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We consider a quiz show game in which a contestant is presented with a sequence of questions. Each time the contestant answers a question correctly, she receives a prize and proceeds to the next question; the probability of answering each question correctly is given. If the contestant answers a question incorrectly, she receives a consolation prize and the game ends. The contestant's problem of determining the optimal order in which to answer questions, for known fixed parameters, is a classic one studied in Kadane (1969), and admits a simple index-based solution. We consider game-theoretic versions of this problem in which a game show host can choose how to allocate a fixed prize budget. Our models are motivated by operational search problems in national security involving reconnaissance missions and inspecting for evidence of nuclear enrichment, as well as certain scheduling problems. We study three variants of the game, corresponding to different ways in which the host can distribute the prize money. For the first variant, we provide complete closed-form solutions, including equilibrium strategies and the value of the game. We reduce the second variant to a game solved in the li

Founded in 2007, the Foothill College Physics Show has served nearly a quarter of a million attendees in the two decades that have followed. This demo show features both performances for the public and field trips for students from local Title 1 schools. The college's students play an important role, acting as both on-stage talent, leading tours of the college, and helping build equipment. From a small beginning, it now hosts over twenty-five thousand attendees a year, and is an important part of the college's outreach efforts.

Diffusion Denoising models demonstrated impressive results across generative Computer Vision tasks, but they still fail to outperform standard autoregressive solutions in the discrete domain, and only match them at best. In this work, we propose a different paradigm by adopting diffusion models to provide suggestions to the autoregressive generation rather than replacing them. By doing so, we combine the bidirectional and refining capabilities of the former with the strong linguistic structure provided by the latter. To showcase its effectiveness, we present Show, Suggest and Tell (SST), which achieves State-of-the-Art results on COCO, among models in a similar setting. In particular, SST achieves 125.1 CIDEr-D on the COCO dataset without Reinforcement Learning, outperforming both autoregressive and diffusion model State-of-the-Art results by 1.5 and 2.5 points. On top of the strong results, we performed extensive experiments to validate the proposal and analyze the impact of the suggestion module. Results demonstrate a positive correlation between suggestion and caption quality, overall indicating a currently underexplored but promising research direction. Code will be available a

Amazon Echo is one of the most popular product families of smart speakers and displays. Considering their growing presence in modern households as well as the digital traces associated with residents' interactions with these devices, analyses of Echo products are likely to become more common for forensic investigators at "smart home" crime scenes. With this in mind, we present the first forensic examination of the Echo Show 15, Amazon's largest smart display running on Fire OS and the first Echo device with Visual ID, a face recognition feature. We unveil a non-invasive method for accessing the unencrypted file system of the Echo Show 15 based on an undocumented pinout for the eMMC interface which we discovered on the main logic board. On the device, we identify various local usage artifacts, such as searched products, streamed movies, visited websites, metadata of photos and videos as well as logged events of Visual ID about movements and users detected by the built-in camera. Furthermore, we utilize an insecurely stored token on the Echo Show 15 to obtain access to remote user artifacts in Amazon's cloud, including Alexa voice requests, calendars, contacts, conversations, photos,

The rise of platforms like Netflix has expanded the possibility for audiences worldwide to watch the same content simultaneously, motivating research on cross-country media consumption. We investigate the global dynamics of media consumption by analyzing daily top-ranked Netflix TV shows across 71 countries over a span of 822 days. Using an information-theoretic framework, we measure diversity, similarity, and directional relationships in consumption trends using Shannon entropy, mutual information, and Kullback-Leibler (KL) divergence. According to Shannon entropy analysis, North America and Europe have highly dynamic viewing preferences, whereas East and Southeast Asia (ESA) display more persistent trends, with the same shows often dominating for long periods. Mutual information identifies clear regional clusters of synchronized consumption, with particularly strong alignment among ESA countries. To analyze temporal patterns, we introduce a KL-based asymmetry measure that captures directional patterns between countries, applicable to both inter- and intra-regional pairs. This analysis reveals distinct pathways of content spread. We find inter-regional patterns from ESA and South

Historical maps offer valuable insights into changes on Earth's surface but pose challenges for modern segmentation models due to inconsistent visual styles and symbols. While deep learning models such as UNet and pre-trained foundation models perform well in domains like autonomous driving and medical imaging, they struggle with the variability of historical maps, where similar concepts appear in diverse forms. To address this issue, we propose On-Need Declarative (OND) knowledge-based prompting, a method that provides explicit image-label pair prompts to guide models in linking visual patterns with semantic concepts. This enables users to define and segment target concepts on demand, supporting flexible, concept-aware segmentation. Our approach replaces the prompt encoder of the Segment Anything Model (SAM) with the OND prompting mechanism and fine-tunes it on historical maps, creating SMOL-MapSeg (Show Me One Label). Unlike existing SAM-based fine-tuning methods that are class-agnostic or restricted to fixed classes, SMOL-MapSeg supports class-aware segmentation across arbitrary datasets. Experiments show that SMOL-MapSeg accurately segments user-defined classes and substantiall

We investigate the possibility of a Markovian quantum master equation (QME) that consistently describes a finite-dimensional system, a part of which is weakly coupled to a thermal bath. In order to preserve complete positivity and trace, such a QME must be of Lindblad form. For physical consistency, it should additionally preserve local conservation laws and be able to show thermalization. We search of Lindblad equations satisfying these additional criteria. First, we show that the microscopically derived Bloch-Redfield equation (RE) violates complete positivity unless in extremely special cases. We then prove that imposing complete positivity and demanding preservation of local conservation laws enforces the Lindblad operators and the lamb-shift Hamiltonian to be `local', i.e, to be supported only on the part of the system directly coupled to the bath. We then cast the problem of finding `local' Lindblad QME which can show thermalization into a semidefinite program (SDP). We call this the thermalization optimization problem (TOP). For given system parameters and temperature, the solution of the TOP conclusively shows whether the desired type of QME is possible up to a given precis

There are many types of automata and grammar models that have been studied in the literature, and for these models, it is common to determine whether certain problems are decidable. One problem that has been difficult to answer throughout the history of automata and formal language theory is to decide whether a given system $M$ accepts a bounded language (whether there exist words $w_1, \ldots,w_k$ such that $L(M) \subseteq w_1 \cdots w_k$?). Decidability of this problem has gone unanswered for the majority of automata/grammar models in the literature. Boundedness was only known to be decidable for regular and context-free languages until recently when it was shown to also be decidable for finite-automata and pushdown automata augmented with reversal-bounded counters, and for vector addition systems with states. In this paper, we develop new techniques to show that the boundedness problem is decidable for larger classes of one-way nondeterministic automata and grammar models, by reducing the problem to the decidability of boundedness for simpler classes of automata. One technique involves characterizing the models in terms of multi-tape automata. We give new characterizations of fi

Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video alignment. In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation. Our model first uses pixel-based VDMs to produce a low-resolution video of strong text-video correlation. After that, we propose a novel expert translation method that employs the latent-based VDMs to further upsample the low-resolution video to high resolution, which can also remove potential artifacts and corruptions from low-resolution videos. Compared to latent VDMs, Show-1 can produce high-quality videos of precise text-video alignment; Compared to pixel VDMs, Show-1 is much more efficient (GPU memory usage during inference is 15G vs 72G). Furthermore, our Show-1 model can be readily adapted for motion customization and video stylization applications through simple temporal attention layer finetuning. Our model achieve

Cultural Astronomy sits at the intersection of our study of the universe and the human experience, exploring how we observe the stars, interpret their motions, and incorporate them into our perspectives of the nature of reality. "Astronomy in Appalachia" is a new Planetarium show, developed for the Planetarium at East Tennessee State University, that explores how the inherited experience of the night sky in Appalachia can be used as a part of Astronomy education, and how that can serve as a cross-cultural point of connection. Using Earth's seasons as an anchor, the show highlights several Appalachian folk science narratives, examining how they are rooted in Astronomy, and relating them to modern scientific interpretations of the cosmos. This article describes the lessons our team learned while researching, scripting, and developing this new program.

Patient no-shows is a major burden for health centers leading to loss of revenue, increased waiting time and deteriorated health outcome. Developing machine learning (ML) models for the prediction of no -shows could help addressing this important issue. It is crucial to consider fair ML models for no-show prediction in order to ensure equality of opportunity in accessing healthcare services. In this wo rk, we are interested in developing deep learning models for no-show prediction based on tabular data while ensuring fairness properties. Our baseline model, TabNet, uses on attentive feature transforme rs and has shown promising results for tabular data. We propose Fair-TabNet based on representation learning that disentangles predictive from sensitive components. The model is trained to jointly min imize loss functions on no-shows and sensitive variables while ensuring that the sensitive and prediction representations are orthogonal. In the experimental analysis, we used a hospital dataset of 210, 000 appointments collected in 2019. Our preliminary results show that the proposed Fair-TabNet improves the predictive, fairness performance and convergence speed over TabNet for the task

TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications. However, the majority of the existing work focuses on 2D recognition tasks. In this paper, we make the observation that there is a certain persistence in TV shows, i.e., repetition of the environments and the humans, which makes possible the 3D reconstruction of this content. Building on this insight, we propose an automatic approach that operates on an entire season of a TV show and aggregates information in 3D; we build a 3D model of the environment, compute camera information, static 3D scene structure and body scale information. Then, we demonstrate how this information acts as rich 3D context that can guide and improve the recovery of 3D human pose and position in these environments. Moreover, we show that reasoning about humans and their environment in 3D enables a broad range of downstream applications: re-identification, gaze estimation, cinematography and image editing. We apply our approach on environments from seven iconic TV shows and perform an extensive evaluation of the proposed system.

This study explores strategic decision-making in professional golf's Stroke Play format through a computational lens. We develop a Markov Decision Process (MDP) model-specifically, a stochastic shortest path formulation-to optimize a golfer's strategy on any given course, incorporating both course layout and player skill data. While MDPs have been widely used in sports analytics, applying them to golf presents significant scalability challenges due to the curse of dimensionality. Our primary objective is not to predict player performance with high precision, but rather to demonstrate that an exact, data-driven MDP approach is computationally tractable on full scale, real-world instances. We show that, with careful problem structuring, low-level coding, and efficient memory management, it is possible to solve such large-scale models without resorting to heuristics or Q-learning approximations, as used in existing approaches. To illustrate the model's potential, we show how one can use PGA Tour data and aerial course imagery to simulate strategic outcomes and analyze how different skill profiles influence performance. In particular, we assess the relative impact of driving and puttin

We present the screenplay of a physics show on particle physics, by the Physikshow of Bonn University. The show is addressed at non-physicists aged 14+ and communicates basic concepts of elementary particle physics including the discovery of the Higgs boson in an entertaining fashion. It is also demonstrates a successful outreach activity heavily relying on the university physics students. This paper is addressed at anybody interested in particle physics and/or show physics. This paper is also addressed at fellow physicists working in outreach, maybe the experiments and our choice of simple explanations will be helpful. Furthermore, we are very interested in related activities elsewhere, in particular also demonstration experiments relevant to particle physics, as often little of this work is published. Our show involves 28 live demonstration experiments. These are presented in an extensive appendix, including photos and technical details. The show is set up as a quest, where 2 students from Bonn with the aid of a caretaker travel back in time to understand the fundamental nature of matter. They visit Rutherford and Geiger in Manchester around 1911, who recount their famous experim

We present a physics show musical with live physics experiments and live performed songs with live orchestral accompaniment. The musical was first put on stage in German at the Physikalische Institut, University of Bonn, on the 24th of March, 2019. Here we present the original German script as well as an English translation, including a translation of the songs. We also give brief descriptions of all the experiments employed, with photos as well as how we present the experiments. The musical tells the story of a couple, Life and Death, nicknamed Vita and Mortis, who want to buy a planet where they can settle down and raise a family. They arrive at the store Planetamos, run by the manager Luna Callisto and the scientist Jupi Mercury. The latter two present to Vita and Mortis what is required for life on a planet, as well as all of the extra features for sale, to make it that very special place for their family. Through Vita and Mortis the audience learns about the physical requirements for life on a planet orbiting a star. The show involves 14 live physics experiments. The four actors sing 7 songs, which are to well-known tunes with new lyrics, in German. During the show the singers

Online microblogging services that have been increasingly used by people to share and exchange information, have emerged as a promising way to profiling multimedia contents, in a sense to provide users a socialized abstraction and understanding of these contents. In this paper, we propose a microblogging profiling framework, to provide a social demonstration of TV shows. Challenges for this study lie in two folds: First, TV shows are generally offline, i.e., most of them are not originally from the Internet, and we need to create a connection between these TV shows with online microblogging services; Second, contents in a microblogging service are extremely noisy for video profiling, and we need to strategically retrieve the most related information for the TV show profiling.To address these challenges, we propose a MAP, a microblogging-assisted profiling framework, with contributions as follows: i) We propose a joint user and content retrieval scheme, which uses information about both actors and topics of a TV show to retrieve related microblogs; ii) We propose a social-aware profiling strategy, which profiles a video according to not only its content, but also the social relation

We present a network-based recommender system for live shows (concerts, theater, circus, etc) that finds a set of people probably interested in a given, new show. We combine collaborative and content-based filtering to take benefit of past activity of users and of the features of the new show. Indeed, as this show is new we cannot rely on collaborative filtering only. To solve this cold-start problem, we perform network alignment and insert the new show in a way consistent with collaborative filtering. We refine the obtained similarities using spreading in the network. We illustrate the performances of our system on a large scale real-world dataset.