Mutual **Information**-based State-Control for Intrinsically Motivated
Reinforcement Learning

**information**between the goal states and the controllable states. Expand abstract.

**information**between the goal states and the controllable states. This objective encourages the agent to take control of its environment. Subsequently, we derive a surrogate objective of the proposed reward function, which can be optimized efficiently. Lastly, we evaluate the developed framework in different robotic manipulation and navigation tasks and demonstrate the efficacy of our approach. A video showing experimental results is available at \url{https://youtu.be/CT4CKMWBYz0}.

6/10 relevant

arXiv

Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories

**information**can complement and reinforce various place embedding generating methods. Expand abstract.

**information**according to the local density of observed data points. The effectiveness of our fine grained place embeddings are compared to baseline methods via next place prediction tasks using real world trajectory data from 3 cities in Japan. In addition, we demonstrate the value of our fine grained place embeddings for land use classification applications. We believe that our technique of incorporating spatial hierarchical

**information**can complement and reinforce various place embedding generating methods.

4/10 relevant

arXiv

On Geometry of **Information** Flow for Causal Inference

**information**flow, which includes the Nobel prize winning work on Granger-causality, and the recently highly popular transfer entropy, these being probabilistic in nature. Our main contribution will be to develop analysis tools that will allow a geometric interpretation of

**information**flow as a causal inference indicated by positive transfer entropy. We will describe the effective dimensionality of an underlying manifold as projected into the outcome space that summarizes

**information**flow. Therefore contrasting the probabilistic and geometric perspectives, we will introduce a new measure of causal inference based on the fractal correlation dimension conditionally applied to competing explanations of future forecasts, which we will write $GeoC_{y\rightarrow x}$. This avoids some of the boundedness issues that we show exist for the transfer entropy, $T_{y\rightarrow x}$. We will highlight our discussions with data developed from synthetic models of successively more complex nature: then include the H\'{e}non map example, and finally a real physiological example relating breathing and heart rate function. Keywords: Causal Inference; Transfer Entropy; Differential Entropy; Correlation Dimension; Pinsker's Inequality; Frobenius-Perron operator.

4/10 relevant

arXiv

Rental Housing Spot Markets: How Online **Information** Exchanges Can Supplement Transacted-Rents Data

5/10 relevant

SocArXiv

Informational Reinterpretation of the Mechanics Notions and Laws

**information**al interpretation of the equivalence principle is proposed: the informational content of the inertial and gravitational masses is the same. Expand abstract.

**information**is transferred; thus, the informational affinity of the rest state and the rectilinear motion with a constant speed is established. The analysis of the minimal Szilard thermal engine as seen from the non-inertial frame of references is carried out. The Szilard single-particle minimal thermal engine undergoes the isobaric expansion relatively to the accelerated frame of references, enabling the erasure of 1 bit of

**information**. The energy ΔQ spent by the inertial force for the erasure of 1 bit of

**information**is estimated as: ΔQ≅5/3 k_B T ̅, which is larger than the Landauer bound but qualitatively close to it. The informational interpretation of the equivalence principle is proposed: the informational content of the inertial and gravitational masses is the same.

5/10 relevant

Preprints.org

The Entropy Function for Non Polynomial Problems and Its Applications for Turing Machines

**information**content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. Expand abstract.

**information**rate proposed in this work is a method that models the solution space boundaries of any decision problem (and non polynomial problems in general) as a communication channel by means of

**Information**Theory. We described a sort method that order objects using the intrinsic

**information**content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. The limits of the search space are defined by the Kolmogorov-Chaitin complexity of the sequences encoded as Shannon-Bernoulli strings. We conclude with a discussion about the implications for general decision problems in Turing machines.

4/10 relevant

Preprints.org

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic
Universes in the Area of Volunteered Geographic **Information**

**information**on the aboutness level of texts. Expand abstract.

**information**onto the level of intertextuality. To this end, we explore Volunteered Geographic

**Information**(VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. This is done to examine how language encodes and networks geographic

**information**on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar - regardless of their distances and the underlying communities of authors. To investigate this we introduce Multiplex Topic Networks (MTN), which we automatically derive from Linguistic Multilayer Networks (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zipfian organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this finding in the context of cognitive maps, a notion which we extend by so-called thematic maps. According to our interpretation of this finding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way we come to the conclusion: Places, whether close to each other or not, are located in neighboring places that span similar subnetworks in the topic universe.

8/10 relevant

arXiv

The Privacy Funnel from the viewpoint of Local Differential Privacy

**information**as a leakage metric. The downsides to this approach are that mutual

**information**does not give worst-case guarantees, and that finding optimal sanitisation protocols can be computationally prohibitive. We tackle these problems by using differential privacy metrics, and by considering local protocols which operate on one entry at a time. We show that under both the Local Differential Privacy and Local

**Information**Privacy leakage metrics, one can efficiently obtain optimal protocols; however, Local

**Information**Privacy is both more closely aligned to the privacy requirements of the Privacy Funnel scenario, and more efficiently computable. We also consider the scenario where each user has multiple attributes (i.e. $X_i = (X^1_i,\cdots,X^m_i)$), for which we define \emph{Side-channel Resistant Local

**Information**Privacy}, and we give efficient methods to find protocols satisfying this criterion while still offering good utility. Exploratory experiments confirm the validity of these methods.

5/10 relevant

arXiv

Rental Housing Spot Markets: How Online **Information** Exchanges Can Supplement Transacted-Rents Data

5/10 relevant

NEP RePEc

A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis

**information**yields an analysis similar to Mendelian Randomization (MR) and TWAS approaches such as FUSION and S-PrediXcan. Expand abstract.

**information**in genetic association studies. When this prior

**information**consists of effect estimates from association analyses of single nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a two-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively. Methods: We propose to extend our previous approach for the joint analysis of marginal summary statistics (JAM) to incorporate prior

**information**via a hierarchical model (hJAM). In this framework, the use of appropriate effect estimates as prior

**information**yields an analysis similar to Mendelian Randomization (MR) and TWAS approaches such as FUSION and S-PrediXcan. However, hJAM is applicable to multiple correlated SNPs and multiple correlated intermediates to yield conditional estimates of effect for the intermediate on the outcome, thus providing advantages over alternative approaches. Results: We investigate the performance of hJAM in comparison to existing MR approaches (inverse-variance weighted MR and multivariate MR) and existing TWAS approaches (S-PrediXcan) for effect estimation, type-I error and empirical power. We apply hJAM to two examples: estimating the conditional effects of body mass index and type 2 diabetes on myocardial infarction and estimating the effects of the expressions of gene NUCKS1 and PM20D1 on the risk of prostate cancer. Conclusions: Across numerous causal simulation scenarios, we demonstrate that hJAM is unbiased, maintains correct type-I error and has increased power.

5/10 relevant

bioRxiv