Driven by four applications of Plackett-Burman (PB) designs, this paper proposes a causal inference framework based on potential outcomes. First, we define the causal effects of the PB designs under finite populations. The Neymanian estimator of causal effects is then obtained, including the estimated variance and covariance. Furthermore, we conduct a sharp null-hypothesis test and construct the Fisherian interval using an algorithm. Finally, the proposed methods are illustrated through these applications.

We study the problem of testing whether two conditional distributions are equal using generative models. The proposed method learns a conditional generator from each sample and uses it to create responses at covariate values observed in the other sample, allowing generated and observed responses to be compared directly. By aligning covariates through cross-generation, the approach avoids conditional density-ratio estimation and local smoothing over high-dimensional covariates. The population version of this construction yields a conditional discrepancy that characterizes equality of the two conditional distributions under suitable overlap conditions, while the sample version leads to a test statistic defined as the supremum of an RKHS-indexed empirical process with multiplier bootstrap calibration. A computationally efficient algorithm for evaluating the statistic and its bootstrap analogue is developed based on alternating maximization and the kernel trick. Theoretically, we derive the limiting distribution of the test statistic under both the null and alternative hypotheses, prove bootstrap validity and consistency of the resulting test, and show that the proposed procedure attains a double-robustness property with respect to conditional generator estimation errors. Simulations and real data applications suggest that the proposed method performs well for multivariate responses and high-dimensional covariates.

DeepKriging-style models, such as Spatio-Temporal DeepKriging, improve scalability through basis-function embeddings and stochastic gradient learning; however, fixed regular-grid spatial bases remain inefficient under highly non-uniform sampling patterns, often over-allocating capacity to sparse regions while under-resolving dense clusters. To address this limitation, we propose a practical extension of DeepKriging for reliable spatio-temporal distributional forecasting, incorporating cluster-adaptive spatial bases - whose centers and scales are initialized from {the spatial sampling density} - to better capture heterogeneous spatial sampling, together with cluster-aware conformal calibration that determines prediction-interval widths within spatial clusters (with a global fallback when calibration samples are insufficient). The resulting calibration pipeline explicitly targets spatial heterogeneity and local miscalibration, and experiments, including simulation studies and PM$_{2.5}$ data analysis, demonstrate substantially improved coverage accuracy and tail reliability under clustered observation patterns compared with a global conformal baseline.

With the increasing availability of ranking data, there has been a growing demand for appropriate unsupervised rank-based inferential frameworks capable of handling high-dimensional datasets and providing uncertainty quantification for all estimates. Rank-based methods have also seen a growing popularity in -omics pipelines, as ranking continuous measurements provides a robust means of handling non-normally distributed data. The Bayesian Mallows model (BMM) has emerged as a promising choice because of its adaptability to various types of ranking data and its flexible framework, integrating cluster-wise rank aggregation with inference at the individual level. However, the scalability of BMM to ultra-high-dimensional settings, such as -omics analyses, has remained limited. The present paper addresses this issue by introducing the first rank-based model generalizing BMM to jointly handle clustering and variable selection, namely the lower-dimensional Bayesian Mallows Model Mixture (lowBM3). The proposed method provides a novel Bayesian framework that simultaneously handles heterogeneity in the sample, unsupervised parameter estimation, and model selection in a scalable manner for ultra-high-dimensional data. Additionally, a companion postprocessing framework is introduced to provide posterior summaries of the discrete posterior distributions of both the consensus ranking and the variable selector. Simulation studies are performed to assess the performance of the method. The usefulness of the method is also shown in an application to signature discovery for cancer genomics, where RNA-seq bulk gene expression data obtained from breast cancer patients are clustered genome-wide.

Highly reliable products are often tested under accelerated conditions to provoke failures within a feasible timeframe. For products whose service life involves repeated alternation between two stress levels, such as automotive air-conditioners, batteries, and aerospace components, cyclic-stress accelerated life testing (CyALT) provides a more realistic loading profile than conventional accelerated tests. In practice, failures are often recorded only at scheduled inspection times, leading to interval-censored counts rather than exact lifetimes. Moreover, traditional maximum likelihood estimation is sensitive to data contamination, which is a genuine concern in small-sample industrial experiments. This paper develops robust inferential procedures for CyALT models with lognormal lifetimes under interval monitoring. Robust estimators are obtained by minimizing a weighted density power divergence (WDPD), leading to the weighted minimum density power divergence estimator (WMDPDE). We establish the asymptotic distribution of the WMDPDE, derive influence function expressions to characterize the robustness, and present asymptotic and bootstrap confidence intervals for important lifetime characteristics. A simulation study confirms that the WMDPDE provides substantial protection against outliers while retaining high efficiency under clean data. The methodology is illustrated through the analysis of an air-conditioner reliability dataset, demonstrating the practical advantages of robust inference in the CyALT framework.

Forecasting systems are commonly refreshed at every review period, and that refresh usually bundles two distinct operations: estimating parameters and selecting the model form. Recent evidence suggests the second operation is often unnecessary, since intermediate updating strategies can hold forecast accuracy roughly fixed while cutting computational cost and forecast instability. This technical note takes up the complementary question. Once a system has adopted a reduced-update policy, when should it interrupt that policy and re-specify the model form? We define specification debt as the evidence accumulated against the deployed model form, and we use it to build a cost-sensitive trigger for re-specification. In a closed discrete model space the trigger reduces to a threshold on the negative log posterior probability of the deployed specification. In open production settings the same decision rule can be run with predictive score gaps, stacking weights, or calibrated monitoring diagnostics. Fixed update frequencies turn out to be a special case of the rule, recovered when evidence against the deployed form accumulates at a constant rate. We illustrate the idea on 500 monthly M4 series, comparing full updating, fixed model-form update frequencies, parameter-only updating, and capped adaptive score-triggered updating, and within the finite ETS grid we also compute information-criterion analogues of specification debt from AIC and BIC weights over the candidate forms. In that illustration the best capped adaptive policy is comparable to full updating in accuracy, runs in about 28 percent of full-update computational time, lowers forecast instability, and behaves like a fixed schedule with a small number of evidence-based exceptions.

In various application domains, there is a certain `null cell', inside a multinomial setup, where observations are recorded for the other cells, but where one cannot count the number of occurrences for the null cell. I develop inference theory for assessing such unknown numbers, counting the uncounted, in situations where counts are available for the other cells, via parametric modelling. The methods are used to estimate the number of persons killed in Guatemala during the Genocidio guatemalteco years 1978--1995. There are three carefully curated lists of killed people, where the information can be mapped to a Venn diagram with $2^3=8$ cells. Summing over the seven observed cells, $R=\hbox{47,803}$ killed individuals can be identified, but how big is $N_{0,0,0}$, and hence $N=N_{0,0,0}+R$?

This paper presents an open-economy macroeconomic equilibrium model for Proof-of-Stake (PoS) networks with fee-burn mechanics (EIP-1559) that formalizes the strategic interplay between a Kelly-optimizing rational institutional investor and a utility-driven retail consumer. We analyze network dynamics across two behavioral regimes. In The Unbounded Accumulation Model, the consumer purely accumulates tokens, creating an exclusive buy-side pressure that interacts with institutional portfolio rebalancing to fuel an ever-expanding speculative bubble and generate compounding excess returns for investors. Conversely, in The Utility-Consumption Model, the consumer dynamically buys and sells tokens to balance crypto wealth against real-world fiat consumption. Within this framework, we derive an explicit steady-state equilibrium price for ETH, demonstrating how token valuation anchors to a stable fundamental baseline that scales directly with network adoption while completely dissolving the institutional yield premium. Our numerical simulations show that while exogenous traditional finance (TradFi) shocks propagate through portfolio rebalancing to drive high token price volatility, network inflation remains highly stable. Furthermore, we prove that network security is insulated from institutional monopoly by counter-cyclical consumer behavior. Our findings reveal that institutional excess wealth creation in PoS ecosystems is not native to the staking protocol itself, but is strictly driven by the leveraged extraction of the retail consumer's continuous demand for transactional utility.

Museums can serve as policy tools when their content is purposefully curated. We designed a framed field experiment at the Santa Maria della Scala museum in Siena that leveraged the site's historical role offering care and hospitality.Student visitors randomly assigned to a tour emphasizing this function later donated more to an NGO supporting refugee than those who followed a standard artistic itinerary, with effects concentrated among female participants. These results show that thematically targeted museum experiences can measurably boost charitable behavior toward vulnerable groups, underscoring the untapped potential of cultural institutions in behavioral public policy.

Decomposing impact as the difference between realized and counterfactual returns and requiring both to be diffusive yields an identity that restricts admissible impact scaling at the level of individual participants. This constraint implies the square-root law in the information-neutral regime and a crossover to linear impact under strong informational coupling, consistent with empirical observations. In the weak-coupling regime, cumulative market impact is itself diffusive -- a diagnostic that many propagator and latent liquidity models fail to satisfy.

This article introduces an unconventional model for price processes in finance that emerges from the classical Heston model under Mechkov's fast-reversion limit. This new fast-excursion Heston model exhibits instantaneous (i.e. fast) excursions through an interval of prices at each time, which are invisible to vanilla options but critical for hitting probabilities and continuously monitored exotics. Theoretically, the model provides a rare example of a non-degenerate limit of stochastic volatility models that escapes the Skorokhod topologies. This leads us to a class of interval-valued processes which exist as lifts of subordinated Levy processes, through the concept of selections in the theory of random closed sets. On the practical side, we show how the model can be simulated using price-time parametric representations, and utilise a purpose-built classical Heston simulation scheme in order to visualise convergence. Finally we demonstrate how this model raises hitting probabilities for barrier options considerably (of order 10% for one-month EURUSD options), due to taking excursion risk into account.

This paper develops a binary-gamble framework for characterizing risk sensitivity and loss aversion in Cumulative Prospect Theory (CPT). The proposed probabilistic risk-sensitivity metric is defined as a probability-threshold ratio that determines acceptance and preference thresholds in choice problems involving either a certain outcome and a binary gamble or two binary gambles. We show how standard notions of symmetric and non-symmetric bet aversion can be recovered within this framework, and we compare the resulting threshold-based conditions with utility premia, probability premia, and Arrow--Pratt curvature measures. The analysis clarifies when these criteria coincide and when they diverge, particularly for increasing aversion conditions, binary gambles with unequal probability distributions, and settings involving probability weighting functions. We also identify technical restrictions that arise when CPT-utility functions are used to represent loss aversion at the reference point. The resulting framework provides a decision-theoretic interpretation of risk sensitivity that is directly tied to probability thresholds and complements existing premium-based approaches.

In the demographic framework, mortality payoff function describes the cost of an interaction and fertility payoff function describes its reward. So while mortality cost depends on opponent's strategy, fertility reward can be affected by the density-dependent juvenile recruitment survival. This motivates an analysis of the eco-evolutionary dynamics of the classical Hawk-Dove game. It is shown that the stable and unstable equilibria (determined by the intersections of frequency and density nullclines) are connected by heteroclinic orbits, which attract nearby trajectories. The resulting bundle of trajectories leads to the discovery of the so-called subnullcines (manifolds placed between frequency and density nullcline) before they converge to the stable rest point. The initial isolated system is then extended by adding environmental seasonality (periodic background mortality), which acts as an external factor. This leads to complex cycling behavior and the subnullclines act as barriers to the propagation of the perturbation (resilience/resistance threshold). Thus, in a way, this paper completes, yet extends, previous works on the eco-evolutionary dynamics of games with demographic payoffs.

Brains routinely generate highly flexible and complex behaviors on a relatively stable structure and limited resources. A key mechanism underlying this ability is compositionality, which allows the brain to efficiently decompose complex tasks into simpler, reusable primitives. While network modularity has often been linked to compositionality in biological and artificial networks, a rigorous mathematical characterization of this relationship in nonlinear networks is still lacking. In this work, we formally investigate how structural modularity supports functional compositionality in inhibition-dominated threshold-linear networks (TLNs). We introduce a novel class of modular network assembly called low-rank gluings, where component subnetworks with arbitrary internal connectivity are connected via specific low-rank couplings. We prove that the global fixed points of these networks are constrained to be combinations of the local fixed points of their constituent modules. For a more structured subclass, called rank-1 gluings, we provide a complete characterization that determines which combinations of local fixed points yield global ones. We apply these results to graph-based networks, extending fixed point decomposition rules from combinatorial threshold-linear networks (CTLNs) to the more flexible family of generalized CTLNs (gCTLNs), thereby proving that these structural rules are more robust than initially posited. Finally, we demonstrate that these gluing rules provide a mathematically tractable recipe for engineering compositional dynamics, enabling the construction of networks with a combinatorially large repertoire of predictable attractors that can be understood from simpler component motifs, ranging from compositions of fixed points to compositional limit cycles.

Metatranscriptomic sequencing has expanded our knowledge of the RNA virosphere far more rapidly than novel viruses can be taxonomically classified. Taxonomic assignment above the family level is particularly difficult because the RNA-dependent RNA polymerase (RdRp) is often the only gene retained across RNA viruses yet exhibits little sequence similarity among highly divergent viruses. Here we show that RdRp protein structure retains taxonomic signal at evolutionary depths where RdRp primary sequence similarity has largely collapsed, and that the organization of this signal is consistent with the current ICTV hierarchy. Based on this, we developed ViraClass, a hierarchical framework for RNA virus taxonomic placement that uses RdRp structure for rank-by-rank assignment from phylum to genus, stopping at the deepest rank supported by confidence thresholds, and calibrated structural clustering for viruses that remain outside existing reference space. Across random-split, prospective and taxonomic hold-out benchmarks, ViraClass outperforms sequence-based and genome-content baselines. The largest gains emerge at deep evolutionary distances, in benchmarks that withhold entire families, orders or classes from the reference, where sequence-based methods lose most of their signal. In challenging boundary cases such as the Flaviviridae, ViraClass's structure-based placements capture the taxonomic boundary tensions highlighted by recent phylogenetic studies. When applied to a large collection of previously unclassified RdRp sequences, ViraClass places high-confidence queries into existing phyla and organizes the remainder into compact structural groups. ViraClass therefore provides a scalable approach from large-scale virus discovery to hierarchical taxonomic interpretation, particularly at the deep evolutionary ranges that current sequence-based pipelines cannot reach.

Biocodicology, the study of biological information preserved in manuscripts, offers new opportunities to examine parchment as both a textual and biological artefact. This study applies non-destructive sampling to isolate and sequence mitochondrial genomes (mtGenomes) from a 14th-century manuscript, Ms. Codex 1629, which contains both single-use and palimpsested folios. We sought to evaluate whether palimpsest preparation, including chemical washing, compromised DNA integrity and whether computational methods could aid in identifying reused parchment. DNA sequencing revealed that both single-use and palimpsested parchments retained sufficient mtGenomes for analysis, with no significant differences in genome coverage or depth. To assess the potential of computational biology in manuscript studies, we implemented machine learning classifiers, including logistic regression and neural networks, to distinguish palimpsests from single-use folios. Models achieved high precision but exhibited reduced recall for the minority palimpsest class, reflecting dataset imbalance. While additional ancient mtGenome samples from palimpsest are required and further testing is needed, this study demonstrates how integrating molecular biology and neural networks highlights new approaches for palimpsest detection and underscores the evolving role of data science in biocodicology.

Public viral genome resources such as the National Center for Biotechnology Information (NCBI) Virus database are central to outbreak response, evolutionary analysis, vaccine design, and genomic surveillance. Yet many high-value retrieval workflows remain optimized for interactive use rather than deterministic, reproducible programmatic interfaces. This creates a challenge for Large Language Model (LLM)-based scientific agents, where errors in metadata interpretation, filtering logic, or retrieval can propagate into incorrect datasets. To evaluate agentic viral data retrieval, we built VirBench, a manually curated benchmark of 120 queries spanning diverse pathogens, taxonomic levels, and metadata filters. When autonomous AI systems, including Biomni, Claude, GPT, and Edison Analysis, were tasked with these queries without a dedicated retrieval layer, performance varied widely: mean accuracy ranged from 16.9% for Claude Sonnet 4 to 91.3% for GPT-5.5, with newer frontier models showing progress but residual errors remaining consequential. To address this, we built gget virus, a deterministic query framework that formalizes NCBI Virus-style filtering as a reproducible programmatic system. By staging retrieval, applying metadata constraints before sequence download, and retrieving structured GenBank records, gget virus reduces data transfer by more than 98% for high-volume queries while preserving exact-match semantics. Instructing autonomous AI systems to use gget virus increased accuracy to at least 90.0% across all evaluated systems and up to 99.7% for GPT-5.5, improved response stability to 0.92-1.00, reduced error magnitude, and generally decreased runtime and tool calls. Together, this work establishes deterministic data access as critical infrastructure for reliable agentic science and provides a reproducible retrieval layer for robust human- and AI-driven viral genomics workflows.

Personalised cancer therapy aims to tailor treatment to individual tumour profiles, yet tumour heterogeneity and adaptive resistance continue to limit clinical efficacy. Drug combinations offer a strategy to overcome resistance by simultaneously targeting multiple pathways, but their rational design is constrained by the vast combinatorial search space and experimental cost. Here, we present an AI-guided, QSAR-driven iterative optimisation framework that integrates machine learning with automated experimental screening to enable closed-loop discovery of selective multi-drug therapies. Starting from an initial random screen, the system iteratively predicts, tests, and refines three-drug combinations targeting MCF7 breast cancer cells. Incorporation of non-tumorigenic MCF10A cells enables explicit optimisation of tumour-selective efficacy, prioritising regimens that maximise cancer cell killing while sparing healthy cells. Across successive iterations, the framework rapidly enriched for highly selective, high-efficacy combinations, while maintaining chemical and mechanistic diversity and avoiding convergence on a narrow solution space. By continuously learning from experimental feedback, the approach efficiently navigates millions of combinations to identify a small set of validated, tumour-selective regimens. These results establish a scalable proof-of-concept for AI-driven, closed-loop optimisation of higher-order drug combinations, demonstrating how iterative integration of computation and experimentation can enable adaptive and potentially personalised therapeutic design in precision oncology.

Modelling opinion dynamics typically relies on hand-crafted local interaction rules to study emergent macroscopic phenomena such as consensus and polarisation. In contrast, multi-agent reinforcement learning (MARL) enables agents to learn such behaviours directly by optimising simple rewards. To explore the potential of MARL for opinion dynamics, we introduce a GPU-accelerated consensus and truth-finding game that scales to populations of up to 1000 agents, comparable to many real-world social sub-networks. To prevent unrealistic conventions, we extend other-play to general-sum social interactions. We next validate our model on a subset of the Bluesky network by recovering agent importance structures from graph topology alone via a learned attention layer, finding that highly conforming populations most closely match human data. In large social media networks such high levels of conformity significantly reduce collective accuracy and promote dishonest agents that lie to fit in. By contrast, small, dynamic hunter-gatherer networks are less affected; here, conformity can even improve collective agreement. This suggests a mismatch between evolved human conformity heuristics and modern social media environments as a potential contributor to misinformation.

Urban trip-planning systems are commonly optimized for travel time and cost, but they offer limited support for the heterogeneous needs that real travelers bring, such as personalized preferences, multi-stop itinerary construction, and end-to-end wheelchair accessibility. We present openpaths, a supervisor-specialist multi-agent system that handles all of these tasks within a single architecture. openpaths adopts a deliberate division of labor: LLM agents parse natural-language input, classify request intent, and orchestrate execution, while classical algorithms perform route optimization over curated mobility and accessibility data. This design ensures that the resulting trip honors heterogeneous user preferences and enforces strict accessibility requirements when requested. Beyond per-user planning, openpaths doubles as a measurement instrument for city-scale accessibility analysis: applied to NYC, the system reveals substantial ADA infrastructure gaps and quantifies their effect on job accessibility for wheelchair users. Overall, this study shows how a supervisor-specialist LLM agentic framework can support heterogeneous trip planning and transparent, equitable transportation analysis in real urban environments.

Deploying deep neural network (DNN) inference on low-end edge devices raises two key challenges: protecting model confidentiality against a potentially compromised edge system and enabling verifiable inference without incurring prohibitive overhead. Existing approaches either house partial models and inference software within trusted execution environments (TEEs), resulting in high cost and an application-dependent trusted computing base (TCB), or execute in untrusted environments, providing little security. In this work, we present VECODI, a framework for verifiable and confidential DNN inference on constrained edge devices. At its core, VECODI introduces SHANGRI-LA, a new execution abstraction on TrustZone-M TEEs that establishes a third runtime environment with privileges strictly between the Secure and Non-Secure Worlds. VECODI leverages SHANGRI-LA to execute untrusted inference code in the Non-Secure World while using minimal application-agnostic Secure-World support to protect model confidentiality and enable verifiability (with respect to proper execution of inference code and model parameters) of inference results. We realize VECODI on a real-world NUCLEO-L552ZE-Q development board and open-source its prototype. Our results show VECODI's small TCB, memory footprint, and runtime overhead, making it a practical option for secure inference in low-end edge devices.

Diabetes affects millions of people and requires reliable continuous glucose monitoring for early hypoglycemia warning. However, medical AI systems must be not only accurate and energy efficient, but also explainable, noise robust, and uncertainty aware. This work presents a 65 nm hypoglycemia forecasting engine based on probabilistic decision trees for trustworthy medical inference. The proposed hybrid architecture combines exact arithmetic evaluation for shallow tree layers with sampling based inference for deeper layers, reducing soft decision tree complexity from exponential to sample efficient traversal. A reconfigurable 4 by 24 by 24 probabilistic node array supports arbitrary tree structures with a maximum depth of 12, coordinated by an on chip low power RISC V core. Fabricated in 65 nm CMOS, the chip achieves 11.3 nJ per inference and a state of the art 30 min forecasting F1 score of 0.825 on continuous glucose monitoring data. Compared with conventional decision tree and random forest models, the proposed engine improves robustness to sensor noise and data point drop off by 4.1x to 16.1x. These results demonstrate an energy efficient, explainable, and uncertainty aware edge AI engine for trustworthy hypoglycemia forecasting.

The Odd Cycle Transversal (OCT) problem, which asks for a minimum subset of vertices whose removal renders a graph bipartite, is a central problem in algorithmic graph theory. It is known to be NP-complete even on $P_k$-free graphs for $k \ge 6$. Furthermore, assuming the Unique Games Conjecture (UGC), OCT does not admit a constant-factor approximation algorithm on general graphs. Motivated by these hardness results, we investigate the approximability of OCT on $P_k$-free graphs. We first establish that the problem becomes polynomial-time solvable on specific subclasses of $P_k$-free graphs, most notably $(P_6, C_3)$-free graphs, by exploiting a structural decomposition into rings of bipartite graphs. Leveraging these tractable substructures as a basis, we present a constant-factor approximation algorithm for OCT on general $P_k$-free graphs. We achieve an approximation ratio of $k-2$ when $k$ is odd and $k-3$ when $k$ is even. These results provide the first nontrivial constant-factor approximations for this class dependent on $k$, aligning with the UGC implication that no approximation factor independent of $k$ is likely to exist.

The collective movement of stock prices harbors complex interdependencies that are conventionally simplified only through a linear lens. This paper explores computed structural network representations in the Indonesian capital market by testing the limits of Pearson correlation and Mutual Information (MI) in unveiling the spectral dynamics of the market. Across 2,328 rolling observation windows from 2015 to 2025, we examine 24 methodological configurations that combine three dependency estimators (Pearson, MI adaptive binning, and MI-kNN), two graph filtering schemes (Minimum Spanning Tree/MST and Planar Maximally Filtered Graph/PMFG), and four community decoders. The empirical results unveil a fundamental reality: topological richness does not always resonate with sectoral classification precision. The Pearson, MST, and Infomap configuration is shown to remain the most robust foundation for recovering conventional sectoral taxonomy. Nevertheless, when deeper observation demands the exposition of local structures and the weave of heterogeneous communities, the architectural relaxation through PMFG demonstrates its superiority. In the realm of residual information detection, MI adaptive binning appears far more proportional than kNN; histogram-based regularization successfully tames empirical noise without sweeping away traces of non-linear dependency. Ultimately, the synergy of MI and PMFG is not positioned to dethrone the dominance of linear correlation, but rather to provide an essential analytical lens for excavating hidden economic sub-structures -- such as the cohesion of commodity regimes -- that have long transcended the rigid boundaries of the market's formal sectors.

In previous work, we investigated the adoption of coding agents in GitHub projects, finding that it was very significant. This study follows this line of work, but analyses new projects, that were created after the previous study. In this new sample, we find that the adoption of coding agents is more than twice as high. We also find that the adoption is significantly more intensive, as the proportion of AI-assisted commits is sensibly higher, despite strong signs that we do not detect all of it.

We present a 65-nm risk-aware multimodal Bayesian inference engine for privacy-preserving, fully on-device skin lesion screening under uncontrolled at-home conditions. The proposed compute-in-memory architecture performs in-word Mixture-of-Gaussian sampling, improving uncertainty modeling beyond conventional unimodal Bayesian neural networks. This added probabilistic expressiveness increases equal-risk operating coverage by 1.4x, improves robustness to user-data perturbations by >1.5x, enhances process-variation resilience by 5.5x, and improves balanced accuracy by 1.8% over state-of-the-art unimodal Bayesian neural networks. Hardware robustness is further supported by calibration-free Gaussian random-number generation using complementary process variation, achieving 16.3 fJ/sample and 168.6 GSa/s/mm^2 efficiency. These results demonstrate a practical, energy-efficient, and risk-aware edge-AI solution for privacy-conscious medical screening.

Modern prediction markets face two limitations that restrict their applicability in a range of settings:~(i)~they reveal what the crowd believes but not the evidence or reasoning behind those beliefs, and~(ii)~they require an event with an external ground truth that resolves at a known future date. We address these twin challenges by introducing evidence markets, a generalization of prediction markets that incentivizes the submission of evidence alongside beliefs and can be endogenously resolved using the crowd-sourced evidence if external resolution is not possible. At its core, the market uses a logarithmic market scoring rule whose liquidity parameter changes dynamically with the accumulated evidence quality. We prove that platform loss is bounded, evidence is rewarded proportional to the current market uncertainty, and can be equivalently implemented through an automated market maker. In the case where the marker resolves endogenously based on submitted evidence, we characterize how withholding evidence shifts a trader's belief about resolution and use it to prove truthful belief and evidence reporting is a always an $\varepsilon$-dominant strategy incentive compatible (DSIC) strategy. To address operational considerations, we propose evidence verification via an LLM-as-a-Judge framework with staking and give an asynchronous execution algorithm that is not bottle-necked by verification. Throughout the work, we use LLM evaluations -- determining which model is best for a given task -- as a salient and representative running example for our proposed market.

The numerical solution of the Electric Field Integral Equation (EFIE) via the Boundary Element Method (BEM) can be computationally challenging due to conditioning issues arising in different regimes, such as (i) when the frequency decreases and the discretization density remains constant, (ii) when the frequency is kept constant while the discretization is refined, and (iii) when the frequency increases along with the discretization density. To address these issues, several preconditioning approaches for the related matrix system have been developed in the literature, only a few of which address all regimes simultaneously. This paper investigates one of these techniques and presents a strategy for accelerating the associated matrix-vector products (MVPs). In particular, we propose a novel preconditioning strategy for the shifted Helmholtz operator, for which standard pseudo-inversion techniques have shown unsatisfactory results. Instead, the application of our preconditioning technique stabilizes the number of iterations in all the aforementioned regimes. In view of these achievements, the pseudo-inversion of the shifted Helmholtz operator can be obtained in quasi-linear complexity when proper acceleration strategies are used, thus enabling the numerical solution of the EFIE with the same complexity.

Android has adopted Kotlin alongside Java across apps and core system components. During this shift, we observe parallel implementations in the Android Open Source Project (AOSP) where the same component is implemented in both Java and Kotlin. In principle, their functional purposes are identical. In practice, subtle semantic divergences can appear. Such divergences are not vulnerabilities by themselves, but they provide useful clues that may reveal flaws in surrounding enforcement logic. To the best of our knowledge, this paper presents the first systematic study of Java-Kotlin parallel implementations in the Android framework and examines their security implications. We design and build ParaDroid, an analysis framework that identifies parallel methods at scale and compares their behaviors. ParaDroid normalizes code into a bytecode-level intermediate representation, reconstructs class-to-source mappings, and uses large language models to reason about method semantics and identify behavioral divergences. Evaluated on AOSP Android 14-16, ParaDroid identified 329 parallel method pairs and 37 vulnerable divergences. We responsibly disclosed the exploitable issues to the Android Security Team. Three vulnerabilities and two bugs have been confirmed, and two CVE IDs have been assigned. Our results demonstrate that parallel Java-Kotlin code paths provide a practical surface for discovering security flaws in modern Android.

Streaming allows executing queries over massive JSON or XML documents whose size makes it infeasible to fully parse them into a tree. Earliest query answering is a radical approach to reducing latency and memory footprint. To minimize latency, a document node must be returned as soon as the node is guaranteed to be an answer regardless of how the document ends. Similarly, to minimize memory footprint, a node must be discarded as soon as it cannot become an answer regardless of how the document ends. For simple queries that select nodes based on the path from the root, the decision for each node can be made on the spot, but practical languages such as XPath or JSONpath support filters, which allow selecting nodes based on information collected from various parts of the document, possibly further down the stream. This makes earliest query answering a challenging task, as candidate nodes must be kept in memory until it becomes clear that they can be safely returned or discarded. We show that this can be done for all unary queries expressible in monadic second order logic (MSO), while ensuring constant update time -- provided that nodes are returned by passing a suitable iterator, rather than one by one.