Above I-35

Growth of a city calls for choices to be made, and given its rapid pace, Austin’s growth requires smart solutions. The void created by an insufficient transit system creates the need for more people to drive to work/school. This in turn generates a greater need for wider roads and more lanes for people to drive on. On the 30th of November, 2017, the Texas Department of Transportation announced its plans to lower I-35 in Downtown Austin and add two managed lanes in each direction. The project would have allowed for faster commutes for some of the north- or southbound drivers, provided they chose to pay variable toll rates. This, in the longer run, would have generated substantial revenue for TxDOT but failed to promote east/west connectivity and to solve the traffic congestion problem Austin is dealing with today. There has been a lot of political involvement in the decision-making processes, because of which we do not know if TxDOT plans on rethinking the project. This project, as per Architect, Planner and Urban Designer, Sinclair Black’s Vision, revolves around addressing the primary issue of congestion and emphasizing on how through smarter and farsighted solutions, we can advance towards a more prosperous Austin. The key solutions include depressing and capping the highway, reclaiming valuable downtown land and returning it to the City of Austin for revenue generating real estate development. This will reconnect the city grid, minimize congestion, diminish pollution, and provide dedicated public transit corridor lowering overall commute times. This project largely focuses on estimating the taxable property and the property taxes generated through the deployment of this idea.Community and Regional Plannin

Neural Implicit Surface Reconstruction from Noisy Camera Observations

Representing 3D objects and scenes with neural radiance fields has become very popular over the last years. Recently, surface-based representations have been proposed, that allow to reconstruct 3D objects from simple photographs. However, most current techniques require an accurate camera calibration, i.e. camera parameters corresponding to each image, which is often a difficult task to do in real-life situations. To this end, we propose a method for learning 3D surfaces from noisy camera parameters. We show that we can learn camera parameters together with learning the surface representation, and demonstrate good quality 3D surface reconstruction even with noisy camera observations.Comment: 4 pages - 2 for paper, 2 for supplementar

[Re] Double Sampling Randomized Smoothing

This paper is a contribution to the reproducibility challenge in the field of machine learning, specifically addressing the issue of certifying the robustness of neural networks (NNs) against adversarial perturbations. The proposed Double Sampling Randomized Smoothing (DSRS) framework overcomes the limitations of existing methods by using an additional smoothing distribution to improve the robustness certification. The paper provides a clear manifestation of DSRS for a generalized family of Gaussian smoothing and a computationally efficient method for implementation. The experiments on MNIST and CIFAR-10 demonstrate the effectiveness of DSRS, consistently certifying larger robust radii compared to other methods. Also various ablations studies are conducted to further analyze the hyperparameters and effect of adversarial training methods on the certified radius by the proposed framework

Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach

Coordinating inverters at scale under uncertainty is the desideratum for integrating renewables in distribution grids. Unless load demands and solar generation are telemetered frequently, controlling inverters given approximate grid conditions or proxies thereof becomes a key specification. Although deep neural networks (DNNs) can learn optimal inverter schedules, guaranteeing feasibility is largely elusive. Rather than training DNNs to imitate already computed optimal power flow (OPF) solutions, this work integrates DNN-based inverter policies into the OPF. The proposed DNNs are trained through two OPF alternatives that confine voltage deviations on the average and as a convex restriction of chance constraints. The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions. This is important when OPF has to be solved for an unobservable feeder. DNN weights are trained via back-propagation and upon differentiating the AC power flow equations assuming the network model is known. Otherwise, a gradient-free variant is put forth. The latter is relevant when inverters are controlled by an aggregator having access only to a power flow solver or a digital twin of the feeder. Numerical tests compare the DNN-based inverter control schemes with the optimal inverter setpoints in terms of optimality and feasibility.Comment: To appear in IEEE Transactions on Smart Gri

Plan-and-Fill Scheme for Semantic Parsing

Semantic parsing processes natural language queries to convert them into a structured parse. This disclosure describes a two-stage scheme for semantic parsing, comprising a plan stage and a fill stage. In the plan stage, the intent or plan behind an input query is identified. In the fill stage, a parse is generated by filling the plan with the relevant span from the query. The separation of parsing into plan and fill enables decoupling losses corresponding to basic intent generation (plan) and span identification (fill) stages. The described techniques provide the flexibility to decouple model parameters that correspond to the two stages. The described techniques provide an efficient alternative to sequence-to-sequence models that use both an encoder and a decoder for parsing

Trust, But Verify: A Survey of Randomized Smoothing Techniques

Machine learning models have demonstrated remarkable success across diverse domains but remain vulnerable to adversarial attacks. Empirical defence mechanisms often fall short, as new attacks constantly emerge, rendering existing defences obsolete. A paradigm shift from empirical defences to certification-based defences has been observed in response. Randomized smoothing has emerged as a promising technique among notable advancements. This study reviews the theoretical foundations, empirical effectiveness, and applications of randomized smoothing in verifying machine learning classifiers. We provide an in-depth exploration of the fundamental concepts underlying randomized smoothing, highlighting its theoretical guarantees in certifying robustness against adversarial perturbations. Additionally, we discuss the challenges of existing methodologies and offer insightful perspectives on potential solutions. This paper is novel in its attempt to systemise the existing knowledge in the context of randomized smoothing

A Chance-Constrained Optimal Design of Volt/VAR Control Rules for Distributed Energy Resources

Deciding setpoints for distributed energy resources (DERs) via local control rules rather than centralized optimization offers significant autonomy. The IEEE Standard 1547 recommends deciding DER setpoints using Volt/VAR rules. Although such rules are specified as non-increasing piecewise-affine, their exact shape is left for the utility operators to decide and possibly customize per bus and grid conditions. To address this need, this work optimally designs Volt/VAR rules to minimize ohmic losses on lines while maintaining voltages within allowable limits. This is practically relevant as excessive reactive injections could reduce equipment's lifetime due to overloading. We consider a linearized single-phase grid model. Even under this setting, optimal rule design (ORD) is technically challenging as Volt/VAR rules entail mixed-integer models, stability implications, and uncertainties in grid loading. Uncertainty is handled by minimizing the average losses under voltage chance constraints. To cope with the piecewise-affine shape of the rules, we build upon our previous reformulation of ORD as a deep learning task. A recursive neural network (RNN) surrogates Volt/VAR dynamics and thanks to back-propagation, we expedite this chance-constrained ORD. RNN weights coincide with rule parameters, and are trained using primal-dual decomposition. Numerical tests corroborate the efficacy of this novel ORD formulation and solution methodology