hypersensitivity without affecting heat hypersensitivity, whereas inhibition improved heat hypersensitivity but not cold responses. DiscussionOur findings suggest that, in addition to PV⁺neurons, SST⁺interneurons in the BLA play complex roles in modulating neuropathic pain following nerve injury and may serve as a potential target for future neuromodulation interventions in chronic pain management. 

Abstract Precise modulation of excitable tissues—including neurons and cardiomyocytes—is essential for both understanding physiological functions and developing advanced therapies for neurological and cardiac disorders. Conventional modulation techniques such as electrical stimulation, pharmacological intervention, and optogenetics, face limitations in terms of invasiveness, spatiotemporal resolution, and/or requirement for genetic modulation. Optoelectronic interfaces based on light‐matter interaction have emerged as promising alternatives. These platforms offer wireless, nongenetic modulation capabilities with high spatiotemporal resolution and minimal invasiveness and risks of infection. Here, a summary of recent advances in nongenetic optoelectronic modulation strategies is presented. Aspects such as material selection and processing, device designs, working principles, and fabrication techniques are discussed. Then, key characterization methodologies, including benchtop assessments and validation within the living systems are discussed. Alongside the discussion, representative applications across in vitro and in vivo models of cardiac and central/peripheral nervous systems are highlighted. Finally, future directions and clinical opportunities, aiming to provide a thorough reference for the continued development of this field for both fundamental research and next‐generation therapeutic applications are explored.