Search

The main objective of this paper is to show that balls under invariant metrics on hyperbolic planar domains are finitely-connected. As applications, we give new and transparent proofs of classical results on conformal mappings of planar domains. In particular, we show that any conformal self-map of a hyperbolic planar domain with three fixed points is the identity. We also give a new and very simple proof of the theorem by Aumann and Carath\'eodory that states that the isotropy groups of a hyperbolic planar domain are either finite or the domain is simply-connected.

This research provides a comprehensive overview of adversarial attacks on AI and ML models, exploring various attack types, techniques, and their potential harms. We also delve into the business implications, mitigation strategies, and future research directions. To gain practical insights, we employ the Adversarial Robustness Toolbox (ART) [1] library to simulate these attacks on real-world use cases, such as self-driving cars. Our goal is to inform practitioners and researchers about the challenges and opportunities in defending AI systems against adversarial threats. By providing a comprehensive comparison of different attack methods, we aim to contribute to the development of more robust and secure AI systems.

We consider the polynomial Ideal Membership Problem (IMP) for ideals encoding combinatorial problems that are instances of CSPs over a finite language. In this paper, the input polynomial $f$ has degree at most $d=O(1)$ (we call this problem IMP$_d$). We bridge the gap in \cite{MonaldoMastrolilli2019} by proving that the IMP$_d$ for Boolean combinatorial ideals whose constraints are closed under the minority polymorphism can be solved in polynomial time. This completes the identification of the tractability for the Boolean IMP$_d$. We also prove that the proof of membership for the IMP$_d$ for problems constrained by the dual discriminator polymorphism over any finite domain can be found in polynomial time. Our results can be used in applications such as Nullstellensatz and Sum-of-Squares proofs., Comment: arXiv admin note: text overlap with arXiv:2006.16422; text overlap with arXiv:2011.03700 by other authors

Clouds in satellite images are a deterrent to qualitative and quantitative study. Time compositing methods compare a series of co-registered images and retrieve only those pixels that have comparatively lesser cloud cover for the resultant image. Two different approaches of time compositing were tested. The first method recoded the clouds to value 0 on all the constituent images and ran a 'max' function. The second method directly ran a 'min' function without recoding on all the images for the resultant image. The 'max' function gave a highly mottled image while the 'min' function gave a superior quality image with smoother texture. Persistent clouds on all constituent images were retained in both methods, but they were readily identifiable and easily extractable in the 'max' function image as they were recoded to 0, while that in the 'min' function appeared with varying DN values. Hence a hybrid technique was created which recodes the clouds to value 255 and runs a 'min' function. This method preserved the quality of the 'min' function and the advantage of retrieving clouds as in the 'max' function image. The models were created using Erdas Imagine Modeler 9.1 and MODIS 250 m resolution images of coastal Karnataka in the months of May, June 2008 were used. A detailed investigation on the different methods is described and scope for automating different techniques is discussed., Comment: 10 pages, 8 figures

The maximum unitarily extractable work from a quantum system -- ergotropy -- is a useful and emerging idea in quantum thermodynamics. In this work, ergotropy is studied in quantum chaotic systems to illustrate the effects arising from chaotic dynamics. In an ancilla-assisted scenario, chaos enhances ergotropy when the state is known, a consequence of large entanglement production in the chaotic regime. In contrast, when the state is unknown, chaos impedes work extraction. This downside arises from chaos suppressing information gain about the system from coarse-grained measurements. When both entanglement and coarse-grained measurements are present, there is competition between the two, and ergotropy reaches maximum at an optimal value of the chaos parameter, followed by a decrease. The fall in ergotropy is due to chaos impeding measurements in the chaotic regime. These results are illustrated using two quantum chaotic models; the quantum kicked top and the kicked Ising spin chain., Comment: 7 pages and 5 figures including supplementary

Rapid advances in the capabilities of large language models (LLMs) have raised widespread concerns regarding their potential for malicious use. Open-weight LLMs present unique challenges, as existing safeguards lack robustness to tampering attacks that modify model weights. For example, recent works have demonstrated that refusal and unlearning safeguards can be trivially removed with a few steps of fine-tuning. These vulnerabilities necessitate new approaches for enabling the safe release of open-weight LLMs. We develop a method, called TAR, for building tamper-resistant safeguards into open-weight LLMs such that adversaries cannot remove the safeguards even after thousands of steps of fine-tuning. In extensive evaluations and red teaming analyses, we find that our method greatly improves tamper-resistance while preserving benign capabilities. Our results demonstrate that tamper-resistance is a tractable problem, opening up a promising new avenue to improve the safety and security of open-weight LLMs., Comment: Website: https://www.tamper-resistant-safeguards.com

We employ a Bose Einstein Condensate (BEC) based atom-optic kicked rotor to generate an asymmetrically localized momentum distribution that depends upon initial velocity of the BEC. Asymmetric features are shown to arise from the early-time dynamics induced by the broken parity symmetry and, asymptotically freeze as the dynamical localization stabilizes. The asymmetry in the momentum distribution critically depends upon the initial launch velocity and is sensitive to very small initial velocities ('micromotion') of the BEC. In this work, we also perform a precise measurement of the 'micromotion'. By utilizing the technique of measuring the early-time asymmetry of momentum distribution, we report measurement of micromotion down to (230 \pm 17 , \mu\text{m/s}).

Quantum resonance in the paradigmatic kicked rotor system is a purely quantum effect that ignores the state of underlying classical chaos. In this work, it is shown that quantum resonance leads to superlinear entanglement production. In $N$-interacting kicked rotors set to be at quantum resonance, entanglement growth is super-linear until a crossover timescale $t^*$, beyond which growth slows down to a logarithmic form with superimposed oscillations. By mapping positional interaction to momentum space and analytically assessing the linear entropy, we unravel the mechanism driving these two distinct growth profiles. The analytical results agree with the numerical simulations performed for two- and three-interacting kicked rotors. The late time entanglement oscillation is sensitive to changes in scaled Planck's constant with a high quality factor suitable for high precision measurements. These results are amenable to an experimental realization on atom optics setup., Comment: 6 pages, 3 figures

Stance detection has been widely studied as the task of determining if a social media post is positive, negative or neutral towards a specific issue, such as support towards vaccines. Research in stance detection has however often been limited to a single language and, where more than one language has been studied, research has focused on few-shot settings, overlooking the challenges of developing a zero-shot cross-lingual stance detection model. This paper makes the first such effort by introducing a novel approach to zero-shot cross-lingual stance detection, Multilingual Translation-Augmented BERT (MTAB), aiming to enhance the performance of a cross-lingual classifier in the absence of explicit training data for target languages. Our technique employs translation augmentation to improve zero-shot performance and pairs it with adversarial learning to further boost model efficacy. Through experiments on datasets labeled for stance towards vaccines in four languages English, German, French, Italian. We demonstrate the effectiveness of our proposed approach, showcasing improved results in comparison to a strong baseline model as well as ablated versions of our model. Our experiments demonstrate the effectiveness of model components, not least the translation-augmented data as well as the adversarial learning component, to the improved performance of the model. We have made our source code accessible on GitHub.

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai, Comment: See the project page at https://wmdp.ai