Path Ranking with Attention to Type Hierarchies

The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base. Prior work has demonstrated the effectiveness of path-ranking based methods, which solve the problem by discovering observable patterns in knowledge graphs, consisting of nodes representing entities and edges representing relations. However, these patterns either lack accuracy because they rely solely on relations or cannot easily generalize due to the direct use of specific entity information. We introduce Attentive Path Ranking, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization. Then, we present an end-to-end trained attention-based RNN model to discover the new path patterns from data. Experiments conducted on benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate that the proposed model outperforms existing methods on the fact prediction task by statistically significant margins of 26% and 10%, respectively. Furthermore, quantitative and qualitative analyses show that the path patterns balance between generalization and discrimination.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

Hunting for Dark Matter Coannihilation by Mixing Dijet Resonances and Missing Transverse Energy

Simplified models of the dark matter (co)annihilation mechanism predict striking new collider signatures untested by current searches. These models, which were codified in the coannihilation codex, provide the basis for a dark matter (DM) discovery program at the Large Hadron Collider (LHC) driven by the measured DM relic density. In this work, we study an exemplary model featuring $s$-channel DM coannihilation through a scalar diquark mediator as a representative case study of scenarios with strongly interacting coannihilation partners. We discuss the full phenomenology of the model, ranging from low energy flavor constraints, vacuum stability requirements, and precision Higgs effects to direct detection and indirect detection prospects. Moreover, motivated by the relic density calculation, we find significant portions of parameter space are compatible with current collider constraints and can be probed by future searches, including a proposed analysis for the novel signature of a dijet resonance accompanied by missing transverse energy (MET). Our results show that the $13$ TeV LHC with $100~\mathrm{fb}^{-1}$ luminosity should be sensitive to mediators as heavy as 1 TeV and dark matter in the 400--500 GeV range. The combination of searches for single and paired dijet peaks, non-resonant jets + MET excesses, and our novel resonant dijet + MET signature have strong coverage of the motivated relic density region, reflecting the tight connections between particles determining the dark matter abundance and their experimental signatures at the LHC.Comment: 35 pages, 9 figure

Collective coordinates for D-branes

We develope a formalism for the scattering off D-branes that includes collective coordinates. This allows a systematic expansion in the string coupling constant for such processes, including a worldsheet calculation for the D-brane's mass.Comment: 11 pages, 5 figures. Corrected reference [12], added remark about contributions from virtual recoil of the bran

The Importance of Autonomous Regulation for Students' Successful Translation of Intentions into Behavior Change via Planning

Physical activity has a high prevention potential in adolescents. This study investigated the relations between physical activity and intention, autonomous regulation, and planning. We hypothesized that planning mediates the relationship between intention and behavior and that this mediation should depend on the level of autonomous regulation. Stratified randomization sampling method was administered to assemble a sample of N = 534 students among two schools in China. To test the hypothesis, autonomous regulation, intention, and physical activity were assessed at baseline as well as planning and follow-up physical activity four weeks after the pretest. A moderated mediation model confirmed that planning mediated the intention-behavior relation with the effect of planning being moderated by autonomous regulation. Study results demonstrated that autonomous regulation facilitated the translation of intention into behavior change via planning. To promote physical activity among adolescents, interventions targeting planning and autonomous regulation might facilitate successful translation of intentions into behavior change

StructDiffusion: Object-Centric Diffusion for Semantic Rearrangement of Novel Objects

Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen. In this work, we focus on the problem of building physically-valid structures without step-by-step instructions. We propose StructDiffusion, which combines a diffusion model and an object-centric transformer to construct structures out of a single RGB-D image based on high-level language goals, such as "set the table." Our method shows how diffusion models can be used for complex multi-step 3D planning tasks. StructDiffusion improves success rate on assembling physically-valid structures out of unseen objects by on average 16% over an existing multi-modal transformer model, while allowing us to use one multi-task model to produce a wider range of different structures. We show experiments on held-out objects in both simulation and on real-world rearrangement tasks. For videos and additional results, check out our website: http://weiyuliu.com/StructDiffusion/

Subject-centered multi-view feature fusion for neuroimaging retrieval and classification

Multi-View neuroimaging retrieval and classification play an important role in computer-aided-diagnosis of brain disorders, as multi-view features could provide more insights of the disease pathology and potentially lead to more accurate diagnosis than single-view features. The large inter-feature and inter-subject variations make the multi-view neuroimaging analysis a challenging task. Many multi-view or multi-modal feature fusion methods have been proposed to reduce the impact of inter-feature variations in neuroimaging data. However, there is not much in-depth work focusing on the inter-subject variations. In this study, we propose a subject-centered multi-view feature fusion method for neuroimaging retrieval and classification based on the propagation graph fusion (PGF) algorithm. Two main advantages of the proposed method are: 1) it evaluates the query online and adaptively reshapes the connections between subjects according to the query; 2) it measures the affinity of the query to the subjects using the subject-centered affinity matrices, which can be easily combined and efficiently solved. Evaluated using a public accessible neuroimaging database, our algorithm outperforms the state-of-the-art methods in retrieval and achieves comparable performance in classification

Detection of Multiple Human Papillomavirus Genotypes in Anal Carcinoma

Infection with human papillomavirus (HPV) is a major risk factor for development of anal squamous cell carcinoma. Despite over 100 genotypes of the virus, HPV 16 and 18 are considered pathogenic as they are seen in the majority of cervical and anal cancers. We have employed a custom microarray to examine DNA for several HPV genotypes. We aimed to determine the accuracy of our microarray in anal cancer DNA for HPV genotypes compared to the DNA sequencing gold standard

Longitudinal brain MR retrieval with diffeomorphic demons registration: What happened to those patients with similar changes?

Current medical content-based retrieval (MCBR) systems for neuroimaging data mainly focus on retrieving the cross-sectional neuroimaging data with similar regional or global measurements. The longitudinal pathological changes along different time-points are usually neglected in such MCBR systems. We propose the cross-registration based retrieval for longitudinal MR data to retrieve patients with similar structural changes as an extension to the existing MCBR systems. The diffeomorphic demons registration is used to extract the tissue deformation between two adjacent MR volumes. An asymmetric square dissimilarity matrix is designed for indexing the patient changes within a specific interval. A visual demonstration is given to show the registration displacement fields of the query as compared to the simulated results. The experimental performance with the mean average precision (mAP) and the average top-K accuracy (aACC) are reported for evaluation