Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging – A Symposium Review

Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for translational neuroscience. On October 10, 2023, the Center for Mesoscale Mapping (CMM) at the Massachusetts General Hospital (MGH) Athinoula A. Martinos Center for Biomedical Imaging and the Massachusetts Institute of Technology (MIT) Health Sciences Technology based Neuroimaging Training Program (NTP) hosted a symposium exploring the state-of-the-art in this rapidly growing area of research. “Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging” brought together researchers who use a broad range of imaging techniques to study brain structure and function at the convergence of the microscopic and macroscopic scales. The day-long event centered on areas in which the CMM has established expertise, including the development of emerging technologies and their application to clinical translational needs and basic neuroscience questions. The in-person symposium welcomed more than 150 attendees, including 57 faculty members, 61 postdoctoral fellows, 35 students, and four industry professionals, who represented institutions at the local, regional, and international levels. The symposium also served the training goals of both the CMM and the NTP. The event content, organization, and format were planned collaboratively by the faculty and trainees. Many CMM faculty presented or participated in a panel discussion, thus contributing to the dissemination of both the technologies they have developed under the auspices of the CMM and the findings they have obtained using those technologies. NTP trainees who benefited from the symposium included those who helped to organize the symposium and/or presented posters and gave “flash” oral presentations. In addition to gaining experience from presenting their work, they had opportunities throughout the day to engage in one-on-one discussions with visiting scientists and other faculty, potentially opening the door to future collaborations. The symposium presentations provided a deep exploration of the many technological advances enabling progress in structural and functional mesoscale brain imaging. Finally, students worked closely with the presenting faculty to develop this report summarizing the content of the symposium and putting it in the broader context of the current state of the field to share with the scientific community. We note that the references cited here include conference abstracts corresponding to the symposium poster presentations

Synthetic biology by controller design

Natural biological systems display complex regulation and synthetic biomolecular systems have been used to understand their natural counterparts and to parse sophisticated regulations into core design principles. At the same time, the engineering of biomolecular systems has unarguable potential to transform current and to enable new, yet-to-be-imagined, biotechnology applications. In this review, we discuss the progression of control systems design in synthetic biology, from the purpose of understanding the function of naturally occurring regulatory motifs to that of creating genetic circuits whose function is sufficiently robust for biotechnology applications

Location, Location, Substation? How Battery Energy Storage Systems (BESS) Can Create Value in Unexpected Places

The transition to renewable energy is a critical step in reducing global carbon emissions, yet it introduces new challenges for the aging electrical grid, particularly in urban areas. Battery Energy Storage Systems (BESS) are emerging as key infrastructure in this transition, capable of enhancing grid resiliency, managing peak loads, and facilitating the integration of renewable energy sources. Federal and state incentives and a recent sharp decline in the cost of battery cells have made BESS development economically viable. This thesis explores the potential of BESS to create public and economic value in underutilized urban spaces through the exploration of a hypothetical redevelopment proposal for the Alewife MBTA Complex in Cambridge, Massachusetts. The Alewife MBTA Complex presents significant challenges for redevelopment due to the high cost of demolishing the decaying existing structure. However, its proximity to a major substation and the increasing local demand for electricity make it an ideal candidate for a BESS project. This thesis demonstrates how integrating energy storage into the redevelopment of the site can enable an otherwise financially infeasible project. The paper provides an overview of the BESS development process, detailing each phase from creating a business strategy to disposition. It offers insights into the common challenges encountered, and how these might be navigated to optimize project outcomes. By breaking down the development timeline and key decision points, this thesis serves as a practical guide for real estate professionals to gain familiarity with Battery Energy Storage Systems. Through detailed financial modeling and analysis, including sensitivity testing, this research quantifies the expected financial performance of a BESS project at the Alewife site. The study concludes that BESS can unlock ‘found value’ in sites with little other economic potential. The findings suggest that incorporating BESS into real estate development projects can provide substantial public benefits, including enhanced grid resilience, lower energy costs, and increased property values, making it a strategic tool for urban planners and developers.S.M

Scaling Post-Disaster Housing Capacity: Roundtable Report

In January 2024, the MIT Humanitarian Supply Chain Lab held a roundtable on the theme of scaling construction capacity after disasters. The roundtable convened participants from academia, non-profit organizations, and both the public and private sectors. Participants brought varied perspectives to this issue, including considerations of supply chains, local, state, and federal policies, building codes, and private sector construction operations. The roundtable used recent natural disasters and their subsequent housing challenges to frame discussions around two goals: 1) identify approaches to increase capacity for rapidly deployable housing solutions after disasters, and 2) capture policy and operational constraints that hinder implementation of those rapidly deployable housing solutions. The roundtable and this report seek to catalyze systemic research and provide discrete recommendations to address the challenges and opportunities to restore housing for disaster survivors

Information Design for Platform-Enabled Operations

Information design studies how an informed player (planner) can optimally provision information over an uncertain payoff relevant random variable to influence the actions of less-informed players (agents). Codified through a ``signaling mechanism", the informed player can design distributions over informative signals to reveal depending on the value of the uncertain random variable. Through the design of the signaling mechanism, a planner can affect the agents' posterior beliefs of the uncertainty and the agents' consequent actions. While an abstract representation, solving for the optimal signaling mechanism provides valuable real-world intuition into how platforms or public entities should provision information. However, the practical design of these optimal signaling mechanisms more generally is associated with five key technical challenges. First, the uncertainty set can be continuously-valued which leads to an uncountably large set of decision variables over which to optimize. Second, the objective of the planner and the response of agents to the induced beliefs can be arbitrarily complex which can lead to intractable optimization formulations. Third, in dynamic settings, planners with multi-period objectives need to provision information across time, but provisioning information in the present affects what information the planner can provision in the future. This necessitates the use of computationally intensive multiperiod dynamic programming. Fourth, if agents in these dynamic settings are long-run and subject to subgame perfection, agents anticipate what information the planners will adaptively provision in the future necessitating one to solve a coupled dynamic program. Fifth, planners may find themselves in competition over the provision of information, aiming to gain favor in strategic interactions where both the quality and the content of the information revealed matter. This thesis presents a study of information design as a means to improve platform operations. We formulate models that address each of the five technical challenges described in the context of a particular practical application. Examples of the practical applications we consider include pandemic management, ride-hailing, and incentivizing research and development. In the first chapter, continuous-valued uncertainty and optimization methods robust to planner preference are addressed. We consider a planner using information design to manage a population of hybrid workers amidst the spread of a disease with uncertain infectious risk. We identify closed-form solutions for the optimal signaling mechanism over the risk for a general class of set-based objectives and we identify computationally efficient algorithms to approximate the optimal signaling mechanism for general Lipschitz-continuous objectives. In the second chapter, dynamicity is addressed. We consider a dynamic model where the planner iteratively provisions information to agents with time-varying preferences. The third chapter addresses long-run agents. We focus on a setting where agents serve a transportation platform affected by surge pricing and we identify the optimal signaling mechanisms that provision information over the timing of the uncertain surge. In the final chapter, we address a competitive information design setting. We consider a Bayesian Stackelberg game with a malicious attacker performing a sequential search over a set of firms under costly inspection. Firms can pay to mitigate the probability that the attacker succeeds should they be chosen as a target. Firms can then also choose how much information to reveal on inspection about the attacker's probability of success. We characterize the equilibrium mitigation and signaling strategies of the firms.Ph.D

An Optimal MPC Algorithm for Subunit-Monge Matrix Multiplication, with Applications to LIS

SPAA ’24, June 17–21, 2024, Nantes, FranceWe present an O(1)-round fully-scalable deterministic massively parallel algorithm for computing the min-plus matrix multiplication of unit-Monge matrices. We use this to derive a O(łog n)-round fully-scalable massively parallel algorithm for solving the exact longest increasing subsequence (LIS) problem. For a fully-scalable MPC regime, this result substantially improves the previously known algorithm of O(łog^4 n)-round complexity, and matches the best algorithm for computing the (1+ε)-approximation of LIS

Faster Feedback with AI? A Test Prioritization Study

‹Programming›Companion ’24, March 11–15, 2024, Lund, SwedenFeedback during programming is desirable, but its usefulness depends on immediacy and relevance to the task. Unit and regression testing are practices to ensure programmers can obtain feedback on their changes; however, running a large test suite is rarely fast, and only a few results are relevant. Identifying tests relevant to a change can help programmers in two ways: upcoming issues can be detected earlier during programming, and relevant tests can serve as examples to help programmers understand the code they are editing. In this work, we describe an approach to evaluate how well large language models (LLMs) and embedding models can judge the relevance of a test to a change. We construct a dataset by applying faulty variations of real-world code changes and measuring whether the model could nominate the failing tests beforehand. We found that, while embedding models perform best on such a task, even simple information retrieval models are surprisingly competitive. In contrast, pre-trained LLMs are of limited use as they focus on confounding aspects like coding styles. We argue that the high computational cost of AI models is not always justified, and tool developers should also consider non-AI models for code-related retrieval and recommendation tasks. Lastly, we generalize from unit tests to live examples and outline how our approach can benefit live programming environments

Innovative Floating Wind Turbine with Synthetic Mooring System and Feasibility Analysis of a Solar-Wind-Battery Hybrid System

Synthetic mooring lines, characterized by their neutral buoyancy and high strength, are crucial for maintaining the station-keeping of Floating Wind Turbines (FWTs) by providing the necessary restoring forces while minimizing the vertical loads on the platform. This thesis explores the evolution of mooring systems from traditional catenary chains to taut synthetic fiber ropes, using the VolturnUS-S semi-submersible platform as a case study. The investigation delves into the viscoelastic properties of synthetic ropes and the challenges in accurately modeling their stiffness characteristics. Detailed analysis of the mooring system for the VolturnUS-S platform includes configuration, inclination, and composition of the mooring lines. Environmental conditions at the prospective mooring site are analyzed to evaluate the platform’s responses. A mesh sensitivity study determines the optimal balance between computational efficiency and accuracy. Various stiffness models of polyester mooring ropes are compared, highlighting the impact of rope diameter and inclination on mooring system performance, examining pretension, static and dynamic tensions, and safety margins. The major conclusions of this study are discussed, emphasizing the key findings. Acomprehensive feasibility analysis and preliminary economic assessment of a solar-windbattery hybrid system designed to supply power to a remote island is presented. Multiple configurations are evaluated to identify the most cost-effective and efficient system. The findings indicate that a hybrid system is both technically viable and economically feasible, with wind energy contributing significantly during winter months and solar energy during summer, yielding a reliable power supply throughout the year. Additionally, an overview of offshore wind submarine cabling is provided, focusing on types of cables, route planning, installation, operational considerations, and environmental impacts. Comprehensive planning for cable routes is covered, including site assessments, hydrographic surveys, and regulatory requirements.Nav.E.S.M

Mitigating Generative Agent Social Dilemmas

In social dilemmas, individuals would be better off cooperating but fail to do so due to conflicting interests that discourage cooperation. Existing work on social dilemmas in AI has focused on standard agent design paradigms, most recently in the context of multi-agent reinforcement learning (MARL). However, with the rise of large language models (LLMs), a new design paradigm for AI systems has started to emerge—generative agents, in which actions performed by agents are chosen by prompting LLMs. This paradigm has seen recent success, such as Voyager, a highly capable Minecraft agent. In this work, we perform an initial study of outcomes that arise when deploying generative agents in social dilemmas. To do this, we build a multi-agent Voyager framework with a contracting and judgement mechanism based on formal contracting, which has been effective in mitigating social dilemmas in MARL. Wethen construct social dilemmas in Minecraft as the testbed for our open-source¹ framework. Finally, we conduct preliminary experiments using our framework to provide evidence that contracting helps improve outcomes for generative agents in social dilemmas.M.Eng

Accurate and Fast Approximate Graph Mining at Scale

Approximate graph pattern mining (A-GPM) is an important data analysis tool for numerous graph-based applications. There exist sampling-based A-GPM systems to provide automation and generalization over a wide variety of use cases. Despite improved usability, there are two major obstacles that prevent existing A-GPM systems being adopted in practice. First, the termination mechanism that decides when to terminate sampling lacks theoretical backup on confidence, and performs significantly unstable and thus slow in practice. Second, they particularly suffer poor performance when dealing with the “needle-in-the-hay” cases, because a huge number of samples are required to converge, given the extremely low hit rate of their lazy-pruning strategy and fixed sampling schemes. We build ScaleGPM, an accurate and fast A-GPM system that removes the two obstacles. First, we propose a novel on-the-fly convergence detection mechanism to achieve stable termination and provide theoretical guarantee on the confidence, with negligible online overhead. Second, we propose two techniques to deal with the “needle-in-the-hay” problem, eager-verify and hybrid sampling. Our eager-verify method drastically improves sampling hit rate by pruning unpromising candidates as early as possible. Hybrid sampling further improves performance by automatically choosing the better scheme between fine-grained and coarse-grained sampling schemes. Experiments show that our online convergence detection mechanism can precisely detect convergence, and results in stable and rapid termination with theoretically guaranteed confidence. We also show the effectiveness of eager-verify in improving the hit rate, and the scheme-selection mechanism in correctly choosing the better scheme for various cases. Overall, ScaleGPM achieves an geomean average of 565× (up to 610,169×) speedup over the state-of-the-art A-GPM system, Arya. ScaleGPM is also four orders of magnitude faster than state-of-the-art exact GPM system, GraphZero. In particular, ScaleGPM handles billion-scale graphs in seconds, where existing systems either run out of memory or fail to complete in hours.M.Eng