First results in terrain mapping for a roving planetary explorer

To perform planetary exploration without human supervision, a complete autonomous rover must be able to model its environment while exploring its surroundings. Researchers present a new algorithm to construct a geometric terrain representation from a single range image. The form of the representation is an elevation map that includes uncertainty, unknown areas, and local features. By virtue of working in spherical-polar space, the algorithm is independent of the desired map resolution and the orientation of the sensor, unlike other algorithms that work in Cartesian space. They also describe new methods to evaluate regions of the constructed elevation maps to support legged locomotion over rough terrain

Driver Attitudes and Choices: Speed Limits, Seat Belt Use, and Drinking-and-Driving

A better understanding of attitudes and behavioral principles underlying driving behavior and traffic safety issues can contribute to design and policy solutions, such as speed limits and seat belt legislation. This work examines the Motor Vehicle Occupant Safety Surveys (MVOSS) dataset to illuminate drivers' seatbelt use, driving speed choices, drinking-and-driving tendencies, along with their attitudes towards speed limits and seat belt laws. Ordered probit, negative binomial, and linear regression models were used for the data analysis, and several interesting results emerged. The number and variety of results feasible with this single dataset are instructive as well as intriguing

Vision loss following snakebite in a patient with controlled aplastic anemia

Viper venoms act mainly on blood and blood vessels. Reports of ophthalmic manifestations after snakebite include ptosis and ophthalmoplegia. In the current study, we describe a case that developed bilateral retinal and subretinal hemorrhage following snakebite. Bilateral retinal hemorrhage is a rare ocular complication of snake envenomation and has not been reported with fundus photographs in the literature so far

Radiating dipoles in photonic crystals

The radiation dynamics of a dipole antenna embedded in a Photonic Crystal are modeled by an initially excited harmonic oscillator coupled to a non--Markovian bath of harmonic oscillators representing the colored electromagnetic vacuum within the crystal. Realistic coupling constants based on the natural modes of the Photonic Crystal, i.e., Bloch waves and their associated dispersion relation, are derived. For simple model systems, well-known results such as decay times and emission spectra are reproduced. This approach enables direct incorporation of realistic band structure computations into studies of radiative emission from atoms and molecules within photonic crystals. We therefore provide a predictive and interpretative tool for experiments in both the microwave and optical regimes.Comment: Phys. Rev. E, accepte

The Role of Repeated Exposure to Multimodal Input in Incidental Acquisition of Foreign Language Vocabulary

Prior research has reported incidental vocabulary acquisition with complete beginners in a foreign language (FL), within 8 exposures to auditory and written FL word forms presented with a picture depicting their meaning. However, important questions remain about whether acquisition occurs with fewer exposures to FL words in a multimodal situation and whether there is a repeated exposure effect. Here we report a study where the number of exposures to FL words in an incidental learning phase varied between 2, 4, 6, and 8 exposures. Following the incidental learning phase, participants completed an explicit learning task where they learned to recognize written translation equivalents of auditory FL word forms, half of which had occurred in the incidental learning phase. The results showed that participants performed better on the words they had previously been exposed to, and that this incidental learning effect occurred from as little as 2 exposures to the multimodal stimuli. In addition, repeated exposure to the stimuli was found to have a larger impact on learning during the first few exposures and decrease thereafter, suggesting that the effects of repeated exposure on vocabulary acquisition are not necessarily constant

Fibrosis Evaluation by Transient Elastography in Patients With Long-Term Sustained HCV Clearance

BACKGROUND: Reversibility of advanced fibrosis after HCV-clearance is an important goal of therapy. OBJECTIVES: Measuring liver stiffness (LS) by transient elastography (TE) might be helpful in this setting. PATIENTS AND METHODS: We evaluated 104 patients with biopsy-proven chronic hepatitis C (CHC) and sustained virological response (SVR) after Peg-Interferon (IFN) plus ribavirin since at least 18 months. HCV-eradication was confirmed searching for serum HCV-RNA (TMA® sensitivity > 5-10 IU/ml). Data from literature reported the best LS cut-off values for different stages of liver fibrosis were 7.1 kPa for Metavir stage 2 (F2), 9.5 kPa for F3 and 12.5 for cirrhosis (F4). RESULTS: TE was not reliable in four SVR obese patients. Metavir-stage of biopsy was F0-1 in 28, F2 in 47, F3 in 17 and F4 in eight patients. The median interval elapsed since achieving SVR was 36 months (range: 18-77, SD¬¬:18). Stratifying patients according to the histological stage assessed before treatment, a clear-cut gradient of LS values was observed from F0-1: median: 3.8 kPa (range: 3.5-4.9) to F2: 4.6 kPa (3.8-6.0), F3: 6.2 kPa (4.8-8.6) and F4: 8.4 kPa (6.2-9.2) (P = 0.001). Overall, 86 patients had lower values of LS than the expected LS values according to Metavir-stage. At multivariate logistic analysis γ-GT and histological steatosis were independently associated with persistence of higher values of LS. CONCLUSION: Long term responders to IFN-based therapies have lower LS values than those who are untreated and still viraemic. High levels of γ-GT and liver steatosis, all markers of insulin resistance, may hamper reduction of liver stiffness after HCV-clearance

Antipsychotic Use in Pregnancy: Patient Mental Health Challenges, Teratogenicity, Pregnancy Complications, and Postnatal Risks

Pregnant women constitute a vulnerable population, with 25.3% of pregnant women classified as suffering from a psychiatric disorder. Since childbearing age typically aligns with the onset of mental health disorders, it is of utmost importance to consider the effects that antipsychotic drugs have on pregnant women and their developing fetus. However, the induction of pharmacological treatment during pregnancy may pose significant risks to the developing fetus. Antipsychotics are typically introduced when the nonpharmacologic approaches fail to produce desired effects or when the risks outweigh the benefits from continuing without treatment or the risks from exposing the fetus to medication. Early studies of pregnant women with schizophrenia showed an increase in perinatal malformations and deaths among their newborns. Similar to schizophrenia, women with bipolar disorder have an increased risk of relapse in antepartum and postpartum periods. It is known that antipsychotic medications can readily cross the placenta, and exposure to antipsychotic medication during pregnancy is associated with potential teratogenicity. Potential risks associated with antipsychotic use in pregnant women include congenital abnormalities, preterm birth, and metabolic disturbance, which could potentially lead to abnormal fetal growth. The complex decision-making process for treating psychosis in pregnant women must evaluate the risks and benefits of antipsychotic drugs

MATE: Masked Autoencoders are Online 3D Test-Time Learners

We propose MATE, the first Test-Time-Training (TTT) method designed for 3D data. It makes deep networks trained in point cloud classification robust to distribution shifts occurring in test data, which could not be anticipated during training. Like existing TTT methods, which focused on classifying 2D images in the presence of distribution shifts at test-time, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: Each test point cloud has a large portion of its points removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. Further, we show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, which reduces its memory footprint and makes it lightweight. We also highlight that MATE achieves competitive performance by adapting sparingly on the test data, which further reduces its computational overhead, making it ideal for real-time applications.Comment: Minor fix in citation