We study the standard four-stroke regenerative quantum Stirling heat engine cycle, which assumes local thermal equilibrium at each stage, within the standard weak-coupling, Markovian open quantum system framework. We point out that the regeneration process is not thermodynamically free in a reduced open-system description, and we treat the required work input as an explicit regeneration cost by modifying the cycle efficiency accordingly. We consider two working substances--a single spin-$1/2$ and a pair of interacting spin-$1/2$ particles--and investigate the cycle performance by taking the regeneration cost at its minimum value set by the Carnot heat-pump limit. For comparison, we also analyze the conventional Stirling cycle without regeneration under the same conditions. The super-Carnot efficiencies reported under the cost-free regeneration assumption disappear once the regeneration cost is included: the modified efficiency stays below the Carnot bound, while still remaining higher than the efficiency of the conventional Stirling cycle. For the conventional Stirling cycle, we provide a rigorous Carnot bound using quantum relative entropy, whereas for the regenerative cycle we de
Training datasets have tremendous proprietary value and are vulnerable to unauthorized copying. Existing defenses mainly focus on tracking individual data points, but pay little attention to the threat of dataset regeneration. Through a measurement study of public tumor datasets, we identify substantial real-world partial-dataset replication, raising concerns about potential license noncompliance. To counter the challenge of tracking previously unknown adversarial regeneration, our key insight is that regeneration that preserves model utility inevitably preserves measurable signals across multiple feature scales. We categorize these dataset features into sample-, set-, and distribution-level features and design four similarity metrics to accurately identify regeneration. Based on these metrics, we develop DIPBox, which to our knowledge is the first testing framework that tracks regeneration suspects via multi-scale similarity testing across a spectrum of defender access settings, from limited to full information. We further provide a learning-theoretic analysis that justifies these multi-scale metrics and formalizes an inherent utility--divergence trade-off, implying fundamental li
Regeneration of the nervous system after injury remains an important therapeutic objective, especially in the central nervous system (CNS), in which regeneration is restricted by both neuronal limitations as well as adverse extracellular environments. Conversely, the peripheral nervous system (PNS) displays enhanced regenerative capability in the presence of supportive Schwann cells (SC) and pro-growth stimuli. While the structure and molecular mechanisms are thoroughly understood, functional biomarkers that can non-invasively monitor regeneration in real time are limited. In this review, we discuss the promise of electroencephalography (EEG) as well as electromyography (EMG) as real-time, non-invasive biomarkers to monitor damage to nerves and regeneration in both CNS and PNS contexts. First, we contrast biological and electrophysiological indicators of CNS/PNS injury, showing how EEG signs, including oscillatory power, connectivity, and evoked potential changes, reflect dysfunction due to injury as well as neuroplastic reorganization. Also, EMG provides direct insight into muscle activation and peripheral output, providing useful EEG complementation in neuromuscular pathway integ
Iron accumulates in the neural tissue during peripheral nerve degeneration. Some studies have already been suggested that iron facilitates Wallerian degeneration (WD) events such as Schwann cell de-differentiation. On the other hand, intracellular iron levels remain elevated during nerve regeneration and gradually decrease. Iron enhances Schwann cell differentiation and axonal outgrowth. Therefore, there seems to be a paradox in the role of iron during nerve degeneration and regeneration. We explain this contradiction by suggesting that the increase in intracellular iron concentration during peripheral nerve degeneration is likely to prepare neural cells for the initiation of regeneration. Changes in iron levels are the result of changes in the expression of iron homeostasis proteins. In this review, we will first discuss the changes in the iron/iron homeostasis protein levels during peripheral nerve degeneration and regeneration and then explain how iron is related to nerve regeneration. This data may help better understand the mechanisms of peripheral nerve repair and find a solution to prevent or slow the progression of peripheral neuropathies.
We consider the problem of regenerating 3D objects from 2D images and initial 3D shapes. Most 3D generators operate in a one-shot fashion, converting text or images to a 3D object with limited controllability. We introduce instead MeshReGen, a 3D regenerator that is conditioned on an initial 3D shape. This conceptually simple formulation allows us to support numerous useful tasks, including 3D enhancement, reconstruction, and editing. MeshReGen uses a new conditioning mechanism based on VecSet, which allows the regenerator to update or improve the input geometry with consistent fine-grained details. MeshReGen learns a widely applicable regeneration prior from off-the-shelf 3D datasets via self-supervised pretext tasks and augmentations, without additional annotations. We evaluate both the geometric consistency and fine-grained quality of MeshReGen, achieving state-of-the-art performance in controllable 3D generation across several tasks.
Conventionally, atomic vapor is perceived as a non-living system governed by the principles of thermodynamics and statistical physics. However, the demarcation line between life and non-life appears to be less distinct than previously thought. In a study of amplifying spin waves, we observe a phenomenon reminiscent of life: The atomic spin wave stored in atomic vapor has a capability of absorbing energy from an external light source, and exhibits behaviors akin to active regeneration. We demonstrate that this regeneration significantly enhances the lifetime and retrieval efficiency of the spin wave, while concurrently the noise is effectively suppressed. Our results suggest that the regeneration mechanism holds promise for mitigating the pronounced decoherence typically encountered in spin waves carried by room-temperature media, therefore offering potential applications in the realms of quantum information and precision measurements at ambient conditions.
The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users. Significant efforts have been made to enhance the capabilities of SR systems. These methods typically follow the model-centric paradigm, which involves developing effective models based on fixed datasets. However, this approach often overlooks potential quality issues and flaws inherent in the data. Driven by the potential of data-centric AI, we propose a novel data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. This framework enables the regeneration of a dataset with exceptional cross-architecture generalizability. Additionally, we introduce the DR4SR+ framework, which incorporates a model-aware dataset personalizer to tailor the regenerated dataset specifically for a target model. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets. Furthermore, we conduct in-depth analyses to explore the potential
Machine learning methods for conditional data generation usually build a mapping from source conditional data X to target data Y. The target Y (e.g., text, speech, music, image, video) is usually high-dimensional and complex, and contains information that does not exist in source data, which hinders effective and efficient learning on the source-target mapping. In this paper, we present a learning paradigm called regeneration learning for data generation, which first generates Y' (an abstraction/representation of Y) from X and then generates Y from Y'. During training, Y' is obtained from Y through either handcrafted rules or self-supervised learning and is used to learn X-->Y' and Y'-->Y. Regeneration learning extends the concept of representation learning to data generation tasks, and can be regarded as a counterpart of traditional representation learning, since 1) regeneration learning handles the abstraction (Y') of the target data Y for data generation while traditional representation learning handles the abstraction (X') of source data X for data understanding; 2) both the processes of Y'-->Y in regeneration learning and X-->X' in representation learning can be le
Stem cell regeneration is a vital biological process in self-renewing tissues, governing development and tissue homeostasis. Gene regulatory network dynamics are pivotal in controlling stem cell regeneration and cell type transitions. However, integrating the quantitative dynamics of gene regulatory networks at the single-cell level with stem cell regeneration at the population level poses significant challenges. This study presents a computational framework connecting gene regulatory network dynamics with stem cell regeneration through a data-driven formulation of the inheritance function. The inheritance function captures epigenetic state transitions during cell division in heterogeneous stem cell populations. Our scheme allows the derivation of the inheritance function based on a hybrid model of cross-cell-cycle gene regulation network dynamics. The proposed scheme enables us to derive the inheritance function based on the hybrid model of cross-cell-cycle gene regulation network dynamics. By explicitly incorporating gene regulatory network structure, it replicates cross-cell-cycling gene regulation dynamics through individual-cell-based modeling. The numerical scheme holds the p
Quarkonium production in ultra-relativistic collisions plays a crucial role in probing the existence of hot QCD matter. This study explores quarkonia states dissociation and regeneration in the hot QCD medium while considering momentum anisotropy. The net quarkonia decay width ($Γ_{D}$) arises from two essential processes: collisional damping and gluonic dissociation. The quarkonia regeneration includes the transition from octet to singlet states within the anisotropic medium. Our study utilizes a medium-modified potential that incorporates anisotropy via particle distribution functions. This modified potential gives rise to collisional damping for quarkonia due to the surrounding medium, as well as the transition of quarkonia from singlet to octet states due to interactions with gluons. Furthermore, we employ the detailed balance approach to investigate the regeneration of quarkonia within this medium. Our comprehensive analysis spans various temperature settings, transverse momentum values, and anisotropic strengths. Notably, we find that, in addition to medium temperatures and heavy quark transverse momentum, anisotropy significantly influences the dissociation and regeneration
We demonstrate an all-optical phase regeneration technique based on Kerr soliton combs, which can realize degraded quaternary phase shift keying (QPSK) signal regeneration through phase-sensitive amplification. A Kerr soliton comb is generated at the receiver side of optical communication systems based on a carrier recovery scheme and is used as coherent dual pumps to achieve phase regeneration. Our study will enhance the relay and reception performance of all-optical communication systems.
Distributed storage systems introduce redundancy to protect data from node failures. After a storage node fails, the lost data should be regenerated at a replacement storage node as soon as possible to maintain the same level of redundancy. Minimizing such a regeneration time is critical to the reliability of distributed storage systems. Existing work commits to reduce the regeneration time by either minimizing the regenerating traffic, or adjusting the regenerating traffic patterns, whereas nodes participating data regeneration are generally assumed to be given beforehand. However, such regeneration time also depends heavily on the selection of the participating nodes. Selecting different participating nodes actually involve different data links between the nodes. Real-world distributed storage systems usually exhibit heterogeneous link capacities. It is possible to further reduce the regeneration time via exploiting such link capacity differences and avoiding the link bottlenecks. In this paper, we consider the minimization of the regeneration time by selecting the participating nodes in heterogeneous networks. We analyze the regeneration time and propose node selection algorithm
In this paper, we consider certain linear-fractional branching processes with immigration in varying environments. For $n\ge0,$ let $Z_n$ counts the number of individuals of the $n$-th generation, which excludes the immigrant which enters into the system at time $n.$ We call $n$ a regeneration time if $Z_n=0.$ We give first a criterion for the finiteness or infiniteness of the number of regeneration times. Then, we construct some concrete examples to exhibit the strange phenomena caused by the so-called varying environments. It may happen that the process is extinct but there are only finitely many regeneration times. Also, when there are infinitely many regeneration times, we show that for each $\varepsilon>0,$ the number of regeneration times in $[0,n]$ is no more than $(\log n)^{1+\varepsilon}$ as $n\rightarrow\infty.$
We make use of published yields for $D$-mesons and $J/ψ$ in Pb+Pb collisions at ALICE and a schematic description of the expansion of the hadron gas to study $D$-meson collisions during the hadronic break-up phase as a production mechanism for charmonium in relativistic heavy ion collisions at the Large Hadron Collider. Our calculation is based on chemical reaction rates with thermal cross sections for an effective meson interaction among pseudoscalar and vector mesons. We find that due to regeneration, the newly measured $J/ψ$ yields are consistent with anywhere from roughly $25\%$ to $110\%$ of the total yield present at hadronization time. This allows us to bound the fractional abundance of $J/ψ$ immediately after hadronization: $0.28 \leq \frac{dN_0^{J/ψ}/dy}{dN_{\rm eq}^{J/ψ}/dy} \leq 1.13$. Our results are robust under the relaxation of the particulars of our schematic description and imply that it will be difficult to distinguish regeneration during hadronization from regeneration by final-state hadronic interactions. Therefore, regeneration must be taken into account when modelling.
Adenosine-5'-triphosphate (ATP) plays a crucial role in many biocatalytic reactions and its regeneration can influence the performance of a related enzymatic reaction significantly. Here, we established an electrochemically coupled ATP regeneration by pyruvate oxidase and acetate kinase (ACK) for the phosphorylation of mevalonate catalyzed by mevalonate kinase. A yield of 84% for the product mevalonate phosphate was reached and a total turnover number for ADP of 68. These metrics are promising for the development of an economic feasible bioprocess and surpass many other enzymatic ATP regeneration systems. A comparison was made to polyphosphate kinases (PPKs), ACK, pyruvate kinase, and creatine kinase in terms of the phosphate donor properties and biocatalytic metrics of exemplary reactions. Furthermore, our system was expanded by a PPK that enables the phosphorylation of AMP, which can broaden the spectrum of applications even further.
Distributed storage systems provide large-scale reliable data storage services by spreading redundancy across a large group of storage nodes. In such a large system, node failures take place on a regular basis. When a storage node breaks down, a replacement node is expected to regenerate the redundant data as soon as possible in order to maintain the same level of redundancy. Previous results have been mainly focused on the minimization of network traffic in regeneration. However, in practical networks, where link capacities vary in a wide range, minimizing network traffic does not always yield the minimum regeneration time. In this paper, we investigate two approaches to the problem of minimizing regeneration time in networks with heterogeneous link capacities. The first approach is to download different amounts of repair data from the helping nodes according to the link capacities. The second approach generalizes the conventional star-structured regeneration topology to tree-structured topologies so that we can utilize the links between helping nodes with bypassing low-capacity links. Simulation results show that the flexible tree-structured regeneration scheme that combines the
Partial hepatectomy (PHx) is a surgical intervention where a part of the liver is removed. Due to its extraordinary capacity to regenerate, the liver is able to regenerate about two-thirds of its mass within a few weeks. Nevertheless, in some patients regeneration fails. Understanding the principles and limitations underlying regeneration may permit to control this process and prospectively improve the regeneration. Here, we established a simulation model to mimic the process of regeneration in the liver lobe of a mouse. This model represents each hepatocyte individually and builds upon a previous computational model of regeneration of drug induced damage in a single liver lobule. The present study simulates entire liver lobes that consist of hundreds to thousands of lobules. It accounts for biomechanical control of cell cycle progression (Biomechanical Growth Control), which has not been considered in that previous work. The model reproduced the available experimental observations only if BGC was taken into account. Interestingly, the model predicted that BGC minimizes the number of proliferating neighbor cells of a proliferating cell resulting in a checkerboard-like proliferation
Silicon-substituted hydroxyapatite (SiHA) macroporous scaffolds have been prepared by robocasting. In order to optimize their bone regeneration properties, we have manufactured these scaffolds presenting different microstructures: nanocrystalline and crystalline. Moreover, their surfaces have been decorated with vascular endothelial growth factor (VEGF) to evaluate the potential coupling between vascularization and bone regeneration. In vitro cell culture tests evidence that nanocrystalline SiHA hinders pre-osteblast proliferation, whereas the presence of VEGF enhances the biological functions of both endothelial cells and pre-osteoblasts. The bone regeneration capability has been evaluated using an osteoporotic sheep model. In vivo observations strongly correlate with in vitro cell culture tests. Those scaffolds made of nanocrystalline SiHA were colonized by fibrous tissue, promoted inflammatory response and fostered osteoclast recruitment. These observations discard nanocystalline SiHA as a suitable material for bone regeneration purposes. On the contrary, those scaffolds made of crystalline SiHA and decorated with VEGF exhibited bone regeneration properties, with high ossificati
Chen-Gounelas-Liedtke recently introduced a powerful regeneration technique, a process opposite to specialization, to prove existence results for rational curves on projective $K3$ surfaces. We show that, for projective irreducible holomorphic symplectic manifolds, an analogous regeneration principle holds and provides a very flexible tool to prove existence of uniruled divisors, significantly improving known results.
A new approach to investigate noise spikes due to regeneration in a relaxation oscillator is proposed. Noise spikes have not been satisfactorily accounted for in traditional phase noise models. This paper attempts to explain noise spikes/jump phenomenon by viewing it as phase change in the thermodynamic system(for example, from gas to liquid or magnetization of ferromagnet). Both are due to regeneration (positive feedback in oscillator as well as alignment of spin due to positive feedback in ferromagnet). The mathematical tool used is the partition function in thermodynamics, and the results mapped between thermodynamic system and relaxation oscillator. Theory is developed and formula derived to predict the magnitude of the jump, as a function of design parameter such as regeneration parameter or loop gain. Formulas show that noise increases sharply as regeneration parameter/loop gain approaches one, in much the same way when temperature approaches critical temperature in phase change. Simulations on circuits (Eldo) using CMOS as well as Monte Carlo simulations (Metropolis) on ferromagnet (Ising model) were performed and both show jump behaviour consistent with formula. Measurement