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Unlike junctions in solid-state devices, a plasma-metal junction (pm-junction) is a junction of classical and quantum electrons. The plasma electrons are Maxwellain in nature, while metal electrons obey the Fermi-Dirac distribution. In this experiment, the current-voltage characteristics of solid-state devices that form homo or hetero-junction are compared to the pm-junction. Observation shows that the turn-on voltage for pn-junction is 0.5V and decreases to 0.24V for metal-semiconductor junction. However, the pm-junction's turn-on voltage was lowered to a negative value of -7.0V. The devices with negative turn-on voltage are suitable for high-frequency operations. Further, observations show that the current-voltage characteristics of the pm-junction depend on the metal's work function, and the turn-on voltage remains unchanged. This result validates the applicability of the energy-band model for the pm-junction. We present a perspective metal-oxide-plasma (MOP), a gaseous electronic device, as an alternative to metal-oxide-semiconductor (MOS), based on the new understanding developed., Comment: Manuscript is of total 9 pages and 10 figures. This manuscript is a fundamental study of junction between an ionised gas i.e. plasma and metal supported by experimental demonstration

We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.

Assessing the quality of bovine satellite cells (BSCs) is essential for the cultivated meat industry, which aims to address global food sustainability challenges. This study aims to develop a label-free method for predicting fluorescence markers in isolated BSCs using deep learning. We employed a U-Net-based CNN model to predict multiple fluorescence signals from a single bright-field microscopy image of cell culture. Two key biomarkers, DAPI and Pax7, were used to determine the abundance and quality of BSCs. The image pre-processing pipeline included fluorescence denoising to improve prediction performance and consistency. A total of 48 biological replicates were used, with statistical performance metrics such as Pearson correlation coefficient and SSIM employed for model evaluation. The model exhibited better performance with DAPI predictions due to uniform staining. Pax7 predictions were more variable, reflecting biological heterogeneity. Enhanced visualization techniques, including color mapping and image overlay, improved the interpretability of the predictions by providing better contextual and perceptual information. The findings highlight the importance of data pre-processing and demonstrate the potential of deep learning to advance non-invasive, label-free assessment techniques in the cultivated meat industry, paving the way for reliable and actionable AI-driven evaluations., Comment: 11 pages, 4 figures

Celikbas, Liang and Sadeghi established a one-sided inequality for the relative version of Jorgensen's dependency formula and questioned whether it would be an equality. In this paper, we show that the inequality can be indeed strict, and prove a relative dependency formula. Along the way, we obtain some bounds on s(M,N), a notion related to the vanishing of relative homology of finitely generated modules M and N over a local ring R, under specific assumptions., Comment: 8 pages

Chart corpora, which comprise data visualizations and their semantic labels, are crucial for advancing visualization research. However, the labels in most existing chart corpora are high-level (e.g., chart types), hindering their utility for broader interactive applications like chart reuse, animation, and accessibility. In this paper, we contribute VisAnatomy, a chart corpus containing 942 real-world SVG charts produced by over 50 tools, encompassing 40 chart types and featuring structural and stylistic design variations. Each chart is augmented with multilevel fine-grained labels on its semantic components, including each graphical element's type, role, and position, hierarchical groupings of elements, group layouts, and visual encodings. We demonstrate the richness of the semantic labels by comparing VisAnatomy with existing corpora. We illustrate the usefulness of VisAnatomy through four applications: chart type classification, chart decomposition, animation authoring, and content navigation for accessibility. Finally, we discuss our plan to improve VisAnatomy and the research opportunities VisAnatomy presents.

An $s$-sparse polynomial has at most $s$ monomials with nonzero coefficients. The Equivalence Testing problem for sparse polynomials (ETsparse) asks to decide if a given polynomial $f$ is equivalent to (i.e., in the orbit of) some $s$-sparse polynomial. In other words, given $f \in \mathbb{F}[\mathbf{x}]$ and $s \in \mathbb{N}$, ETsparse asks to check if there exist $A \in \mathrm{GL}(|\mathbf{x}|, \mathbb{F})$ and $\mathbf{b} \in \mathbb{F}^{|\mathbf{x}|}$ such that $f(A\mathbf{x} + \mathbf{b})$ is $s$-sparse. We show that ETsparse is NP-hard over any field $\mathbb{F}$, if $f$ is given in the sparse representation, i.e., as a list of nonzero coefficients and exponent vectors. This answers a question posed in [Gupta-Saha-Thankey, SODA'23] and [Baraskar-Dewan-Saha, STACS'24]. The result implies that the Minimum Circuit Size Problem (MCSP) is NP-hard for a dense subclass of depth-$3$ arithmetic circuits if the input is given in sparse representation. We also show that approximating the smallest $s_0$ such that a given $s$-sparse polynomial $f$ is in the orbit of some $s_0$-sparse polynomial to within a factor of $s^{\frac{1}{3} - \epsilon}$ is NP-hard for any $\epsilon > 0$; observe that $s$-factor approximation is trivial as the input is $s$-sparse. Finally, we show that for any constant $\sigma \geq 5$, checking if a polynomial (given in sparse representation) is in the orbit of some support-$\sigma$ polynomial is NP-hard. Support of a polynomial $f$ is the maximum number of variables present in any monomial of $f$. These results are obtained via direct reductions from the $3$-SAT problem., Comment: A preliminary version of the paper appeared in the proceedings of ICALP 2024. This version is slightly stronger than the ECCC one: a) We show it is NP-hard to test equivalence to support-5 polynomials (instead of support-6). b) We adapt the proof technique to show it is NP-hard to test equivalence to sparse polynomials under translations only; A suitable gap version of the result is also shown

Large Language Models for Code (or code LLMs) are increasingly gaining popularity and capabilities, offering a wide array of functionalities such as code completion, code generation, code summarization, test generation, code translation, and more. To leverage code LLMs to their full potential, developers must provide code-specific contextual information to the models. These are typically derived and distilled using program analysis tools. However, there exists a significant gap--these static analysis tools are often language-specific and come with a steep learning curve, making their effective use challenging. These tools are tailored to specific program languages, requiring developers to learn and manage multiple tools to cover various aspects of the their code base. Moreover, the complexity of configuring and integrating these tools into the existing development environments add an additional layer of difficulty. This challenge limits the potential benefits that could be gained from more widespread and effective use of static analysis in conjunction with LLMs. To address this challenge, we present codellm-devkit (hereafter, `CLDK'), an open-source library that significantly simplifies the process of performing program analysis at various levels of granularity for different programming languages to support code LLM use cases. As a Python library, CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs. With this library, developers can effortlessly integrate detailed, code-specific insights that enhance the operational efficiency and effectiveness of LLMs in coding tasks. CLDK is available as an open-source library at https://github.com/IBM/codellm-devkit.

Patterns arise spontaneously in a range of systems spanning the sciences, and their study typically focuses on mechanisms to understand their evolution in space-time. Increasingly, there has been a transition towards controlling these patterns in various functional settings, with implications for engineering. Here, we combine our knowledge of a general class of dynamical laws for pattern formation in non-equilibrium systems, and the power of stochastic optimal control approaches to present a framework that allows us to control patterns at multiple scales, which we dub the "Hamiltonian bridge". We use a mapping between stochastic many-body Lagrangian physics and deterministic Eulerian pattern forming PDEs to leverage our recent approach utilizing the Feynman-Kac-based adjoint path integral formulation for the control of interacting particles and generalize this to the active control of patterning fields. We demonstrate the applicability of our computational framework via numerical experiments on the control of phase separation with and without a conserved order parameter, self-assembly of fluid droplets, coupled reaction-diffusion equations and finally a phenomenological model for spatio-temporal tissue differentiation. We interpret our numerical experiments in terms of a theoretical understanding of how the underlying physics shapes the geometry of the pattern manifold, altering the transport paths of patterns and the nature of pattern interpolation. We finally conclude by showing how optimal control can be utilized to generate complex patterns via an iterative control protocol over pattern forming pdes which can be casted as gradient flows. All together, our study shows how we can systematically build in physical priors into a generative framework for pattern control in non-equilibrium systems across multiple length and time scales., Comment: 29 pages, 8 figures

The multiplicities of positive and negative pions, kaons and unidentified hadrons produced in deep-inelastic scattering are measured in bins of the Bjorken scaling variable $x$, the relative virtual-photon energy $y$ and the fraction of the virtual-photon energy transferred to the final-state hadron $z$. Data were obtained by the COMPASS Collaboration using a 160 GeV muon beam of both electric charges and a liquid hydrogen target. These measurements cover the kinematic domain with photon virtuality $Q^2 > 1$ (GeV/$c)^2$, $0.004 < x < 0.4$, $0.1 < y < 0.7$ and $0.2 < z < 0.85$, in accordance with the kinematic domain used in earlier published COMPASS multiplicity measurements with an isoscalar target. The calculation of radiative corrections was improved by using the Monte Carlo generator DJANGOH, which results in up to 12\% larger corrections in the low-$x$ region., Comment: 19 pages, 29 figures

Heterogeneous graphs are present in various domains, such as social networks, recommendation systems, and biological networks. Unlike homogeneous graphs, heterogeneous graphs consist of multiple types of nodes and edges, each representing different entities and relationships. Generating realistic heterogeneous graphs that capture the complex interactions among diverse entities is a difficult task due to several reasons. The generator has to model both the node type distribution along with the feature distribution for each node type. In this paper, we look into solving challenges in heterogeneous graph generation, by employing a two phase hierarchical structure, wherein the first phase creates a skeleton graph with node types using a prior diffusion based model and in the second phase, we use an encoder and a sampler structure as generator to assign node type specific features to the nodes. A discriminator is used to guide training of the generator and feature vectors are sampled from a node feature pool. We conduct extensive experiments with subsets of IMDB and DBLP datasets to show the effectiveness of our method and also the need for various architecture components.

We give two characterisations of when a map-germ admits a 1-parameter stable unfolding, one related to the $\mathscr K_e$-codimension and another related to the normal form of a versal unfolding. We then prove that there are infinitely many finitely determined map-germs of multiplicity 4 from $\mathbb K^3$ to $\mathbb K^3$ which do not admit a 1-parameter stable unfolding.

The paper presents REPeat, a Real2Sim2Real framework designed to enhance bite acquisition in robot-assisted feeding for soft foods. It uses `pre-acquisition actions' such as pushing, cutting, and flipping to improve the success rate of bite acquisition actions such as skewering, scooping, and twirling. If the data-driven model predicts low success for direct bite acquisition, the system initiates a Real2Sim phase, reconstructing the food's geometry in a simulation. The robot explores various pre-acquisition actions in the simulation, then a Sim2Real step renders a photorealistic image to reassess success rates. If the success improves, the robot applies the action in reality. We evaluate the system on 15 diverse plates with 10 types of food items for a soft food diet, showing improvement in bite acquisition success rates by 27\% on average across all plates. See our project website at https://emprise.cs.cornell.edu/repeat.

Large language models (LLMs) have demonstrated remarkable capabilities; however, the optimization of their prompts has historically prioritized performance metrics at the expense of crucial safety and security considerations. To overcome this shortcoming, we introduce "Survival of the Safest" (SoS), an innovative multi-objective prompt optimization framework that enhances both performance and security in LLMs simultaneously. SoS utilizes an interleaved multi-objective evolution strategy, integrating semantic, feedback, and crossover mutations to effectively traverse the prompt landscape. Differing from the computationally demanding Pareto front methods, SoS provides a scalable solution that expedites optimization in complex, high-dimensional discrete search spaces while keeping computational demands low. Our approach accommodates flexible weighting of objectives and generates a pool of optimized candidates, empowering users to select prompts that optimally meet their specific performance and security needs. Experimental evaluations across diverse benchmark datasets affirm SoS's efficacy in delivering high performance and notably enhancing safety and security compared to single-objective methods. This advancement marks a significant stride towards the deployment of LLM systems that are both high-performing and secure across varied industrial applications, Comment: EMNLP 2024 Industry Track

Machine unlearning algorithms, designed for selective removal of training data from models, have emerged as a promising approach to growing privacy concerns. In this work, we expose a critical yet underexplored vulnerability in the deployment of unlearning systems: the assumption that the data requested for removal is always part of the original training set. We present a threat model where an attacker can degrade model accuracy by submitting adversarial unlearning requests for data not present in the training set. We propose white-box and black-box attack algorithms and evaluate them through a case study on image classification tasks using the CIFAR-10 and ImageNet datasets, targeting a family of widely used unlearning methods. Our results show extremely poor test accuracy following the attack: 3.6% on CIFAR-10 and 0.4% on ImageNet for white-box attacks, and 8.5% on CIFAR-10 and 1.3% on ImageNet for black-box attacks. Additionally, we evaluate various verification mechanisms to detect the legitimacy of unlearning requests and reveal the challenges in verification, as most of the mechanisms fail to detect stealthy attacks without severely impairing their ability to process valid requests. These findings underscore the urgent need for research on more robust request verification methods and unlearning protocols, should the deployment of machine unlearning systems become more prevalent in the future.

Halo assembly bias is a phenomenon whereby the clustering of dark matter halos is dependent on halo properties, such as age, at fixed mass. Understanding the origin of assembly bias is important for interpreting the clustering of galaxies and constraining cosmological models. One proposed explanation for the origin of assembly bias is the truncation of mass accretion in low-mass halos in the presence of more massive halos, called 'arrested development.' Halos undergoing arrested development would have older measured ages and exhibit stronger clustering than equal mass halos that have not undergone arrested development. We propose a new method to test the validity of this explanation for assembly bias, and correct for it in cosmological N-body simulations. The method is based on the idea that the early mass accretion history of a halo, before arrested development takes effect, can be used to predict the late-time evolution of the halo in the absence of arrested development. We implement this idea by fitting a model to the early portion of halo accretion histories and extrapolating to late times. We then calculate 'corrected' masses and ages for halos based on this extrapolation and investigate how this impacts the assembly bias signal. We find that correcting for arrested development this way leads to a factor of two reduction in the strength of the assembly bias signal across a range of low halo masses. This result provides new evidence that arrested development is a cause of assembly bias and validates our approach to mitigating the effect., Comment: 13 pages, 6 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society

Ground motion prediction equations (GMPEs) play a key role in seismic hazard assessment (SHA). Considering the seismo-tectonic, geophysical and geotectonic characteristics of a target region, all the GMPEs may not be suitable in predicting the observed ground motion effectively. With a fairly large number of published GMPEs, the selection and ranking of suitable GMPEs for the design of logic trees in SHA for a particular target region has become a necessity of late. This paper presents a detailed quantitative evaluation of performance of 16 GMPEs against recorded ground motion data in two target regions, characterized by distinct seismo-tectonic, geophysical and geotectonical nature. The data set comprises of 465 three-component spectral accelerograms corresponding to 122 earthquake events. The suitability of a GMPE is tested by two widely accepted data-driven statistical methods, namely, likelihood (LH) and log-likelihood (LLH) method. Different suites of GMPEs are shown suitable for different periods of interest. The results will be useful to scientists and engineers for microzonation and estimation of seismic design parameters for the design of earthquake-resistant structures in these regions., Comment: 24 pages, 5 figures

The possibility of observing correlation-driven charge migration has been a driving force behind theoretical and experimental developments in the field of attosecond molecular science since its inception. Despite significant accomplishments, the unambiguous experimental observation of this quantum beating remains elusive. In this work, we present a method to selectively trigger such dynamics using molecules predicted to exhibit long-lived electron coherence. We show that these dynamics can be selectively triggered using infrared multi-photon ionization and probed using the spacial resolution of X-ray free-electron laser, proposing a promising experimental scheme to study these pivotal dynamics. Additionally, we demonstrate that real-time time-dependent density-functional theory can describe correlation-driven charge migration resulting from a hole mixing structure involving the HOMO of a molecule.

Generalized additive models (GAMs) have long been a powerful white-box tool for the intelligible analysis of tabular data, revealing the influence of each feature on the model predictions. Despite the success of neural networks (NNs) in various domains, their application as NN-based GAMs in tabular data analysis remains suboptimal compared to tree-based ones, and the opacity of encoders in NN-GAMs also prevents users from understanding how networks learn the functions. In this work, we propose a new deep tabular learning method, termed Prototypical Neural Additive Model (ProtoNAM), which introduces prototypes into neural networks in the framework of GAMs. With the introduced prototype-based feature activation, ProtoNAM can flexibly model the irregular mapping from tabular features to the outputs while maintaining the explainability of the final prediction. We also propose a gradient-boosting inspired hierarchical shape function modeling method, facilitating the discovery of complex feature patterns and bringing transparency into the learning process of each network layer. Our empirical evaluations demonstrate that ProtoNAM outperforms all existing NN-based GAMs, while providing additional insights into the shape function learned for each feature. The source code of ProtoNAM is available at \url{https://github.com/Teddy-XiongGZ/ProtoNAM}.

Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies at test time and provide early warnings of failure are necessary to facilitate scalable deployment. We propose Sentinel, a runtime monitoring framework that splits the detection of failures into two complementary categories: 1) Erratic failures, which we detect using statistical measures of temporal action consistency, and 2) task progression failures, where we use Vision Language Models (VLMs) to detect when the policy confidently and consistently takes actions that do not solve the task. Our approach has two key strengths. First, because learned policies exhibit diverse failure modes, combining complementary detectors leads to significantly higher accuracy at failure detection. Second, using a statistical temporal action consistency measure ensures that we quickly detect when multimodal, generative policies exhibit erratic behavior at negligible computational cost. In contrast, we only use VLMs to detect failure modes that are less time-sensitive. We demonstrate our approach in the context of diffusion policies trained on robotic mobile manipulation domains in both simulation and the real world. By unifying temporal consistency detection and VLM runtime monitoring, Sentinel detects 18% more failures than using either of the two detectors alone and significantly outperforms baselines, thus highlighting the importance of assigning specialized detectors to complementary categories of failure. Qualitative results are made available at https://sites.google.com/stanford.edu/sentinel., Comment: Project page: https://sites.google.com/stanford.edu/sentinel. 35 pages, 9 figures. Accepted to the Conference on Robot Learning (CoRL) 2024

Compound Finance is a decentralized lending protocol that enables the secure and efficient borrowing and lending of cryptocurrencies, utilizing smart contracts and dynamic interest rates based on supply and demand to facilitate transactions. The protocol enables users to supply different crypto assets and accrue interest, while borrowers can avail themselves of loans secured by collateralized assets. Our collaboration with Compound Finance focuses on harnessing the power of the Chainrisk simulation engine to optimize risk parameters of the Compound V3 (Comet) protocol. This report delineates a comprehensive methodology aimed at calculating key risk metrics of the protocol. This optimization framework is pivotal for mitigating systemic risks and enhancing the overall stability of the protocol. By leveraging Chainrisk's Cloud Platform, we conduct millions of simulations to evaluate the protocol's Value at Risk (VaR) and Liquidations at Risk (LaR), ultimately providing recommendations for parameter adjustments.

Procedural video representation learning is an active research area where the objective is to learn an agent which can anticipate and forecast the future given the present video input, typically in conjunction with textual annotations. Prior works often rely on large-scale pretraining of visual encoders and prediction models with language supervision. However, the necessity and effectiveness of extending compute intensive pretraining to learn video clip sequences with noisy text supervision have not yet been fully validated by previous works. In this work, we show that a strong off-the-shelf frozen pretrained visual encoder, along with a well designed prediction model, can achieve state-of-the-art (SoTA) performance in forecasting and procedural planning without the need for pretraining the prediction model, nor requiring additional supervision from language or ASR. Instead of learning representations from pixel space, our method utilizes the latent embedding space of publicly available vision encoders. By conditioning on frozen clip-level embeddings from observed steps to predict the actions of unseen steps, our prediction model is able to learn robust representations for forecasting through iterative denoising - leveraging the recent advances in diffusion transformers (Peebles & Xie, 2023). Empirical studies over a total of five procedural learning tasks across four datasets (NIV, CrossTask, COIN and Ego4D-v2) show that our model advances the strong baselines in long-horizon action anticipation (+2.6% in Verb ED@20, +3.1% in Noun ED@20), and significantly improves the SoTA in step forecasting (+5.0%), task classification (+3.8%), and procedure planning tasks (up to +2.28% in success rate, +3.39% in mAcc, and +0.90% in mIoU)., Comment: 10 pages

Spacecraft trajectory design is a global search problem, where previous work has revealed specific solution structures that can be captured with data-driven methods. This paper explores two global search problems in the circular restricted three-body problem: hybrid cost function of minimum fuel/time-of-flight and transfers to energy-dependent invariant manifolds. These problems display a fundamental structure either in the optimal control profile or the use of dynamical structures. We build on our prior generative machine learning framework to apply diffusion models to learn the conditional probability distribution of the search problem and analyze the model's capability to capture these structures., Comment: This paper was presented at the AAS/AIAA Astrodynamics Specialist Conference

Cu$_2$IrO$_3$ has attracted recent interest due to its proximity to the Kitaev quantum spin liquid state and the complex structural response observed at high pressures. We use x-ray spectroscopy and scattering as well as electrical transport techniques to unveil the electronic structure of Cu$_2$IrO$_3$ at ambient and high pressures. Despite featuring a $\mathrm{Ir^{4+}}$ $J_{\rm{eff}}=1/2$ state at ambient pressure, Ir $L_{3}$ edge resonant inelastic x-ray scattering reveals broadened electronic excitations that point to the importance of Ir $5d$-Cu $3d$ interaction. High pressure first drives an Ir-Ir dimer state with collapsed $\langle \mathbf{L} \cdot \mathbf{S} \rangle$ and $\langle L_z \rangle/\langle S_z \rangle$, signaling the formation of $5d$ molecular orbitals. A novel $\mathrm{Cu \to Ir}$ charge transfer is observed at the onset of phase 5 above 30 GPa at low temperatures, leading to an approximate $\mathrm{Ir^{3+}}$ and $\mathrm{Cu^{1.5+}}$ valence, with persistent insulating electrical transport seemingly driven by charge segregation of Cu 1+/2+ ions into distinct sites. Concomitant x-ray spectroscopy and scattering measurements through different thermodynamic paths demonstrate a strong electron-lattice coupling, with $J_{\rm{eff}}=1/2$ and $\mathrm{Ir^{3+}}$/$\mathrm{Cu^{1.5+}}$ electronic states occurring only in phases 1 and 5, respectively. Remarkably, the charge-transferred state can only be reached if Cu$_2$IrO$_3$ is pressurized at low temperature, suggesting that phonons play an important role in the stability of this phase. These results point to the choice of thermodynamic path across interplanar collapse transition as a key route to access novel states in intercalated iridates., Comment: 8 pages, 4 figures, see ancillary file for Supplemental Material

This paper presents the Language Aided Subset Sampling Based Motion Planner (LASMP), a system that helps mobile robots plan their movements by using natural language instructions. LASMP uses a modified version of the Rapidly Exploring Random Tree (RRT) method, which is guided by user-provided commands processed through a language model (RoBERTa). The system improves efficiency by focusing on specific areas of the robot's workspace based on these instructions, making it faster and less resource-intensive. Compared to traditional RRT methods, LASMP reduces the number of nodes needed by 55% and cuts random sample queries by 80%, while still generating safe, collision-free paths. Tested in both simulated and real-world environments, LASMP has shown better performance in handling complex indoor scenarios. The results highlight the potential of combining language processing with motion planning to make robot navigation more efficient., Comment: 8 pages, 9 figures

The growing interest in the cislunar domain over the past decade has led to an increasing demand for low-thrust missions to key orbits within this region. These low-thrust missions, typically characterized by long thrust arcs, are highly susceptible to operational disruptions such as unforeseen thruster outages or missed thrust events. Consequently, there is a critical need for efficient trajectory design frameworks which incorporate robustness against such anomalies. In this study, we utilize a robust trajectory design framework to explore the solution space for the Power and Propulsion Element (PPE) module to the Earth-Moon L2 Southern 9:2 Near Rectilinear Halo Orbit. We propose algorithmic enhancements to improve the global search for robust solutions, and present a comprehensive analysis of two approaches: a nonconditional approach which involves a purely random search for robust solutions versus a conditional approach which involves warm-starting the search for robust solutions using the non-robust solutions. Our results indicate that by using non-robust solutions as initial guesses for the robust solutions, it is possible to achieve significant improvements in both the rate of convergence and the robustness of the final solutions., Comment: This manuscript was submitted to Journal of Guidance, Control, and Dynamics. This manuscript builds on previous work which was presented as Paper AAS 23-425 at the AAS/AIAA Astrodynamics Specialist Conference, Big Sky, MT, August 13-17 2023, and as Paper AAS 24-220 at the AAS/AIAA Astrodynamics Specialist Conference, Broomfield, CO, August 11-15 2024

As low-thrust space missions increase in prevalence, it is becoming increasingly important to design robust trajectories against unforeseen thruster outages or missed thrust events. Accounting for such events is particularly important in multibody systems, such as the cislunar realm, where the dynamics are chaotic and the dynamical flow is constrained by pertinent dynamical structures. Yet the role of these dynamical structures in robust trajectory design is unclear. This paper provides the first comprehensive statistical study of robust and non-robust trajectories in relation to the invariant manifolds of resonant orbits in a circular restricted three-body problem. For both the non-robust and robust solutions analyzed in this study, the optimal subset demonstrates a closer alignment with the invariant manifolds, while the overall feasible set frequently exhibits considerable deviations. Robust optimal trajectories shadow the invariant manifolds as closely as the non-robust optimal trajectories, and in some cases, demonstrate closer alignment than the non-robust solutions. By maintaining proximity to these structures, low-thrust solutions are able to efficiently utilize the manifolds to achieve optimality even under operational uncertainties., Comment: This manuscript was submitted to Journal of Guidance, Control, and Dynamics. This manuscript builds on previous work which was presented as Paper AAS 23-272 at the AAS/AIAA Astrodynamics Specialist Conference, Big Sky, MT, August 13-17 2023, and as Paper AAS 24-221 at the AAS/AIAA Astrodynamics Specialist Conference, Broomfield, CO, August 11-15 2024

In this paper, we propose an adaptive event-triggered reinforcement learning control for continuous-time nonlinear systems, subject to bounded uncertainties, characterized by complex interactions. Specifically, the proposed method is capable of jointly learning both the control policy and the communication policy, thereby reducing the number of parameters and computational overhead when learning them separately or only one of them. By augmenting the state space with accrued rewards that represent the performance over the entire trajectory, we show that accurate and efficient determination of triggering conditions is possible without the need for explicit learning triggering conditions, thereby leading to an adaptive non-stationary policy. Finally, we provide several numerical examples to demonstrate the effectiveness of the proposed approach.

In this paper, we employ the Density Dependent Relativistic Hadron (DDRH) field theoretical Model in a Bayesian analysis to investigate the equation of state (EOS) of dense matter featuring antikaon condensation for $K^-$ and $\bar{K}^0$ inside neutron stars. The vector coupling parameters within the kaonic sector are determined through the iso-spin counting rule and quark model. Our study integrates various constraints, including $\chi$EFT calculations, nuclear saturation properties, and astrophysical observations from pulsars PSR J0030+0451 and PSR J0740+66 and from the GW170817 event. We present posterior distributions of model parameters derived from these constraints, enabling us to explore the distributions of nuclear matter properties and neutron star (NS) characteristics such as radii, tidal deformabilities, central energy densities, and speed of sound. The antikaon potential at the 68(90)\% confidence intervals is determined to be $-129.36^{+12.53(+32.617)}_{-3.837(-5.696)}$ MeV. This aligns with several studies providing estimates within the range of $-120$ to $-150$ MeV. We find that the maximum neutron star mass is constrained to around 2M$_\odot$ due to the significant softening of the EOS caused by antikaon condensation. This softening results in a considerable decrease in the speed of sound. Although antikaon condensation for $K^-$ is not feasible inside the canonical neutron stars, it becomes feasible for higher NS masses. The condensation of both $K^-$ and $\bar{K}^0$ is probably present in the interior of neutron star with mass greater than 2M$_\odot$. We also discuss the interconnections among input variables, isoscalar and isovector aspects of the EOS, and specific NS properties in the context of antikaon condensation., Comment: 19 pages, 10 figures, Accepted for publication in Physical Review C (in press)

In this paper, we discuss some results on integrable Hamiltonian systems with two coordinate variables. We revisit the much-studied problem of the two-dimensional harmonic oscillator and discuss its (super)integrability in the light of a canonical transformation which can map the anisotropic oscillator to a corresponding isotropic one. Following this, we discuss the computation of first integrals for integrable two-dimensional systems using the framework of the Jacobi last multiplier. Using the latter, we describe some novel physical examples, namely, the classical Landau problem with a scalar-potential-induced hyperbolic mode, the two-dimensional Kepler problem, and a problem involving a linear curl force., Comment: Based on talk delivered at the XXVIII. International Conference on Integrable Systems and Quantum Symmetries (2024)

We present a new approach to bootstrapping string-like theories by exploiting a local crossing symmetric dispersion relation and field redefinition ambiguities. This approach enables us to use mass-level truncation and to go beyond the dual resonance hypothesis. We consider both open and closed strings, focusing mainly on open tree-level amplitudes with integer-spaced spectrum, and two leading Wilson coefficients as inputs. Using entanglement minimization in the form of the minimum of the first finite moment of linear entropy or entangling power, we get an excellent approximation to the superstring amplitudes, including the leading and sub-leading Regge trajectories. We find other interesting S-matrices which do not obey the duality hypothesis, but exhibit a transition from Regge behaviour to power law behaviour in the high energy limit. Finally, we also examine Machine-Learning techniques to do bootstrap and discuss potential advantages over the present approach., Comment: 48 pages, 21 figures

Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels. Furthermore, we introduce a data annotation pipeline for generating text prompts based on natural language instructions for the DeepCAD dataset using Mistral and LLaVA-NeXT. The dataset contains $\sim170$K models and $\sim660$K text annotations, from abstract CAD descriptions (e.g., generate two concentric cylinders) to detailed specifications (e.g., draw two circles with center $(x,y)$ and radius $r_{1}$, $r_{2}$, and extrude along the normal by $d$...). Within the Text2CAD framework, we propose an end-to-end transformer-based auto-regressive network to generate parametric CAD models from input texts. We evaluate the performance of our model through a mixture of metrics, including visual quality, parametric precision, and geometrical accuracy. Our proposed framework shows great potential in AI-aided design applications. Our source code and annotations will be publicly available., Comment: Accepted in NeurIPS 2024 (Spotlight)

This work proposes a kinodynamic motion planning technique for collaborative object transportation by multiple mobile manipulators in dynamic environments. A global path planner computes a linear piecewise path from start to goal. A novel algorithm detects the narrow regions between the static obstacles and aids in defining the obstacle-free region to enhance the feasibility of the global path. We then formulate a local online motion planning technique for trajectory generation that minimizes the control efforts in a receding horizon manner. It plans the trajectory for finite time horizons, considering the kinodynamic constraints and the static and dynamic obstacles. The planning technique jointly plans for the mobile bases and the arms to utilize the locomotion capability of the mobile base and the manipulation capability of the arm efficiently. We use a convex cone approach to avoid self-collision of the formation by modifying the mobile manipulators admissible state without imposing additional constraints. Numerical simulations and hardware experiments showcase the efficiency of the proposed approach., Comment: Pre-print Under Review

The traditional approach to POMDPs is to convert them into fully observed MDPs by considering a belief state as an information state. However, a belief-state based approach requires perfect knowledge of the system dynamics and is therefore not applicable in the learning setting where the system model is unknown. Various approaches to circumvent this limitation have been proposed in the literature. We present a unified treatment of some of these approaches by viewing them as models where the agent maintains a local recursively updateable agent state and chooses actions based on the agent state. We highlight the different classes of agent-state based policies and the various approaches that have been proposed in the literature to find good policies within each class. These include the designer's approach to find optimal non-stationary agent-state based policies, policy search approaches to find a locally optimal stationary agent-state based policies, and the approximate information state to find approximately optimal stationary agent-state based policies. We then present how ideas from the approximate information state approach have been used to improve Q-learning and actor-critic algorithms for learning in POMDPs.

The impact of solar-stellar activity on planetary environments is a topic of great interest within the Sun-Earth system as well as exoplanetary systems. In particular, extreme events such as flares and coronal mass ejections have a profound effect on planetary atmospheres. In May this year, a magnetic active region on the Sun (AR 13664) -- with a size exceeding hundred times that of Earth -- unleashed a large number of high energy X-class flares and associated mass ejections. The resulting Earth impact (geomagnetic storm) on May 10-11 was the strongest in the last two decades. We perform the first comprehensive analysis of the magnetic properties of the active region that spawned these flares and identify this to be a super active region with very rare physical characteristics. We also demonstrate how the rate of energization of the system is related to the flaring process. Our work illuminates how flare productive super active regions on the Sun and stars can be identified and what are their salient physical properties. Specifically, we put AR 13664 in historical context over the cumulative period of 1874 May-2024 June. We find that AR 13664 stands at 99.95 percentile in the distribution of area over 1874 May-2024 June, and at 99.10 percentile in terms of flux content among all ARs over the period 1996 April-2024 June. Our analysis indicates that five of its magnetic properties rank highest among all ARs recorded in SHARP data series during 2010 May-2024 June by the Solar Dynamic Observatory. Furthermore, we demonstrate that AR 13664 reached its most dynamic flare productive state following a rapid rate of rise of its flare-relevant parameters and that the X-class flares it spawned were more frequent near their peak values. Our analyses establish AR 13644 to be solar super active region and provide a paradigm for investigating their flare-relevant physical characteristics., Comment: submitted to The Astrophysical Journal (ApJ)

Machine learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations (PDEs), have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK). While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers, and suggest its use in future work as a strong baseline.

Contribution: This Full paper in the Research Category track describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums, offering a novel approach to augment the instructional capabilities of staff. The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM). This contribution enhances the efficiency and effectiveness of instructional support and significantly improves the quality and speed of responses provided to students, thereby enriching the overall learning experience. Background: Grounded in Vygotsky's socio-cultural theory and the concept of the More Knowledgeable Other (MKO), the study examines how GenAI can act as an auxiliary MKO to enrich educational dialogue between students and instructors. Research Question: How effective is GenAI in reducing the workload of instructional staff when used to pre-answer student questions posted on educational discussion forums? Methodology: Using a mixed-methods approach in large introductory programming courses, human Teaching Assistants (AI-TAs) employed an AI-assisted platform to pre-answer student queries. We analyzed efficiency indicators like the frequency of modifications to AI-generated responses and gathered qualitative feedback from AI-TAs. Findings: The findings indicate no significant difference in student reception to responses generated by AI-TAs compared to those provided by human instructors. This suggests that GenAI can effectively meet educational needs when adequately managed. Moreover, AI-TAs experienced a reduction in the cognitive load required for responding to queries, pointing to GenAI's potential to enhance instructional efficiency without compromising the quality of education., Comment: 8 pages, 1 figure. Accepted for publication in Frontiers in Education 2024

Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. Unlike conventional tuning-based personalization techniques, Imagine yourself operates as a tuning-free model, enabling all users to leverage a shared framework without individualized adjustments. Moreover, previous work met challenges balancing identity preservation, following complex prompts and preserving good visual quality, resulting in models having strong copy-paste effect of the reference images. Thus, they can hardly generate images following prompts that require significant changes to the reference image, \eg, changing facial expression, head and body poses, and the diversity of the generated images is low. To address these limitations, our proposed method introduces 1) a new synthetic paired data generation mechanism to encourage image diversity, 2) a fully parallel attention architecture with three text encoders and a fully trainable vision encoder to improve the text faithfulness, and 3) a novel coarse-to-fine multi-stage finetuning methodology that gradually pushes the boundary of visual quality. Our study demonstrates that Imagine yourself surpasses the state-of-the-art personalization model, exhibiting superior capabilities in identity preservation, visual quality, and text alignment. This model establishes a robust foundation for various personalization applications. Human evaluation results validate the model's SOTA superiority across all aspects (identity preservation, text faithfulness, and visual appeal) compared to the previous personalization models.

We show that the two-dimensional $\mathbb{Z}_2$ invariant for time-reversal invariant insulators can be formulated in terms of the boundary-condition dependence of the ground state wavefunction for both non-interacting and strongly-correlated insulators. By introducing a family of quasi-single particle states associated to the many-body ground state of an insulator, we show that the $\mathbb{Z}_2$ invariant can be expressed as the integral of a certain Berry connection over half the space of boundary conditions, providing an alternative expression to the formulations that appear in [Lee et al., Phys. Rev. Lett. $\textbf{100}$, 186807 (2008)]. We show the equivalence of the different many-body formulations of the invariant, and show how they reduce to known band-theoretic results for Slater determinant ground states. Finally, we apply our results to analytically calculate the invariant for the Kane-Mele model with nonlocal (orbital) Hatsugai-Kohmoto (HK) interactions. This rigorously establishes the topological nontriviality of the Kane-Mele model with HK interactions, and represents one of the few exact calculations of the $\mathbb{Z}_2$ invariant for a strongly-interacting system., Comment: 20 pages, 2 figures

The exponential growth of neuroscientific data necessitates platforms that facilitate data management and multidisciplinary collaboration. In this paper, we introduce Pennsieve - an open-source, cloud-based scientific data management platform built to meet these needs. Pennsieve supports complex multimodal datasets and provides tools for data visualization and analyses. It takes a comprehensive approach to data integration, enabling researchers to define custom metadata schemas and utilize advanced tools to filter and query their data. Pennsieve's modular architecture allows external applications to extend its capabilities, and collaborative workspaces with peer-reviewed data publishing mechanisms promote high-quality datasets optimized for downstream analysis, both in the cloud and on-premises. Pennsieve forms the core for major neuroscience research programs including NIH SPARC Initiative, NIH HEAL Initiative's PRECISION Human Pain Network, and NIH HEAL RE-JOIN Initiative. It serves more than 80 research groups worldwide, along with several large-scale, inter-institutional projects at clinical sites through the University of Pennsylvania. Underpinning the SPARC.Science, Epilepsy.Science, and Pennsieve Discover portals, Pennsieve stores over 125 TB of scientific data, with 35 TB of data publicly available across more than 350 high-impact datasets. It adheres to the findable, accessible, interoperable, and reusable (FAIR) principles of data sharing and is recognized as one of the NIH-approved Data Repositories. By facilitating scientific data management, discovery, and analysis, Pennsieve fosters a robust and collaborative research ecosystem for neuroscience and beyond., Comment: 71 pages, 12 figures

Visual Question-Answering (VQA) has become a key use-case in several applications to aid user experience, particularly after Vision-Language Models (VLMs) achieving good results in zero-shot inference. But evaluating different VLMs for an application requirement using a standardized framework in practical settings is still challenging. This paper introduces a comprehensive framework for evaluating VLMs tailored to VQA tasks in practical settings. We present a novel dataset derived from established VQA benchmarks, annotated with task types, application domains, and knowledge types, three key practical aspects on which tasks can vary. We also introduce GoEval, a multimodal evaluation metric developed using GPT-4o, achieving a correlation factor of 56.71% with human judgments. Our experiments with ten state-of-the-art VLMs reveals that no single model excelling universally, making appropriate selection a key design decision. Proprietary models such as Gemini-1.5-Pro and GPT-4o-mini generally outperform others, though open-source models like InternVL-2-8B and CogVLM-2-Llama-3-19B demonstrate competitive strengths in specific contexts, while providing additional advantages. This study guides the selection of VLMs based on specific task requirements and resource constraints, and can also be extended to other vision-language tasks., Comment: 8 pages + references + 6 pages of Appendix

In this paper, we consider the tracking of arbitrary curvilinear geometric paths in three-dimensional output spaces of unmanned aerial vehicles (UAVs) without pre-specified timing requirements, commonly referred to as path-following problems, subjected to bounded inputs. Specifically, we propose a novel nonlinear path-following guidance law for a UAV that enables it to follow any smooth curvilinear path in three dimensions while accounting for the bounded control authority in the design. The proposed solution offers a general treatment of the path-following problem by removing the dependency on the path's geometry, which makes it applicable to paths with varying levels of complexity and smooth curvatures. Additionally, the proposed strategy draws inspiration from the pursuit guidance approach, which is known for its simplicity and ease of implementation. Theoretical analysis guarantees that the UAV converges to its desired path within a fixed time and remains on it irrespective of its initial configuration with respect to the path. Finally, the simulations demonstrate the merits and effectiveness of the proposed guidance strategy through a wide range of engagement scenarios, showcasing the UAV's ability to follow diverse curvilinear paths accurately.

The integration of fine-scale multispectral imagery with deep learning models has revolutionized land use and land cover (LULC) classification. However, the atmospheric effects present in Top-of-Atmosphere sensor measured Digital Number values must be corrected to retrieve accurate Bottom-of-Atmosphere surface reflectance for reliable analysis. This study employs look-up-table-based radiative transfer simulations to estimate the atmospheric path reflectance and transmittance for atmospherically correcting high-resolution CARTOSAT-3 Multispectral (MX) imagery for several Indian cities. The corrected surface reflectance data were subsequently used in supervised and semi-supervised segmentation models, demonstrating stability in multi-class (buildings, roads, trees and water bodies) LULC segmentation accuracy, particularly in scenarios with sparsely labelled data.

The paper presents a theoretical study on small-signal stability and damping in bulk power systems with multiple grid-forming inverter-based storage resources. A detailed analysis is presented, characterizing the impacts of inverter droop gains and storage size on the slower eigenvalues, particularly those concerning inter-area oscillation modes. From these parametric sensitivity studies, a set of necessary conditions are derived that the design of droop gain must satisfy to enhance damping performance. The analytical findings are structured into propositions highlighting potential design considerations for improving system stability. The findings are illustrated via numerical studies on an IEEE 68-bus grid-forming storage network., Comment: accepted for presentation at The 63rd IEEE Conference on Decision and Control

Implementing automated unit tests is an important but time consuming activity in software development. Developers dedicate substantial time to writing tests for validating an application and preventing regressions. To support developers in this task, software engineering research over the past few decades has developed many techniques for automating unit test generation. However, despite this effort, usable tools exist for very few programming languages -- mainly Java, C, and C# and, more recently, for Python. Moreover, studies have found that automatically generated tests suffer poor readability and often do not resemble developer-written tests. In this work, we present a rigorous investigation of how large language models (LLMs) can help bridge the gap. We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases. We illustrate how the pipeline can be applied to different programming languages, specifically Java and Python, and to complex software requiring environment mocking. We conducted a through empirical study to assess the quality of the generated tests in terms of coverage, mutation score, and test naturalness -- evaluating them on standard as well as enterprise Java applications and a large Python benchmark. Our results demonstrate that LLM-based test generation, when guided by static analysis, can be competitive with, and even outperform, state-of-the-art test-generation techniques in coverage achieved while also producing considerably more natural test cases that developers find easy to read and understand. We also present the results of a user study, conducted with 161 professional developers, that highlights the naturalness characteristics of the tests generated by our approach.

This paper presents an approach to using decentralized distributed digital (DDD) ledgers like blockchain with multi-level verification. In regular DDD ledgers like Blockchain, only a single level of verification is available, which makes it not useful for those systems where there is a hierarchy and verification is required on each level. In systems where hierarchy emerges naturally, the inclusion of hierarchy in the solution for the problem of the system enables us to come up with a better solution. Introduction to hierarchy means there could be several verification within a level in the hierarchy and more than one level of verification, which implies other challenges induced by an interaction between the various levels of hierarchies that also need to be addressed, like verification of the work of the previous level of hierarchy by given level in the hierarchy. The paper will address all these issues, and provide a road map to trace the state of the system at any given time and probability of failure of the system., Comment: Submitted to IEEE Transactions on Information Forensics and Security Journal

Stars in an open cluster are assumed to have formed from a broadly homogeneous distribution of gas, implying that they should be chemically homogeneous. Quantifying the level to which open clusters are chemically homogeneous can therefore tell us about ISM pollution and gas-mixing in progenitor molecular clouds. Using SDSS-V Milky Way Mapper and SDSS-IV APOGEE DR17 abundances, we test this assumption by quantifying intrinsic chemical scatter in up to 20 different chemical abundances across 26 Milky Way open clusters. We find that we can place 3${\sigma}$ upper limits on open cluster homogeneity within 0.02 dex or less in the majority of elements, while for neutron capture elements, as well as those elements having weak lines, we place limits on their homogeneity within 0.2 dex. Finally, we find that giant stars in open clusters are ~0.01 dex more homogeneous than a matched sample of field stars., Comment: 26 pages, 14 figures

The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages. However, our preliminary studies reveal that a single retrieval process often suffers from low quality results, as the retrieved passages frequently fail to capture all plausible interpretations. Although the iterative RAG approach has been proposed to address this problem, it comes at the cost of significantly reduced efficiency. To address these issues, we propose the diversify-verify-adapt (DIVA) framework. DIVA first diversifies the retrieved passages to encompass diverse interpretations. Subsequently, DIVA verifies the quality of the passages and adapts the most suitable approach tailored to their quality. This approach improves the QA systems accuracy and robustness by handling low quality retrieval issue in ambiguous questions, while enhancing efficiency.

The recent surprising discovery of superconductivity with critical temperature $T_c$ = 32 K in MoB$_2$ above 70 GPa has led to the search for related materials that may superconduct at similarly high $T_c$ values and lower pressures. We have studied the superconducting and structural properties of Re$_{0.10}$Mo$_{0.90}$B$_2$ to 170 GPa. A structural phase transition from R3m to P6/mmm commences at 48 GPa, with the first signatures of superconductivity appearing above 44 GPa. The critical temperature is observed to increase with pressure. A complete resistive transition is observed only above 150 GPa, where the highest onset $T_c$ of 30 K is also achieved. Upon releasing pressure, the high pressure superconducting phase is found to be metastable. During unloading, a complete resistive superconducting transition is observed all the way down to 20 GPa (with onset $T_c \sim 20$ K). Our results suggest that the P6/mmm structure is responsible for the observed superconductivity., Comment: 7 pages, 7 figures, supplemental material. All data and analysis code associated with this work is available at https://doi.org/10.5281/zenodo.13359794

We present a finite-size security proof of the decoy-state BB84 QKD protocol against coherent attacks, using entropic uncertainty relations, for imperfect detectors. We apply this result to the case of detectors with imperfectly characterized basis-efficiency mismatch. Our proof works by obtaining a suitable bound on the phase error rate, without requiring any new modifications to the protocol steps or hardware. It is applicable to imperfectly characterized detectors, and only requires the maximum relative difference in detection efficiencies and dark count rates of the detectors to be characterized. Moreover, our proof allows Eve to choose detector efficiencies and dark count rates in their allowed ranges in each round, thereby addressing an important problem of detector side channels. We prove security in the variable-length framework, where users are allowed to adaptively determine the length of key to be produced, and number of bits to be used for error-correction, based on observations made during the protocol. We quantitatively demonstrate the effect of basis-efficiency mismatch by applying our results to the decoy-state BB84 protocol., Comment: Manuscript has been restructured significantly, new plots and discussion on detector model has been included, citations have been added

Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to guarantee high usability and widespread adoption of a new system. Given the significant investments in leadership-class supercomputers of the exascale era, this is even more important and necessitates alignment with a vision of Open Science and reproducibility. In this work, we present the JUPITER Benchmark Suite, which incorporates 16 applications from various domains. It was designed for and used in the procurement of JUPITER, the first European exascale supercomputer. We identify requirements and challenges and outline the project and software infrastructure setup. We provide descriptions and scalability studies of selected applications and a set of key takeaways. The JUPITER Benchmark Suite is released as open source software with this work at https://github.com/FZJ-JSC/jubench., Comment: To be published in Proceedings of The International Conference for High Performance Computing Networking, Storage, and Analysis (SC '24) (2024)