Extended Energy Vector Prediction of Ambisonically Reproduced Image Direction at Off-Centre Listening Positions

This paper presents an extension to the energy vector, well known in the Ambisonics literature, to improve its predictions of localisation at off-centre listening positions. In determining the source direction, a perceptual weight is assigned to each loudspeaker gain, taking into account the relative arrival times, levels, and directions of the loudspeaker signals. The proposed model is evaluated alongside the original energy vector and two binaural models through comparison with the results of recent perceptual studies. The extended version was found to provide results that were at least 50% more accurate than the second best predictor for two experiments involving off-centre listeners with first- and third-order Ambisonics systems.

Building autonomous sensitive artificial listeners (Extended abstract)

This paper describes a substantial effort to build a real-time interactive multimodal dialogue system with a focus on emotional and non-verbal interaction capabilities. The work is motivated by the aim to provide technology with competences in perceiving and producing the emotional and non-verbal behaviours required to sustain a conversational dialogue. We present the Sensitive Artificial Listener (SAL) scenario as a setting which seems particularly suited for the study of emotional and non-verbal behaviour, since it requires only very limited verbal understanding on the part of the machine. This scenario allows us to concentrate on non-verbal capabilities without having to address at the same time the challenges of spoken language understanding, task modeling etc. We first summarise three prototype versions of the SAL scenario, in which the behaviour of the Sensitive Artificial Listener characters was determined by a human operator. These prototypes served the purpose of verifying the effectiveness of the SAL scenario and allowed us to collect data required for building system components for analysing and synthesising the respective behaviours. We then describe the fully autonomous integrated real-time system we created, which combines incremental analysis of user behaviour, dialogue management, and synthesis of speaker and listener behaviour of a SAL character displayed as a virtual agent. We discuss principles that should underlie the evaluation of SAL-type systems. Since the system is designed for modularity and reuse, and since it is publicly available, the SAL system has potential as a joint research tool in the affective computing research community.

Cationic Bovine Serum Albumin (CBA) conjugated Poly Lactic-co-Glycolic Acid (PLGA) nanoparticles for extended delivery of methotrexate into brain tumor

Current treatment strategies for the treatment of brain tumor have been hindered primarily by the presence of highly lipophilic insurmountable blood-brain barrier (BBB). The purpose of current research was to investigate the efficiency of engineered biocompatible polymeric nanoparticles (NPs) as drug delivery vehicle to bypass the BBB and enhance biopharmaceutical attributes of anti-metabolite methotrexate (MTX) encapsulated NPs. The NPs were prepared by solvent diffusion method using cationic bovine serum albumin (CBA), and characterized for physicochemical parameters such as particle size, polydispersity index, and zeta-potential; while the surface modification was confirmed by FTIR, and NMR spectroscopy. Developed NPs exhibited zestful relocation of FITC tagged NPs across BBB in albino rats. Further, hemolytic studies confirmed them to be non-toxic and biocompatible as compared to free MTX. In vitro cytotoxicity assay of our engineered NPs on HNGC1 tumor cells proved superior uptake in tumor cells; and elicited potent cytotoxic effect as compared to plain NPs and free MTX solution. The outcomes of the study evidently indicate the prospective of CBA conjugated poly (D,L-lactide-co-glycolide) (PLGA) NPs loaded with MTX in brain cancer bomber with amplified capability to circumvent BBB.