Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition

Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs). As an important application, ELMs have been successfully used as a means for calculating sentence scores in speech recognition, but they all use less-modern CNN or LSTM networks. The recent progress in Transformer networks and large pretrained models such as BERT and GPT2 opens new possibility to further advancing ELMs. In this paper, we explore different architectures of energy functions and different training methods to investigate the capabilities of ELMs in rescoring for speech recognition, all using large pretrained models as backbones.Comment: Accepted into INTERSPEECH 202

Highly efficient blue OLED

Based on our work on deep blue OLED, very recently, we have synthesized a deep blue emitter TPEA. The anthracene groups are twisted from the central TPE moiety, which effectively prevents bathochromic shift of emission as shown in its crystallographic structure. In addition, a D-A structure was built by using methoxy and cyano to improve the charge balance in the devices. In addition, the material possesses high thermal stability with a Tg of 155 °C. The non-doped device achieved the high performance with a Von of 2.6 V at a luminance of 1 cd rrr2, a 77PE, max Of 11.1 lm W~1, a TJCE, max of 9.9 cd A '1, and a low 77ce roll-off. The doped device based on TPEA was fabricated to acquire deep blue emission with CIE coordinates of (0.15, 0.09), showing a tje x t, max up to 8.0% and the highest t j p e, max of 7.3 lm W"1 among all the TTF and HLCT deep-blue emitters. Inspired by these preliminary results, we believe that the combination of the merits of TTF-HLCT and AIE would be a promising molecular design principle for exploring highly efficient deep blue emitters

Innate Immune Response to Streptococcus pyogenes Depends on the Combined Activation of TLR13 and TLR2

International audienceInnate immune recognition of the major human-specific Gram-positive pathogen Strepto-coccus pyogenes is not understood. Here we show that mice employ Toll-like receptor (TLR) 2-and TLR13-mediated recognition of S. pyogenes. These TLR pathways are non-redundant in the in vivo context of animal infection, but are largely redundant in vitro, as only inactivation of both of them abolishes inflammatory cytokine production by macrophages and dendritic cells infected with S. pyogenes. Mechanistically, S. pyogenes is initially recognized in a phagocytosis-independent manner by TLR2 and subsequently by TLR13 upon in-ternalization. We show that the TLR13 response is specifically triggered by S. pyogenes rRNA and that Tlr13 −/− cells respond to S. pyogenes infection solely by engagement of TLR2. TLR13 is absent from humans and, remarkably, we find no equivalent route for S. pyogenes RNA recognition in human macrophages. Phylogenetic analysis reveals that TLR13 occurs in all kingdoms but only in few mammals, including mice and rats, which are naturally resistant against S. pyogenes. Our study establishes that the dissimilar expression of TLR13 in mice and humans has functional consequences for recognition of S. pyogenes in these organisms

Perpendicular in-plane negative magnetoresistance in ZrTe5

The unique band structure in topological materials frequently results in unusual magneto-transport phenomena, one of which is in-plane longitudinal negative magnetoresistance (NMR) with the magnetic field aligned parallel to the electrical current direction. This NMR is widely considered as a hallmark of chiral anomaly in topological materials. Here we report the observation of in-plane NMR in the topological material ZrTe5 when the in-plane magnetic field is both parallel and perpendicular to the current direction, revealing an unusual case of quantum transport beyond the chiral anomaly. We find that a general theoretical model, which considers the combined effect of Berry curvature and orbital moment, can quantitatively explain this in-plane NMR. Our results provide new insights into the understanding of in-plane NMR in topological materials