Henan University

Spiking networks that fuse audio and video have lacked a way to regulate information flow inside cross-modal attention according to the input. GSCANet adds a gate to a dual-stream Spikingformer backbone, placing it on the attention output just before spike firing and driving it with concatenated query and value features. On CREMA-D and RAVDESS this configuration beats the no-gate baseline by 2.15 and 1.05 percentage points; both gate position and driving signal change the outcome.

Spiking networks that fuse audio and video have lacked a way to regulate information flow inside cross-modal attention according to the input. GSCANet adds a gate to a dual-stream Spikingformer backbone, placing it on the attention output just before spike firing and driving it with concatenated query and value features. On CREMA-D and RAVDESS this configuration beats the no-gate baseline by 2.15 and 1.05 percentage points; both gate position and driving signal change the outcome.

Thermoelectric screening needs the full zT-versus-temperature curve, because peak height and position set the usable operating window, yet measuring S, σ and κ point by point is costly. A multi-task model with zT as the principal task and S, σ, κ and PF as auxiliary tasks takes only composition descriptors and temperature, predicts all five properties and reconstructs zT from them. In ten-fold material-grouped held-out evaluation zT reaches R² = 0.8765 with peak-temperature MAE of 14.07 K, and 24 external points from four literature materials give R² = 0.9755.

Thermoelectric screening needs the full zT-versus-temperature curve, because peak height and position set the usable operating window, yet measuring S, σ and κ point by point is costly. A multi-task model with zT as the principal task and S, σ, κ and PF as auxiliary tasks takes only composition descriptors and temperature, predicts all five properties and reconstructs zT from them. In ten-fold material-grouped held-out evaluation zT reaches R² = 0.8765 with peak-temperature MAE of 14.07 K, and 24 external points from four literature materials give R² = 0.9755.

The work proposes JMLLM, a jailbreak framework that combines four concealment strategies—alternating translation, word encryption, feature collapse, and harmful injection—across text, visual, and speech modalities, releases the TriJail dataset with 1,250 textual and speech adversarial prompts plus 150 harmful images, reports leading attack success rates on 16 popular LLMs over AdvBench and TriJail at 24.65 seconds for a single query and 6 queries for the multi-round mode, and proposes a Harmful Separator defense that reduces attack success rates.

The work proposes JMLLM, a jailbreak framework that combines four concealment strategies—alternating translation, word encryption, feature collapse, and harmful injection—across text, visual, and speech modalities, releases the TriJail dataset with 1,250 textual and speech adversarial prompts plus 150 harmful images, reports leading attack success rates on 16 popular LLMs over AdvBench and TriJail at 24.65 seconds for a single query and 6 queries for the multi-round mode, and proposes a Harmful Separator defense that reduces attack success rates.