HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding

We consider a contrastive learning approach to knowledge graph embedding (KGE) via InfoNCE. For KGE, efficient learning relies on augmenting the training data with negative triples. However, most KGE works overlook the bias from generating the negative triples-false negative triples (factual triples missing from the knowledge graph). We argue that the generation of high-quality (i.e., hard) negative triples might lead to an increase in false negative triples. To mitigate the impact of false negative triples during the generation of hard negative triples, we propose the Hardness and Structure-aware (\textbf{HaSa}) contrastive KGE method, which alleviates the effect of false negative triples while generating the hard negative triples. Experiments show that HaSa improves the performance of InfoNCE-based KGE approaches and achieves state-of-the-art results in several metrics for WN18RR datasets and competitive results for FB15k-237 datasets compared to both classic and pre-trained LM-based KGE methods

Dlgh1 coordinates actin polymerization, synaptic T cell receptor and lipid raft aggregation, and effector function in T cells.

Lipid raft membrane compartmentalization and membrane-associated guanylate kinase (MAGUK) family molecular scaffolds function in establishing cell polarity and organizing signal transducers within epithelial cell junctions and neuronal synapses. Here, we elucidate a role for the MAGUK protein, Dlgh1, in polarized T cell synapse assembly and T cell function. We find that Dlgh1 translocates to the immune synapse and lipid rafts in response to T cell receptor (TCR)/CD28 engagement and that LckSH3-mediated interactions with Dlgh1 control its membrane targeting. TCR/CD28 engagement induces the formation of endogenous Lck-Dlgh1-Zap70-Wiskott-Aldrich syndrome protein (WASp) complexes in which Dlgh1 acts to facilitate interactions of Lck with Zap70 and WASp. Using small interfering RNA and overexpression approaches, we show that Dlgh1 promotes antigen-induced actin polymerization, synaptic raft and TCR clustering, nuclear factor of activated T cell activity, and cytokine production. We propose that Dlgh1 coordinates TCR/CD28-induced actin-driven T cell synapse assembly, signal transduction, and effector function. These findings highlight common molecular strategies used to regulate cell polarity, synapse assembly, and transducer organization in diverse cellular systems

Do Non-Executive Employees Matter in Curbing Corporate Financial Fraud

Exploiting staggered enactment of employee stock ownership plans (ESOPs) as a quasi-natural shock, we use a difference-in-differences (DiD) approach to investigate whether and how ESOPs mitigate corporate financial fraud in China. We find ESOPs significantly reduce corporate financial fraud. This is because of stock ownership of non-executives rather than executives. The underlying mechanisms are heightened internal monitoring and external monitoring through which ESOPs curb executives’ opportunistic behaviour. Our results are robust to parallel trend test, placebo test, PSM approach, instrument variable test, and considering omitted variable concern, partial observability problem, model specification, stock market crash, and industry effect. Our additional analyses indicate that the effect of ESOPs on corporate financial fraud is more pronounced when firms with weaker corporate governance, poorer information environment, less powerful executives and higher-intensity and broader-based plans. Collectively, our results indicate that ESOPs play a role, as an alternative corporate governance mechanism, in mitigating financial fraud

Resonant inelastic X-ray scattering study of overdoped La2−x_{2-x}Srx_{x}CuO4_{4}

Resonant inelastic x-ray scattering (RIXS) at the copper K absorption edge has been performed for heavily overdoped samples of La$_{2-x}$Sr$_{x}$CuO$_{4}$ with $x= 0.25$ and 0.30. We have observed the charge transfer and molecular-orbital excitations which exhibit resonances at incident energies of $E_i= 8.992$ and 8.998 keV, respectively. From a comparison with previous results on undoped and optimally-doped samples, we determine that the charge-transfer excitation energy increases monotonically as doping increases. In addition, the $E_i$-dependences of the RIXS spectral weight and absorption spectrum exhibit no clear peak at $E_i = 8.998$ keV in contrast to results in the underdoped samples. The low-energy ($\leq 3$ eV) continuum excitation intensity has been studied utilizing the high energy resolution of 0.13 eV (FWHM). A comparison of the RIXS profiles at $(\pi ~0)$ and $(\pi ~\pi)$ indicates that the continuum intensity exists even at $(\pi ~\pi)$ in the overdoped samples, whereas it has been reported only at $(0 ~0)$ and $(\pi ~0)$ for the $x=0.17$ sample. Furthermore, we also found an additional excitation on top of the continuum intensity at the $(\pi ~\pi)$ and $(\pi ~0)$ positions.Comment: 7 pages, 7 figure

Machine Learning For Robot Motion Planning

Robot motion planning is a field that encompasses many different problems and algorithms. From the traditional piano mover\u27s problem to more complicated kinodynamic planning problems, motion planning requires a broad breadth of human expertise and time to design well functioning algorithms. A traditional motion planning pipeline consists of modeling a system and then designing a planner and planning heuristics. Each part of this pipeline can incorporate machine learning. Planners and planning heuristics can benefit from machine learned heuristics, while system modeling can benefit from model learning. Each aspect of the motion planning pipeline comes with trade offs between computational effort and human effort. This work explores algorithms that allow motion planning algorithms and frameworks to find a compromise between the two. First, a framework for learning heuristics for sampling-based planners is presented. The efficacy of the framework depends on human designed features and policy architecture. Next, a framework for learning system models is presented that incorporates human knowledge as constraints. The amount of human effort can be modulated by the quality of the constraints given. Lastly, semi-automatic constraint generation is explored to enable a larger range of trade-offs between human expert constraint generation and data driven constraint generation. We apply these techniques and show results in a variety of robotic systems