Abstract-In diffusion-based molecular communications, messages can be conveyed via the variation in the concentration of molecules in the medium. In this paper, we intend to analyze the achievable capacity in transmission of information from one node to another in a diffusion channel. We observe that because of the molecular diffusion in the medium, the channel possesses memory. We then model the memory of the channel by a two-step Markov chain and obtain the equations describing the capacity of the diffusion channel. By performing a numerical analysis, we obtain the maximum achievable rate for different levels of the transmitter power, i.e., the molecule production rate.

A diffusion-based molecular communication system has two major components: the diffusion in the medium, and the ligand-reception. Information bits, encoded in the time variations of the concentration of molecules, are conveyed to the receiver front through the molecular diffusion in the medium. The receiver, in turn, measures the concentration of the molecules in its vicinity in order to retrieve the information. This is done via ligand-reception process. In this paper, we develop models to study the constraints imposed by the concentration sensing at the receiver side and derive the maximum rate by which a ligand-receiver can receive information. Therefore, the overall capacity of the diffusion channel with the ligand receptors can be obtained by combining the results presented in this paper with our previous work on the achievable information rate of molecular communication over the diffusion channel.

The design of biologically-inspired wireless communication systems using bacteria as the basic element of the system is initially motivated by a phenomenon called Quorum Sensing. Due to high randomness in the individual behavior of a bacterium, reliable communication between two bacteria is almost impossible. Therefore, we have recently proposed that a population of bacteria in a cluster is considered as a bio node in the network capable of molecular transmission and reception. This proposition enables us to form a reliable bio node out of many unreliable bacteria. In this paper, we study the communication between two nodes in such a network where information is encoded in the concentration of molecules by the transmitter. The molecules produced by the bacteria in the transmitter node propagate through the diffusion channel. Then, the concentration of molecules is sensed by the bacteria population in the receiver node which would decode the information and output light or fluorescent as a result. The uncertainty in the communication is caused by all three components of communication, i.e., transmission, propagation and reception. We study the theoretical limits of the information transfer rate in the presence of such uncertainties. Finally, we consider Mary signaling schemes and study their achievable rates and corresponding error probabilities.

The task of identifying out-of-domain (OOD) input examples directly at test-time has seen renewed interest recently due to increased real world deployment of models. In this work, we focus on OOD detection for natural language sentence inputs to task-based dialog systems. Our findings are three-fold:First, we curate and release ROSTD (Real Out-of-Domain Sentences From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, our examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset.Second, we explore likelihood ratio based approaches as an alternative to currently prevalent paradigms. Specifically, we reformulate and apply these approaches to natural language inputs. We find that they match or outperform the latter on all datasets, with larger improvements on non-artificial OOD benchmarks such as our dataset. Our ablations validate that specifically using likelihood ratios rather than plain likelihood is necessary to discriminate well between OOD and in-domain data.Third, we propose learning a generative classifier and computing a marginal likelihood (ratio) for OOD detection. This allows us to use a principled likelihood while at the same time exploiting training-time labels. We find that this approach outperforms both simple likelihood (ratio) based and other prior approaches. We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.

Abstract-THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD.Molecular communication between biological entities is a new paradigm in communications. Recently, we studied molecular communication between two nodes formed from synthetic bacteria. Due to high randomness in behavior of bacteria, we used a population of them in each node. The reliability of such communication systems depends on both the maximum concentration of molecules that a transmitter node is able to produce at the receiver node as well as the number of bacteria in each nodes. This maximum concentration of molecules falls with distance which makes the communication to the far nodes nearly impossible. In order to alleviate this problem, in this paper, we propose to use a molecular relaying node. The relay node can resend the message either by the different or the same type of molecules as the original signal from the transmitter. We study two scenarios of relaying. In the first scenario, the relay node simply senses the received concentration and forwards it to the receiver. We show that this sense and forward scenario, depending on the type of molecules used for relaying, results in either increasing the range of concentration of molecules at the receiver or increasing the effective number of bacteria in the receiver node. For both cases of sense and forward relaying, we obtain the resulting improvement in channel capacity. We conclude that multi-type molecular relaying outperforms the single-type relaying. In the second scenario, we study the decode and forward relaying for the M-ary signaling scheme. We show that this relaying strategy increases the reliability of M-ary communication significantly.