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Recent AI research has given rise to powerful techniques for deep reinforcement learning. In their combination of representation learning with reward-driven behavior, deep reinforcement learning would appear to have inherent interest for psychology and neuroscience. One reservation has been that deep reinforcement learning procedures demand large amounts of training data, suggesting that these algorithms may differ fundamentally from those underlying human learning. While this concern applies to the initial wave of deep RL techniques, subsequent AI work has established methods that allow deep RL systems to learn more quickly and efficiently. Two particularly interesting and promising techniques center, respectively, on episodic memory and meta-learning. Alongside their interest as AI techniques, deep RL methods leveraging episodic memory and meta-learning have direct and interesting implications for psychology and neuroscience. One subtle but critically important insight which these techniques bring into focus is the fundamental connection between fast and slow forms of learning. Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive scientists interested in understanding human learning. However, the concern has been raised that deep RL may be too sample-inefficient – that is, it may simply be too slow – to provide a plausible model of how humans learn. In the present review, we counter this critique by describing recently developed techniques that allow deep RL to operate more nimbly, solving problems much more quickly than previous methods. Although these techniques were developed in an AI context, we propose that they may have rich implications for psychology and neuroscience. A key insight, arising from these AI methods, concerns the fundamental connection between fast RL and slower, more incremental forms of learning. Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive scientists interested in understanding human learning. However, the concern has been raised that deep RL may be too sample-inefficient – that is, it may simply be too slow – to provide a plausible model of how humans learn. In the present review, we counter this critique by describing recently developed techniques that allow deep RL to operate more nimbly, solving problems much more quickly than previous methods. Although these techniques were developed in an AI context, we propose that they may have rich implications for psychology and neuroscience. A key insight, arising from these AI methods, concerns the fundamental connection between fast RL and slower, more incremental forms of learning. 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A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes via (i) identification of the structural changes involved in these processes and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either generic internal coordinates or a user-defined set of parameters. Using a variational formulation of conformational dynamics, it is shown that an existing method-the time-lagged independent component analysis-provides the optional solution to this problem. In addition, optimal indicators-order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates-are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue intrinsically disordered peptide kinase inducible domain (KID). The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis.

Summary The leaf economics spectrum (LES) provides a useful framework for examining species strategies as shaped by their evolutionary history. However, that spectrum, as originally described, involved only two key resources (carbon and nutrients) and one of three economically important plant organs. Herein, I evaluate whether the economics spectrum idea can be broadly extended to water – the third key resource –stems, roots and entire plants and to individual, community and ecosystem scales. My overarching hypothesis is that strong selection along trait trade‐off axes, in tandem with biophysical constraints, results in convergence for any taxon on a uniformly fast, medium or slow strategy (i.e. rates of resource acquisition and processing) for all organs and all resources. Evidence for economic trait spectra exists for stems and roots as well as leaves, and for traits related to water as well as carbon and nutrients. These apply generally within and across scales (within and across communities, climate zones, biomes and lineages). There are linkages across organs and coupling among resources, resulting in an integrated whole‐plant economics spectrum. Species capable of moving water rapidly have low tissue density, short tissue life span and high rates of resource acquisition and flux at organ and individual scales. The reverse is true for species with the slow strategy. Different traits may be important in different conditions, but as being fast in one respect generally requires being fast in others, being fast or slow is a general feature of species. Economic traits influence performance and fitness consistent with trait‐based theory about underlying adaptive mechanisms. Traits help explain differences in growth and survival across resource gradients and thus help explain the distribution of species and the assembly of communities across light, water and nutrient gradients. Traits scale up – fast traits are associated with faster rates of ecosystem processes such as decomposition or primary productivity, and slow traits with slow process rates. Synthesis . Traits matter. A single ‘fast–slow’ plant economics spectrum that integrates across leaves, stems and roots is a key feature of the plant universe and helps to explain individual ecological strategies, community assembly processes and the functioning of ecosystems.

Powdery mildew development on the slow-mildewing wheat cultivar Knox was compared to that on the susceptible cultivar Vermillion over a period of 4 yr in the field at Lafayette, Indiana. Cultivars received three levels of nitrogen fertilizer to determine if high levels of N affected the expression of slow-mildewing in Knox wheat. Knox's resistance was evident under conditions favoring moderate to severe disease on Vermillion. Under low nitrogen fertility or unfavorable weather there was little difference in level of mildew on the two cultivars; under more favorable conditions disease severity increased greatly on Vermillion but increased little on Knox. The area under the disease progress curve had a lower error variance than statistics associated with the logit transformation of severity data and hence was a superior measurement of slow-mildewing. Slow-mildewing remains effective under the highest rates of nitrogen fertilization likely to be applied to wheat. In breeding for slow-mildewing, high rates of N provide optimal conditions for recognition of this resistance.

The use of equilibrium expressions for sorption to natural particles in fate and transport models is often invalid due to slow kinetics. This paper reviews recent research into the causes of slow sorption and desorption rates at the intraparticle level and how this phenomenon relates to contaminant transport, bioavailability, and remediation. Sorption kinetics are complex and poorly predictable at present. Diffusion limitations appear to play a major role. Contending mechanisms include diffusion through natural organic matter matrices and diffusion through intraparticle nanopores. These mechanisms probably operate simultaneously, but the relative importance of each in a given system is indeterminate. Sorption shows anomalous behaviors that are presently not well explained by the simple diffusion models, including concentration dependence of the slow fraction, distributed rate constants, and kinetic hysteresis. Research is needed to determine whether adsorption/desorption bond energies may play a role along with molecular diffusion in slow kinetics. The possible existence of high-energy adsorption sites both within the internal matrix of organic matter and in nanopores is discussed. Sorption can be rate-limiting to biodegradation, bioavailablity, and subsurface transport of contaminants. Characterization of mechanism is thus critical for fate and risk assessment. Studies are needed to measure desorption kinetics under digestive and respiratory conditions in receptor organisms. Conditions under which the constraint of slow desorption may be overcome are discussed, including the addition of biological or chemical agents, the application of heat, and the physical alteration of the soil.

During much of sleep, virtually all cortical neurons undergo a slow oscillation (<1 Hz) in membrane potential, cycling from a hyperpolarized state of silence to a depolarized state of intense firing. This slow oscillation is the fundamental cellular phenomenon that organizes other sleep rhythms such as spindles and slow waves. Using high-density electroencephalogram recordings in humans, we show here that each cycle of the slow oscillation is a traveling wave. Each wave originates at a definite site and travels over the scalp at an estimated speed of 1.2-7.0 m/sec. Waves originate more frequently in prefrontal-orbitofrontal regions and propagate in an anteroposterior direction. Their rate of occurrence increases progressively reaching almost once per second as sleep deepens. The pattern of origin and propagation of sleep slow oscillations is reproducible across nights and subjects and provides a blueprint of cortical excitability and connectivity. The orderly propagation of correlated activity along connected pathways may play a role in spike timing-dependent synaptic plasticity during sleep.

Slow earthquakes are characterized by a wide spectrum of fault slip behaviors and seismic radiation patterns that differ from those of traditional earthquakes. However, slow earthquakes and huge megathrust earthquakes can have common slip mechanisms and are located in neighboring regions of the seismogenic zone. The frequent occurrence of slow earthquakes may help to reveal the physics underlying megathrust events as useful analogs. Slow earthquakes may function as stress meters because of their high sensitivity to stress changes in the seismogenic zone. Episodic stress transfer to megathrust source faults leads to an increased probability of triggering huge earthquakes if the adjacent locked region is critically loaded. Careful and precise monitoring of slow earthquakes may provide new information on the likelihood of impending huge earthquakes.

The meaning of the inflationary slow-roll approximation is formalized. Comparisons are made between an approach based on the Hamilton-Jacobi equations, governing the evolution of the Hubble parameter, and the usual scenario based on the evolution of the potential energy density. The vital role of the inflationary attractor solution is emphasized, and some of its properties described. We propose a new measure of inflation, based upon contraction of the comoving Hubble length as opposed to the usual e-foldings of physical expansion, and derive relevant formulas. We introduce an infinite hierarchy of slow-roll parameters, and show that only a finite number of them are required to produce results to a given order. The extension of the slow-roll approximation into an analytic slow-roll expansion, converging on the exact solution, is provided. Its role in calculations of inflationary dynamics is discussed. We explore rational approximants as a method of extending the range of convergence of the slow-roll expansion up to, and beyond, the end of inflation.

This paper offers some econometric evidence on the sources of slow growth in Sub-Saharan Africa. The evidence suggests that the continent's slow growth can be explained in an international cross-country framework, without the need to invoke a special explanation unique to Sub-Saharan Africa. We find that poor economic policies have played an especially important role in the slow growth, most importantly Africa's lack of openness to international markets. In addition, geographical factors such as lack of access to the sea and tropical climate have also contributed to Africa's slow growth.

In the Earth's history, periods of relatively stable climate have often been interrupted by sharp transitions to a contrasting state. One explanation for such events of abrupt change is that they happened when the earth system reached a critical tipping point. However, this remains hard to prove for events in the remote past, and it is even more difficult to predict if and when we might reach a tipping point for abrupt climate change in the future. Here, we analyze eight ancient abrupt climate shifts and show that they were all preceded by a characteristic slowing down of the fluctuations starting well before the actual shift. Such slowing down, measured as increased autocorrelation, can be mathematically shown to be a hallmark of tipping points. Therefore, our results imply independent empirical evidence for the idea that past abrupt shifts were associated with the passing of critical thresholds. Because the mechanism causing slowing down is fundamentally inherent to tipping points, it follows that our way to detect slowing down might be used as a universal early warning signal for upcoming catastrophic change. Because tipping points in ecosystems and other complex systems are notoriously hard to predict in other ways, this is a promising perspective.

INTRODUCTION: The Mediterranean and dash diets have been shown to slow cognitive decline; however, neither diet is specific to the nutrition literature on dementia prevention. METHODS: We devised the Mediterranean-Dietary Approach to Systolic Hypertension (DASH) diet intervention for neurodegenerative delay (MIND) diet score that specifically captures dietary components shown to be neuroprotective and related it to change in cognition over an average 4.7 years among 960 participants of the Memory and Aging Project. RESULTS: In adjusted mixed models, the MIND score was positively associated with slower decline in global cognitive score (β = 0.0092; P < .0001) and with each of five cognitive domains. The difference in decline rates for being in the top tertile of MIND diet scores versus the lowest was equivalent to being 7.5 years younger in age. DISCUSSION: The study findings suggest that the MIND diet substantially slows cognitive decline with age. Replication of these findings in a dietary intervention trial would be required to verify its relevance to brain health.

The violence wrought by climate change, toxic drift, deforestation, oil spills, and the environmental aftermath of war takes place gradually and often invisibly. Using the innovative concept of &ldquo;slow violence&rdquo; to describe these threats, Rob Nixon focuses on the inattention we have paid to the attritional lethality of many environmental crises, in contrast with the sensational, spectacle-driven messaging that impels public activism today. Slow violence, because it is so readily ignored by a hard-charging capitalism, exacerbates the vulnerability of ecosystems and of people who are poor, disempowered, and often involuntarily displaced, while fueling social conflicts that arise from desperation as life-sustaining conditions erode. In a book of extraordinary scope, Nixon examines a cluster of writer-activists affiliated with the environmentalism of the poor in the global South. By approaching environmental justice literature from this transnational perspective, he exposes the limitations of the national and local frames that dominate environmental writing. And by skillfully illuminating the strategies these writer-activists deploy to give dramatic visibility to environmental emergencies, Nixon invites his readers to engage with some of the most pressing challenges of our time.

Nerve cells communicate with each other through two mechanisms, referred to as fast and slow synaptic transmission. Fast-acting neurotransmitters, e.g., glutamate (excitatory) and gamma-aminobutyric acid (GABA) (inhibitory), achieve effects on their target cells within one millisecond by virtue of opening ligand-operated ion channels. In contrast, all of the effects of the biogenic amine and peptide neurotransmitters, as well as many of the effects of glutamate and GABA, are achieved over hundreds of milliseconds to minutes by slow synaptic transmission. This latter process is mediated through an enormously more complicated sequence of biochemical steps, involving second messengers, protein kinases, and protein phosphatases. Slow-acting neurotransmitters control the efficacy of fast synaptic transmission by regulating the efficiency of neurotransmitter release from presynaptic terminals and by regulating the efficiency with which fast-acting neurotransmitters produce their effects on postsynaptic receptors.

Journal Article To Slow or Not to Slow: The Economics of The Greenhouse Effect Get access William D. Nordhaus William D. Nordhaus Yale University and the Cowles Foundation Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 101, Issue 407, 1 July 1991, Pages 920–937, https://doi.org/10.2307/2233864 Published: 01 July 1991

The speed of absorption of dietary amino acids by the gut varies according to the type of ingested dietary protein. This could affect postprandial protein synthesis, breakdown, and deposition. To test this hypothesis, two intrinsically 13C-leucine-labeled milk proteins, casein (CAS) and whey protein (WP), of different physicochemical properties were ingested as one single meal by healthy adults. Postprandial whole body leucine kinetics were assessed by using a dual tracer methodology. WP induced a dramatic but short increase of plasma amino acids. CAS induced a prolonged plateau of moderate hyperaminoacidemia, probably because of a slow gastric emptying. Whole body protein breakdown was inhibited by 34% after CAS ingestion but not after WP ingestion. Postprandial protein synthesis was stimulated by 68% with the WP meal and to a lesser extent (+31%) with the CAS meal. Postprandial whole body leucine oxidation over 7 h was lower with CAS (272 +/- 91 micromol.kg-1) than with WP (373 +/- 56 micromol.kg-1). Leucine intake was identical in both meals (380 micromol.kg-1). Therefore, net leucine balance over the 7 h after the meal was more positive with CAS than with WP (P < 0.05, WP vs. CAS). In conclusion, the speed of protein digestion and amino acid absorption from the gut has a major effect on whole body protein anabolism after one single meal. By analogy with carbohydrate metabolism, slow and fast proteins modulate the postprandial metabolic response, a concept to be applied to wasting situations.

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

Modern implementations of TCP contain four intertwined algorithms that have never been fully documented as Internet standards: slow start, congestion avoidance, fast retransmit, and fast recovery. [2] and [3] provide some details on these algorithms, [4] provides examples of the algorithms in action, and [5] provides the source code for the 4.4BSD implementation. RFC 1122 requires that a TCP must implement slow start and congestion avoidance (Section 4.2.2.15 of [1]), citing [2] as the reference, but fast retransmit and fast recovery were implemented after RFC 1122. The purpose of this document is to document these four algorithms for the Internet.

This paper discusses a family of non‐linear sequence‐to‐sequence transformations designated as e k , e k m , ẽ k , and e d . A brief history of the transforms is related and a simple motivation for the transforms is given. Examples are given of the application of these transformations to divergent and slowly convergent sequences. In particular the examples include numerical series, the power series of rational and meromorphic functions, and a wide variety of sequences drawn from continued fractions, integral equations, geometry, fluid mechanics, and number theory. Theorems are proven which show the effectiveness of the transformations both in accelerating the convergence of (some) slowly convergent sequences and in inducing convergence in (some) divergent sequences. The essential unity of these two motives is stressed. Theorems are proven which show that these transforms often duplicate the results of well‐known, but specialized techniques. These special algorithms include Newton's iterative process, Gauss's numerical integration, an identity of Euler, the Padé Table, and Thiele's reciprocal differences. Difficulties which sometimes arise in the use of these transforms such as irregularity, non‐uniform convergence to the wrong answer, and the ambiguity of multivalued functions are investigated. The concepts of antilimit and of the spectra of sequences are introduced and discussed. The contrast between discrete and continuous spectra and the consequent contrasting response of the corresponding sequences to the e 1 transformation is indicated. The characteristic behaviour of a semiconvergent (asymptotic) sequence is elucidated by an analysis of its spectrum into convergent components of large amplitude and divergent components of small amplitude.

Abstract When a soluble substance is introduced into a fluid flowing slowly through a small-bore tube it spreads out under the combined action of molecular diffusion and the variation of velocity over the cross-section. It is shown analytically that the distribution of concentration produced in this way is centred on a point which moves with the mean speed of flow and is symmetrical about it in spite of the asymmetry of the flow. The dispersion along the tube is governed by a virtual coefficient of diffusivity which can be calculated from observed distributions of concentration. Since the analysis relates the longitudinal diffusivity to the coefficient of molecular diffusion, observations of concentration along a tube provide a new method for measuring diffusion coefficients. The coefficient so obtained was found, with potassium permanganate, to agree with that measured in other ways. The results may be useful to physiologists who may wish to know how a soluble salt is dispersed in blood streams.