This paper studies the Dynamic Electric Dial-A-Ride Problem (DEDARP), which is a combinatorial optimisation problem that has applications in real-world ridesharing services with electric vehicles. In addition to the challenges from classical scheduling and route planning, we consider here the extra challenge of making real-time dispatching decisions in dynamic environments with new requests arriving over time and selecting proper times for the vehicles to recharge. To solve DEDARP effectively, we propose a Genetic Programming Hyper-Heuristic (GPHH) that evolves heuristics/policies to dispatch vehicles in real time. We have developed a simulation process that generates a solution for any given instance by two policies, one for vehicle allocation and the other for request allocation, and design fitness evaluations based on the simulation. Moreover, we propose a multi-tree GP to evolve these two policies simultaneously, which makes use of advanced terminals to comprehensively represent the state. Experimental results on a wide range of instances show that GPHH can evolve effective policies that make significantly better real-time dispatching decisions than human-designed policies based on prior knowledge.

With use, Accessibility Scout becomes an increasingly capable "accessibility scout", tailoring accessibility scans to an individual's mobility level, preferences, and specific environmental interests through collaborative Human-AI assessments.We present findings from three studies: a formative study with six participants to inform the design of Accessibility Scout, a technical evaluation of 500 images of built environments, and a user study with 10 participants of varying mobility.Results from our technical evaluation and user study show that Accessibility Scout can generate personalized accessibility scans that extend beyond traditional ADA considerations.Finally, we conclude with a discussion on the implications of our work and future steps for building more scalable and personalized accessibility assessments of the physical world.

Existing pose estimation models perform poorly on wheelchair users due to a lack of representation in training data. We present a data synthesis pipeline to address this disparity in data collection and subsequently improve pose estimation performance for wheelchair users. Our configurable pipeline generates synthetic data of wheelchair users using motion capture data and motion generation outputs simulated in the Unity game engine. We validated our pipeline by conducting a human evaluation, investigating perceived realism, diversity, and an AI performance evaluation on a set of synthetic datasets from our pipeline that synthesized different backgrounds, models, and postures. We found our generated datasets were perceived as realistic by human evaluators, had more diversity than existing image datasets, and had improved person detection and pose estimation performance when fine-tuned on existing pose estimation models. Through this work, we hope to create a foothold for future efforts in tackling the inclusiveness of AI in a data-centric and human-centric manner with the data synthesis techniques demonstrated in this work. Finally, for future works to extend upon, we open source all code in this research and provide a fully configurable Unity Environment used to generate our datasets. In the case of any models we are unable to share due to redistribution and licensing policies, we provide detailed instructions on how to source and replace said models. All materials can be found at https://github.com/hilab-open-source/wheelpose.

Diffusion models have demonstrated significant promise in various generative tasks; however, they often struggle to satisfy challenging constraints. Our approach addresses this limitation by rethinking training-free loss-guided diffusion from an optimization perspective. We formulate a series of constrained optimizations throughout the inference process of a diffusion model. In each optimization, we allow the sample to take multiple steps along the gradient of the proxy constraint function until we can no longer trust the proxy, according to the variance at each diffusion level. Additionally, we estimate the state manifold of diffusion model to allow for early termination when the sample starts to wander away from the state manifold at each diffusion step. Trust sampling effectively balances between following the unconditional diffusion model and adhering to the loss guidance, enabling more flexible and accurate constrained generation. We demonstrate the efficacy of our method through extensive experiments on complex tasks, and in drastically different domains of images and 3D motion generation, showing significant improvements over existing methods in terms of generation quality. Our implementation is available at https://github.com/will-s-h/trust-sampling.

Clara Vania, Phu Mon Htut, William Huang, Dhara Mungra, Richard Yuanzhe Pang, Jason Phang, Haokun Liu, Kyunghyun Cho, Samuel R. Bowman. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of OOD examples and how to best detect them.We categorize these examples by whether they exhibit a background shift or a semantic shift, and find that the two major approaches to OOD detection, model calibration and density estimation (language modeling for text), have distinct behavior on these types of OOD data.Across 14 pairs of in-distribution and OOD English natural language understanding datasets, we find that density estimation methods consistently beat calibration methods in background shift settings, while performing worse in semantic shift settings.In addition, we find that both methods generally fail to detect examples from challenge data, highlighting a weak spot for current methods.Since no single method works well across all settings, our results call for an explicit definition of OOD examples when evaluating different detection methods.

Performance on the Winograd Schema Challenge (WSC), a respected English commonsense reasoning benchmark, recently rocketed from chance accuracy to 89% on the Super-GLUE leaderboard, with relatively little corroborating evidence of a correspondingly large improvement in reasoning ability.We hypothesize that much of this improvement comes from recent changes in task formalizationthe combination of input specification, loss function, and reuse of pretrained parametersby users of the dataset, rather than improvements in the pretrained model's reasoning ability.We perform an ablation on two Winograd Schema datasets that interpolates between the formalizations used before and after this surge, and find (i) framing the task as multiple choice improves performance by 2-6 points and (ii) several additional techniques, including the reuse of a pretrained language modeling head, can mitigate the model's extreme sensitivity to hyperparameters.We urge future benchmark creators to impose additional structure to minimize the impact of formalization decisions on reported results.

Currently, researchers study face-to-face interactions using wearable sensors and smartphones which provide 2 to 5 m proximity sensing every 20 to 300 s. However, studying interaction distance, which is known to impact disease spread, communication behavior, and other phenomenon, has proven challenging. Smartphones are limited by their inaccurate and/or impractical ranging capabilities, while wearable sensors are limited by their need for infrastructure nodes, bulkiness, and/or inaccurate ranging. To address these challenges, we present Opo, a 14 cm2, 11.4 g "lapel pin" built from commercial components. Opo sensors range neighbors every 2 s up to 2 m away with 5% average error, all while requiring zero infrastructure and improving upon current wearable sensors' accuracy and power usage. The cornerstone of Opo is an ultrasonic wakeup circuit that draws 19 μA when no neighbors are present. This enables Opo sensors to discover and range neighbors without the need for infrastructure nodes and slow or power-hungry RF discovery protocols. Thus, Opo is able to sense interaction distance with high accuracy (5 cm) and temporal fidelity (2 s) on a limited power budget.

PureVoice™ VR is a speech recognition system characterized by noise robustness performance, small footprint, and limited computing requirement. The system was embedded into Qualcomm's Code Division Multiple Access (CDMA) Mobile Station Modem (MSM) chipsets. The PureVoice™ VR engine enabled many manufactures of CDMA cellular phones to add highly accurate voice-activated dialing features to their handsets and hands-free car kits without significant increase in hardware cost. Users of these cellular phones could make calls by simply speaking names or phone numbers. The current embedded system has the capacity to recognize 100 speaker-dependent (SD) nametags and user defined commands, or 30 speaker-independent (SI) digits and commands in realtime. It also works in a hybrid mode, in which an input utterance can be either SD namtags or SI commands. PureVoice™ VR has successfully supported the voice-activated dialing function for millions of CDMA handsets sold in the world market.

Artificial neural networks are of interest because algorithms used in many speech recognizers can be implemented using highly parallel neural net architectures and because new parallel algorithms are being development that are inspired by biological nervous systems. Some neural net approaches are resented for the problem of static pattern classification and time alignment. For static pattern classification, multi-layer perceptron classifiers trained with back propagation can form arbitrary decision regions, are robust, and train rapidly for convex decision regions. For time alignment, the Viterbi net is a neural net implementation of the Viterbi decoder used very effectively in recognition systems based on hidden Markov models (HMMs).>