Analyzing and Improving Generative Adversarial Training for Generative Modeling and Out-of-Distribution Detection

Generative adversarial training (GAT) is a recently introduced adversarial defense method. Previous works have focused on empirical evaluations of its application to training robust predictive models. In this paper we focus on theoretical understanding of the GAT method and extending its application to generative modeling and out-of-distribution detection. We analyze the optimal solutions of the maximin formulation employed by the GAT objective, and make a comparative analysis of the minimax formulation employed by GANs. We use theoretical analysis and 2D simulations to understand the convergence property of the training algorithm. Based on these results, we develop an incremental generative training algorithm, and conduct comprehensive evaluations of the algorithm's application to image generation and adversarial out-of-distribution detection. Our results suggest that generative adversarial training is a promising new direction for the above applications

Representation Engineering: A Top-Down Approach to AI Transparency

In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.Comment: Code is available at https://github.com/andyzoujm/representation-engineerin

Effect of Different Land Use Types on the Taxonomic and Functional Diversity of Macroinvertebrates in an Urban Area of Northern China

The urbanization of riverine landscapes is an increasing threat to river ecosystems. However, it is unclear which metrics can best assess the response of macroinvertebrates to the conversion of forested lands to urban and agricultural lands. The main goal of this study is to examine whether trait-based approaches are more sensitive than taxonomic approaches in distinguishing macroinvertebrate responses to different land use types in a highly urbanized area of northern China. Results based on 14 environmental variables showed a significant difference across a human-induced environmental gradient. The results showed that no significant differences were observed in terms of taxonomic diversity indices between the different land use types. Functional evenness (FEve) and Rao’s quadratic entropy decreased with the increase in urban area caused by the intensification of human activity, demonstrating that functional diversity is more sensitive than taxonomic diversity in discriminating between different land use types. In addition, the results based on RLQ (physical–chemical variables (R), macroinvertebrate taxa (L), and species traits (Q)) and fourth-corner analyses indicated that the trait states of bi- or multivoltine, high dispersal capacity, and not-streamlined body shape were much higher in the agricultural area and positively related to farmland percentage. Taxa with large body size were dominant in urban areas and were positively correlated with EC. Overall, the observed responses of traits to environmental variables suggest that trait-based approaches should be incorporated into land use management for river restoration

An Efficient Implementation of Track-Oriented Multiple Hypothesis Tracker Using Graphical Model Approaches

The multiple hypothesis tracker (MHT) is currently the preferred method for addressing data association problem in multitarget tracking (MTT) application. MHT seeks the most likely global hypothesis by enumerating all possible associations over time, which is equal to calculating maximum a posteriori (MAP) estimate over the report data. Despite being a well-studied method, MHT remains challenging mostly because of the computational complexity of data association. In this paper, we describe an efficient method for solving the data association problem using graphical model approaches. The proposed method uses the graph representation to model the global hypothesis formation and subsequently applies an efficient message passing algorithm to obtain the MAP solution. Specifically, the graph representation of data association problem is formulated as a maximum weight independent set problem (MWISP), which translates the best global hypothesis formation into finding the maximum weight independent set on the graph. Then, a max-product belief propagation (MPBP) inference algorithm is applied to seek the most likely global hypotheses with the purpose of avoiding a brute force hypothesis enumeration procedure. The simulation results show that the proposed MPBP-MHT method can achieve better tracking performance than other algorithms in challenging tracking situations

Effect of Different Land Use Types on the Taxonomic and Functional Diversity of Macroinvertebrates in an Urban Area of Northern China

The urbanization of riverine landscapes is an increasing threat to river ecosystems. However, it is unclear which metrics can best assess the response of macroinvertebrates to the conversion of forested lands to urban and agricultural lands. The main goal of this study is to examine whether trait-based approaches are more sensitive than taxonomic approaches in distinguishing macroinvertebrate responses to different land use types in a highly urbanized area of northern China. Results based on 14 environmental variables showed a significant difference across a human-induced environmental gradient. The results showed that no significant differences were observed in terms of taxonomic diversity indices between the different land use types. Functional evenness (FEve) and Rao’s quadratic entropy decreased with the increase in urban area caused by the intensification of human activity, demonstrating that functional diversity is more sensitive than taxonomic diversity in discriminating between different land use types. In addition, the results based on RLQ (physical–chemical variables (R), macroinvertebrate taxa (L), and species traits (Q)) and fourth-corner analyses indicated that the trait states of bi- or multivoltine, high dispersal capacity, and not-streamlined body shape were much higher in the agricultural area and positively related to farmland percentage. Taxa with large body size were dominant in urban areas and were positively correlated with EC. Overall, the observed responses of traits to environmental variables suggest that trait-based approaches should be incorporated into land use management for river restoration

Diet Diversity of the Fluviatile Masu Salmon, <i>Oncorhynchus masou</i> (Brevoort 1856) Revealed via Gastrointestinal Environmental DNA Metabarcoding and Morphological Identification of Contents

Masu salmon, Oncorhynchus masou (Brevoort 1856), a commercially important fish species endemic to the North Pacific Ocean, attained national second-level protected animal status in China in 2021. Despite this recognition, knowledge about the trophic ecology of this fish remains limited. This study investigated the diet diversity of fluviatile Masu salmon in the Mijiang River, China, utilizing the gastrointestinal tract environmental DNA (GITeDNA) metabarcoding and morphological identification. The results revealed a diverse prey composition, ranging from terrestrial and aquatic invertebrates to small fishes. The fluviatile Masu salmon in general consumed noteworthily more aquatic prey than terrestrial prey. There were much more prey taxa and a higher diet diversity detected by GITeDNA metabarcoding than by morphological identification. GITeDNA metabarcoding showed that larger and older Masu salmon consumed significantly more terrestrial insects than aquatic prey species did, with 7366 verses 5012 sequences in the group of ≥20 cm, 9098 verses 4743 sequences in the group of ≥100 g and 11,540 verses 729 sequences in the group of age 3+. GITeDNA metabarcoding also showed size- and age-related diet diversity, indicating that the dietary niche breadth and trophic diversity of larger and older Masu salmon increased with food resources expanding to more terrestrial prey. Terrestrial invertebrates of riparian habitats play a vital role in the diet of fluviatile Masu salmon, especially larger individuals, highlighting their importance in connecting aquatic and terrestrial food webs. Conservation plans should prioritize the protection and restoration of riparian habitats. This study advocates the combined use of GITeDNA metabarcoding and morphological observation for a comprehensive understanding of fish diet diversity

HIV-1 NNRTIs: structural diversity, pharmacophore similarity, and implications for drug design

Nonnucleoside reverse transcriptase inhibitors (NNRTIs) nowadays represent very potent and most promising anti-AIDS agents that specifically target the HIV-1 reverse transcriptase (RT). However, the effectiveness of NNRTI drugs can be hampered by rapid emergence of drug-resistant viruses and severe side effects upon long-term use. Therefore, there is an urgent need to develop novel, highly potent NNRTIs with broad spectrum antiviral activity and improved pharmacokinetic properties, and more efficient strategies that facilitate and shorten the drug discovery process would be extremely beneficial. Fortunately, the structural diversity of NNRTIs provided a wide space for novel lead discovery, and the pharmacophore similarity of NNRTIs gave valuable hints for lead discovery and optimization. More importantly, with the continued efforts in the development of computational tools and increased crystallographic information on RT/NNRTI complexes, structure-based approaches using a combination of traditional medicinal chemistry, structural biology, and computational chemistry are being used increasingly in the design of NNRTIs. First, this review covers two decades of research and development for various NNRTI families based on their chemical scaffolds, and then describes the structural similarity of NNRTIs. We have attempted to assemble a comprehensive overview of the general approaches in NNRTI lead discovery and optimization reported in the literature during the last decade. The successful applications of medicinal chemistry strategies, crystallography, and computational tools for designing novel NNRTIs are highlighted. Future directions for research are also outlined.status: publishe