The mean value of the stress-energy tensor of a given quantum field theory at global thermodynamic equilibrium in a curved space-time can be expressed in terms of the derivatives of the Killing four-temperature field and the derivatives of the metric tensor. Its asymptotic expansion about zero includes an analytic part made of integer powers of these derivatives - corresponding to the so-called gradient expansion - as well as non-perturbative corrections. By using available exact solutions for the free real massless scalar field, we show that in the case of Minkowski, de Sitter, anti-de Sitter, and closed Einstein universe, the analytic part - obtained through the procedure of analytic distillation - has a finite number of terms and it is the same once expressed in a covariant form. On the other hand, non-universal terms are non-analytic in these derivatives and correspond to boundary conditions or to specific global properties of the space-time. We argue that the universality of the analytic part extends to any quantum field theory on a curved background.

In the pinwheel problem, one is given an $m$-tuple of positive integers $(a_1, \ldots, a_m)$ and asked whether the integers can be partitioned into $m$ color classes $C_1,\ldots,C_m$ such that every interval of length $a_i$ has non-empty intersection with $C_i$, for $i = 1, 2, \ldots, m$. It was a long-standing open question whether the pinwheel problem is NP-hard. We affirm a prediction of Holte et al. (1989) by demonstrating, for the first time, NP-hardness of the pinwheel problem. This enables us to prove NP-hardness for a host of other problems considered in the literature: pinwheel covering, bamboo garden trimming, windows scheduling, recurrent scheduling, and the constant gap problem. On the positive side, we develop a PTAS for an approximate version of the pinwheel problem. Previously, the best approximation factor known to be achievable in polynomial time was $\frac{9}{7}$.

Randomized controlled trials (RCTs) often suffer from limited inferential efficiency in estimating treatment effects due to their small sample sizes. In recent years, incorporating external controls (ECs) has gained increasing attention as an effective way to augment small RCTs and thereby enhance estimation efficiency. However, ECs are not always comparable to RCTs, and direct borrowing without careful evaluation can introduce substantial bias and, paradoxically, undermine the accuracy of treatment effect estimation. In this paper, we propose a novel adaptive influence-based sample borrowing framework to improve average treatment effect (ATE) estimation in RCTs. The framework quantifies the ``comparability'' of each sample in ECs using influence functions and identifies the optimal subset of ECs that minimizes the mean squared error of the ATE estimator. The proposed framework is assumption-lean regarding the distribution of ECs and is robust to outliers, making it broadly applicable across diverse settings. Moreover, we develop an outcome calibration method to improve the data utilization efficiency of ECs, further strengthening the adaptive influence-based sample-borrowing framework. We demonstrate the effectiveness of the proposed method using both simulated and real-world datasets.

In this article, we revisit the thermodynamics of Hayward black holes [1] in asymptotic flat spacetime and obtain the bounds on the final mass post merger after head-on collision event of two equal mass black holes. We revisit thermal properties of these black holes from a perspective that the laws of black hole thermodynamics remain valid. Under this condition a novel entropy formula appears naturally with a logarithmic correction term along with one extra term. We discuss phase structure of the black holes. Next, we obtain the bounds on the final black hole mass parameter using the validity of the second law of black hole thermodynamics with the new entropy formula. We discuss the impact of the Hayward parameter on thermal and merger properties of these black holes.

We study the Max-Cut semidefinite programming (SDP) relaxation in the regime where a near-optimal solution admits a low-dimensional realization. While the Goemans--Williamson hyperplane rounding achieves the worst-case optimal approximation ratio $α_{GW}\approx 0.87856$, it is natural to ask whether one can beat $α_{GW}$ when the SDP solution lives in $\mathbb{R}^d$ for a small dimension $d$. We answer this in the affirmative for every fixed $d$: there is a polynomial-time rounding algorithm that, given a $d$-dimensional feasible solution to the standard Max-Cut SDP strengthened with triangle inequalities, produces a cut of expected value at least $(α_{GW}+2^{-O(d)})$ times the SDP value. Our improvement is driven by a new geometric anti-concentration lemma for signs of low-dimensional Gaussian projections.

With the rapid growth of deep neural networks (DNNs), compute-in-memory (CIM) has emerged as a promising energy-efficient paradigm for accelerating multiply-and-accumulate (MAC) operations. Yet, current CIM architectures are largely limited to dot-product computations and struggle to efficiently support general-purpose matrix operations, such as transpose, element-wise addition, and multiplication. This work presents a 3D-integrated, memory-on-memory SRAM-eDRAM hybrid CIM architecture, implemented in GlobalFoundries 22~nm FDSOI technology, capable of performing general matrix operations directly within the memory crossbar with 4-bit precision. By leveraging a specialized transpose-based architecture, in-memory arithmetic operations, peripheral-aware design, and 3D SRAM--eDRAM integration, the proposed architecture balances latency, energy efficiency, and compute density for general purpose matrix operations while remaining compatible with the conventional CIM dot product architectures. Overall, this memory-on-memory CIM framework generalizes CIM beyond dot products, enabling versatile matrix processing and paving the way for broader applications in AI acceleration and general-purpose high performance computing.

We analyze an optimal stopping problem for random walk in random scenery on general graphs, and determine when it has a finite optimum. We use this to extend a theorem of Levine, Murugan, Peres, and Ugurcan [2016]. They proved that on a vertex-transitive graph, the divisible sandpile with i.i.d. initial masses of mean $μ$ stabilizes almost surely if $μ< 1$, explodes if $μ> 1$, and explodes if $μ= 1$ with positive finite variance. Their proofs rely on conservation of mean mass under toppling. This conservation extends to unimodular random graphs, but fails on general graphs. We prove explosion for all infinite bounded-degree graphs whenever $μ\geq 1$, and stabilization for $μ<1$ provided the initial masses have finite $p$-th moment for some $p>3$. Our conditions are nearly sharp: we exhibit unbounded-degree graphs on which sandpiles with $μ> 1$ stabilize, and for every $p < 3$ we construct bounded-degree graphs on which sandpiles with~$μ< 1$ and finite $p$-th moment explode.

Niho exponents have found important applications in sequence design, coding theory, and cryptography. Determining the differential spectrum of a power function with Niho exponent is a topic of considerable interest. In this paper, we investigate the power function $F(x) = x^{3q - 2}$ over $\mathbb{F}_{q^2}$, where $q = 2^m$ and $m\geq 4$ is an even integer. Notably, the exponent $3q - 2$ is a Niho exponent. By analyzing the properties of certain polynomials over $\mathbb{F}_{q^2}$, we determine the differential spectrum of $F$. Our results show that $F$ is locally differentially $4$-uniform, which complements existing results on the differential spectra of power functions with Niho exponents.

This paper studies the variance dichotomy in continuous simulation optimization (CSO). Existing literature shows a sharp contrast between deterministic CSO and stochastic CSO, with convergence rates in stochastic settings appearing insensitive to the magnitude of the noise variance. However, this asymptotic view does not fully explain the behavior of CSO under finite simulation budgets, especially in low-noise settings. To address this gap, this work develops a minimax lower-bound analysis and shows that the complexity is decided by the maximum of a variance-dependent term and a variance-independent term. When the simulation budget is not very large and the noise variance is low, the variance-independent term dominates, implying that low-noise stochastic CSO has essentially the same complexity as deterministic CSO. As the budget increases, the variance-dependent term becomes dominant, and the convergence behavior of stochastic CSO transitions to a slower regime determined jointly by the noise variance and the simulation budget.

The quantum Internet envisions a network where information is transmitted through entanglement, with Einstein-Podolsky-Rosen (EPR) pairs serving as one of the fundamental carriers. In this work, we propose a framework for dimensioning quantum memories capable of storing distilled EPR pairs useful to transmitting and manage quantum error correcting codes. Using a Markov chain model, we capture the stochastic evolution of stored entangled states in quantum memories, linking memory performance to system parameters such as technology characteristics and initial entanglement fidelity. Building on this framework, we provide analytical tools and design principles for optimizing memory architectures that preserve high-fidelity entanglement over time, ensuring the availability of encoded quantum resources necessary for several operations in future quantum Internet infrastructures transmitting EPR packets.

Gene-sharing networks provide a powerful framework to study the evolution of viruses and mobile genetic elements. These bipartite networks, which link genes to the genomes that contain them, exhibit characteristic degree distributions: a scale-free distribution for genes and an exponential-like decay for genomes. Here, we propose a mechanistic model that explains these patterns through fundamental evolutionary processes including horizontal gene transfer, capture of new genes, emergence of new genomes, and gene loss. Using a mean-field approximation, we derive analytical expressions for the asymptotic gene and genome degree distributions, recapitulating a power-law distribution for genes and an exponential distribution for genomes. Numerical simulations validate these predictions and yield parameter values that closely fit empirical data from dsDNA viruses, RNA viruses, and prokaryotic pangenomes. This simple model with only two parameters provides a generative framework for bipartite gene-sharing networks, offering qualitative and quantitative insights into the main evolutionary forces driving genome plasticity. Setting the gene loss rate to zero, the gene and genome degree distributions of the model closely fit the empirically observed distributions. Thus, evolution of viruses appears to be dominated by gene gain, in agreement with the results of independent reconstructions of viral evolution.

We present a detailed investigation of the shock properties associated with solar energetic particle (SEP) events that exhibit a concave (``nose-like'') shape in their energy spectrogram, characterized by inverse velocity arrival (IVA) of the particles, where high-energy particles arrive later than mid-energy ones. Using measurements from Solar Orbiter and Parker Solar Probe between 2018 and 2025, we identify 26 such SEP events and reconstruct the observed shock fronts in three dimensions. We derive shock parameters along the magnetic field lines connected to each spacecraft using kinematic modeling and coronal magnetohydrodynamic simulations. Our analysis indicates that IVA-SEP events arise due to the spatial and temporal evolution of the shock properties and magnetic connectivity. In most cases analyzed here, the magnetic connectivity starts on the flanks of CME-driven shocks, where shocks tend to be weak, and shifts toward the shock apex sampling stronger portions of the shock front. This evolution of the shock properties at the connected field lines likely leads to the delayed arrival of high-energy particles and the progressive hardening of the SEP energy spectrum, observed in some of the events. We find a correlation between the transition energy at which the IVA begins and the shock speed along the connected field lines, consistent with expectations from time-dependent diffusive shock acceleration. Our results underscore the importance of the evolving shock properties, magnetic connectivity, and instrumental sensitivity in shaping SEP intensity profiles and the formation of IVA signatures.

Oil spills represent a severe threat, making early-stage thickness estimation crucial for guiding remediation efforts. Unmanned Aerial Vehicles (UAVs) are an attractive platform for environmental monitoring. However, due to their limited computation and power budgets, real-time onboard processing requires optimized algorithms or lightweight machine learning models. While the standard U-Net architecture is often too large for constrained UAV hardware, the compressed Tiny U-Net variant fits on FPGA platforms and achieves competitive estimation performance (0.79 in the metric Intersection over Union, or IoU). Despite this success, Tiny U-Net processes every radar image through the complete inference pipeline, resulting in unnecessary computation for simple cases. To address this inefficiency, we integrate an early exit feature into the Tiny U-Net architecture. We introduce an early exit branch that returns an early prediction when a compact confidence score exceeds a tunable threshold, bypassing deeper layers for high-confidence evaluations. Our experiments demonstrate that this design achieves comparable IoU to the full baseline model. Crucially, the technique is shown to reduce the average number of multiplications by up to 42% for an aggressive threshold, reducing the dynamic power consumption. Choosing a threshold that ensures extreme confidence reduces the complexity-reduction gains for an improved IoU. This early exit approach substantially improves computational efficiency in Tiny U-Net, enabling more practical deployment in UAV-based environmental monitoring systems.

Twist-engineering of topological phases in two-dimensional materials offers a powerful route to modulate electronic structure beyond conventional strain or chemical control. In particular, group 15 (pnictogens) monolayers such as bismuthene provide an ideal platform due to their strong intrinsic spin-orbit coupling (SOC) and robust topological character. Here, we investigate a previously unexplored heterostructure consisting of a $β$-bismuthene monolayer rotated by 30$^\circ$ on a planar bismuthene layer stabilized on a SiC(0001) substrate. Using first-principles calculations, we demonstrate that this specific rotational alignment induces a unique interlayer orbital hybridization which, combined with the strong SOC and the naturally broken inversion symmetry, gives rise to a pronounced Rashba spin-splitting, absent in the isolated monolayers. The topological nature of the system is confirmed through the calculation of the Z2 topological invariant and Spin Hall Conductivity (SHC), revealing a robust Quantum Spin Hall (QSH) phase with an enhanced topological response compared to the individual layers. Furthermore, we explore the chemical tunability of this system via Sb substitution, showing that the gradual reduction of SOC systematically narrows the band gap while preserving the non-trivial topology. Our results establish large-angle twisted group 15 heterostructures as a versatile platform for engineering spin-orbit-driven phenomena and advancing topological spintronics.

Many-body effects in condensed matter yield novel quantum states when the electronic density of states is enhanced. A vivid example is flat bands, which suppress kinetic energy and let interactions dominate, when they are filled with an integer number of electrons in moire systems. Yet flat bands and commensurate fillings are not the only conditions for correlated phenomena. Situations may occur where the band structure develops locally enhanced density of states, leading to strong correlations even at non-integer fillings, although such cases often yield pseudogaps that make detection elusive. Here we demonstrate that small-angle twisted monolayer-bilayer graphene combines moire-induced global flat band and additional local band flattening. Their coexistence allows direct comparison of correlated effects. The global route stabilizes commensurate states, while the local mechanism produces nearly flat bands, lifting degeneracy and generating symmetry breaking at non-integer fillings, yet without opening a global gap. Because there is no global gapped signature, the system remains metallic, but the effect reveals itself in anomalous Hall responses, signaling time-reversal symmetry breaking and valley polarization. Our results demonstrate dual-flatness as a guiding principle, extending moire physics beyond commensurate fillings and identifying topological transport as a probe of gapless correlated metals.

Graph theory is a cornerstone of Computer Science education, yet entry-level students often struggle to map abstract node-edge relationships to practical applications. This paper presents the design and architecture of a Minecraft-based educational tool specifically built to visualize graph traversal and shortest-path algorithms. We propose a three-layer system: (1) a Grid Traversal module where terrain types (e.g., soul sand, ice) represent edge weights, allowing for the gamified study of shortest path algorithms; (2) a "Sky Graph" module for interactive 3D manipulation of both directed and undirected graphs; and (3) lessons and quizzes available through books. The system grounds its design in Constructionist learning theory, transitioning students from passive observers to active protagonists who physically manipulate algorithmic behavior. We additionally present a planned empirical evaluation using NASA-TLX and in-game telemetry to validate the system's pedagogical efficacy.

According to constructivist theory, students learn software security more effectively when examples are grounded in their own code. Generic examples often fail to connect with students' prior work, limiting engagement and understanding. Advances in LLMs are now making it possible to automatically generate personalized examples by embedding security vulnerabilities directly into student-authored code. This paper introduces a method that uses LLMs to inject instances of specific Common Weakness Enumerations (CWEs) into students' own assignment code, creating individualized instructional materials. We present an agentic AI framework, using autonomous LLM-based agents equipped with task-specific tools to orchestrate injection, evaluation, ranking, and learning outcome generation. We report the experience of deploying this system in two undergraduate computer science courses (N=71), where students reviewed code samples containing LLM-injected vulnerabilities and completed a post-project survey. We compared responses with a baseline using a widely adopted set of generic security instructional materials. Students qualitatively reported finding CWE injections into their own code more relevant, clearer, and more engaging than the textbook-style examples. However, our quantitative findings revealed limited statistically significant differences, suggesting that while students valued the personalization, further studies and refinement of the approach are needed to establish stronger empirical support.

In this paper, we exhibit $\textsf{AC}^{3}$ isomorphism tests for coprime extensions $H \ltimes N$ where $H$ is elementary Abelian and $N$ is Abelian; and groups where $\text{Rad}(G) = Z(G)$ is elementary Abelian and $G = \text{Soc}^{*}(G)$. The fact that isomorphism testing for these families is in $\textsf{P}$ was established respectively by Qiao, Sarma, and Tang (STACS 2011), and Grochow and Qiao (CCC 2014, SIAM J. Comput. 2017). The polynomial-time isomorphism tests for both of these families crucially leveraged small (size $O(\log |G|)$) instances of Linear Code Equivalence (Babai, SODA 2011). Here, we combine Luks' group-theoretic method for Graph Isomorphism (FOCS 1980, J. Comput. Syst. Sci. 1982) with the fact that $G$ is given by its multiplication table, to implement the corresponding instances of Linear Code Equivalence in $\textsf{AC}^{3}$. As a byproduct of our work, we show that isomorphism testing of arbitrary central-radical groups is decidable using $\textsf{AC}$ circuits of depth $O(\log^3 n)$ and size $n^{O(\log \log n)}$. This improves upon the previous bound of $n^{O(\log \log n)}$-time due to Grochow and Qiao (ibid.).

The rise of Artificial Intelligence (AI)-driven services, machine-type communications, and massive Internet of Things (IoT) deployments is reshaping wireless traffic toward dense, uplink-oriented, bursty, and latency-critical patterns. In these regimes, Multi-User Multiple-Input Multiple-Output (MU-MIMO) is essential to support massive concurrent connectivity through spatial multiplexing. However, the need for frequent, low-latency scheduling decisions exposes fundamental scalability barriers in existing user selection approaches. The inherently combinatorial nature of MU-MIMO user selection leads computational complexity to grow rapidly with both the number of candidate users and spatial layers, rendering existing near-optimal heuristic methods impractical in dense and highly dynamic scenarios. This paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a complexity barrier-breaking, massively parallelizable method that departs from subset-based selection by constructing orthonormal spatial bases and independently matching users to spatial directions. Simulation results across diverse MIMO configurations, channel conditions, and user densities show that SS-US reduces computational complexity by over three orders of magnitude while achieving spectral efficiency comparable to state-of-the-art practical baselines.

The coexistence of topological states with different dimensionalities in a single crystalline system offers a unique platform to study the interplay of distinct fermionic excitations. Here, integrating first-principles calculations with symmetry analysis, we propose the three-dimensional boron allotrope $P6_3$-$\text{B}_{30}$ as an ideal, structurally stable candidate for exploring multidimensional topological physics. Benefiting from the practically negligible spin-orbit coupling of the light-element framework, $P6_3$-$\text{B}_{30}$ operates as a pristine spinless topological semimetal. We show that the combined time-reversal and twofold screw symmetry ($\mathcal{T}S_{2z}$) enforces a robust two-dimensional nodal surface on the $k_z = π$ plane via a Kramers-like degeneracy. Concurrently, the system hosts a diverse set of zero-dimensional Weyl fermions -- including an unconventional double-Weyl point ($\mathcal{C} = -2$), conventional Type-I WPs ($\mathcal{C} = -1$), and completely tilted Type-II WPs ($\mathcal{C} = +1$) -- emerging at the high-symmetry points $Γ$ and K, as well as along the H-K path, protected by $C_6$ and $C_3$ crystalline rotational symmetries. Crucially, the substantial momentum-space separation between the nodal surface and Weyl points allows for their unambiguous independent resolution. Calculations of the (100) surface states reveal distinct, nontrivial Fermi arcs connecting Weyl nodes of opposite chirality. This work establishes $P6_3$-$\text{B}_{30}$ as a compelling material platform for investigating the physics of multidimensional hybrid topological fermions and their interplay.

Kitaev chains realized in quantum dots coupled via superconducting segments provide a controllable platform for engineering Majorana zero modes (MZMs). In these systems, subgap states in the hybrid region mediate the effective coupling between quantum dots and determine the emergence of sweet-spots where MZMs are strongly localized. However, existing minimal treatments often oversimplify the mesoscopic hybrid region. We perform a full microscopic treatment of this hybrid segment, capturing the quasiparticle continuum and spin-split Andreev bound states (ABSs), and show that it fundamentally alters the minimal picture. We derive analytical expressions for the renormalized couplings and sweet-spot conditions, establishing a direct link between microscopic chain parameters and Majorana optimization and identifying experimentally relevant regimes for improved device performance. Critically, we find that parity-crossings of the ABS, marking the onset of an odd-parity spin-polarized regime in the segment, identify the optimal operating windows where MZMs are simultaneously well localized with a large gap to excited states.

High-dimensional data arise routinely in modern statistics, econometrics, finance, genomics, and machine learning. While a large body of existing methodology is developed under Gaussian or light-tailed assumptions, many real data sets exhibit heavy tails, heterogeneity, and departures from classical covariance-based models. This book provides a systematic treatment of high-dimensional data analysis under elliptically symmetric distributions, with an emphasis on robust inference based on spatial signs, spatial ranks, multivariate Kendall's tau matrices, and related shape-based methods.The book covers the basic theory of elliptical symmetry, high-dimensional location inference, estimation and testing for covariance and precision matrices, sphericity and proportionality testing, high-dimensional alpha testing in factor pricing models, change-point analysis, white-noise and independence testing, high-dimensional discriminant analysis, and dimension reduction through principal component analysis and factor models. Throughout, we review classical low-dimensional and high-dimensional benchmark methods and then develop robust alternatives tailored to elliptical models. Particular attention is paid to the interplay between sum-type, max-type, and adaptive procedures, as well as to the role of scatter, shape, and rank-based dependence measures in heavy-tailed settings. This book is intended as a unified overview of robust high-dimensional methods under elliptical symmetry and as a synthesis of the author's recent research contributions in this area. It is written for researchers and graduate students in statistics, econometrics, and related fields who are interested in modern high-dimensional inference beyond the Gaussian paradigm.

The Quantum Leading-Zero/One Counter (QLZOC) is a fundamental component in quantum arithmetic, playing a critical role in normalization, floating-point units, dynamic range scaling, and logarithmic approximations. Conventional designs primarily rely on direct Boolean-to-quantum mapping, which results in inefficient resource utilization such as irregular gate growth and width-dependent resource overhead. In this work, we propose a scalable, modular, and resource efficient architecture for QLZOC by reformulating the counting process into a sequence of systematic conditional bit-flip operations. Moreover, our design achieves functional polymorphism so that the same design can be easily toggled between zero and one detection, while ensuring seamless scalability to any bit-width without manual re-tuning. We further introduce a Parallel QLZOC (PQLZOC) variant and a Fan-Out optimized (FO-PQLZOC) design. In this work, we evaluate resource efficiency based on the classic criteria about T gates, including the number of total T gates being used (T-count) and the number of sequential T gate layers (T-depth). By exploiting the properties of all-zero/one qubit blocks and a hierarchical merge strategy, the proposed FO-PQLZOC reduces the T-depth from O(m) to O(log m), where m is the input size. Comparative analysis demonstrates that our optimized architecture achieves a 40% reduction in T-count and a 60% reduction in T-depth over state-of-the-art designs, providing a high-performance, T-gate efficient solution for general-purpose quantum arithmetic processors.

We formulate and analyse an optimal control problem for the coagulation-fragmentation equation, where a scalar, time-dependent control modulates the coagulation rate by multiplying the coagulation kernel. The objective functional consists of a quadratic penalisation of the control and a terminal cost depending on the final size distribution. In a weighted $L^1$ framework, we prove weak-to-weak continuity of the control-to-state map under perturbations of the coefficients and obtain existence of optimal controls by the direct method. We then establish $Γ$-convergence of the corresponding cost functionals, providing stability of optimal controls and justifying truncation of unbounded kernels in the optimisation setting. For bounded coagulation kernels we show differentiability of the dynamics, derive an adjoint equation, and obtain a Pontryagin-type minimum principle. Lipschitz continuity of the gradient with respect to the control yields, at the continuous level, convergence of a projected-gradient algorithm with Armijo backtracking. A proof-of-concept finite-volume implementation is then used in a numerical study targeting the number of particles within a prescribed size window, demonstrating that a single low-dimensional actuator can effectively reshape an infinite-dimensional particle-size distribution.

We study a Su-Schrieffer-Heeger chain coupled to a single mode photonic cavity. Considering an off-resonant regime we use the high-frequency expansion in order to obtain an effective fermionic Hamiltonian with cavity-mediated interactions. We characterize the effects of the cavity on topology in a finite size chain by studying three different markers adapted for interacting systems: correlation functions between edges in a chain with open boundary conditions, and a winding number based on the single-particle Green's function and bulk electric polarization via the many-body formula by Resta for a chain with periodic boundary conditions. There is excellent agreement between the winding number and polarization approaches to compute the phase diagram, with the presence of the edge states being confirmed through the calculations of the two-point correlation function. Our approach provides an alternative perspective on cavity-modified topological phases through a study of an effective interacting electronic Hamiltonian and complements methods that treat the full light-matter Hamiltonian directly.

The STAR experiment at the Relativistic Heavy Ion Collider presents measurements of correlations between charged hadron triggers of high transverse momenta ($7 < p_{\rm T} < 30$ GeV/$c$) with recoiling charged hadrons ($3 < p_{\rm T} < 7$ GeV/$c$) or charged--particle jets ($p_{\rm T, jet} > 8$ GeV/$c$) in event--activity selected O+O collisions at $\sqrt{s_{\mathrm {NN}}}=200$ GeV. Yields of associated hadrons and jets, normalized by the number of trigger hadrons, are suppressed by approximately 20\% in high event activity relative to low event activity collisions, with an absence of suppression excluded with high significance. This suppression corresponds to a shift in p_{\rm T} of $0.70\pm0.15~(\rm stat.)~\pm0.10~(\rm syst.)$ GeV/$c$ for large--radius charged--particle jets ($R=0.5$), quantifying their energy redistribution due to final--state interactions. These measurements provide strong evidence for jet quenching in O+O collisions at $\sqrt{s_\mathrm{NN}}=200$ GeV, offering new insight into quark--gluon plasma formation in small collision systems.

Software engineering research has focused on automating maintenance and evolution processes to reduce costs and improve reliability. The emergence of foundation models (FMs) with strong code understanding and reasoning abilities offers new opportunities for autonomous software behavior. Inspired by Artificial Life (ALife), we propose a fundamental shift in the Software Development Life-Cycle (SDLC) by introducing self-construction mechanisms that enable software to evolve and maintain autonomously. This position paper explores the potential of Autopoietic Architectures, specifically Psi-Arch, as a foundational framework for self-constructing software. We first analyze the limitations of traditional maintenance approaches and identify gaps in current SDLC automation. Subsequently, we outline the core challenges in achieving self-construction, including the integration of foundation-model-based reasoning units and the establishment of novel architectural paradigms. Although this paper does not present a definitive solution, it seeks to catalyze discourse and inspire research toward a new paradigm in software engineering, one in which self-constructing software represents the next frontier in SDLC automation.

Crack segmentation on edge devices can support continuous infrastructure monitoring and maintenance and thereby help to preserve public safety. Furthermore, autonomous infrastructure monitoring by using Unmanned Aerial Vehicles (UAVs) can reduce inspection risks, as human operators no longer need to enter hazardous areas. Edge processing reduces the cost of inspection by eliminating the need for high resolution image storage for offline processing and mitigates the security risks and bandwidth requirements of streaming to cloud servers. Edge inference is difficult due to the limited memory and computational capabilities of edge devices, which can affect both accuracy and latency. Furthermore, battery-powered devices are subject to strict power and energy constraints. Together, these limitations impose restrictions on the model size and computational complexity that can be deployed close to the sensor. In recent years, Transformers have achieved state-of-the-art accuracy in a variety of applications, including semantic segmentation. However, Transformer-based models are typically large and computationally intensive, making efficient edge deployment difficult. To address this, we first apply knowledge distillation to enhance the performance of the base models. We then use PTQ to compress the models further. Additionally, we consider the deployment of these models across multiple edge platforms. To maximize energy efficiency, we design and implement a custom hardware architecture for the models on an FPGA. Our results show that Knowledge Distillation (KD) improves all tested U-Net variants. Among the evaluated platforms, the selected FPGA implementation achieves 398 FPS at 204.99 Frames/J while maintaining a mean IoU of 69.42%. In addition, our best model reaches 71.92% mean IoU, which is 8.82 percentage points (pps) higher than the previously reported result on the CrackVision12K dataset.

Colour coherence affects the radiation pattern of hard partons both in vacuum and in a dense coloured background formed in heavy ion collisions. In vacuum evolution it leads to the well-known phenomenon of angular ordering, and in heavy ion collisions the appearance of a medium resolution scale strongly affects the way in which a fragmenting hard parton interacts with the background medium. In this paper I present the implementation of colour coherence in the JEWEL event generator for jet evolution in a dense medium. In each interaction between a hard parton and the medium it is checked whether the momentum transfer of the scattering is sufficient to resolve the colour dipole. In this way it is dynamically decided which structures stay coherent. Importantly, scatterings that resolve an individual parton disrupt the colour coherence, which affects the next splitting via the loss of angular ordering. This leads to a suppression of hard radiation, and consequently a reduction in overall scattering rate, which is the dominant source of effects of colour coherence observable in reconstructed jets. I discuss these modifications using the examples of nuclear modification factor, jet fragmentation function and jet-hadron correlations.

Topologically nontrivial magnetic textures such as skyrmions offer promising opportunities for spintronic applications. In recent years, it has been shown that the magnetic properties of layered materials can be affected by depositing chiral molecules on the surface, while the influence of chiral overlayers on skyrmion properties such as their stability and interactions remains largely unexplored. To address this challenge, we employ wide-field nitrogen-vacancy (NV) magnetometry to directly image skyrmions in chiral-molecule-functionalized magnetic thin films, enabling quantitative mapping of magnetic stray fields over extended areas under ambient conditions. Using pixel-resolved optically detected magnetic resonance (ODMR) combined with controlled magnetic fields, we reproducibly nucleate and probe skyrmion states in CoFeB ferromagnetic samples, enabling quantitative investigation of their properties. We find evidence for enantioselective and magnetic-field-polarity-dependent modifications of skyrmion diameter, spacing, and shape, pointing to a possibility of molecular control of topological spin textures via magneto-chiral coupling.