dataesr/scientific-paragraphs-categorization huggingface dataset:
The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGN right in the act of changing-state. Here we present an anomaly detection (AD) technique designed to identify AGN light curves with anomalous behaviors in massive datasets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive datasets for AGN variability analyses.
We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behaviour of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm, that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8,809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
(2019a). This event is particularly interesting because it is a CSAGN both in the optical and X-ray regimes. The authors claim that a tidal disruption event (TDE; tidal disruption of a star by the accreting SMBH) provoked an increase in the accretion rate at the inner-most regions of the accretion disk, which emptied the inner disk, and led to the destruction of the X-ray corona. This corresponds to the first CSAGN extensively monitored at X-rays and optical through a transition state, placing strong constraints on the physics of the system. That work demonstrated the importance of the rapid detection and rapid followup of changing-state events, which can give us the opportunity to probe accretion physics. Previous searches of CSAGN in massive archival datasets have employed simple statistical analysis to find them, using for instance measurements of the variability amplitude (e.
g. ;  MacLeod et al. 2016 MacLeod et al. , 2019)) , or more complex analysis like Bayesian blocks representation (BB;  Scargle et al. 2013; Graham et al. 2020 ). However, these methods are not suitable for the realtime detection of CSAGN events, since they favor the detection of events well after they have occurred, and not the detection of the transition phase. More promising techniques involve time series forecasting or the use of Anomaly Detection algorithms (AD; e. g. ,  Mehrotra et al. 2017). AD techniques have been used in astronomy to search for unusual objects within astronomical datasets, however this search has largely focused to date on transient and periodic classes (e. g. Ishida et al. 2019; Lochner & Bassett 2020; Malanchev et al. 2020; Villar et al. 2021). Pérez-Carrasco et al.
(in prep)  presents the outlier detector for the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker  (Förster et al. 2021; Carrasco-Davis et al. 2020; Sánchez-Sáez et al. 2021). Its main goal is to find multiple types of variable and transient sources, that are not included in the taxonomy tree of the ALeRCE broker light curve classifier  (Sánchez-Sáez et al. 2021). However, AD algorithms can also be used to detect light curves of objects with previous known classifications, that suddenly start presenting unusual variability behaviours (also known as contextual AD). Recently,  Suberlak et al. (2021)  showed that CSAGN candidates appear as outliers when they are compared with the damped random walk (DRW) parameters (timescale and variance;  Kelly et al. 2009 ) obtained from light curves of the same objects, that cover different timespans (and using different datasets).
Thus, we can expect that the early detection of CSAGNs would benefit from the use of AD techniques on current large monitoring surveys. In this work we present an unsupervised AD algorithm to search for AGN light curves with anomalous variability behaviours in massive datasets, focusing in particular on data from the Zwicky Transient Facility (ZTF;  Bellm et al. 2019). The main goal of the proposed algorithm is to find CSAGN candidates at different stages of the changing-state event (i. e. , either early or late stages), although the methodology should be generally useful to find other classes of anomalous light curves, like atypical flaring activity (e. g. ,  Graham et al. 2017; Trakhtenbrot et al. 2019b; Frederick et al. 2020) , extremely variable AGNs (e. g. ,  Rumbaugh et al. 2018; Guo et al.
2020; Luo et al. 2020) , or sources with incorrect labels or photometric issues, as we show in the following sections. Our method is inspired by the work presented in  Tachibana et al. (2020) , who used a Recurrent Autoencoder (RAE) architecture to model AGN light curves observed by the Catalina Real-time Transient Survey (CRTS;  Drake et al. 2009). We instead use a Variational Recurrent Autoencoder (VRAE;  Fabius et al. 2015 ) architecture together with an Isolation Forest (IF;  Liu et al. 2008)  algorithm, following a two-stage approach, to search for anomalous light curves. As far as we know, this is the first attempt to search for anomalous AGN variability behaviors using AD techniques. The paper is organized as follows. In Section 2 we describe the datasets used for this work, including the AGN catalogs considered, and the procedure to define the final sample of ZTF light curves.
Optical light curves For this work we used the point spread function (PSF) fit-based optical light curves available in the ZTF public data release 5 1  (ZTF DR5;  Masci et al. 2019). In particular, we used the ZTF Application Programming Interface (ZTF API) to obtain light curves for all the sources in our sample of known AGNs described in Section 2. 1. We retrieved the light curves of each source, centred in the coordinates of the catalogs presented in Section 2. 1, and using a radius of 1. 5 arcseconds, which is the standard cross-matching radius used by ZTF. When doing this for a particular object, the ZTF API can return more than one light curve in a given filter. This happens because ZTF treats independently the light curves observed in a particular field, filter, and CCD-quadrant, and thus, if a source appears in more than one combination of these three, ZTF will provide more than one light curve.
METHODS In this work we followed an unsupervised approach (i. e. , without considering the labels of our dataset) to search for anomalous AGN variability behaviors. We followed a two step approach, where we first train a VRAE architecture to reconstruct the light curves in our sample, and used the reconstruction error as an AD score. Then, we used the VRAE mean latent space as a vector of features for an IF algorithm, and selected anomalies using the anomaly score provided by the IF method. In the following section we provide a detailed description of our AD methodology.
VRAE architecture Autoencoders (AEs) are artificial neural networks that are able to learn compressed representations of an input dataset, called the "latent representations", in an unsupervised way. The latent representations are then used to reconstruct or predict the original data. Basically, AEs are composed by an encoder set of layers, that compress the data, a latent space, that contains the compressed information, and a decoder set of layers that produces a reconstruction or prediction. RAEs, on the other hand, are recurrent AEs that use recurrent neural networks (RNN) to encode and decode sequential data, and are normally used for sequence-to-sequence learning. Tachibana et al. (2020)  presented a novel algorithm to empirically model AGN light curves using an RAE (e. g. ,  Naul et al.
AD methodology A common way to do time series AD with RAEs or VRAEs is to train a model with a training set that is assumed to be normal, and then use this model to reconstruct the data of a test set and measure the reconstruction error. If the reconstruction error is above a given threshold, then the time series is labeled as anomalous (e. g. ,  Malhotra et al. 2016; Pereira & Silveira 2018). Another option, is to use the RAE or VRAE latent space as features for an AD algorithm, such as IF. IF is a tree-based ensemble method that isolates anomalies by making random selection of features, and dividing the dataset according to randomly selected thresholds. In general, anomalies tend to be isolated in fewer steps compared to normal objects, thus an anomaly score can be defined as proportional to the number of splits required to isolate a given object.
3. The results for the full sample when using the RAE architecture are shown in panel c of Figure  6  and in panel b of Figure  7  (black plots in both figures). We can see in both figures that the correlation with n det is highly reduced. A similar result is observed for the VRAE architecture, whose results are shown in panel d of Figure  6  and in panel d of Figure  7  (blue plots in both figures). In general, the correlation with n det is nonexistent or very weak (e. g. , VRAE e 2 , and VRAE e 10 ), although the improvement with respect to the previous VRAE model is small, and the main differences are observed in the IF score obtained for light curves with n det > 600 (see panels c and d of Figure  7 ). From these results, we can conclude that a VRAE architecture provides better performance when dealing with datasets that are highly imbalanced in their observational properties, like n det.
VRAE reconstruction error is R ≥ 3 or if its IF score ≤ IF th. Figure  10  shows the fraction of light curves labeled as anomalous for each of the classes described in Section 2. 1. The low fraction of anomalous objects belonging to the class Q is not surprising, since we trained our model with a dataset that is dominated by this class. The fraction of anomalous Q sources is close to the 2% that we defined as threshold in our AD strategy. On the other hand, the classes with the largest fraction of anomalies are the three blazar classes (BLLac, BZU, and FSRQ), the host-dominated classes (A and AR), and the type 2 with radio emission (type2 R). As mentioned in Section 2. 1, blazars were included in the analysis to test the efficiency of our AD methodology, and we expected these classes to be classified as anomalous.
2021) , and 26% of these are classified as blazars. In addition, 36 of these anomalies are classified as BLLac in the SIMBAD database  (Wenger et al. 2000). After the visual inspection we identified 10 promising CSAGN candidates. • Class A (873 anomalies): the anomalies in this class have lower redshifts than those labeled as normal, and therefore are probably highly dominated by the emission from the host galaxies. Their light curves show in general low-amplitude rapid variations, which are not properly recovered by the VRAE reconstructions. We found one source classified as AGN by the SDSS pipeline (ZTF DR5 ID 408114200003141), but classified as YSO in SIMBAD. We identified 10 promising CSAGN candidates. • Class AR (190 anomalies): as with class A, the anomalies in this class have lower redshifts than those labeled as normal, and thus, their light curves could be more contaminated by the emission from the hosts.
Similar to the QR class, some sources show variability behavior close to those observed for BLLacs. 88% of these anomalies have classification in the ALeRCE light curve classifier, and 18 are classified as blazar. In addition, 10 are classified as blazar in the SIMBAD database. We identified three promising CSAGN candidates. • Classes type2 and type2 R (20 anomalies): several of the type2 anomalies are probably type 1. 8 or 1. 9 sources, since their SDSS spectra show evidence of a broad emission line component, which may help to explain their optical variations. We identify two promising CSAGN candidates. • Class X (246 anomalies): when we inspected the light curves of these anomalies, we noticed that several of them seem to be variable stars. 46% of the anomalies have classification in the ALeRCE light curve classifier, and 87 are classified as variable star (including several cataclysmic variables and young stellar objects).
About the use of AD techniques to search for CSAGNs In this work we used an AD approach to search for CSAGNs. A high fraction of the anomalies found by our method are bogus candidates (e. g. , noisy light curves, or light curves with problems in the photometry), or sources with incorrect labels in the original catalogs (e. g. , variable stars classified as quasars by the SDSS pipeline, or X-ray detected variable stars). This type of issue has been found in previous works that used AD algorithms to search for anomalous light curves (e. g. ,  Malanchev et al. 2020, Pérez-Carrasco et al. in prep). Thus, our algorithm can be a powerful tool to identify light curves with photometric issues in massive datasets, and to identify mislabeled sources. Moreover, as mentioned in Section 4. 2, the light curves of host-dominated AGNs are not properly reconstructed by our VRAE architecture, producing a large fraction of anomalous candidates, which hinders the discovery of true outliers such as CSAGNs from this population.
CONCLUSIONS In this work we presented an AD algorithm to search for AGN with anomalous light curves in massive datasets (like ZTF DR5), whose main aim is to find CSAGN candidates. We used an architecture inspired by the work with RAE architecture presented by  Tachibana et al. (2020) , but with two modifications: we used GRUs instead of LSTMs, and we used a VRAE architecture, instead of RAE. Our dataset is composed of 230,451 g band light curves from ZTF DR5. The light curves are associated with different classes of AGNs, although 90. 7% are regular quasars (class Q). The rest of the sources are type 2 AGNs, Seyfert galaxies, blazars, and sources selected by their X-ray or radio emission. We used the ZTF PSFfit-based light curves from DR5, with more than 2 years of data, and more than 50 epochs.
Therefore, we highlight that our AD algorithm is useful not only to find intrinsic anomalous light curves, but also to detect problems in the datasets used for AGN variability analyses. We visually inspected the anomalies found by our AD algorithm and selected a sample of 75 promising CSAGN candidates. The full list of candidates is presented in Appendix A. The visual inspection is required, as a large fraction of our anomalies correspond to bogus candidates (like light curves with photometric issues, or incorrect labels reported in the literature). Further spectroscopic confirmation would be required to confirm the real nature of our sample. A high fraction of our candidates present light curve shapes similar to those observed in X-ray light curves of X-ray binaries, which are generally associated to changes in the accretion state.
The timescales of these variations in our set of candidates are of the order of hundreds of days, much smaller than the typical viscous timescales for accretion disks around SMBHs (order of thousands of years), and thus we need further analyses to discover the origin of these atypical variations. This work corresponds to our first attempt to search for CSAGN candidates in massive datasets. In the future, we plan to use this architecture, together with other techniques, to forecast the light curves of known type 1 and type 2 AGNs, and from this, detect variability behaviors that deviate from the normal variations expected for each source. For this aim, we will use data from the ZTF data releases, but also data from the ZTF alert stream, which should allow us to detect CSAGN candidates in real-time, and trigger follow-up to better pin down the nature of this enigmatic phenomenon.
Software: Google Colaboratory (Bisong 2019), Keras  (Chollet et al. 2015) , Scikit-learn  (Pedregosa et al. 2011), and Tensorflow (Abadi et al. 2016). For graphical representations and data manipulation: Jupyter  (Kluyver et al. 2016) , Matplotlib  (Hunter 2007 ), Numpy and Scipy (van der Walt et al. 2011 ), Pandas (McKinney et al. 2010 ), Python (Van Rossum & Drake Jr 1995) , and Seaborn  (Waskom et al. 2017 ).
obtained with the Samuel Oschin 48-inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under Grant No. AST-1440341 and a collaboration including Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, and Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and UW.
Data Collection and Instruments The participant's characterization was performed through: (1) a structured questionnaire-based interview to assess socio-demographic, clinical, and lifestyle information; (2) a neuropsychological and quality of life evaluation; (3) an anthropometric and body composition evaluation; and (4) physical performance evaluation. Detailed information regarding the study evaluation moments is present in Figure  1. Based on the assessment type, measures were assessed always by the same attributed team member/researcher, following a study manual and/or established practices. Participants were instructed not to change their habitual daily living habits/routines.
Physical Performance The assessment of physical performance included gait speed (6-m walking test) and handgrip strength (Jamar Hand Dynamometer). In older individuals, the cut-off point for slow gait speed was defined as walking less than 1 m/s, which identifies persons at high risk of health-related outcomes in wellfunctioning  (Cesari et al. , 2005). The normative data for handgrip strength ranges from 22. 5 to 25. 6 kg for women and 36. 2 to 41. 7 kg for men  (Bohannon et al. , 2006). Functional status was evaluated using the Lawton Instrumental Activities of Daily Living (IADL) Scale to assess independent living skills  (Lawton and Brody, 1969) , Katz Activities of Daily Living (ADL) Scale to assess six primary and psychosocial functions (bathing, dressing, going to the toilet, transferring, feeding, and continence)  (Katz et al.
Data Reduction Microsoft Excel was used to perform data reduction and cleaning. Seven consecutive valid days of data were collected from the total wear time. For the analysis, we excluded the first day of records to ensure a complete day in each data collection period. Regarding data validation, each participant must have a minimum of five valid days, including at least one weekend day  (Nicaise et al. , 2014; O'Neill et al. , 2017) , wherein a valid day consisted of at least 10 h of records. An invalid hour was defined as more than 30 consecutive minutes with zero activity  (Troiano et al. , 2008; Ferrari et al. , 2020). The PA intensities were defined using the following intensity-specific cut-points: sedentary 0-19 steps/min; light intensity: 60-99 steps/min; moderate intensity: 100-119 steps/min; and vigorous-intensity: ≥120 steps/min  (Tudor-Locke et al.
Anatomical Data Preprocessing T1w image was corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection  (Tustison et al. , 2010) , distributed with ANTs 2. 2. 0 RRID:SCR_004757  (Avants et al. , 2008) , and used as T1w-reference throughout the workflow. The T1wreference was then skull-stripped with a Nipype implementation of the antsBrainExtraction. sh workflow (from ANTs), with OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and graymatter (GM) was performed on the brain-extracted T1w using fast (FSL 5. 0. 9, RRID:SCR_002823)  (Zhang et al. , 2001). Brain surfaces were reconstructed using recon-all (FreeSurfer 6. 0. 1, RRID:SCR_001847)  (Dale et al. , 1999) , and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle (RRID:SCR_002438)  (Klein et al.
Resting-State Data Preprocessing For each subject, the following preprocessing steps were performed: First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. A deformation field to correct for susceptibility distortions was estimated based on a field map that was coregistered to the BOLD reference, using a custom workflow of fMRIPrep derived from D. Greve's epidewarp. fsl script and further improvements of HCP Pipelines  (Glasser et al. , 2013). Based on the estimated susceptibility distortion, an unwarped BOLD reference was calculated for a more accurate coregistration with the anatomical reference. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration  (Greve and Fischl, 2009).
Components were calculated separately within the WM and CSF masks. For each CompCor decomposition, the k components with the largest singular values were retained, such that the retained components' time series were sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components were dropped from consideration. The mean CSF and WM signals, as well as the first 6 aCompCor components, the FD and the DVARS were regressed as confounds from the BOLD data using fslregfilt. Excessive movement was considered with a mean FD >0. 25. Thus, subjects that exceeded this threshold were excluded. Finally, fslmaths was used to spatially smooth (with a FWHM kernel of 6 mm) and band-pass filter (between 0. 01 and 0. 08 Hz) the resulting time-series.
Voxel-Based Morphometry FMRIB software library (FSL)-voxel-based morphometry (VBM) was used to analyze structural data  (Douaud et al. , 2007)  an optimized VBM protocol  (Good et al. , 2001)  conducted with FSL  (Smith et al. , 2004). First, structural images (which were already brain-extracted and gray matter-segmented by FMRIPrep) were registered to the MNI 152 standard space using non-linear registration  (Andersson et al. , 2007). The images obtained were averaged and flipped along the x-axis to create a left-right symmetric, study-specific GM template. Second, all native GM images were non-linearly registered to this study-specific template and "modulated" to correct for local expansion (or contraction) due to the non-linear component of the spatial transformation. The modulated GMimages were subsequently smoothed with an isotropic Gaussian kernel with a sigma of 3 mm.
DISCUSSION Multiple neuroimaging studies have provided evidence that PA has protective effects on brain structure and function in older adults  (Domingos et al. , 2021b). However, the use of subjective measures of PA and the lack of a multimodal MRI approach have limited the understanding of this association. Therefore, in this study, we used objective PA data (obtained from a Xiaomi Mi Band 2) combined with neuroimaging acquisitions to test whether PA is related to the preservation of brain volume and better brain function in brain-healthy older adults (without neuropathological disease or dementia). The main findings from this study were as follows: (1) higher time spent in vigorous PA associated with larger volumes in left parahippocampal gyrus and right hippocampus; (2) light, moderate and total PA time associated with greater FC between the frontal gyrus (superior, middle, and medial), cingulate gyrus, occipital inferior lobe and parahippocampal gyrus, and (3) sedentary time associated with lower FC in the same networks.