Papers

Cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) is widely adopted as a reference waveform for integrated sensing and communication (ISAC). However, its high peak-to-average power ratio (PAPR) requires power amplifier back-off, thereby reducing the available sensing link budget. Frequency-modulated OFDM (FM-OFDM) provides a constant-envelope signal with 0 dB PAPR and has demonstrated reliable communication performance in high-mobility scenarios, but its sensing characteristics remain largely unexplored. This paper characterizes FM-OFDM as a sensing waveform based on its bandwidth, ambiguity function, sidelobe behavior, and Doppler estimation capability. The analysis shows that its data dependent sidelobe floor is incoherent and decreases through across frame integration, whereas the corresponding CP-OFDM floor remains unchanged. Consequently, under equal occupied bandwidth and transmit power, frame-level integration reverses the single-symbol performance ordering and provides FM-OFDM with approximately 20 dB of additional dynamic range for weak-target detection. Moreover, the zero-delay cut of the FMOFDM ambiguity function is shown to be deterministic and independent of the transmitted data realization, in contrast to linearly modulated waveforms, while a closed-form expression is derived for the sidelobe floor away from the zero-delay cut. Since the nonlinear mapping between the data symbols and time-domain samples prevents the direct application of the conventional CP-OFDM range-Doppler processing chain, a weighted phase-increment Doppler estimator is developed. The proposed estimator enables closed-form prediction of the sensing floor and its crossover point with a minimum computational complexity.

Rumor and word of mouth spread at the same speed as the highway of information diffusion in the age of the internet. Social networks play quite an important role in the huge internet. Nowadays, social networks have become indispensable in our lives, especially for the government and enterprises. A social network becomes a complex information diffusion network with users working as nodes and the relationships between users working as the vehicle. In this paper, we propose three kinds of algorithms for computing user influence based on the behavior of a user's forwarding microblogs and the symbol of @ in microblogs. We evaluate the effectiveness of the algorithms by comparing the results of our work with the training data in the dataset, and in the end, it proves that our algorithms work well.

Visual localization (VL) is a fundamental technology for vision applications such as virtual reality. Recently, a novel VL paradigm, Camera Ray Regression (CRR), has emerged, which maps 2D image patches to 3D camera rays, but its accuracy is limited. To improve CRR accuracy, we notice a compelling duality: the inverse of this mapping is inherently performed by the novel view synthesis model, \ie, Neural Radiance Fields (NeRF). While NeRF renders image patches from camera rays via differentiable ray marching, CRR predicts the rays from image patches. Motivated by this complementary relationship, we propose NeRF-aided Camera Ray Regression (NaCR), a unified framework that seamlessly bridges NeRF and CRR at the ray level. First, NaCR incorporates three simple yet effective enhancements into the CRR baseline. Second, leveraging a pre-trained NeRF, NaCR augments the training data by synthesizing novel views tailored for efficient, patch-level consumption. Finally, exploiting the differentiability of NeRF, NaCR forms a closed-loop supervision pipeline where photometric rendering errors are back-propagated to optimize the predicted camera rays. To ensure stable convergence within the highly non-convex image space, we introduce a two-stage training curriculum. Extensive experiments across indoor and outdoor benchmarks demonstrate that NaCR achieves competitive accuracy. Comprehensive ablation studies validate the efficacy of each proposed component.

In this paper, a very compact 2 x 2 wideband beamsteering array with monocone antennas is designed and validated for NATO Band III (1350-2400 MHz) tactical communications with interference suppressing ability. The small 4 x 4 x 4 cm$^3$ size of the array enables placing it for example on vehicles with limited space. The array is manufactured and measured, and the interference suppressing capability thereof is studied with a minimum variance distortionless response beamformer and a phase-only beamformer in the presence of a single interferer. It is shown that the designed antenna array can significantly improve the signal-to-noise ratio over 10 dB for angles separated over 15$^\circ$ from the signal-of-interest, and over 20 dB for separation larger than 43$^\circ$ across the operational band.

This paper presents a modular control barrier function (CBF) framework for safe free-flying robotic spacecraft operations during tumbling target capture. Motivated by latest ESA guidelines for safe close proximity operations, safety zones and requirements are translated into dedicated CBFs. The 13-DoF system is decomposed into translational, attitude, and robotic subsystems, each equipped with a safety filter that minimally modifies nominal control inputs in a lightweight quadratic program. The filters enforce a conical approach corridor, collision avoidance zone, attitude line-of-sight pointing, angular velocity limits, robotic joint limits, link-base collision avoidance, and actuator constraints. Dynamic coupling between subsystems is handled by treating upstream safe control commands as known interconnection inputs in the downstream safety filters, preserving modularity while supporting system-level safety. The framework is validated in an on-orbit servicing scenario, including final approach, angular rate synchronization, and tumbling target grasping, using the high-fidelity astrodynamics simulator Basilisk. Monte Carlo simulation results demonstrate runtime efficiency and operational safety for various tumbling rates.

Coronal mass ejections (CMEs) exhibit poorly understood internal velocity distributions. In this work, we introduce an optical flow-based method, DOFCAT (Dense Optical Flow CME Analysis Tool), applied to high-resolution data from ASPIICS/Proba-3 and METIS/Solar Orbiter to examine the internal velocity dispersion of different substructures within CMEs in the Middle Corona. We also introduce a Gaussian-tapered Fourier (GTF) filter that suppresses brightness artifacts in running-difference images from the ASPIICS wideband channel. We then apply DOFCAT to four CME events observed across ASPIICS and METIS, extracting their internal velocity information. Our results show that the velocity profiles of both the CME front and core change with position angle and are non-uniform. For impulsive CMEs, the height offset between the front and core begins to increase following the impulsive acceleration phase, while CMEs without a clear impulsive phase show a more gradual evolution of the height offset. We find that this increase in height difference occurs rapidly between 2.3–3.0\,R$_\odot$. Furthermore, the front shows a higher velocity spread than the core, with impulsiveness evident in the CME leading half. We further compute the radial self-similarity coefficient separately for the front and the core, finding that the front expands non-self-similarly initially and tends toward self-similarity at larger heights, while the core remains relatively self-similar. These findings confirm that CMEs contain complex internal flows and layered substructures within the traditional three-part morphology. Our results demonstrate the utility of DOFCAT on high-resolution coronagraphs for probing CME dynamics and provide new insights into CME initiation and evolution.

In this paper, it is shown that in spacetimes of maximal proper acceleration, there is a reduction of the mean lifetime of the muon. We consider several candidates for the maximal proper acceleration. For all of them, the induced corrections are small, but for the case when the maximal proper acceleration is identified with the Schwinger's acceleration, the effect is detectable in future muon collider facilities.

Fano resonances based on coherent interference between localized Lorentz modes and Fabry-Perot radiation continua have been widely explored for microwave metamaterial sensors, filters, and photonic devices. However, conventional Fano metasurfaces suffer from angular dispersion and polarization-dependent spectral variations, limiting robustness under dynamic illumination. Here, we propose a band-folding-enabled flatband engineering strategy to realize robust Fano resonances in a planar microwave metasurface with a 2x2 enlarged cell of orthogonally arranged H-shaped metallic resonators. The enlarged cell induces Brillouin-zone folding, generating a nearly dispersionless flatband near 15.3 GHz with suppressed momentum dependence and enhanced photonic confinement. This flatband state provides a high-Q localized resonance strongly coupled to the FP radiation continuum, producing a stable asymmetric Fano response. Furthermore, the orthogonal resonator configuration supports two polarization-decoupled dipole modes with comparable excitation efficiency, reducing polarization-induced spectral distortion. Near-field measurements and simulations verify localized flatband features, while far-field experiments demonstrate stable Fano responses under oblique incidence from 0{\deg} to 30{\deg} with minor resonance variation. A temporal coupled-mode theory model is developed to quantitatively describe Fano spectral evolution under different illumination conditions. This work establishes a flatband-mediated route toward robust microwave Fano metasurfaces and offers a general strategy for stable resonant photonic device design.

Many Security Operations Centers rely on signature-based Network Intrusion Detection Systems like Suricata, yet detection rule engineering remains understudied. We investigate this process by introducing SuriCap, a platform for rule engineering exercises, and hosting CTF-style workshops where 60 participants, trained MSc students, and experienced SOC professionals, created rules for four scenarios. Participants produced 3146 valid rules, enabling analysis of their methods, performance, and iteration patterns. Surprisingly, prior experience had limited impact on rule quality, suggesting that less experienced engineers can produce rules comparable to experts. We also observed challenges in generalizing rules beyond available tests, underscoring the need for sufficient labeled data. From our study, we identify three phases and a common pattern in rule engineering, offering SOC managers insights to improve their processes and expectations of engineer expertise.

When we evaluate the performance of our odometry, it is common practice to score the estimated track against a ground truth. Unfortunately, scoring uses point metrics, such as the root mean square error, that ignore the covariance matrix which estimators like filters and smoothers already report. Using the covariance matters for two reasons. First, the covariance encodes the estimator's uncertainty, so it tells us whether the estimator trusts its own output. An overconfident estimator will not report itself lost. Second, the covariance weights the error in each direction of the estimate. Without the covariance, an estimator is unduly penalized for a high error in an uncertain direction. Instead of point metrics, we should use strictly proper scoring rules. These rules score the estimate together with its reported uncertainty. Strictly proper scoring rules recover the point metrics when no covariance is reported, and they diagnose covariance inconsistency when covariance is reported. Using a one-sided pairwise test, we show that two estimators can expose overconfidence in at least one of them without a ground truth. Strictly proper scoring rules and our pairwise test are available in our open-source framework smfeval. As a case study, we use smfeval to assess the uncertainty quality of the translational component of ground-based LiDAR-inertial odometry. Across four filters we find overconfidence - the worst case reports centimeter certainty with kilometer error. Knowing the filters are overconfident, we investigate the mechanism. The investigation traces overconfidence to filters crediting LiDAR measurements with more new information than they carry.

Pressure can drive Weyl semimetals toward superconductivity through qualitatively distinct reconstructions of their lattices and normal-state electronic structures. Here, we report the first observation of superconductivity in compressed TaRhTe4. This finding enables a direct comparison of the distinct reconstruction pathways leading to superconductivity in TaRhTe4 and the previously studied TaIrTe4, both of which belong to the TaXTe4 (X = Rh, Ir) family of type-II Weyl semimetals. For TaRhTe4, high-pressure electrical-resistance, Hall effect, and magnetoresistance measurements, together with synchrotron X-ray diffraction and first-principles calculations, reveal a superconducting transition that emerges near 20 GPa, with onset Tc increasing to approximately 2.6 K at 65.2 GPa and zero resistance achieved above 63 GPa. The onset of superconductivity coincides with a progressive lattice distortion, a strong suppression of the positive magnetoresistance, and a continuous decrease of the Hall coefficient toward zero. Calculations further show that additional electron-like bands cross the Fermi level (EF) and that N(EF) increases upon compression. This evolution contrasts with TaIrTe4, where the Hall coefficient initially increases before reversing its pressure dependence near the superconducting threshold, while the structural anomaly is confined to a narrower pressure interval. This comparison indicates that superconductivity in the TaXTe4 family is not tied to a unique critical pressure or a single Fermi-surface reconstruction, but can emerge through distinct material-specific pathways once pressure sufficiently reconstructs the low-carrier Weyl-semimetal-derived state into a multiband metallic regime.

Aerial-ground cooperation requires real-time UAV–UGV relative-state information. Instead of maintaining global estimates for both robots, direct control in a UGV-attached non-inertial frame avoids reliance on global localization. Vision-based relative pose estimation with a passive marker offers a low-cost and effective solution. However, a fixed camera may lose sight of the moving UGV when the required UAV attitude conflicts with the field-of-view (FOV) constraint. To address this, we propose COPA, a robust active-perception framework for global-state-free aerial-ground cooperation. We use a single-axis gimbal to decouple the camera optical axis from the UAV pitch attitude. We derive an active-perception model that relates UAV motion, gimbal angle, and UGV motion to the target image-plane state.A Temporal Convolutional Network (TCN) predicts short-horizon UGV acceleration and angular velocity from recent motion history without global-state measurements. The model predictive control (MPC) uses these predictions to jointly optimize UAV and gimbal control. Simulations show that COPA maintains continuous target visibility, while ablation studies confirm that the TCN reduces peak errors during UGV motion transitions. Real-world experiments with UGV accelerations up to 3m/s^2 and yaw rates up to 1.0rad/s demonstrate robust tracking.

We study SPDEs associated with $2$D active scalars driven by incompressible transport noise of Kraichnan type, with regularity exponent $\alpha\in (0,1)$; our examples include the Euler, SQG and IPM systems. We investigate whether a number of “turbulent” phenomenologies, which are well understood in the linear Kraichnan model, persist in this nonlinear setting, uniformly in vanishing viscosity approximations. First, for suitable values of $\alpha$ and initial data in $L^p_x$, we establish anomalous regularization estimates, measured in appropriate endpoint Besov-type spaces of regularity $\beta=\beta(\alpha,p)>0$. Remarkably, these results allow for some scaling supercritical regimes of the parameters $\alpha,p$; on the other hand, for (sub)critical parameters, we recover the same regularity exponent $\beta=1-\alpha$ as in the linear case. Second, in the (sub)critical case, we further prove anomalous integrability, namely solutions becoming instantaneously $L^\infty_x$-valued at positive times, uniformly in the viscosity; moreover, in this case we establish strong existence and pathwise uniqueness of solutions to the inviscid SPDE, which are recovered as the unique vanishing viscosity limit. Finally, in the $2$D Euler case, for $\alpha\in (0,1/2)$, we establish anomalous dissipation of enstrophy and sharpness of anomalous regularization.

In this paper, we systematically exhibit the roles of logistic source on global boundedness and convergence of classical solutions, and global existence and eventual smoothness as well as convergence of generalized solutions for a singular chemotaxis urban crime model. Roughly speaking, this work shows that suitably (can be precise) strong logistic damping ensures boundedness and convergence of classical solutions, and weak logistic damping ensures global existence of generalized solution and its eventual smoothness as well as convergence in two and three dimensions. The former in particular implies that strong logistic damping can prevent the formation of crime hotspots (spatial patterns). Previous known results in this respect are thus significantly improved and extended.

We establish a structure theorem for measures satisfying left-invariant PDE constraints in Carnot groups. Our generalization, that takes inspiration from the paper arxiv:1601.06543 by the first named author and F. Rindler, requires the introduction of the hypoelliptic wave cone, a sub-Riemannian version of the wave cone from the theory of Compensated Compactness, and it employs classical results in Harmonic Analysis on homogeneous spaces. Then, we utilize this result to extend the Rank-one Theorem for functions of bounded horizontal variation ($BV_H$) to all Carnot groups. This is achieved by considering a suitable curl-type left-invariant operator, that provides necessary differential constraints for horizontal gradients, and by studying the hypoellipticity of said constraints.

Relegation, not the championship, decides the outcome of many European water polo leagues: one club has won every recent national title in Italy, Spain and Hungary. We make the relegation decision our object of inference. Serie A1 relegates one club for finishing last and a second through a three-match play-out, and we ask at each stage how much can be known and when. Promoted clubs arrive with no top-flight record, so we also compare four ways of setting their prior. These analyses rest on the first Bayesian hierarchical model of water polo, which splits scoring into even-strength, man-up and penalty components, models opportunities and conversions separately, and lets abilities evolve between seasons. Treating the components as independent proves untenable, since they compete for a common budget of possessions. Fitted to a new dataset of 940 matches, the model identifies the directly relegated club from the ninth round, before the league table does, predicts the play-out field better than the table, and shows the play-out itself to be close to a coin toss. Promoted clubs start below the league on every component except drawing exclusions: knowing a club was promoted improves forecasts; knowing how does not.

We introduce a Green–potential heat flow for maps from a closed Riemannian surface. Inspired by Parker–Wolfson renormalization~\cite{ParkerWolfson1993,Parker1996}, the conformal factor is reconstructed at each time from an energy-based source determined by the current map. The resulting parabolic–elliptic system incorporates renormalization into the evolution while leaving the Dirichlet energy functional and its harmonic critical points unchanged. We establish local well-posedness for $H^3$ initial data and a continuation criterion in terms of energy concentration. For sufficiently large coupling, we exclude finite-time bubbling and obtain global solutions. At infinite time, the energy identity provides only weighted $L^2$ control of the tension field. Localizing to regular regions of concentration charts yields harmonic profiles along subsequences of prescribed times. We represent these profiles in the flow's packet charts, organize them into a finite bubble tree, and decompose the limiting energy into harmonic component energies and neck energies. A critically normalized radial example illustrates how the modified flow can prevent finite-time blow-up and produce harmonic bubbles with no-neck property.

In recent years, the threat of application-layer (L7) distributed denial-of-service (DDoS) attacks is ever increasing. To defend against them, network operators deploy web application firewalls (WAFs). WAFs are stateful scoring systems which are configured with a rule set that specifies what malicious traffic looks like, and how to handle it. While effective, WAFs are expensive and can increase the request latency of realistic applications by up to $4\times$. This paper introduces Shimmer, a highly optimized WAF. Shimmer JIT-compiles the rule set and applies advanced optimizations to avoid unnecessary work in the scoring pipeline.

Multimodal large language models (MLLMs) often struggle with fine-grained visual perception when processing complete images, as critical evidence may only appear in local regions. On-policy self-distillation (OPD) enables transferring privileged visual knowledge from informative views to full-image policies, but querying the teacher for every rollout introduces substantial supervision costs. In this work, we propose BAS-OPD, a budget-aware selective OPD framework that allocates teacher supervision under limited query budgets. Instead of querying all rollouts, BAS-OPD selects informative samples while maintaining full-batch student generation. We explore random, uncertainty-based, and learned utility-based selection strategies, where the learned selector estimates query value from detached rollout statistics and online utility signals derived from student–teacher agreement and teacher confidence without additional student forward passes. BAS-OPD only changes training-time supervision allocation and preserves single-pass full-image inference. Experiments on fine-grained multimodal perception benchmarks demonstrate that BAS-OPD achieves strong performance while substantially reducing teacher supervision costs, highlighting the effectiveness of selective OPD under constrained budgets.

Short-to-long training is a simple curriculum for speech models, but its gains can be difficult to interpret. In speech token language models, length-based training can change the shuffle policy, batch composition, token retention, and token weights under batch-mean loss. We disentangle these factors through matched comparisons. In the tested settings, short-to-long ordering shows no independent benefit when batch composition and token exposure are fixed. First-epoch grouping lowers perplexity for Mimi under batch-mean loss, but this gain is not observed under token-balanced loss. The cross-tokenizer results are consistent with a link between chunk-length variation and token weighting. This work provides a systematic analysis protocol for studying length-based training in variable-length speech models.

AI-based autonomous agents, typically hosted at data centers, must acquire state information from robots or edge devices in order to issue informed control decisions. Managing uncertainty about the state is particularly consequential in safety-critical settings, in which average-case guarantees are insufficient. In this context, we study a sequential decision maker process that jointly decides which actions to take and when to probe given access to an arbitrary state prediction model. We propose online conformal state probing (OCSP), an action and probing policy that certifies worst-case reliability levels without relying on distributional assumptions. OCSP is designed to provably control the missed query error (MQE), i.e., the fraction of instances where probing would have been beneficial, while minimizing the probing rate. OCSP can be applied to existing pre-trained value-based control policies without requiring retraining or fine-tuning. We validate OCSP through numerical simulations to verify theoretical guarantees and to assess performance trade-offs as a function of the calibration of the state predictor.

Potential energy surfaces and collective inertial functions are essential microscopic inputs for describing nuclear large-amplitude collective motions such as rotation, vibration, and fission. We develop the Nuclear Collective Generator (NCG), a machine-learning framework that predicts the collective potential and six collective inertial functions on the quadrupole deformation $(\beta,\gamma)$ plane from proton and neutron numbers and shell-effect descriptors, providing the microscopic inputs for the five-dimensional collective Hamiltonian (5DCH) used to describe low-lying spectra in even-even nuclei. The NCG combines weighted supervised learning, adversarial refinement, and ensemble averaging to improve reconstruction fidelity and prediction stability. Across 568 even-even nuclei, the reconstructed collective potentials reproduce the covariant density functional theory (CDFT) results with a mean root-mean-square deviation of 0.58~MeV. When propagated through the 5DCH solver, the NCG inputs reproduce the global systematics of equilibrium deformations, low-lying excitation spectra, and electric-quadrupole transition strengths. Near the $Z=82$ shell closure, the NCG softens the collective potential along the $\gamma$ direction, reducing the overestimated collectivity of the original CDFT+5DCH calculations and bringing the $B(E2)$ values closer to experimental data. These results demonstrate that the NCG provides an accurate surrogate for global microscopic collective calculations while retaining their essential physical content.

How should a robot learn to manipulate objects so fragile that sub-Newton contact forces can cause irreversible damage? Existing visuo-tactile policy learning typically treats tactile sensing as an additional policy input. In direct-contact force-sensitive manipulation, however, the bottleneck can arise earlier, during data collection: manual gripper control is too delayed and coarse-grained to reliably maintain the narrow force range required for stable grasping. We therefore use a deterministic 25 Hz tactile reflex controller as a collection-time teacher, producing demonstrations with controller-shaped grasping behavior for tactile-free policy learning. On Action Chunking with Transformers (ACT), policies trained from reflex-shaped demonstrations recover the teacher's grasping profile and achieve 95% stable grasps on the nominal plastic-cup task, substantially outperforming visually screened manual demonstrations. The same intervention improves in-distribution stability on $\pi_{0.5}$ and shows a favorable exploratory trend on an unseen paper-cup variant. Under randomized external disturbance, however, the reflex-data $\pi_{0.5}$ policy still fails in 45% of policy-only trials, whereas a deployment-time reflex arbiter retains all grasps. These results reveal a new role for tactile feedback in force-sensitive manipulation: rather than integrating tactile into the policy, we use it as a collection-time teacher that shapes grasping behavior in demonstrations for policy learning, while disturbance rejection remains controller-dependent, revealing the boundary of tactile-free policy.

We present a systematic study of the rest-frame ultraviolet (UV) and optical morphologies of Ly$\alpha$ emitters (LAEs) at $z \simeq 2$-$7$ from the SILVERRUSH multi-narrowband catalog, using JWST/NIRCam imaging from COSMOS-Web. We fit Sérsic profiles in four NIRCam filters (F115W, F150W, F277W, F444W) for LAEs spanning $\sim 2.2$ Gyr of cosmic time. LAE sizes show a mild increase from rest-frame UV to optical wavelengths, with a median optical-to-UV size ratio of $R_{e,\rm opt}/R_{e,\rm UV} = 1.14^{+0.01}_{-0.02}$, indicating a small morphological $K$-correction; this offset is significant at $z \leq 5.7$ but not at $z = 6.6$. The rest-frame optical sizes follow a power-law evolution, given by $R_e \propto (1+z)^{-0.94 \pm 0.09}$, decreasing from $0.91^{+0.07}_{-0.07}$ kpc at $z = 2.2$ to $\sim 0.5$ kpc at $z \gtrsim 5$, in close agreement with a recent JWST study of spectroscopically confirmed LAEs. The rest-frame optical size-mass relation slopes are broadly consistent with those of the general star-forming galaxy (SFG) population, while LAEs lie below it in normalization over $z \sim 3$-$6$. More compact LAEs tend to exhibit higher Ly$\alpha$ equivalent widths, a trend driven mainly by the most compact, marginally resolved sources. These compact systems also occupy elevated SFR surface-density regimes, consistent with a scenario in which concentrated star formation may facilitate Ly$\alpha$ escape. No significant correlation of LAE rest-frame optical size with projected large-scale environment is found over $z \simeq 2$-$7$, suggesting that LAE morphology may be governed primarily by internal galaxy properties, except possibly in the most extreme overdensities.

We modify the approach to the arithmetical form of the large sieve by relying on the Parseval identity rather than on an approximate Bessel inequality and as a consequence, improve on the weighted large sieve inequality. We compute in passing the $L^2$-norm of the Beurling-Selberg majorant of the characteristic function of an interval.

Users commonly combine multiple Low-Rank Adaptation (LoRA) adapters to personalize images with different subjects, styles, and visual attributes. Yet inspecting adapters individually does not establish the safety of their composition. We identify and characterize a pair-conditioned attack in text-to-image diffusion: individually useful and benign-appearing adapters redirect image generation when co-loaded with a specifically matched partner, whose identity serves as the trigger. We introduce LoRango to realize this attack through complementary Signature and Payload adapters. The Signature writes a pair-specific code into intermediate carrier representations, while the Payload uses code-selective responses and opposing signal/reference branches. These branches approximately cancel for standalone adapters and mismatched pairs; matched code-reader alignment breaks cancellation within native GEGLU blocks and releases the programmed action. Both adapters are exported as ordinary static LoRA files compatible with standard loaders, requiring no prompt trigger or base-pipeline modification. LoRango achieves matched-pair attack success rates of 97.9% on SD v1.5 and 98.7% on SDXL, compared with 2.8–4.6% when implanted adapters are loaded individually. Further experiments evaluate pair selectivity, standalone fidelity, robustness to deployment variations, and applicability across denoiser architectures. These findings show that individual-adapter inspection is insufficient to assess the security of multi-LoRA personalization and motivate auditing adapter compositions.

Prolonged sedentary behavior, a pervasive issue in modern workplaces, has been closely linked to musculoskeletal disorders (MSDs) and reduced productivity. This study evaluates the effectiveness of Deep Care Isa, an advanced digital health assistant, in addressing these challenges. Utilizing data from over 2,300 participants across 50 corporations, the study demonstrates significant improvements in ergonomic practices, physical activity, hydration habits, and overall productivity, with notable reductions in MSD-related sick leave. The findings highlight the role of innovative ergonomic interventions in enhancing employee well-being and organizational efficiency. Comprehensive statistical analysis underscores the reliability and practical significance of these outcomes.

The use of malicious domains is central to cyberattacks such as phishing, malware distribution, impersonation, and fraudulent transactions. Because domains are inexpensive to register and easy to deploy at scale, they remain one of the most common and damaging tools used in cybercrime across industries. Proactive detection is essential to reducing this window of vulnerability and preventing harm to users. In this work, we propose COBRA: a content-agnostic, registration-time detection framework for identifying and analyzing suspicious domains from day zero. Our approach does not rely on any content-based features, allowing us to classify a domain even before it is populated with content. We analyze the names of newly registered domains and employ a clustering technique to group them based on lexical and structural similarity. We evaluate our methodology using real-world data consisting of 1.5M newly created domains, demonstrating that COBRA detects suspicious domains with a precision of 98.5%, identifying more than 47K distinct newly registered suspicious domains. Furthermore, our results show that domain-name clustering enables accurate early detection, allowing us to identify 80% of suspicious or malicious domains earlier than one of the most widely used threat-intelligence services, which in some cases may require up to 7 days.

Virtual try-on (VTON) requires precise pixel-level fidelity, yet mainstream Diffusion Transformers (DiTs) often suffer from texture degradation and structural drift. We identify symmetric interactions in standard joint-attention mechanisms as a source of these failures. Although such interactions support semantic flexibility in general-purpose editing, they allow stochastic noise to corrupt deterministic garment features in VTON. We analyze this problem through asymmetric information dynamics and introduce two diagnostic indicators: Conditional Attention Entropy (CAE) for feature unbiasedness and Injected Information Flux (IIF) for injection effectiveness. Our analysis suggests that symmetric bidirectional attention can corrupt conditional features and attenuate the conditional signal. To address these limitations, we propose RealFit, a framework that combines Unidirectional Information Flow (UIF) with Decoupled Timestep Modulation (DTM). UIF isolates the garment condition from stochastic noise to preserve garment identity, while DTM optimizes the modulation scale to maintain a strong conditional signal. The resulting time-invariant condition branch enables a conditional KV cache that reduces inference time by approximately 75%. RealFit offers a principled approach to conditional generation with state-of-the-art fidelity and efficiency.

We study the structure of compact Vaisman and l.c.K. manifolds with nonnegative bisectional curvature in this paper. Our first main result (cf. Theorem 1.3) is a uniformization theorem for compact Vaisman manifolds, extending Mok's uniformization theorem in the Kähler setting (cf. [Mok88]). Our second result (cf. Theorem 1.6) gives a structural trichotomy for compact l.c.K. manifolds with nonnegative Chern bisectional curvature: the underlying complex manifold admits a Kähler metric, admits a Vaisman metric, or its universal cover is conformally equivalent to the product of an incomplete Kähler manifold and a positive-dimensional complex Euclidean space.