The proverb ``see something, say something'' captures a core responsibility of autonomous mobile robots in safety-critical situations: when they detect a hazard, they must communicate--and do so quickly. In emergency scenarios, delayed or miscalibrated responses directly increase the time to action and the risk of damage. We argue that a systematic context-sensitive assessment of the criticality level, time sensitivity, and feasibility of mitigation is necessary for AMRs to reduce time to action and respond effectively. This paper presents a framework in which VLM/LLM-based perception drives adaptive message generation, for example, a knife in a kitchen produces a calm acknowledgment; the same object in a corridor triggers an urgent coordinated alert. Validation in 60+ runs using a patrolling mobile robot not only empowers faster response, but also brings user trusts to 82\% compared to fixed-priority baselines, validating that structured criticality assessment improves both response speed and mitigation effectiveness.
This work introduces a novel training paradigm that draws from affective neuroscience. Inspired by the interplay of emotions and cognition in the human brain and more specifically the SEEKING motivational state, we design a dual-model framework where a smaller base model is trained continuously, while a larger motivated model is activated intermittently during predefined "motivation conditions". The framework mimics the emotional state of high curiosity and anticipation of reward in which broader brain regions are recruited to enhance cognitive performance. Exploiting scalable architectures where larger models extend smaller ones, our method enables shared weight updates and selective expansion of network capacity during noteworthy training steps. Empirical evaluation on the image classification task demonstrates that, not only does the alternating training scheme efficiently and effectively enhance the base model compared to a traditional scheme, in some cases, the motivational model also surpasses its standalone counterpart despite seeing less data per epoch. This opens the possibility of simultaneously training two models tailored to different deployment constraints with competi
The current cosmological paradigm, $Λ$CDM, is characterized its expansive description of the history of the Universe, its deep connections to particle physics and the large amounts of data that support it. Nonetheless, $Λ$CDM's critics argue that it has been falsified or must be discarded for various reasons. Critics and boosters alike do agree on one thing: it is the not the final cosmological theory and they are anxious to see it replaced by something better! I review the status of $Λ$CDM, provide my views of the path forward, and discuss the role that the ``Hubble tension'' might play.
As artificial intelligence (AI) becomes embedded in healthcare, trust in medical decision-making is changing fast. Nowhere is this shift more visible than in radiology, where AI tools are increasingly embedded across the imaging workflow - from scheduling and acquisition to interpretation, reporting, and communication with referrers and patients. This opinion paper argues that trust in AI isn't a simple transfer from humans to machines - it is a dynamic, evolving relationship that must be built and maintained. Rather than debating whether AI belongs in medicine, it asks: what kind of trust must AI earn, and how? Drawing from philosophy, bioethics, and system design, it explores the key differences between human trust and machine reliability - emphasizing transparency, accountability, and alignment with the values of good care. It argues that trust in AI should not be built on mimicking empathy or intuition, but on thoughtful design, responsible deployment, and clear moral responsibility. The goal is a balanced view - one that avoids blind optimism and reflexive fear. Trust in AI must be treated not as a given, but as something to be earned over time.
We suggest that the question of why is there something rather than nothing can be answered by the existence of two types of nothing. We propose that matter occurs at the boundaries of intersection of both nothings. This accords with the common view that there are three worlds; the platonic world of concepts, the material world (i.e. of matter), and the non-material world (i.e. of consciousness). Both the material and non-material worlds have their own type of nothing, thus leading to the proposal. The interpretation provides an alternative type of duality distinct from property or substance dualism, and may unify the two. The interpretation also has implications for the understanding of physical causation, and Mach's principle as the boundary of intersection provides a return to the aether concept.
General-purpose object placement is a fundamental capability of an intelligent generalist robot: being capable of rearranging objects following precise human instructions even in novel environments. This work is dedicated to achieving general-purpose object placement with ``something something'' instructions. Specifically, we break the entire process down into three parts, including object localization, goal imagination and robot control, and propose a method named SPORT. SPORT leverages a pre-trained large vision model for broad semantic reasoning about objects, and learns a diffusion-based pose estimator to ensure physically-realistic results in 3D space. Only object types (movable or reference) are communicated between these two parts, which brings two benefits. One is that we can fully leverage the powerful ability of open-set object recognition and localization since no specific fine-tuning is needed for the robotic scenario. Moreover, the diffusion-based estimator only need to ``imagine" the object poses after the placement, while no necessity for their semantic information. Thus the training burden is greatly reduced and no massive training is required. The training data for
This pictorial aims to critically consider the nature of text-to-audio and text-to-music generative tools in the context of explainable AI. As a group of experimental musicians and researchers, we are enthusiastic about the creative potential of these tools and have sought to understand and evaluate them from perspectives of prompt creation, control, usability, understandability, explainability of the AI process, and overall aesthetic effectiveness of the results. One of the challenges we have identified that is not explicitly addressed by these tools is the inherent semantic gap in using text-based tools to describe something as abstract as music. Other gaps include explainability vs. useability, and user control and input vs. the human creative process. The aim of this pictorial is to raise questions for discussion and make a few general suggestions on the kinds of improvements we would like to see in generative AI music tools.
We investigate structural implications arising from the condition that a given directed graph does not interpret, in the sense of primitive positive interpretation with parameters or orbits, every finite structure. Our results generalize several theorems from the literature and yield further algebraic invariance properties that must be satisfied in every such graph. Algebraic properties of this kind are tightly connected to the tractability of constraint satisfaction problems, and we obtain new such properties even for infinite countably categorical graphs. We balance these positive results by showing the existence of a countably categorical hypergraph that fails to interpret some finite structure, while still lacking some of the most essential algebraic invariance properties known to hold for finite structures.
Distributed-Something coordinates the distribution of any Dockerized workflow using on-demand computational infrastructure from Amazon Web Services to enable at-scale workflows where neither computing power nor data storage are limited by local availability while minimizing the time-consuming and confusing aspects of architecture coordination. We also provide Distributed-Something implementations of several bioimaging tools: Distributed-CellProfiler, -Fiji, and -OmeZarrCreator. All are open-source and available at http://GitHub.com/DistributedScience.
There was a lot of controversy about corollary 3.12, which was described in the paper Inter-universal Teichmuller Theory III. In this article, another proof of Corollary 3.12 will be derived, where the basis of the proof will be the Erdos-Kac theorem. Also in Inter-universal Teichmuller Theory IV it was said that the theory has a strong connection with the theory of Weil cohomology, based on this connection, very important physical applications will be derived in this article: generalization of non-Abelian Hodge correspondence, non-Abelian gauge theory, proof of the mass gap based on corollary 3.12, T duality is $Θ^{\pm ell}$NF Hodge Theater, the limit at which a black hole appears, the inflationary growth of the universe for the observer.
For much of February 2023, the world was in panic as repeated balloon-like unidentified flying objects (UFOs) were reported over numerous countries by governments that often responded with military action. As a result, most of these craft either escaped or were destroyed, making any further observation of them nearly impossible. These were not the first time balloon-like objects have loomed over Earth, nor are they likely to be the last. This has prompted us to push for a better understanding of UFOs. First we demonstrate that the distribution of balloon incidents and other UFO reports are consistent with being drawn from the same geographic distribution, and further that both of these distributions are consistent with the areas of the Earth that feature the jet stream. Second we show that there are more UFO sightings during meteor showers, as we would expect if meteor showers, already a known source of extraterrestrial material, are being used to provide some manner of distraction to help alien craft enter the Earth's atmosphere without drawing undue attention. These links between alleged balloon incidents, UFO reports, and meteor showers establish a transport pipeline for alien c
Many believe that the deep question of "why is there something rather than nothing?" is unanswerable. The universe just is and no further explanation for its existence is possible. In this paper I explain why this question must have an answer, and why that answer must establish that physical existence is inescapable and necessary. Based on the conclusion that if the universe is eternal rather than having a beginning some finite time in the past, the universe has to exist rather than not because its possible non-existence is never an option, such an explanation is put forward. As a logical extension of only an eternal universe being capable of providing an answer to the question of why there is something rather than nothing, the argument necessitates that the universe must be eternal. The consequences of this conclusion for cosmology are then briefly discussed.
We solve the Dirichlet problem in the unit disc and derive the Poisson formula using very elementary methods and explore consequent simplifications in other foundational areas of complex analysis.
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply about complex scenes and situations, and from integrating visual knowledge with natural language, like humans do, is their lack of common sense knowledge about the physical world. Videos, unlike still images, contain a wealth of detailed information about the physical world. However, most labelled video datasets represent high-level concepts rather than detailed physical aspects about actions and scenes. In this work, we describe our ongoing collection of the "something-something" database of video prediction tasks whose solutions require a common sense understanding of the depicted situation. The database currently contains more than 100,000 videos across 174 classes, which are defined as caption-templates. We also describe the challenges in crowd-sourcing this data at scale.
We present a novel approach for parallel computation in the context of machine learning that we call "Tell Me Something New" (TMSN). This approach involves a set of independent workers that use broadcast to update each other when they observe "something new". TMSN does not require synchronization or a head node and is highly resilient against failing machines or laggards. We demonstrate the utility of TMSN by applying it to learning boosted trees. We show that our implementation is 10 times faster than XGBoost and LightGBM on the splice-site prediction problem.
Facing the upcoming era of Internet-of-Things and connected intelligence, efficient information processing, computation, and communication design becomes a key challenge in large-scale intelligent systems. Recently, Over-the-Air (OtA) computation has been proposed for data aggregation and distributed computation of functions over a large set of network nodes. Theoretical foundations for this concept exist for a long time, but it was mainly investigated within the context of wireless sensor networks. There are still many open questions when applying OtA computation in different types of distributed systems where modern wireless communication technology is applied. In this article, we provide a comprehensive overview of the OtA computation principle and its applications in distributed learning, control, and inference systems, for both server-coordinated and fully decentralized architectures. Particularly, we highlight the importance of the statistical heterogeneity of data and wireless channels, the temporal evolution of model updates, and the choice of performance metrics, for the communication design in OtA federated learning (FL) systems. Several key challenges in privacy, securit
Conventional referring expression comprehension (REF) assumes people to query something from an image by describing its visual appearance and spatial location, but in practice, we often ask for an object by describing its affordance or other non-visual attributes, especially when we do not have a precise target. For example, sometimes we say 'Give me something to eat'. In this case, we need to use commonsense knowledge to identify the objects in the image. Unfortunately, these is no existing referring expression dataset reflecting this requirement, not to mention a model to tackle this challenge. In this paper, we collect a new referring expression dataset, called KB-Ref, containing 43k expressions on 16k images. In KB-Ref, to answer each expression (detect the target object referred by the expression), at least one piece of commonsense knowledge must be required. We then test state-of-the-art (SoTA) REF models on KB-Ref, finding that all of them present a large drop compared to their outstanding performance on general REF datasets. We also present an expression conditioned image and fact attention (ECIFA) network that extract information from correlated image regions and commonsen
In this paper I aim to defend one version at least of Hume's dictum: roughly, the idea that possibility is determined by ontology through something like independent variation. My defence is broadly pragmatic, in the sense that adherence to something like Hume's dictum delivers at least three benefits. The first benefit is that, through Hume's dictum, a physical theory's ontology delimits a range of possibilities, that I call \emph{kinematical possibilities}, which serves as a sufficiently permissive notion of possibility to sustain something like an intensional semantics for its claims, and a sufficiently demanding notion of supervenience to sustain plausible claims of inter-theoretic reduction and theoretical equivalence. The second benefit is that Hume's dictum allows us to work backwards from a range of kinematical possibilities to an ontology. This is especially useful when aiming to glean an interpretation of a physical theory, since often we are more confident that we have arrived at the right space of possibilities than that we have arrived at the right ontology. The third benefit is that Hume's dictum -- at least the version I aim to defend here -- may be applied to physica
Illusions are entertaining, but they are also a useful diagnostic tool in cognitive science, philosophy, and neuroscience. A typical illusion shows a gap between how something "really is" and how something "appears to be", and this gap helps us understand the mental processing that lead to how something appears to be. Illusions are also useful for investigating artificial systems, and much research has examined whether computational models of perceptions fall prey to the same illusions as people. Here, I invert the standard use of perceptual illusions to examine basic processing errors in current vision language models. I present these models with illusory-illusions, neighbors of common illusions that should not elicit processing errors. These include such things as perfectly reasonable ducks, crooked lines that truly are crooked, circles that seem to have different sizes because they are, in fact, of different sizes, and so on. I show that many current vision language systems mistakenly see these illusion-illusions as illusions. I suggest that such failures are part of broader failures already discussed in the literature.
Results of direct numerical simulations (DNS) of porous-wall turbulent flows in open channels with conjugate heat transfer are reported in this work. For the conductive porous walls considered here, the change in heat transfer is not monotonic. The heat flux initially decreases when going from a conductive smooth wall to slightly porous walls. In this initial porous-wall turbulence regime, the near-wall flow remains smooth-wall like and the heat transfer is dominated by molecular diffusion. As such, a reduction of the more favorably conducting solid material diminishes the overall heat transfer performance. Beyond a certain level of permeability however, the near-wall flow transitions to the K-H-like regime marked by the presence of cross-stream rollers, and the heat flux undergoes an increasing trend until it eventually surpasses that of smooth-wall turbulence. Neglecting the thermal behavior of the solid material can therefore result in overestimation of any gains in heat transfer. Additionally, thermal performance is assessed in terms of the Reynolds analogy breakdown, which is the disparity between the fractional increases in the Stanton number, $St$, and the fractional increas