Towards Constraining Parity-Violations in Gravity with Satellite Gradiometry

Parity violation in gravity, if existed, could have important implications, and it is meaningful to search and test the possible observational effects. Chern-Simons modified gravity serves as a natural model for gravitational parity-violations. Especially, considering extensions to Einstein-Hilbert action up to second order curvature terms, it is known that theories of gravitational parity-violation will reduce to the dynamical Chern-Simons gravity. In this letter, we outline the theoretical principles of testing the dynamical Chern-Simons gravity with orbiting gravity gradiometers, which could be naturally incorporated into future satellite gravity missions. The secular gravity gradient signals, due to the Mashhoon-Theiss (anomaly) effect, in dynamical Chern-Simons gravity are worked out, which can improve the constraint of the corresponding Chern-Simons length scale $\xi^{\frac{1}{4}}_{cs}$ obtained from such measurement scheme. For orbiting superconducting gradiometers or gradiometers with optical readout, a bound $\xi^{\frac{1}{4}}_{cs}\leq 10^6 \ km$ (or even better) could in principle be obtained, which will be at least 2 orders of magnitude stronger than the current one based on the observations from the GP-B mission and the LAGEOS I, II satellites.Comment: 15 pages, 6 figures. arXiv admin note: text overlap with arXiv:1606.0818

Psychological crisis intervention for the family members of patients in a vegetative state

OBJECTIVES: Family members of patients in a vegetative state have relatively high rates of anxiety and distress. It is important to recognize the problems faced by this population and apply psychological interventions to help them. This exploratory study describes the psychological stress experienced by family members of patients in a vegetative state. We discuss the effectiveness of a psychological crisis intervention directed at this population and offer suggestions for future clinical work. METHODS: A total of 107 family members of patients in a vegetative state were included in the study. The intervention included four steps: acquisition of facts about each family, sharing their first thoughts concerning the event, assessment of their emotional reactions and developing their coping abilities. The Symptom Check List-90 was used to evaluate the psychological distress of the participants at baseline and one month after the psychological intervention. Differences between the Symptom Check List-90 scores at the baseline and follow-up evaluations were analyzed. RESULTS: All participants in the study had significantly higher Symptom Check List-90 factor scores than the national norms at baseline. There were no significant differences between the intervention group and the control group at baseline. Most of the Symptom Check List-90 factor scores at the one-month follow-up evaluation were significantly lower than those at baseline for both groups; however, the intervention group improved significantly more than the control group on most subscales, including somatization, obsessive-compulsive behavior, depression, and anxiety. CONCLUSION: The results of this study indicate that the four-step intervention method effectively improves the mental health of the family members who received this treatment and lessens the psychological symptoms of somatization, obsessive-compulsive behavior, depression and anxiety

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an $F_1$-score of 83.7\%, higher than competing methods in the literature.Comment: EMNLP '1

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1