Dimensional Action Research Articles

Wormholes and Fermionic Super Radiance

In this article, I propose that since Hawking radiation may be a type of super radiation, the action of super radiation with a preset boundary satisfies some higher dimensional action structure, for example, the super radiance of a boson on a preset boundary for a Kerr black hole satisfies the algebraic structure of a boson on a Kerr-Schild black hole. I think the super radiance of a bounded fermion to a Kerr-structured black hole satisfies the mapping structure of the wormhole.

Suspicious Action Detection in Intelligent Surveillance System Using Action Attribute Modelling

Research in the field of image processing and computer vision for recognition of suspicious activity is growing actively. Surveillance systems play a key role in monitoring of sensitive places such as airports, railway stations, shopping complexes, roads, parking areas, roads, banks. For a human it is very difficult to monitor surveillance videos continually, therefore a smart and intelligent system is required that can do real time monitoring of all activities and can categories between usual and some abnormal activities. In this paper many different abnormal activities has been discussed. More focuses is given to violence activity like hitting, slapping, punching etc. For this large human action dataset like UCF101, Kaggel is required. This paper proposes a method to model violence actions using Gaussian Mixture Model with Universal Attribute Model. In this action vector is used to remove redundant attributes and get a low dimensional relevant action vectors.

Bouncing cosmology in a curved braneworld

We explore the possibility of a non-singular bounce in our universe from a warped braneworld scenario with dynamical branes and a non-zero brane cosmological constant. Such models naturally incorporate a scalar sector known as the radion originating from the modulus of the theory. The presence of brane cosmological constant renders the branes to be non-flat and gives rise to a potential and a non-canonical kinetic term for the radion field in the four dimensional effective action. The kinetic term exhibits a phantom-like behavior within the domain of evolution of the modulus which leads to a violation of the null-energy condition often observed in a bouncing universe. The interplay of the radion potential and kinetic term enables the evolution of the radion field from a normal to a phantom regime where the universe transits from a contracting era to an expanding epoch through a non-singular bounce. Analysis of the scalar and tensor perturbations over such background evolution reveal that the primordial observables e.g., the amplitude of scalar perturbations \U0001d49cs, tensor to scalar ratio r and the scalar spectral index ns are in agreement with the current constraints reported by the Planck satellite. The implications are discussed.

Topology of complexity one quotients

We describe of the topology of the geometric quotients of 2n dimensional compact connected symplectic manifolds with n-1 dimensional torus actions. When the isotropy weights at each fixed point are in general position, the quotient is homeomorphic to a sphere.

ANTI- PYRETIC ACTIVITY OF SRIRAMA RASA IN ALBINO RATS

Jwara appears as an independent disease and also as a Lakshana of various diseases. Because of this, it is the common health problem met within medical practice. That is why, Ayurveda gives much importance to Jwara and considered Jwara as Sarvarogaadhipati.
 Abnormal food habits, style of living etc. factors disturb Jatharagni and health, which leads to Jwara and Vedana. Srirama Rasa is a unique herbo-mineral formulation explained in the Jwara Chikitsa Prakaranam of Bhaishajya Ratnavali having a multi- dimensional therapeutic action i.e., Ama jwara, Shoola, Vishtambha and Vata vikaras. It is a potent formulation having Shuddha Parada, Shuddha Gandhaka, ShuddhaJayapala Beeja and Maricha given Bhavana with Dantimoola kwatha.
 Methodology: Raw materials were screened and collected and the formulation selected for the present study Srirama Rasa was prepared accordingly. Experimental study was conducted in 3 group of animals for Anti-pyretic study. Anti-pyretic study was done by Yeast induced pyrexia method in Albino rats.
 Results: In Anti-pyretic study, a significant result was observed in standard group and trial group. But in standard group, it was more significant than control & trial group. In trial drug, a significant decrease in rectal temperature was observed from 5th to 6th hr, while in standard group it was from 2nd to 6th hr.
 Conclusion: Srirama Rasa has demonstrated significant Anti-pyretic activity. This study has provided evidence base and given scope for clinical research.

Antisymmetric Tensor Fields in Modified Gravity: A Summary

We provide various aspects of second rank antisymmetric Kalb–Ramond (KR) field in modified theories of gravity. The KR field energy density is found to decrease with the expansion of our universe at a faster rate in comparison to radiation and matter components. Thus as the universe evolves and cools down, the contribution of the KR field on the evolutionary process reduces significantly, and at present it almost does not affect the universe evolution. However the KR field has a significant contribution during early universe; in particular, it affects the beginning of inflation as well as increases the amount of primordial gravitational radiation and hence enlarges the value of tensor-to-scalar ratio in respect to the case when the KR field is absent. In regard to the KR field couplings, it turns out that in four dimensional higher curvature inflationary model the couplings of the KR field to other matter fields is given by 1/MPl (where MPl is known as the “reduced Planck mass” defined by MPl=18πG with G is the “Newton’s constant”) i.e., same as the usual gravity–matter coupling; however in the context of higher dimensional higher curvature model the KR couplings get an additional suppression over 1/MPl. Thus in comparison to the four dimensional model, the higher curvature braneworld scenario gives a better explanation of why the present universe carries practically no footprint of the Kalb–Ramond field. The higher curvature term in the higher dimensional gravitational action acts as a suitable stabilizing agent in the dynamical stabilization mechanism of the extra dimensional modulus field from the perspective of effective on-brane theory. Based on the evolution of KR field, one intriguing question can be—“sitting in present day universe, how do we confirm the existence of the Kalb–Ramond field which has considerably low energy density (with respect to the other components) in our present universe but has a significant impact during early universe?” We try to answer this question by the phenomena “cosmological quantum entanglement” which indeed carries the information of early universe. Finally, we briefly discuss some future perspectives of Kalb–Ramond cosmology at the end of the paper.

Crossed products of C∗-algebras for singular actions with spectrum conditions

We analyze existence of crossed product constructions for singular group actions on C∗-algebras, i.e. where the group need not be locally compact, or the action need not be strongly continuous. This is specialized to the case where spectrum conditions are required for the implementing unitary groups in covariant representations. The existence of a crossed product construction is guaranteed by the existence of "cross representations". For one-parameter automorphism groups, this existence property is stable with respect to many perturbations of the action. The structure of cross representations of inner actions on von Neumann algebras is obtained. We analyze the cross property for covariant representations of one-parameter automorphism groups, where the generator of the implementing unitary group is positive. If the Borchers-Arveson minimal implementing group is cross, then so are all other implementing groups.s This analysis is extended here to higher dimensional Lie group actions, including several examples of interest to physics.

Holographic RG and exact RG in O(N) model

In this paper an Exact Renormalization Group (ERG) equation is written for the critical O(N) model in D-dimensions (with D≈3) at the Wilson-Fisher fixed point perturbed by a scalar composite operator. The action is written in terms of an auxiliary scalar field and reproduces correlation functions of a scalar composite operator. The equation is derived starting from the Polchinski ERG equation for the fundamental scalar field. As described in Sathiapalan and Sonoda (2017) [1] an evolution operator for the Polchinski ERG equation can be written in the form of a functional integral, with a D+1 dimensional scalar field theory action. In the case of the fundamental scalar field this action only has a kinetic term and therefore looks quite different from Holographic RG where there are potential terms. But in the composite operator case discussed in this paper, the ERG equation and consequently the D+1 dimensional action contains higher order potential terms for the scalar field and is therefore very similar to the case of Holographic RG. Furthermore this action can be mapped to a scalar field action in AdSD+1 using the techniques of ([1]). The leading cubic term of the potential is computed in this paper for D≈3 and expectedly vanishes in D=3 in agreement with results in the AdS/CFT literature.

Model primitives for hierarchical lifelong reinforcement learning

Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult. Some traditional hierarchical reinforcement learning techniques enforce this decomposition in a top-down manner, while meta-learning techniques require a task distribution at hand to learn such decompositions. This paper presents a framework for using diverse suboptimal world models to decompose complex task solutions into simpler modular subpolicies. This framework performs automatic decomposition of a single source task in a bottom up manner, concurrently learning the required modular subpolicies as well as a controller to coordinate them. We perform a series of experiments on high dimensional continuous action control tasks to demonstrate the effectiveness of this approach at both complex single task learning and lifelong learning. Finally, we perform ablation studies to understand the importance and robustness of different elements in the framework and limitations to this approach.

Canonical analysis ofn-dimensional Palatini action without second-class constraints

We carry out the canonical analysis of the $n$-dimensional Palatini action with or without a cosmological constant $(n\geq3)$ introducing neither second-class constraints nor resorting to any gauge fixing. This is accomplished by providing an expression for the spatial components of the connection that allows us to isolate the nondynamical variables present among them, which can later be eliminated from the action by using their own equation of motion. As a result, we obtain the description of the phase space of general relativity in terms of manifestly $SO(n-1,1)$ [or $SO(n)$] covariant variables subject to first-class constraints only, with no second-class constraints arising during the process. Afterwards, we perform, at the covariant level, a canonical transformation to a set of variables in terms of which the above constraints take a simpler form. Finally, we impose the time gauge and make contact with the $SO(n-1)$ ADM formalism.

Superpotential of three dimensional mathcal{N} = 1 heterotic supergravity

We dimensionally reduce the ten dimensional heterotic supergravity action on spacetimes of the form mathcal{M} (1, 2) x Y, where mathcal{M} (1, 2) is three dimensional maximally symmetric Anti de Sitter or Minkowski space, and Y is a compact seven dimensional manifold with G2 structure. In doing so, we derive the real superpotential functional of the corresponding three dimensional mathcal{N} = 1 theory. We confirm that extrema of this functional correspond to supersymmetric heterotic compacti:fications on manifolds of G2 structure in the large volume, weak coupling limit to first order in a'. We make some comments on the role of the superpotential functional with respect to the coupled moduli problem of instanton bundles over G2 manifolds.

Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning

With the development of artificial intelligence and integrated sensor technologies, unmanned aerial vehicles (UAVs) are more and more applied in the air combats. A bottleneck that constrains the capability of UAVs against manned vehicles is the autonomous maneuver decision, which is a very challenging problem in the short-range air combat undergoing highly dynamic and uncertain maneuvers of enemies. In this paper, an autonomous maneuver decision model is proposed for the UAV short-range air combat based on reinforcement learning, which mainly includes the aircraft motion model, one-to-one short-range air combat evaluation model and the maneuver decision model based on deep Q network (DQN). However, such model includes a high dimensional state and action space which requires huge computation load for DQN training using traditional methods. Then, a phased training method, called “basic-confrontation”, which is based on the idea that human beings gradually learn from simple to complex is proposed to help reduce the training time while getting suboptimal but efficient results. Finally, one-to-one short-range air combats are simulated under different target maneuver policies. Simulation results show that the proposed maneuver decision model and training method can help the UAV achieve autonomous decision in the air combats and obtain an effective decision policy to defeat the opponent.

Deep Deterministic Policy Gradient (DDPG)-Based Resource Allocation Scheme for NOMA Vehicular Communications

This paper investigates the resource allocation problem in vehicular communications based on multi-agent Deep Deterministic Policy Gradient (DDPG), in which each Vehicle-to-Vehicle (V2V) communication acts as agent and adopts Non-Orthogonal Multiple Access (NOMA) technology to share the frequency spectrum that pre-allocated to Vehicle-to-Infrastructure (V2I) communications. Different with conventional D2D communications, the fast varying channel condition due to the high mobility in vehicular environment causes the difficulty of collecting instantaneous Channel State Information (CSI) at base station. Meanwhile, one tremendous challenge faced by vehicular communications is how to maximize the sum-rate of V2I communications simultaneously guaranteeing the latency and reliability requirements for the transmission of safety-critical information in V2V communications. In response, we formulate the resource allocation problem as a decentralized Discrete-time and Finite-state Markov Decision Process (DFMDP), in which allocation decisions are made by multiple agents that do not have complete and global network information. Due to the complexity of the problem, we propose a DDPG algorithm which is capable of handling continuous high dimensional action spaces to find the optimal allocation strategy. Numerical results verify that each agent can effectively learn from the environment by means of the proposed DDPG algorithm to maximize the sum-rate of V2I communications while satisfying the stringent latency and reliability constraints of V2V communications.

Extended supersymmetry with central charges in Dirac action with curved extra dimensions

We discuss a new realization of $\mathcal{N}$-extended quantum-mechanical supersymmetry (QM SUSY) with central charges hidden in the four-dimensional (4D) mass spectrum of higher dimensional Dirac action with curved extra dimensions. We show that this $\mathcal{N}$-extended QM SUSY results from symmetries in extra dimensions, and the supermultiplets in this supersymmetry algebra correspond to the Bogomol'nyi--Prasad--Sommerfield states. Furthermore, we examine the model of the $S^2$ extra dimension with a magnetic monopole background and confirm that the $\mathcal{N}$-extended QM SUSY explains the degeneracy of the 4D mass spectrum.

Lack of Automatic Vocal Response Learning While Reading Aloud.

Research on implicit sequence learning with the Serial Reaction Task (SRT) has demonstrated that people automatically acquire knowledge about fixed repeating sequences of responses and can transfer response sequence knowledge to novel stimuli. Such demonstrations are, however, mostly limited to setups with visual stimuli and manual responses. Here we systematically follow up on scarce attempts to demonstrate implicit sequence learning in word reading. While the literature on implicit sequence learning can be taken to suggest that sequence knowledge is acquired and affecting performance in word reading, we show that neither is the case in a series of four experiments. Sequence knowledge was acquired and affecting performance in color naming but not in word reading. On the one hand, we observed slowing of voice-onset times in off-sequence as compared to regularly sequenced trials when people named the color of a centrally presented disk. Yet, hardly any effect was observed when the very same sequence of words was verbalized in word reading instead. Transfer of sequence knowledge to and from color naming was not observed, either. This contrasts with sequence learning studies with manual responses, which have been taken to suggest that a fixed and repeating sequence of responses is sufficient for learning to occur even in fast choice reaction tasks and to transfer across stimuli as long as the sequence of responses remains intact. Rather, in line with dimensional action accounts of task performance, the results underline the role of translation between processing streams for implicit sequence learning.

Einstein-Gauss-Bonnet Gravity with Extra Dimensions

We consider a theory of modified gravity possessing d extra spatial dimensions with a maximally symmetric metric and a scale factor, whose ( 4 + d ) -dimensional gravitational action contains terms proportional to quadratic curvature scalars. Constructing the 4D effective field theory by dimensional reduction, we find that a special case of our action where the additional terms appear in the well-known Gauss-Bonnet combination is of special interest as it uniquely produces a Horndeski scalar-tensor theory in the 4D effective action. We further consider the possibility of achieving stabilised extra dimensions in this scenario, as a function of the number and curvature of extra dimensions, as well as the strength of the Gauss-Bonnet coupling. Further questions that remain to be answered such as the influence of matter-coupling are briefly discussed.

Extended supersymmetry with central charges in higher dimensional Dirac action

A new realization of extended quantum-mechanical supersymmetry (QM SUSY) with central extension is investigated. We first show that two different sets of $d+2$ ($d+1$) supercharges for $d=$ even (odd), each of which satisfies an $\mathcal{N}=d+2$ ($d+1$) extended QM SUSY algebra without central extension, are hidden in the four-dimensional (4D) mass spectrum of the $(4+d)$-dimensional Dirac action. We then find that the whole set of the supercharges forms an $\mathcal{N}=2d+4$ ($2d+2$) extended QM SUSY algebra with central charges for $d=$ even (odd). The representation of the supersymmetry algebra is shown to be $1/2$-Bogomol'nyi--Prasad--Sommerfield states that correspond to a short representation for the supersymmetry algebra with central extension. We explicitly examine the 4D mass spectrum of the models with the hyperrectangle and the torus extra dimensions, and discuss their supersymmetric structures.

Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network

This paper presents the deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctors and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts’ decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.

O(D,D) constraint on D -dimensional effective actions

Double Field Theory is a manifestly T-duality invariant formulation of string theory in which the effective theory at any order of $\alpha'$ is invariant under global $O(D,D)$ transformations and ought to be invariant under gauge transformations which receive $\alpha'$-corrections. On the other hand, the effective theory in the usual $D$-dimensional formulation of string theory is manifestly gauge invariant and ought to be invariant under T-duality transformations which receive $\alpha'$-corrections. We speculate that the combination of these two constraints may fix both the $2D$-dimensional and the $D$-dimensional effective actions without knowledge of the $\alpha'$-corrections of the gauge and the T-duality transformations. In this paper, using generalized fluxes, we construct arbitrary $O(D,D)$-invariant actions at orders $\alpha'^0$ and $\alpha'$, and then dimensionally reduce them to the $D$-dimensional spacetime. On the other hand, at these orders, we construct arbitrary covariant $D$-dimensional actions. Constraining the two $D$-dimensional actions to be equal up to non-covariant field redefinitions, we find that both actions are fixed up to overall factors and up to field redefinitions.

Recognizing Involuntary Actions from 3D Skeleton Data Using Body States

Human action recognition has been one of the most active fields of research in computer vision for last years. Two dimensional action recognition methods are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method of action recognition which uses temporal 3D skeletal Kinect data. This method introduces the definition of body states and then every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods, with recognition rate of 88.64% for eight different actions and up to 96.18% for classifying fall actions.