Papers

Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying uniform time-based pricing across fundamentally heterogeneous quantum resources significantly complicates task orchestration, particularly when addressing the tradeoff between execution costs and system performance. While heuristic methods rely on predefined scheduling rules, classical deep reinforcement learning (DRL) models may require more trainable parameters in this setting. Motivated by the potential of parameterised quantum circuits (PQCs) as compact function approximators, we propose QRLQ, a cost-delay-aware quantum cloud scheduling framework integrating PQCs with a dueling double deep Q-network (D3QN) to dynamically account for both cost and delay. Our simulation results show that QRLQ achieves lower mean cost and delay than the heuristic baselines, achieving a 5-11% lower mean cost relative to availability-based and rotation-based heuristics and reducing mean delay by 17% and 82% relative to the strongest and weakest heuristic baselines, respectively, while retaining execution fidelity within 2% of a fidelity-greedy policy. Compared with the classical DRL baseline, QRLQ achieves comparable scheduling performance while using 72% fewer trainable parameters. This work explores the feasibility of using QRL for task orchestration in quantum cloud environments and demonstrates its potential for cost-delay-aware quantum resource management.

Short-term vacation rentals, as promoted by platforms such as Airbnb, Homeaway, Vrbo, etc., are a growing component of the travel industry. This paper provides a unique, large dataset on global market occupancy for the short-term rental market using data from the American company Wheelhouse. The dataset consists of data for $500$ markets around the world. For each market, a daily occupancy time series from January $2017$ to December $2022$ is provided, allowing for studies of local and global patterns in the evolution of the short-term rental market. Additionally, the dataset includes curves representing the booking trajectory of each market and stay date up to one year prior to the stay date. This large dataset comprises a unique combination of time series and survival analysis data, and is suitable as a methodological benchmark for both classical statistical and machine learning models.

We study positive mass and related Brown–York type inequalities for three-dimensional manifolds modeled on a static space with flat toroidal slices. Using inverse mean curvature flow, we first derive an inequality relating the asymptotic geometry at infinity to the size of an interior singularity. This inequality admits a natural interpretation as a positive mass type theorem. We then combine this global inequality with a Shi–Tam type construction to obtain a Brown–York type inequality for boundaries isometric to flat tori. In contrast to the Schwarzschild setting, our argument does not extend directly to general boundary surfaces, and we identify several obstructions to such a generalization. Finally, we discuss examples illustrating the relationship between negative mass, the strength of interior singularities, and the topology of the interior.

The sub-kilometer Super-Fast Rotator and Potentially Hazardous Asteroid (436724) 2011 UW158 exhibits an extreme dynamical environment driven by its rotational period of 0.61072 h. Using a 3-D polyhedral shape model, we investigate the surface dynamic stability under both cohesionless and cohesion-modified conditions, while also addressing families of periodic orbits, station-keeping maneuvers, and energy states in this rapidly rotating, irregular-body regime. Our results show that, with the inclusion of cohesion, the effective slopes decrease to ~45{\deg}, permitting the retention of boulders with maximum sizes of ~50 m near the equatorial region. The dominance of the centrifugal term in the effective potential shifts the exterior equilibrium points inward, creating highly unstable geopotential regions where loose regolith is susceptible to ejection, preventing long-term surface retention outside the polar regions. To quantify this instability, we introduce the equivalent instability speed ($v_{\mathrm{ei}}$), extending conventional escape speed analyses to the SFR regime. These results are consistent with significant internal cohesion in 2011 UW158, which enables the body to maintain structural integrity under rotational stresses exceeding the classical spin barrier. Additionally, thermophysical modeling reveals that the super-fast rotation produces a nearly uniform surface temperature distribution. Finally, we apply a spherical harmonics method to compute families of periodic orbits around the asteroid to assess the fuel cost of station-keeping maneuvers at reduced computational cost. Overall, this study characterizes the dynamical environment of 2011 UW158, contributing to the understanding of Super-Fast Rotator asteroids recently identified in LSST survey images.

3D reconstruction from satellite imagery is essential for large-scale topographic analysis, yet the lack of high-fidelity training datasets with accurate occlusion labels remains a primary bottleneck. Existing benchmarks, such as US3D and WHU-Stereo, face inherent challenges in spatio-temporal mismatch – environmental changes and shadow displacements between multi-view acquisitions – and provide ambiguous ground truth in occluded regions due to LiDAR sparsity. In this paper, we propose SatUnreal, a high-precision synthetic dataset designed to fundamentally overcome these limitations through an Unreal Engine-based simulation pipeline. SatUnreal provides 10,000 stereo pairs with high resolution (0.3m GSD) and is characterized by: (1) Physical Geometry Simulation, replicating realistic satellite orbits by systematically varying baselines and azimuths; (2) Spatio-temporal Consistency, eliminating temporal noise through fixed virtual environments; (3) Topographic Diversity, spanning dense urban canyons to low-texture natural terrains; and (4) Mathematical Label Integrity, utilizing a novel two-step linetrace algorithm to generate flawless occlusion masks. Experimental results using SOTA iterative models demonstrate that models trained exclusively on SatUnreal achieve superior zero-shot transfer performance on real-world benchmarks (US3D, WHU-Stereo) compared to those trained on real datasets. Our findings prove that physically accurate synthetic data provides a more effective supervisory signal for learning geometric features than complex real-world observations, establishing a new paradigm for Sim-to-Real transfer in Earth Observation. Code and dataset are available at https://github.com/jmp-Telepix/SatUnreal_A_High-Precision_Synthetic_Dataset_for_Satellite_Stereo_Matching_via_UnrealEngine

Achieving stable long-term neural decoding in invasive brain-machine interfaces (BMIs) remains challenging due to variations in recorded neural populations across sessions. Current latent alignment approaches may overlook task-dependent structure during cross-session adaptation. We propose Task-Conditioned Latent Alignment (TCLA), a framework that stabilizes neural decoding by learning a shared latent space. TCLA learns a low-dimensional source representation using neural reconstruction and continuous behavioral supervision. During target-session adaptation, the shared representation is fixed, while target neural activity is mapped into the source latent space by aligning source and target distributions separately for each task condition. We evaluated TCLA on seven nonhuman primate datasets spanning multiple tasks. In long-term cross-session evaluation, TCLA achieved a mean $R^2$ of $0.476\pm0.014$ with a negative $R^2$ failure rate of only 6.8%. Across 1,356 within-subject session pairs, TCLA achieved a mean $R^2$ of $0.371\pm0.009$ with a failure rate of 6.8%. Across 2,134 cross-subject session pairs, TCLA achieved a mean $R^2$ of $0.218\pm0.004$ with a failure rate of 12.9%, substantially better than those of the comparison methods. These results demonstrate that by preserving behaviorally relevant and task-dependent latent structure, TCLA improves the robustness of neural decoding across recording sessions and subjects. The source code is publicly available at \href{https://github.com/FAMD-CASIA/TCLA}{https://github.com/FAMD-CASIA/TCLA}.

Let $G$ be the line graph of a finite simple graph, with $n\geq1$ vertices and maximum degree $\Delta$. We prove that single-site Glauber dynamics for uniform proper $q$-colorings mixes in $O_\Delta(n\log(n/\varepsilon))$ steps for every integer $q\geq\Delta+5$. Our proof uses the Bochner framework of Chen and Liu (2026).

Delayed acoustic position measurements describe past states but may reach an underwater vehicle after its navigation estimate has advanced. This paper presents compressed delayed-information projection (CDIP), a causal delayed-update method implemented in a 15-dimensional error-state Kalman filter (ESKF). CDIP retains source-associated inertial measurement unit (IMU) snapshots and maintains current-to-snapshot cross-covariances, projecting delayed corrections without repeated full-from-start filtering. In 154 paired simulation recordings with synthetic horizontal-position fixes delayed by 1.5~s, CDIP reduced mean trajectory-position root-mean-square error (RMSE) from 1.062~m for a reception-time Baseline to 0.456~m (57.1%). Its mean RMSE approached that of full-from-start Replay (0.451~m), with 99.2% lower mean core-loop time per processed IMU step for the tested implementations. A six-delay sweep, scheduled permanent-loss outages of 50–300~s, and packet-erasure tests further characterized sensitivity to measurement timing and availability. A separate 256-trial nonlinear synthetic assessment identified departures from the prespecified calibration criterion for CDIP and two fully augmented ESKF references. Together, these results demonstrate an accuracy–computation trade-off for causal underwater navigation under the evaluated delayed-measurement simulation conditions.

Memory-polynomial digital predistortion (DPD) evaluates a small fixed coefficient set over a sliding input history, so its reduction step is a complex-MAC workload with local reuse. We map this DPD reduction kernel onto In-Memory Accelerator eXtension (IMAX), a programmable CPU-Grounded Linear Array (CGLA) composed of a one-dimensional processing-element/local-memory pipeline. For a (P,M)=(5,5) odd-order memory-polynomial instance, the mapping keeps the 120 B coefficient set in local memory, advances the five-tap history over 1024-sample tiles, and realizes the 15 order-delay terms as a 33-stage streaming complex-MAC reduction. The evaluation measures kernel latency and modeled energy. All measured paths use the same single-precision complex workload of 32 sequences, each with 2048 complex samples, across an IMAX FPGA prototype, a CUDA implementation on an RTX 4090 system, and an ARM-NEON implementation on Jetson AGX Orin. With this 1024-sample tile configuration, the IMAX FPGA prototype reports 20.201 ms end-to-end latency and 1.948 ms kernel-only latency. Using the previously reported 28 nm IMAX frequency and power model, the projected IMAX configuration gives 3.14 ms end-to-end latency and 0.34 ms kernel-only latency. The RTX 4090 baseline has the lowest end-to-end latency at 0.484 ms. Under model-based platform power accounting and the stated power assumptions, the projected IMAX configuration gives 169.1 times smaller modeled end-to-end energy per batch than the RTX 4090 baseline. This value uses platform power assumptions rather than workload-dependent runtime power or a direct silicon power measurement. A controlled synthetic PA-model validation checks that the same 15-term form improves test-set NMSE by 26.1 dB and ACLR by 26.0 dB. These results characterize the mapped memory-polynomial DPD reduction on IMAX for the evaluated tile configuration and power model.

Edge and embedded inference is constrained by power and data movement. Mamba-family state-space models replace attention with sequence-linear recurrence, but their inference path combines dense projections, short-reduction SSD kernels, and recurrent-state updates. This paper maps these kernel groups onto IMAX, a programmable CPU-Grounded Linear Array (CGLA), and measures them from kernel execution to token-level integration. Projection kernels match the long-reduction IMAX pipeline, whereas SSD Step-1 is limited by short reductions and kernel-boundary overheads. Mamba-130M token-level integration identifies projection GEMV as the decode bottleneck. These results show that programmable CGLAs fit long-reduction projection kernels, while SSD and decode-time projection support require boundary reduction and persistent-weight execution.

Fault detection in rotating machinery under varying operating conditions remains a challenging problem. Existing approaches are mainly based either on non-parametric vibration signal representations, parametric deep learning models, or on parametric ARMA-type models. While deep learning approaches often lack transparency and require large training datasets, ARMA-type models rely on the assumption of a purely rational spectrum, which can be restrictive for rotating machinery vibration signals exhibiting mixed spectral characteristics. To address these limitations, a novel Harmonic Regression ARMA (HR-ARMA) model is presented for rotating machinery dynamics identification, which combines an explicit harmonic regression part for representing the dominant deterministic cyclical components with an ARMA part for the remaining broadband stochastic dynamics. Based on this model and within a Multiple Model framework, fault detection is achieved under varying operating conditions. The HR-ARMA modelling performance is validated through simulation and experimental studies on a single-stage gearbox, demonstrating accurate representation of both cyclical and broadband dynamics, improved parsimony and significantly higher fault detection performance compared with conventional ARMA models, achieving at least 98% True Positive Rate at 5% False Positive Rate in the experimental study.

Cross-field turbulent transport remains one of the largest uncertainties in edge plasma simulations and is commonly approximated through empirical transport coefficients. In this work, we calibrate and assess several cross-field transport models implemented in SOLPS-ITER using measurements from the TCV-X21 reference case. The considered models range from conventional constant-diffusivity descriptions to the self-consistent k-model, in which anomalous diffusivities evolve along with plasma conditions. Model parameters are estimated through gradient-based optimization by minimizing discrepancies between simulated and experimental upstream and divertor profiles in forward field configuration. The calibration results show that increasing the number of free parameters substantially improves agreement with the calibration dataset. However, these more flexible models exhibit poor predictive capability when applied to the reversed field configuration, indicating overfitting. In contrast, the simplest constant-diffusivity model provides the best overall predictive performance while requiring only a small number of calibrated parameters. The k-model achieves a calibration quality comparable to the constant-diffusivity model and reproduces the experimental profiles with similar accuracy, while simultaneously providing a physics-based description of the spatial variation of anomalous transport. Predictions for a density scan reveal differences that are not apparent near the calibration point. The k-model predicts increasing transport levels around the separatrix with increasing density, leading to broader upstream profiles and an earlier onset of divertor rollover compared to the constant-diffusivity model. The presented framework provides a systematic and efficient route for model calibration in SOLPS-ITER, and a set of calibrated k-model parameters to be employed in future studies.

For any integers \(N \ge 2\) and \(m \ge 1\), we prove that there exist infinitely many pairs of real quadratic fields \(\QQ(\sqrt{D}), \QQ(\sqrt{D + m})\), with \(D \in \ZZ\) and \(D > 0\), such that the class numbers of both fields are divisible by \(N\). Write \(N = 2^{e}n\) with \(n\) odd. If \(n > 1\), then the class groups of both fields in each such pair contain an element of order \(n\).

Let $B_1,\ldots,B_n$ be independent Bernoulli random variables with parameters $p_1,\ldots,p_n$, and let $S=\sum_i B_i$. Hillion and Johnson proved that the Shannon entropy of $S$ is jointly concave in the parameter vector and proposed corresponding critical-order conjectures for R'enyi and Tsallis entropies, with predicted thresholds $2$ and approximately $3.65986$, respectively. We determine both thresholds exactly. For every $0<q<1$, the power sum $\sum_k \mathbb P(S=k)^q$ is jointly concave in $(p_1,\ldots,p_n)$, and strictly concave on the open parameter cube. Consequently, the R'enyi and Tsallis entropies of order $q$ are jointly concave. At $q=1$ this agrees with the Shannon theorem. For every $q>1$, joint concavity fails already for the sum of two Bernoulli variables: a transverse interpolation in which the two parameters move in opposite directions gives strict local convexity for both entropies. Hence the universal joint-concavity range for both families is exactly $0<q\leq 1$. Below order one, the proof combines the Hillion–Johnson transport inequality with an explicit nonlinear telescoping correction. The corrected local curvature reduces to a two-dimensional quadratic form. An exact Riccati identity, together with a one-sided zero-crossing argument, proves positivity of its determinant throughout the full range $0<q<1$.

The tight scaling relation between absolute magnitude and internal velocity dispersion ($\sigma$) for globular clusters (GCs), referred to as the GC velocity dispersion (GCVD) relation, has shown great potential as a distance estimator. However, at the high-luminosity end of the GC luminosity function, GCs mix with ultra-compact dwarfs (UCDs). As GCs appear to smoothly transition into UCDs with dynamical mass, we investigate the possibility of a unified distance estimation relation applicable to both GCs and UCDs. To this end, we exploit the transition between scaling relations with dynamical mass, using the Milky Way and M31 GCs alongside literature UCDs. Additionally, we look at the influence of UCDs, and of GC size, on GCVD-derived distances. Using the GCVD for UCDs gives a systematic distance underestimation of $\sim$33 per cent, but a cut to select UCDs with GC-like sizes can eliminate this bias. The minor systematics on GC size produce systematic offsets smaller than the uncertainty of the GCVD. Using the bilinear relation formed by the GCs and UCDs in the absolute magnitude - log dynamical mass plane for distance estimation yields per-object distance uncertainties of $\sim$30-35 per cent (0.12-0.16 dex). We find that the GC - UCD division occurs at $M_V \simeq -10.8$ mag and $M_{dyn} \simeq 3.1\times10^6$ $M_{\odot}$, consistent with previous findings. Applied to the dwarf galaxy NGC1052-DF2, whose distance is strongly debated, the relation returns 19.1 $\pm$ 4.3 Mpc, consistent with literature values.

We consider $K$-dimensional Bayesian vector autoregressions (BVARs) with Cholesky stochastic volatility (SV), in which the innovation covariance matrix is a lower-triangular linear transform of $K$ independent univariate SV processes. Such models are widely used in empirical macroeconomics to capture time-varying uncertainty and improve forecast accuracy, but the cost of posterior simulation is the binding constraint on the size of the system, and a major bottleneck for empirical work. We introduce a Markov chain Monte Carlo (MCMC) kernel that mixes better than existing samplers at the same computational complexity. It rests on a reparametrisation that makes the $K$ volatility trajectories conditionally independent, and on Particle Gibbs to update each trajectory. The kernel targets the exact posterior, rather than an approximation of it, and in an application with $K = 15$ it raises the mean effective sample size per second, relative to the benchmark corrected triangular algorithm, by a factor of approximately $14$ for the VAR coefficients and $3.4$ for the volatilities. We also introduce a a Sequential Monte Carlo squared (\smcsq{}) sampler, which used our MCMC kernel as a building block, and which delivers at every $t$ the one-step-ahead predictive density and the marginal likelihood of the data up to $t$, and hence on-line forecasting and model choice. To our knowledge, this is the first algorithm that delivers sequential marginal likelihoods for Cholesky-SV BVARs with static contemporaneous coefficients and a non-conjugate prior. We illustrate both on US monthly macroeconomic data, with $K=15$ for posterior inference and $K=6$ for the sequential sampler and model choice.

Dynamic multi-objective optimization with a variable number of objectives is difficult because objective-dimensional variations may significantly change the Pareto front and degrade algorithm adaptability. This paper proposes an unbounded archive-based transfer strategy (UATS), which maintains an unbounded archive of offspring solutions within each environment stage and extracts feasible nondominated solutions as transferable elites when objective changes occur. UATS is embedded into SPEA2SDE to construct UATS-SPEA2SDE, enabling the algorithm to reuse historical evolutionary information while retaining the convergence and diversity advantages of shift-based density estimation. Experiments are conducted on four benchmark problems under three objective-changing settings, where UATS-SPEA2SDE is compared with a restart-based SPEA2SDE baseline and four representative dynamic multi-objective optimization algorithms. The results indicate that the archive-guided transfer improves recovery after environmental changes and enhances adaptability to objective-number variations.

Scattering singularities are features conventionally associated with resonant scattering bodies, which correspond to the zeros and poles of a scattering matrix, and manifest as coherent perfect absorption and lasing respectively. Here, we show that they can emerge solely from an interface, eliminating the need for any resonant body. We introduce boundary singular scattering, whereby an interface between two non-Hermitian media supports coherent perfect absorption, lasing, or both. Despite these singular responses, the two adjoining media are in parity-time (PT)-unbroken phases, with entirely real bulk spectra. This phenomenon originates from a singular boundary mode-matching condition unique to non-Hermitian systems, not from any underlying resonant modes. We demonstrate it experimentally in a programmable synthetic photonic lattice, and show coherent switching between perfect absorption and lasing by controlling only the relative phase of the incident beams. The same mechanism further generalizes from mirror-symmetric interfaces to asymmetric interfaces with dissimilar bulk dispersions. Our work establishes boundaries as a previously unexplored origin of scattering singularities, opening a resonator-free route to coherent perfect absorption, lasing, and other singular wave phenomena.

Cybercriminal operations increasingly depend on reusable digital infrastructure—including hosting, proxies, and virtual private networks (VPNs)—rented through Cybercrime-as-a-Service markets and advertised on platforms such as Telegram. We present a taxonomy for identifying Telegram messages advertising cybercriminal Infrastructure-as-a-Service (IaaS). The taxonomy comprises six service categories across compute, network, and communication infrastructure, together with three trust attributes: Bulletproof, Payment Security, and Transparency. Using 261 human-annotated messages, we evaluate keyword-based and TF–IDF classifiers and examine prompt-based large language models as exploratory baselines. We select a TF–IDF pipeline and apply it to 1,116,071 messages from 167 cybercrime-related Telegram communities. The pipeline assigns at least one infrastructure category to 207,244 messages (18.57%) spanning 113 communities. Classified advertising is highly concentrated: a single community accounts for 50.3% of infrastructure-positive messages, while the trust-attribute classifiers identify Bulletproof claims in 37.66% of those messages. These findings characterize the scale, composition, and concentration of infrastructure advertising on Telegram and can inform the prioritization of communities and actors for monitoring and investigation.

This paper studies the cryptanalytic extraction of convolutional neural networks (CNNs). Existing cryptanalytic extraction attacks on CNNs assume that the network architecture is known, and try to recover model parameters.In this paper, we prove for the first time that the architecture assumption can be removed for CNNs with both max and average pooling. Our core finding is that the spatial geometry of the weight vectors recovered by existing parameter-recovery attacks naturally leaks the architecture. We formalize this geometry and establish its correspondence with the architectural knowledge of a convolutional layer: (1) The sparsity consistency with the convolution receptive field reveals the layer type, the kernel size, and the stride; (2) The numerical consistency with the kernel parameters reveals the padding mode and the output-channel number; (3) The structural consistency with the pooling operation reveals the pooling type, the window size, and the stride. Although the recovered vectors are obtained using different methods in the raw-output and hard label settings, their spatial geometry remains the same. Therefore, our architecture recovery is feedback-agnostic: combined with a parameter-recovery attack, it yields a complete cryptanalytic extraction framework that recovers both the architecture and the parameters in the black-box setting. Extensive experiments, including both layer-wise and end-to-end ones, on a wide range of CNNs demonstrate that simultaneously recovering the network architecture and the model parameters is practical.

We obtain the relation between non-hermite skew information $NI_{\rho,\alpha}(A)$, $NU_{\rho,\alpha}(A)$, variance $NV_{\rho}(A)$ and $NJ_{\rho,\alpha}(A)$ by using the maximal eigenvalue and the minimal eigenvalue of density $\rho$. And also we obtain their refinements.

First passage processes in active systems combine stochastic transport with self-propulsion and orientational persistence, making motion along and against an external bias sensitive to the internal active dynamics. For passive biased diffusion, opposite exits can have different splitting probabilities while their conditional first passage time distributions remain identical. We study how this relation changes for an active Brownian particle driven by a constant external force between two absorbing boundaries. Self-propulsion breaks the equality of the uphill and downhill first passage time distributions and modifies the splitting probabilities. Nevertheless, the two directional path ensembles remain exactly related by spatial reflection, which pairs downhill and uphill first passage paths of the same duration while preserving their orientational history. The log-ratio of the probabilities of a path and its reflected partner defines a path-dependent asymmetry functional and provides an exact reweighting between the two ensembles. In the symmetric half-weighted representation, the uphill and downhill first passage time distributions coincide for arbitrary orientational persistence. The same path relation also allows rare uphill statistics to be reconstructed from the more frequently sampled downhill trajectories. Numerical simulations confirm the weighted equality across the explored bias and persistence regimes, while perturbative and asymptotic analyses clarify how orientational persistence produces the directional asymmetry of the unweighted statistics. The exact relation between the two directional path ensembles suggests that similar symmetry-based reconstruction protocols may be found in other nonequilibrium first-passage problems.

Maximal Extractable Value (MEV) has evolved into a major economic force in blockchain ecosystems, yet its capture is dominated by experienced teams, and both strategy design and implementation rely on manual expert work that scales poorly across heterogeneous protocols and chains. We present EVAGE, the first fully autonomous multi-agent framework for end-to-end MEV strategy generation and adaptation. Equipped with three specialized operation modes, it automatically discovers novel MEV variants, adapts execution logic across disparate protocols, and ports strategies between chains, including Layer-1 and Layer-2 networks. To avoid inference latency on the critical MEV execution path, EVAGE generates and refines MEV bot code offline rather than making real-time decisions directly. Under the coordination of an orchestrator agent, three specialized subagents collectively implement and repair the full MEV bot workflow via closed-loop diagnostics, eliminating human intervention while producing validated and deterministic Proof-of-Concept implementations. We evaluate EVAGE on over 1.5M blocks from each of Ethereum, Base, and BNB Smart Chain (BSC). On Ethereum, EVAGE uncovers five novel MEV strategy variants, yielding a profit increase of 1.02$\times$ to 15.97$\times$. It also successfully adapts 11 MEV strategies from CPMM to both CLMM and Balancer V2 and ports strategies from Ethereum to Base and BSC, all with less than 60 dollars in LLM token costs.

Most computer-vision systems organize visual input toward a predefined interpretation, such as semantic categories, prompted regions, learned object-like representations, or a single spatial partition. This work considers an earlier stage of visual organization: the formation of candidate perceptual units directly from sensor measurements before their identity, meaning, or task relevance is known. We introduce Domain Parent Grouping (DPG), a sensor-grounded grouping method in which complementary measurement relationships are represented in separate processing domains. Spatially connected groups formed within these domains are related through cross-domain overlap, yielding a non-exclusive grouping representation rather than a single mutually exclusive segmentation. This representation retains broader and more localized groups, as well as alternative grouping boundaries over the same image locations, simultaneously available. DPG also includes a native mechanism for reprocessing selected group content, in which input-relative measurement ranges allow the observational resolution to change while preserving previously formed groups. DPG is implemented using three domains representing locally contextualized luminance, direct chromatic relationships, and contextual chromatic relationships. Experiments on the BSDS500 dataset demonstrate the benefit of combining the three domains. The results further show that DPG forms measurement-supported groups corresponding to low-level image structure, and that these groups exhibit measurable correspondence with human-annotated regions and boundaries. This demonstrates that structured visual organization can emerge directly from relationships among sensor measurements.

Deep learning-based malware detectors are commonly updated by fine-tuning on newly collected samples, but this practical update pipeline also creates an attack surface for training-time backdoor attacks. In realistic crowdsourced data collection, however, strict label vetting typically restricts attackers to the clean-label setting, in which poisoned samples must retain benign labels and functionality, making effective backdoor injection substantially harder. We present a new attack perspective based on feature-space manipulation: instead of relying solely on stronger trigger designs or selecting benign samples that are naturally similar to malware, we deliberately construct benign programs whose representations shift toward the malware region before trigger injection, thereby creating stronger feature-label conflicts during training. Based on this insight, we propose RAMP, an attack enhancement method that uses a genetic algorithm to optimize reversed adversarial perturbations under black-box access and then injects them through functionality-preserving binary manipulations. Extensive experiments show that RAMP substantially improves attack effectiveness over trigger-only baselines, with especially pronounced gains at low poisoning ratios, while maintaining accuracy on clean data. Moreover, RAMP can be combined with advanced trigger designs.

Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tuned models still routinely violate some of them. Existing approaches either augment supervision with sequence- or token-level RL rewards from external verifiers or learned graders, or use on-policy distillation (OPD) against a single full-context teacher whose probability mass becomes diluted as more constraints become simultaneously active. We propose CC-OPD (Counterfactual Constraint-Conditioned On-Policy Distillation), which inverts the standard supervision-generation direction in distillation. Rather than enriching the teacher with information beyond what the student sees, CC-OPD ablates each constraint from the teacher's conditioning in turn, and constructs the per-constraint signal from the resulting per-token probability differentials. The resulting per-token leave-one-out log-likelihood shifts are summed, clipped, and added to the vanilla OPD reward as a token-level shaping term. All shaping terms are obtained from the frozen teacher, without an external verifier during distillation, and the reward equals vanilla OPD wherever the aggregate shift is zero. Across two Qwen model pairs and seven benchmarks, CC-OPD achieves the highest average among all evaluated student-training methods. A 1.5B student trained with CC-OPD surpasses its own 7B RL-trained teacher on the MulDimIF benchmark.

We construct a graded Lie algebra structure on the space of cyclically skew-symmetric cochains whose Maurer-Cartan elements characterize double Lie algebra structures. Twisting by a fixed double Lie bracket yields a cochain complex that is isomorphic to the positive arity part of the complex introduced by Fairon and Valeri after reindexing and degreewise sign adjustment. Using the Koszul resolution of the double Lie properad, we identify the resulting differential graded Lie algebra with the convolution differential graded Lie algebra on the deformation complex of the fixed double Lie algebra. We also construct a graded Lie algebra governing skew-symmetric Rota-Baxter operators on a finite-dimensional symmetric Frobenius algebra. For a finite-dimensional vector space $V$ and $A=\en(V)$, we lift the known correspondence between double Lie algebra structures on $V$ and skew-symmetric Rota-Baxter operators on $A$ to an isomorphism of their governing graded Lie algebras. After twisting by corresponding Maurer-Cartan elements, this isomorphism becomes an isomorphism of differential graded Lie algebras and hence induces an isomorphism between the associated cohomology groups. We further prove that the cohomology of a skew-symmetric Rota-Baxter operator on a symmetric Frobenius algebra can be seen as the cyclic cohomology of its descendent associative algebra. Thus the cohomology of a double Lie algebra is identified with the cyclic cohomology of the descendent associative algebra associated with the skew-symmetric Rota-Baxter operator on the matrix algebra.

In this paper, we construct axially symmetric kernels on the sphere. We start by taking an isotropic kernel and transform it into an axially symmetric kernel via a specific deformation. For a general deformation, an explicit spherical-harmonic representation is not necessarily available. For our deformation, we can obtain such a representation of the deformed kernel in terms of the expansion coefficients of the original isotropic kernel. We derive this representation by explicitly describing the effect of the deformation on the spherical harmonic basis. From this, an explicit finite sum formula for the coupling coefficients is deduced and we show that, for each fixed input mode, the coefficients decay geometrically at a rate controlled by our deformation parameter. We further note that if the native space of the isotropic kernel is norm equivalent to a Sobolev-space, then the deformed kernel has the same native space with an equivalent norm. In our numerical experiments, we show that our deformation can improve prediction when the underlying random field has an axially symmetric covariance.

Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two reviewers extract each study independently, an adjudicator resolves disagreements, and the team keeps an auditable record of how every value was produced. Large language models can assist with extraction, but that assistance must fit established review protocols and preserve reproducibility. We present EviStreams, a live, open-source, no-code web platform that puts review teams in control of AI-assisted extraction at three key stages: program design (a structured decomposition approved before any code runs), field specification (typed field definitions calibrated from a pilot), and extracted predictions (reviewer-blinded dual review with adjudication). Working through a form builder, a domain expert defines typed fields rather than prompts, runs extraction over uploaded PDFs, inspects every value alongside the supporting passage it came from, and resolves a reviewer-blinded dual review into an auditable consensus export. An evaluation across four clinical corpora and three frontier model families, released with the system, shows that extraction quality is shaped far more by the field specification than by the choice of model. EviStreams is live at https://evistreams.com/demo and released under Apache-2.0.

Symbiosis between humans and digital beings offers a vision for the future of human–machine interaction. In enduring human–machine relationships, personality provides a foundation for continuity of identity, individuality in interaction, and development through experience. We investigate this capacity through persona agents as computational implementations and introduce Emergi-PersonaOS, a psychology-grounded operating system for managing persona objects throughout their lifecycle. The system organizes dispositional traits, characteristic adaptations, and narrative identity into a three-layer persona representation, distinguishing relatively enduring persona beliefs from their activation in the current persona state. During situational adaptation, it integrates the current interlocutor, relationship, event, and retrieved memories to infer a persona state and generate actions and replies; during long-term development, it records experiences and outcomes, and develops and evaluates revision candidates through change attribution, meaning-making, and behavioral testing. Belief updates are managed through explicit review, traceable evidence and version records, and the ability to reject candidates, making persona evolution controllable. Using television-character dialogue as longitudinal material, we demonstrate long-horizon system operation and examine its principal mechanisms in a concrete implementation. This work provides a computational framework for persona agents to maintain individual continuity, produce situation-specific expression, and develop through experience over sustained interaction.