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

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

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease

Body-Weight–Supported Treadmill Rehabilitation after Stroke

BACKGROUND Locomotor training, including the use of body-weight support in treadmill stepping, is a physical therapy intervention used to improve recovery of the ability to walk after stroke. The effectiveness and appropriate timing of this intervention have not been established. METHODS We stratified 408 participants who had had a stroke 2 months earlier according to the extent of walking impairment — moderate (able to walk 0.4 to \u3c0.8 m per second) or severe (able to walk \u3c0.4 m per second) — and randomly assigned them to one of three training groups. One group received training on a treadmill with the use of body-weight support 2 months after the stroke had occurred (early locomotor training), the second group received this training 6 months after the stroke had occurred (late locomotor training), and the third group participated in an exercise program at home managed by a physical therapist 2 months after the stroke (home-exercise program). Each intervention included 36 sessions of 90 minutes each for 12 to 16 weeks. The primary outcome was the proportion of participants in each group who had an improvement in functional walking ability 1 year after the stroke. RESULTS At 1 year, 52.0% of all participants had increased functional walking ability. No significant differences in improvement were found between early locomotor training and home exercise (adjusted odds ratio for the primary outcome, 0.83; 95% confidence interval [CI], 0.50 to 1.39) or between late locomotor training and home exercise (adjusted odds ratio, 1.19; 95% CI, 0.72 to 1.99). All groups had similar improvements in walking speed, motor recovery, balance, functional status, and quality of life. Neither the delay in initiating the late locomotor training nor the severity of the initial impairment affected the outcome at 1 year. Ten related serious adverse events were reported (occurring in 2.2% of participants undergoing early locomotor training, 3.5% of those undergoing late locomotor training, and 1.6% of those engaging in home exercise). As compared with the home-exercise group, each of the groups receiving locomotor training had a higher frequency of dizziness or faintness during treatment (P=0.008). Among patients with severe walking impairment, multiple falls were more common in the group receiving early locomotor training than in the other two groups (P=0.02). CONCLUSIONS Locomotor training, including the use of body-weight support in stepping on a treadmill, was not shown to be superior to progressive exercise at home managed by a physical therapist. (Funded by the National Institute of Neurological Disorders and Stroke and the National Center for Medical Rehabilitation Research; LEAPS ClinicalTrials.gov number, NCT00243919.