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Rural America has long been perceived as "left behind" by policies that leave it struggling while benefiting bustling urban cities and suburbs. That narrative holds some truth: rural America has a declining population, along with lower educational attainment and lower workforce participation than urban America. But while rural America certainly faces challenges, it also has its own strengths and assets. "Small Towns, Big Opportunities: Many Workers in Rural Areas Have Good Jobs, but These Areas Need Greater Investment in Education, Training, and Career Counseling" counters some of the negative stereotypes and finds that working adults in rural America are almost as likely (50 percent) as working adults in urban America (54 percent) to have a good job.

Concerns about rising college costs and uncertain economic returns have combined with a wave of populist backlash to reduce public trust in higher education, which plummeted to new lows in 2023. President Biden, who ran his 2020 campaign on a platform that included student loan forgiveness and free community college, has focused some of his recent public messaging on high-paying jobs for workers without college degrees--despite the fact that such jobs are rare. The data shows that time and again a college degree is the most reliable pathway to the middle class: 74 percent of workers with college degrees have good jobs, compared with 42 percent of workers with no more than a high school diploma. These statistics indicate that Americans need both more access to affordable college education and more and better pathways to economic opportunity for workers without college degrees. But they also demonstrate that college degrees remain valuable both to individuals and to society. This report documents the economic benefits associated with increases in college degree attainment that occurred between 2010 and 2020, both nationally and within each state. It also describes the nonmonetary ways in which education contributes to human flourishing.

We devise an explicit method for computing combinatorial formulae for Hadamard products of certain rational generating functions. The latter arise naturally when studying so-called ask zeta functions of direct sums of modules of matrices or class- and orbit-counting zeta functions of direct products of nilpotent groups. Our method relies on shuffle compatibility of coloured permutation statistics and coloured quasisymmetric functions, extending recent work of Gessel and Zhuang., Comment: 19 pages

Here, we report the experimental demonstration of a novel multi-mode linear bus interferometer (LBI) coupler in a six qubit superconducting quantum processor. A key feature of this coupler is an engineered multi-path interference which eliminates transverse coupling between qubits over a wide frequency range. This negligible static coupling is achieved without any flux bias tuning, and greatly reduces the impact of qubit frequency collisions. We achieve good simultaneous single qubit gate operation and low ZZ rates (below 600 Hz) across the device without staggering qubit frequencies, even in cases where qubits are as close as 10 MHz. Multi-qubit interactions are still possible through the coupler using microwave-driven resonator induced phase gates, which we utilize to demonstrate simultaneous two qubit gates with fidelities as high as 99.4%

The large-conductance, calcium-activated potassium (BK) channel lacks the typical intracellular bundle-crossing gate present in most ion channels of the 6TM family. This observation, initially inferred from Ca$^{2+}$-free-pore accessibility experiments and recently corroborated by a CryoEM structure of the non-conductive state, raises a puzzling question: how can gating occur in absence of steric hindrance? To answer this question, we carried out molecular simulations and accurate free energy calculations to obtain a microscopic picture of the sequence of events that, starting from a Ca$^{2+}$-free state leads to ion conduction upon Ca$^{2+}$ binding. Our results highlight an unexpected role for annular lipids, which turn out to be an integral part of the gating machinery. Due to the presence of fenestrations, the "closed" Ca$^{2+}$-free pore can be occupied by the methyl groups from the lipid alkyl chains. This dynamic occupancy triggers and stabilizes the nucleation of a vapor bubble into the inner pore cavity, thus hindering ion conduction. By contrast, Ca$^{2+}$ binding results into a displacement of these lipids outside the inner cavity, lowering the hydrophobicity of this region and thus allowing for pore hydration and conduction. This lipid-mediated hydrophobic gating rationalizes several seemingly problematic experimental observations, including the state-dependent pore accessibility of blockers.

Work is fundamental to societal prosperity and mental health, providing financial security, identity, purpose, and social integration. The emergence of generative artificial intelligence (AI) has catalysed debate on job displacement. Some argue that many new jobs and industries will emerge to offset the displacement, while others foresee a widespread decoupling of economic productivity from human input threatening jobs on an unprecedented scale. This study explores the conditions under which both may be true and examines the potential for a self-reinforcing cycle of recessionary pressures that would necessitate sustained government intervention to maintain job security and economic stability. A system dynamics model was developed to undertake ex ante analysis of the effect of AI-capital deepening on labour underutilisation and demand in the economy. Results indicate that even a moderate increase in the AI-capital-to-labour ratio could increase labour underutilisation to double its current level, decrease per capita disposable income by 26% (95% interval, 20.6% - 31.8%), and decrease the consumption index by 21% (95% interval, 13.6% - 28.3%) by mid-2050. To prevent a reduction in per capita disposable income due to the estimated increase in underutilization, at least a 10.8-fold increase in the new job creation rate would be necessary. Results demonstrate the feasibility of an AI-capital- to-labour ratio threshold beyond which even high rates of new job creation cannot prevent declines in consumption. The precise threshold will vary across economies, emphasizing the urgent need for empirical research tailored to specific contexts. This study underscores the need for governments, civic organisations, and business to work together to ensure a smooth transition to an AI- dominated economy to safeguard the Mental Wealth of nations., Comment: 10 pages, 5 figures, 1 table, an Appendix

The breakup of liquid threads into droplets is crucial in various applications, such as nanoprinting, nanomanufacturing, and inkjet printing, where a detailed understanding of the thinning neck dynamics allows for a precise droplet control. Here, the role of surfactant in the breakup process is studied by many-body dissipative particle dynamics, in particular, the various regime transitions and thread profiles, shedding light on molecular-level intricacies of this process hitherto inaccessible to continuum theory and experiments. Moreover, the role of surfactant in the most unstable perturbation, the formed droplet size, and surfactant distributions have been unraveled. As surfactant concentration rises, both the wavelength and time to breakup steadily increase due to the lowering of surface tension below the critical micelle concentration (CMC) and viscous effects introduced by micelles above the CMC. These changes prior to the breakup lead to larger droplets being formed in cases with higher surfactant concentration. We also compared the thinning dynamics to existing theoretical predictions, revealing that the surfactant-laden breakup starts at the inertial regime and transitions into the thermal fluctuation regime when the concentration is increased. Thus, we illuminate the hitherto poorly investigated and intricate breakup process of surfactant-laden liquid threads driven by thermal fluctuations, contributing to a deeper understanding of this process at molecular scales., Comment: 10 figures, 24 pages

In this paper, we investigate a data-driven framework to solve Linear Quadratic Regulator (LQR) problems when the dynamics is unknown, with the additional challenge of providing stability certificates for the overall learning and control scheme. Specifically, in the proposed on-policy learning framework, the control input is applied to the actual (unknown) linear system while iteratively optimized. We propose a learning and control procedure, termed RELEARN LQR, that combines a recursive least squares method with a direct policy search based on the gradient method. The resulting scheme is analyzed by modeling it as a feedback-interconnected nonlinear dynamical system. A Lyapunov-based approach, exploiting averaging and singular perturbations theory for nonlinear systems, allows us to provide formal stability guarantees for the whole interconnected scheme. The effectiveness of the proposed strategy is corroborated by numerical simulations, where RELEARN LQR is deployed on an aircraft control problem, with both static and drifting parameters.

This paper introduces a systematic methodological framework to design and analyze distributed algorithms for optimization and games over networks. Starting from a centralized method, we identify an aggregation function involving all the decision variables (e.g., a global cost gradient or constraint) and introduce a distributed consensus-oriented scheme to asymptotically approximate the unavailable information at each agent. Then, we delineate the proper methodology for intertwining the identified building blocks, i.e., the optimization-oriented method and the consensus-oriented one. The key intuition is to interpret the obtained interconnection as a singularly perturbed system. We rely on this interpretation to provide sufficient conditions for the building blocks to be successfully connected into a distributed scheme exhibiting the convergence guarantees of the centralized algorithm. Finally, we show the potential of our approach by developing a new distributed scheme for constraint-coupled problems with a linear convergence rate.

Much progress has been made in elucidating the inner workings of voltage-gated ion channels, but less understood is the influence of lipid rafts on gating kinetics. Here we propose that state-dependent channel affinity for different lipid species provides a unified explanation for the experimentally observed behaviors of clustering, cooperativity, and hysteresis. We develop models of diffusing lipids and channels engaged in Ising-like interactions to investigate the collective behaviors driven by raft formation in critical membranes close to the demixing transition. The model channels demonstrate lipid-mediated long-range interactions, activation curve steepening, and long-term memory in ionic currents. These behaviors likely play a role in channel-mediated cellular signaling and suggest a universal mechanism for self-organization of biomolecular assemblies., Comment: 14 pages, 6 figures

Over the past century, the United States workforce has undergone a massive structural shift. Technological change has moved the economy toward skilled labor and away from unskilled labor--a phenomenon known as skill-biased technical change. This structural shift has increased the relative demand for educated and skilled labor, leading to commensurate increases in the relative wages of skilled workers, and changes in the nature of work itself. The authors project that the United States will have 171 million jobs in 2031, compared to 155 million in 2021. This total is even more impressive when compared to the low of 138 million jobs during the COVID-19 pandemic. This report shows: (1) A breakdown of job projections by 13 major industries; and (2) A breakdown of job projections by nine major occupational clusters and 22 total occupational groups. All of the industry and occupational sections include projections for jobs through 2031 by needed level of educational attainment. This report also accounts for the increasing role of technology in American society, particularly in the world of work. The authors find that the nature of work has changed dramatically to incorporate technology not only as a complement to human labor but also as a substitute for tasks within jobs and sometimes even workers. This report includes a national overview of job projections and their educational requirements across industries, occupational clusters, and detailed occupational groups.

As young people progress with their education and their early careers, they find themselves pushed forward or held back at critical junctures without full regard for their individual capabilities. Their paths are too often defined less by their talents and more by characteristics such as their race/ethnicity, gender, and socioeconomic or class status. By default, too many young people encounter barriers based on these characteristics, narrowing the scope of their educational and career options. The Georgetown University Center on Education and the Workforce (CEW) built the Pathways-to-Career policy simulation model, which uses longitudinal data to identify promising actions for increasing the likelihood of working in a good job--as defined as providing minimum annual earnings of about $38,000 per year, with a median of $57,000 at age 30. The Pathways-to-Career model establishes an actionable, solution-oriented framework for improving the economic lives of young adults by simulating the potential impacts of different pathway changes at critical junctures along the route from adolescence to early adulthood. The model relies on data from the US Bureau of Labor Statistics' National Longitudinal Survey of Youth 1997 (NLSY97)--an ongoing study that tracks a nationally representative sample of individuals born in the early 1980s from ages 12-16 to adulthood. The data set allows researchers to estimate the expected labor-market effects of different pathway changes for young people overall and separately by race/ethnicity, gender, and class. It also allows them to layer these pathway changes and examine the gains associated with comprehensive policy efforts to expand access to good jobs. Using the Pathways-to-Career model, they examined 38 pathway changes involving hypothetical adjustments to individuals' education, sectoral training, and work-based experiences at different life stages, from adolescence to their mid-20s. They then narrowed down these 38 pathway changes to the 10 that could most improve the likelihood of having a good job at age 30. The report outlines how the expected impacts of each of these 10 pathway changes differ by race/ethnicity, gender, and class, as well as how these 10 pathway changes could influence opportunity gaps in good jobs at age 30. It also considers the enhanced impact of strategically combining pathway changes for maximum effect. [For the executive summary, see ED628029.]

This is the executive summary of the report, "What Works: Ten Education, Training, and Work-Based Pathway Changes That Lead to Good Jobs. Findings by Race, Gender, and Class from the Georgetown University Pathways-to-Career Policy Simulation Model." To identify the pathway changes with the greatest potential, the Georgetown University Center on Education and the Workforce (CEW) developed the Pathways-to-Career policy simulation model. The model uses longitudinal data to identify promising junctures at which a strategic intervention could increase the likelihood of working in a good job--one define as providing minimum annual earnings of about $38,000 per year, with a median of $57,000, at age 30. The Pathways-to-Career model establishes an actionable, solution-oriented framework for improving the economic lives of young adults by simulating the potential impacts of different pathway changes at critical junctures along the route from adolescence to early adulthood. Using the model, the researchers identified 10 pathway changes involving education, training, and work experience that could most improve the likelihood of having a good job at age 30. [For the full report, see ED628027.]

An expected national ban on the consideration of race in college admissions will threaten the racial and ethnic diversity of students at selective colleges unless these colleges fundamentally alter their admissions practices. This report finds that selective colleges barred from considering race and ethnicity in their admissions decisions may be able to partially claw back some racial/ethnic diversity using class-conscious admissions practices, but they will be extremely unlikely to enroll student bodies that come close to mirroring the demographic diversity of the high school class. The authors examine the following six admissions models and the impact they would likely have on racial/ethnic and socioeconomic diversity if used consistently across selective colleges: (1) Academic merit, socioeconomic status, and race/ethnicity; (2) High school class rank, socioeconomic status, and race/ethnicity; (3) Academic merit only; (4) Academic merit and socioeconomic status; (5) High school class rank only; and (6) High school class rank and socioeconomic status. All six models assume the elimination of preferences for legacy applicants, student athletes, and other groups that receive admissions boosts for reasons unrelated to academic merit, race/ethnicity, or socioeconomic status (SES).

If the Supreme Court bans race-conscious affirmative action, as expected, selective higher education institutions almost certainly will become less diverse, reducing the rates of degree attainment among students from historically underrepresented racial/ethnic groups. "Race, Elite College Admissions, and the Courts: The Pursuit of Racial Equality in Education Retreats to K-12 Schools" explores the legal history of racial equity in education, evaluates alternatives to using race/ethnicity in college admissions, and considers changes to the K-12 education system that would improve educational opportunity. In the long term, the only way to ensure diversity at selective higher education institutions is to confront the segregation and inequity in K-12 education and society at large.

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation are vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Due to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model., Comment: 6 pages, 10 figures, IFAC World Congress 2023

MARTINI is a popular coarse-grained force-field that is mainly used in molecular dynamics (MD) simulations. It is based on the ``Lego'' approach where intermolecular interactions between coarse-grained beads representing chemical units of different polarity are obtained through water--octanol partition coefficients. This enables the simulation of a wide range of molecules by only using a finite number of parametrized coarse-grained beads, similar to the Lego game, where a finite number of bricks are used to create larger structures. Moreover, the MARTINI force-field is based on the Lennard-Jones potential with the shortest possible cutoff including attractions, thus rendering it very efficient for MD simulations. However, MD simulation is in general a computationally expensive method. Here, we demonstrate that using the MARTINI ``Lego'' approach is suitable for many-body dissipative particle (MDPD) dynamics, a method that can simulate multi-component and multi-phase soft matter systems in a much faster time (about 4--7 times) than MD. In this study, a DPPC lipid bilayer is chosen to provide evidence for the validity of this approach and various properties are compared to highlight the potential of the method. Thus, we anticipate that our study opens new possibilities for faster simulations of a wide range of soft matter systems by using the MDPD method., Comment: 9 pages, 5 figures

In this paper, we propose a novel distributed algorithm for consensus optimization over networks and a robust extension tailored to deal with asynchronous agents and packet losses. Indeed, to robustly achieve dynamic consensus on the solution estimates and the global descent direction, we embed in our algorithms a distributed implementation of the Alternating Direction Method of Multipliers (ADMM). Such a mechanism is suitably interlaced with a local proportional action steering each agent estimate to the solution of the original consensus optimization problem. First, in the case of ideal networks, by using tools from system theory, we prove the linear convergence of the scheme with strongly convex costs. Then, by exploiting the averaging theory, we extend such a first result to prove that the robust extension of our method preserves linear convergence in the case of asynchronous agents and packet losses. Further, by using the notion of Input-to-State Stability, we also guarantee the robustness of the schemes with respect to additional, generic errors affecting the agents' updates. Finally, some numerical simulations confirm our theoretical findings and compare our algorithms with other distributed schemes in terms of speed and robustness.

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots.

The breakup of liquid threads into smaller droplets is a fundamental problem in fluid dynamics. In this study, we estimate the characteristic wavelength of the breakup process by means of many-body dissipative particle dynamics. This wavelength shows a power-law dependence on the Ohnesorge number in line with results from stability analysis. We also discover that the number of satellite droplets exhibits a power-law decay with exponent $0.72 \pm 0.04$ in the product of the Ohnesorge and thermal capillary numbers, while the overall size of main droplets is larger than that based on the characteristic wavelength thanks to the asynchronous breakup of the thread. Finally, we show that the formation of satellite droplets is the result of the advection of pinching points towards the main droplets in a remaining thinning neck, when the velocity gradient of the fluid exhibits two symmetric maxima., Comment: 18 pages, 9 figures

A subgroup of $\mathrm{Homeo}_+(\mathbb{S}^1)$ is M\"obius-like if every element is conjugate to an element of $\mathrm{PSL}(2,\mathbb{R})$. In general, a M\"obius-like subgroup of $\mathrm{Homeo}_+(\mathbb{S}^1)$ is not necessarily (semi-)conjugate to a subgroup of $\mathrm{PSL}(2,\mathbb{R})$, as discovered by N. Kova\v{c}evi\'{c} [Trans. Amer. Math. Soc. 351 (1999), 4823-4835]. Here we determine simple dynamical criteria for the existence of such a (semi-)conjugacy. We show that M\"obius-like subgroups of $\mathrm{Homeo}_+(\mathbb{S}^1)$ which are elementary (namely, preserving a Borel probability measure), are semi-conjugate to subgroups of $\mathrm{PSL}(2,\mathbb{R})$. On the other hand, we provide an example of elementary subgroup of $\mathrm{Diff}^\infty_+(\mathbb{S}^1)$ satisfying that every non-trivial element fixes at most 2 points, which is not isomorphic to any subgroup of $\mathrm{PSL}(2,\mathbb{R})$. Finally, we show that non-elementary, non-locally discrete subgroups acting with at most $N$ fixed points are conjugate to a dense subgroup of some finite central extension of $\mathrm{PSL}(2,\mathbb{R})$., Comment: 16 pages. This work is part of the second author's PhD thesis at Universit\'e de Bourgogne; to appear in Math. Z

In this paper, we propose a distributed algorithm to control a team of cooperating robots aiming to protect a target from a set of intruders. Specifically, we model the strategy of the defending team by means of an online optimization problem inspired by the emerging distributed aggregative framework. In particular, each defending robot determines its own position depending on (i) the relative position between an associated intruder and the target, (ii) its contribution to the barycenter of the team, and (iii) collisions to avoid with its teammates. We highlight that each agent is only aware of local, noisy measurements about the location of the associated intruder and the target. Thus, in each robot, our algorithm needs to (i) locally reconstruct global unavailable quantities and (ii) predict its current objective functions starting from the local measurements. The effectiveness of the proposed methodology is corroborated by simulations and experiments on a team of cooperating quadrotors.

Distributed aggregative optimization is a recently emerged framework in which the agents of a network want to minimize the sum of local objective functions, each one depending on the agent decision variable (e.g., the local position of a team of robots) and an aggregation of all the agents' variables (e.g., the team barycentre). In this paper, we address a distributed feedback optimization framework in which agents implement a local (distributed) policy to reach a steady-state minimizing an aggregative cost function. We propose Aggregative Tracking Feedback, i.e., a novel distributed feedback optimization law in which each agent combines a closed-loop gradient flow with a consensus-based dynamic compensator reconstructing the missing global information. By using tools from system theory, we prove that Aggregative Tracking Feedback steers the network to a stationary point of an aggregative optimization problem with (possibly) nonconvex objective function. The effectiveness of the proposed method is validated through numerical simulations on a multi-robot surveillance scenario.