Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts

Text-to-image models like Stable Diffusion have had a profound impact on daily life by enabling the generation of photorealistic images from textual prompts, fostering creativity, and enhancing visual experiences across various applications. However, these models also pose risks. Previous studies have successfully demonstrated that manipulated prompts can elicit text-to-image models to generate unsafe images, e.g., hateful meme variants. Yet, these studies only unleash the harmful power of text-to-image models in a passive manner. In this work, we focus on the proactive generation of unsafe images using targeted benign prompts via poisoning attacks. We propose two poisoning attacks: a basic attack and a utility-preserving attack. We qualitatively and quantitatively evaluate the proposed attacks using four representative hateful memes and multiple query prompts. Experimental results indicate that text-to-image models are vulnerable to the basic attack even with five poisoning samples. However, the poisoning effect can inadvertently spread to non-targeted prompts, leading to undesirable side effects. Root cause analysis identifies conceptual similarity as an important contributing factor to the side effects. To address this, we introduce the utility-preserving attack as a viable mitigation strategy to maintain the attack stealthiness, while ensuring decent attack performance. Our findings underscore the potential risks of adopting text-to-image models in real-world scenarios, calling for future research and safety measures in this space.
Certifying Bimanual RRT Motion Plans in a Second

We present an efficient method for certifying non-collision for piecewise-polynomial motion plans in algebraic reparametrizations of configuration space. Such motion plans include those generated by popular randomized methods including RRTs and PRMs, as well as those generated by many methods in trajectory optimization. Based on Sums-of-Squares optimization, our method provides exact, rigorous certificates of non-collision; it can never falsely claim that a motion plan containing collisions is collision-free. We demonstrate that our formulation is practical for real world deployment, certifying the safety of a twelve degree of freedom motion plan in just over a second. Moreover, the method is capable of discriminating the safety or lack thereof of two motion plans which differ by only millimeters.
Cortical Functional architectures as contact and sub-Riemannian geometry

In a joint paper, Jean Petitot together with the authors of the present paper described the functional geometry of the visual cortex as the symplectization of a contact form to describe the family of cells sensitive to position, orientation and scale. In the present paper, as a"homage"to the enormous contribution of Jean Petitot to neurogeometry, we will extend this approach to more complex functional architectures built as a sequence of contactization or a symplectization process, able to extend the dimension of the space. We will also outline a few examples where a sub-Riemannian lifting is needed.
Learning Robust Deep Visual Representations from EEG Brain Recordings

Decoding the human brain has been a hallmark of neuroscientists and Artificial Intelligence researchers alike. Reconstruction of visual images from brain Electroencephalography (EEG) signals has garnered a lot of interest due to its applications in brain-computer interfacing. This study proposes a two-stage method where the first step is to obtain EEG-derived features for robust learning of deep representations and subsequently utilize the learned representation for image generation and classification. We demonstrate the generalizability of our feature extraction pipeline across three different datasets using deep-learning architectures with supervised and contrastive learning methods. We have performed the zero-shot EEG classification task to support the generalizability claim further. We observed that a subject invariant linearly separable visual representation was learned using EEG data alone in an unimodal setting that gives better k-means accuracy as compared to a joint representation learning between EEG and images. Finally, we propose a novel framework to transform unseen images into the EEG space and reconstruct them with approximation, showcasing the potential for image reconstruction from EEG signals. Our proposed image synthesis method from EEG shows 62.9% and 36.13% inception score improvement on the EEGCVPR40 and the Thoughtviz datasets, which is better than state-of-the-art performance in GAN.
ConvNets Match Vision Transformers at Scale

Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web-scale. We challenge this belief by evaluating a performant ConvNet architecture pre-trained on JFT-4B, a large labelled dataset of images often used for training foundation models. We consider pre-training compute budgets between 0.4k and 110k TPU-v4 core compute hours, and train a series of networks of increasing depth and width from the NFNet model family. We observe a log-log scaling law between held out loss and compute budget. After fine-tuning on ImageNet, NFNets match the reported performance of Vision Transformers with comparable compute budgets. Our strongest fine-tuned model achieves a Top-1 accuracy of 90.4%.
Temporal variation and stoichiometric ratios of organic matter remineralization in bottom waters of the northern Gulf of Mexico during late spring and summer

An improved extended optimum multiparameter (eOMP) analysis was applied to hydrographic (temperature and salinity), and water chemistry data, including dissolved oxygen (O2), nutrients (nitrate plus nitrite, phosphate, and silicate), dissolved inorganic carbon (DIC), and total alkalinity (TAlk) data collected during late spring and summer from 2006 to 2012 in bottom waters off the Louisiana coast, to explore the dynamics and stoichiometry of DIC production during the development and maintenance of summer hypoxia. Our analysis demonstrated that DIC in bottom water was relatively low from April to June, but increased significantly in July, peaked in August, and dropped slightly in September. Furthermore, DIC production resulted from both aerobic organic carbon (OC) respiration and denitrification, as well as substantial loss due to vertical mixing with surface water. The average summer gross OC respiration rate was estimated to be 0.19 g C m−2 d−1, with the highest values occurring in late summer when hypoxic conditions dominated. We also found that Corg/N/P/-O2 remineralization ratios for aerobic respiration were generally consistent with the classic Redfield ratio (106/16/1/138) except individual C/N and C/P ratios were slightly lower, indicating that marine OC was the major source of the DIC production in the bottom water. This study quantified the role of temporal bottom-water microbial respiration to seasonal DIC dynamics and provided a means for studying the stoichiometry of biogeochemical processes in coastal waters.
An Infinite Needle in a Finite Haystack: Finding Infinite Counter-Models in Deductive Verification

First-order logic, and quantifiers in particular, are widely used in deductive verification of programs and systems. Quantifiers are essential for describing systems with unbounded domains, but prove difficult for automated solvers. Significant effort has been dedicated to finding quantifier instantiations that establish unsatisfiability of quantified formulas, thus ensuring validity of a system’s verification conditions. However, in many cases the formulas are satisfiable—this is often the case in intermediate steps of the verification process, e.g., when an invariant is not yet inductive. For such cases, existing tools are limited to finding finite models as counterexamples. Yet, some quantified formulas are satisfiable but only have infinite models, which current solvers are unable to find. Such infinite counter-models are especially typical when first-order logic is used to approximate the natural numbers, the integers, or other inductive definitions such as linked lists, which is common in deductive verification. The inability of solvers to find infinite models makes them diverge in these cases, providing little feedback to the user as they try to make progress in their verification attempts. In this paper, we tackle the problem of finding such infinite models, specifically, finite representations thereof that can be presented to the user of a deductive verification tool. These models give insight into the verification failure, and allow the user to identify and fix bugs in the modeling of the system and its properties. Our approach consists of three parts. First, we introduce symbolic structures as a way to represent certain infinite models, and show they admit an efficient model checking procedure. Second, we describe an effective model finding procedure that symbolically explores a given (possibly infinite) family of symbolic structures in search of an infinite model for a given formula. Finally, we identify a new decidable fragment of first-order logic that extends and subsumes the many-sorted variant of EPR, where satisfiable formulas always have a model representable by a symbolic structure within a known family, making our model finding procedure a decision procedure for that fragment. We evaluate our approach on examples from the domains of distributed consensus protocols and of heap-manipulating programs (specifically, linked lists). Our implementation quickly finds infinite counter-models that demonstrate the source of verification failures in a simple way, while state-of-the-art SMT solvers and theorem provers such as Z3, cvc5, and Vampire diverge or return “unknown”.
Latent event history models for quasi-reaction systems (preprint)

Various processes can be modelled as quasi-reaction systems of stochastic differential equations, such as cell differentiation and disease spreading. Since the underlying data of particle interactions, such as reactions between proteins or contacts between people, are typically unobserved, statistical inference of the parameters driving these systems is developed from concentration data measuring each unit in the system over time. While observing the continuous time process at a time scale as fine as possible should in theory help with parameter estimation, the existing Local Linear Approximation (LLA) methods fail in this case, due to numerical instability caused by small changes of the system at successive time points. On the other hand, one may be able to reconstruct the underlying unobserved interactions from the observed count data. Motivated by this, we first formalise the latent event history model underlying the observed count process. We then propose a computationally efficient Expectation-Maximation algorithm for parameter estimation, with an extended Kalman filtering procedure for the prediction of the latent states. A simulation study shows the performance of the proposed method and highlights the settings where it is particularly advantageous compared to the existing LLA approaches. Finally, we present an illustration of the methodology on the spreading of the COVID-19 pandemic in Italy.
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks

While complex simulations of physical systems have been widely used in engineering and scientific computing, lowering their often prohibitive computational requirements has only recently been tackled by deep learning approaches. In this paper, we present GraphSplineNets, a novel deep-learning method to speed up the forecasting of physical systems by reducing the grid size and number of iteration steps of deep surrogate models. Our method uses two differentiable orthogonal spline collocation methods to efficiently predict response at any location in time and space. Additionally, we introduce an adaptive collocation strategy in space to prioritize sampling from the most important regions. GraphSplineNets improve the accuracy-speedup tradeoff in forecasting various dynamical systems with increasing complexity, including the heat equation, damped wave propagation, Navier-Stokes equations, and real-world ocean currents in both regular and irregular domains.
Polar Kerr effect in multiband spin-orbit coupled superconductors

We undertake a theoretical analysis to probe the Kerr spectrum within the superconducting phase of strontium ruthenate, where the Kerr rotation experiments demonstrate the existence of a superconducting state with broken time reversal symmetry. We find that spin-orbit coupling changes the hybridization along the Fermi surface's diagonal zone mainly affects Hall transport. We show that the dominant Hall response arises mainly from the quasi-1D orbitals $d_{yz}$ and $d_{xz}$, linked to their hybridization, while other contributions are negligible. This establishes that, the breaking of time reversal symmetry of quasi-1D orbitals can account for the existence of the Kerr angle, irrespective of the order symmetry specific to the $d_{xy}$ orbital. Moreover, the optical Hall conductivity and Kerr angle estimated for the hypothesised superconducting orders also closely match the experimental findings, providing important novel insight on the role of the spin-orbit coupling, hybridization, and emergent order.
Assessing the relationship between subjective trust, confidence measurements, and mouse trajectory characteristics in an online task

Trust is essential for our interactions with others but also with artificial intelligence (AI) based systems. To understand whether a user trusts an AI, researchers need reliable measurement tools. However, currently discussed markers mostly rely on expensive and invasive sensors, like electroencephalograms, which may cause discomfort. The analysis of mouse trajectory has been suggested as a convenient tool for trust assessment. However, the relationship between trust, confidence and mouse trajectory is not yet fully understood. To provide more insights into this relationship, we asked participants (n = 146) to rate whether several tweets were offensive while an AI suggested its assessment. Our results reveal which aspects of the mouse trajectory are affected by the users subjective trust and confidence ratings; yet they indicate that these measures might not explain sufficiently the variance to be used on their own. This work examines a potential low-cost trust assessment in AI systems.
Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles

In the dynamic realm of deepfake detection, this work presents an innovative approach to validate video content. The methodology blends advanced 2-dimensional and 3-dimensional Convolutional Neural Networks. The 3D model is uniquely tailored to capture spatiotemporal features via sliding filters, extending through both spatial and temporal dimensions. This configuration enables nuanced pattern recognition in pixel arrangement and temporal evolution across frames. Simultaneously, the 2D model leverages EfficientNet architecture, harnessing auto-scaling in Convolutional Neural Networks. Notably, this ensemble integrates Voting Ensembles and Adaptive Weighted Ensembling. Strategic prioritization of the 3-dimensional model's output capitalizes on its exceptional spatio-temporal feature extraction. Experimental validation underscores the effectiveness of this strategy, showcasing its potential in countering deepfake generation's deceptive practices.
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches

Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks have been shown to be very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but empirical studies show that some heuristics improve performance whereas others can make models more brittle or have other side effects. This work aims to shed light on how different pruning methods alter the network's internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We decomposed and evaluated a set of critical geometric concepts from the common adopted classification loss, and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for in-depth comparison of pruning methods and a comprehensive understanding of how model response to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundant in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption to model pruning and data corruption but also obtain insights and explanations on how some pruned models achieve superior robustness performance.
Controllable magnetic states in chains of coupled 
φ0
 Josephson junctions with ferromagnetic weak links

A superconductor/ferromagnet/superconductor Josephson junction with anomalous phase shift (phi_0-S/F/S JJ) is a system, where the anomalous ground state shift phi_0 provides a direct magnetoelectric coupling between a magnetic moment and a phase of the superconducting condensate. If a chain of such phi_0-S/F/S JJs are coupled via superconducting leads, the condensate phase, being a macroscopic quantity, mediates a long-range interaction between the magnetic moments of the weak links. We study static and dynamic magnetic properties of such a system. It is shown that it manifests properties of n-level system, where the energies of the levels are only determined by projections of the total magnetic moment onto the easy magnetic axis. It is similar to a magnetic atom in a Zeeman field, but the role of the field is played by the magnetoelectric coupling. However, unlike an atom in a magnetic field, the relative order of energies of different states is controlled by electrical means. It is also demonstrated that the total magnetic moment can be fully controlled by a supercurrent and the response of the magnetic system to local external perturbations is highly nonlocal.
Mapping Observations of Peptide-like Molecules around Sagittarius B2

Peptide-like molecules, which have a close connection with the origin of life, have been detected in the Universe. Mapping observations of HCONH2 and CH3CONH2, two of the simplest peptide-like molecules, are performed toward the Sagittarius B2 (Sgr B2) complex with the IRAM 30 m telescope. Seven transitions of HCONH2 and five transitions of CH3CONH2 are used in the analysis. The spatial distributions of the excitation temperature and column density of HCONH2 in the molecular envelope of Sgr B2 are obtained by rotation diagrams. Assuming the same excitation temperature of HCONH2, the column densities of CH3CONH2 are also calculated. The results show that the excitation temperature ranges from 6 to 46 K in the molecular envelope of Sgr B2. The abundance ratios between HCONH2 and CH3CONH2 are calculated to explore the relationship between them, as are those between HCONH2 and HNCO. The abundance ratio of CH3CONH2/HCONH2 varies from 10% to 20%, while that of HCONH2/HNCO ranges from 1.5% to 10%. CH3CONH2 is enhanced with respect to HCONH2 in the northwest region of Sgr B2. One transition of H13CONH2 is detected toward 12 positions of Sgr B2, from which a 12C/13C ratio of 28.7 is obtained. A time-dependent chemical model with a short-duration X-ray burst is used to explain the observed abundances of HCONH2 and CH3CONH2, with the best-fitting result at T dust = 53–56 K. More chemical reactions are required to be included in the model since the modeled abundance is lower than the observed one at the observed T dust.
Vacuum energy of scalar fields on spherical shells with general matching conditions

In this work we analyze the spectral zeta function for massless scalar fields propagating in a $D$-dimensional flat space under the influence of a shell potential. The static nature of the potential, and the spherical symmetry, allows us to focus on the spatial part of the field which satisfies a one-dimensional Schrodinger equation endowed with a point potential. The shell potential is defined in terms of the two-interval self-adjoint extensions of the one-dimensional Schrodinger equation that describes the radial part of the scalar field. After performing the necessary analytic continuation, we utilize the spectral zeta function of the system to compute the vacuum energy of the field.
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation

Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to regularize model predictions. Since the predictions of unlabeled data can be unreliable, uncertainty-aware schemes are typically employed to gradually learn from meaningful and reliable predictions. Uncertainty estimation methods, however, rely on multiple inferences from the model predictions that must be computed for each training step, which is computationally expensive. Moreover, these uncertainty maps capture pixel-wise disparities and do not consider global information. This work proposes a novel method to estimate segmentation uncertainty by leveraging global information from the segmentation masks. More precisely, an anatomically-aware representation is first learnt to model the available segmentation masks. The learnt representation thereupon maps the prediction of a new segmentation into an anatomically-plausible segmentation. The deviation from the plausible segmentation aids in estimating the underlying pixel-level uncertainty in order to further guide the segmentation network. The proposed method consequently estimates the uncertainty using a single inference from our representation, thereby reducing the total computation. We evaluate our method on two publicly available segmentation datasets of left atria in cardiac MRIs and of multiple organs in abdominal CTs. Our anatomically-aware method improves the segmentation accuracy over the state-of-the-art semi-supervised methods in terms of two commonly used evaluation metrics.
Implications of species and management interactions for ranking perennial ryegrass (Lolium perenne L.) cultivars: a progress report from the two hemispheres

Experiments with a common design were established in County Cork, Ireland and in four dairying regions in New Zealand during 2012 to determine if forage value indices developed in both countries to rank perennial ryegrass cultivars should account for genotype x management interactions. Eight perennial ryegrass cultivars representing a range of phenotypes were sown in monocultures or in mixtures with white clover and received low or high rates of nitrogen fertiliser. Results from the first year of the Irish and Canterbury (NZ) experiments indicate no evidence that ryegrass cultivars re-ranked or that the scale of the difference between highest-yielding and lowest-yielding cultivars changed, when cultivars were sown in ryegrass-clover mixtures compared with ryegrass monocultures. All experiments will continue for three years to confirm whether or not these initial findings remain valid through different climate years and potential persistence differences among cultivars.