Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigation

In Vision-and-Language Navigation (VLN), researchers typically take an image encoder pre-trained on ImageNet without fine-tuning on the environments that the agent will be trained or tested on. However, the distribution shift between the training images from ImageNet and the views in the navigation environments may render the ImageNet pre-trained image encoder suboptimal. Therefore, in this paper, we design a set of structure-encoding auxiliary tasks (SEA) that leverage the data in the navigation environments to pre-train and improve the image encoder. Specifically, we design and customize (1) 3D jigsaw, (2) traversability prediction, and (3) instance classification to pre-train the image encoder. Through rigorous ablations, our SEA pre-trained features are shown to better encode structural information of the scenes, which ImageNet pre-trained features fail to properly encode but is crucial for the target navigation task. The SEA pre-trained features can be easily plugged into existing VLN agents without any tuning. For example, on Test-Unseen environments, the VLN agents combined with our SEA pre-trained features achieve absolute success rate improvement of 12% for Speaker-Follower, 5% for Env-Dropout, and 4% for AuxRN

The Atacama Cosmology Telescope: Extragalactic Sources at 148 GHz in the 2008 Survey

We report on extragalactic sources detected in a 455 square-degree map of the southern sky made with data at a frequency of 148 GHz from the Atacama Cosmology Telescope 2008 observing season. We provide a catalog of 157 sources with flux densities spanning two orders of magnitude: from 15 to 1500 mJy. Comparison to other catalogs shows that 98% of the ACT detections correspond to sources detected at lower radio frequencies. Three of the sources appear to be associated with the brightest cluster galaxies of low redshift X-ray selected galaxy clusters. Estimates of the radio to mm-wave spectral indices and differential counts of the sources further bolster the hypothesis that they are nearly all radio sources, and that their emission is not dominated by re-emission from warm dust. In a bright (>50 mJy) 148 GHz-selected sample with complete cross-identifications from the Australia Telescope 20 GHz survey, we observe an average steepening of the spectra between 5, 20, and 148 GHz with median spectral indices of $\alpha_{\rm 5-20} = -0.07 \pm 0.06$, $\alpha_{\rm 20-148} = -0.39 \pm0.04$, and $\alpha_{\rm 5-148} = -0.20 \pm 0.03$. When the measured spectral indices are taken into account, the 148 GHz differential source counts are consistent with previous measurements at 30 GHz in the context of a source count model dominated by radio sources. Extrapolating with an appropriately rescaled model for the radio source counts, the Poisson contribution to the spatial power spectrum from synchrotron-dominated sources with flux density less than 20 mJy is C^{\rm Sync} = (2.8 \pm 0.3) \times 10^{-6} \micro\kelvin^2.Comment: Accepted to Ap

The Vehicle, Fall 1984

Vol. 26, No. 1 Table of Contents Thoughts on I-57Jim Caldwellpage 3 A Night Between Lonely and BlindJennifer K. Soulepage 4 What is Unnatural is Sometimes MagicAngelique Jenningspage 4 Cutting ClosenessBecky Lawsonpage 5 PhotoBrian Ormistonpage 6 The Sensuality of Corn One Week in AugustMichelle Mitchellpage 7 American MusicJim Caldwellpage 7 Water is WaitingMichael Kuopage 8 WhereJennifer K. Soulepage 8 The Fishing HoleJan Kowalskipage 9 Miller\u27s PondSue Gradypage 9 PhotoCathy Stonerpage 11 Young Man Reading To His LoverMaggie Kennedypage 11 ShellsChristopher R. Albinpage 12 In The ShadeJohn Fehrmannpage 12 FallLynanne Feilenpage 13 IndecisionDave L. Brydenpage 13 Dark Falls SoftlyAngelique Jenningspage 14 Not a Parked \u2757 Chevy in the Summer in the CountryMichelle Mitchellpage 20 BirdAnnie Heisepage 20 Clouds Created Only For Poets And Certain WomenJennifer K. Soulepage 21 SandGraham Lewispage 22 PhotoFred Zwickypage 23 Judgment CallCathy Moepage 23 I was hip that night Dan Hintzpage 24 A Sight Of WindDan Von Holtenpage 25 Tillard Isabel M. Parrottpage 26 The WidowMaggie Kennedypage 27 The SeparationMichelle Mitchellpage 27 The Garden Hose TrialMaggie Kennedypage 28 InterruptionsJennifer K. Soulepage 28 On Happening Across Jesus While Cleaning the BasementMaggie Kennedypage 29 GileonMichelle Mitchellpage 30 If My Father Were A Writer, He Would Still BuildAngelique Jenningspage 36 A Visit to Grandpa Gib\u27s HouseTammy Veachpage 37 For Having SeenAngelique Jenningspage 38 PhotoJudy Klancicpage 39 The Earth in BlueSusan J. Bielskypage 39 Things I Could Have SaidAngelique Jenningspage 40 AcrosticsAnnie Heisepage 40https://thekeep.eiu.edu/vehicle/1044/thumbnail.jp

The Vehicle, Fall 1984

Vol. 26, No. 1 Table of Contents Thoughts on I-57Jim Caldwellpage 3 A Night Between Lonely and BlindJennifer K. Soulepage 4 What is Unnatural is Sometimes MagicAngelique Jenningspage 4 Cutting ClosenessBecky Lawsonpage 5 PhotoBrian Ormistonpage 6 The Sensuality of Corn One Week in AugustMichelle Mitchellpage 7 American MusicJim Caldwellpage 7 Water is WaitingMichael Kuopage 8 WhereJennifer K. Soulepage 8 The Fishing HoleJan Kowalskipage 9 Miller\u27s PondSue Gradypage 9 PhotoCathy Stonerpage 11 Young Man Reading To His LoverMaggie Kennedypage 11 ShellsChristopher R. Albinpage 12 In The ShadeJohn Fehrmannpage 12 FallLynanne Feilenpage 13 IndecisionDave L. Brydenpage 13 Dark Falls SoftlyAngelique Jenningspage 14 Not a Parked \u2757 Chevy in the Summer in the CountryMichelle Mitchellpage 20 BirdAnnie Heisepage 20 Clouds Created Only For Poets And Certain WomenJennifer K. Soulepage 21 SandGraham Lewispage 22 PhotoFred Zwickypage 23 Judgment CallCathy Moepage 23 I was hip that night Dan Hintzpage 24 A Sight Of WindDan Von Holtenpage 25 Tillard Isabel M. Parrottpage 26 The WidowMaggie Kennedypage 27 The SeparationMichelle Mitchellpage 27 The Garden Hose TrialMaggie Kennedypage 28 InterruptionsJennifer K. Soulepage 28 On Happening Across Jesus While Cleaning the BasementMaggie Kennedypage 29 GileonMichelle Mitchellpage 30 If My Father Were A Writer, He Would Still BuildAngelique Jenningspage 36 A Visit to Grandpa Gib\u27s HouseTammy Veachpage 37 For Having SeenAngelique Jenningspage 38 PhotoJudy Klancicpage 39 The Earth in BlueSusan J. Bielskypage 39 Things I Could Have SaidAngelique Jenningspage 40 AcrosticsAnnie Heisepage 40https://thekeep.eiu.edu/vehicle/1044/thumbnail.jp

Accuracy of CT Colonography for Detection of Large Adenomas and Cancers

Background Computed tomographic (CT) colonography is a noninvasive option in screening for colorectal cancer. However, its accuracy as a screening tool in asymptomatic adults has not been well defined. Methods We recruited 2600 asymptomatic study participants, 50 years of age or older, at 15 study centers. CT colonographic images were acquired with the use of standard bowel preparation, stool and fluid tagging, mechanical insufflation, and multidetector-row CT scanners (with 16 or more rows). Radiologists trained in CT colonography reported all lesions measuring 5 mm or more in diameter. Optical colonoscopy and histologic review were performed according to established clinical protocols at each center and served as the reference standard. The primary end point was detection by CT colonography of histologically confirmed large adenomas and adenocarcinomas (10 mm in diameter or larger) that had been detected by colonoscopy; detection of smaller colorectal lesions (6 to 9 mm in diameter) was also evaluated. Results Complete data were available for 2531 participants (97%). For large adenomas and cancers, the mean (±SE) per-patient estimates of the sensitivity, specificity, positive and negative predictive values, and area under the receiver-operating-characteristic curve for CT colonography were 0.90±0.03, 0.86±0.02, 0.23±0.02, 0.99± Conclusions In this study of asymptomatic adults, CT colonographic screening identified 90% of subjects with adenomas or cancers measuring 10 mm or more in diameter. These findings augment published data on the role of CT colonography in screening patients with an average risk of colorectal cancer. (ClinicalTrials.gov number, NCT00084929; American College of Radiology Imaging Network [ACRIN] number, 6664.

AI recognition of patient race in medical imaging: a modelling study

Background Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. Interpretation The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. Funding National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology

Reading Race: AI Recognises Patient's Racial Identity In Medical Images

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to

Correlations Between Gene Expression and Mercury Levels in Blood of Boys With and Without Autism

Gene expression in blood was correlated with mercury levels in blood of 2- to 5-year-old boys with autism (AU) compared to age-matched typically developing (TD) control boys. This was done to address the possibility that the two groups might metabolize toxicants, such as mercury, differently. RNA was isolated from blood and gene expression assessed on whole genome Affymetrix Human U133 expression microarrays. Mercury levels were measured using an inductively coupled plasma mass spectrometer. Analysis of covariance (ANCOVA) was performed and partial correlations between gene expression and mercury levels were calculated, after correcting for age and batch effects. To reduce false positives, only genes shared by the ANCOVA models were analyzed. Of the 26 genes that correlated with mercury levels in both AU and TD boys, 11 were significantly different between the groups (P(Diagnosis*Mercury) ≤ 0.05). The expression of a large number of genes (n = 316) correlated with mercury levels in TD but not in AU boys (P ≤ 0.05), the most represented biological functions being cell death and cell morphology. Expression of 189 genes correlated with mercury levels in AU but not in TD boys (P ≤ 0.05), the most represented biological functions being cell morphology, amino acid metabolism, and antigen presentation. These data and those in our companion study on correlation of gene expression and lead levels show that AU and TD children display different correlations between transcript levels and low levels of mercury and lead. These findings might suggest different genetic transcriptional programs associated with mercury in AU compared to TD children

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival