Music Recommendations in Hyperbolic Space: An Application of Empirical Bayes and Hierarchical Poincar\'e Embeddings

Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean embedding space to represent two entity types, recent work has suggested that a hyperbolic Poincar\'e ball may be more well suited to representing multiple entity types, and in particular, hierarchies. We describe a novel method to embed a hierarchy of related music entities in hyperbolic space. We also describe how a parametric empirical Bayes approach can be used to estimate link reliability between entities in the hierarchy. Applying these methods together to build personalized playlists for users in a digital music service yielded a large and statistically significant increase in performance during an A/B test, as compared to the Euclidean model

A Controlled Investigation of Optimal Internal Medicine Ward Team Structure at a Teaching Hospital

BACKGROUND: The optimal structure of an internal medicine ward team at a teaching hospital is unknown. We hypothesized that increasing the ratio of attendings to housestaff would result in an enhanced perceived educational experience for residents. METHODS: Harbor-UCLA Medical Center (HUMC) is a tertiary care, public hospital in Los Angeles County. Standard ward teams at HUMC, with a housestaff∶attending ratio of 5:1, were split by adding one attending and then dividing the teams into two experimental teams containing ratios of 3:1 and 2:1. Web-based Likert satisfaction surveys were completed by housestaff and attending physicians on the experimental and control teams at the end of their rotations, and objective healthcare outcomes (e.g., length of stay, hospital readmission, mortality) were compared. RESULTS: Nine hundred and ninety patients were admitted to the standard control teams and 184 were admitted to the experimental teams (81 to the one-intern team and 103 to the two-intern team). Patients admitted to the experimental and control teams had similar age and disease severity. Residents and attending physicians consistently indicated that the quality of the educational experience, time spent teaching, time devoted to patient care, and quality of life were superior on the experimental teams. Objective healthcare outcomes did not differ between experimental and control teams. CONCLUSIONS: Altering internal medicine ward team structure to reduce the ratio of housestaff to attending physicians improved the perceived educational experience without altering objective healthcare outcomes

Mapping nonlinear receptive field structure in primate retina at single cone resolution

The function of a neural circuit is shaped by the computations performed by its interneurons, which in many cases are not easily accessible to experimental investigation. Here, we elucidate the transformation of visual signals flowing from the input to the output of the primate retina, using a combination of large-scale multi-electrode recordings from an identified ganglion cell type, visual stimulation targeted at individual cone photoreceptors, and a hierarchical computational model. The results reveal nonlinear subunits in the circuity of OFF midget ganglion cells, which subserve high-resolution vision. The model explains light responses to a variety of stimuli more accurately than a linear model, including stimuli targeted to cones within and across subunits. The recovered model components are consistent with known anatomical organization of midget bipolar interneurons. These results reveal the spatial structure of linear and nonlinear encoding, at the resolution of single cells and at the scale of complete circuits

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far—ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or “priors.” In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available

Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina

When visual contrast changes, retinal ganglion cells adapt by adjusting their sensitivity as well as their temporal filtering characteristics. The latter has classically been described by contrast-induced gain changes that depend on temporal frequency. Here, we explored a new perspective on contrast-induced changes in temporal filtering by using spike-triggered covariance analysis to extract multiple parallel temporal filters for individual ganglion cells. Based on multielectrode-array recordings from ganglion cells in the isolated salamander retina, we found that contrast adaptation of temporal filtering can largely be captured by contrast-invariant sets of filters with contrast-dependent weights. Moreover, differences among the ganglion cells in the filter sets and their contrast-dependent contributions allowed us to phenomenologically distinguish three types of filter changes. The first type is characterized by newly emerging features at higher contrast, which can be reproduced by computational models that contain response-triggered gain-control mechanisms. The second type follows from stronger adaptation in the Off pathway as compared to the On pathway in On-Off-type ganglion cells. Finally, we found that, in a subset of neurons, contrast-induced filter changes are governed by particularly strong spike-timing dynamics, in particular by pronounced stimulus-dependent latency shifts that can be observed in these cells. Together, our results show that the contrast dependence of temporal filtering in retinal ganglion cells has a multifaceted phenomenology and that a multi-filter analysis can provide a useful basis for capturing the underlying signal-processing dynamics

Structured hierarchical models for neurons in the early visual system

The early visual system is composed of a set of anatomically distinct areas that are linked together in a hierarchy. This structure uses simple rules at each stage but supports an impressive array of processing capabilities. In order to capture the full range of these computations, neuronal models in these areas should include this hierarchical architecture. Neurons in the earliest stages receive information directly from sensory transducers, yielding linear-like visual representations that are closely tied to visual stimulation. Neurons further downstream are more abstract and nonlinear in their representation, being both more selective for relevant stimulus visual and invariant across irrelevant features. Despite these computational differences, individual neurons among all areas are anatomically similar and they can be described in simple terms; inputs are summed across dendritic synapses and arbors and outputs are generated by a spiking nonlinearity in the soma and axon hillock. This regularity can be exploited to build simple but powerful hierarchical models that approximate the stages of visual processing in cortex. A realistic model architecture can reduce, and in some cases eliminated altogether, the need for ad-hoc priors or regularizers. Incorporating physiological and anatomical constraints, and careful experimental design (including the choice of stimuli), simplifies models and allows for more direct and efficient estimation procedures. In this thesis I present a series of hierarchical models for neurons in the early visual system (V1 & V2) and show that they can accurately capture the computations performed by real neurons. I also demonstrate that a stage-wise structure avoids overfitting and that it allows for a more efficient estimation procedure than generic statistical models

Acute kidney injury caused by intravascular hemolysis after mechanical thrombectomy.

A 43-year-old African-American female (gravida 5 para 0) with an 8-week intrauterine pregnancy presented to the emergency room with crampy abdominal pain, shortness of breath, and shoulder pain. She had normal renal function on admission. CT angiography of the chest revealed bilateral pulmonary emboli; therefore, the AngioJet (Possis Medical, Inc., Minneapolis, MN) device was used to perform mechanical thrombolysis. The patient subsequently developed hyperkalemia, red urine and anuria.Physical examination, measurement of serum creatinine level and electrolytes, dipstick urinalysis and centrifugation of urine and blood.Acute kidney injury due to hemoglobinuria as a result of non-immune-mediated intravascular hemolysis following the use of a percutaneous mechanical thrombectomy device (AngioJet).Hydration, alkalinization of urine and initiation of hemodialysis (temporarily switched to continuous venovenous hemodiafiltration). Urine output improved after the 20th day of hospitalization, at which point dialysis was discontinued. The patient's renal function completely recovered by day 25

Current diognosis and treatment critical care

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