julilili42/papers huggingface dataset:
Rethinking the elusive construct of global self-concept: A latent composite score as the apex of the Shavelson model
2020-05-31

The multidimensional, hierarchical model of self-concept by Shavelson et al. (1976) is a cornerstone of modern self-concept research. Given the comprehensive research interest in it, it is surprising that one core aspect of this model has yet to be clarified: What is the best way to operationalize the elusive construct of global self-concept as the apex of the hierarchy? Previous research has implemented global self-concept by applying reflective modeling procedures (e.g., second-order factor models) that followed a top-down logic, which assumes that global self-concept affects lower order self-concepts. However, theoretical considerations have often equally emphasized bottom-up processes, in which lower order self-concepts form a global self-concept. Yet, a bottom-up approach has not garnered much empirical interest, most likely because the requisite statistical models have not been available. The recently proposed model-based latent composite score can fill this gap. Therefore, we contrasted top-down and bottom-up representations of global self-concept by comparing traditional one-dimensional second-order factors and model-based latent composite scores. Across three independent, large-scale studies (Study 1: N = 8,063; Study 2: N = 3,081; Study 3: N = 2,106), the second-order factors reproduced only small amounts of interindividual differences, which boosted the correlations with external criteria (i.e., self-esteem, enjoyment of school, academic outcomes) to theoretically and somewhat empirically implausible levels. By contrast, the composite scores showed plausible patterns of stabilities and correlations. We discuss the consequences of the two approaches and propose the latent composite score as a new perspective on the apex of the Shavelson model.
Evaluating Weakly Supervised Object Localization Methods Right
2020-06-01

Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal WSOL work of class activation mapping (CAM), the field has focused on how to expand the attention regions to cover objects more broadly and localize them better. However, these strategies rely on full localization supervision to validate hyperparameters and for model selection, which is in principle prohibited under the WSOL setup. In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set. We observe that, under our protocol, the five most recent WSOL methods have not made a major improvement over the CAM baseline. Moreover, we report that existing WSOL methods have not reached the few-shot learning baseline, where the full-supervision at validation time is used for model training instead. Based on our findings, we discuss some future directions for WSOL.
Phase 2 study of cemiplimab in patients with metastatic cutaneous squamous cell carcinoma: primary analysis of fixed-dosing, long-term outcome of weight-based dosing
2020-06-01

Background Cemiplimab, a high-affinity, potent human immunoglobulin G4 monoclonal antibody to programmed cell death-1 demonstrated antitumor activity in a Phase 1 advanced cutaneous squamous cell carcinoma (CSCC) expansion cohort ( NCT02383212 ) and the pivotal Phase 2 study ( NCT02760498 ). Here we report the primary analysis of fixed dose cemiplimab 350 mg intravenously every 3 weeks (Q3W) (Group 3) and provide a longer-term update after the primary analysis of weight-based cemiplimab 3 mg/kg intravenously every 2 weeks (Q2W) (Group 1) among metastatic CSCC (mCSCC) patients in the pivotal study ( NCT02760498 ). Methods The primary objective for each group was objective response rate (ORR) per independent central review (ICR). Secondary endpoints included ORR by investigator review (INV), duration of response (DOR) per ICR and INV, and safety and tolerability. Results For Group 3 (n=56) and Group 1 (n=59), median follow-up was 8.1 (range, 0.6 to 14.1) and 16.5 (range, 1.1 to 26.6) months, respectively. ORR per ICR was 41.1% (95% CI, 28.1% to 55.0%) in Group 3, 49.2% (95% CI, 35.9% to 62.5%) in Group 1, and 45.2% (95% CI, 35.9% to 54.8%) in both groups combined. Per ICR, Kaplan–Meier estimate for DOR at 8 months was 95.0% (95% CI, 69.5% to 99. 3%) in responding patients in Group 3, and at 12 months was 88.9% (95% CI, 69.3% to 96.3%) in responding patients in Group 1. Per INV, ORR was 51.8% (95% CI, 38.0% to 65.3%) in Group 3, 49.2% (95% CI, 35.9% to 62.5%) in Group 1, and 50.4% (95% CI, 41.0% to 59.9%) in both groups combined. Overall, the most common adverse events regardless of attribution were fatigue (27.0%) and diarrhea (23.5%). Conclusion In patients with mCSCC, cemiplimab 350 mg intravenously Q3W produced substantial antitumor activity with durable response and an acceptable safety profile. Follow-up data of cemiplimab 3 mg/kg intravenously Q2W demonstrate ongoing durability of responses. Trial registration number Clinicaltrials.gov, NCT02760498 . Registered May 3, 2016, https://clinicaltrials.gov/ct2/show/NCT02760498
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
2020-06-01

In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.
Data Digitizing: Accurate and Precise Data Extraction for Quantitative Systems Pharmacology and Physiologically‐Based Pharmacokinetic Modeling
2020-06-01

In quantitative systems pharmacology (QSP) and physiologically‐based pharmacokinetic (PBPK) modeling, data digitizing is a valuable tool to extract numerical information from published data presented as graphs. To quantify their relevance, a literature search revealed a remarkable mean increase of 16% per year in publications citing digitizing software together with QSP or PBPK. Accuracy, precision, confounder influence, and variability were investigated using scaled median symmetric accuracy (ζ), thus finding excellent accuracy (mean ζ = 0.99%). Although significant, no relevant confounders were found (mean ζ ± SD circles = 0.69% ± 0.68% vs. triangles = 1.3% ± 0.62%). Analysis of 181 literature peak plasma concentration values revealed a considerable discrepancy between reported and post hoc digitized data with 85% having ζ &gt; 5%. Our findings suggest that data digitizing is precise and important. However, because the greatest pitfall comes from pre‐existing errors, we recommend always making published data available as raw values.
Cell Division Protein FtsZ: From Structure and Mechanism to Antibiotic Target
2020-06-01

Antimicrobial resistance to virtually all clinically applied antibiotic classes severely limits the available options to treat bacterial infections. Hence, there is an urgent need to develop and evaluate new antibiotics and targets with resistance-breaking properties. Bacterial cell division has emerged as a new antibiotic target pathway to counteract multidrug-resistant pathogens. New approaches in antibiotic discovery and bacterial cell biology helped to identify compounds that either directly interact with the major cell division protein FtsZ, thereby perturbing the function and dynamics of the cell division machinery, or affect the structural integrity of FtsZ by inducing its degradation. The impressive antimicrobial activities and resistance-breaking properties of certain compounds validate the inhibition of bacterial cell division as a promising strategy for antibiotic intervention.
Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving
2020-06-01

Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e.g., learning to drive in a specific simulation condition. However, as on-policy data is sequentially sampled and added in an iterative manner, the policy can specialize and overfit to the training conditions. For real-world applications, it is useful for the learned policy to generalize to novel scenarios that differ from the training conditions. To improve policy learning while maintaining robustness when training end-to-end driving policies, we perform an extensive analysis of data aggregation techniques in the CARLA environment. We demonstrate how the majority of them have poor generalization performance, and develop a novel approach with empirically better generalization performance compared to existing techniques. Our two key ideas are (1) to sample critical states from the collected on-policy data based on the utility they provide to the learned policy in terms of driving behavior, and (2) to incorporate a replay buffer which progressively focuses on the high uncertainty regions of the policy's state distribution. We evaluate the proposed approach on the CARLA NoCrash benchmark, focusing on the most challenging driving scenarios with dense pedestrian and vehicle traffic. Our approach improves driving success rate by 16% over state-of-the-art, achieving 87% of the expert performance while also reducing the collision rate by an order of magnitude without the use of any additional modality, auxiliary tasks, architectural modifications or reward from the environment.
Two-Shot Spatially-Varying BRDF and Shape Estimation
2020-06-01

Capturing the shape and spatially-varying appearance (SVBRDF) of an object from images is a challenging task that has applications in both computer vision and graphics. Traditional optimization-based approaches often need a large number of images taken from multiple views in a controlled environment. Newer deep learning-based approaches require only a few input images, but the reconstruction quality is not on par with optimization techniques. We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. The previous predictions guide each estimation, and a joint refinement network later refines both SVBRDF and shape. We follow a practical mobile image capture setting and use unaligned two-shot flash and no-flash images as input. Both our two-shot image capture and network inference can run on mobile hardware. We also create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images. Comparisons with recent approaches demonstrate the superior performance of the proposed approach.
Comparative Genomics and Metabolomics in the Genus Nocardia
2020-06-01

Our work emphasizes that Nocardia represent a prolific source for natural products rivaling better-characterized genera such as Streptomyces or Amycolatopsis . Furthermore, we showed that large-scale analysis of biosynthetic gene clusters using similarity networks with high stringency allows the distinction and prediction of natural product structural variations. This will facilitate future genomics-driven drug discovery campaigns.
Learning Situational Driving
2020-06-01

Human drivers have a remarkable ability to drive in diverse visual conditions and situations, e.g., from maneuvering in rainy, limited visibility conditions with no lane markings to turning in a busy intersection while yielding to pedestrians. In contrast, we find that state-of-the-art sensorimotor driving models struggle when encountering diverse settings with varying relationships between observation and action. To generalize when making decisions across diverse conditions, humans leverage multiple types of situation-specific reasoning and learning strategies. Motivated by this observation, we develop a framework for learning a situational driving policy that effectively captures reasoning under varying types of scenarios. Our key idea is to learn a mixture model with a set of policies that can capture multiple driving modes. We first optimize the mixture model through behavior cloning and show it to result in significant gains in terms of driving performance in diverse conditions. We then refine the model by directly optimizing for the driving task itself, i.e., supervised with the navigation task reward. Our method is more scalable than methods assuming access to privileged information, e.g., perception labels, as it only assumes demonstration and reward-based supervision. We achieve over 98% success rate on the CARLA driving benchmark as well as state-of-the-art performance on a newly introduced generalization benchmark.
Overall survival at 5 years of follow-up in a phase III trial comparing ipilimumab 10 mg/kg with 3 mg/kg in patients with advanced melanoma
2020-06-01

Background We have previously reported significantly longer overall survival (OS) with ipilimumab 10 mg/kg versus ipilimumab 3 mg/kg in patients with advanced melanoma, with higher incidences of adverse events (AEs) at 10 mg/kg. This follow-up analysis reports a 5-year update of OS and safety. Methods This randomized, multicenter, double-blind, phase III trial included patients with untreated or previously treated unresectable stage III or IV melanoma. Patients were randomly assigned (1:1) to ipilimumab 10 mg/kg or 3 mg/kg every 3 weeks for 4 doses. The primary end point was OS. Results At a minimum follow-up of 61 months, median OS was 15.7 months (95% CI 11.6 to 17.8) at 10 mg/kg and 11.5 months (95% CI 9.9 to 13.3) at 3 mg/kg (HR 0.84, 95% CI 0.71 to 0.99; p=0.04). In a subgroup analysis, median OS of patients with asymptomatic brain metastasis was 7.0 months (95% CI 4.0 to 12.8) in the 10 mg/kg group and 5.7 months (95% CI 4.2 to 7.0) in the 3 mg/kg group. In patients with wild-type or mutant BRAF tumors, median OS was 13.8 months (95% CI 10.2 to 17.0) and 33.2 months (95% CI 19.4 to 45.2) in the 10 mg/kg group, and 11.2 months (95% CI 9.2 to 13.8) and 19.7 months (95% CI 11.6 to 25.3) in the 3 mg/kg group, respectively. The incidence of grade 3/4 treatment-related AEs was 36% in the 10 mg/kg group vs 20% in the 3 mg/kg group, and deaths due to treatment-related AEs occurred in four (1%) and two patients (1%), respectively. Conclusions This 61-month follow-up of a phase III trial showed sustained long-term survival in patients with advanced melanoma who started metastatic treatment with ipilimumab monotherapy, and confirmed the significant benefit for those who received ipilimumab 10 mg/kg vs 3 mg/kg. These results suggest the emergence of a plateau in the OS curve, consistent with previous ipilimumab studies. Trial registration number NCT01515189 .
Registered Replication Report on Fischer, Castel, Dodd, and Pratt (2003)
2020-06-01

The attentional spatial-numerical association of response codes (Att-SNARC) effect (Fischer, Castel, Dodd, &amp; Pratt, 2003)—the finding that participants are quicker to detect left-side targets when the targets are preceded by small numbers and quicker to detect right-side targets when they are preceded by large numbers—has been used as evidence for embodied number representations and to support strong claims about the link between number and space (e.g., a mental number line). We attempted to replicate Experiment 2 of Fischer et al. by collecting data from 1,105 participants at 17 labs. Across all 1,105 participants and four interstimulus-interval conditions, the proportion of times the effect we observed was positive (i.e., directionally consistent with the original effect) was .50. Further, the effects we observed both within and across labs were minuscule and incompatible with those observed by Fischer et al. Given this, we conclude that we failed to replicate the effect reported by Fischer et al. In addition, our analysis of several participant-level moderators (finger-counting habits, reading and writing direction, handedness, and mathematics fluency and mathematics anxiety) revealed no substantial moderating effects. Our results indicate that the Att-SNARC effect cannot be used as evidence to support strong claims about the link between number and space.
Examining the mechanisms underlying the acquisition of animal tool behaviour
2020-06-01

Despite major advances in the study of animal tool behaviour, researchers continue to debate how exactly certain behaviours are acquired. While specific mechanisms, such as genetic predispositions or action copying, are sometimes suspected to play a major role in behavioural acquisition, controlled experiments are required to provide conclusive evidence. In this opinion piece, we refer to classic ethological methodologies to emphasize the need for studying the relative contributions of different factors to the emergence of specific tool behaviours. We describe a methodology, consisting of a carefully staged series of baseline and social-learning conditions, that enables us to tease apart the roles of different mechanisms in the development of behavioural repertoires. Experiments employing our proposed methodology will not only advance our understanding of animal learning and culture, but as a result, will also help inform hypotheses about human cognitive, cultural and technological evolution. More generally, our conceptual framework is suitable for guiding the detailed investigation of other seemingly complex animal behaviours.
On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner
2020-06-01

We propose a novel formulation for joint recovery of camera pose, object geometry and spatially-varying BRDF. The input to our approach is a sequence of RGB-D images captured by a mobile, hand-held scanner that actively illuminates the scene with point light sources. Compared to previous works that jointly estimate geometry and materials from a hand-held scanner, we formulate this problem using a single objective function that can be minimized using off-the-shelf gradient-based solvers. By integrating material clustering as a differentiable operation into the optimization process, we avoid pre-processing heuristics and demonstrate that our model is able to determine the correct number of specular materials independently. We provide a study on the importance of each component in our formulation and on the requirements of the initial geometry. We show that optimizing over the poses is crucial for accurately recovering fine details and show that our approach naturally results in a semantically meaningful material segmentation.
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision
2020-06-01

Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.
Distinctions in the Acquisition of Vocabulary and Grammar: An Individual Differences Approach
2020-06-01

Abstract Learning language requires acquiring the grammatical categories of words in the language, but learning those categories requires understanding the role of words in the syntax. In this study, we examined how this chicken and egg problem is resolved by learners of an artificial language comprising nouns, verbs, adjectives, and case markers following syntactic rules. We also measured individual differences in declarative and procedural memory processing, which have been linked to vocabulary and grammar learning, respectively. The results showed that grammar and vocabulary can be acquired simultaneously, but with distinctive patterns of acquisition: the syntactic role of verbs and their referents first, then other lexical categories, and finally the syntactic function of case markers. Interdependencies in learning were found for word order and verbs, which related to verbal declarative memory, and also for nouns, adjectives, and case markers, which related to procedural memory.
Systems biology graphical notation markup language (SBGNML) version 0.3
2020-06-01

Abstract This document defines Version 0.3 Markup Language (ML) support for the Systems Biology Graphical Notation (SBGN), a set of three complementary visual languages developed for biochemists, modelers, and computer scientists. SBGN aims at representing networks of biochemical interactions in a standard, unambiguous way to foster efficient and accurate representation, visualization, storage, exchange, and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. SBGN is defined neutrally to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML, the eXtensible Markup Language. The notable changes from the previous version include the addition of attributes for better specify metadata about maps, as well as support for multiple maps, sub-maps, colors, and annotations. These changes enable a more efficient exchange of data to other commonly used systems biology formats (e. g., BioPAX and SBML) and between tools supporting SBGN (e. g., CellDesigner, Newt, Krayon, SBGN-ED, STON, cd2sbgnml, and MINERVA). More details on SBGN and related software are available at http://sbgn.org . With this effort, we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.