Information Security Field Event Detection Technology Based on SAtt-LSTM

Detecting information security events from multimodal data can help analyze the evolution of events in the security field. The Tree-LSTM network that introduces the self-attention mechanism was used to construct the sentence-vectorized representation model (SAtt-LSTM: Tree-LSTM with self-attention) and then classify the candidate event sentences through the representation results of the SAtt-LSTM model to obtain the event of the candidate event sentence types. Event detection using sentence classification methods can solve the problem of error cascade based on pipeline methods, and the problem of CNN or RNN cannot make full use of the syntactic information of candidate event sentences in methods based on joint learning. The paper treats the event detection task as a sentence classification task. In order to verify the effectiveness and superiority of the method in this paper, the DuEE data set was used for experimental verification. Experimental results show that this model has better performance than methods that use chain structure LSTM, CNN, or only Tree-LSTM.

An Electrocortical Measure Associated With Metarepresentation Mediates the Relationship Between Autism Symptoms and Theory of Mind

Impairments in theory of mind (ToM)—long considered common among individuals with autism spectrum disorder (ASD)—are in fact highly heterogeneous across this population. Although such heterogeneity should be reflected in differential recruitment of neural mechanisms during ToM reasoning, no research has yet uncovered a mechanism that explains these individual differences. In this study, 78 (48 with ASD) adolescents viewed ToM vignettes and made mental-state inferences about characters’ behavior while participant electrophysiology was concurrently recorded. Two candidate event-related-potentials (ERPs)—the late positive complex (LPC) and the late slow wave (LSW)—were successfully elicited. LPC scores correlated positively with ToM accuracy and negatively with ASD symptom severity. Note that the LPC partially mediated the relationship between ASD symptoms and ToM accuracy, which suggests that this ERP component, thought to represent cognitive metarepresentation, may help explain differences in ToM performance in some individuals with ASD.

Disforming the Kerr metric

Starting from a recently constructed stealth Kerr solution of higher order scalar tensor theory involving scalar hair, we analytically construct disformal versions of the Kerr spacetime with a constant degree of disformality and a regular scalar field. While the disformed metric has only a ring singularity and asymptotically is quite similar to Kerr, it is found to be neither Ricci flat nor circular. Non-circularity has far reaching consequences on the structure of the solution. As we approach the rotating compact object from asymptotic infinity we find a static limit ergosurface similar to the Kerr spacetime with an enclosed ergoregion. However, the stationary limit of infalling observers is found to be a timelike hypersurface. A candidate event horizon is found in the interior of this stationary limit surface. It is a null hypersurface generated by a null congruence of light rays which are no longer Killing vectors. Under a mild regularity assumption, we find that the candidate surface is indeed an event horizon and the disformed Kerr metric is therefore a black hole quite distinct from the Kerr solution.

Constructing Narrative Event Evolutionary Graph for Script Event Prediction

Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.

Global Identification of Previously Undetected Pre-Satellite-Era Tropical Cyclone Candidates in NOAA/CIRES Twentieth-Century Reanalysis Data

Prior to the satellite era, limited synoptic observation networks led to an indefinite number of tropical cyclones (TCs) remaining undetected. This period of decreased confidence in the TC climatological record includes the first two-thirds of the twentieth century. While prior studies found that this undersampling exists, disagreement regarding its magnitude has caused difficulties in interpreting multidecadal changes in TC activity. Previous research also demonstrated that reanalyses can be used to extend TC climatology, utilizing the NOAA/Cooperative Institute for Research in Environmental Sciences (CIRES) Twentieth-Century Reanalysis to manually identify previously unknown Atlantic Ocean basin potential TCs. This study expands the spatiotemporal scope of the earlier work by presenting a filtering algorithm that dramatically improves the efficiency with which candidate events are identified in the reanalysis. This algorithm was applied to all tropical basins for the years 1871–1979, resulting in the first quantitative and objective global TC candidate event counts for the decades prior to formal recordkeeping. Observational verification performed on a subset of these events indicates that the algorithm identifies potential missing TCs at a success rate approximating that of earlier work with a significant decrease in the amount of time required. Extrapolating these proportions to all of the candidate events identified suggests that this method may help to locate hundreds of previously unknown TCs worldwide for future study and cataloging. As such, the dataset produced by this research is a source of independent guidance for use in ongoing and future TC climatology revision efforts to produce a more complete historical record more quickly than with current methods.

FIRST RESULTS FROM BNL E949

The first result from BNL E949 for the rare kaon decay [Formula: see text] is described. One candidate event was seen with a signal-to-noise ratio of 0.9. Combined with the two events previously reported by E787, the best estimate of the branching ratio is [Formula: see text].