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In this paper we introduce a multi-agent deep-learning method which trades in the Futures markets based on the US S&P 500 index. The method (referred to as Model A) is an innovation founded on existing well-established machine-learning models which sample market prices and associated derivatives in order to decide whether the investment should be long/short or closed (zero exposure), on a day-to-day decision. We compare the predictions with some conventional machine-learning methods namely, Long Short-Term Memory, Random Forest and Gradient-Boosted-Trees. Results are benchmarked against a passive model in which the Futures contracts are held (long) continuously with the same exposure (level of investment). Historical tests are based on daily daytime trading carried out over a period of 6 calendar years (2018-23). We find that Model A outperforms the passive investment in key performance metrics, placing it within the top quartile performance of US Large Cap active fund managers. Model A also outperforms the three machine-learning classification comparators over this period. We observe that Model A is extremely efficient (doing less and getting more) with an exposure to the market of only 41.95% compared to the 100% market exposure of the passive investment, and thus provides increased profitability with reduced risk., Comment: 12 pages, 5 figures

Clinical practice guidelines, care pathways, and protocols are designed to support evidence-based practices for clinicians; however, their adoption remains a challenge. We set out to investigate why clinicians deviate from the ``Wake Up and Breathe'' protocol, an evidence-based guideline for liberating patients from mechanical ventilation in the intensive care unit (ICU). We conducted over 40 hours of direct observations of live clinical workflows, 17 interviews with frontline care providers, and 4 co-design workshops at three different medical intensive care units. Our findings indicate that unlike prior literature suggests, disagreement with the protocol is not a substantial barrier to adoption. Instead, the uncertainty surrounding the application of the protocol for individual patients leads clinicians to deprioritize adoption in favor of tasks where they have high certainty. Reflecting on these insights, we identify opportunities for technical systems to help clinicians in effectively executing the protocol and discuss future directions for HCI research to support the integration of protocols into clinical practice in complex, team-based healthcare settings., Comment: to appear at CHI 2024

Prior robot painting and drawing work, such as FRIDA, has focused on decreasing the sim-to-real gap and expanding input modalities for users, but the interaction with these systems generally exists only in the input stages. To support interactive, human-robot collaborative painting, we introduce the Collaborative FRIDA (CoFRIDA) robot painting framework, which can co-paint by modifying and engaging with content already painted by a human collaborator. To improve text-image alignment, FRIDA's major weakness, our system uses pre-trained text-to-image models; however, pre-trained models in the context of real-world co-painting do not perform well because they (1) do not understand the constraints and abilities of the robot and (2) cannot perform co-painting without making unrealistic edits to the canvas and overwriting content. We propose a self-supervised fine-tuning procedure that can tackle both issues, allowing the use of pre-trained state-of-the-art text-image alignment models with robots to enable co-painting in the physical world. Our open-source approach, CoFRIDA, creates paintings and drawings that match the input text prompt more clearly than FRIDA, both from a blank canvas and one with human created work. More generally, our fine-tuning procedure successfully encodes the robot's constraints and abilities into a foundation model, showcasing promising results as an effective method for reducing sim-to-real gaps.

Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation., Comment: to appear at CHI 2024

Tactile skins made from textiles enhance robot-human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human-robot interaction in industrial environments, and can provide unique opportunities in robot control and learning, making them a promising technology for enhancing robot perception and interaction.

Tactile sensing is essential for robots to perceive and react to the environment. However, it remains a challenge to make large-scale and flexible tactile skins on robots. Industrial machine knitting provides solutions to manufacture customizable fabrics. Along with functional yarns, it can produce highly customizable circuits that can be made into tactile skins for robots. In this work, we present RobotSweater, a machine-knitted pressure-sensitive tactile skin that can be easily applied on robots. We design and fabricate a parameterized multi-layer tactile skin using off-the-shelf yarns, and characterize our sensor on both a flat testbed and a curved surface to show its robust contact detection, multi-contact localization, and pressure sensing capabilities. The sensor is fabricated using a well-established textile manufacturing process with a programmable industrial knitting machine, which makes it highly customizable and low-cost. The textile nature of the sensor also makes it easily fit curved surfaces of different robots and have a friendly appearance. Using our tactile skins, we conduct closed-loop control with tactile feedback for two applications: (1) human lead-through control of a robot arm, and (2) human-robot interaction with a mobile robot.

Painting is an artistic process of rendering visual content that achieves the high-level communication goals of an artist that may change dynamically throughout the creative process. In this paper, we present a Framework and Robotics Initiative for Developing Arts (FRIDA) that enables humans to produce paintings on canvases by collaborating with a painter robot using simple inputs such as language descriptions or images. FRIDA introduces several technical innovations for computationally modeling a creative painting process. First, we develop a fully differentiable simulation environment for painting, adopting the idea of real to simulation to real (real2sim2real). We show that our proposed simulated painting environment is higher fidelity to reality than existing simulation environments used for robot painting. Second, to model the evolving dynamics of a creative process, we develop a planning approach that can continuously optimize the painting plan based on the evolving canvas with respect to the high-level goals. In contrast to existing approaches where the content generation process and action planning are performed independently and sequentially, FRIDA adapts to the stochastic nature of using paint and a brush by continually re-planning and re-assessing its semantic goals based on its visual perception of the painting progress. We describe the details on the technical approach as well as the system integration.

We introduce a method to design lightweight shell objects that are structurally robust under the external forces they may experience during use. Given an input 3D model and a general description of the external forces, our algorithm generates a structurally-sound minimum weight shell object. Our approach works by altering the local shell thickness repeatedly based on the stresses that develop inside the object. A key issue in shell design is that large thickness values might result in self-intersections on the inner boundary creating a significant computational challenge during optimization. To address this, we propose a shape parametrization based on the solution to the Laplace's equation that guarantees smooth and intersection-free shell boundaries. Combined with our gradient-free optimization algorithm, our method provides a practical solution to the structural design of hollow objects with a single inner cavity. We demonstrate our method on a variety of problems with arbitrary 3D models under complex force configurations and validate its performance with physical experiments., Comment: Eurographics Symposium on Geometry Processing (SGP) 2019 / Computer Graphics Forum

We introduce a lightweight structure optimization approach for problems in which there is uncertainty in the force locations. Such uncertainty may arise due to force contact locations that change during use or are simply unknown a priori. Given an input 3D model, regions on its boundary where arbitrary normal forces may make contact, and a total force-magnitude budget, our algorithm generates a minimum weight 3D structure that withstands any force configuration capped by the budget. Our approach works by repeatedly finding the most critical force configuration and altering the internal structure accordingly. A key issue, however, is that the critical force configuration changes as the structure evolves, resulting in a significant computational challenge. To address this, we propose an efficient critical instant analysis approach. Combined with a reduced order formulation, our method provides a practical solution to the structural optimization problem. We demonstrate our method on a variety of models and validate it with mechanical tests., Comment: SIGGRAPH 2017

Accurate photodissociation cross sections have been computed for transitions from the X $^1\Sigma^+$ ground electronic state of CS to six low-lying excited electronic states. New ab initio potential curves and transition dipole moment functions have been obtained for these computations using the multi-reference configuration interaction approach with the Davidson correction (MRCI+Q) and aug-cc-pV6Z basis sets. State-resolved cross sections have been computed for transitions from nearly the full range of rovibrational levels of the X $^1\Sigma^+$ state and for photon wavelengths ranging from 500 $\text{\AA}$ to threshold. Destruction of CS via predissociation in highly excited electronic states originating from the rovibrational ground state is found to be unimportant. Photodissociation cross sections are presented for temperatures in the range between 1000 and 10,000 K, where a Boltzmann distribution of initial rovibrational levels is assumed. Applications of the current computations to various astrophysical environments are briefly discussed focusing on photodissociation rates due to the standard interstellar and blackbody radiation fields., Comment: 16 pages, 9 figures, 4 tables

Single-molecule F\"orster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards ensuring both the reproducibility and accuracy of measuring FRET efficiencies are currently lacking. Here we report the results of a worldwide, comparative, blind study, in which 20 labs determined the FRET efficiencies of several dye-labeled DNA duplexes. Using a unified and straightforward method, we show that FRET efficiencies can be obtained with a standard deviation between ${\Delta}$E = +-0.02 and +-0.05. We further suggest an experimental and computational procedure for converting FRET efficiencies into accurate distances. We discuss potential uncertainties in the experiment and the modelling. Our extensive quantitative assessment of intensity-based smFRET measurements and correction procedures serve as an essential step towards validation of distance networks with the ultimate aim to archive reliable structural models of biomolecular systems obtained by smFRET-based hybrid methods., Comment: 27 pages, 5 figures, supplementary material, corrections: p20 last sentence; eqn. 13 & 14