Tentative evidence of spatially extended GeV emission from SS433/W50

We analyze 10 years of Fermi-LAT data towards the SS433/W50 region. With the latest source catalog and diffuse background models, the gamma-ray excess from SS433/W50 is detected with a significance of 6{\sigma} in the photon energy range of 500 MeV - 10 GeV. Our analysis indicates that an extended flat disk morphology is preferred over a point-source description, suggesting that the GeV emission region is much larger than that of the TeV emission detected by HAWC. The size of the GeV emission is instead consistent with the extent of the radio nebula W50, a supernova remnant being distorted by the jets, so we suggest that the GeV emission may originate from this supernova remnant. The spectral result of the GeV emission is also consistent with an supernova remnant origin. We also derive the GeV flux upper limits on the TeV emission region, which put moderate constrains on the leptonic models to explain the multiwavelength data.Comment: 7 pages, 4 figures, accepted for publication in A&

VLBI astrometry of two millisecond pulsars

We present astrometric results on two millisecond pulsars, PSR B1257+12 and PSR J1022+1001, as carried out through VLBI. For PSR B1257+12, a model-independent distance of $710_{\rm -38}^{\rm +43}$ pc and proper motion of ($\mu_{\rm \alpha}=46.44\pm0.08$ mas/yr, $\mu_{\rm \delta}=-84.87\pm0.32$ mas/yr) were obtained from 5 epochs of VLBA and 4 epochs of EVN observations, spanning about 2 years. The two dimensional proper motion of PSR J1022+1001 ($\mu_{\rm \alpha} \sim -10.13$ mas/yr, $\mu_{\delta} \sim 16.89$ mas/yr) was also estimated, using 3 epochs of EVN observations. Based on our results, the X-ray efficiency of PSR B1257+12 should be in the same range as other millisecond pulsars, and not as low as previously thought.Comment: Proceedings of IAUS 291 "Neutron Stars and Pulsars: Challenges and Opportunities after 80 years", J. van Leeuwen (ed.); 3 page

Certified Monotonic Neural Networks

Learning monotonic models with respect to a subset of the inputs is a desirable feature to effectively address the fairness, interpretability, and generalization issues in practice. Existing methods for learning monotonic neural networks either require specifically designed model structures to ensure monotonicity, which can be too restrictive/complicated, or enforce monotonicity by adjusting the learning process, which cannot provably guarantee the learned model is monotonic on selected features. In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem.This provides a new general approach for learning monotonic neural networks with arbitrary model structures. Our method allows us to train neural networks with heuristic monotonicity regularizations, and we can gradually increase the regularization magnitude until the learned network is certified monotonic. Compared to prior works, our approach does not require human-designed constraints on the weight space and also yields more accurate approximation. Empirical studies on various datasets demonstrate the efficiency of our approach over the state-of-the-art methods, such as Deep Lattice Networks