Dead Men Do Tell Tales: How computers can help us hear them

Jessica Lam explains how forensic anthropology and computer science can work together to identify and recover information from dead bodies. As she puts it, whenever we deal with the dead, we remember the living

Noninvasive depth estimation using tissue optical properties and a dual-wavelength fluorescent molecular probe in vivo

Translation of fluorescence imaging using molecularly targeted imaging agents for real-time assessment of surgical margins in the operating room requires a fast and reliable method to predict tumor depth from planar optical imaging. Here, we developed a dual-wavelength fluorescent molecular probe with distinct visible and near-infrared excitation and emission spectra for depth estimation in mice and a method to predict the optical properties of the imaging medium such that the technique is applicable to a range of medium types. Imaging was conducted at two wavelengths in a simulated blood vessel and an in vivo tumor model. Although the depth estimation method was insensitive to changes in the molecular probe concentration, it was responsive to the optical parameters of the medium. Results of the intra-tumor fluorescent probe injection showed that the average measured tumor sub-surface depths were 1.31 ± 0.442 mm, 1.07 ± 0.187 mm, and 1.42 ± 0.182 mm, and the average estimated sub-surface depths were 0.97 ± 0.308 mm, 1.11 ± 0.428 mm, 1.21 ± 0.492 mm, respectively. Intravenous injection of the molecular probe allowed for selective tumor accumulation, with measured tumor sub-surface depths of 1.28 ± 0.168 mm, and 1.50 ± 0.394 mm, and the estimated depths were 1.46 ± 0.314 mm, and 1.60 ± 0.409 mm, respectively. Expansion of our technique by using material optical properties and mouse skin optical parameters to estimate the sub-surface depth of a tumor demonstrated an agreement between measured and estimated depth within 0.38 mm and 0.63 mm for intra-tumor and intravenous dye injections, respectively. Our results demonstrate the feasibility of dual-wavelength imaging for determining the depth of blood vessels and characterizing the sub-surface depth of tumors in vivo

A re-analysis of the isolated black hole candidate OGLE-2011-BLG-0462/MOA-2011-BLG-191

There are expected to be $\sim 10^8$ isolated black holes (BHs) in the Milky Way. OGLE-2011-BLG-0462/MOA-2011-BLG-191 (OB110462) is the only such BH with a mass measurement to date. However, its mass is disputed: Lam et al. (2022a,b) measured a lower mass of $1.6 - 4.4 M_\odot$, while Sahu et al. (2022); Mr\'{o}z et al. (2022) measured a higher mass of $5.8 - 8.7 M_\odot$. We re-analyze OB110462, including new data from the Hubble Space Telescope (HST) and re-reduced Optical Gravitational Lensing Experiment (OGLE) photometry. We also re-reduce and re-analyze the HST dataset with newly available software. We find significantly different ($\sim 1$ mas) HST astrometry than Lam et al. (2022a,b) in the de-magnified epochs due to the amount of positional bias induced by a bright star $\sim$0.4 arcsec from OB110462. After modeling the updated photometric and astrometric datasets, we find the lens of OB110462 is a $6.0^{+1.2}_{-1.0} M_\odot$ BH. Future observations with the Nancy Grace Roman Space Telescope, which will have an astrometric precision comparable or better to HST but a field of view $100\times$ larger, will be able to measure hundreds of isolated BH masses via microlensing. This will enable the measurement of the BH mass distribution and improve understanding of massive stellar evolution and BH formation channels.Comment: 23 pages, 18 figures, 8 tables. Accepted for publication in ApJ on 2 Aug 2023 [Same as v1, just fixed typo in email address

CATVI: conditional and adaptively truncated variational inference for hierarchical Bayesian nonparametric models

Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation struc- ture among latent variables due to the mean- eld setting, nor infer the true posterior dimension because of the universal trunca- tion. To overcome these limitations, we pro- pose the conditional and adaptively trun- cated variational inference method (CATVI) by maximizing the nonparametric evidence lower bound and integrating Monte Carlo into the variational inference framework. CATVI enjoys several advantages over tra- ditional methods, including a smaller diver- gence between variational and true posteri- ors, reduced risk of undertting or overt- ting, and improved prediction accuracy. Em- pirical studies on three large datasets re- veal that CATVI applied in Bayesian non- parametric topic models substantially out- performs competing models, providing lower perplexity and clearer topic-words clustering

EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model

Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of over-smoothing and under-reaching to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, mis-simplification, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network (EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model (DMPGM), a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. We propose a Markov chain Monte Carlo inference framework for DMPGM. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines

puddl: Gamifying Water Conservation

As the global population surges, the demand for fresh water will only increase. With no technological breakthrough on the horizon, it is more important than ever to conserve water and not waste this precious resource. Water conservation is a serious issue, and will only become more severe as global warming worsens. As the global temperature increases, and rainfall becomes more sporadic, it will be important for communities to find new ways to save water. This push has already been made in certain areas in the united states, California, where they were forced to reduce their water consumption by 25%. Through our fieldwork and other research, we discovered three important factors that would shape our project. The first was all about flow! The flow in a sewage system is integral to its ability to function. If the flow of a sewage system gets too low, you encounter a number of logistical problems. Additionally, if you use too much water, you could adversely affect the ecosystems from which you took the water. Salmon, and their food chain, are affected the most by the overuse of water. When talking with Catherine Howells, we also learned about water conservation. Certain areas in the world, like Perth, Australia, are very good at conserving water, whereas other areas, like Phoenix, Arizona are not.. However, no matter where you are, it is very hard to conserve more than thirty percent of water usage. The average American family of four uses 400 gallons of water per day. On average, approximately 70 percent of that water is used indoors, with the bathroom being the largest consumer. This leaves a large amount of water to be conserve, especially when you consider the average Australian family of the same size uses on 238 gallons of water per day! Also, certain applications use a huge portion of a person’s daily water usage, for instance the toilet and washer equal forty‐eight percent of a household\u27 water consumption, and by learning new ways to reduce this, families could save a lot of water