Papers

Poor indoor air quality can cause up to five times more direct health problems to occupants than outdoor air. In particular, it may cause headaches, fatigue, eye/throat irritation, and long-time exposure is linked to respiratory and heart as well as some forms of cancer. Despite the importance of indoor health and well-being, most current monitoring devices and systems (usually for offices and workspaces) are passive. The Environmental Quality Monitor (EnQyMo) platform is a generic Internet of Things (IoT) middleware designed to process several sensor data related to air quality in indoor spaces and correlate this data with health exposure risks of users/workplace employees. Using Bluetooth Low Energy (BLE) beacons and a mobile IoT middleware it is able to identify the (smartphone) users exposed to these polluted air or high CO2 (carbon dioxide) levels, and generate location-specific alarms only to the users at the places with the unhealthy air conditions. At the core of EnQyMo is an agency of Large Language Models (LLMs) capable of interpreting regulatory standards and scientific literature to automatically identify critical health exposure levels.

Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence they cite. We present Explanation-Driven Counterfactual Testing (EDCT), an intervention-based protocol that extracts visual concepts cited in a model's explanation, applies verified minimal edits to them, and tests whether the resulting answer and explanation remain consistent with the edited image. Using this protocol, we create EDCT-Bench, a comprehensive benchmark spanning three complementary domains: knowledge-intensive visual question answering (OK-VQA), safety-critical driving (DriveLM), and 3D spatial reasoning (3DSRBench). Across the evaluated VLMs, EDCT reveals substantial faithfulness gaps, with models frequently producing responses inconsistent with verified visual changes. Finally, our fine-tuning study suggests that EDCT-generated counterfactuals provide high-impact training signals.

Inspired by the recent observation of the $\Sigma_c(3200)^0$ baryon in the $\Lambda_c^+\pi^-$ invariant mass spectrum by the LHCb Collaboration, we perform a comprehensive investigation of the $D^{(*)}\Delta$ interactions within the one-boson-exchange framework. Our results show that the $D^*\Delta$ molecule with $I(J^P)=1(1/2^-)$ can be regarded as the most natural candidate for the $\Sigma_c(3200)$, and predict the existence of several $D^*\Delta$ molecular partners with $I(J^P)=1(3/2^-, 5/2^-)$ and $2(1/2^{\pm}, 3/2^{\pm}, 5/2^-)$. After including the coupled channel effects between $D\Delta$ and $D^*\Delta$, several shallowly bound $D\Delta$-dominated molecular candidates also emerge, especially for the $I(J^P)=1,2(3/2^-)$ and the $2(1/2^+,3/2^+)$ systems. These results provide a unified molecular interpretation of the newly observed $\Sigma_c(3200)^0$ and useful guidance for future experimental searches for exotic charmed-baryon states. We expect the high-statistics data from the LHCb Upgrade and Belle~II runs will directly test these predictions.

We study the maximum strong independent set problem in a finite hypergraph: find the largest vertex set that intersects every hyperedge in at most one vertex. This objective arises whenever each observed block is a local incompatibility constraint but transitive closure across overlapping blocks is not justified. A motivating example is multi-band LSH-MinHash deduplication, where each collision bucket gives local evidence, while connected-component contraction can impose spurious global equivalences. The paper develops an incidence-structural toolkit for this problem. We prove exact reductions for dominance, incidence twins, and weight-1 blocks; derive closed-form and low-weight upper bounds; introduce puncturing and covering certificates that sharpen those bounds; and analyze a layered greedy clustering algorithm driven by block weights and residual incidence. The algorithmic analysis includes feasibility, maximality, conditional optimality, a layered witness-matching upper bound, and incidence-local complexity bounds. The results give correctness, termination, fixed-point, and optimality certificates for broad incidence families, together with examples showing when different certificates separate or coincide.

The Taylor diagram is used to evaluate and compare predictive models with observed data and has many applications in climate and environmental sciences. Three statistics of interest–the centered root-mean-squared error, standard deviation, and the product-moment correlation coefficient–are related by the law of cosines; Taylor diagrams leverage this fact to allow visualization of all three statistics in a two-dimensional plot without any loss of information. In this work, we present a novel model evaluation tool–the Wasserstein-Taylor diagram–formed from integrating the Wasserstein distance from optimal transport into a Taylor diagram framework. This tool is built upon the fact that the law of cosines still holds in the Taylor diagram if the centered root-mean-squared error and product-moment correlation coefficient are replaced by the centered 2-Wasserstein distance and quantile correlation coefficient, respectively. The advantage of this Wasserstein-Taylor diagram is that one can use the distribution perspective afforded by the Wasserstein distance to compare observed and predicted data sets of different sizes and even to compare statistical models with only observed data. We further show that the quantile correlation coefficient on empirical measures converges to the quantile correlation coefficient on the sampled measures.

Experimental tools are a key factor in both academic and industrial research communities to create design evaluations of new networking technologies that involve troubleshooting or changing the planning of deployed networks. Physical Software-Defined Radio (SDR) experimental platforms enable a design solution for the quick prototyping of wireless communication systems. However, SDR-based experimental platforms incur high costs, which leads to scalability limitations in the experimental settings. Having said this, network simulators, emulators, and new testbeds have attracted increasing attention. Emulation-based research prototyping can be distinguished from real communication networks and SDR-based platforms by allowing a tradeoff between cost and flexibility. This paper examines the Mininet-RAN emulation tool, which, as well as Radio Access Network (RAN) modeling, provides a way to test Open RAN Intelligent Controller (RIC) services without the need to deploy an entire RAN infrastructure. The Mininet-RAN creates virtual network elements, such as hosts, L2/L3 devices, controllers, and links, by combining some of the best emulator features, hardware testbeds, and simulators. By running the current code of standard practice Unix/Linux network applications and network stack, the Mininet-RAN enables real-world network data traffic patterns to be delivered to the RIC, regarding the most significant aspect of the dynamic generation of wireless system's KPIs. We provide the basic code of Mininet-RAN for the first two O-RAN Alliance-defined use cases involving V2X and UAV. The xApps are being implemented in O-RAN SC near-RT RIC, with Mininet-RAN which provides a closed-loop validation environment.

Doing inclusive design in HCI practice can be labor-intensive, a costly barrier that some companies and HCI practitioners may be unwilling or unable to overcome. Yet, not doing inclusive design is costly too, in the form of UX barriers that disproportionately disadvantage under-served user populations. To address this problem, we introduce Apply-<x>Mag, an LLM-powered tool to support HCI practitioners' work to design their products inclusively to wide ranges of users. Apply-<x>Mag is general, supporting any inclusive design method that can be expressed as <x>Mags (i.e., using attribute ranges and heuristics). It is also effective: Empirical results with researcher and practitioner teams using various combinations of two <x>Mags on 7 products showed Apply-<x>Mag precision averaging 90-99% and recall averaging 82-89%. Further, its environmental costs were reasonable, costing about the same resources as 2-4 ordinary Google searches.

Adaptive cross approximation (ACA) constructs low-rank CUR approximations from selected rows and columns of a matrix, making it attractive when individual entries are inexpensive to query but forming or repeatedly multiplying by the full matrix is not. Its practical performance, however, is not well explained by existing worst-case bounds, which can be exponentially large and often fail to predict when greedy largest-entry pivoting performs poorly. By viewing the matrix as a cross-gram matrix, we reinterpret the classical determinantal formula for the CUR residual through the lens of exterior algebra. In this representation, each residual entry is a weighted inner product between two $(k+1)$-blades divided by the normalized volume of the pivot block. The formula separates three effects: the weighted magnitudes of singular-vector rows, their angular geometry, and the conditioning of the selected pivots. From this we recover a classical $\sigma_{k+1}$-type estimate, characterize when one pivot annihilates or nearly annihilates additional rows and columns, obtain a quasi-optimality estimate for positive-semidefinite matrices, and bound the rank-one update through an explicit angle-based formula. We use this framework to explain the failure of greedy ACA on an asymmetric spiral-kernel example and to motivate a geometry-aware weighted-mass pivoting rule for radial-basis Galerkin stiffness matrices. In the reported experiments, weighted-mass pivoting often reduces the Frobenius residual relative to greedy pivoting and randomly pivoted Cholesky while retaining comparable runtime on parallel hardware.

Learned quadruped locomotion policies commonly map observations directly to joint-level actions, leaving the periodic structure of locomotion implicit in the policy. We investigate an alternative representation in which each joint trajectory is expressed as a command-conditioned Fourier series and modified online using feedback from the robot state. A context network generates the Fourier coefficients and the weights of a per-step feedback network, whose outputs adjust joint offsets, harmonic gains, frequency, and phase during execution. In simulation, we examine this explicit frequency structure alongside the hidden activations of an MLP policy that directly outputs joint targets. The harmonic waveforms change frequency and shape with commanded speed. Dynamic mode decomposition of selected MLP rollouts reveals dominant activation modes near the foot-height oscillation frequency and its second harmonic, showing periodic structure without an explicit Fourier generator. On a Unitree Go2, the simulation-trained harmonic controller records a provisional onboard-estimated peak speed of 3.67 meter per second and carries added loads up to 5.883 kilogram in separate trials.

We report the synthesis and comprehensive investigation of iodine-incorporated $Cr_2Se_3$, a non-van der Waals quasi-two-dimensional magnetic material, using structural, magnetic, transport, spectroscopic, and first-principles methods. Magnetization and electron spin resonance measurements reveal an antiferromagnetically ordered state below $T_N \approx 52$ K. At higher temperatures, a second, broader anomaly emerges near $T^* \approx 150$ K, coinciding with a shallow minimum in the temperature-dependent resistivity that resembles a metal-to-insulator-like crossover. Hall measurements indicate predominantly hole-like conduction at high temperatures ($\geq$ 150 K), while the nonlinear Hall response below T*, together with an anomaly in the third-harmonic electrical signal, suggests the emergence of mobility-dependent multichannel and spatially inhomogeneous transport. A large, non-saturating negative magnetoresistance reaching approximately -78% at 25 K and 12 T further demonstrates strong coupling between charge transport and the magnetic state. Temperature-dependent Raman spectroscopy reveals no symmetry-changing structural transition near T*, whereas angle resolved photoemission spectroscopy (ARPES) measurements show no major reconstruction of the electronic structure occupied across the crossover. Taken together, these results identify the high-temperature anomaly as a broad magnetically coupled transport crossover arising from the interplay of short-range magnetic correlations, chemical disorder, and redistribution among competing conduction channels. These findings demonstrate that anion incorporation provides an effective route to tuning the coupled electronic and magnetic properties of non-layered Cr$_2$Se$_3$, establishing this system as a promising platform for investigating correlated transport and emergent spintronic functionalities in transition-metal chalcogenides.

Alpha-decay provides an important probe of nuclear structure and the underlying nucleon-nucleon interaction, while its rigorous microscopic description from first principles remains challenging. For $\alpha$+magic core systems, a microscopic treatment of $\alpha$-cluster formation and decay has been achieved by considering both the four-body correlation on top of the core and Pauli blocking. Extending this microscopic description to open-shell nuclei should account for the configuration mixing caused by the residual interactions between valence nucleons near the Fermi surface. In this work, we improve the quartetting wave function approach (QWFA) by incorporating the pairing-induced configuration mixing using the particle-number-projected Bardeen-Cooper-Schrieffer (PBCS) method. We find that the pairing enhance the formation amplitudes of the $\alpha$-cluster in open-shell nuclei while the closed shells suppress the $\alpha$-clustering.

Long-context LLM inference is bottlenecked by attention, whose repeated KV-cache reads make decoding memory-bound. Self-speculative decoding alleviates this by drafting tokens with sparse attention and verifying them with full attention, but existing batched methods remain synchronized: all requests in a batch share a single draft-verify schedule, even though the optimal draft length varies widely across requests and changes dynamically within each request. We propose ASPIRE, a non-synchronized batched self-speculative decoding framework built on three components. First, a unified mixed forward allows drafting and verifying requests to coexist in the same batched forward pass, removing the need for global draft-verify phases. Second, a lightweight online speculation scheduler uses per-request acceptance-rate estimates and a batch-aware cost model to let each request independently choose when to verify. Third, an intra-draft refresh layer performs full attention at a single designated layer during drafting, updating the sparse context at every draft step to reduce staleness during drafting. Across three models and five reasoning and long-context benchmarks, ASPIRE achieves $1.70$-$4.58\times$ speedup in decoding throughput over autoregressive baselines and improves average speedup by approximately $27\%$ over the strongest prior self-speculative baselines.

The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponential family is evaluated. The results go beyond classical structural equation models as well as results for nodes with observations from a homogeneous family of distributions. The main result establishes that the orientation of every edge joining an ordinal node to an exponential-family node is identifiable from the joint distribution alone at every parameter value, provided the ordinal node has at least three categories and the exponential-family node at least three points of support, with no restriction on the sufficient statistic. Converses show that both requirements are necessary: the three-category requirement is binding only for affine sufficient statistics, and the three-point requirement is binding under the canonical link. The guarantee extends to orienting every such mixed ordinal-exponential family edge of a given $d$-node undirected skeleton. Numerical experiments illustrate the theoretical results by successfully separating orientations within a Markov equivalence class, which are indistinguishable by conditional independence alone.

In this paper, we study Renyi and Tsallis information entropies for a Hamiltonian system with position-dependent mass confined in harmonic oscillator potential. Gegenbauer polynomials are used to obtain the position eigenfunction of such a system, and the modified Bessel function of the second kind is used to determine the equivalent momentum eigenfunction. By means of probability densities of both representations, we analytically and numerically evaluate the Heisenberg-like uncertainty of this system. Because the Renyi and Tsallis information entropies in position representation are described by integral functionals of the Gegenbauer polynomials, these quantities are much more difficult to calculate. To get around this problem, we evaluate these information entropies at the system s asymptotical limit, which corresponds to the behavior of an undeformed harmonic oscillator. Nevertheless, no approximation method is used to obtain the Tsallis and Renyi information entropies in momentum representation. In both representations, we find that these information entropies approach the Shannon entropy when the entropic parameter alpha-> 1 and are closed to the results of similar models of the literature. Finally, we evaluate the related entropic uncertainty relations numerically to validate the latter obersevations.

A success in adversarial testing of scientific theories reaches only as deep as its matching failure would have cut. I call this the Reach Principle, and the goal of this chapter is to build the instrument that enforces it. Adversarial tests of consciousness theories can turn into a one-way evidential ratchet. When a preregistered prediction passes, the success is claimed for the research program; when it fails, the failure lands on an auxiliary involved in measurement or implementation. The ratchet turns wherever attribution is settled after the outcome is known. Against it, I define a preregisterable test object, putting the attribution policy on the record before the result, comprising what is compared, under which assumptions, when data suffice to settle an outcome, and which targets of a dated theory version each outcome exposes. The Measure Centrality Index 2.0 extends the original version to the positive-evidence side, recording for every possible outcome what it would count against and what its proponent has agreed to change. I run the framework on Cogitate's third prediction, where the preregistered target was the posterior-connectivity implementation and the mathematical or computational core of both Global Neuronal Workspace Theory and Integrated Information Theory stood outside the test. The record shows that a pass could have been claimed at any depth, because nothing fenced success the way the failure was bound. The next preregistration can set reach in advance, and support for a theory then reaches only what was exposed ex ante.

Tiered inference distributes large language model computation across a hierarchy of devices, from on-device and edge hardware to the datacenter, and is increasingly offered as the route to cheaper, lower-latency, and more private serving. Most such systems orchestrate frozen model checkpoints, and most decide what to resolve locally and what to escalate using a confidence signal produced by a forward-deployed model. We give a closed-form condition for when no threshold on that signal makes a tiered cascade worthwhile relative to sending every query to the backstop. Modeling the routing decision as a binary test and separating local from escalation cost through an architecture cost ratio $\kappa$, we derive a feasibility condition bracketed by a signal-free floor, below which a cascade is feasible at any signal quality, and a perfect-signal ceiling, above which none is. Read along the $\kappa$ axis the condition inverts: where local compute is billed, cost binds, matching a recent empirical finding; where it is sunk, on decentralized hardware, feasibility becomes unconditional and signal quality instead sets what the cascade achieves. We turn the condition into three design requirements that existing trained-in primitives satisfy only in isolation, and we propose, as future work, a co-designed architecture and a protocol to test it. The condition and the inversion hold independently of that architecture; they are the paper's contribution.

Accurate heart rate (HR) and interbeat interval (IBI) estimation from wearable electrocardiogram (ECG) is essential for continuous physiological monitoring, but motion artifacts can create false QRS-like peaks and obscure true R-peaks. Conventional detectors and local interval-repair rules process detections independently and may become unreliable when several consecutive beats are corrupted. We present ARTA (Artifact-Robust Temporal Alignment), a graph-based combinatorial framework that estimates the heartbeat sequence through constrained temporal optimization. ARTA combines candidates from multiple ECG detectors, represents them as nodes in a directed acyclic graph and connects candidates through physiologically plausible RR intervals. Candidate confidence uses detector agreement and local ECG quality, while history-dependent transition costs capture recent IBI consistency and a soft heart-rate prior. Beam-search dynamic programming selects a coherent low-cost path across the recording, suppressing false detections and recovering missed beats when plausible auxiliary candidates are available. On the IEEE Signal Processing Cup exercise dataset with electrode-motion artifacts from -2 to 12 dB, ARTA achieved an HR mean absolute error of 1.79 beats per minute, 54% lower than the strongest single-detector baseline, with 92.38% of windows within +/-5 beats per minute, an R-peak F1 score of 0.95, and an IBI mean absolute error of 6.52 ms. Cross-dataset evaluation on the European ST-T Database further assessed transfer to long clinical ambulatory recordings and a label-free reliability analysis examined conditional performance without using reference annotations for segment selection. ARTA is single-channel, training-free, interpretable and computationally lightweight, operating approximately 27 times faster than real time with peak memory below 2.1 MB in a single-thread CPU simulation.

A matrix method for constructing a discrete cosine-sine transform of order N is proposed, upon which discrete and integer cosine-sine transforms of order 16 are constructed. A fast algorithm for computing an integer cosine-sine transform of order-16 with low multiplicative complexity, which is 8.25 times smaller and requires 115% more addition operations compared to the fast discrete cosine-sine transform algorithm, is presented.

Does a widening gap between company revenue and staff expense per employee provide evidence of declining rent-sharing? We examine how reporting selection and sector composition affect that inference, as a follow-up to Acemoglu, He, and le Maire's Eclipse of Rent-Sharing. We assemble 7,000 company-years for 695 firms over 2015-2025. In a fixed sample of 75 staff-expense reporters, nominal revenue per employee rises 83.6%, while staff expense per employee rises 51.0%. The pooled expense-to-revenue ratio falls, but the pattern is concentrated in financial firms; among the other 25 reporters, aggregate staff expense per employee grows slightly faster than aggregate revenue per employee. These accounting measures identify neither real productivity nor the allocation of its gains between labor and capital. A separate audit of the original paper's reported estimates finds that some US output effects remain imprecisely estimated, while several Danish estimates support tighter bounds. The fitted reporting-probability model exhibits concentrated weights, substantial two-group imbalance and sectors without reporters; it does not justify generalizing payroll results. We extend the original study's measurement discussion to public-company accounts and qualify a blanket interpretation of no output effects: precision varies across its estimates. Our sector-dependent accounting results neither confirm nor overturn its causal wage and labor-share findings.

This paper presents a study of dispersion for the two-dimensional Friedlander model with Neumann and Robin boundary conditions, thereby extending previous works treating only Dirichlet boundary conditions. By considering an Airy-type special function, we obtain a parametrix construction similar to the Dirichlet case but with additional terms requiring careful control. By studying the dispersion of this parametrix, we obtain the same dispersive estimates as in previous results for the Dirichlet case, and therefore the same Strichartz estimates.

Accurate prediction does not establish that a world model has recovered a physical law: distinct dynamics can generate identical records under the same observation protocol. We formulate law recovery on a fixed physical domain under an explicit catalog of experiments, sensor uncertainty, and an acquisition budget. A rate–distortion converse separates the information needed to describe a law from the information the apparatus can reveal. Its constructive counterpart gives a finite response codebook and an explicit decoding budget. On compact world classes, uniform recovery is possible exactly when every pair of different laws is experimentally distinguishable; equivalently, the apparatus can recover all the entropy of every finite law source. An inverse response modulus quantifies stability. For Lipschitz fields on a $d$-dimensional state–action domain, noisy full-state readouts after resets require minimax budget $\Theta(\varepsilon^{-(d+4)/2})$ for squared law error $\varepsilon$, compared with $\Theta(\varepsilon^{-(d+2)/2})$ for direct field observations. Exact crossing-time symmetries establish the lower bound under adaptive experiment selection and arbitrary durations with constant inputs. Reproducible synthetic cases illustrate the separate roles of intervention, calibration, and repeated measurement. Together, the results identify which evidence supports a claim of physical-law recovery and the cost of acquiring it.

Probabilistic downscaling must represent kilometer-scale structure left unresolved by a deterministic regional prediction. We introduce an analog-initialized latent transport method that uses historical regional residuals as a meteorologically informed empirical source. A frozen graph neural network predicts the deterministic state from the Integrated Forecasting System (IFS). For each input, residuals from 20 training dates with similar IFS conditions initialize the ensemble. A conditional flow-matching model transports these fields in a carefully designed 65-factor, 100,000-node latent space, and a grid decoder reconstructs 81 atmospheric channels including precipitation at 2.5km spacing. On a season- and cycle-balanced 2025 test, ensemble CRPS improves over deterministic mean absolute error for all 81 variables. Matched analogs improve point and spatial errors over Gaussian initialization and reduce spectral error compared with covariance matching. Comparisons with released CorrDiff, a local CorrDiff-mini model and full grid EDM provide context on the 2023 validation sample. Total training of the downscaling model takes approximately 12h on one NVIDIA H200, and inference takes about 12s per 81-field member. The frozen networks also generate fields over France when supplied with local static data and a high-resolution IFS-derived analog bank. The method combines empirical initialization with whole-domain latent transport for computationally practical regional ensembles.

The free-energy principle (FEP) and active inference describe adaptive systems through probabilistic generative models and free-energy functionals, whereas integrated information theory (IIT) characterizes intrinsic cause-effect organization. We separate two questions: whether revised IIT quantities admit local free-energy-like representations and whether scalar expected free energy can determine system integrated information. We show that the 2026 IIT intrinsic-differentiation and specification terms admit exact surprisal representations, and hence variational-free-energy representations under exact inference. We then construct noisy binary permutation networks whose causal topology varies while local transition reliability is fixed. In a homogeneous state, all permutations yield the same one-step predictive distribution and state-risk expected free energy under common preferences and observation models, yet revised IIT distinguishes them: multicycle permutations have zero whole-system integration, whereas a single noisy cycle has positive integration at intermediate reliability. For an n-unit noisy single cycle, we derive a closed-form expression for revised system integrated information, prove that the minimum-information partition cuts exactly two causal edges, and show that as n grows the optimal reliability approaches unity, the joint specified-state probability approaches one half, and the maximum integration approaches one ibit. Numerical IIT calculations for n = 2-5 and exhaustive partition enumeration through n = 8 reproduce the analytical results. Thus, local surprisal/free-energy correspondence coexists with global non-equivalence: equal expected free energy does not imply equal revised IIT system integrated information. Any synthesis of IIT with FEP/active inference must retain counterfactual causal information beyond a scalar free-energy value.

We clarify the relation between the time-optimal quantum evolution of Carlini et al. and the coherent two-boundary optimal-control formulation recently proposed for quantum algorithm design.

Multi-objective optimization (MOO) problems are common in logistics, where routing decisions must balance conflicting objectives such as travel distance, delivery time, and operational risk. A recently proposed Quantum Approximate Optimization Algorithm (QAOA) parameter-transfer strategy solves multi-objective MAX-CUT problems by reusing parameters trained on smaller instances, avoiding costly reoptimization for each scalarized problem. However, its effectiveness has only been demonstrated on proof-of-concept instances tailored to quantum hardware connectivity. In this work, we evaluate the applicability of this strategy to realistic routing problems. We formulate the Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) as Quadratic Unconstrained Binary Optimization (QUBO) models, reduce them to MAX-CUT, and assess the parameter-transfer framework under conditions matching the original study. Validation is performed through classical simulations and experiments on IBM quantum hardware. The resulting Pareto fronts are compared with those obtained using an adapted classical {\epsilon}-constraint method, using hypervolume as the primary quality metric. Results indicate that parameter transfer remains effective, frequently achieving higher hypervolume and often finding competitive solutions earlier. However, performance depends on problem structure. The approach is consistently effective for TSP instances but less stable for the more constrained VRP, suggesting that QAOA parameter transferability decreases as the optimization landscape becomes more complex. These results provide the first comprehensive evaluation of QAOA parameter transfer on realistic multi-objective routing problems and demonstrate its potential beyond proof-of-concept MAX-CUT benchmarks.

Establishing a lasting human settlement beyond Earth requires a habitable destination at arrival and sustained renewal of its population/infrastructure. We compare Earth delivery, robotic predeployment, local resource processing, localized manufacture under coupled protection, power, recovery, health requirements. In a 1,000-resident case, specific retention times and 1% annual growth require ~1.8 tonnes of equipment replacement/expansion per resident-year. Local manufacture raises mean site electricity from 11.8 to 19.5 MW; the declared retention range gives 17-26 MW at fixed processing intensity. A separate 100-ton/year pilot reduces 10-year deliveries from 1,000 to 419 tonnes after a 3-year commissioning ramp, while retaining imported inputs. Local production thus reduces freight by adding maintained capacity at the destination. Receiving capacity is bounded by replacement-adjusted supply, not passenger throughput. Published Martian transport gives ~63,000 tonnes of planar cover per hectare for the selected 10-mSv/year GCR allocation; Earth-like acceleration at 2~rpm requires a 224-m rotation radius. These scales favor local shielding and modular rotating habitats. Repeated 10-day wastewater outages every 35 days require capacity at least 1.4 times inflow; storage cannot correct a persistent deficit. Existing evidence leaves lifelong partial-gravity, mixed-field radiation responses, gestation, development and subsequent fertility unresolved. With sustained investment, recoverable habitats, resource pilots, controlled-gravity investigations are credible 10-20-year objectives; broad industrial renewal requires multidecade development. Earth-supported habitation is a credible development path. Independent population continuity requires demonstrated health, recovery, demographic resilience, essential-product renewal within one exposure/resource history; its completion cannot yet be dated.

The core-diagonal closure of Bothe and Druet identifies multicomponent Darken diffusion as a thermodynamically consistent Maxwell-Stefan model in which the constitutive data are species tracer diffusivities. Building on the Stieltjes-coordinate and De Giorgi regularity framework introduced in our recent preprint for additive Maxwell-Stefan frictions, we prove global maximal-$L^p$ strong well-posedness, without smallness assumptions, for the affine Darken class with composition-dependent scalar coefficients and uniformly positive tracer diffusivities. We show that this is exactly the maximal Darken class preserving the fixed Stieltjes eigenframe underlying the global argument. For general smooth, uniformly positive Darken tracer diffusivities we also derive the direct constrained flux form, exact mixing-entropy symmetrization, Fisher dissipation, composition-uniform normal ellipticity, and local strong well-posedness. Solutions in the affine class remain uniformly separated from the simplex boundary, become classical for positive times, and converge exponentially to equilibrium. Constant multiplicative frictions, including repeated factors, are covered by grouping species with equal factors.

Event-contract settlement is a state path rather than a universal timestamp. Using a registered seven-day enrollment and seven-day administrative follow-up on Kalshi, this paper distinguishes market-object membership, lifecycle messages, endpoint states, and observation gaps. The MVE layer contains 7,611,594 tickers created inside the registered half-open interval; the reconstructed ordinary stock contains 152,694 markets at risk at the enrollment boundary, 3,477,227 already terminal records, and 11,410 boundary-uncertain records. The follow-up identifies exact public endpoints for 7,357,576 MVE market objects: 7,237,993 through REST settlement time alone, 119,212 agreeing across WebSocket and REST, and 371 WebSocket-only; 254,018 lack an admissible exact endpoint. In the ordinary boundary cohort, 71,657 markets have an exact WebSocket endpoint and 81,037 do not. These are venue-level public-finality outcomes, not member-cash observations. Exact determination-to-endpoint pairs are available for 126,806 MVE and 70,979 ordinary tickers, with respective mean durations of 9.676 and 322.50 seconds and observed maxima of 15 and 7,133 seconds. The contrast is descriptive, not causal. Transport gaps preserve two exact endpoint clocks but prevent the same rows from proving complete intermediate paths or absence of revisions; current settlement timers cannot be bound to historical rule versions. Public endpoint completion is therefore extensive, especially on REST, while path completeness remains narrower and family-specific. The exact-key historical crosswalk records earlier interim observations without changing the final cohort or endpoint facts.

Young's law on a rigid solid and the Neumann balance at a three-fluid junction describe different admissible motions. We connect them through an ordered continuum construction. An affine viscoelastic third phase first acquires permanent deformation memory in the no-relaxation limit at fixed modulus. Independently specified material-surface laws distinguish surface free energy from mechanical surface stress. On the deformable support, displacement of solid material and migration of the wet-dry partition give separate equilibrium conditions. Stiffening an anchored support suppresses displacement at a fixed macroscopic observation distance but leaves reversible migration possible. The surviving variation gives Young's law; the constrained mechanics remains as a reaction carrying normal and, in general, tangential capillary loads. Two complementary results substantiate this reduction. A clamped finite-strain model with circular liquid interfaces yields uniform convergence of reduced energies and recovery of the spatial Young angle for global almost minimisers. A separate quadratic half-space model gives convergence of stationary migration derivatives and a nonzero limiting traction distribution. The results explain how a local Neumann-like ridge can coexist with a Young angle measured against the distant wall, while distinguishing geometric convergence from the control of migration and reaction forces.

Event cameras are well suited to pedestrian crossing detection, and spiking neural networks (SNNs) can process their output natively, but current SNN detectors are trained with supervised backpropagation and therefore require costly frame-level crossing labels. We investigate crossing detection with no labels in feature learning and report the first unsupervised results on the recent DVS-PedX pedestrian-crossing benchmark. A single spiking layer trained with winner-take-all spike-timing-dependent plasticity learns a dictionary from unlabelled event-frame patches; frames are encoded by cosine similarity to the learned filters with spatial pooling and read out by a linear classifier, the only supervised component. On the 24,454-frame test split the method attains 90.3% accuracy and an area under the receiver operating characteristic curve (AUROC) of 0.936, compared with 92.0% and 0.943 for a supervised spiking network trained end-to-end on the same frames; under adverse weather the AUROC is 0.913. The result is insensitive to the choice of plasticity rule but depends strongly on the readout protocol: with the classical neuron-assignment readout the same network attains only 0.70 AUROC. On the benchmark's real converted portion, a readout refit lifts performance from chance (0.53) to 0.67 AUROC, within the published supervised range. These findings indicate that the accuracy cost of removing labels from feature learning is small on this benchmark, and that reported weaknesses of unsupervised spiking networks may be attributable to the readout protocol rather than to the learning rule.