A knowledge regularized hierarchical approach for emotion cause analysis

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure

Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations

Approximately half of the world's 500,000 new oesophageal squamous-cell carcinoma (ESCC) cases each year occur in China. Here, we show whole-genome sequencing of DNA and RNA in 94 Chinese individuals with ESCC. We identify six mutational signatures (E1–E6), and Signature E4 is unique in ESCC linked to alcohol intake and genetic variants in alcohol-metabolizing enzymes. We discover significantly recurrent mutations in 20 protein-coding genes, 4 long non-coding RNAs and 10 untranslational regions. Functional analyses show six genes that have recurrent copy-number variants in three squamous-cell carcinomas (oesophageal, head and neck and lung) significantly promote cancer cell proliferation, migration and invasion. The most frequently affected genes by structural variation are LRP1B and TTC28. The aberrant cell cycle and PI3K-AKT pathways seem critical in ESCC. These results establish a comprehensive genomic landscape of ESCC and provide potential targets for precision treatment and prevention of the cancer

Tree-based machine learning performed in-memory with memristive analog CAM

Tree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer improved interpretability compared to Deep Neural Networks (DNN). However, while easier to train, they are difficult to optimize for fast inference without accuracy loss in von Neumann architectures due to non-uniform memory access patterns. Recently, we proposed a novel analog, or multi-bit, content addressable memory(CAM) for fast look-up table operations. Here, we propose a design utilizing this as a computational primitive for rapid tree-based inference. Large random forest models are mapped to arrays of analog CAMs coupled to traditional analog random access memory (RAM), and the unique features of the analog CAM enable compression and high performance. An optimized architecture is compared with previously proposed tree-based model accelerators, showing improvements in energy to decision by orders of magnitude for common image classification tasks. The results demonstrate the potential for non-volatile analog CAM hardware in accelerating large tree-based machine learning models

Differentiable Content Addressable Memory with Memristors

Memristors, Flash, and related nonvolatile analog device technologies offer in-memory computing structures operating in the analog domain, such as accelerating linear matrix operations in array structures. These take advantage of analog tunability and large dynamic range. At the other side, content addressable memories (CAM) are fast digital lookup tables which effectively perform nonlinear Boolean logic and return a digital match/mismatch value. Recently, nonvolatile analog CAMs have been presented merging analog storage and analog search operations with digital match/mismatch output. However, CAM blocks cannot easily be inserted within a larger adaptive system due to the challenges of training and learning with binary outputs. Here, a missing link between analog crossbar arrays and CAMs, namely a differentiable content addressable memory (dCAM), is presented. Utilizing nonvolatile memories that act as a “soft” memory with analog outputs, dCAM enables learning and fine-tuning of the memory operation and performance. Four applications are quantitatively evaluated to highlight the capabilities: improved data pattern storage, improved robustness to noise and variability, reduced energy and latency performance, and an application to solving Boolean satisfiability optimization problems. The use of dCAM is envisioned as a core building block of fully differentiable computing systems employing multiple types of analog compute operations and memories

A Conantokin Peptide Con-T[M8Q] Inhibits Morphine Dependence with High Potency and Low Side Effects

N-methyl-D-aspartate receptor (NMDAR) antagonists have been found to be effective to inhibit morphine dependence. However, the discovery of the selective antagonist for NMDAR GluN2B with low side-effects still remains challenging. In the present study, we report a selective NMDAR GluN2B antagonist con-T[M8Q](a conantokin-T variant) that potently inhibits the naloxone-induced jumping and conditioned place preference of morphine-dependent mice at nmol/kg level, 100-fold higher than ifenprodil, a classical NMDAR NR2B antagonist. Con-T[M8Q] displays no significant impacts on coordinated locomotion function, spontaneous locomotor activity, and spatial memory mice motor function at the dose used. Further molecular mechanism experiments demonstrate that con-T[M8Q] effectively inhibited the transcription and expression levels of signaling molecules related to NMDAR NR2B subunit in hippocampus, including NR2B, p-NR2B, CaMKII-α, CaMKII-β, CaMKIV, pERK, and c-fos. The high efficacy and low side effects of con-T[M8Q] make it a good lead compound for the treatment of opiate dependence and for the reduction of morphine usage

Tunable exchange bias effects in perovskite YFe0.5Cr0.5O3

Exchange bias (EB) effects in YFe0.5Cr0.5O3, which exhibits magnetization reversal, were systemically investigated. Above the compensation temperature (T-comp), only negative EB field appears, whereas the sign of the EB field below T-comp can be reversed from positive to negative by applying a large cooling field. The difference between the EB behaviors above and below T-comp, which are strictly related to magnetization reversal, may originate from the competing magnetic anisotropy of the system. The sign reversal of both magnetization and EB field coexisting in a single system can be tuned in a preselected and convenient way. (C) 2011 Elsevier Ltd. All rights reserved