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We present a novel quantum circuit that genuinely implements the Scully-Dr\"uhl-type delayed-choice quantum eraser, where the two recorders of the which-way information directly interact with the signal qubit and remain spatially separated. Experiments conducted on IBM Quantum and IonQ processors demonstrate that the recovery of interference patterns, to varying degrees, aligns closely with theoretical predictions, despite the presence of systematic errors. This quantum circuit-based approach, more manageable and versatile than traditional optical experiments, facilitates arbitrary adjustment of the erasure and enables a true random choice in a genuine delayed-choice manner. On the IBM Quantum platform, delay gates can be employed to further defer the random choice, thereby amplifying the retrocausal effect. Since gate operations are executed sequentially in time, the system does not have any involvement of random choice until after the signal qubit has been measured, therefore eliminating any potential philosophical loopholes regarding retrocausality that might exist in other experimental setups. Remarkably, quantum erasure is achieved with delay times up to $\sim1\,\mu\text{s}$ without noticeable decoherence, a feat challenging to replicate in optical setups.

This paper presents a mini-review of the current state of research in mobile manipulators with variable levels of autonomy, emphasizing their associated challenges and application environments. The need for mobile manipulators in different environments is evident due to the unique challenges and risks each presents. Many systems deployed in these environments are not fully autonomous, requiring human-robot teaming to ensure safe and reliable operations under uncertainties. Through this analysis, we identify gaps and challenges in the literature on Variable Autonomy, including cognitive workload and communication delays, and propose future directions, including whole-body Variable Autonomy for mobile manipulators, virtual reality frameworks, and large language models to reduce operators' complexity and cognitive load in some challenging and uncertain scenarios., Comment: Presented at Variable Autonomy for Human-Robot Teaming (VAT) at IEEE RO-MAN 2024 Workshop

Remote Photoplethysmography (rPPG) aims to measure physiological signals and Heart Rate (HR) from facial videos. Recent unsupervised rPPG estimation methods have shown promising potential in estimating rPPG signals from facial regions without relying on ground truth rPPG signals. However, these methods seem oblivious to interference existing in rPPG signals and still result in unsatisfactory performance. In this paper, we propose a novel De-interfered and Descriptive rPPG Estimation Network (DD-rPPGNet) to eliminate the interference within rPPG features for learning genuine rPPG signals. First, we investigate the characteristics of local spatial-temporal similarities of interference and design a novel unsupervised model to estimate the interference. Next, we propose an unsupervised de-interfered method to learn genuine rPPG signals with two stages. In the first stage, we estimate the initial rPPG signals by contrastive learning from both the training data and their augmented counterparts. In the second stage, we use the estimated interference features to derive de-interfered rPPG features and encourage the rPPG signals to be distinct from the interference. In addition, we propose an effective descriptive rPPG feature learning by developing a strong 3D Learnable Descriptive Convolution (3DLDC) to capture the subtle chrominance changes for enhancing rPPG estimation. Extensive experiments conducted on five rPPG benchmark datasets demonstrate that the proposed DD-rPPGNet outperforms previous unsupervised rPPG estimation methods and achieves competitive performances with state-of-the-art supervised rPPG methods.

Variable autonomy equips a system, such as a robot, with mixed initiatives such that it can adjust its independence level based on the task's complexity and the surrounding environment. Variable autonomy solves two main problems in robotic planning: the first is the problem of humans being unable to keep focus in monitoring and intervening during robotic tasks without appropriate human factor indicators, and the second is achieving mission success in unforeseen and uncertain environments in the face of static reward structures. An open problem in variable autonomy is developing robust methods to dynamically balance autonomy and human intervention in real-time, ensuring optimal performance and safety in unpredictable and evolving environments. We posit that addressing unpredictable and evolving environments through an addition of rule-based symbolic logic has the potential to make autonomy adjustments more contextually reliable and adding feedback to reinforcement learning through data from mixed-initiative control further increases efficacy and safety of autonomous behaviour.

Many remote photoplethysmography (rPPG) estimation models have achieved promising performance in the training domain but often fail to accurately estimate physiological signals or heart rates (HR) in the target domains. Domain generalization (DG) or domain adaptation (DA) techniques are therefore adopted during the offline training stage to adapt the model to either unobserved or observed target domains by utilizing all available source domain data. However, in rPPG estimation problems, the adapted model usually encounters challenges in estimating target data with significant domain variation. In contrast, Test-Time Adaptation (TTA) enables the model to adaptively estimate rPPG signals in various unseen domains by online adapting to unlabeled target data without referring to any source data. In this paper, we first establish a new TTA-rPPG benchmark that encompasses various domain information and HR distributions to simulate the challenges encountered in real-world rPPG estimation. Next, we propose a novel synthetic signal-guided rPPG estimation framework to address the forgetting issue during the TTA stage and to enhance the adaptation capability of the pre-trained rPPG model. To this end, we develop a synthetic signal-guided feature learning method by synthesizing pseudo rPPG signals as pseudo ground truths to guide a conditional generator in generating latent rPPG features. In addition, we design an effective spectral-based entropy minimization technique to encourage the rPPG model to learn new target domain information. Both the generated rPPG features and synthesized rPPG signals prevent the rPPG model from overfitting to target data and forgetting previously acquired knowledge, while also broadly covering various heart rate (HR) distributions. Our extensive experiments on the TTA-rPPG benchmark show that the proposed method achieves superior performance.

Multi-modal large language models (MLLMs) have been given free rein to explore exciting medical applications with a primary focus on radiology report generation. Nevertheless, the preliminary success in 2D radiology captioning is incompetent to reflect the real-world diagnostic challenge in the volumetric 3D anatomy. To mitigate three crucial limitation aspects in the existing literature, including (1) data complexity, (2) model capacity, and (3) evaluation metric fidelity, we collected an 18,885 text-scan pairs 3D-BrainCT dataset and applied clinical visual instruction tuning (CVIT) to train BrainGPT models to generate radiology-adherent 3D brain CT reports. Statistically, our BrainGPT scored BLEU-1 = 44.35, BLEU-4 = 20.38, METEOR = 30.13, ROUGE-L = 47.6, and CIDEr-R = 211.77 during internal testing and demonstrated an accuracy of 0.91 in captioning midline shifts on the external validation CQ500 dataset. By further inspecting the captioned report, we reported that the traditional metrics appeared to measure only the surface text similarity and failed to gauge the information density of the diagnostic purpose. To close this gap, we proposed a novel Feature-Oriented Radiology Task Evaluation (FORTE) to estimate the report's clinical relevance (lesion feature and landmarks). Notably, the BrainGPT model scored an average FORTE F1-score of 0.71 (degree=0.661; landmark=0.706; feature=0.693; impression=0.779). To demonstrate that BrainGPT models possess objective readiness to generate human-like radiology reports, we conducted a Turing test that enrolled 11 physician evaluators, and around 74% of the BrainGPT-generated captions were indistinguishable from those written by humans. Our work embodies a holistic framework that showcased the first-hand experience of curating a 3D brain CT dataset, fine-tuning anatomy-sensible language models, and proposing robust radiology evaluation metrics., Comment: 6 figures, 5 supplementary figures, 8 supplementary tables

At high redshifts ($z\gtrsim12$), the relative velocity between baryons and dark matter (the so-called streaming velocity) significantly affects star formation in low-mass objects. Streaming substantially reduces the abundance of low-mass gas objects while simultaneously allowing for the formation of supersonically-induced gas objects (SIGOs) and their associated star clusters outside of dark matter halos. Here, we present a study of the population-level effects of streaming on star formation within both halos and SIGOs in a set of simulations with and without streaming. Notably, we find that streaming actually enhances star formation within individual halos of all masses at redshifts between $z=12$ and $z=20$. This is demonstrated both as an increased star formation rate per object as well as an enhancement of the Kennicutt-Schmidt relation for objects with streaming. We find that our simulations are consistent with some observations at high redshift, but on a population level, they continue to under-predict star formation relative to the majority of observations. Notably, our simulations do not include feedback, and so can be taken as an upper limit on the star formation rate, exacerbating these differences. However, simulations of overdense regions (both with and without streaming) agree with observations, suggesting a strategy for extracting information about the overdensity and streaming velocity in a given survey volume in future observations., Comment: 16 pages, 8 figures

Emotions are an integral part of being human, and experiencing a range of emotions is what makes life rich and vibrant. From basic emotions like anger, fear, happiness, and sadness to more complex ones like excitement and grief, emotions help us express ourselves and connect with the world around us. In recent years, researchers have begun adopting virtual reality (VR) technology to evoke emotions as realistically as possible and quantify the strength of emotions from the electroencephalogram (EEG) signals measured from the brain to understand human emotions in realistic situations better. This is achieved by creating a sense of presence in the virtual environment, the feeling that the user is there. For instance, [6] studied the excitement of a rollercoaster ride in VR, and [5] studied the fear of navigating in a VR cave., Comment: This is an accepted position statement of CHI 2024 Workshop (Novel Approaches for Understanding and Mitigating Emerging New Harms in Immersive and Embodied Virtual Spaces: A Workshop at CHI 2024)

Integrating mixed reality (MR) with artificial intelligence (AI) technologies, including vision, language, audio, reasoning, and planning, enables the AI-powered MR assistant [1] to substantially elevate human efficiency. This enhancement comes from situational awareness, quick access to essential information, and support in learning new skills in the right context throughout everyday tasks. This blend transforms interactions with both the virtual and physical environments, catering to a range of skill levels and personal preferences. For instance, computer vision enables the understanding of the user's environment, allowing for the provision of timely and relevant digital overlays in MR systems. At the same time, language models enhance comprehension of contextual information and support voice-activated dialogue to answer user questions. However, as AI-driven MR systems advance, they also unveil new vulnerabilities, posing a threat to user safety by potentially exposing them to grave dangers [5, 6]., Comment: This is an accepted position statement of CHI 2024 Workshop (Novel Approaches for Understanding and Mitigating Emerging New Harms in Immersive and Embodied Virtual Spaces: A Workshop at CHI 2024)

The Metaverse, an immersive virtual world, has emerged as a shared space where people engage in various activities ranging from social interactions to commerce. Cryptocurrencies [3] and Non-Fungible Tokens (NFTs) [6] play pivotal roles within this virtual realm, reshaping interactions and transactions. Cryptocurrencies, utilizing cryptographic techniques for security, enable decentralized and secure transactions, and NFTs represent ownership or proof of authenticity of unique digital assets through the blockchain technology. While NFTs and cryptocurrencies offer innovative opportunities for ownership, trading, and monetization within the metaverse, their use also introduces potential risks and negative consequences, such as financial scams and fraud, highlighting the need for users to exercise caution and diligence in their virtual transactions., Comment: This is an accepted position statement of CHI 2024 Workshop (Novel Approaches for Understanding and Mitigating Emerging New Harms in Immersive and Embodied Virtual Spaces: A Workshop at CHI 2024)

As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in subsequent machine learning tasks. Given the lack of ground truth in most cases of crowdsourcing, we refer to data quality as annotators' consistency and credibility. Unlike the simple scenarios where Kappa coefficient and intraclass correlation coefficient usually can apply, online crowdsourcing requires dealing with more complex situations. We introduce a systematic method for evaluating data quality and detecting spamming threats via variance decomposition, and we classify spammers into three categories based on their different behavioral patterns. A spammer index is proposed to assess entire data consistency and two metrics are developed to measure crowd worker's credibility by utilizing the Markov chain and generalized random effects models. Furthermore, we showcase the practicality of our techniques and their advantages by applying them on a face verification task with both simulation and real-world data collected from two crowdsourcing platforms., Comment: Preprint paper, under review on Behavior Research Methods. 45 pages, 10 figures

This paper addresses the challenge of preparing arbitrary mixed quantum states, an area that has not been extensively studied compared to pure states. Two circuit design methods are presented: one via a mixture of pure states and the other via purification. A novel strategy utilizing the Cholesky decomposition is proposed to improve both computational efficiency during preprocessing and circuit efficiency in the resulting circuits, offering significant advantages, especially when the targeted density matrix is low-ranked or sparse. By leveraging the incomplete Cholesky decomposition with threshold dropping, we also propose an appealing strategy for generating a high-fidelity approximation of the targeted density matrix, enabling substantial efficiency enhancement at the cost of mild fidelity loss. Additionally, as a closely related issue, we prove the "no-superposing theorem": given a certain number of arbitrary unknown pure states as input, it is impossible to devise an operation that produces an output state as the superposition of the input states with predefined coefficients unless all but one of the coefficients vanish., Comment: 25 pages, 8 figures

We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the covariates and the labels. In this setting, neither the covariate shift nor the label shift assumptions apply. Our approach to adaptation employs proximal causal learning, a technique for estimating causal effects in settings where proxies of unobserved confounders are available. We demonstrate that proxy variables allow for adaptation to distribution shift without explicitly recovering or modeling latent variables. We consider two settings, (i) Concept Bottleneck: an additional ''concept'' variable is observed that mediates the relationship between the covariates and labels; (ii) Multi-domain: training data from multiple source domains is available, where each source domain exhibits a different distribution over the latent confounder. We develop a two-stage kernel estimation approach to adapt to complex distribution shifts in both settings. In our experiments, we show that our approach outperforms other methods, notably those which explicitly recover the latent confounder.

Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often involves extracting standardized entities, creating an inverted index, and performing term matching for retrieval. Such traditional methods require manual and time-consuming development of query models. In this paper, we discuss applying learning-to-retrieve technology to enhance LinkedIns job search and recommendation systems. In the realm of promoted jobs, the key objective is to improve the quality of applicants, thereby delivering value to recruiter customers. To achieve this, we leverage confirmed hire data to construct a graph that evaluates a seeker's qualification for a job, and utilize learned links for retrieval. Our learned model is easy to explain, debug, and adjust. On the other hand, the focus for organic jobs is to optimize seeker engagement. We accomplished this by training embeddings for personalized retrieval, fortified by a set of rules derived from the categorization of member feedback. In addition to a solution based on a conventional inverted index, we developed an on-GPU solution capable of supporting both KNN and term matching efficiently.

Patients managing a complex illness such as cancer face a complex information challenge where they not only must learn about their illness but also how to manage it. Close interaction with healthcare experts (radiologists, oncologists) can improve patient learning and thereby, their disease outcome. However, this approach is resource intensive and takes expert time away from other critical tasks. Given the recent advancements in Generative AI models aimed at improving the healthcare system, our work investigates whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data. We conducted a formative need-finding study in which participants discussed chest computed tomography (CT) scans and associated radiology reports of a fictitious close relative with a cardiothoracic radiologist. Using thematic analysis of the conversation between participants and medical experts, we identified commonly occurring themes across interactions, including clarifying medical terminology, locating the problems mentioned in the report in the scanned image, understanding disease prognosis, discussing the next diagnostic steps, and comparing treatment options. Based on these themes, we evaluated two state-of-the-art generative visual language models against the radiologist's responses. Our results reveal variability in the quality of responses generated by the models across various themes. We highlight the importance of patient-facing generative AI systems to accommodate a diverse range of conversational themes, catering to the real-world informational needs of patients.

Designing for AI trustworthiness is challenging, with a lack of practical guidance despite extensive literature on trust. The Multisource AI Scorecard Table (MAST), a checklist rating system, addresses this gap in designing and evaluating AI-enabled decision support systems. We propose the Principled Approach for Designing Trustable Human-centered AI systems using MAST Methodology (PADTHAI-MM), a nine-step framework what we demonstrate through the iterative design of a text analysis platform called the REporting Assistant for Defense and Intelligence Tasks (READIT). We designed two versions of READIT, high-MAST including AI context and explanations, and low-MAST resembling a "black box" type system. Participant feedback and state-of-the-art AI knowledge was integrated in the design process, leading to a redesigned prototype tested by participants in an intelligence reporting task. Results show that MAST-guided design can improve trust perceptions, and that MAST criteria can be linked to performance, process, and purpose information, providing a practical and theory-informed basis for AI system design.

As a realistic model of a quantum system of matter, this paper investigates the gravitational-wave effects on a hydrogen-like atom. By formulating the tetrad formalism of linearized gravity, we naturally incorporate the gravitational-wave effects through minimal coupling in the covariant Dirac equation. The atomic electron transition rates induced by the gravitational wave are calculated using the first-order perturbation theory, revealing a distinctive selection rule along with Fermi's golden rule. This rule can be elegantly understood in terms of gravitons as massless spin-2 particles. Our results suggest the existence of gravitons and may lead to a novel approach to probe Ultra-High-Frequency Gravitational Waves (UHF-GWs)., Comment: 19 pages

Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, possibly with the presence of an informative terminal event. The regression framework is a general scale-change model which encompasses the popular Cox-type model, the accelerated rate model, and the accelerated mean model as special cases. Informative censoring is accommodated through a subject-specific frailty without any need for parametric specification. Different regression models are allowed for the recurrent event process and the terminal event. Also included are visualization and simulation tools.

This paper investigates learning effects and human operator training practices in variable autonomy robotic systems. These factors are known to affect performance of a human-robot system and are frequently overlooked. We present the results from an experiment inspired by a search and rescue scenario in which operators remotely controlled a mobile robot with either Human-Initiative (HI) or Mixed-Initiative (MI) control. Evidence suggests learning in terms of primary navigation task and secondary (distractor) task performance. Further evidence is provided that MI and HI performance in a pure navigation task is equal. Lastly, guidelines are proposed for experimental design and operator training practices., Comment: This paper is a preprint of the paper published on the IEEE International Conference on Systems, Man and Cybernetics (SMC) 2019

The precipitous rise and adoption of Large Language Models (LLMs) have shattered expectations with the fastest adoption rate of any consumer-facing technology in history. Healthcare, a field that traditionally uses NLP techniques, was bound to be affected by this meteoric rise. In this paper, we gauge the extent of the impact by evaluating the performance of LLMs for the task of medical coding on real-life noisy data. We conducted several experiments on MIMIC III and IV datasets with encoder-based LLMs, such as BERT. Furthermore, we developed Segmented Harmonic Loss, a new loss function to address the extreme class imbalance that we found to prevail in most medical data in a multi-label scenario by segmenting and decoupling co-occurring classes of the dataset with a new segmentation algorithm. We also devised a technique based on embedding similarity to tackle noisy data. Our experimental results show that when trained with the proposed loss, the LLMs achieve significant performance gains even on noisy long-tailed datasets, outperforming the F1 score of the state-of-the-art by over ten percentage points., Comment: 16 pages,3 figures, 3 tables

The James Webb Space Telescope (JWST) is capable of probing extremely early eras of our Universe when the supersonic relative motions between dark matter and baryonic overdensities modulate structure formation ($z>\sim 10$). We study low-mass galaxy formation including this "stream velocity" using high resolution AREPO hydrodynamics simulations, and present theoretical predictions of the UV luminosity function (UVLF) and galaxy stellar mass function (GSMF) down to extremely faint and low mass galaxies ($M_{UV}>\sim-15$, $10^4M_\odot<=M_*<=10^8 M_\odot)$. We show that, although the stream velocity suppresses early star formation overall, it induces a short period of rapid star formation in some larger dwarfs, leading to an enhancement in the faint-end of the UVLF at $z=12$. We demonstrate that JWST observations are close to this enhanced regime, and propose that the UVLF may constitute an important probe of the stream velocity at high redshift for JWST and future observatories., Comment: 13 pages, 7 figures. Accepted ApJL

To reduce the dimensionality of the functional covariate, functional principal component analysis plays a key role, however, there is uncertainty on the number of principal components. Model averaging addresses this uncertainty by taking a weighted average of the prediction obtained from a set of candidate models. In this paper, we develop an optimal model averaging approach that selects the weights by minimizing a $K$-fold cross-validation criterion. We prove the asymptotic optimality of the selected weights in terms of minimizing the excess final prediction error, which greatly improves the usual asymptotic optimality in terms of minimizing the final prediction error in the literature. When the true regression relationship belongs to the set of candidate models, we provide the consistency of the averaged estimators. Numerical studies indicate that in most cases the proposed method performs better than other model selection and averaging methods, especially for extreme quantiles., Comment: Any comments are welcome

This research introduces an enhanced version of the multi-objective speech assessment model--MOSA-Net+, by leveraging the acoustic features from Whisper, a large-scaled weakly supervised model. We first investigate the effectiveness of Whisper in deploying a more robust speech assessment model. After that, we explore combining representations from Whisper and SSL models. The experimental results reveal that Whisper's embedding features can contribute to more accurate prediction performance. Moreover, combining the embedding features from Whisper and SSL models only leads to marginal improvement. As compared to intrusive methods, MOSA-Net, and other SSL-based speech assessment models, MOSA-Net+ yields notable improvements in estimating subjective quality and intelligibility scores across all evaluation metrics in Taiwan Mandarin Hearing In Noise test - Quality & Intelligibility (TMHINT-QI) dataset. To further validate its robustness, MOSA-Net+ was tested in the noisy-and-enhanced track of the VoiceMOS Challenge 2023, where it obtained the top-ranked performance among nine systems., Comment: Accepted to IEEE ICME 2024

Automated speech intelligibility assessment is pivotal for hearing aid (HA) development. In this paper, we present three novel methods to improve intelligibility prediction accuracy and introduce MBI-Net+, an enhanced version of MBI-Net, the top-performing system in the 1st Clarity Prediction Challenge. MBI-Net+ leverages Whisper's embeddings to create cross-domain acoustic features and includes metadata from speech signals by using a classifier that distinguishes different enhancement methods. Furthermore, MBI-Net+ integrates the hearing-aid speech perception index (HASPI) as a supplementary metric into the objective function to further boost prediction performance. Experimental results demonstrate that MBI-Net+ surpasses several intrusive baseline systems and MBI-Net on the Clarity Prediction Challenge 2023 dataset, validating the effectiveness of incorporating Whisper embeddings, speech metadata, and related complementary metrics to improve prediction performance for HA., Comment: Accepted to Interspeech 2024