The random coding capacity of a vector Gaussian arbitrarily varying channel (VGAVC) is determined, along with a simple general method for computing this capacity. The VGAVC is a discrete-time memoryless vector channel with an input power constraint and additive Gaussian noise that is further corrupted by an additive “jamming signal.” The statistics of this jamming signal are unknown and may be ...

We consider the problem of robust Bayesian inference on the mean regression function allowing the residual density to change flexibly with predictors. The proposed class of models is based on a Gaussian process prior for the mean regression function and mixtures of Gaussians for the collection of residual densities indexed by predictors. Initially considering the homoscedastic case, we propose ...

A new approach for controlling a system of multiple agents by choosing a time-varying density function is presented, employing optimal coverage ideas. In this approach, we specify a timevarying density function that represents where it is that want the agents to monitor, and how important it is for each point to be covered. A new algorithm is presented under which the agents track the time-vary...

In real-world crowd counting applications, the densities in an image vary greatly. When facing density variation, humans tend to locate and count targets low-density regions, reason number high-density regions. We observe that CNN focus on local information correlation using a fixed-size convolution kernel Transformer could effectively extract semantic by global self-attention mechanism. Thus, ...

The Gaussian arbitrarily varying channel with input constraint r and state constraint 2 admits input sequences x = ( x l , , x , ~ ) of real numbers with Cxf 5 nT and state sequences s = ( 5 ,. , T , ~ ) of real numbers with L Y ~ 5 H A ; the output sequence x + s + V , where V = ( VI, , y , ) is a sequence of independent and identically distributed Gaussian random variables with mean 0 and var...