TOWARDS HIGH RESOLUTION IDENTIFICATION OF VARIETY-SEEKING BEHAVIOR

Because smartphones are now ubiquitous, it becomes for the first time economically feasible to operationalize personalized marketing measures also for physical grocery retailing. A particularly interesting and high valu target group in this domain is the one of variety seeking, since this group is most likely to respond positively to new offers and recommendations. However, present methods in identifying variety seekers rely on qustionnaires and ignore that variety seeking may differ between product categories. In this paper, we present a model for measuring variety seeking behavior on a high level of granularity, based on a consumer´s purchases in individual product categories. Our study has three main contributions. Firstly, we contribute to the customer segmentation research stream by providing a novel way for identifying customers´ overall extent of variety seeking as well as their specific variety seeking at a category level. Second, for the most important retail categories we characterize the extent of variety seeking and provide a data-driven approach that is easy to operationalize by practitioners “ especially for deploying large-scale personalized marketing measures in social or mobile commerce in physical stores. Finally, we provide a method to reconcile the highly granular category-level results with existing per person typologies

A Spitzer Spectroscopic Survey of Low Ionization Nuclear Emission-line Regions: Characterization of the Central Source

We have conducted a comprehensive mid-IR spectroscopic investigation of 67 Low Ionization Nuclear Emission Line Regions (LINERs) using archival observations from the high resolution modules of the Infrared Spectrograph on board the Spitzer Space Telescope. Using the [NeV] 14 and 24um lines as active galactic nuclei (AGN) diagnostics, we detect active black holes in 39% of the galaxies in our sample, many of which show no signs of activity in either the optical or X-ray bands. In particular, a detailed comparison of multi-wavelength diagnostics shows that optical studies fail to detect AGN in galaxies with large far-IR luminosities. These observations emphasize that the nuclear power source in a large percentage of LINERs is obscured in the optical. Indeed, the majority of LINERs show mid-IR [NeV]14/[NeV]24um flux ratios well below the theoretical low-density limit, suggesting that there is substantial extinction toward even the [NeV]-emitting region . Combining optical, X-ray, and mid-IR diagnostics, we find an AGN detection rate in LINERs of 74%, higher than previously reported statistics of the fraction of LINERs hosting AGN. The [NeV]24um /[OIV]26um mid-IR line flux ratio in "AGN-LINERs" is similar to that of standard AGN, suggesting that the spectral energy distribution (SED) of the intrinsic optical/UV continuum is similar in the two. This result is in contrast to previous suggestions of a UV deficit in the intrinsic broadband continuum emission in AGN-LINERs. Consistent with our finding of extinction to the [NeV]-emitting region, we propose that extinction may also be responsible for the observed optical/UV deficit seen in at least some AGN-LINERs.Comment: Accepted for publication in Ap

Preventing Neonatal Abstinence Syndrome within the Opioid Epidemic: A Uniform Facilitative Policy

The United States is currently in the midst of an opioid epidemic that has hit states in the southern New England regions particularly hard — with Massachusetts as one primary example. One of the many unfortunate results of the epidemic is a dramatic upsurge in cases of opioid dependency by expectant women that result in children born with Neonatal Abstinence Syndrome (NAS). NAS is a clinical syndrome that occurs when a newborn suffers withdrawal symptoms as a consequence of abrupt discontinuation of prenatal substance exposure. The expenses of treating and rehabilitating these drug-dependent newborns, predominantly shouldered by state taxpayers, are extremely costly, with a mean cost per stay of $93,400 for pharmacologically-treated cases. This Article illustrates a policy, grounded in facilitative principles, designed to reduce incidents of NAS. Key components to the solution’s success should rely on early identification of opioid abuse or dependence during pregnancy, as well as adherence to a standardized protocol implemented uniformly throughout public hospitals state-wide. The Article concludes by reemphasizing the importance of acting promptly and assertively to protect society’s most vulnerable members from the tragic epidemic

Planar immersion lens with metasurfaces

The solid immersion lens is a powerful optical tool that allows light entering material from air or vacuum to focus to a spot much smaller than the free-space wavelength. Conventionally, however, they rely on semispherical topographies and are non-planar and bulky, which limits their integration in many applications. Recently, there has been considerable interest in using planar structures, referred to as metasurfaces, to construct flat optical components for manipulating light in unusual ways. Here, we propose and demonstrate the concept of a planar immersion lens based on metasurfaces. The resulting planar device, when placed near an interface between air and dielectric material, can focus electromagnetic radiation incident from air to a spot in material smaller than the free-space wavelength. As an experimental demonstration, we fabricate an ultrathin and flexible microwave lens and further show that it achieves wireless energy transfer in material mimicking biological tissue

XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video object segmentation typically only uses one type of feature memory. For videos longer than a minute, a single feature memory model tightly links memory consumption and accuracy. In contrast, following the Atkinson-Shiffrin model, we develop an architecture that incorporates multiple independent yet deeply-connected feature memory stores: a rapidly updated sensory memory, a high-resolution working memory, and a compact thus sustained long-term memory. Crucially, we develop a memory potentiation algorithm that routinely consolidates actively used working memory elements into the long-term memory, which avoids memory explosion and minimizes performance decay for long-term prediction. Combined with a new memory reading mechanism, XMem greatly exceeds state-of-the-art performance on long-video datasets while being on par with state-of-the-art methods (that do not work on long videos) on short-video datasets. Code is available at https://hkchengrex.github.io/XMemComment: Accepted to ECCV 2022. Project page: https://hkchengrex.github.io/XMe