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Imitation learning from human demonstrations enables robots to perform complex manipulation tasks and has recently witnessed huge success. However, these techniques often struggle to adapt behavior to new preferences or changes in the environment. To address these limitations, we propose Fine-tuning Diffusion Policy with Human Preference (FDPP). FDPP learns a reward function through preference-based learning. This reward is then used to fine-tune the pre-trained policy with reinforcement learning (RL), resulting in alignment of pre-trained policy with new human preferences while still solving the original task. Our experiments across various robotic tasks and preferences demonstrate that FDPP effectively customizes policy behavior without compromising performance. Additionally, we show that incorporating Kullback-Leibler (KL) regularization during fine-tuning prevents over-fitting and helps maintain the competencies of the initial policy.

Structured optical fields have led to several ground-breaking techniques in classical imaging and microscopy. At the same time, in the quantum domain, position-momentum entangled photon fields have been shown to have several unique features that can lead to beyond-classical imaging and microscopy capabilities. Therefore, it is natural to expect that position-momentum entangled two-photon fields that are structured can push the boundaries of quantum imaging and microscopy even further beyond. Nonetheless, the existing experimental schemes are able to produce either structured two-photon fields without position-momentum entanglement, or position-momentum entangled two-photon fields without structures. In this article, by manipulating the phase-matching condition of the spontaneous parametric down-conversion process, we report experimental generation of two-photon fields with various structures in their spatial correlations. We experimentally measure the minimum bound on the entanglement of formation and thereby verify the position-momentum entanglement of the structured two-photon field. We expect this work to have important implications for quantum technologies related to imaging and sensing., Comment: 9 pages, 6 figures

Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels. This makes minute-length video generation extremely expensive, limiting most existing models to generating videos of only 10-20 seconds length. We propose a Linear-complexity text-to-video Generation (LinGen) framework whose cost scales linearly in the number of pixels. For the first time, LinGen enables high-resolution minute-length video generation on a single GPU without compromising quality. It replaces the computationally-dominant and quadratic-complexity block, self-attention, with a linear-complexity block called MATE, which consists of an MA-branch and a TE-branch. The MA-branch targets short-to-long-range correlations, combining a bidirectional Mamba2 block with our token rearrangement method, Rotary Major Scan, and our review tokens developed for long video generation. The TE-branch is a novel TEmporal Swin Attention block that focuses on temporal correlations between adjacent tokens and medium-range tokens. The MATE block addresses the adjacency preservation issue of Mamba and improves the consistency of generated videos significantly. Experimental results show that LinGen outperforms DiT (with a 75.6% win rate) in video quality with up to 15$\times$ (11.5$\times$) FLOPs (latency) reduction. Furthermore, both automatic metrics and human evaluation demonstrate our LinGen-4B yields comparable video quality to state-of-the-art models (with a 50.5%, 52.1%, 49.1% win rate with respect to Gen-3, LumaLabs, and Kling, respectively). This paves the way to hour-length movie generation and real-time interactive video generation. We provide 68s video generation results and more examples in our project website: https://lineargen.github.io/., Comment: 20 pages, 20 figures

The Unruh effect is a well-understood phenomenon, where one considers a vacuum state of a quantum field in Minkowski spacetime, which appears to be thermally populated for a uniformly accelerating Rindler observer. In this article, we derive a variant of the Unruh effect involving two distinct accelerating observers and aim to address the following questions: (i) Is it possible to selectively thermalize a subset of momentum modes for the case of massless scalar fields, and (ii) Is it possible to excite only the left-handed massless fermions while keeping right-handed fermions in a vacuum state or vice versa? To this end, we consider a Rindler wedge $R_1$ constructed from a class of accelerating observers and another Rindler wedge $R_2$ (with $R_2 \subset R_1$) constructed from another class of accelerating observers such that the wedge $R_2$ is displaced along a null direction w.r.t $R_1$ by a parameter $\Delta$. By first considering a massless scalar field in the $R_1$ vacuum, we show that if we choose the displacement $\Delta$ along one null direction, the positive momentum modes are thermalized, whereas negative momentum modes remain in vacuum (and vice versa if we choose the displacement along the other null direction). We then consider a massless fermionic field in a vacuum state in $R_1$ and show that the reduced state in $R_2$ is such that the left-handed fermions are excited and are thermal for large frequencies. In contrast, the right-handed fermions have negligible particle density and vice versa. We argue that the toy models involving shifted Rindler spacetime may provide insights into the particle excitation aspects of evolving horizons and the possibility of Rindler spacetime having a quantum strand of hair. Additionally, based on our work, we hypothesize that massless fermions underwent selective chiral excitations during the radiation-dominated era of cosmology., Comment: 17 pages, 4 figures

Electron-phonon (E-p) coupling incorporated density functional theory (DFT) based investigation of structural, electronic and vibrational properties of bulk MgSe and MgTe is presented. Electron-phonon coupling is incorporated to understand its effect on charge carrier dynamics. It is observed that the MgTe possesses room temperature hole and electron mobility of 18.7 cm2/Vs and 335 cm2/Vs, respectively; in contrast to this, the bulk MgSe follows reverse trend in temperature dependent carrier mobilities owing to its different scattering rate and electron-phonon coupling profiles. The key feature of the study was to showcase the importance of electron-phonon coupling in determining the carrier mobility and the relative dynamics in the material. Further, the incorporation of e-p coupling softens the electronic and phonon dispersions which is subjected to the inclusion of the interaction between the electrons and the phonons of the systems. The overall results indicate that the incorporation of the electron-phonon coupling is crucial in determining the carrier dynamics of the system which is in excellent agreement with the experimental findings. Both materials possess moderate magnitudes of mobilities that are subjected to the modification by means of changing the dimensional confinement and/or chemical composition that can strongly influence the carrier dynamics by means of altered edge states and coupling parameters., Comment: 19 pages, 3 Figures

Model-based planners and controllers are commonly used to solve complex manipulation problems as they can efficiently optimize diverse objectives and generalize to long horizon tasks. However, they are limited by the fidelity of their model which oftentimes leads to failures during deployment. To enable a robot to recover from such failures, we propose to use hierarchical reinforcement learning to learn a separate recovery policy. The recovery policy is triggered when a failure is detected based on sensory observations and seeks to take the robot to a state from which it can complete the task using the nominal model-based controllers. Our approach, called RecoveryChaining, uses a hybrid action space, where the model-based controllers are provided as additional \emph{nominal} options which allows the recovery policy to decide how to recover, when to switch to a nominal controller and which controller to switch to even with \emph{sparse rewards}. We evaluate our approach in three multi-step manipulation tasks with sparse rewards, where it learns significantly more robust recovery policies than those learned by baselines. Finally, we successfully transfer recovery policies learned in simulation to a physical robot to demonstrate the feasibility of sim-to-real transfer with our method., Comment: 8 pages, 9 figures

Wearable medical sensors (WMSs) are revolutionizing smart healthcare by enabling continuous, real-time monitoring of user physiological signals, especially in the field of consumer healthcare. The integration of WMSs and modern machine learning (ML) enables unprecedented solutions to efficient early-stage disease detection. Despite the success of Transformers in various fields, their application to sensitive domains, such as smart healthcare, remains underexplored due to limited data accessibility and privacy concerns. To bridge the gap between Transformer-based foundation models and WMS-based disease detection, we propose COMFORT, a continual fine-tuning framework for foundation models targeted at consumer healthcare. COMFORT introduces a novel approach for pre-training a Transformer-based foundation model on a large dataset of physiological signals exclusively collected from healthy individuals with commercially available WMSs. We adopt a masked data modeling (MDM) objective to pre-train this health foundation model. We then fine-tune the model using various parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and its variants, to adapt it to various downstream disease detection tasks that rely on WMS data. In addition, COMFORT continually stores the low-rank decomposition matrices obtained from the PEFT algorithms to construct a library for multi-disease detection. The COMFORT library enables scalable and memory-efficient disease detection on edge devices. Our experimental results demonstrate that COMFORT achieves highly competitive performance while reducing memory overhead by up to 52% relative to conventional methods. Thus, COMFORT paves the way for personalized and proactive solutions to efficient and effective early-stage disease detection for consumer healthcare., Comment: 25 pages, 10 figures. This work has been submitted to the ACM for possible publication

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans. Pre-defined attenuation coefficients are assigned to these regions, yielding the attenuation map used for AC. We objectively evaluated this method in a retrospective study with anonymized clinical SPECT/CT stress MPI images on the clinical task of detecting defects with an anthropomorphic model observer. CTLESS yielded statistically non-inferior performance compared to a CT-based AC (CTAC) method and significantly outperformed a non-AC (NAC) method on this clinical task. Similar results were observed in stratified analyses with different sexes, defect extents and severities. The method was observed to generalize across two SPECT scanners, each with a different camera. In addition, CTLESS yielded similar performance as CTAC and outperformed NAC method on the metrics of root mean squared error and structural similarity index measure. Moreover, as we reduced the training dataset size, CTLESS yielded relatively stable AUC values and generally outperformed another DL-based AC method that directly estimated the attenuation coefficient within each voxel. These results demonstrate the capability of the CTLESS method for transmission-less AC in SPECT and motivate further clinical evaluation.

Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparation, and the complex nature of the large intestine contribute to high polyp miss-rate. These missed polyps can develop into cancer later, underscoring the importance of improving the detection methods. To address this gap of lack of publicly available, multi-center large and diverse datasets for developing automatic methods for polyp detection and segmentation, we introduce PolypDB, a large scale publicly available dataset that contains 3934 still polyp images and their corresponding ground truth from real colonoscopy videos. PolypDB comprises images from five modalities: Blue Light Imaging (BLI), Flexible Imaging Color Enhancement (FICE), Linked Color Imaging (LCI), Narrow Band Imaging (NBI), and White Light Imaging (WLI) from three medical centers in Norway, Sweden, and Vietnam. We provide a benchmark on each modality and center, including federated learning settings using popular segmentation and detection benchmarks. PolypDB is public and can be downloaded at \url{https://osf.io/pr7ms/}. More information about the dataset, segmentation, detection, federated learning benchmark and train-test split can be found at \url{https://github.com/DebeshJha/PolypDB}., Comment: 3 Figures, 6 tables

The equation of state (EOS) for neutron stars is modeled using the Relativistic Mean Field (RMF) approach with a mesonic nonlinear (NL) interaction, a modified sigma cut potential (NL-$\sigma$ cut), and the influences of dark matter in the NL (NL DM). Using a Bayesian analysis framework, we evaluate the plausibility and impact of each scenario. Experimental constraints on the general properties of finite nuclei and heavy ion collisions, along with astrophysical observational data on neutron star radii and tidal deformation, have been taken into account. It was shown that all models, including the PREX-II data, were less favored, indicating that this experimental data seemed to be in tension with the other constraints included in the inference procedure, and were incompatible with chiral effective field theoretical calculations of pure neutron matter. Considering the models with no PREX-II constraints, we find the model NL-$\sigma$ cut with the largest Bayes evidence, indicating that the constraints considered favor the stiffening of the EOS at large densities. Conversely, the neutron star with a dark matter component is the least favorable case in light of recent observational constraints, among different scenarios considered here. The $f$ and $p$ modes were calculated within the Cowling approximation, and it can be seen that $f$ modes are sensitive to the EOS. An analysis of the slopes of the mass-radius curves and $f$-mode mass curves has indicated that these quantities may help distinguish the different scenarios.We also analyzed the impact of new PSR J0437-4715 measurements on neutron star mass-radius estimates, noting a $\sim$ 0.2 km reduction in the 90\% CI upper boundary across all models and a significant Bayes evidence decrease, indicating potential conflicts with previous data or the necessity for more adaptable models., Comment: 18 pages, 10 figures and 6 tables

In this paper, we report the excitation of coherent density and potential fluctuations induced by magnetohydrodynamic (MHD) activity in the edge plasma region of ADITYA-U Tokamak. When the amplitude of the MHD mode, mainly the m/n = 2/1, increases beyond a threshold value of 0.3-0.4 %, coherent oscillations in the density and potential fluctuations are observed having the same frequency as that of the MHD mode. The mode numbers of these MHD induced density and potential fluctuations are obtained by Langmuir probes placed at different radial, poloidal, and toroidal locations in the edge plasma region. Detailed analyses of these Langmuir probe measurements reveal that the coherent mode in edge potential fluctuation has a mode structure of m/n = 2/1 whereas the edge density fluctuation has an m/n = 1/1 structure. It is further observed that beyond the threshold, the coupled power fraction scales almost linearly with the magnitude of magnetic fluctuations. Furthermore, the rise rates of the coupled power fraction for coherent modes in density and potential fluctuations are also found to be dependent on the growth rate of magnetic fluctuations. The disparate mode structures of the excited modes in density and plasma potential fluctuations suggest that the underlying mechanism for their existence is most likely due to the excitation of the global high-frequency branch of zonal flows occurring through the coupling of even harmonics of potential to the odd harmonics of pressure due to 1/R dependence of the toroidal magnetic field.

Contact-implicit trajectory optimization (CITO) is an effective method to plan complex trajectories for various contact-rich systems including manipulation and locomotion. CITO formulates a mathematical program with complementarity constraints (MPCC) that enforces that contact forces must be zero when points are not in contact. However, MPCC solve times increase steeply with the number of allowable points of contact, which limits CITO's applicability to problems in which only a few, simple geometries are allowed to make contact. This paper introduces simultaneous trajectory optimization and contact selection (STOCS), as an extension of CITO that overcomes this limitation. The innovation of STOCS is to identify salient contact points and times inside the iterative trajectory optimization process. This effectively reduces the number of variables and constraints in each MPCC invocation. The STOCS framework, instantiated with key contact identification subroutines, renders the optimization of manipulation trajectories computationally tractable even for high-fidelity geometries consisting of tens of thousands of vertices., Comment: arXiv admin note: text overlap with arXiv:2306.06465

Iron chalcogenides superconductors, such as Fe(Te,Se) have recently garnered significant attention due to their simple crystal structure with a relatively easy synthesis process, high-temperature superconductivity, intrinsic topological band structure, and an unconventional pairing of superconductivity with ferromagnetism. Here, we report the complex in-plane refractive index measurement of nanometer-thin Fe(Te,Se) flake exfoliated from a single crystal FeTe$_{\text{0.6}}$Se$_{\text{0.4}}$ for photon wavelengths from 450 to 1100 nm over a temperature range from 4 K to 295 K. The results were obtained by employing a two-Drude model for the dielectric function of Fe(Te,Se), a multiband superconductor, and fitting the absolute optical reflection spectra using the transfer matrix method. A high extinction coefficient in the visible to near-infrared range makes nanometer-thin Fe(Te,Se) flakes a promising material for photodetection applications., Comment: 18 Pages, 4 Figures

The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data. However, NNs come with notable disadvantages, such as their "black-box" nature, which hampers interpretability, as well as their tendency to overfit the training data. We introduce a novel method for learning interpretable differentiable logic networks (DLNs) that are architectures that employ multiple layers of binary logic operators. We train these networks by softening and differentiating their discrete components, e.g., through binarization of inputs, binary logic operations, and connections between neurons. This approach enables the use of gradient-based learning methods. Experimental results on twenty classification tasks indicate that differentiable logic networks can achieve accuracies comparable to or exceeding that of traditional NNs. Equally importantly, these networks offer the advantage of interpretability. Moreover, their relatively simple structure results in the number of logic gate-level operations during inference being up to a thousand times smaller than NNs, making them suitable for deployment on edge devices.

Clinical randomized controlled trials (RCTs) collect hundreds of measurements spanning various metric types (e.g., laboratory tests, cognitive/motor assessments, etc.) across 100s-1000s of subjects to evaluate the effect of a treatment, but do so at the cost of significant trial expense. To reduce the number of measurements, trial protocols can be revised to remove metrics extraneous to the study's objective, but doing so requires additional human labor and limits the set of hypotheses that can be studied with the collected data. In contrast, a planned missing design (PMD) can reduce the amount of data collected without removing any metric by imputing the unsampled data. Standard PMDs randomly sample data to leverage statistical properties of imputation algorithms, but are ad hoc, hence suboptimal. Methods that learn PMDs produce more sample-efficient PMDs, but are not suitable for RCTs because they require ample prior data (150+ subjects) to model the data distribution. Therefore, we introduce a framework called Measurement EfficienT Randomized Controlled Trials using Transformers with Input MasKing (METRIK), which, for the first time, calculates a PMD specific to the RCT from a modest amount of prior data (e.g., 60 subjects). Specifically, METRIK models the PMD as a learnable input masking layer that is optimized with a state-of-the-art imputer based on the Transformer architecture. METRIK implements a novel sampling and selection algorithm to generate a PMD that satisfies the trial designer's objective, i.e., whether to maximize sampling efficiency or imputation performance for a given sampling budget. Evaluated across five real-world clinical RCT datasets, METRIK increases the sampling efficiency of and imputation performance under the generated PMD by leveraging correlations over time and across metrics, thereby removing the need to manually remove metrics from the RCT., Comment: 18 pages, 11 figures

Pre-existing density of states for a Quark-Gluon Phase, based on Thomas-Fermi and Bethe mode, is expanded by incorporation of new variables. Results from recent study indicate that perturbations in the form of a finite non-zero chemical potential T, B, dynamic thermal masses M and of course Temperature T are indeed vital to fully comprehend the formation and dynamics of QGP. Simulations depict an overall increase in the stability of QGP in the paradigm of the statistical model. On the top of Free Energy, Entropy and heat capacity are calculated for the phase transition. The overall qualitative behavior, of entropy or Heat Capacity determines the order of phase transition of the QGP. Investigation of order of phase transition is carried out in this study through Monte-Carlo based differential element, which ensures the inclusion of the randomness of the collisions at the particle colliders.

Imagine a robot that can assemble a functional product from the individual parts presented in any configuration to the robot. Designing such a robotic system is a complex problem which presents several open challenges. To bypass these challenges, the current generation of assembly systems is built with a lot of system integration effort to provide the structure and precision necessary for assembly. These systems are mostly responsible for part singulation, part kitting, and part detection, which is accomplished by intelligent system design. In this paper, we present autonomous assembly of a gear box with minimum requirements on structure. The assembly parts are randomly placed in a two-dimensional work environment for the robot. The proposed system makes use of several different manipulation skills such as sliding for grasping, in-hand manipulation, and insertion to assemble the gear box. All these tasks are run in a closed-loop fashion using vision, tactile, and Force-Torque (F/T) sensors. We perform extensive hardware experiments to show the robustness of the proposed methods as well as the overall system. See supplementary video at https://www.youtube.com/watch?v=cZ9M1DQ23OI., Comment: Under submission

The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was: AI in Action. Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript.

Deep neural networks exhibit remarkable performance, yet their black-box nature limits their utility in fields like healthcare where interpretability is crucial. Existing explainability approaches often sacrifice accuracy and lack quantifiable measures of prediction uncertainty. In this study, we introduce Conformal Prediction for Interpretable Neural Networks (CONFINE), a versatile framework that generates prediction sets with statistically robust uncertainty estimates instead of point predictions to enhance model transparency and reliability. CONFINE not only provides example-based explanations and confidence estimates for individual predictions but also boosts accuracy by up to 3.6%. We define a new metric, correct efficiency, to evaluate the fraction of prediction sets that contain precisely the correct label and show that CONFINE achieves correct efficiency of up to 3.3% higher than the original accuracy, matching or exceeding prior methods. CONFINE's marginal and class-conditional coverages attest to its validity across tasks spanning medical image classification to language understanding. Being adaptable to any pre-trained classifier, CONFINE marks a significant advance towards transparent and trustworthy deep learning applications in critical domains.

A K-mirror is a device that rotates the wavefront of an incident optical field. It has recently gained prominence over Dove prism, another commonly used wavefront rotator, due to the fact that while a K-mirror has several controls for adjusting the internal reflections, a Dove prism is made of a single glass element with no additional control. Thus, one can obtain much lower angular deviations of transmitting wavefronts using a K-mirror than with a Dove prism. However, the accompanying polarization changes in the transmitted field due to rotation persist even in the commercially available K-mirrors. A recent theoretical work [Applied Optics, 61, 8302 (2022)] shows that it is possible to optimize the base angle of a K-mirror for a given refractive index such that the accompanying polarization changes are minimum. In contrast, we show in this article that by optimizing the refractive index it is possible to design a K-mirror at any given base angle and with any given value for the mean polarization change, including near-zero values. Furthermore, we experimentally demonstrate a K-mirror with an order-of-magnitude lower mean polarization change than that of the commercially available K-mirrors. This can have important practical implications for OAM-based applications that require precise wavefront rotation control., Comment: Manuscript: 9 pages, 9 figures

Diffusion Models (DMs) have exhibited superior performance in generating high-quality and diverse images. However, this exceptional performance comes at the cost of expensive architectural design, particularly due to the attention module heavily used in leading models. Existing works mainly adopt a retraining process to enhance DM efficiency. This is computationally expensive and not very scalable. To this end, we introduce the Attention-driven Training-free Efficient Diffusion Model (AT-EDM) framework that leverages attention maps to perform run-time pruning of redundant tokens, without the need for any retraining. Specifically, for single-denoising-step pruning, we develop a novel ranking algorithm, Generalized Weighted Page Rank (G-WPR), to identify redundant tokens, and a similarity-based recovery method to restore tokens for the convolution operation. In addition, we propose a Denoising-Steps-Aware Pruning (DSAP) approach to adjust the pruning budget across different denoising timesteps for better generation quality. Extensive evaluations show that AT-EDM performs favorably against prior art in terms of efficiency (e.g., 38.8% FLOPs saving and up to 1.53x speed-up over Stable Diffusion XL) while maintaining nearly the same FID and CLIP scores as the full model. Project webpage: https://atedm.github.io., Comment: Accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024

Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single photon measurements often requires efficient detectors and longer measurement times to obtain high-quality photon statistics. Here we introduce a deep learning-based variational autoencoder (VAE) method for classifying single photon added coherent state (SPACS), single photon added thermal state (SPACS), mixed states between coherent/SPACS and thermal/SPATS of light. Our semisupervised learning-based VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi-instantaneous classification with low average photon counts. The proposed VAE method is robust and maintains classification accuracy in the presence of losses inherent in an experiment, such as finite collection efficiency, non-unity quantum efficiency, finite number of detectors, etc. Additionally, leveraging the transfer learning capabilities of VAE enables successful classification of data of any quality using a single trained model. We envision that such a deep learning methodology will enable better classification of quantum light and light sources even in the presence of poor detection quality.

Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we propose DynaMo, a suite of multi-token prediction language models that reduce net inference times. Our models $\textit{dynamically}$ predict multiple tokens based on their confidence in the predicted joint probability distribution. We propose a lightweight technique to train these models, leveraging the weights of traditional autoregressive counterparts. Moreover, we propose novel ways to enhance the estimated joint probability to improve text generation quality, namely co-occurrence weighted masking and adaptive thresholding. We also propose systematic qualitative and quantitative methods to rigorously test the quality of generated text for non-autoregressive generation. One of the models in our suite, DynaMo-7.3B-T3, achieves same-quality generated text as the baseline (Pythia-6.9B) while achieving 2.57$\times$ speed-up with only 5.87% and 2.67% parameter and training time overheads, respectively., Comment: Accepted at NAACL 2024

In this paper, our focus lies on the Merton's jump diffusion model, employing jump processes characterized by the compound Poisson process. Our primary objective is to forecast the drift and volatility of the model using a variety of methodologies. We adopt an approach that involves implementing different drift, volatility, and jump terms within the model through various machine learning techniques, traditional methods, and statistical methods on price-volume data. Additionally, we introduce a path-dependent Monte Carlo simulation to model cryptocurrency prices, taking into account the volatility and unexpected jumps in prices., Comment: 21 pages

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy. Solutions to improve image quality for these patients outside of increased dose or total acquisition time remain challenging. Thus, there is an important need for new methodologies to improve image quality for such patients. One approach to improving this performance is adapting the image acquisition protocol specific to each patient. For this study, we first designed and implemented a personalized patient-specific protocol-optimization strategy, which we term precision SPECT (PRESPECT). This strategy integrates ideal observer theory with the constraints of tomographic reconstruction to optimize the acquisition time for each projection view, such that MPS defect detection performance is maximized. We performed a clinically realistic simulation study on patients with outlier anatomies on the task of detecting perfusion defects on various realizations of low-dose scans by an anthropomorphic channelized Hotelling observer. Our results show that using PRESPECT led to improved performance on the defect detection task for the considered patients. These results provide evidence that personalization of MPS acquisition protocol has the potential to improve defect detection performance, motivating further research to design optimal patient-specific acquisition and reconstruction protocols for MPS, as well as developing similar approaches for other medical imaging modalities., Comment: To be published in the Proceedings of SPIE, Medical Imaging 2024

SPECT can enable the quantification of activity uptake in lesions and at-risk organs in {\alpha}-particle-emitting radiopharmaceutical therapies ({\alpha}-RPTs). But this quantification is challenged by the low photon counts, complicated isotope physics, and the image-degrading effects in {\alpha}-RPT SPECT. Thus, strategies to optimize the SPECT system and protocol designs for the task of regional uptake quantification are needed. Objectively performing this task-based optimization requires a reliable (accurate and precise) regional uptake quantification method. Conventional reconstruction-based quantification (RBQ) methods have been observed to be erroneous for {\alpha}-RPT SPECT. Projection-domain quantification methods, which estimate regional uptake directly from SPECT projections, have demonstrated potential in providing reliable regional uptake estimates, but these methods assume constant uptake within the regions, an assumption that may not hold. To address these challenges, we propose WIN-PDQ, a Wiener-estimator-based projection-domain quantitative SPECT method. The method accounts for the heterogeneity within the regions of interest while estimating mean uptake. An early-stage evaluation of the method was conducted using 3D Monte Carlo-simulated SPECT of anthropomorphic phantoms with radium-223 uptake and lumpy-model-based intra-regional uptake heterogeneity. In this evaluation with phantoms of varying mean regional uptake and intra-regional uptake heterogeneity, the WIN-PDQ method yielded ensemble unbiased estimates and significantly outperformed both reconstruction-based and previously proposed projection-domain quantification methods. In conclusion, based on these preliminary findings, the proposed method is showing potential for estimating mean regional uptake in {\alpha}-RPTs and towards enabling the objective task-based optimization of SPECT system and protocol designs., Comment: The work has been accepted for publication in 2024 SPIE Medical Imaging conference proceedings

Objective evaluation of quantitative imaging (QI) methods with patient data, while important, is typically hindered by the lack of gold standards. To address this challenge, no-gold-standard evaluation (NGSE) techniques have been proposed. These techniques have demonstrated efficacy in accurately ranking QI methods without access to gold standards. The development of NGSE methods has raised an important question: how accurately can QI methods be ranked without ground truth. To answer this question, we propose a Cramer-Rao bound (CRB)-based framework that quantifies the upper bound in ranking QI methods without any ground truth. We present the application of this framework in guiding the use of a well-known NGSE technique, namely the regression-without-truth (RWT) technique. Our results show the utility of this framework in quantifying the performance of this NGSE technique for different patient numbers. These results provide motivation towards studying other applications of this upper bound.

We report on the quantum photon statistics of hexagonal boron nitride (h-BN) quantum emitters by analyzing the Mandel Q parameter. We have measured the Mandel Q parameter for h-BN quantum emitters under various temperatures and pump power excitation conditions. Under pulsed excitation we can achieve a Mandel Q of -0.002 and under continuous-wave (CW) excitation this parameter can reach -0.0025. We investigate the effect of cryogenic temperatures on Mandel Q and conclude that the photon statistics vary weakly with temperature. Through calculation of spontaneous emission from an excited two-level emitter model, we demonstrate good agreement between measured and calculated Mandel Q parameter when accounting for the experimental photon collection efficiency. Finally, we illustrate the usefulness of Mandel Q in quantum applications by the example of random number generation and analyze the effect of Mandel Q on the speed of generating random bits via this method., Comment: Main text: 22 pages, 8 figures, 1 table. Supplemental document: 2 pages, 1 figure. Submitted to APL Quantum

We propose PAGE, a domain-incremental adaptation strategy with past-agnostic generative replay for smart healthcare. PAGE enables generative replay without the aid of any preserved data or information from prior domains. When adapting to a new domain, it exploits real data from the new distribution and the current model to generate synthetic data that retain the learned knowledge of previous domains. By replaying the synthetic data with the new real data during training, PAGE achieves a good balance between domain adaptation and knowledge retention. In addition, we incorporate an extended inductive conformal prediction (EICP) method into PAGE to produce a confidence score and a credibility value for each detection result. This makes the predictions interpretable and provides statistical guarantees for disease detection in smart healthcare applications. We demonstrate PAGE's effectiveness in domain-incremental disease detection with three distinct disease datasets collected from commercially available WMSs. PAGE achieves highly competitive performance against state-of-the-art with superior scalability, data privacy, and feasibility. Furthermore, PAGE can enable up to 75% reduction in clinical workload with the help of EICP., Comment: 30 pages, 7 figures. arXiv admin note: text overlap with arXiv:2305.05738

This work considers the nodal finite element approximation of peridynamics, in which the nodal displacements satisfy the peridynamics equation at each mesh node. For the nonlinear bond-based peridynamics model, it is shown that, under the suitable assumptions on an exact solution, the discretized solution associated with the central-in-time and nodal finite element discretization converges to the exact solution in $L^2$ norm at the rate $C_1 \Delta t + C_2 h^2/\epsilon^2$. Here, $\Delta t$, $h$, and $\epsilon$ are time step size, mesh size, and the size of the horizon or nonlocal length scale, respectively. Constants $C_1$ and $C_2$ are independent of $h$ and $\Delta t$ and depend on norms of the solution and nonlocal length scale. Several numerical examples involving pre-crack, void, and notch are considered, and the efficacy of the proposed nodal finite element discretization is analyzed., Comment: 35 pages, 22 figures

Spatial coherence plays an important role in several real-world applications ranging from imaging to communication. As a result, its accurate characterization and measurement are extremely crucial for its optimal application. However, efficient measurement of an arbitrary complex spatial coherence function is still very challenging. In this letter, we propose an efficient, noise-insensitive interferometric technique that combines wavefront shearing and inversion for measuring the complex cross-spectral density function of the class of fields, in which the cross-spectral density function depends either on the difference of the spatial coordinates, or the squares of spatial coordinates, or both. This class of fields are most commonly encountered, and we experimentally demonstrate high-fidelity measurement of many stationary and non-stationary fields.

Several accounts of human cognition posit that our intelligence is rooted in our ability to form abstract composable concepts, ground them in our environment, and reason over these grounded entities. This trifecta of human thought has remained elusive in modern intelligent machines. In this work, we investigate whether slot representations extracted from visual scenes serve as appropriate compositional abstractions for grounding and reasoning. We present the Neural Slot Interpreter (NSI), which learns to ground object semantics in slots. At the core of NSI is an XML-like schema that uses simple syntax rules to organize the object semantics of a scene into object-centric schema primitives. Then, the NSI metric learns to ground primitives into slots through a structured objective that reasons over the intermodal alignment. We show that the grounded slots surpass unsupervised slots in real-world object discovery and scale with scene complexity. Experiments with a bi-modal object-property and scene retrieval task demonstrate the grounding efficacy and interpretability of correspondences learned by NSI. Finally, we investigate the reasoning abilities of the grounded slots. Vision Transformers trained on grounding-aware NSI tokenizers using as few as ten tokens outperform patch-based tokens on challenging few-shot classification tasks.