Pulsar polarization profiles are very basic database for understanding the emission processes in
pulsar magnetosphere. After careful polarization calibration of the 19-beam L-band receiver and verification
of beam-offset observation results, we obtain polarization profiles of 682 pulsars from observations by the
Five-hundred-meter Aperture Spherical radio Telescope (FAST) during the survey tests for the Galactic
Plan Pulsar Snapshot (GPPS) survey and other normal FAST projects. Among them, polarization profiles
of about 460 pulsars are observed for the first time. The profiles exhibit diverse features. Some pulsars have
a polarization position angle curve with a good S-shaped swing, and some with orthogonal modes; some
have components with highly linearly components or strong circularly polarized components; some have
a very wide profile, coming from an aligned rotator, and some have an interpulse from a perpendicular
rotator; some wide profiles are caused by interstellar scattering. We derive geometry parameters for 190
pulsars from the S-shaped position angle curves or with orthogonal modes. We find that the linear and
circular polarization or the widths of pulse profiles have various frequency dependencies. Pulsars with large
fraction of linear polarization are more likely to have a large Edot.

We are carrying out the Galactic Plane Pulsar Snapshot (GPPS) survey by using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the most sensitive systematic pulsar survey in the Galactic plane.
In addition to about 500 pulsars already discovered through normal periodical search, we report here the discovery of 76 new transient radio sources with sporadic strong pulses, detected by using the newly developed module for a sensitive single pulse search. Their small DM values suggest that they all are the Galactic rotating radio transients (RRATs). They show different properties in the following-up observations. 
More radio pulses have been detected from 26 transient radio sources but no periods can be found due to a limited small number of pulses from all FAST observations.
The following-up observations show that 16 transient sources are newly identified as being the prototypes of RRATs with a period already determined from more detected sporadic pulses, and 10 sources are extremely nulling pulsars, and 24 sources are weak pulsars with sparse strong pulses.
On the other hand, 48 previously known RRATs have been detected by the FAST either during verification observations for the GPPS survey or through targeted observations of applied normal FAST projects. Except for 1 RRAT with four pulses detected in a session of five minute observation and 4 RRATs with only one pulse detected in a session, sensitive FAST observations reveal that 43 RRATs are just generally weak pulsars with sporadic strong pulses or simply very nulling pulsars, so that the previously known RRATs always have an extreme emission state together with a normal hardly detectable weak emission state. This is echoed by the two normal pulsars J1938+2213 and J1946+1449 with occasional brightening pulses.
Though strong pulses of RRATs are very outstanding in the energy distribution, their polarization angle variations follow the polarization angle curve of the averaged normal pulse profile, suggesting that the predominant sparse pulses of RRATs are emitted in the same region with the same geometry as normal weak pulsars.

Radio astronomy observations are frequently impacted by radio frequency interference (RFI). We propose a novel method, named 2$\sigma$CRF, for cleaning RFI in the folded data of pulsar observations, utilizing a Bayesian-based model called conditional random fields (CRFs). This algorithm minimizes the ``energy" of every pixel given an initial label. The standard deviations (i.e. RMS values) of the folded pulsar data are utilized as pixels for all subintegrations and channels. Non-RFI data without obvious interference is treated as ``ackground noise," while RFI-affected data have different classes due to their exceptional RMS values. This initial labeling can be automated and is adaptive to the actual data. The CRF algorithm optimizes the label category for each pixel of the image with the prior initial labels. We demonstrate the efficacy of the proposed method on pulsar folded data obtained from Five-hundred-meter Aperture Spherical radio Telescope (FAST) observations. It can effectively recognize and tag various categories of RFIs, including broadband or narrowband, constant or instantaneous, and even weak RFIs that are unrecognizable in some pixels but picked out based on their neighborhoods. The results are comparable to those obtained via manual labeling but without the need for human intervention, saving time and effort.