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The organization of latent knowledge within large-scale models poses unique challenges when addressing overlapping representations and optimizing contextual accuracy. Conceptual redundancies embedded across layers often result in inefficiencies that affect both computational demands and task-specific outcomes. A framework was proposed to restructure these redundancies through advanced clustering techniques and dynamic thresholding, ensuring that critical semantic relationships are preserved while removing unnecessary overlaps. Evaluations revealed improved memory efficiency and faster inference times, alongside better alignment in latent knowledge clusters that enhanced interpretability. Improvements in error rates and adversarial robustness suggest that restructuring redundancies has broader implications for increasing model reliability across diverse applications. Comparative analyses highlighted reductions in resource consumption and notable gains in performance, particularly in translation and summarization tasks. Energy metrics demonstrated significant savings during training phases, further validating the practicality of the approach for real-world deployments. Representational fidelity was also enhanced, with latent space evaluations indicating better cluster alignment and higher semantic consistency. The methodology bridges a key gap in model optimization through directly addressing redundancies at the structural level. Its application opens avenues for scalable, efficient, and contextually aware systems that can adapt to complex, domain-specific tasks without compromising on performance.

Attention can be used to inform choice selection in contextual bandit tasks even when context features have not been previously experienced. One example of this is in dimensional shifts, where additional feature values are introduced and the relationship between features and outcomes can either be static or variable. Attentional mechanisms have been extensively studied in contextual bandit tasks where the feedback of choices is provided immediately, but less research has been done on tasks where feedback is delayed or in counterfactual feedback cases. Some methods have successfully modeled human attention with immediate feedback based on reward prediction errors (RPEs), though recent research raises questions of the applicability of RPEs onto more general attentional mechanisms. Alternative models suggest that information theoretic metrics can be used to model human attention, with broader applications to novel stimuli. In this paper, we compare two different methods for modeling how humans attend to specific features of decision making tasks, one that is based on calculating an information theoretic metric using a memory of past experiences, and another that is based on iteratively updating attention from reward prediction errors. We compare these models using simulations in a contextual bandit task with both intradimensional and extradimensional domain shifts, as well as immediate, delayed, and counterfactual feedback. We find that calculating an information theoretic metric over a history of experiences is best able to account for human-like behavior in tasks that shift dimensions and alter feedback presentation. These results indicate that information theoretic metrics of attentional mechanisms may be better suited than RPEs to predict human attention in decision making, though further studies of human behavior are necessary to support these results.

Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) depth maps, making detailed scene perception challenging. To address this, enhancing LR depth maps to high-resolution (HR) ones has become essential, guided by HR-structured inputs like RGB or grayscale images. We propose a novel sensor fusion methodology for guided depth super-resolution (GDSR), a technique that combines LR depth maps with HR images to estimate detailed HR depth maps. Our key contribution is the Incremental guided attention fusion (IGAF) module, which effectively learns to fuse features from RGB images and LR depth maps, producing accurate HR depth maps. Using IGAF, we build a robust super-resolution model and evaluate it on multiple benchmark datasets. Our model achieves state-of-the-art results compared to all baseline models on the NYU v2 dataset for $\times 4$, $\times 8$, and $\times 16$ upsampling. It also outperforms all baselines in a zero-shot setting on the Middlebury, Lu, and RGB-D-D datasets. Code, environments, and models are available on GitHub.

There are various examples of phenotypic plasticity in ecosystems that serve as the basis for a wide range of inducible defences against predation. These strategies include camouflage, burrowing, mimicry, evasive actions, and even counterattacks that enhance survival under fluctuating predatory threats. Additionally, the ability to exhibit plastic responses often influences ecological balances, shaping predator-prey coexistence over time. This study introduces a predator-prey model where prey species show inducible defences, providing new insights into the role of adaptive strategies in these complex interactions. The stabilizing impact of the defensive mechanism is one of several intriguing outcomes produced by the dynamics. Moreover, the predator population rises when the interference rate increases to a moderate value even in the presence of lower prey defence but decreases monotonically for stronger defence levels. Furthermore, we identify a bistable domain when the handling rate is used as a control parameter, emphasizing the critical role of initial population sizes in determining system outcomes. By considering the species diffusion in a bounded region, the study is expanded into a spatio-temporal model. The numerical simulation reveals that the Turing domain decreases as the level of protection increases. The study is subsequently extended to incorporate taxis, known as the directed movement of species toward or away from another species. Our investigation identifies the conditions under which pattern formation emerges, driven by the interplay of inducible defences, taxis as well as species diffusion. Numerical simulations demonstrate that including taxis within the spatio-temporal model exerts a stabilizing influence, thereby diminishing the potential for pattern formation in the system., Comment: 31 pages, 30 figures

Active Inference is a closed-loop computational theoretical basis for understanding behaviour, based on agents with internal probabilistic generative models that encode their beliefs about how hidden states in their environment cause their sensations. We review Active Inference and how it could be applied to model the human-computer interaction loop. Active Inference provides a coherent framework for managing generative models of humans, their environments, sensors and interface components. It informs off-line design and supports real-time, online adaptation. It provides model-based explanations for behaviours observed in HCI, and new tools to measure important concepts such as agency and engagement. We discuss how Active Inference offers a new basis for a theory of interaction in HCI, tools for design of modern, complex sensor-based systems, and integration of artificial intelligence technologies, enabling it to cope with diversity in human users and contexts. We discuss the practical challenges in implementing such Active Inference-based systems.

Organic photovoltaics are a promising solar cell technology well-suited to mass production using roll-to-roll processes. The efficiency of lab-scale solar cells has exceeded 20% and considerable attention is currently being given to understanding and minimising the remaining loss mechanisms preventing higher efficiencies. While recent efficiency improvements are partly owed to reducing non-radiative recombination losses at open-circuit, the low fill factor due to a significant transport resistance is becoming the Achilles heel of organic photovoltaics. The term transport resistance refers to a voltage and light intensity dependent charge collection loss in low-mobility materials. In this Perspective, we demonstrate that even the highest efficiency organic solar cells reported to-date have significant performance losses that can be attributed to transport resistance and that lead to high fill factor losses. We provide a closer look at the transport resistance and the material properties influencing it. We describe how to experimentally characterise and quantify the transport resistance by providing easy to follow instructions. Furthermore, the causes and theory behind transport resistance are detailed. In particular, we integrate the relevant figures of merit and different viewpoints on the transport resistance. Finally, we outline strategies that can be followed to minimise these charge collection losses in future solar cells., Comment: Perspective (31 page, 13 figures)

Although entangled state vectors cannot be described in terms of classically realistic variables, localized in space and time, any given entanglement experiment can be built from basic quantum circuit components with well-defined locations. By analyzing the (local) weak values for any given run of a quantum circuit, we present evidence for a localized account of any circuit's behavior. Specifically, even if the state is massively entangled, the weak values are found to evolve only when they pass through a local circuit element. They otherwise remain constant and do not evolve when other qubits pass through their circuit elements. A further surprise is found when two qubits are brought together in an exchange interaction, as their weak values then evolve according to a simple classical equation. The weak values are subject to both past and future constraints, so they can only be determined by considering the entire circuit "all-at-once", as in action principles. In the context of a few basic quantum gates, we show how an all-at-once model of a complete circuit could generate weak values without using state vectors as an intermediate step. Since these gates comprise a universal quantum gate set, this lends support to the claim that any quantum circuit can plausibly be underpinned by localized variables, providing a realistic, lower-level account of generic quantum systems., Comment: 17 pages, 6 figures, Comments and References welcome

Quantum key distribution (QKD) allows two users to exchange a provably secure key for cryptographic applications. In prepare-and-measure QKD protocols, the states must be indistinguishable to prevent information leakage to an eavesdropper performing a side-channel attack. Here, we measure the indistinguishability of quantum states in a prepare-and-measure three-state BB84 polarization-based decoy state protocol using resonant-cavity light-emitting diodes (RC-LEDs) as the source in the transmitter. We make the spatial, spectral, and temporal DOF of the generated quantum states nearly indistinguishable using a spatial filter single-mode fiber, a narrow-band spectral filter, and adjustable timing of the electrical pulses driving the RC-LEDs, respectively. The sources have fully indistinguishable transverse spatial modes. The measured fractional mutual information between an assumed eavesdropper and the legitimate receiver is $2.39\times10^{-5}$ due to the spectral distinguishability and $4.31\times10^{-5}$ for the temporal distinguishability. The source is integrated into a full QKD system operating in a laboratory environment, where we achieve a raw key rate of 532 Kbits/s with an average quantum bit error rate of 1.83\%. The low system size, weight, and power make it suitable for mobile platforms such as uncrewed aerial vehicles (drones) or automobiles.

Phenotypic plasticity is a key factor in driving the evolution of species in the predator-prey interaction. The natural environment is replete with phenotypic plasticity, which is the source of inducible defences against predators, including concealment, cave-dwelling, mimicry, evasion, and revenge. In this work, a predator-prey model is proposed where the prey species shows inducible defence against their predators. The dynamics produce a wide range of non-trivial and impactful results, including the stabilizing effect of the defence mechanism. The model is also analyzed in the presence of spatio-temporal diffusion in a bounded domain. It is found in the numerical simulation that the Turing domain shrinks with the increase of defence level. The work is extended further by introducing a nonlocal term in the intra-specific competition of the prey species. The Turing instability condition has been studied for the local model around the coexisting steady state, followed by the Turing and non-Turing patterns in the presence of the nonlocal interaction term. The work reveals how an increase in inducible defence reduces the Turing domain in the local interaction model but expands it when the range of nonlocal interactions is extended, suggesting a higher likelihood of species colonization., Comment: arXiv admin note: text overlap with arXiv:2310.01392

Glass networks model systems of variables that interact via sharp switching. A body of theory has been developed over several decades that, in principle, allows rigorous proof of dynamical properties in high dimensions that is not normally feasible in nonlinear dynamical systems. Previous work has, however, used examples of dimension no higher than 6 to illustrate the methods. Here we show that the same tools can be applied in dimensions at least as high as 20. An important application of Glass networks is to a recently-proposed design of a True Random Number Generator that is based on an intrinsically chaotic electronic circuit. In order for analysis to be meaningful for the application, the dimension must be at least 20. Bifurcation diagrams show what appear to be periodic and chaotic bands. Here we demonstrate that the analytic tools for Glass networks can be used to rigorously show where periodic orbits are lost, and the types of bifurcations that occur there. The main tools are linear algebra and the stability theory of Poincar\'e maps. All main steps can be automated, and we provide computer code. The methods reviewed here have the potential for many other applications involving sharply switching interactions, such as artificial neural networks.

It is essential to understand the complex structure of the human brain to develop new treatment approaches for neurodegenerative disorders (NDDs). This review paper comprehensively discusses the challenges associated with modelling the complex brain networks and dynamic processes involved in NDDs, particularly Alzheimer's disease (AD), Parkinson's disease (PD), and cortical spreading depression (CSD). We investigate how the brain's biological processes and associated multiphysics interact and how this influences the structure and functionality of the brain. We review the literature on brain network models and dynamic processes, highlighting the need for sophisticated mathematical and statistical modelling techniques. Specifically, we go through large-scale brain network models relevant to AD and PD, highlighting the pathological mechanisms and potential therapeutic strategies investigated in the literature. Additionally, we investigate the propagation of CSD in the brain and its implications for neurological disorders. Furthermore, we discuss how data-driven approaches and artificial neural networks refine and validate models related to NDDs. Overall, this review underscores the significance of coupled multiscale models in deciphering disease mechanisms, offering potential avenues for therapeutic development and advancing our understanding of pathological brain dynamics., Comment: 5 figures, 26 pages

Parkinson's disease (PD) belongs to the class of neurodegenerative disorders that affect the central nervous system. It is usually defined as the gradual loss of dopaminergic neurons in the substantia nigra pars compacta, which causes both motor and non-motor symptoms. Understanding the neuronal processes that underlie PD is critical for creating successful therapies. This study combines machine learning (ML), stochastic modelling, and Bayesian inference with connectomic data to analyze the brain networks involved in PD. We use modern computational methods to study large-scale neural networks to identify neuronal activity patterns related to PD development. We aim to define the subtle structural and functional connection changes in PD brains by combining connectomic with stochastic noises. Stochastic modelling approaches reflect brain dynamics' intrinsic variability and unpredictability, shedding light on the origin and spread of pathogenic events in PD. We employ a novel hybrid model to assess how stochastic noise impacts the cortex-basal ganglia-thalamus (CBGTH) network, using data from the Human Connectome Project (HCP). Bayesian inference allows us to quantify uncertainty in model parameters, improving the accuracy of our predictions. Our findings reveal that stochastic disturbances increase thalamus activity, even under deep brain stimulation (DBS). Bayesian analysis suggests that reducing these disturbances could enhance healthy brain states, providing insights for potential therapeutic interventions. This approach offers a deeper understanding of PD dynamics and paves the way for personalized treatment strategies. This is an extended version of our work presented at the ICCS-2024 conference., Comment: 22 pages, 10 figures

Neuronal oscillations are related to symptoms of Parkinson's disease. The random inputs could affect such oscillations in the brain states that translate collective activities of neurons interconnected via synaptic connections. In this paper, we study coupled effects of channels and synaptic dynamics under the stochastic influence, together with spike-timing-dependent plasticity (STDP) of healthy brain cells with applications to Parkinson's disease (PD). In particular, we investigate the effects of random inputs and input correlations in a subthalamic nucleus (STN) cell membrane potential model. Our numerical results show that the random inputs strongly affect the spiking activities of the STN neuron not only in the case of healthy cells but also in the case of PD cells in the presence of DBS treatment. The STDP increases the interspike interval (ISI) regularity of spike trains of the output neurons. However, the existence of a random refractory period and random input current in the system may substantially influence an increased irregularity of spike trains of the output neurons. Furthermore, the presence of the stochastic influence together with spike-timing-dependent plasticity could increase the correlation of the neurons. These effects would potentially contribute to the management of PD symptoms., Comment: 18 pages, 8 figures

We present a novel Hand-pose Embedding Interactive System (HpEIS) as a virtual sensor, which maps users' flexible hand poses to a two-dimensional visual space using a Variational Autoencoder (VAE) trained on a variety of hand poses. HpEIS enables visually interpretable and guidable support for user explorations in multimedia collections, using only a camera as an external hand pose acquisition device. We identify general usability issues associated with system stability and smoothing requirements through pilot experiments with expert and inexperienced users. We then design stability and smoothing improvements, including hand-pose data augmentation, an anti-jitter regularisation term added to loss function, stabilising post-processing for movement turning points and smoothing post-processing based on One Euro Filters. In target selection experiments (n=12), we evaluate HpEIS by measures of task completion time and the final distance to target points, with and without the gesture guidance window condition. Experimental responses indicate that HpEIS provides users with a learnable, flexible, stable and smooth mid-air hand movement interaction experience., Comment: 6 pages, 8 figures, 3 tables

In recent yerars, Bismuth sodium titanate (BNT) and BNT-based piezoelectric materials have proved potential for lead-free piezoelectric technologies. Such materials' complicated phase structure is crucial to their thermo-electromechanical behaviour. Pure BNT has a rhombohedral (R3c) lattice structure at room temperature. The phase transition to an orthorhombic phase (Pnma) at 200\textcelsius is mainly due to octahedral tilting. This transition is called the depolarization temperature ($T_{d}$). The R3c phase ferroelectric domains have a higher spontaneous polarization, which declines with increasing temperature and reaches a local minimum at $T_{d}$, causing a phase change. Additionally, BNT transitions from Pnma to P4bm at 320\textcelsius and from P4bm to Pm3m at 520\textcelsius. These transitions also result in the changes in the spontaneous polarization. Therefore, the investigation of domain switching and phase change dynamics in relation to temperature and electromechanical coupling has acquired considerable importance. The complex phase regimes and their activation under different temperature settings will be examined to improve the use of these materials in sensors, actuators, energy harvesting devices, and haptic technologies. The micro-sized BNT inclusions are implanted in polydimethylsiloxane (PDMS) to reperesent the practical scenario. A two-dimensional phase-field thermo-electromechanical computational model has been created to investigate the complex phase transition and domain switching behaviour of BNT-PDMS composite under varying temperature conditions. The model uses Landau-Ginzburg and thermo-electromechanical free energy to accurately simulate phase shift and domain switching. Data from pure BNT experiments validated the model. It can predict thermo-electromechanical response in different phase regimes and temperature-induced phase coexistence., Comment: 36 pages, 12 figures

TOI-1266 is a benchmark system of two temperate ($<$ 450 K) sub-Neptune-sized planets orbiting a nearby M dwarf exhibiting a rare inverted architecture with a larger interior planet. In this study, we characterize transit timing variations (TTVs) in the TOI-1266 system using high-precision ground-based follow-up and new TESS data. We confirm the presence of a third exterior non-transiting planet, TOI-1266 d (P = 32.5 d, $M_d$ = 3.68$^{+1.05}_{-1.11} M_{\oplus}$), and combine the TTVs with archival radial velocity (RV) measurements to improve our knowledge of the planetary masses and radii. We find that, consistent with previous studies, TOI-1266 b ($R_b$ = 2.52 $\pm$ 0.08 $R_{\oplus}$, $M_b$ = 4.46 $\pm$ 0.69 $M_{\oplus}$) has a low bulk density requiring the presence of a hydrogen-rich envelope, while TOI-1266 c ($R_c$ = 1.98 $\pm$ 0.10 $R_{\oplus}$, $M_c$ = 3.17 $\pm$ 0.76 $M_{\oplus}$) has a higher bulk density that can be matched by either a hydrogen-rich or water-rich envelope. Our new dynamical model reveals that this system is arranged in a rare configuration with the inner and outer planets located near the 3:1 period ratio with a non-resonant planet in between them. Our dynamical fits indicate that the inner and outer planet have significantly nonzero eccentricities ($e_b + e_d = 0.076^{+0.029}_{-0.019}$), suggesting that TOI-1266 b may have an inflated envelope due to tidal heating. Finally, we explore the corresponding implications for the formation and long-term evolution of the system, which contains two of the most favorable cool ($<$ 500 K) sub-Neptunes for atmospheric characterization with JWST., Comment: 36 pages, 19 figures, 5 tables, submitted to The Astronomical Journal

Recent advances in Natural Language Interfaces (NLIs) and Large Language Models (LLMs) have transformed our approach to NLP tasks, shifting the focus towards a more Pragmatics-based approach. This shift enables more natural interactions between humans and voice assistants, which have been historically difficult to achieve. Pragmatics involves understanding how users often talk out of turn, interrupt one another, or provide relevant information without being explicitly asked (maxim of quantity). To explore this, we developed a digital assistant that continuously listens to conversations and proactively generates relevant visualizations during data exploration tasks. In a within-subject study, participants interacted with both proactive and non-proactive versions of a voice assistant while exploring the Hawaii Climate Data Portal (HCDP). Results suggest that the proactive assistant enhanced user engagement and facilitated quicker insights. Our study highlights the potential of Pragmatic, proactive AI in NLIs and identifies key challenges in its implementation, offering insights for future research., Comment: 14 pages, 8 figures, 1 table

With the decreasing cost of consumer display technologies making it easier for universities to have larger displays in classrooms, and the ubiquitous use of online tools such as collaborative whiteboards for remote learning during the COVID-19 pandemic, combining the two can be useful in higher education. This is especially true in visually intensive classes, such as data visualization courses, that can benefit from additional "space to teach," coined after the "space to think" sense-making idiom. In this paper, we reflect on our approach to using SAGE3, a collaborative whiteboard with advanced features, in higher education to teach visually intensive classes, provide examples of activities from our own visually-intensive courses, and present student feedback. We gather our observations into usage patterns for using content-rich canvases in education., Comment: To be published in the EDUVIS workshop at IEEE VIS 2024

For public health programs with limited resources, the ability to predict how behaviors change over time and in response to interventions is crucial for deciding when and to whom interventions should be allocated. Using data from a real-world maternal health program, we demonstrate how a cognitive model based on Instance-Based Learning (IBL) Theory can augment existing purely computational approaches. Our findings show that, compared to general time-series forecasters (e.g., LSTMs), IBL models, which reflect human decision-making processes, better predict the dynamics of individuals' states. Additionally, IBL provides estimates of the volatility in individuals' states and their sensitivity to interventions, which can improve the efficiency of training of other time series models.

This paper explores the intersection of data visualization and Large Language Models (LLMs). Driven by the need to make a broader range of data visualization types accessible for novice users, we present a guided LLM-based pipeline designed to transform data, guided by high-level user questions (referred to as macro-queries), into a diverse set of useful visualizations. This approach leverages various prompting techniques, fine-tuning inspired by Abela's Chart Taxonomy, and integrated SQL tool usage.

In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich the information obtained from a dynamic scene. One application of this sensor fusion is in public security and surveillance, whose efficacy and privacy protection measures must be continually evaluated. We present a novel deployment of sensor fusion for the discrete detection of concealed metal objects on persons whilst preserving their privacy. This is achieved by coupling off-the-shelf mmWave radar and depth camera technology with a novel neural network architecture that processes the radar signals using convolutional Long Short-term Memory (LSTM) blocks and the depth signal, using convolutional operations. The combined latent features are then magnified using a deep feature magnification to learn cross-modality dependencies in the data. We further propose a decoder, based on the feature extraction and embedding block, to learn an efficient upsampling of the latent space to learn the location of the concealed object in the spatial domain through radar feature guidance. We demonstrate the detection of presence and inference of 3D location of concealed metal objects with an accuracy of up to 95%, using a technique that is robust to multiple persons. This work provides a demonstration of the potential for cost effective and portable sensor fusion, with strong opportunities for further development.

In recent years, Large Language Models (LLM) have demonstrated impressive capabilities in the field of natural language processing (NLP). This paper explores the application of LLMs in negotiation transcript analysis by the Vanderbilt AI Negotiation Lab. Starting in September 2022, we applied multiple strategies using LLMs from zero shot learning to fine tuning models to in-context learning). The final strategy we developed is explained, along with how to access and use the model. This study provides a sense of both the opportunities and roadblocks for the implementation of LLMs in real life applications and offers a model for how LLMs can be applied to coding in other fields.

Monte Carlo (MC) simulations have been performed to refine the estimation of the correction-to-scaling exponent $\omega$ in the 2D $\varphi^4$ model, which belongs to one of the most fundamental universality classes. If corrections have the form $\propto L^{-\omega}$, then we find $\omega=1.546(30)$ and $\omega=1.509(14)$ as the best estimates. These are obtained from the finite-size scaling of the susceptibility data in the range of linear lattice sizes $L \in [128,2048]$ at the critical value of the Binder cumulant and from the scaling of the corresponding pseudocritical couplings within $L \in [64,2048]$. These values agree with several other MC estimates at the assumption of the power-law corrections and are comparable with the known results of the $\epsilon$-expansion. In addition, we have tested the consistency with the scaling corrections of the form $\propto L^{-4/3}$, $\propto L^{-4/3} \ln L$ and $\propto L^{-4/3} /\ln L$, which might be expected from some considerations of the renormalization group and Coulomb gas model. The latter option is consistent with our MC data. Our MC results served as a basis for a critical reconsideration of some earlier theoretical conjectures and scaling assumptions. In particular, we have corrected and refined our previous analysis by grouping Feynman diagrams. The renewed analysis gives $\omega \approx 4-d-2 \eta$ as some approximation for spatial dimensions $d<4$, or $\omega \approx 1.5$ in two dimensions., Comment: 24 pages, 10 tables, 3 figures

Non-charge based logic in single-hole spin of semiconductor quantum dots (QDs) can be controlled by anisotropic gate potentials providing a notion for making next generation solid-state quantum devices. In this study, we investigate the isotropic and anisotropic behavior of phonon mediated spin relaxation of heavy-hole spin hot spots in QDs. For the electron spin in isotropic QDs, hot spots are known to be always present due to the Rashba spin-orbit coupling. But for heavy holes in isotropic dots, we show that the occurrences of spin hot spots are sensitive to the bulk g-factor. The hot spot for Rashba coupling in InAs and GaSb dots arises because these materials possess negative bulk g-factor, while that for the Dresselhaus coupling in GaAs and InSb dots is found due to their positive bulk g-factor. For anisotropic QDs, on the other hand, the spin hot spot is universally present due to their broken in-plane rotational symmetry. Further, the increasing electric field, that strengthens the Rashba coupling, is shown to cover a wide range of magnetic field by the hot spots. Results demonstrate that the magnetic field, choice of dot materials and size anisotropy can act as effective control parameters which can be experimentally used to design the device for detecting the phonon mediated heavy-hole spin-relaxation behavior of III-V semiconductor QDs., Comment: 15 pages, 11 figures

In a recent Note (Am. J. Phys. 92:397, 2024; arXiv:2309.10826), Vallejo and Bove provide a physical argument based nominally on the second law of thermodynamics as a way of resolving the mathematical question appearing in the title. A remarkable aspect of their argument is that it does not depend on the numerical value of $\pi$, because $e^{x} \ge x^{e}$ for all positive $x$, with equality occurring only when $x = e$. Moreover, their argument does not depend on the validity of the second law but is rather a limited proof of it for this particular case., Comment: 5 pages, 1 figure, accepted for publication by Am. J. Phys., comment on arXiv:2309.10826

In bio-social models, cooperative behaviour has evolved as an adaptive strategy, playing multi-functional roles. One of such roles in populations is to increase the success of survival and reproduction of individuals and their families or social groups. Moreover, collective decision-making in cooperative behaviour is an aspect that is used to study the dynamic behaviour of individuals within a social group. In this paper, we have focused on population dynamics by considering a predator-prey model as our main exemplification, where the generalist predator has adopted a cooperative hunting strategy while consuming their prey. In particular, we have analyzed the dynamic nature of the system when a nonlocal term is introduced in the cooperation. First, the Turing instability condition has been studied for the local model around the coexisting steady-state, followed by the Turing and non-Turing patterns in the presence of the nonlocal interaction term. This work is also concerned with the existence of travelling wave solutions for predator-prey interaction with the nonlocal cooperative hunting strategy. Such solutions are reported for local as well as for nonlocal models. We have characterized the invading speed of the predator with the help of the minimal wave speed of travelling wave solutions connecting the predator-free state to the co-existence state. The travelling waves are found to be non-monotonic in this system. The formation of wave trains has been demonstrated for an extended range of nonlocal interactions. Finally, the importance of psychological effects in shaping the dynamics of nonlocal collective behaviour is demonstrated with several representative examples., Comment: 39 pages, 34 figures

In this work, we introduce Pixelsmith, a zero-shot text-to-image generative framework to sample images at higher resolutions with a single GPU. We are the first to show that it is possible to scale the output of a pre-trained diffusion model by a factor of 1000, opening the road for gigapixel image generation at no additional cost. Our cascading method uses the image generated at the lowest resolution as a baseline to sample at higher resolutions. For the guidance, we introduce the Slider, a tunable mechanism that fuses the overall structure contained in the first-generated image with enhanced fine details. At each inference step, we denoise patches rather than the entire latent space, minimizing memory demands such that a single GPU can handle the process, regardless of the image's resolution. Our experimental results show that Pixelsmith not only achieves higher quality and diversity compared to existing techniques, but also reduces sampling time and artifacts. The code for our work is available at https://github.com/Thanos-DB/Pixelsmith.

We propose that chaotic Glass networks (a class of piecewise-linear Ordinary Differential Equations) are good candidates for the design of true random number generators. A Glass network design has the advantage of involving only standard Boolean logic gates. Furthermore, an already chaotic (deterministic) system combined with random ``jitter'' due to thermal noise can be used to generate random bit sequences in a more robust way than noisy limit-cycle oscillators. Since the goal is to generate bit sequences with as large a positive entropy as possible, it is desirable to have a theoretical method to assess the irregularity of a large class of networks. We develop a procedure here to calculate good upper bounds on the entropy of a Glass network, by means of symbolic representations of the continuous dynamics. Our method improves on a result by Farcot (2006), and allows in principle for an arbitrary level of precision by refinements of the estimate, and we show that in the limiting case, these estimates converge to the true entropy of the symbolic system corresponding to the continuous dynamics. As a check on the method, we demonstrate for an example network that our upper bound after only a few refinement steps is very close to the entropy estimated from a long numerical simulation., Comment: 35 pages, 14 figures

Quality non-degree credentials can lead to good jobs and family-sustaining wages. But not all non-degree credentials lead to good outcomes -- and when they don't, they entrench economic inequalities that impact people of color and women. National Skills Coalition's new report "The Non-Degree Credential Quality Imperative" encourages state policymakers to invest in quality NDCs; shares how we've worked with states to define, measure, and track quality; and offers takeaways for how to advance quality assurance so that policymakers feel confident in investing in non-degree credentials, students have confidence in their training programs, and employers know which programs effectively prepare people for careers.

Automated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design process. In particular, it allows prediction of user movements and ergonomic variables, such as fatigue, prior to conducting user studies. However, there is a fundamental disconnect between simulators hosting state-of-the-art biomechanical user models and simulators used to develop and run VR applications. Existing user simulators often struggle to capture the intricacies of real-world VR applications, reducing ecological validity of user predictions. In this paper, we introduce SIM2VR, a system that aligns user simulation with a given VR application by establishing a continuous closed loop between the two processes. This, for the first time, enables training simulated users directly in the same VR application that real users interact with. We demonstrate that SIM2VR can predict differences in user performance, ergonomics and strategies in a fast-paced, dynamic arcade game. In order to expand the scope of automated biomechanical testing beyond simple visuomotor tasks, advances in cognitive models and reward function design will be needed., Comment: 23 pages, 10 figures, 1 tables; Supplementary Material: 5 pages, 4 figures, 1 table

In functionally complex systems, higher-order connectivity is often revealed in the underlying geometry of networked units. Furthermore, such systems often show signatures of self-organized criticality, a specific type of non-equilibrium collective behaviour associated with an attractor of internal dynamics with long-range correlations and scale invariance, which ensures the robust functioning of complex systems, such as the brain. Here, we highlight the intertwining of features of higher-order geometry and self-organized critical dynamics as a plausible mechanism for the emergence of new properties on a larger scale, representing the central paradigm of the physical notion of complexity. Considering the time scale of the structural evolution with the known separation of the time scale in self-organized criticality, i.e., internal dynamics and external driving, we distinguish three classes of geometries that can shape the self-organized dynamics on them differently. We provide an overview of current trends in the study of collective dynamics phenomena, such as the synchronization of phase oscillators and discrete spin dynamics with higher-order couplings embedded in the faces of simplicial complexes. For a representative example of self-organized critical behaviour induced by higher-order structures, we present a more detailed analysis of the dynamics of field-driven spin reversal on the hysteresis loops in simplicial complexes composed of triangles. These numerical results suggest that two fundamental interactions representing the edge-embedded and triangle-embedded couplings must be taken into account in theoretical models to describe the influence of higher-order geometry on critical dynamics., Comment: 12 pages, 4 figures

This study presents a computational framework to investigate and predict the complicated multiphase properties of eco-friendly lead-free piezoelectric materials, which are crucial for sustainable technological progress. Although their electromechanical properties vary by phase, lead-free piezoelectric materials show a considerable thermo-electromechanical response. Lead-free materials such as Bi$_{0.5}$Na$_{0,5}$TiO$_{3}$ (BNT) and other BNT-type piezoelectric materials transition to rhombohedral (R3c), orthorhombic (Pnma), tetragonal (P4bm), and cubic (Cc) phases with temperature variation. These phases are determined by the symmetry and alignment of the ferroelectric domains. Multiple phases can occur simultaneously under specific thermal, electrical, and mechanical conditions, leading in complex multiphase behaviour. These materials' performance must be assessed by studying such behaviour. This study uses Landau-Ginzburg-Devonshire theory to simulate material micro-domain phase transitions. The computational model for BNT-type piezoelectric material covers temperature-induced ferroelectric domain switching and phase transitions. Therefore, the developed computational approach will assist us in better understanding the influence of these materials' complex multiphase behaviour on creating sustainable solutions with green technologies.

Augmented Reality (AR) provides a safe and low-cost option for hazardous safety training that allows for the visualization of aspects that may be invisible, such as radiation. Effectively visually communicating such threats in the environment around the user is not straightforward. This work describes visually encoding radiation using the spatial awareness mesh of an AR Head Mounted Display. We leverage the AR device's GPUs to develop a real time solution that accumulates multiple dynamic sources and uses stencils to prevent an environment being over saturated with a visualization, as well as supporting the encoding of direction explicitly in the visualization. We perform a user study (25 participants) of different visualizations and obtain user feedback. Results show that there are complex interactions and while no visual representation was statistically superior or inferior, user opinions vary widely. We also discuss the evaluation approaches and provide recommendations.

Living tissues experience various external forces on cells, influencing their behaviour, physiology, shape, gene expression, and destiny through interactions with their environment. Despite much research done in this area, challenges remain in our better understanding of the behaviour of the cell in response to external stimuli, including the arrangement, quantity, and shape of organelles within the cell. This study explores the electromechanical behaviour of biological cells, including organelles like microtubules, mitochondria, nuclei, and cell membranes. Two distinct cell structures have been developed to explore the cell responses to mechanical displacement, resembling actual cell shapes. The finite element method has been utilized to integrate the linear piezoelectric and non-local flexoelectric effects accurately. It is found that the longitudinal stress is absent and only the transverse stress plays a crucial role when the mechanical load is imposed on the top side of the cell through compressive displacement. The impact of flexoelectricity is elucidated by introducing a new parameter called the maximum electric potential ratio ($V_{\text{R,max}}$). It has been found that $V_{\text{R,max}}$ depends upon the orientation angle and shape of the microtubules. Further, the study reveals that the number of microtubules significantly impacts effective elastic and piezoelectric coefficients, affecting cell behaviour based on structure, microtubule orientation, and mechanical stress direction. The insight obtained from the current study can assist in advancements in medical therapies such as tissue engineering and regenerative medicine.

We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance. We replace the self-attention mechanism with a combination of global-local filter blocks to optimize model efficiency. The global filters extract features from the whole feature map whereas the local filters are being adaptively created as 4x4 patches of the same feature map and add restricted scale information. In particular, the feature extraction takes place in the frequency domain rather than the commonly used spatial (image) domain to facilitate faster computations. The fusion of information from both spatial and frequency spaces creates an efficient model with regards to complexity, required data and performance. We test GLFNet on three benchmark datasets achieving state-of-the-art performance on all of them while being almost twice as efficient in terms of GFLOP operations.