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In this paper, the inverse potentials for the resonant f states of {\alpha}-3H and {\alpha}-3He are constructed using the phase function method by utilizing an ab-initio approach. A combination of three Morse functions are joined smoothly to prepare the reference potential. While the regular Morse function captures the nuclear and Coulomb interactions at short and medium ranges, an inverse Morse function is chosen to obtain the Coulomb barrier that arises because of the long-range Coulomb interaction. This reference potential is representative of a large family of curves consisting of eight distinct model parameters and two intermediate points that define the boundaries that exist between the three regions. The phase equation is solved using the Runge-Kutta 5th order method for the input reference potential to obtain the scattering phase shifts at various center of mass energies. The model parameters are then adjusted using the genetic algorithm in an iterative fashion to minimize the mean square error between the simulated and expected phase shift values. Our approach successfully constructed the inverse potentials for the resonant f states of the {\alpha}-3H and {\alpha}-3He systems, achieving convergence with a minimized mean square error. The resonance energies and widths for the {\alpha}-3H system for the f-5/2 and f-7/2 states are determined to be [4.19 (4.14), 1.225 (0.918)] MeV and [2.20 (2.18), 0.099 (0.069)] MeV, respectively. For the f-5/2 and f-7/2 states of the {\alpha}-3He system, the resonance energies and widths are [5.03 (5.14), 1.6 (1.2)] MeV and [2.99 (2.98), 0.182(0.175)] MeV, respectively. Our ab-initio approach to solve the phase equation utilizing a combination of smoothly joined Morse functions effectively captures both short-range nuclear and long-range Coulomb interactions, providing an accurate model for nuclear scattering involving charged particles., Comment: 14 pages, 5 figures, 5 tables

Medical eye-tracking data is an important information source for understanding how radiologists visually interpret medical images. This information not only improves the accuracy of deep learning models for X-ray analysis but also their interpretability, enhancing transparency in decision-making. However, the current eye-tracking data is dispersed, unprocessed, and ambiguous, making it difficult to derive meaningful insights. Therefore, there is a need to create a new dataset with more focus and purposeful eyetracking data, improving its utility for diagnostic applications. In this work, we propose a refinement method inspired by the target-present visual search challenge: there is a specific finding and fixations are guided to locate it. After refining the existing eye-tracking datasets, we transform them into a curated visual search dataset, called GazeSearch, specifically for radiology findings, where each fixation sequence is purposefully aligned to the task of locating a particular finding. Subsequently, we introduce a scan path prediction baseline, called ChestSearch, specifically tailored to GazeSearch. Finally, we employ the newly introduced GazeSearch as a benchmark to evaluate the performance of current state-of-the-art methods, offering a comprehensive assessment for visual search in the medical imaging domain. Code is available at \url{https://github.com/UARK-AICV/GazeSearch}., Comment: Aceepted WACV 2025

The occurrence of neutrino oscillations demands the existence of flavour violation in charged lepton sector. The relation between the branching ratios of different charged lepton flavour violating (CLFV) decay modes depends on the details of the neutrino mass model. In this work, we consider the three types of simple seesaw mechanisms of neutrino masses and study the correlation between the radiative CLFV decays and the meson CLFV decays. We find that the meson CLFV decay branching ratios are negligibly small in type-II seesaw mechanism whereas they are constrained to be at least three (two) orders of magnitude smaller than the radiative CLFV decay branching ratios in the case of type-I (type-III) seesaw mechanism. Thus the relationship between these two modes of CLFV decays helps in distinguishing between different types of seesaw mechanism. If, the branching ratios of CLFV decays of mesons are larger than those of radiative CLFV decays, it provides a strong hint that the neutrino mass generating mechanism is more complicated than simple seesaw., Comment: new references added

The present study investigates the behaviour of the far-field sound radiated by low Mach number tip clearance flow induced by placing a stationary cambered airfoil adjacent to a stationary wall. The tip clearance heights ranged from 14% to 30% of the incoming, undisturbed boundary layer thickness and the clearance heights based Reynolds numbers were between 2,600 and 16,000. The far-field sound measured using a microphone array was beamformed to reveal the dominant noise sources and how they behave when the flow Mach number, angle of attack and the clearance height were varied. The near-field behaviour was also examined through PIV measurements and surface pressure fluctuation measurements on the tip. The results show that the mid-to-high frequency noise generated by tip clearances is dominated by the leakage flow in the mid-chord and leading-edge regions, while a distinct low-frequency noise source with a different scaling behaviour exists close to the trailing-edge of the tip clearance. The origin of this low-frequency noise source is believed to be the tip separation vortex that resides close to the trailing-edge and induces significant turbulence levels in the region. The strength of this noise source decreases with clearance height which is consistent with a reduction in turbulence levels associated with the separation vortex. The magnitude of the mid-frequency clearance noise which scales with the sixth power of the Mach number, decreases with tip clearance height due to a reduction in the fluctuating pressure on the airfoil tip surface. The time-scale of this sound was independent of the flow velocity, implying that the source is non-compact. Smaller tip clearances were also found to generate louder high-frequency noise due to intense turbulence and pressure fluctuation levels concentrated near the leading-edge of the clearance., Comment: Submitted to Applied Acoustics Journal

This work is a benchmark study for quantum-classical computing method with a real-world optimization problem from industry. The problem involves scheduling and balancing jobs on different machines, with a non-linear objective function. We first present the motivation and the problem description, along with different modeling techniques for classical and quantum computing. The modeling for classical solvers has been done as a mixed-integer convex program, while for the quantum-classical solver we model the problem as a binary quadratic program, which is best suited to the D-Wave Leap's Hybrid Solver. This ensures that all the solvers we use are fetched with dedicated and most suitable model(s). Henceforth, we carry out benchmarking and comparisons between classical and quantum-classical methods, on problem sizes ranging till approximately 150000 variables. We utilize an industry grade classical solver and compare its results with D-Wave Leap's Hybrid Solver. The results we obtain from D-Wave are highly competitive and sometimes offer speedups, compared to the classical solver., Comment: Accepted at IEEE QCE 2024 Workshop

Predicting human gaze behavior within computer vision is integral for developing interactive systems that can anticipate user attention, address fundamental questions in cognitive science, and hold implications for fields like human-computer interaction (HCI) and augmented/virtual reality (AR/VR) systems. Despite methodologies introduced for modeling human eye gaze behavior, applying these models to medical imaging for scanpath prediction remains unexplored. Our proposed system aims to predict eye gaze sequences from radiology reports and CXR images, potentially streamlining data collection and enhancing AI systems using larger datasets. However, predicting human scanpaths on medical images presents unique challenges due to the diverse nature of abnormal regions. Our model predicts fixation coordinates and durations critical for medical scanpath prediction, outperforming existing models in the computer vision community. Utilizing a two-stage training process and large publicly available datasets, our approach generates static heatmaps and eye gaze videos aligned with radiology reports, facilitating comprehensive analysis. We validate our approach by comparing its performance with state-of-the-art methods and assessing its generalizability among different radiologists, introducing novel strategies to model radiologists' search patterns during CXR image diagnosis. Based on the radiologist's evaluation, MedGaze can generate human-like gaze sequences with a high focus on relevant regions over the CXR images. It sometimes also outperforms humans in terms of redundancy and randomness in the scanpaths., Comment: Submitted to the Journal

Human-AI collaboration to identify and correct perceptual errors in chest radiographs has not been previously explored. This study aimed to develop a collaborative AI system, CoRaX, which integrates eye gaze data and radiology reports to enhance diagnostic accuracy in chest radiology by pinpointing perceptual errors and refining the decision-making process. Using public datasets REFLACX and EGD-CXR, the study retrospectively developed CoRaX, employing a large multimodal model to analyze image embeddings, eye gaze data, and radiology reports. The system's effectiveness was evaluated based on its referral-making process, the quality of referrals, and performance in collaborative diagnostic settings. CoRaX was tested on a simulated error dataset of 271 samples with 28% (93 of 332) missed abnormalities. The system corrected 21% (71 of 332) of these errors, leaving 7% (22 of 312) unresolved. The Referral-Usefulness score, indicating the accuracy of predicted regions for all true referrals, was 0.63 (95% CI 0.59, 0.68). The Total-Usefulness score, reflecting the diagnostic accuracy of CoRaX's interactions with radiologists, showed that 84% (237 of 280) of these interactions had a score above 0.40. In conclusion, CoRaX efficiently collaborates with radiologists to address perceptual errors across various abnormalities, with potential applications in the education and training of novice radiologists., Comment: Under Review in Journal

A core component present in many successful neural network architectures, is an MLP block of two fully connected layers with a non-linear activation in between. An intriguing phenomenon observed empirically, including in transformer architectures, is that, after training, the activations in the hidden layer of this MLP block tend to be extremely sparse on any given input. Unlike traditional forms of sparsity, where there are neurons/weights which can be deleted from the network, this form of {\em dynamic} activation sparsity appears to be harder to exploit to get more efficient networks. Motivated by this we initiate a formal study of PAC learnability of MLP layers that exhibit activation sparsity. We present a variety of results showing that such classes of functions do lead to provable computational and statistical advantages over their non-sparse counterparts. Our hope is that a better theoretical understanding of {\em sparsely activated} networks would lead to methods that can exploit activation sparsity in practice., Comment: Proceedings of the 37th Conference on Learning Theory (COLT 2024), 20 pages

Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results. This work delves into the understudied aspect of receptive field (RF) size and its impact on the U-Net and Attention U-Net architectures. This work explores several critical elements including the relationship between RF size, characteristics of the region of interest, and model performance, as well as the balance between RF size and computational costs for U-Net and Attention U-Net methods for different datasets. This work also proposes a mathematical notation for representing the theoretical receptive field (TRF) of a given layer in a network and proposes two new metrics - effective receptive field (ERF) rate and the Object rate to quantify the fraction of significantly contributing pixels within the ERF against the TRF area and assessing the relative size of the segmentation object compared to the TRF size respectively. The results demonstrate that there exists an optimal TRF size that successfully strikes a balance between capturing a wider global context and maintaining computational efficiency, thereby optimizing model performance. Interestingly, a distinct correlation is observed between the data complexity and the required TRF size; segmentation based solely on contrast achieved peak performance even with smaller TRF sizes, whereas more complex segmentation tasks necessitated larger TRFs. Attention U-Net models consistently outperformed their U-Net counterparts, highlighting the value of attention mechanisms regardless of TRF size. These novel insights present an invaluable resource for developing more efficient U-Net-based architectures for medical imaging and pave the way for future exploration. A tool is also developed that calculates the TRF for a U-Net (and Attention U-Net) model, and also suggest an appropriate TRF size for a given model and dataset.

With the continuous advancement of large language models (LLMs), it is essential to create new benchmarks to effectively evaluate their expanding capabilities and identify areas for improvement. This work focuses on multi-image reasoning, an emerging capability in state-of-the-art LLMs. We introduce ReMI, a dataset designed to assess LLMs' ability to Reason with Multiple Images. This dataset encompasses a diverse range of tasks, spanning various reasoning domains such as math, physics, logic, code, table/chart understanding, and spatial and temporal reasoning. It also covers a broad spectrum of characteristics found in multi-image reasoning scenarios. We have benchmarked several cutting-edge LLMs using ReMI and found a substantial gap between their performance and human-level proficiency. This highlights the challenges in multi-image reasoning and the need for further research. Our analysis also reveals the strengths and weaknesses of different models, shedding light on the types of reasoning that are currently attainable and areas where future models require improvement. To foster further research in this area, we are releasing ReMI publicly: https://huggingface.co/datasets/mehrankazemi/ReMI.

We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datasets relied on crowd-sourcing, but the emergence of transformers with a context size of 1 million or more tokens now enables entirely automatic approaches. Our objective is to test the capabilities of LLMs to analyze, understand, and reason over problems that require a detailed comprehension of long spans of text, such as questions involving character arcs, broader themes, or the consequences of early actions later in the story. We propose a holistic pipeline for automatic data generation including question generation, answering, and model scoring using an ``Evaluator''. We find that a relative approach, comparing answers between models in a pairwise fashion and ranking with a Bradley-Terry model, provides a more consistent and differentiating scoring mechanism than an absolute scorer that rates answers individually. We also show that LLMs from different model families produce moderate agreement in their ratings. We ground our approach using the manually curated NarrativeQA dataset, where our evaluator shows excellent agreement with human judgement and even finds errors in the dataset. Using our automatic evaluation approach, we show that using an entire book as context produces superior reading comprehension performance compared to baseline no-context (parametric knowledge only) and retrieval-based approaches.

Even for simple arithmetic tasks like integer addition, it is challenging for Transformers to generalize to longer sequences than those encountered during training. To tackle this problem, we propose position coupling, a simple yet effective method that directly embeds the structure of the tasks into the positional encoding of a (decoder-only) Transformer. Taking a departure from the vanilla absolute position mechanism assigning unique position IDs to each of the tokens, we assign the same position IDs to two or more "relevant" tokens; for integer addition tasks, we regard digits of the same significance as in the same position. On the empirical side, we show that with the proposed position coupling, our models trained on 1 to 30-digit additions can generalize up to 200-digit additions (6.67x of the trained length). On the theoretical side, we prove that a 1-layer Transformer with coupled positions can solve the addition task involving exponentially many digits, whereas any 1-layer Transformer without positional information cannot entirely solve it. We also demonstrate that position coupling can be applied to other algorithmic tasks such as Nx2 multiplication and a two-dimensional task., Comment: Accepted to NeurIPS 2024. 76 pages. 23 figures. 90 tables

We study a job shop scheduling problem for an automatized robot in a high-throughput laboratory and a travelling salesperson problem with recently proposed digitized counterdiabatic quantum optimization (DCQO) algorithms. In DCQO, we find the solution of an optimization problem via an adiabatic quantum dynamics, which is accelerated with counterdiabatic protocols. Thereafter, we digitize the global unitary to encode it in a digital quantum computer. For the job-shop scheduling problem, we aim at finding the optimal schedule for a robot executing a number of tasks under specific constraints, such that the total execution time of the process is minimized. For the traveling salesperson problem, the goal is to find the path that covers all cities and is associated with the shortest traveling distance. We consider both hybrid and pure versions of DCQO algorithms and benchmark the performance against digitized quantum annealing and the quantum approximate optimization algorithm (QAOA). In comparison to QAOA, the DCQO solution is improved by several orders of magnitude in success probability using the same number of two-qubit gates. Moreover, we experimentally implement our algorithms on superconducting and trapped-ion quantum processors. Our results demonstrate that circuit compression using counterdiabatic protocols is amenable to current NISQ hardware and can solve logistics scheduling problems, where other digital quantum algorithms show insufficient performance., Comment: 14 pages, 9 figures

The COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications. As such, the Forecast Hub is a critical centralized resource to promote transparent decision making. Nevertheless, by focusing exclusively on prediction accuracy, the Forecast Hub fails to evaluate whether the proposed models have similar performance across social determinants that have been known to play a role in the COVID-19 pandemic including race, ethnicity and urbanization level. In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. We hope this work will encourage COVID-19 modelers and the CDC to report fairness metrics together with accuracy, and to reflect on the potential harms of the models on specific social groups and contexts.

Early in the pandemic, counties and states implemented a variety of non-pharmacological interventions (NPIs) focused on mobility, such as national lockdowns or work-from-home strategies, as it became clear that restricting movement was essential to containing the epidemic. Due to these restrictions, businesses were severely affected and in particular, small, urban restaurant businesses. In addition to that, COVID-19 has also amplified many of the socioeconomic disparities and systemic racial inequities that exist in our society. The overarching objective of this study was to examine the changes in small urban restaurant visitation patterns following the COVID-19 pandemic and associated mobility restrictions, as well as to uncover potential disparities across different racial/ethnic groups in order to understand inequities in the impact and recovery. Specifically, the two key objectives were: 1) to analyze the overall changes in restaurant visitation patterns in US metropolitan areas during the pandemic compared to a pre-pandemic baseline, and 2) to investigate differences in visitation pattern changes across Census Block Groups with majority Asian, Black, Hispanic, White, and American Indian populations, identifying any disproportionate effects. Using aggregated geolocated cell phone data from SafeGraph, we document the overall changes in small urban restaurant businesses' visitation patterns with respect to racial composition at a granularity of Census Block Groups. Our results show clear indications of reduced visitation patterns after the pandemic, with slow recoveries. Via visualizations and statistical analyses, we show that reductions in visitation patterns were the highest for small urban restaurant businesses in majority Asian neighborhoods.

One of the central difficulties of addressing the COVID-19 pandemic has been accurately measuring and predicting the spread of infections. In particular, official COVID-19 case counts in the United States are under counts of actual caseloads due to the absence of universal testing policies. Researchers have proposed a variety of methods for recovering true caseloads, often through the estimation of statistical models on more reliable measures, such as death and hospitalization counts, positivity rates, and demographics. However, given the disproportionate impact of COVID-19 on marginalized racial, ethnic, and socioeconomic groups, it is important to consider potential unintended effects of case correction methods on these groups. Thus, we investigate two of these correction methods for their impact on a downstream COVID-19 case prediction task. For that purpose, we tailor an auditing approach and evaluation protocol to analyze the fairness of the COVID-19 prediction task by measuring the difference in model performance between majority-White counties and majority-minority counties. We find that one of the correction methods improves fairness, decreasing differences in performance between majority-White and majority-minority counties, while the other method increases differences, introducing bias. While these results are mixed, it is evident that correction methods have the potential to exacerbate existing biases in COVID-19 case data and in downstream prediction tasks. Researchers planning to develop or use case correction methods must be careful to consider negative effects on marginalized groups.

COVID-19 forecasting models have been used to inform decision making around resource allocation and intervention decisions e.g., hospital beds or stay-at-home orders. State of the art deep learning models often use multimodal data such as mobility or socio-demographic data to enhance COVID-19 case prediction models. Nevertheless, related work has revealed under-reporting bias in COVID-19 cases as well as sampling bias in mobility data for certain minority racial and ethnic groups, which could in turn affect the fairness of the COVID-19 predictions along race labels. In this paper, we show that state of the art deep learning models output mean prediction errors that are significantly different across racial and ethnic groups; and which could, in turn, support unfair policy decisions. We also propose a novel de-biasing method, DemOpts, to increase the fairness of deep learning based forecasting models trained on potentially biased datasets. Our results show that DemOpts can achieve better error parity that other state of the art de-biasing approaches, thus effectively reducing the differences in the mean error distributions across more racial and ethnic groups.

Human mobility data has been extensively used in covid-19 case prediction models. Nevertheless, related work has questioned whether mobility data really helps that much. We present a systematic analysis across mobility datasets and prediction lookaheads and reveal that adding mobility data to predictive models improves model performance only for about two months at the onset of the testing period, and that performance improvements -- measured as predicted vs. actual correlation improvement over non-mobility baselines -- are at most 0.3., Comment: Presented as a poster in ACM COMPASS 2023

Background: The study of np and pp scattering, central to understanding nuclear force, remains an optional topic in many undergraduate nuclear physics curriculum. Purpose: The main thrust of this paper is to study pp scattering using the phase function method to obtain the observed S-wave phase shifts and cross-sections at various energies. Methods: The pp interaction has been modeled by choosing the Malfliet-Tjon potential for the nuclear part along with the screened Coulomb potential. The phase equation has been solved to obtain scattering phase shifts using the fourth-order RK method (RK-4). Results: The interaction potential obtained from optimized parameters matches well with the realistic Argonne V18 potential for 1S0 state of pp scattering and the scattering phase shifts as well as the cross-section for energies ranging from 1-350 MeV are in good agreement with expected data. Conclusion: Introducing the phase function method for S-wave (l=0) could bring this interesting study of nucleon-nucleon scattering to the undergraduate classroom., Comment: 18, 5 Figures, 2 Tables

In the realm of chest X-ray (CXR) image analysis, radiologists meticulously examine various regions, documenting their observations in reports. The prevalence of errors in CXR diagnoses, particularly among inexperienced radiologists and hospital residents, underscores the importance of understanding radiologists' intentions and the corresponding regions of interest. This understanding is crucial for correcting mistakes by guiding radiologists to the accurate regions of interest, especially in the diagnosis of chest radiograph abnormalities. In response to this imperative, we propose a novel system designed to identify the primary intentions articulated by radiologists in their reports and the corresponding regions of interest in CXR images. This system seeks to elucidate the visual context underlying radiologists' textual findings, with the potential to rectify errors made by less experienced practitioners and direct them to precise regions of interest. Importantly, the proposed system can be instrumental in providing constructive feedback to inexperienced radiologists or junior residents in the hospital, bridging the gap in face-to-face communication. The system represents a valuable tool for enhancing diagnostic accuracy and fostering continuous learning within the medical community., Comment: Accepted in ISBI 2024

Background: The phase-shift analysis for proton-proton scattering has been studied by various research groups using the realistic potentials to be comprised of various internal interactions based on an exchange of pions and mesons, involving a large number of parameters. Purpose: The goal of the research is to construct inverse potentials for various l-channels of proton-proton (pp) elastic scattering using the 3-parameter Morse function in combination with atomic Hulthen by utilizing the phase function method and variational optimization technique. Methodology: The implementation of variational optimization begins with randomly assigning initial values to the Morse model parameters. Utilizing the Morse + Hulthen potential as input, the phase equations for various l-channels are numerically solved using the RK-5 method for obtaining the simulated Scattering Phase Shift (SPS). Mean Squared error between simulated and expected SPS has been chosen as the cost function. Variational optimization proceeds iteratively by adjusting potential parameters and re-evaluating the cost function until convergence is achieved. Results: All the obtained scattering phase shifts for various l-channels have been found to converge to a mean squared error <= 0.3. The computed cross-sections matched the experimental ones to less than 1% for energies up to 25 MeV. The scattering parameters are also found to closely match the experimental data. Conclusion: The inverse potentials constructed for various l-channels using Morse + atomic Hulthen are on par with the currently available high-precision realistic potentials., Comment: 13 pages, 5 figures, 5 Tables

Cryptography plays a pivotal role in safeguarding sensitive information and facilitating secure communication. Classical cryptography relies on mathematical computations, whereas quantum cryptography operates on the principles of quantum mechanics, offering a new frontier in secure communication. Quantum cryptographic systems introduce novel dimensions to security, capable of detecting and thwarting eavesdropping attempts. By contrasting quantum cryptography with its classical counterpart, it becomes evident how quantum mechanics revolutionizes the landscape of secure communication.

An algorithm$^{\ref{Fig1}}$ has been developed with the purpose of obtaining inverse potentials, where the Riccati-type non-linear differential equation, also called phase equation, has been kept in tandem with the Variational Monte Carlo method. The optimization of Gaussian function parameters is achieved such that the experimental phase shifts are reproduced. The obtained SPS for various $\ ell$ channels has been compared with experimental ones with mean absolute percentage error (MAPE) as a measure. The model parameters have been optimised by suitable optimisation technique by looking for minimum value of MAPE. The results for $\ell$=0$^+$, 2$^+$ and 4$^+$ partial waves have been obtained, to match with experimental SPS, with MAPE values of $2.9$, $4.6$ and $6.2$ respectively for data up to $23$ MeV, while for higher states 6$^+$, 8$^+$ and 10$^+$ has MAPE of $3.2, 4.5$ and $5.9$ respectively for data from $53-120$ MeV. On extrapolation for data in range E$_{\ell ab.}$ = $23-120$ MeV, using the optimised parameters, the SPS are found to be in close agreement with experimental ones for the first three channels., Comment: 18 pages, 7 figures, 25 references

In variational quantum algorithms, constraints are usually added to the problem objective via penalty terms. For linear inequality constraints, this procedure requires additional slack qubits. Those extra qubits tend to blow up the search space and complicate the parameter landscapes to be navigated by the classical optimizers. In this work, we explore approaches to model linear inequalities for quantum algorithms without these drawbacks. More concretely, our main suggestion is to omit the slack qubits completely and evaluate the inequality classically during parameter tuning. We test our methods on QAOA as well as on Trotterized adiabatic evolution, and present empirical results. As a benchmark problem, we consider different instances of the multi-knapsack problem. Our results show that removing the slack bits from the circuit Hamiltonian and considering them only for the expectation value yields better solution quality than the standard approach. The tests have been carried out using problem sizes up to 26 qubits. Our methods can in principle be applied to any problem with linear inequality constraints, and are suitable for variational as well as digitized versions of adiabatic quantum computing.

This systematic literature review explores sustainability assessment frameworks (SAFs) across diverse industries. The review focuses on SAF design approaches including the methods used for Sustainability Indicator (SI) selection, relative importance assessment, and interdependency analysis. Various methods, including literature reviews, stakeholder interviews, questionnaires, Pareto analysis, SMART approach, and adherence to sustainability standards, contribute to the complex SI selection process. Fuzzy-AHP stands out as a robust technique for assessing relative SI importance. While dynamic sustainability and performance indices are essential, methods like DEMATEL, VIKOR, correlation analysis, and causal models for interdependency assessment exhibit static limitations. The review presents strengths and limitations of SAFs, addressing gaps in design approaches and contributing to a comprehensive understanding. The insights of this review aim to benefit policymakers, administrators, leaders, and researchers, fostering sustainability practices. Future research recommendations include exploring multi-criteria decision-making models and hybrid approaches, extending sustainability evaluation across organizational levels and supply chains. Emphasizing adaptability to industry specifics and dynamic global adjustments is proposed for holistic sustainability practices, further enhancing organizational sustainability.

Stacking, a heuristic technique for training deep residual networks by progressively increasing the number of layers and initializing new layers by copying parameters from older layers, has proven quite successful in improving the efficiency of training deep neural networks. In this paper, we propose a theoretical explanation for the efficacy of stacking: viz., stacking implements a form of Nesterov's accelerated gradient descent. The theory also covers simpler models such as the additive ensembles constructed in boosting methods, and provides an explanation for a similar widely-used practical heuristic for initializing the new classifier in each round of boosting. We also prove that for certain deep linear residual networks, stacking does provide accelerated training, via a new potential function analysis of the Nesterov's accelerated gradient method which allows errors in updates. We conduct proof-of-concept experiments to validate our theory as well.

Periodically driven thermodynamic systems support stable non-equilibrium oscillating states with properties drastically different from equilibrium. They exhibit even more exotic features for low viscous drives, which is a regime that is hard to probe due to singular behavior of the underlying Langevin dynamics near vanishing viscosity. We propose a method, based on singular perturbation and Floquet theories, that allows us to obtain oscillating states in this limit. We then find two distinct classes of distributions, each exhibiting interesting features that can be exploited for a range of practical applicability, including cooling a system and triggering chemical reactions through weakly interacting driven environments., Comment: 6+13 pages, 2+4 figures

Many learning problems hinge on the fundamental problem of subset selection, i.e., identifying a subset of important and representative points. For example, selecting the most significant samples in ML training cannot only reduce training costs but also enhance model quality. Submodularity, a discrete analogue of convexity, is commonly used for solving subset selection problems. However, existing algorithms for optimizing submodular functions are sequential, and the prior distributed methods require at least one central machine to fit the target subset. In this paper, we relax the requirement of having a central machine for the target subset by proposing a novel distributed bounding algorithm with provable approximation guarantees. The algorithm iteratively bounds the minimum and maximum utility values to select high quality points and discard the unimportant ones. When bounding does not find the complete subset, we use a multi-round, partition-based distributed greedy algorithm to identify the remaining subset. We show that these algorithms find high quality subsets on CIFAR-100 and ImageNet with marginal or no loss in quality compared to centralized methods, and scale to a dataset with 13 billion points.