Time and Causality: A Thermocontextual Perspective

The Thermocontextual Interpretation (TCI) is proposed here as an alternative to existing interpretations of physical states and time. Prevailing interpretations are based on assumptions rooted in classical mechanics. Logical implications include the determinism and reversibility of change, and an immediate conflict. Determinism underlies causality, but causality implies a distinction between cause and effect and an arrow of time, conflicting with reversibility. Prevailing interpretations also fail to explain the empirical irreversibility of wavefunction collapse without untestable and untenable metaphysical implications. They fail to reconcile nonlocality and relativity without invoking superdeterminism or unexplained superluminal correlations. The Thermocontextual Interpretation defines a system’s state with respect to its actual surroundings at a positive ambient temperature. The TCI bridges existing physical interpretations and thermodynamics as special cases, which define states either with respect to an absolute-zero reference or with respect to a thermally equilibrated reference. The TCI defines system time as a complex property of state spanning both reversible mechanical time and irreversible thermodynamic time, and it distinguishes between system time and the reference time of relativity and causality, as measured by an observer’s clock. And, the TCI provides a physical explanation for nonlocality, consistent with relativity, without hidden variables, superdeterminism, or “spooky action.”

Time and Causality: A Thermocontextual Perspective

The Thermocontextual Interpretation (TCI) is proposed here as an alternative to existing interpretations of physical states and time. Prevailing interpretations are based on assumptions rooted in classical mechanics. Logical implications include the determinism and reversibility of change, and an immediate conflict. Determinism underlies causality, but causality implies a distinction between cause and effect and an arrow of time, conflicting with reversibility. Prevailing interpretations also fail to explain the empirical irreversibility of wavefunction collapse without untestable and untenable metaphysical implications. They fail to reconcile nonlocality and relativity without invoking superdeterminism or unexplained superluminal correlations. The Thermocontextual Interpretation defines a system’s state with respect to its actual surroundings at a positive ambient temperature. The TCI bridges existing physical interpretations and thermodynamics as special cases, which define states either with respect to an absolute-zero reference or with respect to a thermally equilibrated reference. The TCI defines system time as a complex property of state spanning both reversible mechanical time and irreversible thermodynamic time, and it distinguishes between system time and the reference time of relativity and causality, as measured by an observer’s clock. And, the TCI provides a physical explanation for nonlocality, consistent with relativity, without hidden variables, superdeterminism, or “spooky action.”

4. The arrow of time

‘The arrow of time’ discusses where the arrow of time comes from. The fundamental laws of motion do not distinguish past and future. And yet the everyday world is full of manifestly asymmetric processes. This chapter discusses the apparent mismatch between the fundamental laws of nature and the manifest asymmetry of the everyday world. The temporal asymmetry is made precise by the second law of thermodynamics and the tension between the second law and the fundamental laws is addressed by the development of statistical mechanics.

Discovering the Arrow of Time in Machine Learning

Machine learning (ML) is increasingly useful as data grow in volume and accessibility. ML can perform tasks (e.g., categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways. Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting ML models for tasks depends on many factors as they vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for tasks that use explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are (implicitly) ordered or time-dependent, potentially allowing a hidden `arrow of time’ to affect ML performance on non-temporal tasks. This research explores the interaction of ML and implicit order using two ML models to automatically classify (a non-temporal task) tweets (temporal data) under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when matching appropriate ML models to tasks, even when time is only implicitly included.

Assessment of Arrow-of-Time Metrics for the Characterization of Underwater Explosions

Anthropogenic impulsive sound sources with high intensity are a threat to marine life and it is crucial to keep them under control to preserve the biodiversity of marine ecosystems. Underwater explosions are one of the representatives of these impulsive sound sources, and existing detection techniques are generally based on monitoring the pressure level as well as some frequency-related features. In this paper, we propose a complementary approach to the underwater explosion detection problem through assessing the arrow of time. The arrow of time of the pressure waves coming from underwater explosions conveys information about the complex characteristics of the nonlinear physical processes taking place as a consequence of the explosion to some extent. We present a thorough review of the characterization of arrows of time in time-series, and then provide specific details regarding their applications in passive acoustic monitoring. Visibility graph-based metrics, specifically the direct horizontal visibility graph of the instantaneous phase, have the best performance when assessing the arrow of time in real explosions compared to similar acoustic events of different kinds. The proposed technique has been validated in both simulations and real underwater explosions.

On Quantum Gravity As A Recorded Quantum Arrow Of Time

I take non-locality to be the Michaelson Morley experiment of the early 21st Century, assume its universal validity, and try to derive its consequences. Spacetime, with its locality, cannot be fundamental, but must somehow be emergent from entangled coherent quantum variables and their behaviors. There are, then, two immediate consequences: i. If we start with non-locality, we need not explain non-locality. We must instead explain an emergence of locality and spacetime. ii. There can be no emergence of spacetime without matter. These propositions flatly contradict General Relativity, which is foundationally local, can be formulated without matter, and in which there is no "emergence" of spacetime.It these be true, then quantum gravity cannot be a minor alteration of General Relativity, but must demand its deep reformulation. This will almost inevitably lead to: Matter not only deforms spacetime, but "creates" spacetime. We will see independent grounds for the assertion that matter both deforms and creates spacetime that may invite a new union of quantum gravity and General Relativity.This quantum creation of spacetime consists in: i. Fully non-local entangled coherent quantum variables. ii. The onset of locality via decoherence. iii. A metric in Hilbert Space among entangled quantum variables by the sub-additive von Neumann Entropy between pairs of variables. iv. Mapping from metric distances in Hilbert Space to metric distances in classical spacetime by episodic actualization events. v. Discrete spacetime is the relations among these discrete actualization events. vi. "Now" is the shared moment of actualization of one among the entangled variables when the amplitudes of the remaining entangled variables change instantaneously. vii. The discrete, successive, episodic, irreversible actualization events constitute a quantum arrow of time. viii. The arrow of time history of these events is recorded in the very structure of the spacetime constructed. ix. Actual Time is a succession of two or more actual events.This quantum creation of spacetime modifies general relativity and may account for Dark Energy, Dark Matter, and the possible elimination of the singularities of General Relativity. Possible experimental tests in both the attractive and repulsive Casimir effect setting are described. A quantum actualization enhancement of repulsive Casimir would be anti-gravitational, and of possible practical use. Relations to Causal Set Theory, faithful Lorentzian manifolds, and past and future light cones joined at ``Actual Now'' are discussed.The ideas and concepts discussed here are not yet a theory, but at most a framework that may be useful.

Discovering the Arrow of Time in Machine Learning

Machine learning (ML) is increasingly useful as data grows in volume and accessibility as it can perform tasks (e.g. categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways , . Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting a ML algorithm depends on many factors as algorithms vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are at least implicitly ordered, potentially allowing a hidden `arrow of time’ to affect non-temporal ML performance. This research explores the interaction of ML and implicit order by training two ML algorithms on Twitter data before performing automatic classification tasks under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when selecting appropriate ML algorithms, even when time is only implicitly included.

The arrow of time in operational formulations of quantum theory

The operational formulations of quantum theory are drastically time oriented. However, to the best of our knowledge, microscopic physics is time-symmetric. We address this tension by showing that the asymmetry of the operational formulations does not reflect a fundamental time-orientation of physics. Instead, it stems from built-in assumptions about the users of the theory. In particular, these formalisms are designed for predicting the future based on information about the past, and the main mathematical objects contain implicit assumption about the past, but not about the future. The main asymmetry in quantum theory is the difference between knowns and unknowns.