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Modern generative modeling has grown into a broad collection of related but often separately implemented paradigms, including denoising diffusion models, score-based stochastic differential equations, flow matching, variational autoencoders, normalizing flows, adversarial models, and energy-based models. For newcomers, this fragmentation makes it difficult to compare training objectives, inference procedures, sampling algorithms, and conditioning mechanisms within a single coherent codebase. We introduce V ENOM, an educational PyTorch toolkit that implements representative generative modeling families under a unified, MNIST-first interface. V ENOM emphasizes breadth, readability, reproducible entry points, and consistent training and sampling APIs rather than large-scale performance engineering. The package currently includes diffusion and score-based models, flow matching and one-step generators, variational autoencoders, normalizing flows, generative adversarial networks, and energy-based models. It provides separate training and sampling scripts, classifier and classifier-free guidance examples, bilingual tutorial notebooks, and a model-family organization that supports teaching

Achieving expert-level expressive full-body motion tracking across multiple humanoids solely from demonstration data remains a challenging and relatively an underexplored problem in humanoid robot learning. Cross-embodiment motion tracking policies are mostly trained by decoupling the control problem into upper and lower body control. This work proposes VENOM, a cross-embodiment full-body motion tracking model for humanoids in simulation. VENOM is a GPT-based motion tracker trained on multiple humanoid data that can track the entire body without the requirement to split into upper and lower body control. We curate a multi-humanoid motion tracking dataset called the VENOM dataset that contains states, actions, and rewards and train VENOM and the baselines on this dataset. In this letter, we evaluate VENOM's performance against baselines and show that we can achieve a stable motion tracker across different humanoids more capable than an MLP trained on multiple humanoid data with supervised learning alone, and also show that despite lack of reward feedback, VENOM closely matches the tracking capability of experts that were trained using asymmetric-actor critic reinforcement learning.

Trainings of Large Language Models are generally bottlenecked by matrix multiplications. In the Transformer architecture, a large portion of these operations happens in the Feed Forward Network (FFN), and this portion increases for larger models, up to 50% of the total pretraining floating point operations. We show that we can leverage hardware-accelerated sparsity to accelerate all matrix multiplications in the FFN, with 2:4 sparsity for weights and v:n:m (Venom) sparsity for activations. Our recipe relies on sparse training steps to accelerate a large part of the pretraining, associated with regular dense training steps towards the end. Overall, models trained with this approach exhibit the same performance on our quality benchmarks, and can speed up training end-to-end by 1.4 to 1.7x. This approach is applicable to all NVIDIA GPUs starting with the A100 generation, and is orthogonal to common optimization techniques, such as, quantization, and can also be applied to mixture-of-experts model architectures.

The introduction of mongooses from Indian subcontinent to Amami Oshima Island, Japan, aimed at controlling the population of venomous Habu snakes, has led to significant ecological disruptions, raising concerns about the long-term sustainability of the islands biodiversity. To highlight the unintended consequences of such interventions and the necessity of understanding predator-prey dynamics in preserving ecological balance, a mathematical model incorporating snake, mongooses, mouse and natural resources has been proposed to explore their role in the ongoing ecological disaster and analysis the other scenarios if the authorities applied different approaches in place of already implemented strategy. Determining the model's existence and uniqueness, stability at equilibrium points, and state variable characteristics are some of the parts of the analytical analysis of the model. Additionally, sensitivity analysis is conducted to identify sensitive factors. In addition, the Runge-Kutta 4th order has been used to execute the numerical simulations. Our research reveals that although the government began killing and trapping mongooses almost 20 years after their introduction, but if trap

Bluebottles (\textit{Physalia} spp.) are marine stingers resembling jellyfish, whose presence on Australian beaches poses a significant public risk due to their venomous nature. Understanding the environmental factors driving bluebottles ashore is crucial for mitigating their impact, and machine learning tools are to date relatively unexplored. We use bluebottle marine stinger presence/absence data from beaches in Eastern Sydney, Australia, and compare machine learning models (Multilayer Perceptron, Random Forest, and XGBoost) to identify factors influencing their presence. We address challenges such as class imbalance, class overlap, and unreliable absence data by employing data augmentation techniques, including the Synthetic Minority Oversampling Technique (SMOTE), Random Undersampling, and Synthetic Negative Approach that excludes the negative class. Our results show that SMOTE failed to resolve class overlap, but the presence-focused approach effectively handled imbalance, class overlap, and ambiguous absence data. The data attributes such as the wind direction, which is a circular variable, emerged as a key factor influencing bluebottle presence, confirming previous inference

Adversarial attacks have proven effective in deceiving machine learning models by subtly altering input images, motivating extensive research in recent years. Traditional methods constrain perturbations within $l_p$-norm bounds, but advancements in Unrestricted Adversarial Examples (UAEs) allow for more complex, generative-model-based manipulations. Diffusion models now lead UAE generation due to superior stability and image quality over GANs. However, existing diffusion-based UAE methods are limited to using reference images and face challenges in generating Natural Adversarial Examples (NAEs) directly from random noise, often producing uncontrolled or distorted outputs. In this work, we introduce VENOM, the first text-driven framework for high-quality unrestricted adversarial examples generation through diffusion models. VENOM unifies image content generation and adversarial synthesis into a single reverse diffusion process, enabling high-fidelity adversarial examples without sacrificing attack success rate (ASR). To stabilize this process, we incorporate an adaptive adversarial guidance strategy with momentum, ensuring that the generated adversarial examples $x^*$ align with the

Biotoxins, mainly produced by venomous animals, plants and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia. A number of venom-derived drugs are already available on the market, with many more candidates currently undergoing clinical and laboratory studies. However, drug design resources related to biotoxins are insufficient, particularly a lack of accurate and extensive activity data. To fulfill this demand, we develop the Biotoxins Database (BioTD). BioTD is the largest open-source database for toxins, offering open access to 14,607 data records (8,185 activity records), covering 8,975 toxins sourced from 5,220 references and patents across over 900 species. The activity data in BioTD is categorized into five groups: Activity, Safety, Kinetics, Hemolysis and other physiological indicators. Moreover, BioTD provides data on 986 mutants, refines the whole sequence and signal peptide sequences of toxins, and annotates disulfide bond information. Given the importance of biotoxins and their associated data, this new database was expected to attract broad interests from diverse research fields in drug discovery. BioTD i

Reinforcement Learning from Human Feedback (RLHF) is a popular method for aligning Language Models (LM) with human values and preferences. RLHF requires a large number of preference pairs as training data, which are often used in both the Supervised Fine-Tuning and Reward Model training and therefore publicly available datasets are commonly used. In this work, we study to what extent a malicious actor can manipulate the LMs generations by poisoning the preferences, i.e., injecting poisonous preference pairs into these datasets and the RLHF training process. We propose strategies to build poisonous preference pairs and test their performance by poisoning two widely used preference datasets. Our results show that preference poisoning is highly effective: injecting a small amount of poisonous data (1-5\% of the original dataset), we can effectively manipulate the LM to generate a target entity in a target sentiment (positive or negative). The findings from our experiments also shed light on strategies to defend against the preference poisoning attack.

Background: Two strains of the endoparasitoid Cotesia typhae present a differential parasitism success on the host, Sesamia nonagrioides. One is virulent on both permissive and resistant host populations, and the other only on the permissive host. This interaction provides a very interesting frame for studying virulence factors. Here, we used a combination of comparative transcriptomic and proteomic analyses to unravel the molecular basis underlying virulence differences between the strains.Results: First, we report that virulence genes are mostly expressed during the nymphal stage of the parasitoid. Especially, proviral genes are broadly up-regulated at this stage, while their expression is only expected in the host. Parasitoid gene expression in the host increases with time, indicating the production of more virulence factors. Secondly, comparison between strains reveals differences in venom composition, with 12 proteins showing differential abundance. Proviral expression in the host displays a strong temporal variability, along with differential patterns between strains. Notably, a subset of proviral genes including protein-tyrosine phosphatases is specifically over-expressed in

Advances in AI, particularly LLMs, have dramatically shortened drug discovery cycles by up to 40% and improved molecular target identification. However, these innovations also raise dual-use concerns by enabling the design of toxic compounds. Prompting Moremi Bio Agent without the safety guardrails to specifically design novel toxic substances, our study generated 1020 novel toxic proteins and 5,000 toxic small molecules. In-depth computational toxicity assessments revealed that all the proteins scored high in toxicity, with several closely matching known toxins such as ricin, diphtheria toxin, and disintegrin-based snake venom proteins. Some of these novel agents showed similarities with other several known toxic agents including disintegrin eristostatin, metalloproteinase, disintegrin triflavin, snake venom metalloproteinase, corynebacterium ulcerans toxin. Through quantitative risk assessments and scenario analyses, we identify dual-use capabilities in current LLM-enabled biodesign pipelines and propose multi-layered mitigation strategies. The findings from this toxicity assessment challenge claims that large language models (LLMs) are incapable of designing bioweapons. This rei

Backdoor attacks have been one of the emerging security threats to deep neural networks (DNNs), leading to serious consequences. One of the mainstream backdoor defenses is model reconstruction-based. Such defenses adopt model unlearning or pruning to eliminate backdoors. However, little attention has been paid to survive from such defenses. To bridge the gap, we propose Venom, the first generic backdoor attack enhancer to improve the survivability of existing backdoor attacks against model reconstruction-based defenses. We formalize Venom as a binary-task optimization problem. The first is the original backdoor attack task to preserve the original attack capability, while the second is the attack enhancement task to improve the attack survivability. To realize the second task, we propose attention imitation loss to force the decision path of poisoned samples in backdoored models to couple with the crucial decision path of benign samples, which makes backdoors difficult to eliminate. Our extensive evaluation on two DNNs and three datasets has demonstrated that Venom significantly improves the survivability of eight state-of-the-art attacks against eight state-of-the-art defenses wit

From microscopic fungi to colossal whales, fluidic ejections are a universal and intricate phenomenon in biology, serving vital functions such as animal excretion, venom spraying, prey hunting, spore dispersal, and plant guttation. This review delves into the complex fluid physics of ejections across various scales, exploring both muscle-powered active systems and passive mechanisms driven by gravity or osmosis. We introduce a framework using dimensionless numbers to delineate transitions from dripping to jetting and elucidate the governing forces. Highlighting the understudied area of complex fluid ejections, this work not only rationalizes the biophysics involved but also uncovers potential engineering applications in soft robotics, additive manufacturing, and drug delivery. By bridging biomechanics, the physics of living systems, and fluid dynamics, this review offers valuable insights into the diverse world of fluid ejections and paves the way for future bioinspired research across the spectrum of life.

The increasing success and scaling of Deep Learning models demands higher computational efficiency and power. Sparsification can lead to both smaller models as well as higher compute efficiency, and accelerated hardware is becoming available. However, exploiting it efficiently requires kernel implementations, pruning algorithms, and storage formats, to utilize hardware support of specialized sparse vector units. An example of those are the NVIDIA's Sparse Tensor Cores (SPTCs), which promise a 2x speedup. However, SPTCs only support the 2:4 format, limiting achievable sparsity ratios to 50%. We present the V:N:M format, which enables the execution of arbitrary N:M ratios on SPTCs. To efficiently exploit the resulting format, we propose Spatha, a high-performance sparse-library for DL routines. We show that Spatha achieves up to 37x speedup over cuBLAS. We also demonstrate a second-order pruning technique that enables sparsification to high sparsity ratios with V:N:M and little to no loss in accuracy in modern transformers.

Intramolecular ion-pair interactions yield shape and functionality to many molecules. With proper orientation, these interactions overcome steric factors and are responsible for the compact structures of several peptides. In this study, we present a thermodynamic cycle based on isoelectronic and alchemical mutation to estimate intramolecular ion-pair interaction energy. We determine these energies for 26 benchmark molecules with common ion-pair combinations and compare them with results obtained using intramolecular symmetry-adapted perturbation theory. For systems with long linkers, the ion-pair energies evaluated using both approaches deviate by less than 2.5% in vacuum phase. The thermodynamic cycle based on density functional theory facilitates calculations of salt-bridge interactions in model tripeptides with continuum/microsolvation modeling, and four large peptides: 1EJG (crambin), 1BDK (bradykinin), 1L2Y (a mini-protein with a tryptophan cage), and 1SCO (a toxin from the scorpion venom).

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