Rutgers University

About: Rutgers University is a education organization based out in New Brunswick, New Jersey, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 68736 authors who have published 159418 publications receiving 6713860 citations. The organization is also known as: Rutgers, The State University of New Jersey & Rutgers.

The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease.

Abstract: Multiple sclerosis is a demyelinating disease, characterized by inflammation in the brain and spinal cord, possibly due to autoimmunity. Large-scale sequencing of cDNA libraries, derived from plaques dissected from brains of patients with multiple sclerosis (MS), indicated an abundance of transcripts for osteopontin (OPN). Microarray analysis of spinal cords from rats paralyzed by experimental autoimmune encephalomyelitis (EAE), a model of MS, also revealed increased OPN transcripts. Osteopontin-deficient mice were resistant to progressive EAE and had frequent remissions, and myelin-reactive T cells in OPN-/- mice produced more interleukin 10 and less interferon-gamma than in OPN+/+ mice. Osteopontin thus appears to regulate T helper cell-1 (TH1)-mediated demyelinating disease, and it may offer a potential target in blocking development of progressive MS.

New vistas on amygdala networks in conditioned fear.

Abstract: It is currently believed that the acquisition of classically conditioned fear involves potentiation of conditioned thalamic inputs in the lateral amygdala (LA). In turn, LA cells would excite more neurons in the central nucleus (CE) that, via their projections to the brain stem and hypothalamus, evoke fear responses. However, LA neurons do not directly contact brain stem-projecting CE neurons. This is problematic because CE projections to the periaqueductal gray and pontine reticular formation are believed to generate conditioned freezing and fear-potentiated startle, respectively. Moreover, like LA, CE may receive direct thalamic inputs communicating information about the conditioned and unconditioned stimuli. Finally, recent evidence suggests that the CE itself may be a critical site of plasticity. This review attempts to reconcile the current model with these observations. We suggest that potentiated LA outputs disinhibit CE projection neurons via GABAergic intercalated neurons, thereby permitting associative plasticity in CE. Thus plasticity in both LA and CE would be necessary for acquisition of conditioned fear. This revised model also accounts for inhibition of conditioned fear after extinction.

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Abstract: Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

Branes within branes

Abstract: Superstring theory has a rich spectrum of solitonic states, and over the last years much has been learned about their important roles in the theory, in strong-weak coupling duality and in resolving singularities [1]. The most important are the BPS states (those in reduced multiplets of supersymmetry) as one can make exact statements about them even at strong coupling, and thus dualities can predict their existence. Many dualities exchange elementary string states with states having Ramond-Ramond gauge charge, which strongly suggests that the two should be considered equally fundamental.

Tensor decompositions for learning latent variable models

Abstract: This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation---which exploits a certain tensor structure in their low-order observable moments (typically, of second- and third-order). Specifically, parameter estimation is reduced to the problem of extracting a certain (orthogonal) decomposition of a symmetric tensor derived from the moments; this decomposition can be viewed as a natural generalization of the singular value decomposition for matrices. Although tensor decompositions are generally intractable to compute, the decomposition of these specially structured tensors can be efficiently obtained by a variety of approaches, including power iterations and maximization approaches (similar to the case of matrices). A detailed analysis of a robust tensor power method is provided, establishing an analogue of Wedin's perturbation theorem for the singular vectors of matrices. This implies a robust and computationally tractable estimation approach for several popular latent variable models.