Search Results (403)

Comparisons of Bayesian log-odds and Dempster’s combination rule for occupancy grid mapping typically parameterize the two sensor models independently, so that observed performance differences confound the fusion rule with the sensor parameterization. We develop a pignistic-transform-based matching methodology that derives belief function masses producing identical per-observation decision probabilities, isolating the accumulation rule as the sole variable. We show that the confound is large: in multi-robot experiments under two noise conditions, applying the match reversed boundary sharpness from a +6% to +14% advantage for belief functions to a −17% to −22% deficit favoring Bayesian log-odds—a 23 to 36 percentage-point reversal, consistent across both conditions—motivating per-observation matching as the basis for controlled comparison. Under BetP-matched comparison in single-agent simulation (15 independent runs) and on two real indoor lidar datasets (Intel Research Lab, Freiburg Building 079), the two frameworks produce practically equivalent maps on the reported point-probability metrics (cell accuracy, boundary sharpness, Brier score), with a small directional advantage for Bayesian log-odds (absolute differences 0.001–0.022 on [0, 1] scales). Under normalized plausibility ($P_{\mathrm{Pl}}$) matching, the direction reverses for boundary sharpness and Brier score, indicating that the ranking depends on the probability transform used for matching, not solely on the fusion rule. All evaluation is restricted to point-probability metrics on 2D binary grids with Dempster’s and Yager’s rules. The interval-valued representation $\left[\mathrm{Bel}\right(A),\mathrm{Pl}(A\left)\right]$ unique to belief functions is not assessed. The matching methodology is applicable to other Bayesian/belief function comparisons. Full article

To achieve non-contact and continuous monitoring of body length in Micropterus salmoides and overcome the stress damage and subjective error associated with traditional manual measurement, this paper proposes an improved YOLOv8-based multi-target tracking framework for intensive recirculating aquaculture systems. The system employs a geometric measurement framework based on monocular vision that achieves conversion from pixel coordinates to actual body length through camera calibration, water-surface refraction correction, and pose projection correction. Under a collaborative optimization framework integrating detection and tracking, the model incorporates multi-scale feature enhancement, lightweight re-identification (ReID), and a robust data association mechanism, which improves system stability under conditions of high fish density, variable illumination, and turbid water. A shallow feature fusion path is introduced to enhance small-target perception, and a MobileNetV3_ReID model is adopted to extract highly discriminative appearance features, which improves identity consistency while maintaining model compactness. In the data association stage, a hybrid cost matrix integrating IoU, cosine similarity, and motion consistency is constructed, and optimal matching is realized through the Hungarian algorithm. Dynamic threshold adjustment and an exponential moving-average feature-update strategy are introduced to effectively suppress identity switching. Experiments were conducted on an overhead video dataset of Micropterus salmoides collected at a recirculating aquaculture system facility. The results show that the proposed method achieves 82.7% mAP50 while maintaining a real-time throughput of 88 FPS, with MOTA reaching 76.9% and IDF1 reaching 81.5%—the latter representing an improvement of 3.2 percentage points over BoT-SORT and 5.3 percentage points over the YOLOv8 baseline tracker. The number of identity switches (IDSW) decreased from 89 in the baseline configuration to 39, a reduction of 56.2%. Crucially, these component-level improvements translate into a body length error (BLE) of 5.2 ± 1.8% (MAE = 1.35 cm, Pearson r = 0.972), representing a 38.8% improvement over the baseline BLE of 8.5% and satisfying the 5–10% tolerance required for aquaculture growth monitoring. Ablation analysis confirms that both detection enhancements (contributing −1.3% BLE) and tracking optimizations (contributing −2.0% BLE) are necessary to achieve this application-level accuracy. Full article

Visible-infrared person re-identification (VI-ReID) aims to retrieve pedestrian images of the same identity across visible and infrared modalities, but remains challenging due to the large modality gap and unstable local correspondence. Existing methods mainly rely on visual cues, which may be insufficient when infrared images lack color and fine-grained texture information. To address this issue, this paper proposes a CLIP-Guided Progressive Body-Part Semantic Alignment Network, termed PBSA-Net. The proposed method introduces CLIP-derived textual semantics as modality-agnostic guidance for both global representation learning and local body-part feature extraction. Specifically, a global semantic branch first learns identity-level textual anchors to regularize global visual features. Then, a body-part semantic branch exploits identity-aware body-part prompt learning, multi-level feature fusion, and text-guided cross-attention to guide fine-grained local representation learning. A progressive three-stage optimization strategy is further adopted to decouple global semantic learning, body-part semantic correspondence learning, and retrieval-oriented feature optimization. Experiments on SYSU-MM01, RegDB, and LLCM demonstrate the effectiveness of PBSA-Net. It achieves 76.5% Rank-1 and 74.2% mAP on SYSU-MM01, 82.5% Rank-1 and 76.0% mAP on RegDB, and 61.8% Rank-1 and 65.8% mAP on LLCM. Ablation studies further show that the proposed body-part semantic alignment and progressive optimization provide complementary improvements. Full article

Group identification refers to a psychological connection with a group in which individuals incorporate group-defining characteristics into their self-concept. The scale developed by Mael and Ashforth is one of the most used, but it has not been examined in Chile. This study analyzed the psychometric properties of this scale in a Chilean sample using the country as the reference group. A one-factor structure and convergent validity were expected. The one-factor fit of the scale was evaluated in a non-probability sample (n = 523) through confirmatory factor analysis. The results were consistent with an essentially unidimensional structure (χ²/df = 3.99, p < 0.001, CFI = 0.973, TLI = 0.955, SRMR = 0.032, and RMSEA = 0.091), but they must be taken with caution. The model presented adequate factor loadings (>0.500), a high reliability (α = 0.87 and ω = 0.87) and convergent validity (identification with Chile and identity fusion with Chile measurements are related, r = 0.46–0.48, p < 0.001), although they represent different constructs. Finally, the model showed invariance for gender. The scale’s relevance was discussed according to the possible positive and negative effects of identification with the country, particularly in contexts with important migration processes. Full article

Occlusion introduces severe structural asymmetry into pedestrian representations by corrupting body topology, breaking cross-scale semantic continuity, and destabilizing identity geometry. Rather than treating occluded person re-identification (ReID) as a local visibility completion problem, this work reformulates it as a topology-preserved symmetry restoration problem: recovering symmetric identity structure from asymmetrically corrupted observations. Under this view, we present the Topology-Stable Person Re-identification Network (TSP-Net), a unified visual framework with three coordinated components: structural restoration, cross-scale symmetry alignment, and prototype-stabilized identity geometry. Specifically, Topology-Guided Occlusion and Visibility Modeling (TOVM) serves as the structural restoration component, and is realized by a closed loop of the Topology-Aware Occlusion Simulator (TOS) and the Topology-Aware Visibility Estimation (TVE) branch; Semantic-Anchored Cross-Scale Fusion (SACF) performs symmetry-consistent semantic recovery across hierarchical features; and the Prototype-Stabilized Supervision Loss (PSS Loss) regularizes identity embeddings toward topology-consistent manifold centers through momentum-updated prototypes. Experimental results on both occluded and holistic benchmarks show that TSP-Net is effective for learning occlusion-robust person representations. These findings suggest that restoring topology-preserved symmetry is a promising route for robust person re-identification under structural corruption. Full article

The central charge occupies a unique role in conformal field theory, simultaneously serving as a measure of degrees of freedom, as the determinant of Casimir energy through modular transformations, and as an obstruction to the naive extension of the Witt algebra. The Virasoro central extension itself is rigid: it fixes c as a label of a given conformal field theory. In this work, we propose that higher categorical coherence—the pentagon and hexagon constraints governing fusion and braiding data, one level above the cocycle responsible for the Virasoro extension—supplies an additional, physically controllable handle. We show that controlled deformations of this higher coherence (higher categorical coherence breakdown, HCCB), implemented consistently through anomaly inflow, shift the effective central charge read out by anomaly-sensitive observables in quantized steps, opening the possibility of treating the measured central charge not as a fixed label but as an experimentally addressable piecewise-quantized quantity. We then focus on the fractional quantum Hall effect (FQHE), where the chiral central charge $c_{-}$ directly governs the quantized thermal Hall conductance. After reviewing the role of edge conformal field theories and current bounds on thermal transport, we propose experimental modifications—such as engineering multi-component edge states, coupling to non-Abelian quasiparticles, or introducing controlled categorical perturbations—that could render higher coherence breakdown detectable as shifts in the effective central charge. Two further elements complete the program. First, we show that within the consistent framework, all route- and bracketing-dependent observables vanish identically (route blindness), so that the pentagon and hexagon interferometers and thermal Y-junction networks we design operate as precision null tests of the modular-functor axioms themselves—the axioms stating that anyonic amplitudes are determined by the topology of a process rather than by the bookkeeping route used to compose it. Second, we show that a quantized remnant of route sensitivity survives in exactly one consistent form: the holonomy of closed cycles of categorical controls, realizing a central-charge pump for which the integer count per cycle is a family invariant beyond any static stacking description. The resulting framework provides both a conceptual reinterpretation of the central charge as a higher obstruction in categorical terms and a concrete experimental route for probing its dynamical behavior. Beyond the quantum Hall setting, these ideas suggest a broader program: anomalies, topological phases, and even string worldsheet central charges may admit reinterpretation through higher coherence. We conclude by outlining a research agenda in which categorical methods yield new experimental observables, potentially transforming the interplay between mathematics, condensed matter physics, and high-energy theory. Full article

Implantable medical device (IMD) and Internet of Medical Things (IoMT) environments need intrusion detection systems that learn across distributed hospitals without centralizing sensitive data, while controlling admission, protecting shared model artifacts, filtering unreliable contributors, and supporting post-run auditability. However, many secure federated learning designs treat identity, privacy, robustness, and evidence verification as separate layers, leaving a gap between privacy-preserving execution and public accountability. This paper presents an implemented zero-trust hierarchical federated learning-based intrusion detection system (FL-IDS) framework for IMD/IoMT security analytics. Hospital clients train eXtreme Gradient Boosting (XGBoost) detectors; self-sovereign identity gates participation; contribution-level differential privacy (DP) perturbs exported booster leaf weights; country aggregators apply adaptive Krum-inspired selection; and the global server performs trust-weighted prediction-level fusion. The evidence layer binds artifacts using Module-Lattice-Based Digital Signature Algorithm signatures, canonical hashes, Merkle roots, decentralized publication, Ethereum Sepolia anchoring, and standalone auditor verification. The framework is evaluated on WUSTL-EHMS-2020, ECU-IoHT, and CICIoMT2024 under paired DP-disabled and DP-enabled modes. Under DP-enabled execution, CICIoMT2024 achieved an F1-score of 0.998789 and area under the receiver operating characteristic curve (AUROC) of 0.999814, ECU-IoHT achieved an AUROC of 0.999337, and WUSTL-EHMS-2020 remained DP-sensitive with an F1-score of 0.422880 and AUROC of 0.776685. All paired evidence runs passed standalone auditor verification, demonstrating that privacy-preserving learning and public accountability can be integrated within a single experimental FL-IDS pipeline. Full article

The development of steady-state advanced operation modes with high fusion gain (Q) is a primary objective of magnetic confinement fusion research. The advancement of high-temperature superconducting (HTS) magnet technology has introduced a new development path using devices like SPARC. This path contrasts with the conventional low-temperature superconducting (LTS) approach represented by devices such as BEST. This study utilizes the fast integrated modeling code METIS to compare the physical conditions required for HTS-based (SPARC-like) and LTS-based (BEST-like) devices to achieve an energy gain of Q ≈ 5. Furthermore, we simulated the achievable fusion power for both devices under an identical set of core physics parameters to isolate the effect of magnetic field strength. Simulation results show that at a similar Q ≈ 5, the HTS device leverages its high magnetic field to require significantly lower auxiliary heating power (approximately 50–60% less). Additionally, it operates at a lower Greenwald density fraction (f_GW ≈ 0.37) than the LTS device (f_GW ≈ 0.87). This well validates the strong dependence of the fusion triple product on magnetic field strength (∝B³). Under identical high-density (“BEST-like”) parameters, the HTS device achieves much higher fusion power but faces a drastically increased L-H transition power threshold. This increase may force operation in L-mode. Crucially, even in L-mode, there remains the possibility for high-field HTS devices to achieve Q > 5 via high-density operation. Full article

Additive manufacturing (AM) requalification and change-control workflows often require evidence that a candidate machine, parameter set, scanner subsystem, facility, or measurement workflow remains comparable to a stable reference process after a change, but fabrication and testing costs limit exhaustive multifeature studies. The aim of this study was to address this engineering design problem by developing a practical multifeature equivalency screening framework for AM settings in which prior engineering evidence already suggests that the candidate process should be comparable to the reference process. Building on prior work focused on the univariate problem, the proposed framework uses reference-defined percentile bins, feature-wise distributional tests, and family-wise error-rate control to screen for evidence of non-equivalency across multiple measured attributes. A direct joint-binning approach was first shown to become sample-intensive as dimensionality increases, after which an independent feature-wise method and an exploratory dependent bivariate extension were developed. Simulation-based power analyses quantified the trade-offs among power, detectable effect size, distributional resolution, feature count, and the combined costs of fabrication and measurement. In a laser-based powder bed fusion validation study with 40 observations per process and three corner-deviation features, the expected-equivalent AconityMIDI+ candidate satisfied all feature-wise equivalency criteria ($\tilde{V}=0.207$–$0.214<C{I}^{+}=0.276$), whereas the expected non-equivalent SLM280 HL candidate failed all three feature-wise tests ($\tilde{V}=0.357$–$1.000>C{I}^{+}=0.276$). These results support multivariate equivalency as a requalification screening tool for AM process comparability and change control, while confirming that it should not be interpreted as proof of physical-process identity or as a replacement for first-time formal qualification. Core procedures are implemented in the open-source R package MultivariateEquivalency. Full article

Porosity-related quantities from industrial X-ray CT depend on segmentation and inference choices. When inference defaults are omitted from the report, void or phase fractions can shift by amounts comparable to slice-to-slice variability. The contribution is metrological rather than architectural: we document a reproducible nnU-Net 2D workflow on Dataset601 CTVoid from semantic labels to slice-wise void fraction, optional two-dimensional connected-component pore summaries, isotropic three-dimensional stacking at 0.058 mm spacing, and spatial axis diagnostics, with region of interest and voxel spacing stated explicitly. The main results pair a weak export policy, defined as a single forward pass per slice without multi-scale fusion or test-time augmentation, with a strong policy that enables multi-scale fusion and flip-based augmentation on the same slice exports and identical weights, on one hundred consecutive slices from one cementitious industrial stack of 1028 × 1028 pixels. In parallel we report trainer validation on eight named Dataset601 validation cases and mirroring-based test-time augmentation off versus on re-inference on those same cases; case identifiers and the cross-validation split appear in the main text. These quantities answer different questions and must not be substituted for one another or for independent full-stack ground truth. Porosity-related scalars from industrial X-ray CT depend on how segmentation and inference are configured; when defaults are omitted, void fractions can shift by amounts comparable to slice-to-slice variability. For fixed nnU-Net weights on one cementitious industrial slice stack (1028 × 1028 pixels), we benchmark weak inference (single forward pass, no multi-scale fusion or test-time augmentation) against a strong export policy (multi-scale fusion and flip-based augmentation) on 100 paired slices, and report parallel trainer validation and TTA-off versus TTA-on re-inference on eight Dataset601 hold-out cases. For the industrial dataset, mean void-class IoU between modes is 0.716 (SD 0.043), while strong inference is ~2.6× slower and predicts lower mean void area (2.37% vs. 3.04%). The full weak export gives a 3D void ratio of 2.44% and integrated void volume of 5175 mm³. On validation patches, mean void Dice/IoU against the reference are 0.835/0.728, while weak–strong void IoU reaches 0.924 under the nnU-Net-native TTA contrast—quantities that must not be interchanged across domains or definitions. The present benchmark does not include a systematic polymer dosage series, and the study does not equate semantic void with open porosity but provides a reproducible disclosure template relevant to porous and polymer-modified cementitious CT reporting. Full article

Transformer-based deformable brain MRI registration achieves high overlap accuracy, but predicted displacement fields can contain voxels with a non-positive Jacobian determinant—local foldings that violate the diffeomorphism assumption required by tensor-based morphometry and atlas-fusion segmentation workflows. We introduce HypEReg, a non-linear hyperelastic regularizer that acts directly on the Jacobian determinant of the predicted displacement field. HypEReg couples a clamped-rational volume-distortion penalty ${(det{J}_{\varphi }-1)}^2/max(det{J}_{\varphi },ϵ)$ with an explicit per-voxel anti-folding hinge ${[max(0,ϵ-det{J}_{\varphi })]}^2$, integrated as a purely loss-side module into a TransMorph backbone with no inference-graph modifications. On the IXI atlas-to-subject benchmark (115 test subjects), HypEReg-TransMorph maintains grouped Dice (0.7537) while reducing the $det\left(J_{\varphi }\right)\le 0$ voxel ratio from $1.502\times {10}^{-2}$ (TransMorph) to $1.5\times {10}^{-5}$, with identical per-case runtime and parameter count to the unregularized baseline. In strict zero-shot transfer to OASIS Learn2Reg test pairs (no fine-tuning), HypEReg-TransMorph achieves Dice 0.7756 with a $det\left(J_{\varphi }\right)\le 0$ ratio of $7.6\times {10}^{-5}$, roughly two orders of magnitude below plain TransMorph zero-shot (Dice 0.7691; ratio $9.6\times {10}^{-3}$); downstream multi-atlas label fusion further confirms the practical benefit of fold suppression (fused Dice 0.8271 vs. 0.8201 for TransMorph). OASIS-2 longitudinal and ROI analyses support deformation plausibility (lower folding/SDlogJ and stronger ventricular ROI agreement), while clinical-covariate associations remain exploratory rather than biomarker-validating. Determinant-level, non-linear hyperelastic regularization substantially suppresses folding in Transformer dense-flow brain MRI registration while preserving alignment accuracy and adding zero inference cost, providing a practical drop-in regularization strategy that improves the reliability of deformation fields for morphometry-oriented deformable registration. Full article

Partial occlusion can substantially impair the accuracy and stability of face recognition systems. Although existing methods perform well on unobstructed face images, their performance often degrades under partial occlusion, because occluded regions may obscure discriminative identity cues and introduce noise into feature representations. To address this issue, this paper proposes an occlusion-aware face recognition framework that integrates lightweight feature extraction, local-region reliability modeling, adaptive feature fusion, and joint loss optimization. Specifically, the face is divided into three sub-regions according to common occlusion patterns, and an MLP-based module is used to estimate the reliability of each region. The estimated reliability weights are then used to adaptively fuse local features, thereby emphasizing visible and discriminative regions. In addition, a joint loss combining ArcFace and InfoNCE is constructed to enhance inter-class separability and intra-class feature consistency. Experimental results under masks, hats, sunglasses, and random occlusion conditions show that the proposed method achieves a recognition accuracy of 92.3%. Compared with ArcFace, CurricularFace, and AdaFace, the proposed method improves accuracy by 9.9%, 6.5%, and 4.1%, respectively. In addition, the FAR is reduced by 5.8%, 4.9%, and 3.7%, respectively, while the FRR is reduced by 2.2%, 6.5%, and 1.3%, respectively. These results demonstrate that the proposed framework effectively enhances the robustness of face recognition under partial occlusion. Full article

This study evaluates the accuracy of various preprocessing methods applied to scanned archival aerial photographs for reconstructing historical terrain in the Czech Republic. Seven workflows were tested on identical imagery and control points, varying parameters such as resolution unification, brightness normalization, focal length calibration, and AI-based denoising. Accuracy was assessed using GNSS checkpoints and high-resolution LiDAR data. Results show that basic brightness correction reduced the vertical RMSE by 59% (to 5.69 m). In contrast, standalone AI preprocessing was associated with increased geometric instability (RMSE 16.48 m) due to over-smoothing and the loss of essential micro-texture. However, the evaluated “Fusion AI” workflow—combining AI enhancement with strict focal length constraints—successfully mitigated this degradation. By restricting the internal orientation, it stabilized the vertical accuracy at 6.48 m, closely matching the best traditional approaches. Statistical analysis revealed strong spatial autocorrelation and non-normal error distributions, highlighting the need for robust validation. Ultimately, this study confirms that AI can be effectively utilized to enhance visual clarity in data-scarce historical reconstruction without sacrificing spatial reliability, provided it is strictly geometrically constrained. This offers an optimal compromise and a tested, reproducible workflow that supports heritage preservation and long-term environmental analysis. Full article

Reliable fault diagnosis of rotating machinery is critical for averting serious failures in modern industrial systems. While data-driven deep learning has advanced condition monitoring, its success is fundamentally predicated on the availability of independent and identically distributed (I.I.D.) datasets. In realistic operational environments, machinery frequently experiences massive domain shifts induced by varying rotational speeds. Concurrently, acquiring high-fidelity fault instances is limited compared to abundant healthy baseline data, often resulting in a long-tailed distribution. Under such data-starved conditions, conventional few-shot domain adaptation (FSDA) methodologies often may be affected by distributional erasure; global alignment objectives are mainly driven by the healthy majority, causing sparse fault signatures to be erroneously absorbed as noise and leading to severe diagnostic performance degradation. To address this setting, this study develops a physics-guided dual-stream fusion framework for extreme few-shot cross-domain fault diagnosis. The method does not treat the Laplace wavelet, STFT, CNNs, or AdaBN as newly introduced techniques. Instead, it integrates these components into a unified diagnostic pipeline designed for long-tailed target support sets under large speed shifts. A learnable Laplace wavelet convolution is used in the temporal branch to emphasize transient impact responses, while STFT spectrograms provide a complementary time-frequency representation for the two-dimensional branch. The two feature streams are then fused for target fault classification. For domain adaptation, a Strict AdaBN strategy is applied using only the target support set, rather than the target test data or a large unlabeled target pool. Under the evaluated 50 healthy + 12 fault support condition, the healthy samples provide target-domain operating-background statistics for BN recalibration, while the limited fault samples are used for supervised classifier adjustment. Experiments on the HUSTbearing and Torino DIRG datasets show that the proposed integrated framework achieves stable performance under the evaluated few-shot cross-speed settings. These results suggest that combining physics-guided Laplace convolution, time-frequency representations, and support-set-restricted BN recalibration can be useful for bearing fault diagnosis when target fault samples are limited. Full article

Accurate segmentation of the ureter on abdominal computed tomography (CT) remains challenging due to its thin tubular structure and limited expert-annotated training data. While recent deep learning approaches have shown promise on non-contrast CT, arterial-phase imaging remains under-researched. We systematically compared nnU-Net-based configurations for ureter segmentation on arterial-phase CT using 25 radiologist-annotated cases from Seoul St. Mary’s Hospital. Seven training strategies were evaluated with five-fold cross-validation: binary ureter-only segmentation, multi-organ training with anatomical context from eight structures, alternative encoder architectures (ResEncM), specialized loss functions (Tversky, clDice), and a multi-phase fusion architecture. Multi-organ training with Tversky-Focal loss (Config 6) achieved the highest mean Dice of 0.743 ± 0.021 with the best clDice connectivity score (0.800 ± 0.046) and lowest fragmentation (6.56 connected components). Multi-phase fusion yielded a mean Dice of 0.713 on the 12-case subset; a controlled arterial-phase single-channel ablation on the identical 12-case subset achieved 0.721, marginally exceeding the two-channel fusion result (0.713). These findings are scoped to a single-institution exploratory cohort and should be interpreted as internally comparative benchmarking results; they may not generalize to other centres, scanners, or patient populations, and do not constitute clinical validation. Full article