Engineering Sciences

Beamforming for near-field extreme massive MIMO integrated sensing and communication usually requires repeatedly solving nonconvex constrained optimization, and online latency grows with array size. PATBO fixes the communication and sensing beam directions geometrically and lets a Transformer predict only the normalized stream powers, trained with Cramér–Rao bound (CRB) sensitivity weighting. On a retained test set with 1024 antennas, 4 users and 2 targets, direct inference reaches 19.54% joint feasibility, physics-constrained power repair raises it to 87.79%, and optimizer fallback for the remainder brings it to 100%, at 1.11 ms per sample.

Beamforming for near-field extreme massive MIMO integrated sensing and communication usually requires repeatedly solving nonconvex constrained optimization, and online latency grows with array size. PATBO fixes the communication and sensing beam directions geometrically and lets a Transformer predict only the normalized stream powers, trained with Cramér–Rao bound (CRB) sensitivity weighting. On a retained test set with 1024 antennas, 4 users and 2 targets, direct inference reaches 19.54% joint feasibility, physics-constrained power repair raises it to 87.79%, and optimizer fallback for the remainder brings it to 100%, at 1.11 ms per sample.

Virtual metrology for chemical mechanical planarization (CMP) material removal rate (MRR) has relied on simplified linear assumptions or handcrafted feature engineering, which needs domain knowledge and can discard temporal information in raw sensor signals. GREP first pretrains self-supervised on unlabeled sensor sequences, having the encoder regress global proxy targets projected from complete and half sequences, then fine-tunes on labeled data. On the PHM 2016 CMP dataset it matches a feature-engineered supervised baseline across most labeled-data fractions, lowers error in selected limited-label settings, and cuts RMSE from 11.23 to 9.32 in the final chronological interval.

Virtual metrology for chemical mechanical planarization (CMP) material removal rate (MRR) has relied on simplified linear assumptions or handcrafted feature engineering, which needs domain knowledge and can discard temporal information in raw sensor signals. GREP first pretrains self-supervised on unlabeled sensor sequences, having the encoder regress global proxy targets projected from complete and half sequences, then fine-tunes on labeled data. On the PHM 2016 CMP dataset it matches a feature-engineered supervised baseline across most labeled-data fractions, lowers error in selected limited-label settings, and cuts RMSE from 11.23 to 9.32 in the final chronological interval.

Paired modalities are usually used to pull two encoders into one shared embedding space. DuoJEPA instead keeps a separate LeJEPA self-supervised space per modality and appends to each encoder one residual-free transformer layer that translates its embedding into the other encoder's space, trained by a contrastive loss computed entirely within that target space. On CheXpert Plus chest radiographs and reports it reaches a zero-shot AUC of 86.6 and a cross-modal retrieval score of 78.5, against 60.0 and 64.2 for a reimplementation of LeVLJEPA with the same encoders and data, while a paired-cosine test detects no direct alignment between the two own spaces.

Paired modalities are usually used to pull two encoders into one shared embedding space. DuoJEPA instead keeps a separate LeJEPA self-supervised space per modality and appends to each encoder one residual-free transformer layer that translates its embedding into the other encoder's space, trained by a contrastive loss computed entirely within that target space. On CheXpert Plus chest radiographs and reports it reaches a zero-shot AUC of 86.6 and a cross-modal retrieval score of 78.5, against 60.0 and 64.2 for a reimplementation of LeVLJEPA with the same encoders and data, while a paired-cosine test detects no direct alignment between the two own spaces.

An engineering design study holds only tens to hundreds of simulations, each on its own mesh, and conventional surrogates are retrained for every study. PhysICL is pretrained once on a procedural prior, then treats a few labeled simulations as context and predicts fields, with quantiles, directly at each query's own unstructured-mesh nodes in one forward pass, with no weight update. On five PLAID datasets unseen in pretraining its field error falls as context grows and drops below the nearest context simulation; on AhmedML surfaces its peak GPU memory stays at 4.27 GiB.

An engineering design study holds only tens to hundreds of simulations, each on its own mesh, and conventional surrogates are retrained for every study. PhysICL is pretrained once on a procedural prior, then treats a few labeled simulations as context and predicts fields, with quantiles, directly at each query's own unstructured-mesh nodes in one forward pass, with no weight update. On five PLAID datasets unseen in pretraining its field error falls as context grows and drops below the nearest context simulation; on AhmedML surfaces its peak GPU memory stays at 4.27 GiB.

Electromagnetic signal processing has long trained a separate expert per task, limiting cross-task reuse, while electromagnetic foundation models pay for large-scale pretraining and shared-backbone inference. GLCR instead routes over a frozen heterogeneous expert pool: a lightweight one-dimensional CNN with an additive-margin head makes the global dispatch, and a local re-evaluation with four physical statistics handles confusable task pairs. On the HeteroSignal-7 benchmark it reaches 96.16% routing accuracy and a 76.30% system score, above all 19 baselines, at 1.43 ms routing latency per sample.

Electromagnetic signal processing has long trained a separate expert per task, limiting cross-task reuse, while electromagnetic foundation models pay for large-scale pretraining and shared-backbone inference. GLCR instead routes over a frozen heterogeneous expert pool: a lightweight one-dimensional CNN with an additive-margin head makes the global dispatch, and a local re-evaluation with four physical statistics handles confusable task pairs. On the HeteroSignal-7 benchmark it reaches 96.16% routing accuracy and a 76.30% system score, above all 19 baselines, at 1.43 ms routing latency per sample.

Engineering design often has only 10 to 1,000 evaluated designs because simulations and experiments are costly. GenPFN trains no new generator: a frozen TabPFN classifier reads labeled designs, candidates are interpolated between desired designs and updated with classifier gradients and Gaussian noise, and every candidate is evaluated by the problem's own constraints and objectives. Across fourteen engineering problems, GenPFN v1 has higher class satisfaction and lower realism distance than the tested CTGAN, TVAE and TabDDPM; in the stern case, 993 of 1,000 candidates passed geometric checks and met the propeller-placement area requirement, versus 28 of 2,000 random context designs.

Engineering design often has only 10 to 1,000 evaluated designs because simulations and experiments are costly. GenPFN trains no new generator: a frozen TabPFN classifier reads labeled designs, candidates are interpolated between desired designs and updated with classifier gradients and Gaussian noise, and every candidate is evaluated by the problem's own constraints and objectives. Across fourteen engineering problems, GenPFN v1 has higher class satisfaction and lower realism distance than the tested CTGAN, TVAE and TabDDPM; in the stern case, 993 of 1,000 candidates passed geometric checks and met the propeller-placement area requirement, versus 28 of 2,000 random context designs.

Probabilistic-bit invertible logic encodes a Boolean circuit as an energy and samples it to compute in any direction, but conventional encodings only enforce legal ground states and widen the minimum gap, while finite-time samplers spend their runs in excited states. This work casts Hamiltonian design as inverse statistical mechanics, fitting interactions up to fourth order by constrained maximum likelihood under a gap floor so that the Gibbs law approaches uniformity on each clamp case's legal states. On 44- to 77-bit multipliers in factor mode, the learned energies cut updates to 99% verified success to 0.55, 0.44, 0.52 and 0.46 of the hand-written encoding across nine tuned samplers, at the cost of more terms evaluated per update.

Probabilistic-bit invertible logic encodes a Boolean circuit as an energy and samples it to compute in any direction, but conventional encodings only enforce legal ground states and widen the minimum gap, while finite-time samplers spend their runs in excited states. This work casts Hamiltonian design as inverse statistical mechanics, fitting interactions up to fourth order by constrained maximum likelihood under a gap floor so that the Gibbs law approaches uniformity on each clamp case's legal states. On 44- to 77-bit multipliers in factor mode, the learned energies cut updates to 99% verified success to 0.55, 0.44, 0.52 and 0.46 of the hand-written encoding across nine tuned samplers, at the cost of more terms evaluated per update.

Event-driven learning turns continuous signals into sparse events, but the triggering threshold has long been chosen empirically. This work maps signed threshold encoding onto discretized learning systems, uses the ratio of normal-condition increment standard deviation to threshold to define an information-sufficiency boundary, then picks the sparsest threshold retaining a prescribed fraction of peak discriminative information. On power-system fault classification, retaining 75% of peak information cut event density about 7.6-fold relative to that boundary while overall accuracy was 94.14%.

Event-driven learning turns continuous signals into sparse events, but the triggering threshold has long been chosen empirically. This work maps signed threshold encoding onto discretized learning systems, uses the ratio of normal-condition increment standard deviation to threshold to define an information-sufficiency boundary, then picks the sparsest threshold retaining a prescribed fraction of peak discriminative information. On power-system fault classification, retaining 75% of peak information cut event density about 7.6-fold relative to that boundary while overall accuracy was 94.14%.

SAR and optical remote sensing images are often treated as largely redundant views, and earlier cross-modal self-supervised methods aligned them only at the whole-image level, leaving fine-grained complementary structure unused. CroCoMAE combines image-level contrastive learning with patch-level masked reconstruction inside a ViT, splitting each patch embedding into a shared subspace aligned across sensors and an unconstrained unshared subspace. On the EuroSAT-SAR scene-classification and DFC2020 semantic-segmentation benchmarks it beats uni-modal self-supervised baselines and retrained cross-modal frameworks in most configurations, with the largest gains on segmentation.

SAR and optical remote sensing images are often treated as largely redundant views, and earlier cross-modal self-supervised methods aligned them only at the whole-image level, leaving fine-grained complementary structure unused. CroCoMAE combines image-level contrastive learning with patch-level masked reconstruction inside a ViT, splitting each patch embedding into a shared subspace aligned across sensors and an unconstrained unshared subspace. On the EuroSAT-SAR scene-classification and DFC2020 semantic-segmentation benchmarks it beats uni-modal self-supervised baselines and retrained cross-modal frameworks in most configurations, with the largest gains on segmentation.

In aerial drone imagery, a referred target often covers only tens of pixels and sits among visually similar vehicles or people, so a generative editor can draw a plausible mask around the wrong instance. DroneSegEdit freezes the open-weight LongCat-Image-Edit, trains only a lightweight LoRA to render candidate regions, then uses a language-grounded spatial verifier to strip foreground inside that candidate that the referring expression does not support. On the complete official RIS-LAD test set it reaches 52.78 mIoU, 7.16 points above the previous best ReSaP-8B, and stays competitive overall on UAVid-RIS and VDD-RIS, with the clearest gains on compact vehicles and humans.

In aerial drone imagery, a referred target often covers only tens of pixels and sits among visually similar vehicles or people, so a generative editor can draw a plausible mask around the wrong instance. DroneSegEdit freezes the open-weight LongCat-Image-Edit, trains only a lightweight LoRA to render candidate regions, then uses a language-grounded spatial verifier to strip foreground inside that candidate that the referring expression does not support. On the complete official RIS-LAD test set it reaches 52.78 mIoU, 7.16 points above the previous best ReSaP-8B, and stays competitive overall on UAVid-RIS and VDD-RIS, with the clearest gains on compact vehicles and humans.