We consider gravitational collapse of a fluid sphere with torsion generated by spin, which forms a black hole. We use the Tolman metric and the Einstein$-$Cartan field equations with a relativistic spin fluid as a source. We show that gravitational repulsion of torsion prevents a singularity, replacing it with a nonsingular bounce. Quantum particle creation during contraction prevents shear from overcoming torsion. Particle creation during expansion can generate a finite period of inflation and produce large amounts of matter. The resulting closed universe on the other side of the event horizon may have several bounces. Such a universe is oscillatory, with each cycle larger than the preceding cycle, until it reaches a size at which dark energy dominates and expands indefinitely. Our Universe might have therefore originated from a black hole existing in another universe.

Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing high-quality, culturally relevant data for SEA languages. By involving contributors from SEA countries, SEA-VL aims to ensure better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages in VL research. Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation. First, we find that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing. Second, despite the substantial progress in generative vision models, synthetic images remain unreliable in accurately reflecting SEA cultures. The generated images often fail to reflect the nuanced traditions and cultural contexts of the region. Collectively, we gather 1.28M SEA culturally-relevant images, more than 50 times larger than other existing datasets. Through SEA-VL, we aim to bridge the representation gap in SEA, fostering the development of more inclusive AI systems that authentically represent diverse cultures across SEA.

Creating multilingual LLMs poses a significant challenge. Pretraining or fine-tuning LLMs to adopt new languages is evidently very costly. Furthermore, there exist limitations concerning benchmark datasets and the metrics used to measure model performance in multilingual settings. This paper proposes cost-effective solutions to both aforementioned challenges. Firstly, we introduce the Multilingual Instruction-Tuning Dataset (MITS), comprised of Alpaca-52K, Dolly-15K, and Vicuna Benchmark translations into 132 languages. Secondly, we propose a new method called \emph{TaCo: Translation-Assisted Cross-Linguality}, which utilizes translations in a chain-of-thought process to instruction-tune LLMs on new languages through a curriculum-learning process. As a proof of concept, we experimented with the instruction-tuned Guanaco-33B model, performing further instruction tuning using our proposed TaCo method in three low-resource languages and one high-resource language. Our results indicate that the TaCo method impresses GPT-4 with an 82\% score for a low-resource language in the Vicuna Benchmark dataset, doubling the performance in contrast to instruction tuning alone. Furthermore, TaCo shows promise in creating multilingual LLMs, even for low-resource languages. We have released our datasets and model adapters\footnote{this https URL} , encouraging the research community to utilize these resources to advance work on multilingual LLMs.

Visual learners think in pictures rather than words and learn best when they utilize representations based on graphs, tables, charts, maps, colors and diagrams. We propose a new pedagogy for teaching pointers in the C programming language using graph transformation systems to visually simulate pointer manipulation. In an Introduction to C course, the topic of pointers is often the most difficult one for students to understand; therefore, we experiment with graph-based representations of dynamic pointer structures to reinforce the learning. Groove, a graph transformation tool, is used to illustrate the behaviour of pointers through modelling and simulation. A study is presented to evaluate the effectiveness of the approach. This paper will also provide a comparison to other teaching methods in this area.

Deep neural networks often achieve high accuracy, but ensuring their reliability under adversarial and distributional shifts remains a pressing challenge. We propose TriGuard, a unified safety evaluation framework that combines (1) formal robustness verification, (2) attribution entropy to quantify saliency concentration, and (3) a novel Attribution Drift Score measuring explanation stability. TriGuard reveals critical mismatches between model accuracy and interpretability: verified models can still exhibit unstable reasoning, and attribution-based signals provide complementary safety insights beyond adversarial accuracy. Extensive experiments across three datasets and five architectures show how TriGuard uncovers subtle fragilities in neural reasoning. We further demonstrate that entropy-regularized training reduces explanation drift without sacrificing performance. TriGuard advances the frontier in robust, interpretable model evaluation.

In the presence of spacetime torsion, the momentum components do not commute; therefore, in quantum field theory, summation over the momentum eigenvalues will replace integration over the momentum. In the Einstein--Cartan theory of gravity, in which torsion is coupled to spin, the separation between the eigenvalues increases with the magnitude of the momentum. Consequently, this replacement regularizes divergent integrals in Feynman diagrams with loops by turning them into convergent sums. In this article, we apply torsional regularization to the self-energy of a charged lepton in quantum electrodynamics. We show that torsion eliminates the ultraviolet divergence of the standard self-energy. We also show that the infrared divergence is absent. In the end, we calculate the finite bare masses of the electron, muon, and tau lepton: $0.4329\,\mbox{MeV}$, $90.95\,\mbox{MeV}$, and $1543\,\mbox{MeV}$, respectively. These values constitute about $85\%$ of the observed, re-normalized masses.

Polymer blends consisting of two or more polymers are important for a wide variety of industries and processes, but, the precise mechanism of their thermomechanical behaviour is incompletely understood. In order to understand clearly, it is essential to determine the miscibility and interactions between the components in polymer blend and its macroscopic thermomechanical properties. In this study, we performed experiments on SEBS and isotactic PP blends (SP) as well as molecular dynamics simulations, aiming to know the role played by molecular interactions on the thermomechanical properties. To investigate the glass transition temperature (Tg) of SEBS, PP and their blends at different ratio, the unit cell of the polymer molecular structure of each was established. The LAMMPS molecular dynamics method was used to predict the density, specific volume, free volume, enthalpy, kinetic energy, potential energy and bond energy. The (Tg) s of the SEBS, PP and SP blends were predicted by analysing these properties. Interestingly, the simulated values of the Tg of SEBS, PP and their blends showed good agreement with our experimental results obtained from dynamic mechanical analysis (DMA). This technique used in this work can be used in studying glass transition of other complex polymer blends.

We analyze the dynamics of a homogeneous and isotropic universe in the Einstein--Cartan theory of gravity. The spin of fermions produces spacetime torsion that prevents gravitational singularities and replaces the big bang with a nonsingular big bounce. We show that a closed universe exists only when a particular function of its scale factor and temperature is higher than some threshold value, whereas an open and a flat universes do not have such a restriction. We also show that a bounce of the scale factor is double: as the temperature increases and then decreases, the scale factor decreases, increases, decreases, and then increases.

In a paper by Umarov, Tsallis and Steinberg (2008), a generalization of the Fourier transform, called the $q$-Fourier transform, was introduced and applied for the proof of a $q$-generalized central limit theorem ($q$-CLT). Subsequently, Hilhorst illustrated (2009 and 2010) that the $q$-Fourier transform for $q>1$ is not invertible in the space of density functions. Indeed, using an invariance principle, he constructed a family of densities with the same $q$-Fourier transform and noted that "as a consequence, the $q$-central limit theorem falls short of achieving its stated goal". The distributions constructed there have compact support. We prove now that the limit distribution in the $q$-CLT is unique and can not have a compact support. This result excludes all the possible counterexamples which can be constructed using the invariance principle and fills the gap mentioned by Hilhorst.

Bytewise approximate matching algorithms have in recent years shown significant promise in de- tecting files that are similar at the byte level. This is very useful for digital forensic investigators, who are regularly faced with the problem of searching through a seized device for pertinent data. A common scenario is where an investigator is in possession of a collection of "known-illegal" files (e.g. a collection of child abuse material) and wishes to find whether copies of these are stored on the seized device. Approximate matching addresses shortcomings in traditional hashing, which can only find identical files, by also being able to deal with cases of merged files, embedded files, partial files, or if a file has been changed in any way. Most approximate matching algorithms work by comparing pairs of files, which is not a scalable approach when faced with large corpora. This paper demonstrates the effectiveness of using a "Hierarchical Bloom Filter Tree" (HBFT) data structure to reduce the running time of collection-against-collection matching, with a specific focus on the MRSH-v2 algorithm. Three experiments are discussed, which explore the effects of different configurations of HBFTs. The proposed approach dramatically reduces the number of pairwise comparisons required, and demonstrates substantial speed gains, while maintaining effectiveness.

If our universe was born as a baby universe on the other side of the event horizon of a black hole existing in a parent universe, then the corresponding white hole at rest provides the absolute frame of reference in the universe. In this frame, the cosmic microwave background radiation is isotropic on large scales. If the parent black hole is rotating, then its axis of rotation becomes a preferred axis in the universe. Accordingly, the absolute frame is non-inertial, although the non-inertial forces are small. To decrease their energies, galaxies tend to align their axes of rotation with the preferred axis, resulting in clockwise-counterclockwise asymmetry. The centrifugal force causes a large-scale bulk flow of galaxy clusters in directions perpendicular to the preferred axis. The astronomical data seem to support these motions. The angular velocity of the universe decreases as the universe expands, which is a consequence of the conservation of the angular momentum of the universe. The centrifugal force in a rotating universe, which also decreases, may be the origin of dark energy, in accordance with recent DES observations showing that dark energy becomes weaker with time.

Multi-Agent Systems (MAS) is the study of multi-agent interactions in a shared environment. Communication for cooperation is a fundamental construct for sharing information in partially observable environments. Cooperative Multi-Agent Reinforcement Learning (CoMARL) is a learning framework where we learn agent policies either with cooperative mechanisms or policies that exhibit cooperative behavior. Explicitly, there are works on learning to communicate messages from CoMARL agents; however, non-cooperative agents, when capable of access a cooperative team's communication channel, have been shown to learn adversarial communication messages, sabotaging the cooperative team's performance particularly when objectives depend on finite resources. To address this issue, we propose a technique which leverages local formulations of Theory-of-Mind (ToM) to distinguish exhibited cooperative behavior from non-cooperative behavior before accepting messages from any agent. We demonstrate the efficacy and feasibility of the proposed technique in empirical evaluations in a centralized training, decentralized execution (CTDE) CoMARL benchmark. Furthermore, while we propose our explicit ToM defense for test-time, we emphasize that ToM is a construct for designing a cognitive defense rather than be the objective of the defense.

The conservation law for the angular momentum in curved spacetime requires that the antisymmetric part of the affine connection (the torsion tensor) is a variable in the principle of least action. The coupling between spin and torsion generates gravitational repulsion in fermionic matter at extremely high densities and avoids the formation of singularities in black holes. We show that every black hole in the presence of torsion forms a nonsingular, closed, nearly flat, homogeneous, and isotropic universe on the other side of its event horizon. Quantum particle production in such a universe can generate a period of exponential expansion which creates an enormous amount of matter in that universe. Accordingly, our Universe may have originated from the interior of a black hole existing in another universe.

We consider gravitational collapse of a sphere of a fluid with torsion generated by spin, which forms a black hole. We use the Tolman metric and the Einstein$-$Cartan field equations with a relativistic spin fluid as a source. We show that gravitational repulsion of torsion prevents a singularity, replacing it with a nonsingular bounce. Quantum particle creation during contraction prevents shear from overcoming torsion. Particle creation during expansion can generate a finite period of inflation and produce large amounts of matter. The resulting closed universe on the other side of the event horizon may have several bounces. Such a universe is oscillatory, with each cycle larger than the preceding cycle, until it reaches a size at which dark energy dominates and expands indefinitely. Our universe might have therefore originated from a black hole existing in another universe.

We use the Tolman metric to describe gravitational collapse of a sphere of a fluid without pressure in spacetime with the Hubble parameter $H$ related to the cosmological constant. We show that the largest radius of a galaxy formed from such a fluid with mass $M$ is given by $(GM/H^2)^{1/3}$.

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of ternary alloys (easily extendable to multi-principal-element alloys), such as Ti-Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co-Ni-X (X = Al, Mg, V). Ti-Nb-Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40-50%. We attributed to slow hydrogen kinetics in molybdenum rich alloys, which is validated by our pressure-composition isotherm (PCT) experiments on pure Ti and Ti5Mo95 alloys. Density functional theory (DFT) and molecular simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

This paper explores the production of a specified object using a combination of machining processes, including milling, shaping, and drilling, while emphasizing the critical role of fixture design in ensuring precision repeatability, and efficiency. The study outlines the systematic approach to transforming raw materials into a finished product through an optimized manufacturing sequence that minimizes waste and maximizes productivity. Different machining operations are carefully selected based on their suitability for achieving the required dimensional accuracy and surface finish. Fixtures are crucial in maintaining workpiece stability, reducing vibration, and ensuring accurate alignment during machining. This paper discusses the selection and design of fixtures, such as jigs and V-blocks, which enhance positioning accuracy and contribute to consistent machining outcomes. Additionally, the integration of CNC technology is examined, highlighting its advantages in automation, process control, and precision enhancement. The proposed methodology not only improves the efficiency of material removal but also ensures compliance with quality standards, reducing machining errors and minimizing rework. Furthermore, potential advancements in fixture automation using pneumatic actuators are considered to further streamline operations. The findings of this study provide valuable insights into optimizing machining sequences and fixture design to achieve a cost-effective and high-quality manufacturing process.

We propose that the four-velocity of a Dirac particle is related to its relativistic wave function by $u^i=\bar{\psi}\gamma^i\psi/\bar{\psi}\psi$. This relativistic wave$-$particle duality relation is demonstrated for a free particle related to a plane wave in a flat spacetime. For a curved spacetime with torsion, the momentum four-vector of a spinor is related to a generator of translation, given by a covariant derivative. The spin angular momentum four-tensor of a spinor is related to a generator of rotation in the Lorentz group. We use the covariant conservation laws for the spin and energy$-$momentum tensors for a spinor field in the presence of the Einstein$-$Cartan torsion to show that if the wave satisfies the curved Dirac equation, then the four-velocity, four-momentum, and spin satisfy the classical Mathisson$-$Papapetrou equations of motion. We show that these equations reduce to the geodesic equation. Consequently, the motion of a particle guided by the four-velocity in the pilot-wave quantum mechanics coincides with the geodesic motion determined by spacetime. We also show how the duality and the operator form of the Mathisson$-$Papapetrou equations arise from the covariant Heisenberg equation of motion in the presence of torsion.