In the third season of "The Comeback," Kudrow has brought back her character Valerie Cherish, which had its roots at the Groundlings. A visitor to Stage 24 on the Warner Bros. lot, in Burbank, last November could be forgiven for thinking that the television show being filmed there was a sitcom called "How's That?!" The parking spaces outside were marked with "How's That?!" signs. Inside, director's chairs with the "How's That?!" logo were arranged around video monitors. The set--a New England bed-and-breakfast, with kitschy floral wallpaper--was surrounded by sitcom cameras and buzzing crew members wearing headsets. A studio audience filed into the bleachers, and a warmup comic urged them to "shake those funny bones." Then, with mounting gusto, he introduced the star of "How's That?!": "Here she is . . . the one and only . . . the living legend . . . She emerged to applause, in a potter's smock, wavy red hair under a bandanna, looking like a cross between Lucy Ricardo and Mrs. Garrett ...

Submitted to ApJS ABSTRACT In modern astronomy, the quantity of data collected has vastly exceeded the capacity for manual analysis, necessitating the use of advanced artificial intelligence (AI) techniques to assist scientists with the most labor-intensive tasks. AI can optimize simulation codes where computational bottlenecks arise from the time required to generate forward models. One such example is PHOEBE, a modeling code for eclipsing binaries (EBs), where simulating individual systems is feasible, but analyzing observables for extensive parameter combinations is highly time-consuming. To address this, we present a fully connected feedforward artificial neural network (ANN) trained on a dataset of over one million synthetic light curves generated with PHOEBE. Optimization of the ANN architecture yielded a model with six hidden layers, each containing 512 nodes, provides an optimized balance between accuracy and computational complexity. Extensive testing enabled us to establish ANN's applicability limits and to quantify the systematic and statistical errors associated with using such networks for EB analysis. Our findings demonstrate the critical role of dilution effects in parameter estimation for EBs, and we outline methods to incorporate these effects in AI-based models. This proposed ANN framework enables a speedup of over four orders of magnitude compared to traditional methods, with systematic errors not exceeding 1%, and often as low as 0.01%, across the entire parameter space. INTRODUCTION number of EBs are found in triple and multiple systems (Conroy et al. 2014; Orosz 2015), hosting circumbinary Fundamental stellar properties are inferred predominantly planets (Welsh et al. 2015), and featuring mass from the study of eclipsing binary stars (EBs) transfer and apsidal motion (Hambleton et al. 2013); (Torres et al. 2010). Their favorable orbital alignment these broaden the domains of study while retaining the with the line of sight, and consequent eclipses, make same tractable modeling principles. In particular, we them ideal astrophysical laboratories: a simple geometry can probe stellar interiors by studying tidally induced coupled with well-understood dynamical laws allow oscillations and gravity-mode pulsations in detached binaries us to obtain fundamental parameters without a-priori (Huber 2015); ubiquitous contact binaries are still assumptions (Prša 2018). Many of the phenomena being observed in hot that, we need samplers such as Markov Chain Monte Jupiters have their foundations in EB studies, e.g., the Carlo (MCMC, Foreman-Mackey et al. 2017) to provide Rossiter-McLaughlin effect, tidal distortions of the host heuristic parameter posteriors. This entails hundreds of star, irradiation effects, Roche lobe overflow and wind thousands if not millions of forward-model runs, which outflows, gravity darkening, apsidal motion, third body puts a hard limit on the number of systems we can solve dynamics, etc. (Barclay et al. 2012).

Humans convey emotions through daily dialogues, making emotion understanding a crucial step of affective intelligence. To understand emotions in dialogues, machines are asked to recognize the emotion for an utterance (Emotion Recognition in Dialogues, ERD); based on the emotion, then find causal utterances for the emotion (Emotion Cause Extraction in Dialogues, ECED). The setting of the two tasks requires first ERD and then ECED, ignoring the mutual complement between emotion and cause. To fix this, some new tasks are proposed to extract them simultaneously. Although the current research on these tasks has excellent achievements, simply identifying emotion-related factors by classification modeling lacks realizing the specific thinking process of causes stimulating the emotion in an explainable way. This thinking process especially reflected in the reasoning ability of Large Language Models (LLMs) is under-explored. To this end, we propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN). EDEN recognizes emotion and causes in an explicitly thinking way. That is, models need to generate an explanation text, which first summarizes the causes; analyzes the inner activities of the speakers triggered by the causes using common sense; then guesses the emotion accordingly. To support the study of EDEN, based on the existing resources in ECED, we construct two EDEN datasets by human effort. We further evaluate different models on EDEN and find that LLMs are more competent than conventional PLMs. Besides, EDEN can help LLMs achieve better recognition of emotions and causes, which explores a new research direction of explainable emotion understanding in dialogues.

The prevalence of large-scale multimodal datasets presents unique challenges in assessing dataset quality. We propose a two-step method to analyze multimodal datasets, which leverages a small seed of human annotation to map each multimodal instance to the modalities required to process it. Our method sheds light on the importance of different modalities in datasets, as well as the relationship between them. We apply our approach to TVQA, a video question-answering dataset, and discover that most questions can be answered using a single modality, without a substantial bias towards any specific modality. Moreover, we find that more than 70% of the questions are solvable using several different single-modality strategies, e.g., by either looking at the video or listening to the audio, highlighting the limited integration of multiple modalities in TVQA. We leverage our annotation and analyze the MERLOT Reserve, finding that it struggles with image-based questions compared to text and audio, but also with auditory speaker identification. Based on our observations, we introduce a new test set that necessitates multiple modalities, observing a dramatic drop in model performance. Our methodology provides valuable insights into multimodal datasets and highlights the need for the development of more robust models.

I am too much of a control freak to let another player screw up my good work. But I really wanted to try It Takes Two because, first, it was in every single top games of 2021 list and, second, the game is about a couple on the verge of divorce who must find a way to work together. And a little over a year ago, my wife and I were in the same situation. In It Takes Two, the spouses become tiny dolls who must work their way through their suddenly gigantic house, solving puzzles to reunite with their weeping daughter. In real life, we did family therapy.

Easy-to-use programming interfaces paired with cloud-scale processing engines have enabled big data system users to author arbitrarily complex analytical jobs over massive volumes of data. However, as the complexity and scale of analytical jobs increase, they encounter a number of unforeseen problems, hotspots with large intermediate data on temporary storage, longer job recovery time after failures, and worse query optimizer estimates being examples of issues that we are facing at Microsoft. To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer. Given a set of constraints and an objective function at compile-time, Phoebe is able to determine the decomposition of job plans, and the optimal set of checkpoints to preserve their outputs to durable global storage. Phoebe consists of three machine learning predictors and one optimization module. For each stage of a job, Phoebe makes accurate predictions for: (1) the execution time, (2) the output size, and (3) the start/end time taking into account the inter-stage dependencies. Using these predictions, we formulate checkpoint optimization as an integer programming problem and propose a scalable heuristic algorithm that meets the latency requirement of the production environment. We demonstrate the effectiveness of Phoebe in production workloads, and show that we can free the temporary storage on hotspots by more than 70% and restart failed jobs 68% faster on average with minimum performance impact. Phoebe also illustrates that adding multiple sets of checkpoints is not cost-efficient, which dramatically reduces the complexity of the optimization.

In my experience, when you ask a person what their favorite film is, you'll often be told the title of the film that made the most impression on them at a most impressionable age. For some, it was "The Monster Squad" (1987). A pastiche of "The Goonies," featuring a group of suburban American kids up against the classic Universal monsters, instead of a pirate, the film mixes horror and comedy in the style of those Abbott & Costello Universal film spoofs (some of which are very good). "The Monster Squad" was not very well received by the critics (me included) or the public, which stayed away in droves to quote Sam Goldwyn ("The Lost Boys" preceded it by two weeks). But a funny thing happened.

Whether they are racing cars that "read" the track, robots that teach coding or ground-drones controlled with the swipe of an iPad, these "connected" playthings have been proclaimed as the future of the toy industry. For parents concerned about the amount of time their kids spend in front of a screen, connected toys offer a welcome and reassuring physicality: the toy is the focus, the app merely the control panel. For their children, it means (effectively) getting a pet robot. Which is why brands such as Sphero and Anki are set to dominate 2016 Christmas lists. But the question remains: how much fun are these digitally driven playthings?