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This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service electricity markets. The proposed framework attempts to address the challenge of including the rainflow algorithm for cycle counting by directly developing a closed-form of marginal degradation as a function of dispatch decisions. Further, this closed-form degradation profile is embedded into an energy portfolio allocation (EPA) problem designed for making the optimal dispatch decisions for all the batteries together, in a shared economy manner. We term the entity taking these decisions as `facilitator' which works as a link between storage units and market operators. The proposed EPA formulation is quipped with a conditional-value-at-risk (CVaR)-based mechanism to bring risk-averseness against uncertainty in market prices. The proposed DI-EPA problem introduces fairness by dividing the profits into various units using the idea of marginal contribution. Simulation results regarding the accuracy of the closed-form of degradation, effectiveness of CVaR in handling uncertainty within the EPA problem, and fairness in the context of degradation awareness are discussed. Numerical results indicate that the DI-EPA framework improves the net profit of the storage units by considering the effect of degradation in optimal market participation.

Glassy dynamics in a dense system of active particles with self-propulsion force $f_0$ and persistence time $\tau_p$ are crucial for many biological processes. Recent studies have shown that, unlike relaxation dynamics, dynamic heterogeneity (DH) in active glasses exhibits nontrivial behavior. However, the mechanism by which activity affects DH remains unknown. We have developed an active inhomogeneous mode-coupling theory (aIMCT) for DH in active glasses. We show that the nontrivial behavior of DH comes from a novel nonequilibrium effect of activity that leads to distinct behaviors of DH and relaxation dynamics in active glasses. When activity is small, DH exhibits equilibrium-like behavior with a power-law divergence of the peak height of the four-point correlation function, $\chi_C^\text{peak}$, and the aIMCT value of the exponent, $\mu\simeq 1.0$, is consistent with the existing and our new simulations of active glasses. However, $\chi_C^\text{peak}$ deviates from the scaling relations at higher $f_0$ values because of the novel effect on DH, although the deviation with varying $\tau_p$ is relatively weak.

Constrained optimization problems arise in various engineering system operations such as inventory management and electric power grids. However, the requirement to repeatedly solve such optimization problems with uncertain parameters poses a significant computational challenge. This work introduces a learning scheme using Bayesian Neural Networks (BNNs) to solve constrained optimization problems under limited labeled data and restricted model training times. We propose a semi-supervised BNN for this practical but complex regime, wherein training commences in a sandwiched fashion, alternating between a supervised learning step (using labeled data) for minimizing cost, and an unsupervised learning step (using unlabeled data) for enforcing constraint feasibility. Both supervised and unsupervised steps use a Bayesian approach, where Stochastic Variational Inference is employed for approximate Bayesian inference. We show that the proposed semi-supervised learning method outperforms conventional BNN and deep neural network (DNN) architectures on important non-convex constrained optimization problems from energy network operations, achieving up to a tenfold reduction in expected maximum equality gap and halving the optimality and inequality (feasibility) gaps, without requiring any correction or projection step. By leveraging the BNN's ability to provide posterior samples at minimal computational cost, we demonstrate that a Selection via Posterior (SvP) scheme can further reduce equality gaps by more than 10%. We also provide tight and practically meaningful probabilistic confidence bounds that can be constructed using a low number of labeled testing data and readily adapted to other applications.

Recent experiments in ABC trilayer graphene detected superconductivity on the border of a phase transition to a symmetry-broken phase. In this work, we use unrestricted Hartree-Fock to study the nature of this phase. We find a close competition between two incommensurate inter-valley coherent (IVC) phases: an IVC crystal where the ordering occurs at multiple wavevectors, and an IVC spiral with a single ordering wavevector. Focusing on one of the regimes where superconductivity is observed experimentally, we find a continuous (or very weakly first order) transition between a half metallic phase to an IVC crystal, followed by a first-order transition into an IVC spiral. Using time-dependent Hartree-Fock, we study the collective mode spectrum in the half-metalic phase. We find a soft inter-valley mode that can mediate superconductivity in a narrow sliver of density near the continuous transition, with a Tc that can reach a few hundreds of mK and a sign-changing s-wave order parameter. The spin stiffness in the half metal phase is found to be surprisingly low, of the order of a few degrees Kelvin.

Self-Distillation is a special type of knowledge distillation where the student model has the same architecture as the teacher model. Despite using the same architecture and the same training data, self-distillation has been empirically observed to improve performance, especially when applied repeatedly. For such a process, there is a fundamental question of interest: How much gain is possible by applying multiple steps of self-distillation? To investigate this relative gain, we propose studying the simple but canonical task of linear regression. Our analysis shows that the excess risk achieved by multi-step self-distillation can significantly improve upon a single step of self-distillation, reducing the excess risk by a factor as large as $d$, where $d$ is the input dimension. Empirical results on regression tasks from the UCI repository show a reduction in the learnt model's risk (MSE) by up to 47%., Comment: 31 pages, 10 figures

The (preorder) traversal conjecture states that starting with an initial tree, the cost to search a sequence $S=(s_1,s_2,\dots,s_n) \in [n]^n$ in a binary search tree (BST) algorithm is $O(n)$, where $S$ is obtained by a preorder traversal of some BST. The sequence $S$ is called a preorder sequence. For Splay trees (candidate for dynamic optimality conjecture), the preorder traversal holds only when the initial tree is empty (Levy and Tarjan, WADS 2019). The preorder traversal conjecture for GREEDY (candidate for dynamic optimality conjecture) was known to be $n2^{\alpha(n)^{O(1)}}$ (Chalermsook et al., FOCS 2015), which was recently improved to $O(n2^{\alpha(n)})$ (Chalermsook et al., SODA 2023), here $\alpha(n)$ is the inverse Ackermann function of $n$. For a special case when the initial tree is flat, GREEDY is known to satisfy the traversal conjecture, i.e., $O(n)$ (Chalermsook et al., FOCS 2015). In this paper, we show that for every preorder sequence $S$, there exists an initial tree called the preorder initial tree for which GREEDY satisfies the preorder traversal conjecture., Comment: 10 pages, 5 figures

Generative AI systems have been heralded as tools for augmenting human creativity and inspiring divergent thinking, though with little empirical evidence for these claims. This paper explores the effects of exposure to AI-generated images on measures of design fixation and divergent thinking in a visual ideation task. Through a between-participants experiment (N=60), we found that support from an AI image generator during ideation leads to higher fixation on an initial example. Participants who used AI produced fewer ideas, with less variety and lower originality compared to a baseline. Our qualitative analysis suggests that the effectiveness of co-ideation with AI rests on participants' chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI's suggestions. We discuss opportunities for designing generative AI systems for ideation support and incorporating these AI tools into ideation workflows., Comment: Accepted at the CHI Conference on Human Factors in Computing Systems (CHI 24),18 pages, 15 figures

Inducing novel quantum phases and topologies in materials using intense light fields is a key objective of modern condensed matter physics, but nonetheless faces significant experimental challenges. Alternately, theory predicts that in the dense limit, excitons - collective excitations composed of Coulomb-bound electron-hole pairs - could also drive exotic quantum phenomena. However, the direct observation of these phenomena requires the resolution of electronic structure in momentum space in the presence of excitons, which became possible only recently. Here, using time- and angle-resolved photoemission spectroscopy of an atomically thin semiconductor in the presence of a high-density of resonantly and coherently photoexcited excitons, we observe the Bardeen-Cooper-Schrieffer (BCS) excitonic state - analogous to the Cooper pairs of superconductivity. We see the valence band transform from a conventional paraboloid into a Mexican-hat like Bogoliubov dispersion - a hallmark of the excitonic insulator phase; and we observe the recently predicted giant exciton-driven Floquet effects. Our work realizes the promise that intense bosonic fields, other than photons, can also drive novel quantum phenomena and phases in materials.

Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time and/or space complexity. The reduction of the power flow (PF) problem into a linear system of equations, allows formulation of quantum power flow (QPF) algorithms, based on quantum linear system solving methods such as the Harrow-Hassidim-Lloyd (HHL) algorithm. The speedup due to QPF algorithms is claimed to be exponential when compared to classical PF solved by state-of-the-art algorithms. We investigate the potential for practical quantum advantage (PQA) in solving QPF compared to classical methods on gate-based quantum computers. We meticulously scrutinize the end-to-end complexity of QPF, providing a nuanced evaluation of the purported quantum speedup in this problem. Our analysis establishes a best-case bound for the HHL-QPF complexity, conclusively demonstrating the absence of any PQA in the direct current power flow (DCPF) and fast decoupled load flow (FDLF) problem. Additionally, we establish that for potential PQA to exist it is necessary to consider DCPF-type problems with a very narrow range of condition number values and readout requirements.

The access lemma (Sleator and Tarjan, JACM 1985) is a property of binary search trees that implies interesting consequences such as static optimality, static finger, and working set property. However, there are known corollaries of the dynamic optimality that cannot be derived via the access lemma, such as the dynamic finger, and any $o(\log n)$-competitive ratio to the optimal BST where $n$ is the number of keys. In this paper, we introduce the group access bound that can be defined with respect to a reference group access tree. Group access bounds generalize the access lemma and imply properties that are far stronger than those implied by the access lemma. For each of the following results, there is a group access tree whose group access bound Is $O(\sqrt{\log n})$-competitive to the optimal BST. Achieves the $k$-finger bound with an additive term of $O(m \log k \log \log n)$ (randomized) when the reference tree is an almost complete binary tree. Satisfies the unified bound with an additive term of $O(m \log \log n)$. Matches the unified bound with a time window $k$ with an additive term of $O(m \log k \log \log n)$ (randomized). Furthermore, we prove simulation theorem: For every group access tree, there is an online BST algorithm that is $O(1)$-competitive with its group access bound. In particular, any new group access bound will automatically imply a new BST algorithm achieving the same bound. Thereby, we obtain an improved $k$-finger bound (reference tree is an almost complete binary tree), an improved unified bound with a time window $k$, and matching the best-known bound for Unified bound in the BST model. Since any dynamically optimal BST must achieve the group access bounds, we believe our results provide a new direction towards proving $o(\log n)$-competitiveness of Splay tree and Greedy.

This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more km-scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions., Comment: Summary of the Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at CERN: https://indico.cern.ch/event/1208783/

This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage-power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50% reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50-250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75% of N-2 contingency network structures, even without historical N-2 outage data. The proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods., Comment: 10 Pages

In this article, we have studied the nuclear modification factor measured in Pb-Pb collisions ($R_{PbPb}$) for $\pi^{\pm}$, $K^{\pm}$, $p+\bar{p}$, $K^{*0} + \bar{K^{*0}}$, $\phi$ and in p-Pb collisions ($R_{pPb}$) for $\pi^{\pm}$, $K^{\pm}$, $p+\bar{p}$ at Large hadron collider (LHC) energy of $\sqrt{s_{NN}}$ = 5.02 TeV for the most central and peripheral collisions. We have also analysed the experimental data of transverse momentum ($p_T$) spectra for these identified hadrons at LHC for Pb-Pb as well as for p-Pb collisions. We have used Boltzmann transport equation (BTE) in relaxation time approximation (RTA) for this analysis. The Tsallis statistics is used as an initial distribution function and The Tsallis blast wave (TBW) model is employed as an equilibrium distribution in BTE. The present model fits the measured transverse momentum spectra, $R_{PbPb}$, and $R_{pPb}$ successfully upto $p_T$ = 8 GeV with a reasonable $\chi^2/ndf$ for all the considered hadrons at various centralities. The experimental data for $R_{pPb}$ are generated using the particle yields at pPb and pp collisions where number of binary collisions are taken from Glauber model calculations. We find that the average transverse flow velocity ($<\beta_r>$) follows the mass and centrality ordering and decreases with the mass as well as when one move from the central collisions to peripheral collisions. These findings are inline with the results of the hydrodynamical calculations., Comment: 10 pages, 5 figures, 1 table, submitted for publication as regular article

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP), their effectiveness on a diverse range of clinical summarization tasks remains unproven. In this study, we apply adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Quantitative assessments with syntactic, semantic, and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with ten physicians evaluates summary completeness, correctness, and conciseness; in a majority of cases, summaries from our best adapted LLMs are either equivalent (45%) or superior (36%) compared to summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care., Comment: 27 pages, 19 figures

This letter introduces a convergence prediction model (CPM) for decentralized market clearing mechanisms. The CPM serves as a tool to detect potential cyber-attacks that affect the convergence of the consensus mechanism during ongoing market clearing operations. In this study, we propose a successively elongating Bayesian logistic regression approach to model the probability of convergence of real-time market mechanisms. The CPM utilizes net-power balance among all the prosumers/market participants as a feature for convergence prediction, enabling a low-dimensional model to operate efficiently for all the prosumers concurrently. The results highlight that the proposed CPM has achieved a net false rate of less than 0.01% for a stressed dataset.

This paper presents a graph-structured Gaussian process (GP) model for data-driven risk assessment of critical voltage constraints. The proposed GP is based on a novel kernel, named the vertex-degree kernel (VDK), that decomposes the voltage-load relationship based on the network graph. To estimate the GP efficiently, we propose a novel active learning scheme that leverages the additive structure of VDK. Further, we prove a probabilistic bound on the error in risk estimation using VDK-GP model that demonstrates that it is statistically comparable to using standard AC power flow (AC-PF), but does not require computing a large number of ACPF solutions. Simulations demonstrate that the proposed VDK-GP achieves more than two fold sample complexity reduction, compared to a generic GP on medium scale 500-Bus and large scale 1354-Bus power systems. Moreover, active learning achieves an impressive reduction of over 15 times in comparison to the time complexity of Monte-Carlo simulations (MCS), and have risk estimation error of order 1E-4 for both 500-Bus and 1354-Bus system, demonstrating its superior efficiency in risk estimation., Comment: 9 pages

The autocorrelation function in many complex systems shows a crossover in the form of its decay: from stretched exponential relaxation (SER) at short times to power law at long times. Studies of the mechanisms leading to such multiple relaxation patterns are rare. Additionally, the inherent complexity of these systems makes it hard to understand the underlying mechanism leading to the crossover. Here we develop a simple one-dimensional spin model, which we call a Domain Wall (DW) to Doublon model, that shows such a crossover as the nature of the excitations governing the relaxation dynamics changes with temperature and time. The relevant excitations are DWs and bound pairs of DWs, which we term `doublons'. The diffusive motion of the DWs govern the relaxation at short times, whereas the diffusive motion of the doublons yields the long time decay. This change of excitations and their relaxation leads to a crossover from SER to power law in the decay pattern of the autocorrelation function. We augment our numerical results with simple physical arguments and analytic derivations.

The large value of non-minimal coupling constant $\xi$ required to satisfy CMB observations in Higgs inflation violates unitarity. In this work we study Higgs-inflation with non-canonical kinetic term of DBI form to find whether $\xi$ can be reduced. To study the inflationary dynamics, we transform the action to the Einstein frame, in which the Higgs is minimally coupled to gravity with a non-canonical kinetic term and modified potential. We choose the Higgs self coupling constant $\lambda=0.14$ for our analysis. We find that the value of $\xi$ can be reduced from $10^{3}-10^{4}$ to $\mathcal{O}(10)$ to satisfy Planck constraints on amplitude of scalar power spectrum. However, this model produces a larger tensor-to-scalar ratio $r$, in comparison to the Higgs inflation with canonical kinetic term. We also find that, to satisfy joint constraints on scalar spectral index $n_s$ and tensor-to-scalar ratio $r$ from Planck-2018 and bounds on $r$ from Planck and BICEP3, the value of $\xi$ should be of the order of $10^4$. Thus, the issue of unitarity violation remains even after considering Higgs inflation with non-canonical kinetic term, Comment: Referee's comments incorporated, New references added, Accepted for publication in Classical and Quantum Gravity