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In this paper, we address Linear Quadratic Regulator (LQR) problems through a novel iterative algorithm named EXtremum-seeking Policy iteration LQR (EXP-LQR). The peculiarity of EXP-LQR is that it only needs access to a truncated approximation of the infinite-horizon cost associated to a given policy. Hence, EXP-LQR does not need the direct knowledge of neither the system matrices, cost matrices, and state measurements. In particular, at each iteration, EXP-LQR refines the maintained policy using a truncated LQR cost retrieved by performing finite-time virtual or real experiments in which a perturbed version of the current policy is employed. Such a perturbation is done according to an extremum-seeking mechanism and makes the overall algorithm a time-varying nonlinear system. By using a Lyapunov-based approach exploiting averaging theory, we show that EXP-LQR exponentially converges to an arbitrarily small neighborhood of the optimal gain matrix. We corroborate the theoretical results with numerical simulations involving the control of an induction motor.

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 design a distributed feedback optimization strategy, embedded into a modular ROS 2 control architecture, which allows a team of heterogeneous robots to cooperatively monitor and encircle a target while patrolling points of interest. Relying on the aggregative feedback optimization framework, we handle multi-robot dynamics while minimizing a global performance index depending on both microscopic (e.g., the location of single robots) and macroscopic variables (e.g., the spatial distribution of the team). The proposed distributed policy allows the robots to cooperatively address the global problem by employing only local measurements and neighboring data exchanges. These exchanges are performed through an asynchronous communication protocol ruled by locally-verifiable triggering conditions. We formally prove that our strategy steers the robots to a set of configurations representing stationary points of the considered optimization problem. The effectiveness and scalability of the overall strategy are tested via Monte Carlo campaigns of realistic Webots ROS 2 virtual experiments. Finally, the applicability of our solution is shown with real experiments on ground and aerial robots.

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.

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.

College typically pays off for low-income students, but not as much as it does for their peers. Low-income students, whose families earn $30,000 or less per year, comprise more than one-third of college students. "The Colleges Where Low-Income Students Get the Highest ROI" finds that low-income students have a lower return on investment (ROI) than all students, largely because they tend to earn less as adults. This holds true across public and private institutions and certificates, associate's degrees, and bachelor's degrees. Among institutions that primarily award bachelor's degrees, public institutions generally lead to the highest ROI for low-income students during a 40-year timeframe ($951,000), followed by private nonprofit institutions ($863,000) and for-profit colleges ($763,000). The ROI for low-income students follows a similar pattern at colleges that primarily grant associate's degrees and certificates, with the highest returns from public institutions, followed by private nonprofit and for-profit institutions. However, low-income students' returns from associate's degrees can exceed $1 million, and certificates can be just as lucrative, depending on which college a student attends.

Majoring in business typically pays off. While graduates' earnings and federal student loan debt vary by institution and degree level, the majority of business programs lead to median earnings that are roughly 10 times graduates' debt payments two years after program completion. "The Most Popular Degree Pays Off: Ranking the Economic Value of 5,500 Business Programs at More Than 1,700 Colleges" finds that the economic value of business programs is high compared to the financial returns from other programs, though not as high as returns associated with health, engineering, and computer and information sciences programs. Two years after graduation, associate's degree holders in business have median annual earnings of $30,000 after debt payments. The financial returns from a business degree rise to $43,200 after debt payments for bachelor's degree holders and $51,600 for master's degree holders.

Americans share a strong belief that the country offers access to opportunity. In 2017, 82 percent of Americans said they had achieved the American Dream or were on their way to achieving it. But do all Americans--regardless of race, ethnicity, gender, or socioeconomic status--have equal access to the American Dream? This report examines racial/ethnic and gender gaps in who has a good job as a young adult. It explores how the likelihood of having a good job is affected by the intersection of race/ethnicity and gender and related opportunity gaps in: (1) educational attainment; (2) field of study; (3) occupation; (4) full-time work; (5) access to high-quality work-based learning; (6) experiences with structural racism and sexism; and (7) intergenerational wealth. These elements work together to perpetuate inequality of opportunity for young Americans. It is part of a two-report exploration of the elongated pathway to a good job. [For the companion report, "The Uncertain Pathway from Youth to a Good Job: How Limits to Educational Affordability, Work-Based Learning, and Career Counseling Impede Progress toward Good Jobs," see ED624515.]

The economic and social landscape that young people encounter today is substantially more complex and challenging than the one faced by earlier generations. Technological change and globalization have transformed the labor market, increasing the value of postsecondary education and hollowing out industries that once employed many workers who had no more than a high school diploma. While access to postsecondary education has expanded, gaps in educational attainment and access to good jobs among demographic groups persist. Together, these factors limit opportunity for many young workers. Within each racial/ethnic group, women are less likely to have a good job than men. Many young people seeking to secure a good job find themselves on winding pathways rife with barriers. Among these barriers are three major hurdles: (1) the rising cost of postsecondary education, (2) limited access to high-quality work-based learning (WBL), and (3) an absence of comprehensive counseling and career navigation services. These three hurdles, exacerbated by discrimination, have heightened disparities in educational attainment and financial security by race, class, and gender. Finally, they have slowed young adults' ability to accumulate wealth, especially for those who acquire substantial debt to cover the rising costs of postsecondary education. [For the full reports, "The Uncertain Pathway from Youth to a Good Job: How Limits to Educational Affordability, Work-Based Learning, and Career Counseling Impede Progress toward Good Jobs," see ED624515 and "The Uncertain Pathway from Youth to a Good Job: How Racial and Gender Bias Impede Progress toward Good Jobs," see ED624516.]

The propensity to believe that good things are likely to happen fuels the enduring belief in the American Dream, including the expectation that each generation will enjoy a better quality of life than the previous one. This report is part of a series on young people's pathways to good jobs. In it, the researchers examine how the route from youth dependency to economic independence in young adulthood has changed over time. It explores how the likelihood of having a good job is affected by (1) educational attainment; (2) field of study; (3) occupation; (4) full-time work; (5) access to high-quality work-based learning; and (6) socioeconomic status. These elements work together to perpetuate inequality of opportunity for young Americans. In this report, the researchers focus on overall historical trends and socioeconomic gaps in opportunity. [For the companion report, "The Uncertain Pathway from Youth to a Good Job: How Racial and Gender Bias Impede Progress toward Good Jobs," see ED624516.]

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.

Using theory and simulations, we carried out a first systematic characterization of DNA unzipping via nanopore translocation. Starting from partially unzipped states, we found three dynamical regimes depending on the applied force, f: (i) heterogeneous DNA retraction and rezipping (f < 17pN), (ii) normal (17pN < f < 60pN) and (iii) anomalous (f > 60pN) drift-diffusive behavior. We show that the normal drift-diffusion regime can be effectively modelled as a one-dimensional stochastic process in a tilted periodic potential. We use the theory of stochastic processes to recover the potential from nonequilibrium unzipping trajectories and show that it corresponds to the free-energy landscape for single base-pairs unzipping. Applying this general approach to other single-molecule systems with periodic potentials ought to yield detailed free-energy landscapes from out-of-equilibrium trajectories., Comment: 6 pages, 4 figures

An important goal in artificial intelligence is to create agents that can both interact naturally with humans and learn from their feedback. Here we demonstrate how to use reinforcement learning from human feedback (RLHF) to improve upon simulated, embodied agents trained to a base level of competency with imitation learning. First, we collected data of humans interacting with agents in a simulated 3D world. We then asked annotators to record moments where they believed that agents either progressed toward or regressed from their human-instructed goal. Using this annotation data we leveraged a novel method - which we call "Inter-temporal Bradley-Terry" (IBT) modelling - to build a reward model that captures human judgments. Agents trained to optimise rewards delivered from IBT reward models improved with respect to all of our metrics, including subsequent human judgment during live interactions with agents. Altogether our results demonstrate how one can successfully leverage human judgments to improve agent behaviour, allowing us to use reinforcement learning in complex, embodied domains without programmatic reward functions. Videos of agent behaviour may be found at https://youtu.be/v_Z9F2_eKk4.

We propose fully-distributed algorithms for Nash equilibrium seeking in aggregative games over networks. We first consider the case where local constraints are present and we design an algorithm combining, for each agent, (i) the projected pseudo-gradient descent and (ii) a tracking mechanism to locally reconstruct the aggregative variable. To handle coupling constraints arising in generalized settings, we propose another distributed algorithm based on (i) a recently emerged augmented primal-dual scheme and (ii) two tracking mechanisms to reconstruct, for each agent, both the aggregative variable and the coupling constraint satisfaction. Leveraging tools from singular perturbations analysis, we prove linear convergence to the Nash equilibrium for both schemes. Finally, we run extensive numerical simulations to confirm the effectiveness of our methods and compare them with state-of-the-art distributed equilibrium-seeking algorithms.

A classical result by Solodov states that if a group acts on the line such that any non-trivial element has at most one fixed point, then the action is either abelian or semi-conjugate to an affine action. We show that the same holds if we relax the assumption, requiring that any non-trivial element has at most 2 fixed points., Comment: 7 pages

Many Americans would agree that all people should have equal educational opportunity and equal pay for equal work. And yet, inequality in postsecondary education access, college completion, and post-college outcomes such as wages stubbornly persists, along with the impression that achieving equal outcomes would be too expensive and would take too much time to accomplish. A thought experiment that the authors conducted in partnership with the Postsecondary Value Commission demonstrates that equality in postsecondary education would have considerable value to society. While equalizing educational attainment would be costly and time-intensive, balancing the costs with the potential societal benefits shows that an investment in postsecondary equality is money well spent. The top-line finding of the analysis is that equalizing postsecondary educational attainment by economic status and race/ethnicity would require an initial public investment of $3.97 trillion. The fiscal case for the societal value of economic and racial/ethnic justice in postsecondary education is even stronger today than it was before COVID-19 devastated American life. As higher education is forced to rethink its business model, it has the opportunity to more fully realize the potential of educational equity and recommit to enhancing the public good. Racial and economic justice need to be central goals, not secondary concerns or politically correct conceits unsupported by action, as postsecondary institutions reinvent themselves for the current era. [For the full report, see ED612641.]

Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data. Through both social language use and internal processes of rehearsal and practice, language learners are able to build high-level, semantic representations that explain their perceptions. Here, we take inspiration from such processes of "inner speech" in humans (Vygotsky, 1934) to better understand the role of intra-agent speech in embodied behavior. First, we formally pose intra-agent speech as a semi-supervised problem and develop two algorithms that enable visually grounded captioning with little labeled language data. We then experimentally compute scaling curves over different amounts of labeled data and compare the data efficiency against a supervised learning baseline. Finally, we incorporate intra-agent speech into an embodied, mobile manipulator agent operating in a 3D virtual world, and show that with as few as 150 additional image captions, intra-agent speech endows the agent with the ability to manipulate and answer questions about a new object without any related task-directed experience (zero-shot). Taken together, our experiments suggest that modelling intra-agent speech is effective in enabling embodied agents to learn new tasks efficiently and without direct interaction experience.

Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research. However, evaluating these interactions is challenging: collecting online human-agent interactions is slow and expensive, yet faster proxy metrics often do not correlate well with interactive evaluation. In this paper, we assess the merits of these existing evaluation metrics and present a novel approach to evaluation called the Standardised Test Suite (STS). The STS uses behavioural scenarios mined from real human interaction data. Agents see replayed scenario context, receive an instruction, and are then given control to complete the interaction offline. These agent continuations are recorded and sent to human annotators to mark as success or failure, and agents are ranked according to the proportion of continuations in which they succeed. The resulting STS is fast, controlled, interpretable, and representative of naturalistic interactions. Altogether, the STS consolidates much of what is desirable across many of our standard evaluation metrics, allowing us to accelerate research progress towards producing agents that can interact naturally with humans. A video may be found at https://youtu.be/YR1TngGORGQ.

Optimisation-based algorithms known as Moving Horizon Estimator (MHE) have been developed through the years. This paper illustrates the implementation of the policy introduced in the companion paper submitted to the 18th IFAC Workshop on Control Applications of Optimization [Oliva and Carnevale, 2022], in which we propose two techniques to reduce the computational cost of MHEs. These solutions mainly rely on output filtering and adaptive sampling. The use of filters reduces the total amount of data used by MHE, shortening the length of the moving window (buffer) and consequently decreasing the time consumption for plant dynamics integration. Meanwhile, the proposed adaptive sampling policy discards those sampled data that do not allow a sensible improvement of the estimation error. Algorithms and numerical simulations are provided to show the effectiveness of the proposed strategies., Comment: 9 pages, 3 figures, 3 algorithms

Nuclear magnetic relaxation is widely used to probe protein dynamics. For decades, most analyses of relaxation in proteins have relied successfully on the model-free approach, forgoing mechanistic descriptions of motions. Model-free types of correlation functions cannot describe a large carbon-13 relaxation dataset in protein sidechains. Here, we use molecular dynamics simulations to design explicit models of motion and solve Fokker-Planck diffusion equations. These models of motion provide better agreement with relaxation data, mechanistic insight and a direct link to configuration entropy.

In partnership with the Postsecondary Value Commission, we conducted a thought experiment on the costs of inequality in the US education system. Our simulation found that the US economy misses out on $956 billion dollars per year, along with numerous nonmonetary benefits, as a result of postsecondary attainment gaps by economic status and race/ethnicity. "The Cost of Economic and Racial Injustice in Postsecondary Education" finds that closing these gaps would require an initial public investment of at least $3.97 trillion, but the benefits would outweigh the costs over time. Equalizing educational attainment without increasing student debt for low-income adults could also boost GDP by a total of $764 billion annually. [During the writing of this report, Kathryn Peltier Campbell, Ban Cheah, Megan L. Fasules, Artem Gulish, Michael C. Quinn, Jenna R. Sablan, Nicole Smith, and Jeff Strohl received funding from the Institute for Higher Education Policy for work done on behalf of the Bill & Melinda Gates Foundation.]

"The College Payoff: More Education Doesn't Always Mean More Earnings" explores how lifetime earnings vary by education level, field of study, occupation, industry, gender, race and ethnicity, and location. The lifetime earnings of a full-time full-year worker with a high school diploma are $1.6 million, while workers with an associate's degree earn $2 million. However, at least one quarter of high school graduates earn more than an associate's degree holder. Bachelor's degree holders earn a median of $2.8 million during their career, 75% more than if they had only a high school diploma. Master's degree holders earn a median of $3.2 million over their lifetimes, while doctoral degree holders earn $4 million and professional degree holders earn $4.7 million. However, one quarter of workers with a bachelor's degree earn more than half of workers with a master's or a doctoral degree.

"If Not Now, When? The Urgent Need for an All-One-System Approach to Youth Policy" makes the case that the United States' disjointed approach to youth policy has failed young people. In the current fragmented system, pre-K-12, postsecondary education, and the workforce operate in silos that allow many young people to fall through the cracks. In its place, the country needs an all-one-system approach that supports youth on their journey through education and training and into careers.

Affirmative action critics argue that race-conscious admissions policies are keeping Asian American enrollment numbers unfairly low because Asian American students are held to higher admissions standards than applicants of any other race or ethnicity. "Selective Bias: Asian Americans, Test Scores, and Holistic Admissions" evaluates the common arguments made by them and Students for Fair Admissions, which is suing Harvard University and has lawsuits pending against the University of North Carolina and the University of Texas at Austin over their admissions practices. The report finds no strong evidence of discrimination against Asian American applicants in admissions to highly selective colleges.

Over the past half century, postsecondary education has taken on an increasingly important role in career preparation in the United States, with profound effects on the life experiences of young adults. Having a college credential has become both more valuable in the labor market and more expensive, with much of the burden falling directly on students and their families. At the same time, postsecondary institutions are not doing enough to equip students to lead economically independent and fulfilling lives. This essay explores several current trends, including the following: the role of the COVID-19 pandemic in both exacerbating the challenges young adults face on the way to economic self-sufficiency and amplifying postsecondary education's relationship to economic security; the increasing number of jobs that will require at least some postsecondary education and training; and the role that separate and unequal postsecondary institutions play in perpetuating and exacerbating racial and socioeconomic injustice. Based on these trends, the report outlines 10 rules of the college game for students and their families as they choose among different postsecondary institutions and programs. Finally, the essay concludes with recommendations to measure educational value to better align the classroom and the workplace, which will ensure that postsecondary education better fulfills its economic, political, and cultural missions.

In this paper we analyze the high friction regime for the Navier Stokes Korteweg equations for multicomponent systems. According to the shape of the mixing and friction terms, we shall perform two limits: the high friction limit toward an equilibrium system for the limit densities and the barycentric velocity, and, after an appropriate time scaling, the diffusive relaxation toward parabolic, gradient flow equations for the limit densities. The rigorous justification of these limits is done by means of relative entropy techniques in the framework of weak, finite energy solutions of the relaxation models, rewritten in the enlarged formulation in terms of the drift velocity, toward smooth solutions of the corresponding equilibrium dynamics. Finally, since our estimates are uniform for small viscosity, the results are also valid for the Euler Korteweg multicomponent models, and the corresponding estimates can be obtained by sending the viscosity to zero.

This paper proposes Asynchronous Triggered Gradient Tracking, i.e., a distributed optimization algorithm to solve consensus optimization over networks with asynchronous communication. As a building block, we devise the continuous-time counterpart of the recently proposed (discrete-time) distributed gradient tracking called Continuous Gradient Tracking. By using a Lyapunov approach, we prove exponential stability of the equilibrium corresponding to agents' estimates being consensual to the optimal solution, with arbitrary initialization of the local estimates. Then, we propose two triggered versions of the algorithm. In the first one, the agents continuously integrate their local dynamics and exchange with neighbors their current local variables in a synchronous way. In Asynchronous Triggered Gradient Tracking, we propose a totally asynchronous scheme in which each agent sends to neighbors its current local variables based on a triggering condition that depends on a locally verifiable condition. The triggering protocol preserves the linear convergence of the algorithm and avoids the Zeno behavior, i.e., an infinite number of triggering events over a finite interval of time is excluded. By using the stability analysis of Continuous Gradient Tracking as a preparatory result, we show exponential stability of the equilibrium point holds for both triggered algorithms and any estimate initialization. Finally, the simulations validate the effectiveness of the proposed methods on a data analytics problem, showing also improved performance in terms of inter-agent communication.

We define a class of partial orders on a Coxeter group associated with sets of reflections. In special cases, these lie between the left weak order and the Bruhat order. We prove that these posets are graded by the length function and that the projections on the right parabolic quotients are always order preserving. We also introduce the notion of $k$-Bruhat graph, $k$-absolute length and $k$-absolute order, proposing some related conjectures and problems., Comment: 14 pages, to appear in Proc. Amer. Math. Soc

This is the executive summary for the report, "The Dollars and Sense of Free College." Policymakers have debated the specifics of free-college programs--including whether free-college eligibility should extend to students at four-year public colleges as well as community colleges, and whether it should be universal or targeted to low-income and middle-income students. Free-college proposals generally reflect support for some form of publicly funded program that makes college attendance affordable for a majority of students. Joe Biden endorsed a free-college plan that was originally part of the party's platform in the previous election. This plan would make public community colleges tuition-free for all students. It also would make four-year public colleges and universities tuition-free for students with family incomes under $125,000, which would mean that approximately 80 percent of in-state undergraduate students at public four-year colleges and universities would not have to pay tuition. The federal government and state governments would share the costs of this program, with the federal government contributing $2 for every $1 contributed by a state. While the Biden plan has immediate relevance to the 2020 election, there are other ways to design free-college plans. This report discusses a variety of free-college models and assesses their relative costs and benefits. [For the full report, see ED608985.]

Education beyond high school is now the preferred currency for workers seeking economic opportunity in the US labor market. Since the 1980s, the bachelor's degree has been the gold standard for stable employment and lifetime earnings and the most promising route to the middle class. The new rules of the college and career game confirm that education level matters, and that more education is generally better when it comes to earnings potential. What is less well known is that program of study and major matter even more to potential earnings than education level. As a result, less education can often be worth more. In fact, some certificate holders can earn more than those with an associate's or bachelor's degree, and some associate's degree holders can earn more than those with a bachelor's degree. In other words, certificates and associate's degrees--credentials on the middle-skills pathway--can be viable routes to economic opportunity. Workers who have taken this pathway to jobs are doing so with certificates, associate's degrees, noncredit education, certifications, licenses, and a host of emerging microcredentials such as badges or coding and technology boot camps. But, while pockets of opportunity exist, not enough is known about the risks and rewards of the particular education and training programs available. Today the combined number of certificates and associate's degrees awarded by colleges is roughly equivalent to the number of bachelor's degrees, around 2 million per year, with certificates and associate's degrees each accounting for about 1 million. The students earning the vast majority of these credentials attend public two-year colleges. While certifications, licenses, microcredentials, and noncredit education are also important options on the middle-skills pathway, much less is known about the workforce outcomes of these options because no data source consistently captures this information. Due to limitations in the data, this report focuses on certificates and associate's degrees.

This report examines the relationship between authoritarianism and postsecondary education, including liberal arts education. This analysis rests on the idea that authoritarianism is part of human nature, but its influence waxes and wanes according to circumstances. Having entered a new era defined by the COVID-19 pandemic, the evidence suggests that there is good reason to worry about the future of democracy. Presenting enormous threats to physical and economic well-being worldwide, this contemporary emergency bears the hallmarks of a possible tipping point into authoritarianism. Key findings include: (1) College graduates are less inclined to express authoritarian preferences and attitudes than their peers with less education; (2) Higher education provides people with a greater sense of security. As a result, it can lead to the development of interpersonal trust, which is associated with a weaker inclination toward expressing authoritarian preferences and attitudes; (3) People with postsecondary education are more likely to be politically active, which in the United States is associated with a lower inclination toward expressing authoritarian preferences and attitudes; (4) Liberal arts majors in particular are less inclined to express authoritarian preferences and attitudes than majors in business-related fields and STEM disciplines (science, technology, engineering, and mathematics); (5) People with postsecondary education are more economically secure than those without it, and people who are economically secure are less inclined to express authoritarian preferences and attitudes; and (6) Postsecondary education tends to expose people to secular values and cultures, leading them to be less inclined to express authoritarian preferences and attitudes. [Foreword by John J. DeGioia.]