Person-independent models for the same lack precision due to anatomical differences of subjects, whereas person-specific calibrated techniques add strict constraints on scalability. To overcome these issues, we propose a novel technique, Facial Landmark Heatmap Activated Multimodal Gaze Estimation (FLAME), as a way of combining eye anatomical information using eye landmark heatmaps to obtain precise gaze estimation without Figure 1: Different types of gaze estimation methods: any person-specific calibration. Our evaluation demonstrates (a) person-independent technique, (b) person-specific technique, a competitive performance of about 10% improvement (c) FLAME. Training subjects are green and test on benchmark datasets ColumbiaGaze and EYEDIAP.

In constructing an econometric or statistical model, we pick relevant features or variables from many candidates. A coalitional game is set up to study the selection problem where the players are the candidates and the payoff function is a performance measurement in all possible modeling scenarios. Thus, in theory, an irrelevant feature is equivalent to a dummy player in the game, which contributes nothing to all modeling situations. The hypothesis test of zero mean contribution is the rule to decide a feature is irrelevant or not. In our mechanism design, the end goal perfectly matches the expected model performance with the expected sum of individual marginal effects. Within a class of noninformative likelihood among all modeling opportunities, the matching equation results in a specific valuation for each feature. After estimating the valuation and its standard deviation, we drop any candidate feature if its valuation is not significantly different from zero. In the simulation studies, our new approach significantly outperforms several popular methods used in practice, and its accuracy is robust to the choice of the payoff function.

The issue of fairness arises when the automatic speech recognition (ASR) systems do not perform equally well for all subgroups of the population. In any fairness measurement studies for ASR, the open questions of how to control the nuisance factors, how to handle unobserved heterogeneity across speakers, and how to trace the source of any word error rate (WER) gap among different subgroups are especially important - if not appropriately accounted for, incorrect conclusions will be drawn. In this paper, we introduce mixed-effects Poisson regression to better measure and interpret any WER difference among subgroups of interest. Particularly, the presented method can effectively address the three problems raised above and is very flexible to use in practical disparity analyses. We demonstrate the validity of proposed model-based approach on both synthetic and real-world speech data.

zoofs is a Python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size. - GitHub - jaswinder9051998/zoofs: zoofs is a Python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.

Recent advancements in deep learning, computer vision, and embodied AI have given rise to synthetic causal reasoning video datasets. These datasets facilitate the development of AI algorithms that can reason about physical interactions between objects. However, datasets thus far have primarily focused on elementary physical events such as rolling or falling. There is currently a scarcity of datasets that focus on the physical interactions that humans perform daily with objects in the real world. To address this scarcity, we introduce SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments. The SPACE simulator allows us to generate the SPACE dataset, a synthetic video dataset in a 3D environment, to systematically evaluate physics-based models on a range of physical causal reasoning tasks. Inspired by daily object interactions, the SPACE dataset comprises videos depicting three types of physical events: containment, stability and contact. These events make up the vast majority of the basic physical interactions between objects. We then further evaluate it with a state-of-the-art physics-based deep model and show that the SPACE dataset improves the learning of intuitive physics with an approach inspired by curriculum learning. Repository: https://github.com/jiafei1224/SPACE

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What is the Mother Idea of All Ideas, Concepts, Rules, Laws or Algorithms? All in all, the idea of all ideas, the rule of all rules, or the law of all laws, the algorithms of all algorithms, or the Mother Discovery is certainly the concept of causality and causation as the real, circular, or reversible causality with the interactive causation, governing the causal order of the world and all its regions, domains and fields. The Six Layer Causal Hierarchy defines the Ladder of Reality, Causality and Mentality, Science and Technology, Human Intelligence and Non-Human Intelligence (MI or AI). The Causal World [levels of causation] is a basis for all real world constructs, as power, force and interactions, agents and substances, states and conditions and situations, events, actions and changes, processes and relations; causality and causation, causal models, causal systems, causal processes, causal mechanisms, causal patterns, causal data or information, causal codes, programs, algorithms, causal analysis, causal reasoning, causal inference, or causal graphs (path diagrams, causal Bayesian networks or DAGs). CAUSALITY AND GLOBAL ARTIFICIAL INTELLIGENCE We need a unifying model of reality/world in terms of causality/actuality, mentality/intelligence and computing/data /virtuality/cyberspace, where neural networks are brain-encoded causal networks.

We examine interval estimation of the effect of a treatment T on an outcome Y given the existence of an unobserved confounder U. Using H\"older's inequality, we derive a set of bounds on the confounding bias |E[Y|T=t]-E[Y|do(T=t)]| based on the degree of unmeasured confounding (i.e., the strength of the connection U->T, and the strength of U->Y). These bounds are tight either when U is independent of T or when U is independent of Y given T (when there is no unobserved confounding). We focus on a special case of this bound depending on the total variation distance between the distributions p(U) and p(U|T=t), as well as the maximum (over all possible values of U) deviation of the conditional expected outcome E[Y|U=u,T=t] from the average expected outcome E[Y|T=t]. We discuss possible calibration strategies for this bound to get interval estimates for treatment effects, and experimentally validate the bound using synthetic and semi-synthetic datasets.

Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to alleviate issues raised from sparse rewards. However, these methods are still limited in efficiency and cannot make full use of experiences. In this paper, we propose Model-based Hindsight Experience Replay (MHER), which exploits experiences more efficiently by leveraging environmental dynamics to generate virtual achieved goals. Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, \emph{model-based relabeling} (MBR). Based on MBR, MHER performs both reinforcement learning and supervised learning for efficient policy improvement. Theoretically, we also prove the supervised part in MHER, i.e., goal-conditioned supervised learning with MBR data, optimizes a lower bound on the multi-goal RL objective. Experimental results in several point-based tasks and simulated robotics environments show that MHER achieves significantly higher sample efficiency than previous state-of-the-art methods.

Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient? Balasubramanian, an associate professor of Mechanical Engineering and Mechanics, studies the basic physics of the materials at the heart of solar energy conversion – the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed – as well as the manufacturing processes that produce commercial solar cells. Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. "When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport," Balasubramanian said.

… consider future-facing investments for the even higher performing machines that … computing, artificial intelligence, and scientific machine learning.”.