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We study the effective dynamics of a polymer quantized scalar field living on an effective polymer quantized homogeneous and isotropic background. We use a presureless dust field as an internal clock, and work with volume variables. Our results show how the quantum bounce in the early universe is affected as the initial conditions and various physical parameters -- including the scalar field polymer scale -- are varied. We also find, generically, that there is asymmetric evolution across the bounce., Comment: 12 pages, 13 figures

This paper introduces quadrature-based approaches that enable offline sampling of the transfer function and its derivative from available frequency response or impulse response data. Unlike quadrature-based data-driven balanced truncation, the proposed methods do not require samples of the transfer function's derivative or the impulse response's derivative. Additionally, a non-intrusive approach to track the error in IRKA as it refines the interpolation data is presented. Furthermore, a non-intrusive data-driven implementation of balanced truncation, equivalent to ADI-based low-rank balanced truncation, is proposed. This approach is not quadrature-based and only requires samples of the transfer function at the mirror images of the ADI shifts to construct the reduced-order model. The quality of the low-rank approximation of the Gramians in this method can also be monitored in a non-intrusive manner. Both the non-intrusive implementations of IRKA and balanced truncation are also extended to discrete-time systems in this paper. For discrete-time IRKA, the impulse response-based implementation uses a truncated summation approach, as the time-domain formulation of discrete-time IRKA involves summations rather than integrals. The paper includes two illustrative examples: one for the non-intrusive data-driven implementations of IRKA and balanced truncation for continuous-time systems, and another for discrete-time systems.

Plant species exhibit significant intra-class variation and minimal inter-class variation. To enhance classification accuracy, it is essential to reduce intra-class variation while maximizing inter-class variation. This paper addresses plant species classification using a limited number of labelled samples and introduces a novel Local Foreground Selection(LFS) attention mechanism. LFS is a straightforward module designed to generate discriminative support and query feature maps. It operates by integrating two types of attention: local attention, which captures local spatial details to enhance feature discrimination and increase inter-class differentiation, and foreground selection attention, which emphasizes the foreground plant object while mitigating background interference. By focusing on the foreground, the query and support features selectively highlight relevant feature sequences and disregard less significant background sequences, thereby reducing intra-class differences. Experimental results from three plant species datasets demonstrate the effectiveness of the proposed LFS attention mechanism and its complementary advantages over previous feature reconstruction methods.

In this short note, a non-intrusive data-driven formulation of ADI-based low-rank balanced truncation is provided. The proposed algorithm only requires transfer function samples at the mirror images of ADI shifts. If some shifts are used in both approximating the controllability Gramian and the observability Gramian, then samples of the transfer function's derivative at these shifts are also needed to enforce Hermite interpolation in the Loewner framework. It is noted that ADI-based low-rank balanced truncation can be viewed as a two-step process. The first step involves constructing an interpolant of the original model at the mirror images of the ADI shifts, which can be done non-intrusively within the Loewner framework. The second step involves reducing this interpolant using low-rank factors of Gramians associated with the interpolation data through the balanced square-root algorithm. This second step does not require any system information, making the overall process non-intrusive with the only required information being samples of the transfer function and/or its derivative at the mirror images of ADI shifts. Furthermore, it is shown that when the order of the reduced model in ADI-based low-rank balanced truncation is selected to match the numerical rank of the low-rank factors of the Gramians, it effectively reduces to standard interpolation at the mirror images of the ADI shift. An illustrative example is provided to explain the proposed approach.

Molecular Communication (MC) utilizes chemical molecules to transmit information, introducing innovative strategies for pharmaceutical interventions and enhanced immune system monitoring. This paper explores Molecular communication based approach to disrupt Quorum Sensing (QS) pathways to bolster immune defenses against antimicrobial-resistant bacteria. Quorum Sensing enables bacteria to coordinate critical behaviors, including virulence and antibiotic resistance, by exchanging chemical signals, known as autoinducers. By interfering with this bacterial communication, we can disrupt the synchronization of activities that promote infection and resistance. The study focuses on RNAIII inhibiting peptide (RIP), which blocks the production of critical transcripts, RNAII and RNAIII, within the Accessory Gene Regulator (AGR) system, thereby weakening bacterial virulence and enhancing host immune responses. The synergistic effects of combining QS inhibitors like RIP with traditional antimicrobial treatments reduce the need for highdose antibiotics, offering a potential solution to antibiotic resistance. This molecular communication-based approach presents a promising path to improved treatment efficacy and more robust immune responses against bacterial infections by targeting bacterial communication.

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

Generative AI has transformed the fields of Design and Manufacturing by providing efficient and automated methods for generating and modifying 3D objects. One approach involves using Large Language Models (LLMs) to generate Computer- Aided Design (CAD) scripting code, which can then be executed to render a 3D object; however, the resulting 3D object may not meet the specified requirements. Testing the correctness of CAD generated code is challenging due to the complexity and structure of 3D objects (e.g., shapes, surfaces, and dimensions) that are not feasible in code. In this paper, we introduce CADCodeVerify, a novel approach to iteratively verify and improve 3D objects generated from CAD code. Our approach works by producing ameliorative feedback by prompting a Vision-Language Model (VLM) to generate and answer a set of validation questions to verify the generated object and prompt the VLM to correct deviations. To evaluate CADCodeVerify, we introduce, CADPrompt, the first benchmark for CAD code generation, consisting of 200 natural language prompts paired with expert-annotated scripting code for 3D objects to benchmark progress. Our findings show that CADCodeVerify improves VLM performance by providing visual feedback, enhancing the structure of the 3D objects, and increasing the success rate of the compiled program. When applied to GPT-4, CADCodeVerify achieved a 7.30% reduction in Point Cloud distance and a 5.0% improvement in success rate compared to prior work, Comment

In this paper, we propose a novel and generalizable zero-shot knowledge transfer framework that distills expert sports navigation strategies from web videos into robotic systems with adversarial constraints and out-of-distribution image trajectories. Our pipeline enables diffusion-based imitation learning by reconstructing the full 3D task space from multiple partial views, warping it into 2D image space, closing the planning loop within this 2D space, and transfer constrained motion of interest back to task space. Additionally, we demonstrate that the learned policy can serve as a local planner in conjunction with position control. We apply this framework in the wheelchair tennis navigation problem to guide the wheelchair into the ball-hitting region. Our pipeline achieves a navigation success rate of 97.67% in reaching real-world recorded tennis ball trajectories with a physical robot wheelchair, and achieve a success rate of 68.49% in a real-world, real-time experiment on a full-sized tennis court., Comment: This manuscript has been submitted to 2025 IEEE International Conference on Robotics & Automation (ICRA)

Advances in robot learning have enabled robots to generate skills for a variety of tasks. Yet, robot learning is typically sample inefficient, struggles to learn from data sources exhibiting varied behaviors, and does not naturally incorporate constraints. These properties are critical for fast, agile tasks such as playing table tennis. Modern techniques for learning from demonstration improve sample efficiency and scale to diverse data, but are rarely evaluated on agile tasks. In the case of reinforcement learning, achieving good performance requires training on high-fidelity simulators. To overcome these limitations, we develop a novel diffusion modeling approach that is offline, constraint-guided, and expressive of diverse agile behaviors. The key to our approach is a kinematic constraint gradient guidance (KCGG) technique that computes gradients through both the forward kinematics of the robot arm and the diffusion model to direct the sampling process. KCGG minimizes the cost of violating constraints while simultaneously keeping the sampled trajectory in-distribution of the training data. We demonstrate the effectiveness of our approach for time-critical robotic tasks by evaluating KCGG in two challenging domains: simulated air hockey and real table tennis. In simulated air hockey, we achieved a 25.4% increase in block rate, while in table tennis, we saw a 17.3% increase in success rate compared to imitation learning baselines.

Gramian-based model order reduction methods, like balanced truncation, and interpolation-based methods, such as H2-optimal reduction, are two important types of model reduction algorithms. Although both are known for their accuracy, they are often seen as two different approaches. This paper shows that these two methods are closely related, with Gramian-based reduction being roughly an interpolation problem, and vice versa. For Galerkin projection, we find that when the reduced model has enough order to capture the significant eigenvalues of the controllability or observability Gramian, preserving these eigenvalues becomes an interpolation problem. In this case, both Gramian-based and interpolation-based model reduction methods produce the same transfer function but with different state-space realizations. When the reduced model's order is too small to capture all significant eigenvalues, the methods begin to differ, and the difference depends on the eigenvalues that were left out. In the case of Petrov-Galerkin projection, if the reduced model's order is large enough to capture the significant Hankel singular values, balanced truncation becomes the same as H2-optimal model order reduction. Again, both methods give the same transfer function but with different state-space realizations. When the order is smaller, the methods diverge, with the difference depending on the truncated Hankel singular values. Numerical examples are provided to support these findings, showing that Gramian-based and interpolation-based model reduction methods are more connected than previously thought and can be viewed as approximations of each other.

The emergence of digital technology has fundamentally transformed all facets of human existence, posing important queries about the safeguarding and implementation of human rights in the digital domain. The research focuses on important topics including privacy, freedom of speech, and information access. The methodology involves an extensive review of existing literature, legal frameworks, and relevant case studies to provide a comprehensive understanding of the intersection between technology and human rights. The paper highlights the challenges posed by surveillance, data breaches, and the digital divide while also exploring the role of international law and policy in safeguarding digital rights. The review highlights the significance of modifying human rights frameworks for the digital era, pointing out gaps in existing research and offering recommendations for future investigations.

$\mathcal{H}_2$-optimal model order reduction algorithms represent a significant class of techniques, known for their accuracy, which has been extensively validated over the past two decades. Among these, the Iterative Rational Krylov Algorithm (IRKA) is widely regarded as a benchmark for constructing $\mathcal{H}_2$-optimal reduced-order models. However, a key challenge in its data-driven implementation lies in the need for transfer function samples and their derivatives, which must be updated iteratively. Conducting new experiments to acquire these samples each time IRKA updates the interpolation data is impractical. Additionally, for discrete-time systems, obtaining transfer function samples at frequencies outside the unit circle is challenging, as these are not easily accessible through measurements. This paper proposes a method to sample the transfer function and its derivative offline using frequency or time-domain data, which is commonly measured for various design and analysis purposes in industry. By leveraging this approach, there is no need to directly measure transfer function samples at interpolation points, as these can be generated offline using the pre-existing data. This facilitates the offline implementation of IRKA within the frequency- or time-domain Loewner framework. The approach is also extended to discrete-time systems in this work. A numerical example is provided to validate the theoretical findings presented.

Linear quadratic output systems constitute an important class of dynamical systems with numerous practical applications. When the order of these models is exceptionally high, simulating and analyzing these systems becomes computationally prohibitive. In such instances, model order reduction offers an effective solution by approximating the original high-order system with a reduced-order model while preserving the system's essential characteristics. In frequency-limited model order reduction, the objective is to maintain the frequency response of the original system within a specified frequency range in the reduced-order model. In this paper, a mathematical expression for the frequency-limited $\mathcal{H}_2$ norm is derived, which quantifies the error within the desired frequency interval. Subsequently, the necessary conditions for a local optimum of the frequency-limited $\mathcal{H}_2$ norm of the error are derived. The inherent difficulty in satisfying these conditions within a Petrov-Galerkin projection framework is also discussed. Based on the optimality conditions and Petrov-Galerkin projection, a stationary point iteration algorithm is proposed that enforces three of the four optimality conditions upon convergence. A numerical example is provided to illustrate the algorithm's effectiveness in accurately approximating the original high-order model within the specified frequency interval.

An important class of dynamical systems with several practical applications is linear systems with quadratic outputs. These models have the same state equation as standard linear time-invariant systems but differ in their output equations, which are nonlinear quadratic functions of the system states. When dealing with models of exceptionally high order, the computational demands for simulation and analysis can become overwhelming. In such cases, model order reduction proves to be a useful technique, as it allows for constructing a reduced-order model that accurately represents the essential characteristics of the original high-order system while significantly simplifying its complexity. In time-limited model order reduction, the main goal is to maintain the output response of the original system within a specific time range in the reduced-order model. To assess the error within this time interval, a mathematical expression for the time-limited $\mathcal{H}_2$-norm is derived in this paper. This norm acts as a measure of the accuracy of the reduced-order model within the specified time range. Subsequently, the necessary conditions for achieving a local optimum of the time-limited $\mathcal{H}_2$ norm error are derived. The inherent inability to satisfy these optimality conditions within the Petrov-Galerkin projection framework is also discussed. After that, a stationary point iteration algorithm based on the optimality conditions and Petrov-Galerkin projection is proposed. Upon convergence, this algorithm fulfills three of the four optimality conditions. To demonstrate the effectiveness of the proposed algorithm, a numerical example is provided that showcases its ability to effectively approximate the original high-order model within the desired time interval.

ReLU, a commonly used activation function in deep neural networks, is prone to the issue of "Dying ReLU". Several enhanced versions, such as ELU, SeLU, and Swish, have been introduced and are considered to be less commonly utilized. However, replacing ReLU can be somewhat challenging due to its inconsistent advantages. While Swish offers a smoother transition similar to ReLU, its utilization generally incurs a greater computational burden compared to ReLU. This paper proposes SwishReLU, a novel activation function combining elements of ReLU and Swish. Our findings reveal that SwishReLU outperforms ReLU in performance with a lower computational cost than Swish. This paper undertakes an examination and comparison of different types of ReLU variants with SwishReLU. Specifically, we compare ELU and SeLU along with Tanh on three datasets: CIFAR-10, CIFAR-100 and MNIST. Notably, applying SwishReLU in the VGG16 model described in Algorithm 2 yields a 6% accuracy improvement on the CIFAR-10 dataset.

This study aims to examine the effect of L\'evy noise on the solutions of the nonlinear Schr\"odinger equation. An improved diversity of stochastic solutions is instinctively located discretely on certain conditions by applying the generalized Kudryashov method. Moreover, the dynamical behaviors of these exact results of the nonlinear Schr\"odinger equation are interpreted in the context of the effect of L\'evy noise. Even mathematical evaluations have been conducted and presented.