Radar-only ego-motion estimation in difficult settings via graph matching

Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.Comment: 6 content pages, 1 page of references, 5 figures, 4 tables, 2019 IEEE International Conference on Robotics and Automation (ICRA

Probably Unknown: Deep Inverse Sensor Modelling In Radar

Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from raw radar power returns is challenging due to complex interactions between sensor noise and occlusion. To counter this we propose to learn an Inverse Sensor Model (ISM) converting a raw radar scan to a grid map of occupancy probabilities using a deep neural network. Our network is self-supervised using partial occupancy labels generated by lidar, allowing a robot to learn about world occupancy from past experience without human supervision. We evaluate our approach on five hours of data recorded in a dynamic urban environment. By accounting for the scene context of each grid cell our model is able to successfully segment the world into occupied and free space, outperforming standard CFAR filtering approaches. Additionally by incorporating heteroscedastic uncertainty into our model formulation, we are able to quantify the variance in the uncertainty throughout the sensor observation. Through this mechanism we are able to successfully identify regions of space that are likely to be occluded.Comment: 6 full pages, 1 page of reference

The Right to be an Exception to a Data-Driven Rule

Data-driven tools are increasingly used to make consequential decisions. They have begun to advise employers on which job applicants to interview, judges on which defendants to grant bail, lenders on which homeowners to give loans, and more. In such settings, different data-driven rules result in different decisions. The problem is: to every data-driven rule, there are exceptions. While a data-driven rule may be appropriate for some, it may not be appropriate for all. As data-driven decisions become more common, there are cases in which it becomes necessary to protect the individuals who, through no fault of their own, are the data-driven exceptions. At the same time, it is impossible to scrutinize every one of the increasing number of data-driven decisions, begging the question: When and how should data-driven exceptions be protected? In this piece, we argue that individuals have the right to be an exception to a data-driven rule. That is, the presumption should not be that a data-driven rule--even one with high accuracy--is suitable for an arbitrary decision-subject of interest. Rather, a decision-maker should apply the rule only if they have exercised due care and due diligence (relative to the risk of harm) in excluding the possibility that the decision-subject is an exception to the data-driven rule. In some cases, the risk of harm may be so low that only cursory consideration is required. Although applying due care and due diligence is meaningful in human-driven decision contexts, it is unclear what it means for a data-driven rule to do so. We propose that determining whether a data-driven rule is suitable for a given decision-subject requires the consideration of three factors: individualization, uncertainty, and harm. We unpack this right in detail, providing a framework for assessing data-driven rules and describing what it would mean to invoke the right in practice.Comment: 22 pages, 0 figure

Matrix Estimation for Individual Fairness

In recent years, multiple notions of algorithmic fairness have arisen. One such notion is individual fairness (IF), which requires that individuals who are similar receive similar treatment. In parallel, matrix estimation (ME) has emerged as a natural paradigm for handling noisy data with missing values. In this work, we connect the two concepts. We show that pre-processing data using ME can improve an algorithm's IF without sacrificing performance. Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions. We then show that, under analogous conditions, SVT pre-processing also yields estimates that are consistent and approximately minimax optimal. As such, the ME pre-processing step does not, under the stated conditions, increase the prediction error of the base algorithm, i.e., does not impose a fairness-performance trade-off. We verify these results on synthetic and real data.Comment: 23 pages, 3 figures, ICML 202

New structural analogues of curcumin exhibit potent growth suppressive activity in human colorectal carcinoma cells

<p>Abstract</p> <p>Background</p> <p>Colorectal carcinoma is one of the major causes of morbidity and mortality in the Western World. Novel therapeutic approaches are needed for colorectal carcinoma. Curcumin, the active component and yellow pigment of turmeric, has been reported to have several anti-cancer activities including anti-proliferation, anti-invasion, and anti-angiogenesis. Clinical trials have suggested that curcumin may serve as a potential preventive or therapeutic agent for colorectal cancer.</p> <p>Methods</p> <p>We compared the inhibitory effects of curcumin and novel structural analogues, GO-Y030, FLLL-11, and FLLL-12, in three independent human colorectal cancer cell lines, SW480, HT-29, and HCT116. MTT cell viability assay was used to examine the cell viability/proliferation and western blots were used to determine the level of PARP cleavages. Half-Maximal inhibitory concentrations (IC₅₀) were calculated using Sigma Plot 9.0 software.</p> <p>Results</p> <p>Curcumin inhibited cell viability in all three of the human colorectal cancer cell lines studied with IC₅₀values ranging between 10.26 μM and 13.31 μM. GO-Y030, FLLL-11, and FLLL-12 were more potent than curcumin in the inhibition of cell viability in these three human colorectal cancer cell lines with IC₅₀values ranging between 0.51 μM and 4.48 μM. In addition, FLLL-11 and FLLL-12 exhibit low toxicity to WI-38 normal human lung fibroblasts with an IC-50 value greater than 1,000 μM. GO-Y030, FLLL-11, and FLLL-12 are also more potent than curcumin in the induction of apoptosis, as evidenced by cleaved PARP and cleaved caspase-3 in all three human colorectal cancer cell lines studied.</p> <p>Conclusion</p> <p>The results indicate that the three curcumin analogues studied exhibit more potent inhibitory activity than curcumin in human colorectal cancer cells. Thus, they may have translational potential as chemopreventive or therapeutic agents for colorectal carcinoma.</p

Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments

Copyright © 2014. Published by Elsevier B.V. Open Access funded by Natural Environment Research CouncilPeer reviewedPublisher PD

CDIM: Cosmic Dawn Intensity Mapper Final Report

The Cosmic Dawn Intensity Mapper (CDIM) will transform our understanding of the era of reionization when the Universe formed the first stars and galaxies, and UV photons ionized the neutral medium. CDIM goes beyond the capabilities of upcoming facilities by carrying out wide area spectro-imaging surveys, providing redshifts of galaxies and quasars during reionization as well as spectral lines that carry crucial information on their physical properties. CDIM will make use of unprecedented sensitivity to surface brightness to measure the intensity fluctuations of reionization on large-scales to provide a valuable and complementary dataset to 21-cm experiments. The baseline mission concept is an 83-cm infrared telescope equipped with a focal plane of 24 x 2048^2 detectors capable of R = 300 spectro-imaging observations over the wavelength range of 0.75 to 7.5 µm using Linear Variable Filters (LVFs). CDIM provides a large field of view of 7.8 deg^2 allowing efficient wide area surveys, and instead of moving instrumental components, spectroscopic mapping is obtained through a shift-and-stare strategy through spacecraft operations. CDIM design and capabilities focus on the needs of detecting faint galaxies and quasars during reionization and intensity fluctuation measurements of key spectral lines, including Lyman-α and Hα radiation from the first stars and galaxies. The design is low risk, carries significant science and engineering margins, and makes use of technologies with high technical readiness level for space observations