The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.

Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant supervision in relation extraction. We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision. The transition matrix can be effectively trained using a novel curriculum learning based method without any direct supervision about the noise. We thoroughly evaluate our approach under a wide range of extraction scenarios. Experimental results show that our approach consistently improves the extraction results and outperforms the state-of-the-art in various evaluation scenarios.

Serotype 4b strains of the food-borne pathogen Listeria monocytogenes are responsible for a large portion of sporadic listeric infections and all major food-borne listeriosis outbreaks in humans. Hybridomas were produced from three fusions with lymphocytes of ND4 mice immunized either with the insoluble antigens of L. monocytogenes serotype 4b or with formalin-killed bacterial cells and screened for monoclonal antibodies (mAbs) reactive to L. monocytogenes serotype 4b. A set of 35 mAbs was identified by ELISA as having reactivity with both the insoluble antigen fraction and the whole-cell antigens. Thirteen of these mAbs belonged to immunoglobulin subclass G1 (IgG1), fifteen were IgG2a and seven mAbs were IgM. Only 20 out of the 35 mAbs were capable of detecting protein bands of various sizes ranging from 20 to 88 kDa in Western blots. Two of these mAbs, M2365 and M2367, were capable of binding to cell-surface antigens of live L. monocytogenes serotype 4b, as demonstrated by immunofluorescence microscopy and immunogold transmission electron microscopy. Immunofluorescence microscopy showed that M2365 and M2367 failed to bind to the cell surfaces of Escherichia coli O157 : H7, Salmonella enterica (serotype Typhimurium DT104) or Campylobacter jejuni. Evaluation of the cross-reactions of all 35 mAbs with whole-cell antigens of E. coli O157 : H7, S. Typhimurium, C. jejuni and Listeria innocua by ELISA indicated that the majority of the mAbs, including M2365 and M2367, did not cross-react with E. coli O157 : H7, S. Typhimurium or C. jejuni and showed no or a very weak reaction with L. innocua. Furthermore, M2365 and M2367 showed no reaction with whole-cell antigens derived from L. monocytogenes serotypes 1/2a, 1/2b and 3a, and from Listeria grayi, Listeria ivanovii and Listeria seeligeri, in an ELISA. Collectively, these data suggest that M2365 and M2367 have potential use in the development of immunological methods of laboratory diagnosis for L. monocytogenes serotype 4b in clinical or food samples.

The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN. flights from Boston to Miami Intent RE: Intent Label: flight /from (__CITY) to (__CITY)/ O O B-fromloc.city O B-toloc.city Sentence: Slot Labels: Slot RE: /^flights? from/ REtag: flight city / toloc.city REtag: city / fromloc.city

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Raman spectroscopy is emerging as an important nondestructive PRACTICAL APPLICATIONSCommercial off-the-shelf components such as a semiconductor laser, a fiber optic probe and a mini spectrometer can be used to construct a portable, low-cost dispersive Raman system. Such system allows for the acquisition of SERS spectra of bacteria adsorbed on silver colloidal nanoparticles, as exemplified by three important bacterial foodborne pathogens L. monocytogenes (serotype 4b), E. coli O157:H7 and S. enterica (serotype Typhimurium DT 104). An unambiguous identification of these pathogens was achieved based 1 Corresponding

The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity. In the game, the answerer first thinks of an object such as a famous person or a kind of animal. Then the questioner tries to guess the object by asking 20 questions. In a Q20 game system, the user is considered as the answerer while the system itself acts as the questioner which requires a good strategy of question selection to figure out the correct object and win the game. However, the optimal policy of question selection is hard to be derived due to the complexity and volatility of the game environment. In this paper, we propose a novel policy-based Reinforcement Learning (RL) method, which enables the questioner agent to learn the optimal policy of question selection through continuous interactions with users. To facilitate training, we also propose to use a reward network to estimate the more informative reward. Compared to previous methods, our RL method is robust to noisy answers and does not rely on the Knowledge Base of objects. Experimental results show that our RL method clearly outperforms an entropy-based engineering system and has competitive performance in a noisyfree simulation environment 1 .

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