Network monitoring in multicast networks using network coding

In this paper we show how information contained in robust network codes can be used for passive inference of possible locations of link failures or losses in a network. For distributed randomized network coding, we bound the probability of being able to distinguish among a given set of failure events, and give some experimental results for one and two link failures in randomly generated networks. We also bound the required field size and complexity for designing a robust network code that distinguishes among a given set of failure events

The Aircraft Carrier in Indian Naval Doctrine

In October 2015, the Indian navy released a document entitled Ensuring Secure Seas: Indian Maritime Security Strategy (IMMS-2015). In contrast to its more conservative predecessor, Freedom to Use the Seas: India\u27s Maritime Military Strategy, published in 2007, the new strategic document propounds a more assertive role for the Indian navy over the next ten years. To that end, New Delhi seeks to build up a force structure centered on three aircraft carriers, each of which would form the nucleus of a carrier battle group (CBG)

Byzantine modification detection in multicast networks using randomized network coding

Distributed randomized network coding, a robust approach to multicasting in distributed network settings, can be extended to provide Byzantine modification detection without the use of cryptographic functions is presented in this paper

Byzantine Modification Detection in Multicast Networks With Random Network Coding

An information-theoretic approach for detecting Byzantine or adversarial modifications in networks employing random linear network coding is described. Each exogenous source packet is augmented with a flexible number of hash symbols that are obtained as a polynomial function of the data symbols. This approach depends only on the adversary not knowing the random coding coefficients of all other packets received by the sink nodes when designing its adversarial packets. We show how the detection probability varies with the overhead (ratio of hash to data symbols), coding field size, and the amount of information unknown to the adversary about the random code

On the utility of network coding in dynamic environments

Many wireless applications, such as ad-hoc networks and sensor networks, require decentralized operation in dynamically varying environments. We consider a distributed randomized network coding approach that enables efficient decentralized operation of multi-source multicast networks. We show that this approach provides substantial benefits over traditional routing methods in dynamically varying environments. We present a set of empirical trials measuring the performance of network coding versus an approximate online Steiner tree routing approach when connections vary dynamically. The results show that network coding achieves superior performance in a significant fraction of our randomly generated network examples. Such dynamic settings represent a substantially broader class of networking problems than previously recognized for which network coding shows promise of significant practical benefits compared to routing

PI3 Kinase Activation and Response to Trastuzumab Therapy: What's neu with Herceptin Resistance?

Trastuzumab is an established therapy for women with breast cancers that overexpress HER2. Despite its proven benefit in treating breast cancer, not all women derive benefit from this monoclonal antibody, and resistant disease can develop. In this issue of Cancer Cell, Berns et al. present evidence that activation of the PI3 kinase pathway, either via loss of the tumor suppressor PTEN or through oncogenic stimulation of PIK3CA, can mediate trastuzumab resistance. This study extends important work of others and forms the rationale for future investigations combining inhibitors of the PI3 kinase pathway in conjunction with trastuzumab therapy

Divergence in Cultural Practices: Tastes as Signals of Identity

Divergence is a fact of social life; people select different tastes to distinguish themselves from others and they abandon tastes when others adopt them. But while we know a great deal about conformity, it predicts convergence, and thus is less equipped to explain why people diverge. We suggest people diverge to maintain clear signals of identity. Our approach emphasizes that the meaning of signals is set at a social rather than individual level. Tastes gain signal value through association with groups or types or individuals, but become diluted when members of more than one type hold them. Thus different types of people will diverge in the tastes they select, and they will abandon tastes they previously liked when they are adopted by members of other social types

Variational Diffusion Models

Diffusion-based generative models have demonstrated a capacity for perceptually impressive synthesis, but can they also be great likelihood-based models? We answer this in the affirmative, and introduce a family of diffusion-based generative models that obtain state-of-the-art likelihoods on standard image density estimation benchmarks. Unlike other diffusion-based models, our method allows for efficient optimization of the noise schedule jointly with the rest of the model. We show that the variational lower bound (VLB) simplifies to a remarkably short expression in terms of the signal-to-noise ratio of the diffused data, thereby improving our theoretical understanding of this model class. Using this insight, we prove an equivalence between several models proposed in the literature. In addition, we show that the continuous-time VLB is invariant to the noise schedule, except for the signal-to-noise ratio at its endpoints. This enables us to learn a noise schedule that minimizes the variance of the resulting VLB estimator, leading to faster optimization. Combining these advances with architectural improvements, we obtain state-of-the-art likelihoods on image density estimation benchmarks, outperforming autoregressive models that have dominated these benchmarks for many years, with often significantly faster optimization. In addition, we show how to use the model as part of a bits-back compression scheme, and demonstrate lossless compression rates close to the theoretical optimum. Code is available at https://github.com/google-research/vdm .Comment: Published at NeurIPS'21. Camera-ready version, with code UR