Explicit Transition Research Articles

Beyond fitness: The information imparted in population states by selection throughout lifecycles

We approach the questions, what part of evolutionary change results from selection, and what is the adaptive information flow into a population undergoing selection, as a problem of quantifying the divergence of typical trajectories realized under selection from the expected dynamics of their counterparts under a null stochastic-process model representing the absence of selection. This approach starts with a formulation of adaptation in terms of information and from that identifies selection from the genetic parameters that generate information flow; it is the reverse of a historical approach that defines selection in terms of fitness, and then identifies adaptive characters as those amplified in relative frequency by fitness. Adaptive information is a relative entropy on distributions of histories computed directly from the generators of stochastic evolutionary population processes, which in large population limits can be approximated by its leading exponential dependence as a large-deviation function. We study a particular class of generators that represent the genetic dependence of explicit transitions around reproductive cycles in terms of stoichiometry, familiar from chemical reaction networks. Following Smith (2023), which showed that partitioning evolutionary events among genetically distinct realizations of lifecycles yields a more consistent causal analysis through the Price equation than the construction from units of selection and fitness, here we show that it likewise yields more complete evolutionary information measures.

Continuous Value Assignment: A Doubly Robust Data Augmentation for Off-Policy Learning.

Deep reinforcement learning (RL) has witnessed remarkable success in a wide range of control tasks. To overcome RL's notorious sample inefficiency, prior studies have explored data augmentation techniques leveraging collected transition data. However, these methods face challenges in synthesizing transitions adhering to the authentic environment dynamics, especially when the transition is high-dimensional and includes many redundant/irrelevant features to the task. In this article, we introduce continuous value assignment (CVA), an innovative optimization-level data augmentation approach that directly synthesizes novel training data in the state-action value space, effectively bypassing the need for explicit transition modeling. The key intuition of our method is that the transition plays an intermediate role in calculating the state-action value during optimization, and therefore directly augmenting the state-action value is more causally related to the optimization process. Specifically, our CVA combines parameterized value prediction and nonparametric value interpolation from neighboring states, resulting in doubly robust target values w.r.t. novel states and actions. Extensive experiments demonstrate CVA's substantial improvements in sample efficiency across complex continuous control tasks, surpassing several advanced baselines.

Matrix Whittaker processes

We study a discrete-time Markov process on triangular arrays of matrices of size dge 1, driven by inverse Wishart random matrices. The components of the right edge evolve as multiplicative random walks on positive definite matrices with one-sided interactions and can be viewed as a d-dimensional generalisation of log-gamma polymer partition functions. We establish intertwining relations to prove that, for suitable initial configurations of the triangular process, the bottom edge has an autonomous Markovian evolution with an explicit transition kernel. We then show that, for a special singular initial configuration, the fixed-time law of the bottom edge is a matrix Whittaker measure, which we define. To achieve this, we perform a Laplace approximation that requires solving a constrained minimisation problem for certain energy functions of matrix arguments on directed graphs.

On the singular values of complex matrix Brownian motion with a matrix drift

Let MatC(K,N) be the space of K×N complex matrices. Let Bt be Brownian motion on MatC(K,N) starting from the zero matrix and M∈MatC(K,N). We prove that, with K≥N, the N eigenvalues of Bt+tM∗Bt+tM form a Markov process with an explicit transition kernel. This generalizes a classical result of Rogers and Pitman (Ann. Probab. 9 (1981) 573–582) for multidimensional Brownian motion with drift which corresponds to N=1. We then give two more descriptions for this Markov process. First, as independent squared Bessel diffusion processes in the wide sense, introduced by Watanabe (Z. Wahrsch. Verw. Gebiete 31 (1975) 115–124) and studied by Pitman and Yor (In Stochastic Integrals (Proc. Sympos., Univ. Durham, Durham, 1980) (1981) 285–370 Springer), conditioned to never intersect. Second, as the distribution of the top row of interacting squared Bessel type diffusions in some interlacting array. The last two descriptions also extend to a general class of one-dimensional diffusions.

Delineating the Intrinsic, Long-Term Path of Land Degradation: A Spatially Explicit Transition Matrix for Italy, 1960-2010.

Vulnerability to land degradation in southern Europe has increased substantially in the last decades because of climate and land-use change, soil deterioration, and rising human pressure. The present work focuses on a quantitative evaluation of changes over time in the level of vulnerability to land degradation of a Mediterranean country (Italy) using a composite indicator, the environmentally sensitive area index (ESAI), which is the final outcome of a complex model conceived to assess land vulnerability on the basis of climate, soil, vegetation, and human pressure. Considering four different levels of vulnerability to land degradation (not affected, potentially affected, fragile, and critical), the main trajectories of this index were highlighted in a long-time perspective (1960-2010), discriminating dynamics over two sub-periods (1960-1990 and 1990-2010). The empirical results at a very detailed spatial scale (1 km2 grid) reflect spatial consolidation of degradation hot-spots over time. However, aggregated trajectories of change indicate an overall improvement in the environmental conditions between 1990 and 2010 compared with what is observed during the first period (1960-1990). Worse environmental conditions concerned southern Italian regions with a dry climate and poor soil conditions in the first time interval, large parts of northern Italy, traditionally recognized as a wet and affluent agricultural region, experienced increasing levels of land vulnerability in the second time interval. Being classified as an unaffected region according with the Italian national action plan (NAP), the expansion of (originally sparse) degradation hot-spots in northern Italy, reflective of an overall increase in critical areas, suggests a substantial re-thinking of the Italian NAP. This may lead to a redesign of individual regional action plans (RAPs) implementing place-specific approaches and comprehensive measures to be adopted to mitigate land degradation.

Transition density of an infinite-dimensional diffusion with the jack parameter

AbstractFrom the Poisson–Dirichlet diffusions to theZ-measure diffusions, they all have explicit transition densities. We show that the transition densities of theZ-measure diffusions can also be expressed as a mixture of a sequence of probability measures on the Thoma simplex. The coefficients are the same as the coefficients in the Poisson–Dirichlet diffusions. This fact will be uncovered by a dual process method in a special case where theZ-measure diffusions are established through an up–down chain in the Young graph.

Securing Approximate Computing Systems via Obfuscating Approximate-Precise Boundary

Approximate computing (AC) techniques have been leveraged to improve computing performance and energy efficiency with minor degradation in accuracy. Recent literature indicates that some AC mechanisms could be exploited by attackers to implement new attack surfaces. To address the emerging attacks in AC systems, we propose to obfuscate the approximate-precise boundary (APB) with entry-blurring and boundary-broadening schemes. The proposed entry-blurring scheme leverages a hidden quality metric, which has a strong correlation with approximation errors, to obscure the entrance of APB and eliminate the explicit transition between approximate and precise modes, thus improving AC systems’ resilience against APB attacks. The proposed boundary-broadening scheme enlarges the transition zone between approximate and precise modes by expanding a single APB threshold to two comparison thresholds, and it further enables a random selection of AC modules in the candidate library. The protection mechanisms provided by our obfuscation method strengthens AC systems’ resilience against APB attacks. Our case studies show that the proposed entry-blurring scheme improves the application quality by up to 168% over the baseline and successfully achieves the desired accuracy. The latency overhead of our method is negligible and the increase on area and power cost can be minimized to 6% and 8%, respectively.

TELS: Evolution patterns of research keywords from the evidence of PNAS Social Sciences topics.

By reviewing scientific literature, researchers may obtain a comprehensive understanding of field developments, keeping abreast of the current research status and hotspot shifts. The evolution pattern of keywords is supposed to be an efficient indicator in revealing the shifting and sustainability configuration of scientific concepts, ideas, and research hotspots. Here we take an extensive investigation of the evolution of keywords among all publications in PNAS Social Sciences from 1990 to 2021. Statistical tests show the keyword mention time series always accompanied by the emergence of a log-normal distribution. Additionally, we introduce a novel schema of four patterns (TELS), which are Transient impact type, Explosive impact type, Large impact type, and Small impact type, respectively, to illustrate the evolution of keywords. The TELS schema can be used to capture the whole life circle feature of any proposed keyword, from a pool of candidates. By dividing the entire time into four periods, we also introduce the concept of elite keywords to reveal the temporal feature of social sciences focus. An explicit transition from anthropology research to neuroscience and social problems research can be observed from the evolution diagram. We argue that the proposed method is of general sense and might be applicable to other fields of science.

Spatialising time: Perceiving multiple layers of time in narrative environment

There is no escape from time. That includes architecture which dominantly discusses space. This paper aims to explore how people can experience time spatially. Notably, this study looks at the context of the narrative environment where stories are imbued in space to enhance their engagement with the audience. Time in a narrative is presented through the narrated time and the real-world time where the audience of the narrative perceive it. Both types of time can be overlapped, whether the audience is consciously aware of that or not. Time is integrated into a narrative through order, plot, pace, and duration. All these four aspects are experienced by the visitor of a narrative environment to connect events and make sense of their journey and the whole story. The relationship between events experienced through time creates a spatial trajectory. Two case studies were conducted to explore this idea: a visit to Museum Kebangkitan Nasional and participation in Secret Cinema. It is found that time can be spatialised through the explicit transition of graphics, a play of lighting, and overall spatial organisation.

Active Grammatical Inference for Non-Markovian Planning

Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L* has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian reward signal, which must be inferred from experiments. We call this setting non-Markovian planning, as opposed to non-Markovian RL. The proposed approach leverages L* to explicate an automaton structure for the underlying planning objective. We exploit the environment model to learn an automaton faster and integrate it with value iteration to accelerate the planning. We compare against recent non-Markovian RL solutions which leverage grammatical inference, and establish complexity results that illustrate the difference in runtime between grammatical inference in planning and RL settings.

Producer organizations as transition intermediaries? Insights from organic and conventional vegetable systems in Uruguay

Increased pressures on agri-food systems have indicated the importance of intermediaries to facilitate sustainability transitions. While producer organizations are acknowledged as intermediaries between individual producers and other food system actors, their role as sustainability transition intermediaries remains understudied. This paper explores the potential of producer organizations as transition intermediaries to support producers in their needs to adopt sustainable production practices. Ten cases of producer organizations in conventional (regime) and organic (niche) vegetable systems in Uruguay were studied qualitatively. Findings show that the classic intermediary roles that producer organizations fulfil in food systems also address the needs of producers in their transition to sustainable food systems. By providing organic inputs, organizing access to output markets, sharing knowledge, and facilitating sustainable production practices, producer organizations support producers within and across regime and niche. Producer organizations mostly function as implicit transition intermediaries, facilitated by their legitimacy among producers, their embeddedness in rural networks, and by refraining from taking a strong normative position. Producer organizations have the potential to be more explicit transition intermediaries, however this position comes with limitations. We provide policy recommendations to optimize the transition intermediary potential of producer organizations in their facilitation towards sustainable food systems.

Exchangeable coalescents, ultrametric spaces, nested interval-partitions: A unifying approach

Kingman’s (1978) representation theorem (J. Lond. Math. Soc. (2) 18 (1978) 374–380) states that any exchangeable partition of ℕ can be represented as a paintbox based on a random mass-partition. Similarly, any exchangeable composition (i.e., ordered partition of ℕ) can be represented as a paintbox based on an interval-partition (Gnedin (1997) Ann. Probab. 25 (1997) 1437–1450). Our first main result is that any exchangeable coalescent process (not necessarily Markovian) can be represented as a paintbox based on a random nondecreasing process valued in interval-partitions, called nested interval-partition, generalizing the notion of comb metric space introduced in Lambert and Uribe Bravo (2017) (p-Adic Numbers Ultrametric Anal. Appl. 9 (2017) 22–38) to represent compact ultrametric spaces. As a special case, we show that any Λ-coalescent can be obtained from a paintbox based on a unique random nested interval partition called Λ-comb, which is Markovian with explicit transitions. This nested interval-partition directly relates to the flow of bridges of Bertoin and Le Gall (2003) (Probab. Theory Related Fields 126 (2003) 261–288). We also display a particularly simple description of the so-called evolving coalescent (Pfaffelhuber and Wakolbinger (2006) Stochastic Process. Appl. 116 (2006) 1836–1859) by a comb-valued Markov process. Next, we prove that any ultrametric measure space U, under mild measure-theoretic assumptions on U, is the leaf set of a tree composed of a separable subtree called the backbone, on which are grafted additional subtrees, which act as star-trees from the standpoint of sampling. Displaying this so-called weak isometry requires us to extend the Gromov-weak topology of Greven, Pfaffelhuber and Winter (2009) (Probab. Theory Related Fields 145 (2009) 285–322), that was initially designed for separable metric spaces, to nonseparable ultrametric spaces. It allows us to show that for any such ultrametric space U, there is a nested interval-partition which is (1) indistinguishable from U in the Gromov-weak topology; (2) weakly isometric to U if U has a complete backbone; (3) isometric to U if U is complete and separable.

Gamma Radiation Chemistry of Polydimethylsiloxane Foam in Radiation-Thermal Environments: Experiments and Simulations.

The γ radiolysis behavior of polydimethylsiloxane (PDMS) in the radiation-thermal environments (dose rate, 0.2 Gy/s) is studied to pinpoint the basic knowledge of the temperature (20-70 °C) effects. The non-monotonous temperature effects on the formation of gas products, paramagnetic species in silica, and cross-linking density are proposed to correlate with the complex chemical reaction mechanisms. Besides, molecular dynamics simulation and theoretical calculation are first performed simultaneously based on the radical chemistry and intricate material composition, making it easier to comprehend and further harness the radiolysis mechanisms and structure deterioration of PDMS. The γ radiation-induced primary gas products and dominant cross-linking phenomena are reproduced by the molecular dynamics simulations with a reactive force field, and the reaction mechanisms and physicochemical interactions among PDMS chains, gas products, reactive radicals, and silica fillers are thoroughly studied at the atomic scale. The thermochemistry of the barrierless radical coupling reactions and reactions with explicit high-barrier transition states is calculated at the M06-2X theoretical level with the 6-311g(d, p) basis set. The barrierless reactions are all exothermal with the heat release of 321-618 kJ/mol, while the potential barriers for reactions with explicit transition states vary between 37 and 229 kJ/mol. The results show that γ radiation-induced radicals are crucial for the ensuing gas formation and cross-linking reactions, especially for the radical coupling reactions. The radical chemistry involved in the radiolytic PDMS is the key to understand and simulate its radiolysis behavior, according to the experimental and simulated results.

Marking Predictability and Prediction in Labeled Petri Nets

This article studies the marking prediction problem in labeled Petri nets. Marking prediction aims to recognize a priori that the plant will inevitably reach a given set of alert markings in finite future steps. Specifically, we require that a marking prediction procedure should have the following properties: i) no missed alarm, i.e., an alarm can always be issued before reaching an alert marking; and ii) no false alarm, i.e., once an alarm is issued, the plant will eventually reach an alert marking in the future. To this end, the notion of marking predictability is proposed as a necessary and sufficient condition for the solvability of the marking prediction problem. A fundamental marking estimation problem in a labeled Petri net is first solved using minimal explanations and basis reachability graphs. Then, we propose two notions of basis markings called boundary basis markings and basis indicators, and prove that a plant is predictable with respect to a set of alert markings if all basis markings confusable with boundary basis markings are basis indicators. By properly selecting a set of explicit transitions, the set of basis indicators can be efficiently computed by structural analysis of the corresponding basis reachability graph. Our method has polynomial complexity in the number of basis markings. Finally, we present an effective algorithm for online marking prediction if the plant is predictable.

Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies

Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs. We propose a simple stochastic time-series model to represent the export regime of a catchment beyond simple regression. We propose how to get the required data with the least effort when the use of high-frequency in situ analyzers is not feasible or restricted. Sampling strategies based on extreme events are essential for reducing the prediction uncertainty of the catchment behavior.

Evolution of the magnetic and charge orders in La0.15Pr0.45Ca0.40MnO3: Assessing the role of particle size and magnetic field

The evolution of the ferromagnetic (FM)/antiferromagnetic (AFM) orders and charge order (CO) as a function of the particle size and applied magnetic field (H) has been studied in detail in the canonical phase-separated system La1−x−yPryCaxMnO3 with larger Pr content (La0.15Pr0.45Ca0.40MnO3). At lower particle size (~ 45 nm) a low TC FM phase dominates with no direct evidence of the AFM and CO phases in the magnetization (M) data. As the particle size increases, the AFM and CO grow with a concomitant decrease in the FM fraction, as shown by a decrease in the magnetization and explicit transitions. All three electronic orders become distinctly visible at particle size ~ 139 nm, with TCO ~ 213 K, TN ~ 173 K, and TC ~ 58 K. At larger sizes, e.g., ~ 370 nm, FM appears as the robust phase while the AFM and CO are quenched. However, the presence of the AFM/CO clusters in this sample is signed by the hysteresis in the FCC and FCW magnetization in the FM regime. The paramagnetic (PM) region susceptibility as a function of temperature exhibits scaling of the type χ=C(T-TCON)γ, where TCON is the temperature at which the FM response becomes visible. The exponent remains γ ≈ 1.17 for samples with less dominant CO. At the ~ 139 nm, the CO is the most robust, and the exponent takes a value γ ≈ 1.31. The effect of the magnetic field in the case of the ~ 139 nm samples shows that the AFM melts at HAFM ~ 40 kOe, while the CO melts at HCO ~ 50 kOe. The scaling of the paramagnetic regime susceptibility (H/M) at different H shows that γ remains confined to the range 1.30–1.34 up to 20 kOe, and then decreases slightly to ≈1.24 at 30 kOe. At the AFM melting magnetic field ~ 40 kOe, it drops to ≈1.13, and at further higher fields, the above scaling breaks down. Thus TCON remains a good substitute for the canonical TC in the scaling law χ=C(T-TCON)γ in strongly phase-separated manganite. Our results show that a phase-separated system with FM dominance deviates from the Curie-Weiss law valid for canonical ferromagnets. However, the presence of explicit CO makes the H/M – T scaling akin to a 3D Heisenberg ferromagnet.

Diffusions on a space of interval partitions: Poisson–Dirichlet stationary distributions

We introduce diffusions on a space of interval partitions of the unit interval that are stationary with the Poisson–Dirichlet laws with parameters α,0 and α,α. The construction has two steps. The first is a general construction of interval partition processes obtained previously by decorating the jumps of a Lévy process with independent excursions. Here, we focus on the second step which requires explicit transition kernels and, what we call, pseudo-stationarity. This allows us to study processes obtained from the original construction via scaling and time-change. In a sequel paper we establish connections to diffusions on decreasing sequences introduced by Ethier and Kurtz (Adv. in Appl. Probab. 13 (1981) 429–452) and Petrov (Funktsional. Anal. i Prilozhen. 43 (2009) 45–66). The latter diffusions are continuum limits of up-down Markov chains on Chinese restaurant processes. Our construction is also a step toward resolving longstanding conjectures by Feng and Sun on measure-valued Poisson–Dirichlet diffusions and by Aldous on a continuum-tree-valued diffusion.

Photoactivity of an anodized biocompatible TiNbSn alloy prepared in sodium tartrate/hydrogen peroxide aqueous solution

A bioactive TiNbSn alloy was anodized in an electrolyte of sodium tartrate containing hydrogen peroxide (H2O2). The photo-induced characteristics of the anodized alloy were explored by focusing on its crystallinity and electrochemical conditions. The electrolysis curve of the anodization exhibited a higher voltage in the TiNbSn substrate than in the pure Ti substrate. Highly crystallized porous rutile-structured TiO2 was formed on TiNbSn, whereas relatively low-crystallized glassy anatase-structured TiO2 was formed on Ti. The absorption spectra of the oxide, formed on TiNbSn, revealed a steep absorption decrease with increasing wavelength while an explicit transition was not observed for the oxide formed on Ti. The anodized TiNbSn alloy exhibited superior photoactivity and generated hydroxyl radicals under ultraviolet (UV) illumination. The photoactivity of the anodized TiNbSn was attributed to TiO2, which was highly crystallized by the application of a high voltage, thus suppressing the recombination of the photogenerated carriers.

A cycle‐jumping method for multicyclic Hubbert modeling of resource production

AbstractThe amount of ultimately recoverable resources and/or timing of peak production have been the central purpose of numerous studies. One broadly applied method is Hubbert modeling, subsequently extended as multicyclic Hubbert modeling. This paper explores a modification to conventional multicyclic Hubbert modeling that we term “cycle‐jumping” wherein the overlap of multiple curves is limited and explicitly accounted for. The model is designed in a way that each curve is described by the same three parameters as a multicyclic Hubbert model, and every two curves are connected through an explicit transition. The transition width indicates the time of the shift from one curve to the next and is controlled by a weighting parameter for the respective curves. Cycle‐jumping provides a superior data fit compared to the conventional cycle‐addition model and, more important, reflects historical production data more realistically as socioeconomic and political factors important to resource production vary in time.Recommendations for Resource Managers Conventional multicyclic Hubbert modeling poorly reflects transitions in production trends. Cycle‐jumping with a finite transition period practically and mathematically provides a superior model for historical resource production by limiting the overlap of multiple curves. Cycle‐jumping with a finite transition period reflects more realistically the production profile affected by external factors including capturing inherent asymmetry in different cycles.

Artificial Intelligence and the Common Sense of Animals.

The problem of common sense remains a major obstacle to progress in artificial intelligence. Here, we argue that common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the understanding of objects, space, and causality. The field of animal cognition has developed numerous experimental protocols for studying these capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply these methods directly to evaluate RL agents in 3D environments. Besides evaluation, the animal cognition literature offers a rich source of behavioural data, which can serve as inspiration for RL tasks and curricula.