Conditional graphic layout generation, which automatically maps user constraints to high-quality layouts, has attracted widespread attention today. Although recent works have achieved promising performance, the lack of versatility and data efficiency hinders their practical applications. In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning. LayoutPrompter is made up of three key components, namely input-output serialization, dynamic exemplar selection and layout ranking. Specifically, the input-output serialization component meticulously designs the input and output formats for each layout generation task.

Conditional graphic layout generation, which automatically maps user constraints to high-quality layouts, has attracted widespread attention today. Although recent works have achieved promising performance, the lack of versatility and data efficiency hinders their practical applications. In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning. LayoutPrompter is made up of three key components, namely input-output serialization, dynamic exemplar selection and layout ranking. Specifically, the input-output serialization component meticulously designs the input and output formats for each layout generation task.

Patients resuscitated from cardiac arrest who enter a coma are at high risk of death. Forecasting neurological outcomes of these patients (the task of neurological prognostication) could help with treatment decisions. In this paper, we propose, to the best of our knowledge, the first dynamic framework for neurological prognostication of post-cardiac-arrest comatose patients using EEG data: our framework makes predictions for a patient over time as more EEG data become available, and different training patients' available EEG time series could vary in length. Predictions are phrased in terms of either time-to-event outcomes (time-to-awakening or time-to-death) or as the patient's probability of awakening or of dying across multiple time horizons. Our framework uses any dynamic survival analysis model that supports competing risks in the form of estimating patient-level cumulative incidence functions. We consider three competing risks as to what happens first to a patient: awakening, being withdrawn from life-sustaining therapies (and thus deterministically dying), or dying (by other causes). We demonstrate our framework by benchmarking three existing dynamic survival analysis models that support competing risks on a real dataset of 922 patients. Our main experimental findings are that: (1) the classical Fine and Gray model which only uses a patient's static features and summary statistics from the patient's latest hour's worth of EEG data is highly competitive, achieving accuracy scores as high as the recently developed Dynamic-DeepHit model that uses substantially more of the patient's EEG data; and (2) in an ablation study, we show that our choice of modeling three competing risks results in a model that is at least as accurate while learning more information than simpler models (using two competing risks or a standard survival analysis setup with no competing risks).

The amoebot model abstracts active programmable matter as a collection of simple computational elements called amoebots that interact locally to collectively achieve tasks of coordination and movement. Since its introduction at SPAA 2014, a growing body of literature has adapted its assumptions for a variety of problems; however, without a standardized hierarchy of assumptions, precise systematic comparison of results under the amoebot model is difficult. We propose the canonical amoebot model, an updated formalization that distinguishes between core model features and families of assumption variants. A key improvement addressed by the canonical amoebot model is concurrency. Much of the existing literature implicitly assumes amoebot actions are isolated and reliable, reducing analysis to the sequential setting where at most one amoebot is active at a time. However, real programmable matter systems are concurrent. The canonical amoebot model formalizes all amoebot communication as message passing, leveraging adversarial activation models of concurrent executions. Under this granular treatment of time, we take two complementary approaches to concurrent algorithm design. We first establish a set of sufficient conditions for algorithm correctness under any concurrent execution, embedding concurrency control directly in algorithm design. We then present a concurrency control framework that uses locks to convert amoebot algorithms that terminate in the sequential setting and satisfy certain conventions into algorithms that exhibit equivalent behavior in the concurrent setting. As a case study, we demonstrate both approaches using a simple algorithm for hexagon formation. Together, the canonical amoebot model and these complementary approaches to concurrent algorithm design open new directions for distributed computing research on programmable matter.

Santiago Ramón y Cajal, a Spanish histologist and anatomist known today as the father of modern neuroscience, was also a committed psychologist who believed psychoanalysis and Freudian dream theory were "collective lies." When Freud published The Interpretation of Dreams in 1900, the science world swooned over his theory of the unconscious. Dreams quickly became synonymous with repressed desire. Puzzling dream images could unlock buried conflicts, the psychoanalyst said, given the correct interpretation. Cajal, who won the 1906 Nobel Prize for discovering neurons and, more remarkably, intuiting the form and function of synapses, set out to prove Freud wrong. To disprove the theory that every dream is the result of a repressed desire, Cajal began keeping a dream journal and collecting the dreams of others, analyzing them with logic and rigor. Translated here into English for the first time, the dreams of Santiago Ramón y Cajal offer insight into the mind of a great scientist. Cajal eventually deemed the project unpublishable.

Listen closely and the ambient soundtrack sounds like a heartbeat rising from within the earth. In Hyper Light Drifter, available now for PC and later this year for consoles, the post-apocalypse is alive--and it's fighting. The story it tells is minimal and wordless. A short opening cutscene reveals everything the game thinks you need to know: a vision of an arcane futuristic utopia, destroyed in a burst of impossibly bright light. In the aftermath, you play as a cloak- and goggle-wearing drifter.

BB-8, the adorable yet heroic droid who made his debut in the latest Star Wars film, is now also the poster child of a hot youth-focused tech trend: toys that improve over time. In this case, the toy robot now has the ability to react to screenings of "The Force Awakens." On Tuesday, Sphero pushed out a smartphone app update giving our current favorite toy robot the ability to listen and respond to the movie's dialog. Before you start the movie--which was just released on home video--you'll need to update then open iPhone or Android Sphero BB-8 control app, then connect it to the baseball-sized robot. You'll want to keep BB-8 in his charging cradle, so he doesn't roll off your table or couch.