scholarly journals Experimental entanglement of temporal order

Quantum ◽  
2022 ◽  
Vol 6 ◽  
pp. 621
Author(s):  
Giulia Rubino ◽  
Lee A. Rozema ◽  
Francesco Massa ◽  
Mateus Araújo ◽  
Magdalena Zych ◽  
...  
Keyword(s):  
Experimental Data ◽  
Experimental Evidence ◽  
Temporal Order ◽  
Causal Structure ◽  
Physical Processes ◽  
Physical Constraints ◽  
Quantum Description ◽  
Causal Processes ◽  
Causal Structures ◽  
Probabilistic Theories

The study of causal relations has recently been applied to the quantum realm, leading to the discovery that not all physical processes have a definite causal structure. While indefinite causal processes have previously been experimentally shown, these proofs relied on the quantum description of the experiments. Yet, the same experimental data could also be compatible with definite causal structures within different descriptions. Here, we present the first demonstration of indefinite temporal order outside of quantum formalism. We show that our experimental outcomes are incompatible with a class of generalised probabilistic theories satisfying the assumptions of locality and definite temporal order. To this end, we derive physical constraints (in the form of a Bell-like inequality) on experimental outcomes within such a class of theories. We then experimentally invalidate these theories by violating the inequality using entangled temporal order. This provides experimental evidence that there exist correlations in nature which are incompatible with the assumptions of locality and definite temporal order.

Validity and the causal structure of a disorder

2017 ◽  
Author(s):  
John Campbell
Keyword(s):  
Latent Variable ◽  
Causal Structure ◽  
Complex Structure ◽  
The Other ◽  
Causal Mechanisms ◽  
Causal Processes ◽  
Physical Variables ◽  
Causal Structures ◽  
As Effects

There are two aspects to validation: one is establishing the very existence of a disorder, the other is determining how good our methods are of determining the presence of the disorder. Both aspects depend on the conception of the disorder as something with a causal structure implicating the symptoms of the disorder. The symptoms may figure in this structure as effects of a single latent variable, or as homeostatically related merely to one another, or in some more complex structure. But without some such conception of the causal structure of the disorder, we have no idea what we are about trying to validate criteria for the disorder. The problem in psychiatry is that we currently seem to be demanding that those causal structures should involve relations between psychological and physical variables; and we have no idea how to think of those relations in terms of causal processes and causal mechanisms.

scholarly journals Analysing causal structures in generalised probabilistic theories

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 236 ◽  
Author(s):  
Mirjam Weilenmann ◽  
Roger Colbeck
Keyword(s):  
Classical Theory ◽  
Convex Cone ◽  
Causal Explanation ◽  
Causal Structure ◽  
Quantum Mechanical ◽  
Probabilistic Theory ◽  
Special Cases ◽  
Classical Quantum ◽  
Causal Structures ◽  
Probabilistic Theories

Causal structures give us a way to understand the origin of observed correlations. These were developed for classical scenarios, but quantum mechanical experiments necessitate their generalisation. Here we study causal structures in a broad range of theories, which include both quantum and classical theory as special cases. We propose a method for analysing differences between such theories based on the so-called measurement entropy. We apply this method to several causal structures, deriving new relations that separate classical, quantum and more general theories within these causal structures. The constraints we derive for the most general theories are in a sense minimal requirements of any causal explanation in these scenarios. In addition, we make several technical contributions that give insight for the entropic analysis of quantum causal structures. In particular, we prove that for any causal structure and for any generalised probabilistic theory, the set of achievable entropy vectors form a convex cone.

Capillary flows and self-assembly of porous hydrate structures

2012 ◽  
Vol 9 (1) ◽  
pp. 22-25
Author(s):  
S.V. Amel’kin ◽  
D.Ye. Igoshin
Keyword(s):  
Experimental Data ◽  
Aqueous Solution ◽  
Self Assembly ◽  
Capillary Flow ◽  
Secondary Crystallization ◽  
Assembly Model ◽  
Physical Processes ◽  
Good Correspondence ◽  
Solution Formation ◽  
Capillary Flows

A self-assembly model for porous hydrate structures is proposed, which takes into account the sequence of basic physical processes: hydrate growth on the surface of the aqueous solution, formation of islet structure, capillary flow, separation and transfer of secondary crystallization nuclei to the meniscus. The model was studied within the cellular automata method. A good correspondence between the results of the simulation and the experimental data is obtained.

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

scholarly journals The Inflation Technique Completely Solves the Causal Compatibility Problem

2020 ◽  
Vol 8 (1) ◽  
pp. 70-91 ◽  
Author(s):  
Miguel Navascués ◽  
Elie Wolfe
Keyword(s):  
Linear Programming ◽  
Latent Variables ◽  
Causal Explanation ◽  
Causal Structure ◽  
Categorical Variables ◽  
False Negatives ◽  
The Given ◽  
Causal Structures ◽  
Compatibility Question ◽  
Structure Graph

AbstractThe causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In 10.1515/jci-2017-0020, one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation is asymptotically tight, i.e., that the hierarchy converges to a zero-error test for causal compatibility. In this sense, the inflation technique fulfills a longstanding desideratum in the field of causal inference. We quantify the rate of convergence by showing that any distribution which passes the nth-order inflation test must be $\begin{array}{} \displaystyle {O}{\left(n^{{{-}{1}}/{2}}\right)} \end{array}$-close in Euclidean norm to some distribution genuinely compatible with the given causal structure. Furthermore, we show that for many causal structures, the (unrelaxed) causal compatibility problem is faithfully formulated already by either the first or second order inflation test.

Convective Losses From Cavity Solar Receivers—Comparisons Between Analytical Predictions and Experimental Results

1983 ◽  
Vol 105 (1) ◽  
pp. 29-33 ◽  
Author(s):  
A. M. Clausing
Keyword(s):  
Experimental Data ◽  
Experimental Evidence ◽  
Analytical Model ◽  
Excellent Agreement ◽  
Experimental Results ◽  
Simple Analytical Model ◽  
Refined Model ◽  
Wall Area

Cavity solar receivers are generally believed to have higher thermal efficiencies than external receivers due to reduced losses. A simple analytical model was presented by the author which indicated that the ability to heat the air inside the cavity often controls the convective loss from cavity receivers. Thus, if the receiver contains a large amount of inactive hot wall area, it can experience a large convective loss. Excellent experimental data from a variety of cavity configurations and orientations have recently become available. These data provided a means of testing and refining the analytical model. In this manuscript, a brief description of the refined model is presented. Emphasis is placed on using available experimental evidence to substantiate the hypothesized mechanisms and assumptions. Detailed comparisons are given between analytical predictions and experimental results. Excellent agreement is obtained, and the important mechanisms are more clearly delineated.

scholarly journals Adaptive Intelligent Welding Manufacturing

2021 ◽  
Vol 100 (01) ◽  
pp. 63-83
Author(s):  
YUMING ZHANG ◽  
◽  
QIYUE WANG ◽  
YUKANG LIU
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Deep Learning ◽  
Welding Process ◽  
Welding Parameters ◽  
Weld Quality ◽  
Robotic Welding ◽  
Process Variables ◽  
Physical Constraints ◽  
Needed Information

Optimal design of the welding procedure gives the desired welding results under nominal welding conditions. During manufacturing, where the actual welding manufacturing conditions often deviate from the nominal ones used in the design, applying the designed procedure will produce welding results that are different from the desired ones. Adaption is needed to make corrections and adjust some of the welding parameters from those specified in the design. This is adaptive welding. While human welders can be adaptive to make corrections and adjustments, their performance is limited by their physical constraints and skill level. To be adaptive, automated and robotic welding systems require abilities in sensing the welding process, extracting the needed information from signals from the sensors, predicting the responses of the welding process to the adjustments on welding parameters, and optimizing the adjustments. This results in the application of classical sensing, modeling of process dynamics, and control system design. In many cases, the needed information for the weld quality and process variables of our concern is not easy to extract from the sensor’s data. Studies are needed to propose the phenomena to sense and establish the scientific foundation to correlate them to the weld quality or process variables of our concern. Such studies can be labor intensive, and a more automated approach is needed. Analysis suggests that artificial intelligence and machine learning, especially deep learning, can help automate the learning such that the needed intelligence for robotic welding adaptation can be directly and automatically learned from experimental data after the physical phenomena being represented by the experimental data has been appropriately selected to make sure they are fundamentally correlated to that with which we are concerned. Some adaptation abilities may also be learned from skilled human welders. In addition, human-robot collaborative welding may incorporate adaptations from humans with the welding robots. This paper analyzes and identifies the challenges in adaptive robotic welding, reviews efforts devoted to solve these challenges, analyzes the principles and nature of the methods behind these efforts, and introduces modern approaches, including machine learning/deep learning, learning from humans, and human-robot collaboration, to solve these challenges.

scholarly journals Is coding a relevant metaphor for the brain?

2018 ◽  
Vol 42 ◽  
Author(s):  
Romain Brette
Keyword(s):  
Brain Function ◽  
Neural Coding ◽  
Causal Structure ◽  
Linear Chain ◽  
Neural Codes ◽  
Experimental Parameters ◽  
Representational Power ◽  
Causal Structures ◽  
Perceptual Systems ◽  
The Brain

Abstract “Neural coding” is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable (e.g., the spike count). Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition.

What kinds of things are psychiatric disorders?

2010 ◽  
Vol 41 (6) ◽  
pp. 1143-1150 ◽  
Author(s):  
K. S. Kendler ◽  
P. Zachar ◽  
C. Craver
Keyword(s):  
Psychiatric Disorders ◽  
Cultural Context ◽  
Causal Structure ◽  
Biological Species ◽  
Physiological Mechanisms ◽  
Socially Constructed ◽  
Causal Processes ◽  
Underlying Mechanisms ◽  
The Mind ◽  
Different Cultures

This essay explores four answers to the question ‘What kinds of things are psychiatric disorders?’Essentialist kindsare classes whose members share an essence from which their defining features arise. Although elegant and appropriate for some physical (e.g. atomic elements) and medical (e.g. Mendelian disorders) phenomena, this model is inappropriate for psychiatric disorders, which are multi-factorial and ‘fuzzy’.Socially constructed kindsare classes whose members are defined by the cultural context in which they arise. This model excludes the importance of shared physiological mechanisms by which the same disorder could be identified across different cultures. Advocates ofpractical kindsput off metaphysical questions about ‘reality’ and focus on defining classes that are useful. Practical kinds models for psychiatric disorders, implicit in the DSM nosologies, do not require that diagnoses be grounded in shared causal processes. If psychiatry seeks to tie disorders to etiology and underlying mechanisms, a model first proposed for biological species,mechanistic property cluster(MPC)kinds, can provide a useful framework. MPC kinds are defined not in terms of essences but in terms of complex, mutually reinforcing networks of causal mechanisms. We argue that psychiatric disorders are objectively grounded features of the causal structure of the mind/brain. MPC kinds are fuzzy sets defined by mechanisms at multiple levels that act and interact to produce the key features of the kind. Like species, psychiatric disorders are populations with central paradigmatic and more marginal members. The MPC view is the best current answer to ‘What kinds of things are psychiatric disorders?’

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