The nonlinear inductance of the Josephson junction has enabled the development of a wide range of continuous-variable amplifiers and qubit-based devices with unprecedented sensitivity. We present an alternative use of the Josephson junction in the context of broadband impedance matching. The idea poses a potential solution to a longstanding problem in the field of high energy particle physics. The axion, a compelling candidate for the dark matter, converts to a weak electromagnetic signal at an as-yet unknown frequency. As such, the ideal axion detector does not compromise bandwidth for sensitivity, a trade-off intrinsic to all linear, time-invariant and passive circuits. We propose a circuit that uses a Josephson junction in an impedance matching network to overcome these gain-bandwidth constraints and increase the scan rate of axion searches. The Josephson junction can be biased to exhibit negative inductance capable of canceling geometric inductance similar to a capacitor but across a wider frequency range.

The Superallowed Transition Beta-Neutrino Decay Ion Coincidence Trap (St. Benedict) is currently under construction at the Nuclear Science Laboratory (NSL) of the University of Notre Dame. It aims to measure the beta-neutrino angular correlation parameter for superallowed mixed mirror beta decay transitions. Measurements of this kind offer unique insight into the electroweak part of the Standard Model through tests of unitarity of the Cabibbo-Kobayashi-Maskawa (CKM) matrix. St. Benedict is comprised of several beam-manipulating components including a radio frequency quadrupole (RFQ) ion guide. This ion guide features an off-line source at $90^\circ$ to the beam path for testing and calibration of downstream components once St. Benedict is online. Off-line commissioning of the ion guide demonstrated a transport efficiency greater than 95% for ions coming from the upstream RF carpet chamber. When taking ions from the $90^\circ$ off-line source a lower efficiency of 60% was obtained.

Chemiresistive gas sensors transduce gas adsorption into changes in the electrical resistance across a pair of electrodes connected by a sensitive layer of material. This type of sensor is used due to its simple operation, high sensitivity, low cost, and convenience for scaled-up manufacturing of microsized devices. The conversion of the electrical resistance to a corresponding gas concentration is often performed through calibration procedures using empirical formulas, overlooking part of the physical phenomena involved in the process, both on the sorption kinetics and on the transduction. Consequently, a direct evaluation of gas concentration is plagued by the response delays and slow recovery intrinsic to these processes. In contrast to this approach, here we first propose a physical model, based on gas-modulated potential barriers, and considering the out-of-equilibrium dynamic response. Based on this model, we derive an original conversion formula able to dynamically convert the resistance changes into a corresponding gas concentration thus eliminating the main drawback related to slow response and recovery. This new strategy is demonstrated for real-time NO2 gas sensing, using chemiresistors based on oxidized PbS nanocrystals. In addition, the broader application of the proposed model and strategy is demonstrated for NH3 sensing, based on polypyrrole/gold junctions.

Theory of Mind benchmarks for large language models typically produce aggregate scores without theoretical grounding, making it unclear whether high performance reflects strategic reasoning or surface-level heuristics. We introduce a game-theoretic evaluation framework grounded in quantal response equilibrium (QRE). We derive closed-form equilibria for four strategic games, each targeting a distinct cognitive capability. We estimate QRE rationality parameters lambda that place model behavior on a continuous scale calibrated against human data (lambda_human in [1.0, 2.5]), and establish finite-sample convergence bounds via martingale concentration. Validation across 1,855 games with seven frontier models (plus four expansion models) confirms predictions: bluff rates converge to within 4% of equilibrium, lambda estimates range from 0.05 to 1.10 across games and models with substantial cross-model variation, and capability profiles differ across cognitive axes. Robustness analyses reveal high sensitivity to prompt framing and version instability in QRE rankings, highlighting the need for standardized protocols.

The rapid integration of digital technology into daily life has prompted sustained concern regarding its impact on human cognition. This integrative review synthesizes documented risks and negative associations across more than 500 empirical studies, including the established literature and the nascent body of work on generative artificial intelligence. Initial evidence suggests a potential evolution in the nature of cognitive risk: while research on earlier technologies predominantly describes disruptions to resource allocation, early findings on generative artificial intelligence point toward a hypothesized erosion of higher-order generative and metacognitive capabilities. We analyze these risks across basic cognitive processes, higher-order cognition, and integrated functional outcomes through four mechanisms: functional interference, neurochemical dysregulation, structural neuroplasticity, and psychosocial displacement. The synthesis further highlights that these associations are frequently moderated or attenuated by socioeconomic status and environmental factors, which often drive both technology use and cognitive outcomes. Finally, by reviewing principles of cognitive epidemiology, the paper examines how habitual digital offloading could theoretically deplete cognitive reserve, creating downstream risks for long-term health. The collective evidence suggests an efficiency-atrophy paradox, where digital tools optimize short-term task performance at the potential expense of the long-term cognitive effort required to maintain unassisted cognition. However, large gaps remain in the literature, particularly the need for longitudinal studies, specifically within adult and professional populations.

This article summarizes the field of consumer protection law, from its historical roots to the contemporary challenges of the digital age. It outlines the legal doctrines governing consumer deception and unfair trade practices, highlighting the interplay between common-law, statutory, and private modes of regulation. The article then addresses the impact of artificial intelligence and big data on consumer markets, focusing on digital advertising and new forms of consumer fraud. Finally, it explores regulatory responses to these challenges, including data privacy laws and prohibitions on dark patterns, which illustrate the trade-offs inherent in consumer protection frameworks.

Gemini said Lawmakers today face continuous calls to "future proof" the legal system against generative artificial intelligence, algorithmic decision-making, targeted advertising, and all manner of emerging technologies. This Article takes a contrarian stance: it is not the law that needs bolstering for the future, but the future that needs protection from the law. From the printing press and the elevator to ChatGPT and online deepfakes, the recurring historical pattern is familiar. Technological breakthroughs provoke wonder, then fear, then legislation. The resulting legal regimes entrench incumbents, suppress experimentation, and displace long-standing legal principles with bespoke but brittle rules. Drawing from history, economics, political science, and legal theory, this Article argues that the most powerful tools for governing technological change--the general-purpose tools of the common law--are in fact already on the books, long predating the technologies they are now called upon to govern, and ready also for whatever the future holds in store. Rather than proposing any new statute or regulatory initiative, this Article offers something far rarer, a defense of doing less. It shows how the law's virtues--generality, stability, and adaptability--are best preserved not through prophylactic regulation, but through accretional judicial decision-making. The epistemic limits that make technological forecasting so unreliable and the hidden costs of early legislative intervention, including biased governmental enforcement and regulatory capture, mean that however fast technology may move, the law must not chase it. The case for legal restraint is thus not a defense of the status quo, but a call to preserve the conditions of freedom and equal justice under which both law and technology can evolve.

Dark patterns in online commerce, especially deceptive user interface designs for apps and websites, undermine consumer autonomy and distort online markets. Although sometimes deception is intentional, the complex app development process can also unintentionally produce manipulative user interfaces. This paper discusses common design pitfalls and proposes strategies for app makers to avoid infringing user autonomy or incurring legal liability under emerging principles of consumer protection law. By focusing on choice architecture and transparent design principles, developers can both facilitate compliance and build user trust and loyalty.

A global challenge in artificial intelligence (AI) regulation lies in achieving effective risk management without compromising innovation and technical progress. The European Union (EU) Artificial Intelligence Act represents the first regulatory attempt worldwide to navigate this tension in the form of a binding, risk-based framework. In August 2025, obligations for providers of general-purpose AI (GPAI) models under the EU AI Act entered into application. They require providers of the most advanced GPAI models to evaluate possible systemic risks stemming from their models. This raises the regulatory challenge of ensuring that the evaluations provide meaningful risk information without imposing excessive burden on providers. The principle of proportionality, a binding requirement under EU law, requires the regulator to calibrate its actions to their intended objectives. The application of proportionality to model evaluations for AI risk opens opportunities to develop scientific methods that operationalize such calibration within concrete evaluation practices.

Open Educational Resources (OER) are freely available teaching and learning materials, such as textbooks, videos, and interactive games, that can be used, reused, adapted, and shared. OER can leverage access, collaboration, and innovation in education; however, their adoption and long-term use remain limited. Motivated by this issue, this manuscript examined the literature and identified 26 social, economic, and technical barriers that hinder teachers, students, and institutions from creating, using, and maintaining OER. These barriers were evaluated through semi-structured interviews with experts to ensure their understandability, correctness, completeness, and relevance. We adopted a four-step research method: (1) a tertiary study that identified barriers from 26 secondary studies; (2) analysis and classification of the barriers according to social, economic, and technical dimensions and the OER lifecycle activities they affect; (3) design of the conceptual model to represent relationships among OER elements, barriers, and mitigation actions; and (4) evaluation through expert interviews. These barriers are also illustrated using a real-world OER. The findings provide insights for the education community, supporting inclusive strategies, institutional actions, and public policies to reduce social, economic, and technical barriers to OER. By addressing factors that affect accessibility, sustainability, and equitable participation, this manuscript advances universal access to educational resources and fosters more inclusive educational ecosystems.

Let $K$ be a function field in positive characteristic, $\infty$ be a fixed place of $K$ and $K_\infty$ be the completion of $K$ at $\infty$. By the work of Serre, it is well known that, for a suitable arithmetic subgroup $Γ\subset GL_2(K)$, the $Γ$-unstable region of the Bruhat-Tits tree for $GL_2(K_\infty)$ is naturally homotopy equivalent to the spherical Tits building for $GL_2(K)$. Grayson, following Quillen's ideas, generalizes this homotopy equivalence to the non-semistable part of the Bruhat-Tits building for $GL_r(K_\infty)$. Modifying the approach described by Grayson, we are also able show a similar homotopy equivalence for the $Γ$-unstable region, for $Γ\subset GL_r(K)$ a principal congruence subgroup.

This paper proposes a change in perspective on the ``transformation of values'' problem: from ``searching for a single constant solution'' to ``characterizing the allocation space under objective constraints imposed by the physical production network.'' Building an input--output model, we show mathematically that whenever the macroeconomy features a physical surplus, the set of skilled-to-simple labor reduction vectors that can sustain the subsistence floor of the entire labor force forms a bounded value-feasible set . Within this multidimensional region, the classical ``two great macro equalities'' necessarily hold simultaneously for a reasonable range of profit rates. Hence, without violating the physical minimum conditions for reproduction, the law of value and the nominal price system can be made logically consistent.

A common problem in private data analysis is the partition selection problem, where each user holds a set of partitions (e.g. keys in a GROUP BY operation) from a possibly unbounded set. The challenge here is in maximizing the set of released partitions while respecting a differential privacy constraint. Previous work [Desfontaines et al., PoPETS 2022] presented an optimal $(\varepsilon, δ)$-DP algorithm when each user submits only a single partition. We generalize this approach to find the optimal algorithm under $δ$-approximate $(α, \varepsilon)$-Rényi differential privacy (RDP), which allows much tighter analysis under composition. Motivated by the non-existence of a general optimality result in the case where users submit multiple partitions each, we present an extension of our optimal algorithm tuned for $L^2$ bounded weighted partition selection which can be used as a drop-in improvement over the Gaussian mechanism any time the partition frequency is not also needed. We show that our primitive can be easily plugged into state of the art partition selection algorithms (PolicyGaussian from [Gopi et al., ICML 2020] and MAD2R from [Chen et al., ICML 2025]), improving performance both for parallel and sequential adaptive algorithms. Finally, we show that there is an inherent cost to algorithms which do support releasing the frequency as well as the partitions. Specifically, we formulate a basic notion of optimal approximate RDP algorithm for partition selection using additive noise, and show that there is a numerical separation between additive and non-additive noise mechanisms for this problem.

We study the problem of differentially private (DP) secure multiplication in distributed computing systems, focusing on regimes where perfect privacy and perfect accuracy cannot be simultaneously achieved. Specifically, N nodes collaboratively compute the product of M private inputs while guaranteeing epsilon-DP against any collusion of up to T nodes. Prior work has characterized the fundamental privacy-accuracy trade-off for the multiplication of two multiplicands. In this paper, we extend these results to the more general setting of computing the product of an arbitrary number M of multiplicands. We propose a secure multiplication framework based on carefully designed encoding polynomials combined with layered noise injection. The proposed construction generalizes existing schemes and enables the systematic cancellation of lower-order noise terms, leading to improved estimation accuracy. We explore two regimes: (M-1)T+1 <= N <= MT and N = T+1. For (M-1)T+1 <= N <= MT, we characterize the optimal privacy--accuracy trade-off. When N = T+1, we derive nontrivial achievability and converse bounds that are asymptotically tight in the high-privacy regime.

Sensemaking in collaborative work and learning is increasingly supported by GenAI systems, however, emerging evidence suggests that poorly designed GenAI systems tend to provide explicit instruction that groups passively follow, fostering over-reliance and eroding autonomous sensemaking. Group awareness tools (GATs) address this challenge through implicit guidance: rather than instructing groups on what to do, GATs externalize observable collaboration data through visualizations that reveal differences between group members to create cognitive conflict, which triggers autonomous elaboration and discussion, thereby implicitly guiding autonomous sensemaking emergence. Drawing on an initial literature search of existing GAT systems, this paper explores the design of GenAI-augmented GATs to support autonomous sensemaking in collaborative work and learning, presenting preliminary design principles for discussion.

Socially shared metacognition (SSM) refers to the collective monitoring and regulation of joint cognitive processes in collaborative problem-solving, and is essential for effective knowledge work and learning. Generative AI (GenAI)-based systems offer new opportunities to support SSM, but emerging evidence suggests that poorly designed systems can encourage over-reliance on AI-generated explicit instruction and erode groups' capacity to develop autonomous regulatory processes. Group awareness tools (GATs) address this challenge through established design principles that make social and cognitive awareness information visible, highlight differences between group members to create cognitive conflict, and trigger autonomous elaboration and discussion, thereby implicitly guiding autonomous SSM emergence. This paper explores the design of GenAI-augmented GATs to support autonomous SSM in collaborative work and learning through an initial literature search, presenting preliminary design principles for discussion.

We revisit the controversial "abscopal" effect in the context of Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR). By allowing long interval between fractions, PULSAR may enhance systemic immune activation and increase the likelihood of abscopal responses compared with conventional daily fractionation. To quantify treatment-induced effects, we introduce an interaction-picture transformation adapted from quantum mechanics, which separates intrinsic tumor growth from radiation and immune-mediated perturbations. In this preliminary study, we tested this method to two preclinical bilateral tumor models (4T1 and MC38). Our model provides a quantitative measure of the interaction strength between primary and secondary tumors at the individual level, capturing dynamics over time rather than relying solely on cohort averages. This approach frames the abscopal effect as a continuous, stochastic phenomenon rather than a binary response. The framework is flexible for future studies, particularly in concurrent radiation and immunotherapy with PULSAR, where different radiation doses and fractionation schedules can be compared, and immune checkpoint inhibitors (ICIs) can be incorporated to further enhance systemic anti-tumor immunity. The framework can also help us make cross-study comparison of abscopal effects and standardizes the reporting of abscopal magnitude beyond simple statistical significance.

Is robot cybersecurity broken by AI? Consumer robots -- from autonomous lawnmowers to powered exoskeletons and window cleaners -- are rapidly entering homes and workplaces, yet their security remains rooted in assumptions of specialized attacker expertise. This paper presents evidence that Generative AI has fundamentally disrupted robot cybersecurity: what historically required deep knowledge of ROS, ROS 2, and robotic system internals can now be automated by anyone with access to state-of-the-art GenAI tools spearheaded by the open source CAI (Cybersecurity AI). We provide empirical evidence through three case studies: (1) compromising a Hookii autonomous lawnmower robot, uncovering fleet-wide vulnerabilities and data protection violations affecting 267+ connected devices, (2) exploiting a Hypershell powered exoskeleton, demonstrating safety-critical motor control weaknesses and credential exposure including access to over 3,300 internal support emails, and (3) breaching a HOBOT S7 Pro window cleaning robot, achieving unauthenticated BLE command injection and OTA firmware exploitation. Across these platforms, CAI discovered in an automated manner 38 vulnerabilities that would have previously required months of specialized security research. Our findings reveal a stark asymmetry: while offensive capabilities have been democratized through AI, defensive measures often remain lagging behind. We argue that traditional defense-in-depth architectures like the Robot Immune System (RIS) must evolve toward GenAI-native defensive agents capable of matching the speed and adaptability of AI-powered attacks.

One possible data encryption scheme is related to stream ciphers, which use a sufficiently long pseudo-random sequence. To increase the cryptographic strength of the cipher, linear shift algorithms (generated by linear recurrent sequences such as the Fibonacci sequence and its generalizations) are additionally used. Two such generalizations are convolved Fibonacci numbers $\{F_n^{(s)}\}_{n=1}^\infty$ and k-sections of the Fibonacci sequence $\{Φ_{n,k}\}_{n=1}^\infty$ $( Φ_{n,k}=F_{nk}/F_k).$ This article considers a further generalization of Fibonacci numbers, namely convolutions of k-sections of the Fibonacci sequence $\{Φ_{n,k}^{(s)}\}_{n=1}^\infty$. These numbers are defined by the relations: $$ Φ_{n,k}^{(1)}=\sum_{j=0}^{n-1}Φ_{j+1,k}Φ_{n-j,k\,},\qquad Φ_{n,k}^{(s)}=\sum_{j=0}^{n-1}Φ_{j+1,k}Φ_{n-j,k}^{(s-1)}\,,\quad s=2,3,...$$Moreover, $Φ_{n,1}=F_n, Φ_{n,1}^{(s)}=F_n^{(s)}$. An explicit formula for the representation of convolutions of k-sections of the Fibonacci sequence and a Binet type formula is established:$$Φ_{n,k}^{(s)}=5^{-s}(F_k)^{-2s-1}\sum_{j=0}^{s}(-1)^{(k-1)j}{n+2s\choose j}{n+s-1-j\choose n-1} F_{k(n+2s-2j)}.$$ Several consequences were also obtained for $F_n$ and $F_n^{(s)}$, based on the connection between the derivatives of Chebyshev polynomials of the second kind $U_n(z)$ and their derivatives, as well as the connection for convolutions of k-sections of the Fibonacci sequence with derivatives of Chebyshev polynomials of the second kind via Lucas numbers $L_k$. Note that the sequences $\{Φ_{n,k}^{(s)}\}_{n=1}^\infty$ for $k=3,4,...$ and $s=1,2,..$ are not included in the OEIS encyclopedia.

Accurate magnetic field direction sensing in compact platforms is critical in applications spanning magnetic navigation, space science, and biomedical imaging. We demonstrate a single-optical-axis vector optically pumped magnetometer based on Rabi oscillations between Zeeman sublevels driven by a series of resonant radiofrequency (RF) polarization ellipses (PEs). A calibration protocol based on controlled rotations of the DC magnetic field determines the spatial orientation of each PE. We develop a detailed theoretical model describing the angular dependence of the Rabi frequencies, incorporating key systematics including RF Stark shifts and Bloch-Siegert shifts. We also account for an RF-based heading-error systematic affecting Rabi-frequency measurements arising from the nonlinear Zeeman effect. Simultaneous Larmor measurements yield the magnitude of the magnetic field, enabling integrated vector-scalar measurements. The magnetometer achieves deadzone-free vector operation with 80 $μ$rad mean angular accuracy and angular noise densities as low as 8 $μ$rad$/\sqrt{\mathrm{Hz}}$, offering a pathway towards miniaturized sensors without requiring 3D optical access or sensor rotations.

A novel experimental platform is developed to investigate the dynamics of inertial particles (micro-droplets) in air turbulence. The goal is to observe particle collision and coalescence in turbulent flows, focusing on its impact on the radial distribution function (RDF) and relative velocity statistics. The main tool is a three-dimensional Lagrangian particle tracking (LPT) system, designed for high-resolution measurements at sub-Kolmogorov scales. The system uses LED illumination with high-speed spinning-disk atomizers, enabling tracking of particles of approximately 10~$μ$m and larger under controlled turbulence. A minimum resolvable particle separation of $r/η\approx 0.1$ is achieved. A central contribution is the identification and mitigation of three dominant sources of spurious particles: FMIS, IIS, and TIF. An angle-based geometric filtering criterion strongly suppresses FMIS artifacts on RDF. These procedures establish a validated workflow for reliable small-scale statistics. Using this framework, RDF and a normalized pseudo-collision rate are measured at near-contact separations for particles with Stokes numbers $St \approx 0.2$--$1.0$. Sub-Kolmogorov clustering increases with Stokes number, and near-contact statistics are consistent through the filtering strategy. This study extends LPT limits and provides a reliable methodology for investigating inertial-particle dynamics at previously inaccessible spatial scales.

Channel decoding is a challenging task in communication channels exhibiting memory effects. In this work, we apply the recently proposed decoding paradigm of guessing random additive noise decoding (GRAND) to channels with memory, focusing on linear Gaussian intersymbol interference (ISI) channels. For describing error patterns (EPs), we introduce the concept of error burst to account for the memory effect, and define sequence reliability to characterize the likelihood of EP. Based on sequence reliability, we obtain the optimal GRAND algorithm as a generalization of soft GRAND (SGRAND) for linear Gaussian ISI channels, termed SGRAND-ISI, which is equivalent to the maximum-likelihood (ML) decoding algorithm. We then develop order-reliability-bit (ORB) GRAND algorithms based on SGRAND-ISI, to facilitate implementation. In numerical experiments, our proposed algorithms achieve multiple-dB improvements compared to GRAND algorithms which ignore channel memory, and can often attain performance within 0.1--0.2dB of the ML lower bound. We also compare our proposed algorithms with the recently proposed ORBGRAND-Approximate Independence algorithm for handling channel memory, and observe a performance gain of at least 0.5dB at block error rate of $10^{-3}$, meanwhile incurring a substantially lower computational complexity.

In this work, we present a semiclassical approach to model Intermolecular Coulombic Electron Capture (ICEC) in aqueous solutions using molecular dynamics simulations with OpenMM. We investigate the behavior of an excess electron in the presence of cations (Fe$^{3+}$) in water, focusing on the influence of electron energy and cation concentration on the ICEC quantum yield. Our simulations reveal that the ICEC quantum yield approaches unity at higher concentrations and initial electron energies, while it decreases at lower concentrations due to electron energy loss before reaching the cation.

We study a family of sorting match puzzles on grids, which we call permutation match puzzles. In this puzzle, each row and column of a $n \times n$ grid is labeled with an ordering constraint -- ascending (A) or descending (D) -- and the goal is to fill the grid with the numbers 1 through $n^2$ such that each row and column respects its constraint. We provide a complete characterization of solvable puzzles: a puzzle admits a solution if and only if its associated constraint graph is acyclic, which translates to a simple "at most one switch" condition on the A/D labels. When solutions exist, we show that their count is given by a hook length formula. For unsolvable puzzles, we present an $O(n)$ algorithm to compute the minimum number of label flips required to reach a solvable configuration. Finally, we consider a generalization where rows and columns may specify arbitrary permutations rather than simple orderings, and establish that finding minimal repairs in this setting is NP-complete by a reduction from feedback arc set.

This paper develops a robust control synthesis method for uncertain linear systems with input saturation in the framework of integral quadratic constraints (IQCs). The system is reformulated as a linear fractional representation (LFR) that captures both dead-zone nonlinearity and time-varying uncertainties. By combining mixed IQC-based dissipation inequalities with quadratic Lyapunov functions, sufficient conditions for robust stabilization are established. Compared with conventional approaches based on a single static sector condition for the dead-zone nonlinearity, the proposed method yields improved $\mathcal{L}_2$-gain performance through the use of scaled mixed IQCs. For systems subject to time-varying structured uncertainties, a new scaled bounded real lemma is further developed based on the IQC characterization. The resulting $\mathcal{H}_\infty$ synthesis conditions are expressed as linear matrix inequalities (LMIs), which are numerically tractable in all decision variables, including the scaling factors in the IQC multipliers. The proposed method is validated using a second-order uncertain system in linear fractional form, and its superiority over an anti-windup design is further illustrated by a cart-pendulum example.

We provide a simplified approach to the the stable Hopf invariant. We provide short elementary proofs of the Cartan Formula, the Composition Formula, and the Transfer formula. In addition, when $π$ is a discrete group, we show how to extend these results to the stable category of $π$-spaces.

We study type II$_0$ loci in the moduli space of type IIB string theory compactified on Calabi-Yau manifolds. We show that around these infinite distance singular loci the leading order behaviour of the gauge kinetic matrix, and of the prepotential, can always be written in the form of a threshold correction from integrating out a BPS state, but one with an effectively complex charge. In order to understand the physical meaning of this, we carefully identify the splitting in the effective supergravity between the graviphoton direction and matter vector multiplets. Within a specific two-parameter example of a Calabi-Yau, we use this to identify a strongly-coupled matter sector involving both light electric and light magnetic states. We propose that the leading gauge kinetic matrix arises as a threshold correction from integrating out this non-perturbative sector, and that the sector has an effective weakly-coupled infrared description in terms of the complex-charged state. The region in moduli space has a Heterotic string dual microscopic description. The light magnetic state in this description corresponds to a Kaluza-Klein monopole, which becomes lighter than the fundamental Heterotic string, leading to the non-perturbative sector. Assuming this picture is correct, it implies the existence of infrared emergent infinite distance loci in moduli spaces of quantum gravity.

Audio-Visual Target Speaker Extraction (AVTSE) aims to separate a target speaker's voice from a mixed audio signal using the corresponding visual cues. While most existing AVTSE methods rely exclusively on frontal-view videos, this limitation restricts their robustness in real-world scenarios where non-frontal views are prevalent. Such visual perspectives often contain complementary articulatory information that could enhance speech extraction. In this work, we propose Multi-View Tensor Fusion (MVTF), a novel framework that transforms multi-view learning into single-view performance gains. During the training stage, we leverage synchronized multi-perspective lip videos to learn cross-view correlations through MVTF, where pairwise outer products explicitly model multiplicative interactions between different views of input lip embeddings. At the inference stage, the system supports both single-view and multi-view inputs. Experimental results show that in the single-view inputs, our framework leverages multi-view knowledge to achieve significant performance gains, while in the multi-view mode, it further improves overall performance and enhances the robustness. Our demo, code and data are available at https://anonymous.4open.science/w/MVTF-Gridnet-209C/

Within the Guessing Random Additive Noise Decoding (GRAND) family, ordered reliability bits GRAND (ORBGRAND) has received considerable attention for its hardware-friendly exploitation of soft information. Existing information-theoretic results for ORBGRAND are asymptotic in blocklength and do not quantify its performance at short-to-moderate blocklengths. This paper develops a finite-blocklength analysis for ORBGRAND over general bit channel, addressing the key challenge that the rank-induced decoding metric is non-additive and coupled across symbols. We first derive an ORBGRAND-specific random-coding union (RCU)-type achievability (ORB-RCU) bound on the ensemble-average error probability. We then characterize two governing decoding metrics: the transmitted-codeword metric is treated as a U-statistic and analyzed via Hoeffding decomposition, while the competing-codeword metric is reduced to a weighted sum of independent and identically distributed Bernoulli random variables and analyzed through strong large-deviation analysis. Combining these ingredients with a Berry-Esseen argument yields a second-order achievable-rate expansion and the associated normal approximation, whose first-order term is shown to equal the ORBGRAND generalized mutual information and whose second-order term defines an ORBGRAND dispersion with a single-letter variance representation. Numerical results for BPSK-modulated additive white Gaussian noise channel validate the tightness of ORB-RCU relative to the maximum-likelihood based RCU benchmark and the accuracy of the normal approximation in the operating regime of practical interest.

Indonesia's nickel ore export ban has driven rapid expansion of smelting and hydrometallurgical processing capacity at the Indonesia Morowali Industrial Park (IMIP), now the world's largest integrated nickel processing complex, on the coast of Central Sulawesi. Whether this industrialization has degraded the adjacent marine environment remains unquantified. We apply Bayesian structural time-series (BSTS) causal inference to a multi-decadal, multi-sensor satellite ocean color record of the diffuse attenuation coefficient at 490 nm, $K_d(490)$, to test for a causal link between IMIP expansion and nearshore turbidity change. A consensus structural breakpoint, a significant posterior causal effect estimated against a Banda Sea counterfactual, and a distribution-free placebo rank test collectively establish that coastal water clarity deteriorated after the transition from initial nickel pig iron production to hyper-expansion of high-pressure acid leaching facilities for battery-grade nickel. Satellite-derived land cover analysis independently corroborates this timing, showing substantial built-area growth and concurrent tree cover loss within the IMIP footprint. The resulting euphotic zone shoaling occurs in oligotrophic waters supporting high marine biodiversity, where even moderate optical degradation may impair coral photosynthesis and compress depth-dependent reef habitat. These findings quantify a marine environmental cost absent from Indonesia's mineral downstreaming policy discourse and demonstrate a transferable, satellite-based quasi-experimental framework for causal impact assessment at coastal industrial sites in data-limited tropical settings.