Souls-like games exemplify how digital play can produce radical forms of pleasure through sustained challenge: players voluntarily invest tens or hundreds of hours in experiences designed to kill them repeatedly. This paper theorizes ordeal pleasure as a community-level phenomenon that emerges most fully when three mechanisms reinforce one another: Ludic Cultivation (mastery through fair adversity), Aspirational Deferment (delayed gratification oriented toward future growth), and Communal Mythopoesis (collective construction of shared meaning). Drawing on game design analysis, empirical player studies, and community discourse, we show how Souls-like games produce pleasure by coordinating difficulty, temporal structure, and social meaning-making. Comparative analysis (Elden Ring, Hollow Knight, Lords of the Fallen, The Surge) illustrates how specific design choices enable or undermine ordeal pleasure. The framework adds a temporal dimension to motivation theory and specifies social mechanisms beyond generic relatedness. It also offers design principles for designers seeking to create challenging games that transform suffering into complex satisfaction.
Globular clusters (GCs) are among the oldest and densest stellar systems in the Universe, yet how they form remains a mystery. Here we present a suite of cosmological simulations in which both dark-matter-free GCs and dark-matter-rich dwarf galaxies naturally emerge in the Standard Cosmology. We show that these objects inhabit distinct locations in the size-luminosity plane and that they have similar ages, age spread, metallicity and metallicity spread to globulars and dwarfs in the nearby Universe. About half of our simulated globulars form by means of regular star formation near the centres of their host dwarf, with the rest forming further out, triggered by mergers. The latter are more tidally isolated and more likely to survive to the present day. Finally, our simulations predict the existence of a new class of object that we call 'globular-cluster-like dwarfs' (GCDs). These form from a single, self-quenching, star-formation event in low-mass dark-matter halos at high redshift and have observational properties intermediate between globulars and dwarfs. We identify several dwarfs in our Galaxy, such as Reticulum II (refs. 2-4), that could be in this new class. If so, they promis
Given a positive noncommutative polynomial $f$, equivalently a sum of Hermitian squares (SOHS), there exists a positive semidefinite Gram matrix that encrypts all the structural essence of $f$. There are no available methods for extending a noncommutative polynomial to a SOHS keeping the Gram matrices unperturbed. As a remedy, we introduce an equally significant notion of Gram-like matrices and provide linear algebraic techniques to get the desired extensions. We further use positive semidefinite completion problem to get SOHS and provide criteria in terms of chordal graphs and 2-regular projective algebraic sets.
Exoplanets are organized in a broad array of orbital configurations that reflect their formation along with billions of years of dynamical processing through gravitational interactions. This history is encoded in the angular momentum architecture of planetary systems--the relation between the rotational properties of the central star and the orbital geometry of planets. A primary observable is the alignment (or misalignment) between the rotational axis of the star and the orbital plane of its planets, known as stellar obliquity. Hundreds of spin-orbit constraints have been measured for giant planets close to their host stars, many of which have revealed planets on misaligned orbits. A leading question that has emerged is whether stellar obliquity originates primarily from gravitational interactions with other planets or distant stars in the same system, or if it is primordial--imprinted during the star-formation process. Here we present a comprehensive assessment of primordial obliquities between the spin axes of young, isolated Sun-like stars and the orientation of the outer regions of their protoplanetary disks. Most systems are consistent with angular momentum alignment but abou
Deep neural networks often suffer from a critical limitation known as catastrophic forgetting, where performance on past tasks degrades after learning new ones. This paper introduces a novel continual learning approach inspired by human learning strategies like Active Recall, Deliberate Practice, and Spaced Repetition, named Task-Focused Consolidation with Spaced Recall (TFC-SR). TFC-SR enhances the standard experience replay framework with a mechanism we term the Active Recall Probe. It is a periodic, task-aware evaluation of the model's memory that stabilizes the representations of past knowledge. We test TFC-SR on the Split MNIST and the Split CIFAR-100 benchmarks against leading regularization-based and replay-based baselines. Our results show that TFC-SR performs significantly better than these methods. For instance, on the Split CIFAR-100, it achieves a final accuracy of 13.17% compared to Standard Experience Replay's 7.40%. We demonstrate that this advantage comes from the stabilizing effect of the probe itself, and not from the difference in replay volume. Additionally, we analyze the trade-off between memory size and performance and show that while TFC-SR performs better i
This paper presents a new class of spatially coupled turbo-like codes (SC-TCs), namely half spatially coupled braided convolutional codes (HSC-BCCs) and half spatially coupled parallel concatenated codes (HSC-PCCs). Different from the conventional SC-TCs, the proposed codes have simpler and deterministic coupling structures. Most notably, the coupling of HSC-BCCs is performed by re-encoding the whole coupling sequence in the component encoder of one time instant, rather than spreading the coupling bits to component encoders of multiple time instants. This simplification not only addresses the window decoding threshold loss issue in existing BCCs, but also allows the proposed codes to attain very close-to-capacity performance with a coupling memory as small as 2. Both theoretical and numerical results are provided to demonstrate the performance advantages of the proposed codes over existing spatially coupled codes.
ChatGPT, GPT-3.5, and other large language models (LLMs) have drawn significant attention since their release, and the abilities of these models have been investigated for a wide variety of tasks. In this research we investigate to what extent GPT-3.5 can generate human-like comments on Dutch news articles. We define human likeness as `not distinguishable from human comments', approximated by the difficulty of automatic classification between human and GPT comments. We analyze human likeness across multiple prompting techniques. In particular, we utilize zero-shot, few-shot and context prompts, for two generated personas. We found that our fine-tuned BERT models can easily distinguish human-written comments from GPT-3.5 generated comments, with none of the used prompting methods performing noticeably better. We further analyzed that human comments consistently showed higher lexical diversity than GPT-generated comments. This indicates that although generative LLMs can generate fluent text, their capability to create human-like opinionated comments is still limited.
Constructing high-definition (HD) maps from sensory input requires accurately mapping the road elements in image space to the Bird's Eye View (BEV) space. The precision of this mapping directly impacts the quality of the final vectorized HD map. Existing HD mapping approaches outsource the projection to standard mapping techniques, such as attention-based ones. However, these methods struggle with accuracy due to generalization problems, often hallucinating non-existent road elements. Our key idea is to start with a geometric mapping based on camera parameters and adapt it to the scene to extract relevant map information from camera images. To implement this, we propose a novel probabilistic projection mechanism with confidence scores to (i) refine the mapping to better align with the scene and (ii) filter out irrelevant elements that should not influence HD map generation. In addition, we improve temporal processing by using confidence scores to selectively accumulate reliable information over time. Experiments on new splits of the nuScenes and Argoverse2 datasets demonstrate improved performance over state-of-the-art approaches, indicating better generalization. The improvements
We show that every infinite, locally finite, and connected graph admitsa translation-like action by $\mathbb{Z}$, and that this action can be takento be transitive exactly when the graph has either one or two ends.The actions constructed satisfy $d(v,v\ast 1)\leq3$ for every vertex$v$. This strengthens a theorem by Brandon Seward. We also study the effective computability of translation-like actionson groups and graphs. We prove that every finitely generated infinitegroup with decidable word problem admits a translation-like actionby $\mathbb{Z}$ which is computable, and satisfies an extra condition whichwe call decidable orbit membership problem. As a nontrivial application of our results, we prove that for everyfinitely generated infinite group with decidable word problem, effectivesubshifts attain all $Π_{1}^{0}$ Medvedev degrees. This extends a classification proved by Joseph Miller for $\mathbb{Z}^{d},$ $d\geq1$.
In this paper, we show that the common hard kernel of double-log-type or threshold-type factorization for certain space-like parton correlators that arise in the context of lattice parton distributions, the heavy-light Sudakov hard kernel, has linear infrared (IR) renormalon. We explicitly demonstrate how this IR renormalon correlates with ultraviolet (UV) renormalons of next-to-leading power operators in two explicit examples: threshold asymptotics of space-like quark-bilinear coefficient functions and transverse momentum dependent (TMD) factorization of quasi wave function amplitude. Theoretically, the pattern of renormalon cancellation complies with general expectations to marginal asymptotics in the UV limit. Practically, this linear renormalon explains the slow convergence of imaginary parts observed in lattice extraction of the Collins-Soper kernel and signals the relevance of next-to-leading power contributions. Fully factorized, fully controlled threshold asymptotic expansion for space-like quark-bilinear coefficient functions in coordinate and moment space has also been proposed.
We show that the analogues of the Hamkins embedding theorems, proved for the countable models of set theory, do not hold when extended to the uncountable realm of $ω_1$-like models of set theory. Specifically, under the $\diamondsuit$ hypothesis and suitable consistency assumptions, we show that there is a family of $2^{ω_1}$ many $ω_1$-like models of ZFC, all with the same ordinals, that are pairwise incomparable under embeddability; there can be a transitive $ω_1$-like model of ZFC that does not embed into its own constructible universe; and there can be an $ω_1$-like model of PA whose structure of hereditarily finite sets is not universal for the $ω_1$-like models of set theory.
In this research, we explore the efficacy and potential of Generative AI models, specifically focusing on their application in role-playing simulations exemplified through Spyfall, a renowned mafia-style game. By leveraging GPT-4's advanced capabilities, the study aimed to showcase the model's potential in understanding, decision-making, and interaction during game scenarios. Comparative analyses between GPT-4 and its predecessor, GPT-3.5-turbo, demonstrated GPT-4's enhanced adaptability to the game environment, with significant improvements in posing relevant questions and forming human-like responses. However, challenges such as the model;s limitations in bluffing and predicting opponent moves emerged. Reflections on game development, financial constraints, and non-verbal limitations of the study were also discussed. The findings suggest that while GPT-4 exhibits promising advancements over earlier models, there remains potential for further development, especially in instilling more human-like attributes in AI.
Motivated by grand unification considerations, we analyze a simple extension of the minimal supersymmetric standard model with additional pairs of vector-like chiral supermultiplets. We focus on the so-called LND setup, which enlarges the particle content of the minimal model by two vector-like pairs of weak doublets (one pair of leptons and one pair of down-type quarks) and one vector-like pair of neutrino singlets. Imposing collider and low-energy constraints, sneutrinos and neutralinos both emerge as possible lightest supersymmetric particles and thus dark matter candidates. We perform a complete analysis of the dark sector and study the viability of these neutralino and sneutrino dark matter options. We show that cosmological considerations (the dark matter relic abundance and its direct and indirect detection signals) restrict neutralino dark matter to exhibit similar properties as in the minimal supersymmetric standard model, and impose the sneutrino dark matter candidate to be singlet-like, rather than doublet-like. Allowing the mixing of the fermionic component of the new supermultiplets with the Standard Model third generation fermions, we moreover demonstrate the existenc
We introduce a new class of groups called wreath-like products. These groups are close relatives of the classical wreath products and arise naturally in the context of group theoretic Dehn filling. Unlike ordinary wreath products, many wreath-like products have Kazhdan's property (T). In this paper, we prove that any group $G$ in a natural family of wreath-like products with property (T) is W$^*$-superrigid: the group von Neumann algebra $\text{L}(G)$ remembers the isomorphism class of $G$. This allows us to provide the first examples (in fact, $2^{\aleph_0}$ pairwise non-isomorphic examples) of W$^*$-superrigid groups with property (T).
The aim of this paper is to develop on the 1-jet space J^1(R, M^n) the Finsler-like geometry (in the sense of distinguished (d-) connection, d-torsions, d-curvatures and some gravitational-like and electromagnetic-like geometrical models) attached to the (t,x)-conformal deformation of the Berwald-Moor metric.
This technical report gives an overview of our work on control algorithms dealing with redundant robot systems for achieving human-like motion characteristics. Previously, we developed a novel control law to exhibit human-motion characteristics in redundant robot arm systems as well as arm-trunk systems for reaching tasks [1], [2]. This newly developed method nullifies the need for the computation of pseudo-inverse of Jacobian while the formulation and optimization of any artificial performance index is not necessary. The time-varying properties of the muscle stiffness and damping as well as the low-pass filter characteristics of human muscles have been modeled by the proposed control law to generate human-motion characteristics for reaching motion like quasi-straight line trajectory of the end-effector and symmetric bell shaped velocity profile. This report focuses on the experiments performed using a 7-DOF redundant robot-arm system which proved the effectiveness of this algorithm in imitating human-like motion characteristics. In addition, we extended this algorithm to a 19-DOF Hand-Arm System for a reach-to-grasp task. Simulations using the 19-DOF Hand-Arm System show the effec
A one to one correspondence between shifts of group-like projections on a locally compact quantum group ${\mathbb{G}}$ which are preserved by the scaling group and contractive idempotent functionals on the dual $\hat{\mathbb{G}}$ is established. This is a generalization of the Illie-Spronk's correspondence between contractive idempotents in the Fourier-Stieltjes algebra of a locally compact group $G$ and cosets of open subgroups of $G$. We also establish a one to one correspondence between non-degenerate, integrable, ${\mathbb{G}}$-invariant ternary rings of operators $X\subset L^\infty({\mathbb{G}})$, preserved by the scaling group and contractive idempotent functionals on ${\mathbb{G}}$. Using our results we characterize coideals in $L^\infty(\hat{\mathbb{G}})$ admitting an atom preserved by the scaling group in terms of idempotent states on ${\mathbb{G}}$. We also establish a one to one correspondence between integrable coideals in $L^\infty({\mathbb{G}})$ and group-like projections in $L^\infty(\hat{\mathbb{G}})$ satisfying an extra mild condition. Exploiting this correspondence we give examples of group like projections which are not preserved by the scaling group.
Accurate identification of charged pions and kaons is essential for precision measurements in relativistic heavy-ion collisions, but becomes increasingly challenging at intermediate and high transverse momentum due to the overlap between time-of-flight mass-square ($m^{2}$) and ionization energy loss ($nσ$) distributions. In this work, we present a two-dimensional shift and rotation method that exploits the correlated information between $m^{2}$ and $nσ$ to enhance particle identification performance. The method is validated using Au+Au collision events generated with the AMPT model, where detector response effects are incorporated through a data-driven smearing procedure tuned to reproduce the particle identification performance of the STAR experiment. The reconstructed pion and kaon transverse momentum distributions show excellent agreement with the AMPT input, maintaining a purity exceeding 98\% at high $p_T$ and extend the reliable identification range up to $p_T \approx$ 3 GeV/$c$. The extracted elliptic flow $v_2$ remains consistent with the input over the extended $p_T$ range, demonstrating that the proposed method provides a robust framework for high precision identified ha
UAV vision-language navigation (VLN) requires an agent to navigate complex 3D environments from an egocentric perspective while following ambiguous multi-step instructions over long horizons. Existing zero-shot methods remain limited, as they often rely on large base models, generic prompts, and loosely coordinated modules. In this work, we propose FineCog-Nav, a top-down framework inspired by human cognition that organizes navigation into fine-grained modules for language processing, perception, attention, memory, imagination, reasoning, and decision-making. Each module is driven by a moderate-sized foundation model with role-specific prompts and structured input-output protocols, enabling effective collaboration and improved interpretability. To support fine-grained evaluation, we construct AerialVLN-Fine, a curated benchmark of 300 trajectories derived from AerialVLN, with sentence-level instruction-trajectory alignment and refined instructions containing explicit visual endpoints and landmark references. Experiments show that FineCog-Nav consistently outperforms zero-shot baselines in instruction adherence, long-horizon planning, and generalization to unseen environments. These
Multimodal large language models have achieved strong performance across diverse vision-language tasks, yet their capabilities in UAV scenarios remain insufficiently explored. Recent UAV-oriented benchmarks have begun to evaluate MLLMs in aerial scenarios, but they typically focus on scene understanding, event recognition, or navigation completion, rather than jointly assessing the dual-cognition capability required for UAV agents: reasoning about both the UAV's own state and the external environment in multiview spatio-temporal contexts. To address this gap, we present UAV-DualCog, a benchmark for aerial multiview spatio-temporal reasoning built on this dual-cognition perspective. UAV-DualCog includes both image and video tasks to jointly evaluate self-state and environment-state reasoning, while requiring spatial or temporal grounding beyond discrete answer prediction. We also develop an automated pipeline that constructs data from scene-level semantic point clouds, yielding a scalable benchmark with diverse scenes, hundreds of landmarks, and thousands of QA samples. Extensive evaluations show that current MLLMs remain far from reliable in UAV dual cognition. Self-state reasoning