Stabilization of the Electroweak Scale in 3-3-1 Models

One way of avoiding the destabilization of the electroweak scale through a strong coupled regime naturally occurs in models with a Landau-like pole at the TeV scale. Hence, the quadratic divergence contributions to the scalar masses are not considered as a problem anymore since a new nonperturbative dynamic emerges at the TeV scale. This scale should be an intrinsic feature of the models and there is no need to invoke any other sort of protection for the electroweak scale. In some models based on the $SU(3)_C\otimes SU(3)_W\otimes U(1)_{X}$ gauge symmetry, a nonperturbative dynamics arise and it stabilizes the electroweak scale.Comment: 10 pages. Version with some improvements and corrections in the tex

Forgery-aware Adaptive Vision Transformer for Face Forgery Detection

With the advancement in face manipulation technologies, the importance of face forgery detection in protecting authentication integrity becomes increasingly evident. Previous Vision Transformer (ViT)-based detectors have demonstrated subpar performance in cross-database evaluations, primarily because fully fine-tuning with limited Deepfake data often leads to forgetting pre-trained knowledge and over-fitting to data-specific ones. To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT). In FA-ViT, the vanilla ViT's parameters are frozen to preserve its pre-trained knowledge, while two specially designed components, the Local-aware Forgery Injector (LFI) and the Global-aware Forgery Adaptor (GFA), are employed to adapt forgery-related knowledge. our proposed FA-ViT effectively combines these two different types of knowledge to form the general forgery features for detecting Deepfakes. Specifically, LFI captures local discriminative information and incorporates these information into ViT via Neighborhood-Preserving Cross Attention (NPCA). Simultaneously, GFA learns adaptive knowledge in the self-attention layer, bridging the gap between the two different domain. Furthermore, we design a novel Single Domain Pairwise Learning (SDPL) to facilitate fine-grained information learning in FA-ViT. The extensive experiments demonstrate that our FA-ViT achieves state-of-the-art performance in cross-dataset evaluation and cross-manipulation scenarios, and improves the robustness against unseen perturbations

Plasma Synthesis of Nanoparticles for Nanocomposite Energy Applications

The nanocomposite energy applications for plasma reactor produced nanoparticles are reviewed. Nanoparticles are commonly defined as particles less than 100 nm in diameter. Due to this small size, nanoparticles have a high surface-to-volume ratio. This increases the surface energy compared to the bulk material. The high surface-to-volume ratio and size effects (quantum effects) give nanoparticles distinctive chemical, electronic, optical, magnetic and mechanical properties from those of the bulk material. Nanoparticles synthesis can be grouped into 3 broad approaches. The first one is wet phase synthesis (sol-gel processing), the second is mechanical attrition, and the third is gas-phase synthesis (aerosol). The properties of the final product may differ significantly depending on the fabrication route. Currently, there are no economical large-scale production processes for nanoparticles. This hinders the widespread applications of nanomaterials in products. The Idaho National Laboratory (INL) is engaging in research and development of advanced modular hybrid plasma reactors for low cost production of nanoparticles that is predicted to accelerate application research and enable the formation of technology innovation alliances that will result in the commercial production of nanocomposites for alternative energy production devices such as fuel cells, photovoltaics and electrochemical double layer capacitors

S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens

Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on deep learning models but their cross-domain generalization capabilities are often hindered by the domain shift problem, which arises due to different distributions between training and testing data. In this study, we develop a generalized FAS method under the Efficient Parameter Transfer Learning (EPTL) paradigm, where we adapt the pre-trained Vision Transformer models for the FAS task. During training, the adapter modules are inserted into the pre-trained ViT model, and the adapters are updated while other pre-trained parameters remain fixed. We find the limitations of previous vanilla adapters in that they are based on linear layers, which lack a spoofing-aware inductive bias and thus restrict the cross-domain generalization. To address this limitation and achieve cross-domain generalized FAS, we propose a novel Statistical Adapter (S-Adapter) that gathers local discriminative and statistical information from localized token histograms. To further improve the generalization of the statistical tokens, we propose a novel Token Style Regularization (TSR), which aims to reduce domain style variance by regularizing Gram matrices extracted from tokens across different domains. Our experimental results demonstrate that our proposed S-Adapter and TSR provide significant benefits in both zero-shot and few-shot cross-domain testing, outperforming state-of-the-art methods on several benchmark tests. We will release the source code upon acceptance

Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning

Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively. To address this issue, we introduce DOLOS\footnote {The name ``DOLOS" comes from Greek mythology.}, the largest gameshow deception detection dataset with rich deceptive conversations. DOLOS includes 1,675 video clips featuring 213 subjects, and it has been labeled with audio-visual feature annotations. We provide train-test, duration, and gender protocols to investigate the impact of different factors. We benchmark our dataset on previously proposed deception detection approaches. To further improve the performance by fine-tuning fewer parameters, we propose Parameter-Efficient Crossmodal Learning (PECL), where a Uniform Temporal Adapter (UT-Adapter) explores temporal attention in transformer-based architectures, and a crossmodal fusion module, Plug-in Audio-Visual Fusion (PAVF), combines crossmodal information from audio-visual features. Based on the rich fine-grained audio-visual annotations on DOLOS, we also exploit multi-task learning to enhance performance by concurrently predicting deception and audio-visual features. Experimental results demonstrate the desired quality of the DOLOS dataset and the effectiveness of the PECL. The DOLOS dataset and the source codes are available at https://github.com/NMS05/Audio-Visual-Deception-Detection-DOLOS-Dataset-and-Parameter-Efficient-Crossmodal-Learning/tree/main.Comment: 11 pages, 6 figure