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Minkowski vacuum entanglement and accelerated oscillator chains
No full textMinkowski vacuum is empty from the perspective of Unruh-Minkowski photons, however, in the Rindler picture, it is filled with entangled pairs of Rindler photons. A ground-state atom uniformly accelerated through Minkowski vacuum can become excited by absorbing a Rindler photon (Unruh effect) or, in the alternative description, by emitting an Unruh-Minkowski photon (Unruh-Wald effect). We find an exact solution for the quantum evolution of a long chain of harmonic oscillators accelerated through Minkowski vacuum and for two chains accelerated in the opposite directions. We show how entanglement of Rindler photons present in Minkowski vacuum is transferred to the oscillators moving in causally disconnected regions. We also show that in the Unruh-Minkowski photon picture the process can be interpreted as if initial correlations between collective oscillator modes are transferred to the generated Unruh-Minkowski photons.16 pages, 6 figure
Observational constraints on freezing quintessence in a non-linear gravity
Get PDFIn this paper, we investigate the freezing quintessence scenario in late-time cosmic expansion using a non-linear gravity model, , where is a free parameter. We consider a solution for this model using an appropriate parametrization of the scale factor, and then the model is constrained by observational datasets, including CC, Pantheon+ (SN), and CC+SN+BAO. Our analysis yields results aligning closely with observational data. The Hubble parameter, deceleration parameter, matter-energy density, and EoS parameter of our model exhibit expected trends over cosmic time, supporting its physical validity. Furthermore, the model demonstrates consistency with the CDM model in late times, displaying freezing behavior in the plane and stability against density perturbations. Our findings suggest that the modified gravity model is a credible approach to describing the universe\u27s accelerating phase.IJGMMP accepted versio
On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models
Get PDFThe increasing reliance on large language models (LLMs) for diverse applications necessitates a thorough understanding of their robustness to adversarial perturbations and out-of-distribution (OOD) inputs. In this study, we investigate the correlation between adversarial robustness and OOD robustness in LLMs, addressing a critical gap in robustness evaluation. By applying methods originally designed to improve one robustness type across both contexts, we analyze their performance on adversarial and out-of-distribution benchmark datasets. The input of the model consists of text samples, with the output prediction evaluated in terms of accuracy, precision, recall, and F1 scores in various natural language inference tasks.
Our findings highlight nuanced interactions between adversarial robustness and OOD robustness, with results indicating limited transferability between the two robustness types. Through targeted ablations, we evaluate how these correlations evolve with different model sizes and architectures, uncovering model-specific trends: smaller models like LLaMA2-7b exhibit neutral correlations, larger models like LLaMA2-13b show negative correlations, and Mixtral demonstrates positive correlations, potentially due to domain-specific alignment. These results underscore the importance of hybrid robustness frameworks that integrate adversarial and OOD strategies tailored to specific models and domains. Further research is needed to evaluate these interactions across larger models and varied architectures, offering a pathway to more reliable and generalizable LLMs
Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach
Get PDFThis paper presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs in two-stage power markets, where the first stage involves a distributionally robust optimal power flow (DR-OPF) model. Unlike traditional methods that adjust forecast parameters and uncertainty quantification (UQ) separately, this framework jointly optimizes both the forecast model parameters and the decision conservativeness, which determines the size of the ambiguity set in the DR-OPF model. The framework aligns UQ with actual uncertainty realizations by directly optimizing downstream operational costs, a process referred to as cost-oriented calibration. The calibration is achieved using a gradient descent approach. To enable efficient differentiation, the DR-OPF problem is reformulated into a convex form, and the Envelope Theorem is leveraged to simplify gradient derivation in the two-stage setting. Additionally, the framework supports distributed implementation, enhancing data privacy and reducing computational overhead. By proactively calibrating forecast parameters and prescribing optimal decision conservativeness, the framework significantly enhances cost efficiency and reliability in power system operations. Numerical experiments on an IEEE 5-bus system demonstrate the effectiveness and efficiency of the proposed approach
Impact of Shoe Parameters on Gait Using Wearables
No full textThe study of biomechanics during locomotion provides valuable insights into the effects of varying conditions on specific movement patterns. This research focuses on examining the influence of different shoe parameters on walking biomechanics, aiming to understand their impact on gait patterns. To achieve this, various methodologies are explored to estimate human body biomechanics, including computer vision techniques and wearable devices equipped with advanced sensors. Given privacy considerations and the need for robust, accurate measurements, this study employs wearable devices with Inertial Measurement Unit (IMU) sensors. These devices offer a non-invasive, precise, and high-resolution approach to capturing biomechanical data during locomotion. Raw sensor data collected from wearable devices is processed using an Extended Kalman Filter to reduce noise and extract meaningful information. This includes calculating joint angles throughout the gait cycle, enabling a detailed analysis of movement dynamics. The analysis identifies correlations between shoe parameters and key gait characteristics, such as stability, mobility, step time, and propulsion forces. The findings provide deeper insights into how footwear design influences walking efficiency and biomechanics. This study paves the way for advancements in footwear technology and contributes to the development of personalized solutions for enhancing gait performance and mobility
How Learner Control and Explainable Learning Analytics on Skill Mastery Shape Student Desires to Finish and Avoid Loss in Tutored Practice
No full textPersonalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined with showing learning analytics on skill mastery and visual what-if explanations, can support students in practice contexts requiring high degrees of self-regulation, such as homework. Semi-structured interviews with six middle school students revealed three key insights: (1) participants highly valued learner control for an enhanced learning experience and better self-regulation, especially because most wanted to avoid losses in skill mastery; (2) only seeing their skill mastery estimates often made participants base problem selection on their weaknesses; and (3) what-if explanations stimulated participants to focus more on their strengths and improve skills until they were mastered. These findings show how explainable learning analytics could shape students\u27 selection strategies when they have control over what to practice. They suggest promising avenues for helping students learn to regulate their effort, motivation, and goals during practice with tutoring systems.Accepted to Learning Analytics and Knowledge (LAK 2025
How negative can null energy be in large N CFTs?
Get PDFSmeared null energy has been shown to be bounded from below for free minimally coupled quantum field theories. This is not the case for conformally coupled free bosonic theories where states of unbounded null energy can be constructed by increasing the particle number. Little is known for interacting conformal field theories (CFTs) in dimensions larger than two. In this work we consider states that are superpositions of scalar primary operators or the stress-energy tensor itself in large N CFTs. Within the large N approximation we present arguments that the negative smeared null energy of such states scales at worst as the central charge of theory, . This provides evidence for a general bound for CFTs in d-dimensions proportional to the central charge
Separation of measurement uncertainty into quantum and classical parts based on Kirkwood-Dirac quasiprobability and generalized entropy
Get PDFMeasurement in quantum mechanics is notoriously unpredictable. The uncertainty in quantum measurement can arise from the noncommutativity between the state and the measurement basis which is intrinsically quantum, but it may also be of classical origin due to the agent\u27s ignorance. It is of fundamental as well as practical importance to cleanly separate the two contributions which can be directly accessed using laboratory operations. Here, we propose two ways of decomposition of the total measurement uncertainty additively into quantum and classical parts. In the two decompositions, the total uncertainty of a measurement described by a POVM (positive-operator-valued measure) over a quantum state is quantified respectively by two generalized nonadditive entropies of the measurement outcomes; the quantum parts are identified, respectively, by the nonreality and the nonclassicality | which captures simultaneously both the nonreality and negativity | of the associated generalized Kirkwood-Dirac quasiprobability relative to the POVM of interest and a PVM (projection-valued measure) and maximized over all possible choices of the latter; and, the remaining uncertainties are identified as the classical parts. Both decompositions are shown to satisfy a few plausible requirements. The minimum of the total measurement uncertainties in the two decompositions over all POVM measurements are given by the impurity of the quantum state quantified by certain generalized quantum entropies, and are entirely classical. We argue that nonvanishing genuine quantum uncertainty in the two decompositions are sufficient and necessary to prove quantum contextuality via weak measurement with postselection. Finally, we suggest that the genuine quantum uncertainty is a manifestation of a specific measurement disturbance.35 page
Shannon information and integrated information: message and meaning
No full textInformation theory, introduced by Shannon, has been extremely successful and influential as a mathematical theory of communication. Shannon\u27s notion of information does not consider the meaning of the messages being communicated but only their probability. Even so, computational approaches regularly appeal to information processing to study how meaning is encoded and decoded in natural and artificial systems. Here, we contrast Shannon information theory with integrated information theory (IIT), which was developed to account for the presence and properties of consciousness. IIT considers meaning as integrated information and characterizes it as a structure, rather than as a message or code. In principle, IIT\u27s axioms and postulates allow one to unfold a cause-effect structure from a substrate in a state, a structure that fully defines the intrinsic meaning of an experience and its contents. It follows that, for the communication of meaning, the cause-effect structures of sender and receiver must be similar
On the low frequency flexural band gaps of a metamaterial plate with low porosity
No full textThis paper demonstrates numerically and experimentally that it is possible to tailor flexural band gaps in the low-frequency regime by appropriate choice of cutout characteristics. The finite element method is used to obtain the numerical dispersion relation and band gaps. The influence of the cutout\u27s shape, size, and location on the band gap is systematically studied. The study demonstrates that the cutout should pass through the center of the unit cell, and a large aspect ratio is required to introduce flexural band gaps in the low-frequency regime. This is validated by experiments on a finite plate with 3 3 unit cells