Miracle-dz/UniFAR-FTUs huggingface dataset:
What You See is What You Get: Exploiting Visibility for 3D Object Detection

Recent advances in 3D sensing have created unique challenges for computer vision. One fundamental challenge is finding a good representation for 3D sensor data. Most popular representations (such as PointNet) are proposed in the context of processing truly 3D data (e.g. points sampled from mesh models), ignoring the fact that 3D sensored data such as a LiDAR sweep is in fact 2.5D. We argue that representing 2.5D data as collections of (x,y,z) points fundamentally destroys hidden information about freespace. In this paper, we demonstrate such knowledge can be efficiently recovered through 3D raycasting and readily incorporated into batch-based gradient learning. We describe a simple approach to augmenting voxel-based networks with visibility: we add a voxelized visibility map as an additional input stream. In addition, we show that visibility can be combined with two crucial modifications common to state-of-the-art 3D detectors: synthetic data augmentation of virtual objects and temporal aggregation of LiDAR sweeps over multiple time frames. On the NuScenes 3D detection benchmark, we show that, by adding an additional stream for visibility input, we can significantly improve the overall detection accuracy of a state-of-the-art 3D detector.
A psychophysics approach for quantitative comparison of interpretable computer vision models

The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general. But how useful the explanations provided by transparent ML methods are for humans remains difficult to assess. Most studies evaluate interpretability in qualitative comparisons, they use experimental paradigms that do not allow for direct comparisons amongst methods or they report only offline experiments with no humans in the loop. While there are clear advantages of evaluations with no humans in the loop, such as scalability, reproducibility and less algorithmic bias than with humans in the loop, these metrics are limited in their usefulness if we do not understand how they relate to other metrics that take human cognition into account. Here we investigate the quality of interpretable computer vision algorithms using techniques from psychophysics. In crowdsourced annotation tasks we study the impact of different interpretability approaches on annotation accuracy and task time. In order to relate these findings to quality measures for interpretability without humans in the loop we compare quality metrics with and without humans in the loop. Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning. Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans. These findings highlight the potential of methods from classical psychophysics for modern machine learning applications. We hope that our results provide convincing arguments for evaluating interpretability in its natural habitat, human-ML interaction, if the goal is to obtain an authentic assessment of interpretability.
Parting with Illusions about Deep Active Learning

Active learning aims to reduce the high labeling cost involved in training machine learning models on large datasets by efficiently labeling only the most informative samples. Recently, deep active learning has shown success on various tasks. However, the conventional evaluation scheme used for deep active learning is below par. Current methods disregard some apparent parallel work in the closely related fields. Active learning methods are quite sensitive w.r. t. changes in the training procedure like data augmentation. They improve by a large-margin when integrated with semi-supervised learning, but barely perform better than the random baseline. We re-implement various latest active learning approaches for image classification and evaluate them under more realistic settings. We further validate our findings for semantic segmentation. Based on our observations, we realistically assess the current state of the field and propose a more suitable evaluation protocol.
Phase Retrieval Using Conditional Generative Adversarial Networks

In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurement process at training time leads to an optimization at test time that is more robust to initialization than existing approaches involving generative models. In addition, conditioning the generator network on the measurements enables us to achieve much more detailed results. We empirically demonstrate that these advantages provide meaningful solutions to the Fourier and the compressive phase retrieval problem and that our method outperforms well-established projection-based methods as well as existing methods that are based on neural networks. Like other deep learning methods, our approach is robust to noise and can therefore be useful for real-world applications.
Dynamical resilience to disorder: The dilute Hubbard model on the Lieb lattice

In itinerant systems, electron-electron interactions may lead to the formation of local magnetic moments and their effective exchange coupling, which in turn gives rise to long-range magnetic order. Therefore, when moment formation is weakened, such as in the single-band Hubbard model on a square lattice with the on-site repulsion being randomly switched off on a fraction $x$ of sites, magnetic order is suppressed beyond some critical $x_c$, which was found to lie below the classical percolation threshold, $x_c^\text{(perc,sq)}$. Here we study dilute magnetism in flat band systems, namely in the Hubbard model on a `Lieb' lattice. Interestingly, we show that magnetic order persists to $x$ almost twice as large as the classical percolation threshold for the lattice, thus emphasizing the central role of electron itinerancy to the magnetic response. The analysis of the orbital-resolved order parameters reveals that the contribution of the four-fold coordinated `d' sites to magnetism is dramatically affected by dilution, while the localized `p' states of the flat band provide the dominant contribution to long-range correlations. We also examine the transport properties, which suggest the existence of an insulator-to-metal transition in the same range of the critical magnetic dilution.
Early-type Host Galaxies of Type Ia Supernovae. II. Evidence for Luminosity Evolution in Supernova Cosmology

The most direct and strongest evidence for the presence of dark energy is provided by the measurement of galaxy distances using SNe Ia. This result is based on the assumption that the corrected brightness of SN Ia through the empirical standardization would not evolve with look-back time. Recent studies have shown, however, that the standardized brightness of SN Ia is correlated with host morphology, host mass, and local star formation rate (SFR), suggesting a possible correlation with stellar population property. To understand the origin of these correlations, we have continued our spectroscopic observations to cover most of the reported nearby early-type host galaxies. From high-quality (signal-to-noise ratio ∼175) spectra, we obtained the most direct and reliable estimates of population age and metallicity for these host galaxies. We find a significant correlation between SN luminosity (after the standardization) and stellar population age at a 99.5% confidence level. As such, this is the most direct and stringent test ever made for the luminosity evolution of SN Ia. Based on this result, we further show that the previously reported correlations with host morphology, host mass, and local SFR are most likely originated from the difference in population age. This indicates that the light-curve fitters used by the SNe Ia community are not quite capable of correcting for the population age effect, which would inevitably cause a serious systematic bias with look-back time. Notably, taken at face values, most of the Hubble residual used in the discovery of the dark energy appears to be affected by the luminosity evolution.
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning

We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor-critic algorithms require an off-policy critic evaluation step, to estimate the value of the new policy after every policy gradient update. Despite enormous success of off-policy policy gradients on control tasks, existing general methods suffer from high variance and instability, partly because the policy improvement depends on gradient of the estimated value function. In this work, we present a new way of off-policy policy evaluation in actor-critic, based on the doubly robust estimators. We extend the doubly robust estimator from off-policy policy evaluation (OPE) to actor-critic algorithms that consist of a reward estimator performance model. We find that doubly robust estimation of the critic can significantly improve performance in continuous control tasks. Furthermore, in cases where the reward function is stochastic that can lead to high variance, doubly robust critic estimation can improve performance under corrupted, stochastic reward signals, indicating its usefulness for robust and safe reinforcement learning.
Investigation of the Effects of Prosthetic Knee Condition on Novice Transfemoral Amputee Runners

INVESTIGATION OF THE EFFECTS OF PROSTHETIC KNEE CONDITION ON NOVICE TRANSFEMORAL AMPUTEE RUNNERS Natalie L. Nelson, B.S. Marquette University, 2018 Some unilateral transfemoral amputees (TFAs) run in a prosthesis with an articulating prosthetic knee, others choose to run without a knee (prosthetic socket and foot are linked via a straight, non-articulating pylon) to increase stability. Research regarding unilateral TFA running with an unlocked versus locked prosthetic knee (approximately equivalent to the no-knee condition) with respect to energy cost and temporal metrics is limited; no studies have investigated the impact of knee condition on kinematic metrics. The purpose of this study was to investigate whether the prosthetic knee of a running-specific prosthesis (RSP) should be unlocked or locked for unilateral TFAs during recreational treadmill distance running. Five male TFA novice runners, aged 52-59 years, completed one training and one testing session running with the knee of a RSP in the unlocked and locked conditions. The testing session included two three-minute self-selected running speed (SSRS) trials (one trial for each knee condition) as well as six peak speed trials (three trials per knee condition). Temporal, kinematic, and oxygen consumption data were acquired during the SSRS trials. Paired t-tests and Wilcoxon signed-rank tests were conducted to identify significant differences between knee conditions. Statistically significant differences were observed between knee conditions for running economy (RE); mean RE was lower (suggesting greater efficiency) for the unlocked knee condition. Peak running speed did not differ significantly between knee conditions. Significant differences between knee conditions were also observed for peak hip flexion and abduction of the prosthetic limb during swing; maximum prosthetic limb hip flexion increased and prosthetic limb hip abduction was reduced for the unlocked knee condition. These differences contributed to reduced kinematic asymmetry at the hip for the unlocked knee condition; the unlocked knee condition also resulted in less asymmetry in hip range of motion. These differences in RE and hip kinematics between knee conditions may be attributed to circumduction of the prosthetic limb during swing to provide foot clearance with the locked knee condition. For novice recreational runners, the unlocked knee condition may be advantageous for TFAs during treadmill running, provided the individuals have sufficient endurance and cognitive focus to prevent knee buckling.
Sequential modeling for mortality prediction in the ICU

Severity of illness scores are commonly used in critical care medicine to guide treatment decisions and benchmark the quality of medical care. These scores operate in part by predicting patient mortality in the ICU using physiological variables including lab values, vital signs, and admission information. However, existing evidence suggests that current mortality predictors are less performant on patients who have an especially high risk of mortality in the ICU. This thesis seeks to reconcile this difference by developing a custom high risk mortality predictor for high risk patients in a process termed sequential modeling. Starting with a base set of features derived from the APACHE IV score, this thesis details the engineering of more complex features tailored to the high risk prediction task and development of a logistic regression model trained on the Philips eICU-CRD dataset. This high risk model is shown to be more performant than a baseline severity of illness score, APACHE IV, on the high risk subpopulation. Moreover, a combination of the baseline severity of illness score and the high risk model is shown to be better calibrated and more performant on patients of all risk types. Lastly, I show that this secondary customization approach has useful applications not only in the general population, but in specific patient subpopulations as well. This thesis thus offers a new perspective and strategy for mortality prediction in the ICU, and when taken in context with the increasing digitization of patient medical records, offers a more personalized predictive model in the ICU. Thesis Supervisor: Peter Szolovits Title: Professor of Computer Science
A novel nanoprobe-based assay for detecting K- ras mutations in plasma and stool samples from patients with pancreatic cancer: value in diagnosis and prognosis evaluation

Background: To develop a novel nanoprobe-based assay for detecting K- ras mutations in plasma and stool samples from patients with pancreatic cancer, and assess its value in the diagnosis and prognosis evaluation of this malignancy. Methods: Fifty-eight pancreatic cancer, 18 chronic pancreatitis, 7 pancreatic cystadenoma, 5 pancreatic cyst, 2 solid pseudopapillary tumor of the pancreas patients, who were treated at the Second Affiliated Hospital of Jiaxing University from June 2013 to October 2015. Thirty-one healthy volunteers were as normal controls. Blood and stool samples were collected from these subjects to detect the K- ras mutation status using a novel nanoprobe-based assay, assess its diagnostic accuracy and analysis the relation between the clinicopathological characteristics and survival in pancreatic cancer. Results: The detection rates of K- ras mutations in stool and plasma samples from patients with pancreatic cancer were 79.3% and 43.1%, respectively, both of which were significantly higher than those for patients with benign pancreatic diseases (15.6% and 6.3%, respectively; P ras mutation status alone, plasma K- ras mutation status alone, and the combination of both in the diagnosis of pancreatic cancer were 79.3% and 84.4%, 43.1% and 93.8%, and 86.2% and 96.9%, respectively. Plasma K- ras mutation status was significantly associated with 1-year survival and TNM stage in patients with pancreatic cancer (P Conclusions: The developed novel nanoprobe-based assay allows for the detection of K- ras mutations in trace amounts of plasma and stool DNA.
Do long waiting times worsen health outcomes of patients with cardiovascular diseases? A study using data from SHARE survey

Long hospital waits are causing frustrations among patients and are a fiercely debated topic in many OECD countries. Peoples are worrying about that long waiting times might cause deterioration in patients’ health. This study investigates whether waiting times have any adverse impact on health in patients with cardiovascular diseases. Using a cross-national sample derived from the Survey of Health, Ageing and Retirement in Europe (SHARE), we estimate the effects of waiting times on three different aspects of health outcomes: self-perceived physical and general health, self-perceived mental health and selfperceived life-expectancy. Our results show that, on one hand, waiting times for non-emergency consultation have generally no adverse impacts on patients’ self-perceived health and, on the other hand, waiting times for emergency consultation and excessive waiting time are associated with several types of deteriorations in self-perceived general health and mental health. Hence, health policy should be designed to reduce emergency waits and excessive waits. Additionally, given that hospital waiting list is an ineffective way to ration excessive healthcare demand and creates a deadweight loss to the society, further research on its replacements are therefore required. Bachelor Thesis in Economics (15hp) Department of Economics, School of Business, Economics and Law University of Gothenburg Supervisor: Prof. Kristian Bolin
Imaging findings of pulmonary diseases in immunocompromised paediatric patients

Poster: "ECR 2018 / C-2877 / Imaging findings of pulmonary diseases in immunocompromised paediatric patients" by: "D. C. Caltabiano1, V. Costanzo1, L. Mammino1, R. Farina2, L. A. Mauro1, A. di cataldo1, P. V. Foti1, A. Basile1, S. Palmucci1; 1Catania/IT, 2Catania, IT/IT"
Host modulation: controlling the inflammation to control the infection

Historically, periodontal disease (gingivitis and periodontitis) has been recognized as being primarily of bacterial origin. However, recent evidence indicates that while bacteria are necessary for disease development they are not sufficient for the clinical manifestation of the many and varied forms of periodontal disease. It is becoming increasingly apparent that it is the host inflammatory response to the subgingival bacteria that is responsible for the tissue damage and, most likely, progression of the disease. We explore the concept that it is the subgingival microenvironment modified by the inflammatory response that leads to a change from a commensal to pathogenic microbiota. In this review, we examine the evidence for the emerging paradigm supporting the central role of inflammation rather than specific microbiota in the pathogenesis of periodontitis, and that by controlling the inflammation, it is possible to control the infection. As an extension of this, we propose a working model for the ongoing monitoring of periodontal patients using the medical model of ‘treat to target’.
Evaluation of the New Zealand Medical Treatment Scheme

With respect to the ORS, a considerable degree of uncertainty around the cost of treatment was well managed, despite factors outside their control such as undiagnosed conditions and complications in theatre or immediately after surgery. Over the evaluation period, our partner has become adept at managing this volatility, which has reduced the variance between budget and expenditure over time. This can be interpreted as a measure of improved efficiency in programme management .
Evidence of Impact of Interventions on Growth and Development during Early and Middle Childhood

Worldwide patterns of linear growth faltering, based on data from many lowand middle-income countries (LMICs) (Victora and others 2010), indicate deterioration of child nutritional status, on average, from age 0 to 24 months; after this period, nutritional status levels off or slightly reverses (for example, Prentice and others 2013; Stein and others 2010). Analyses of the five countries in the Consortium of Health-Orientated Research in Transitioning Societies (COHORTS) study found that low birth weight or undernutrition at age two years (or both) were associated with shorter adult height, less schooling, and lower economic productivity (Victora and others 2008). The 2008 Lancet series on nutrition argued that height-for-age is the best nutritional predictor of adult human capital (Victora and others 2008). These results influenced prioritization of global efforts to combat undernutrition in the first 1,000 days, from conception to age 24 months. More broadly, these 1,000 days are seen as a critical period for establishing the physical, cognitive, and socioemotional foundation for later life (Walker, Wachs, and others 2011) and are viewed as the period of greatest plasticity (Gluckman and others 2009). As reviewed by Halfon and others (2014), new approaches to life course development have integrated biological systems, drawing from genetics as well as epigenetics, with social and behavioral models. The approach in this chapter unites economic theory with health science. We use the lifecycle approach to assess the benefit-cost ratios of interventions in nutrition and child development in LMICs, where undernutrition is a risk factor, with a focus on the first five years of life. Definitions of age groupings and age-specific terminology used in this volume can be found in chapter 1 (Bundy and others 2017). Birth weight and linear growth in the first two years are associated with many beneficial outcomes later in life (Adair and others 2013). The 2013 Lancet nutrition series also acknowledged the need to address both undernutrition and increased obesity in LMICs (Black and others 2013), recognizing that there is a high prevalence of both conditions and that the conditions often are linked. The 2013 series connected the importance of prenatal nutrition and adolescent girls’ nutrition (Bhutta and others 2013). Women’s height affects risks for pregnancy complications (Toh-Adam, Srisupundit, and Tongsong 2012) and low birth weight (Black and others 2013); given associations of birth weight with subsequent undernutrition (Christian and others 2013), these findings bring the discussion full circle. Thus, the 1,000-day window could be made much longer—even going back to mothers’ childhoods.