Reconstruction-as-Feedback Serves as an Effective Attention Mechanism for Object Recognition and Grouping

Our model uses these object reconstructions as a top-down attentional bias for efficiently routing relevant spatial and feature information of the object. This reconstruction-based attention operates on two levels. First, the model has a long-range projection that inhibits irrelevant spatial regions based on the mask generated from the most likely object reconstruction. Second, the model dynamically changes its feature routing weights through local recurrence, where part-whole connection is modulated based on the reconstruction error for each hypothesized object (represented as a slot). This formulation loosely implements biased-competition theory, where the reconstruction error biases a competition between object slots for the visual parts

Affinity-based Attention in Self-supervised Transformers Predicts Dynamics of Object Grouping in Humans

The spreading of attention has been proposed as a mechanism for how humans group features to segment objects. However, such a mechanism has not yet been implemented and tested in naturalistic images. Here, we leverage the feature maps from self-supervised vision Transformers and propose a model of human object-based attention spreading and segmentation. Attention spreads within an object through the feature affinity signal between different patches of the image. We also collected behavioral data on people grouping objects in natural images by judging whether two dots are on the same object or on two different objects. We found that our models of affinity spread that were built on feature maps from the self-supervised Transformers showed significant improvement over baseline and CNN based models on predicting reaction time patterns of humans, despite not being trained on the task or with any other object labels. Our work provides new benchmarks for evaluating models of visual representation learning including Transformers

Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention

Predicting human gaze is important in Human-Computer Interaction (HCI). However, to practically serve HCI applications, gaze prediction models must be scalable, fast, and accurate in their spatial and temporal gaze predictions. Recent scanpath prediction models focus on goal-directed attention (search). Such models are limited in their application due to a common approach relying on trained target detectors for all possible objects, and the availability of human gaze data for their training (both not scalable). In response, we pose a new task called ZeroGaze, a new variant of zero-shot learning where gaze is predicted for never-before-searched objects, and we develop a novel model, Gazeformer, to solve the ZeroGaze problem. In contrast to existing methods using object detector modules, Gazeformer encodes the target using a natural language model, thus leveraging semantic similarities in scanpath prediction. We use a transformer-based encoder-decoder architecture because transformers are particularly useful for generating contextual representations. Gazeformer surpasses other models by a large margin on the ZeroGaze setting. It also outperforms existing target-detection models on standard gaze prediction for both target-present and target-absent search tasks. In addition to its improved performance, Gazeformer is more than five times faster than the state-of-the-art target-present visual search model.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 202

Target-absent Human Attention

The prediction of human gaze behavior is important for building human-computer interactive systems that can anticipate a user's attention. Computer vision models have been developed to predict the fixations made by people as they search for target objects. But what about when the image has no target? Equally important is to know how people search when they cannot find a target, and when they would stop searching. In this paper, we propose the first data-driven computational model that addresses the search-termination problem and predicts the scanpath of search fixations made by people searching for targets that do not appear in images. We model visual search as an imitation learning problem and represent the internal knowledge that the viewer acquires through fixations using a novel state representation that we call Foveated Feature Maps (FFMs). FFMs integrate a simulated foveated retina into a pretrained ConvNet that produces an in-network feature pyramid, all with minimal computational overhead. Our method integrates FFMs as the state representation in inverse reinforcement learning. Experimentally, we improve the state of the art in predicting human target-absent search behavior on the COCO-Search18 datasetComment: Accepted to ECCV202

Characteristics of Classified Aerosol Types in South Korea during the MAPS-Seoul Campaign

During the Megacity Air Pollution Studies-Seoul (MAPS-Seoul) campaign from May to June 2015, aerosol optical properties in Korea were obtained based on the AERONET sunphotometer measurement at five sites (Anmyon, Gangneung_WNU, Gosan_SNU, Hankuk_UFS, and Yonsei_University). Using this dataset, we examine regional aerosol types by applying a number of known aerosol classification methods. We thoroughly utilize five different methods to categorize the regional aerosol types and evaluate the results from each method by inter-comparison. The differences and similarities among the results are also discussed, contingent upon the usage of AERONET inversion products, such as the single scattering albedo. Despite several small differences, all five methods suggest the same general features in terms of the regionally dominant aerosol type: Fine-mode aerosols with highly absorbing radiative properties dominate at HankukUFS and Yonsei_University; non-absorbing fine-mode particles form a large portion of the aerosol at Gosan_SNU; and coarse-mode particles cause some effects at Anmyon. The analysis of 3-day back-trajectories is also performed to determine the relationship between classified types at each site and the regional transport pattern. In particular, the spatiotemporally short-scale transport appears to have a large influence on the local aerosol properties. As a result, we find that the domestic emission in Korea significantly contributes to the high dominance of radiation-absorbing aerosols in the Seoul metropolitan area and the air-mass transport from China largely affects the western coastal sites, such as Anmyon and Gosan_SNU

Biopsychosocial factors of gaming disorder: a systematic review employing screening tools with well-defined psychometric properties

Background and aimsConsidering the growing number of gamers worldwide and increasing public concerns regarding the negative consequences of problematic gaming, the aim of the present systematic review was to provide a comprehensive overview of gaming disorder (GD) by identifying empirical studies that investigate biological, psychological, and social factors of GD using screening tools with well-defined psychometric properties.Materials and methodsA systematic literature search was conducted through PsycINFO, PubMed, RISS, and KISS, and papers published up to January 2022 were included. Studies were screened based on the GD diagnostic tool usage, and only five scales with well-established psychometric properties were included. A total of 93 studies were included in the synthesis, and the results were classified into three groups based on biological, psychological, and social factors.ResultsBiological factors (n = 8) included reward, self-concept, brain structure, and functional connectivity. Psychological factors (n = 67) included psychiatric symptoms, psychological health, emotion regulation, personality traits, and other dimensions. Social factors (n = 29) included family, social interaction, culture, school, and social support.DiscussionWhen the excess amount of assessment tools with varying psychometric properties were controlled for, mixed results were observed with regards to impulsivity, social relations, and family-related factors, and some domains suffered from a lack of study results to confirm any relevant patterns.ConclusionMore longitudinal and neurobiological studies, consensus on a diagnostic tool with well-defined psychometric properties, and an in-depth understanding of gaming-related factors should be established to settle the debate regarding psychometric weaknesses of the current diagnostic system and for GD to gain greater legitimacy in the field of behavioral addiction

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's TEMPO and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Site-Specific Glycan Microheterogeneity Evaluation of Aflibercept Fusion Protein by Glycopeptide-Based LC-MSMS Mapping

The evaluation of the protein glycosylation states of samples of aflibercept obtained from three different regions was conducted by site-specific N-linked glycan microheterogeneity profiling. Glycopeptide-based nano-LC MSMS mapping of tryptic-digested samples of each aflibercept lot provided site-specific information about glycan microheterogeneity on each of the five N-glycosylation sites (two sites in the VEGFR-1 region, two sites in the VEGFR-2 region, and one site in the human IgG Fc region). Next, the glycopeptide-mapping results obtained from the three different aflibercept lots were compared to evaluate the similarity between the samples. Three aflibercept lots showed a high degree of similarity in glycan composition, fucosylation level, sialylation level, and branching, when all five N-glycosylation sites were assessed together as a group. On the other hand, noticeable variations between lots in the glycan types and sialylation levels on the two sites of the VEGFR-2 domain were observed when each of the five N-glycosylation sites were assessed using the glycopeptide-based method. The presence of N-glycolylneuraminic acid (NeuGc) glycans, which may mediate adverse immune reactions in antibody therapeutics, were also detected on the sites of VEGFR1 and VEGFR2 domains, but not on the IgG Fc domain site. These results imply that analyses of the glycosylation profiles of fusion proteins containing multiple N-glycosylation sites, such as aflibercept, being done as a part of quality control for the therapeutics manufacturing process or for biosimilar development, can be done with a more applicable outcome by assessing each site separately

Associated Factors of Spontaneous Hemorrhage in Brain Metastases in Patients with Lung Adenocarcinoma

Background: Hemorrhage in brain metastases (BMs) from lung cancer is common and associated with a poor prognosis. Research on associated factors of spontaneous hemorrhage in patients with BMs is limited. This study aimed to investigate the predictive risk factors for BM hemorrhage and assess whether hemorrhage affects patient survival. Methods: We retrospectively evaluated 159 BMs from 80 patients with lung adenocarcinoma from January 2017 to May 2022. Patients were classified into hemorrhagic and non-hemorrhagic groups. Patient demographics, lung cancer molecular subtype, treatment type, and tumor–node–metastasis stage were compared between the groups. Multivariate generalized estimating equation (GEE) analysis and gradient boosting were performed. To determine whether BM hemorrhage can stratify overall survival after BM (OSBM), univariate survival analysis was performed. Results: In the univariate analysis, hemorrhagic BMs were significantly larger and had a history of receiving combination therapy with tyrosine kinase inhibitor (TKI) and intracranial radiation (p < 0.05). Multivariate GEE showed that tumor size and combination therapy were independent risk factors for BM hemorrhage (p < 0.05). Gradient boosting demonstrated that the strongest predictor of BM hemorrhage was tumor size (variable importance: 49.83), followed by age (16.65) and TKI combined with intracranial radiation (13.81). There was no significant difference in OSBM between the two groups (p = 0.33). Conclusions: Hemorrhage in BMs from lung adenocarcinomas may be associated with BM tumor size and a combination of TKI and intracranial radiotherapy. BM hemorrhage did not affect OSBM