Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: \textit{resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns. } Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to $11.3\%$ in key metrics. The code is available at \href{https://github.com/wxr99/HolisticPU}{https://github.com/wxr99/HolisticPU}.
Toward a Plug-and-Play Vision-Based Grasping Module for Robotics

Despite recent advancements in AI for robotics, grasping remains a partially solved challenge, hindered by the lack of benchmarks and reproducibility constraints. This paper introduces a vision-based grasping framework that can easily be transferred across multiple manipulators. Leveraging Quality-Diversity (QD) algorithms, the framework generates diverse repertoires of open-loop grasping trajectories, enhancing adaptability while maintaining a diversity of grasps. This framework addresses two main issues: the lack of an off-the-shelf vision module for detecting object pose and the generalization of QD trajectories to the whole robot operational space. The proposed solution combines multiple vision modules for 6DoF object detection and tracking while rigidly transforming QD-generated trajectories into the object frame. Experiments on a Franka Research 3 arm and a UR5 arm with a SIH Schunk hand demonstrate comparable performance when the real scene aligns with the simulation used for grasp generation. This work represents a significant stride toward building a reliable vision-based grasping module transferable to new platforms, while being adaptable to diverse scenarios without further training iterations.
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

Time series has been left behind in the era of pre-training and transfer learning. While research in the fields of natural language processing and computer vision are enjoying progressively larger datasets to train massive models, the most popular time series datasets consist of only tens of thousands of time steps, limiting our ability to study the effectiveness of pre-training and scaling. Recent studies have also cast doubt on the need for expressive models and scale. To alleviate these issues, we introduce three large-scale time series forecasting datasets from the cloud operations (CloudOps) domain, the largest having billions of observations, enabling further study into pre-training and scaling of time series models. We build the empirical groundwork for studying pre-training and scaling of time series models and pave the way for future research by identifying a promising candidate architecture. We show that it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method - achieving a 27% reduction in error on the largest dataset. Code and datasets can be found https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops.
An operator preconditioning perspective on training in physics-informed machine learning

In this paper, we investigate the behavior of gradient descent algorithms in physics-informed machine learning methods like PINNs, which minimize residuals connected to partial differential equations (PDEs). Our key result is that the difficulty in training these models is closely related to the conditioning of a specific differential operator. This operator, in turn, is associated to the Hermitian square of the differential operator of the underlying PDE. If this operator is ill-conditioned, it results in slow or infeasible training. Therefore, preconditioning this operator is crucial. We employ both rigorous mathematical analysis and empirical evaluations to investigate various strategies, explaining how they better condition this critical operator, and consequently improve training.
Many-Body-Expansion Based on Variational Quantum Eigensolver and Deflation for Dynamical Correlation.

In this work, we utilize the framework of many-body expansion (MBE) to decompose electronic structures into fragments by incrementing virtual orbitals, aiming to accurately solve the ground and excited state energies of each fragment using the variational quantum eigensolver and deflation algorithms. While our approach is primarily based on unitary coupled cluster singles and doubles (UCCSD) and its generalization, we also introduce modifications and approximations to conserve quantum resources in MBE by partially generalizing the UCCSD operator and neglecting the relaxation of the reference states. As a proof of concept, we investigate the potential energy surfaces for the bond-breaking processes of the ground state of two molecules (H2O and N2) and calculate the ground and excited state energies of three molecules (LiH, CH+, and H2O). The results demonstrate that our approach can, in principle, provide reliable descriptions in all the tests including strongly correlated systems when appropriate approximations are chosen. Additionally, we perform model simulations to investigate the impact of shot noise on the total MBE energy and show that precise energy estimation is crucial for lower-order MBE fragments.
Late-Holocene land-use history and environmental changes at the high altitudes of St Antönien (Switzerland, Northern Alps): Combined evidence from pollen, soil and tree-ring analyses

In climatically sensitive regions such as the Northern Alps, changes in climate and land use have a strong impact on landscapes, vegetation, animals and humans. Multidisciplinary investigations in the high St Antönien Valley (Switzerland) at 1400—3000 m a.s. l. have generated a reconstruction of land-use history. Humans began affecting the landscape of this high mountain region during the Bronze Age. Multiproxy palaeoenvironmental studies show that the present cultural landscape of the valley has been the result of long-term human environmental interactions. Pollen, soil and tree analyses were combined with archaeological and historical archives to provide strong evidence of the complexity of the high-mountain land-use system over the last 3500 years. Phases of agro-pastoral activity from Bronze Age (around 1300 BC), Iron Age (800—15 BC), Roman Period (15 BC—AD 450) and Middle Ages (AD 450—1500) are linked to climate and economic, social and cultural developments. Our results reveal that expansions of pasture land, in combination with climatic fluctuations, led to pronounced ecological changes in St Antönien Valley. Humans adjusted land-use practices according to changing environmental conditions. In this context, the use of fire was an important factor in land management. Forest clearances reached maximum intensity during the late Middle Ages (AD 1300—1500) and triggered natural catastrophes that were amplified during the most severe environmental phase (AD 1600—1850) of the ‘Little Ice Age’ (LIA).
Compositional Servoing by Recombining Demonstrations

Learning-based manipulation policies from image inputs often show weak task transfer capabilities. In contrast, visual servoing methods allow efficient task transfer in high-precision scenarios while requiring only a few demonstrations. In this work, we present a framework that formulates the visual servoing task as graph traversal. Our method not only extends the robustness of visual servoing, but also enables multitask capability based on a few task-specific demonstrations. We construct demonstration graphs by splitting existing demonstrations and recombining them. In order to traverse the demonstration graph in the inference case, we utilize a similarity function that helps select the best demonstration for a specific task. This enables us to compute the shortest path through the graph. Ultimately, we show that recombining demonstrations leads to higher task-respective success. We present extensive simulation and real-world experimental results that demonstrate the efficacy of our approach.
AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement

Growing attention has been paid to Reinforcement Learning (RL) algorithms when optimizing long-term user engagement in sequential recommendation tasks. One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies. When formulated as a Markov Decision Process (MDP), the dynamics and reward functions of the recommendation system are continuously affected by these changes. Existing RL algorithms for recommendation systems will suffer from distribution shift and struggle to adapt in such an MDP. In this paper, we introduce a novel paradigm called Adaptive Sequential Recommendation (AdaRec) to address this issue. AdaRec proposes a new distance-based representation loss to extract latent information from users' interaction trajectories. Such information reflects how RL policy fits to current user behavior patterns, and helps the policy to identify subtle changes in the recommendation system. To make rapid adaptation to these changes, AdaRec encourages exploration with the idea of optimism under uncertainty. The exploration is further guarded by zero-order action optimization to ensure stable recommendation quality in complicated environments. We conduct extensive empirical analyses in both simulator-based and live sequential recommendation tasks, where AdaRec exhibits superior long-term performance compared to all baseline algorithms.
Kagome 
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 metal: Magnetism, lattice dynamics, and anomalous Hall conductivity

Kagome metals are reported to exhibit remarkable properties, including superconductivity, charge density wave order, and a large anomalous Hall conductivity, which facilitate the implementation of spintronic devices. In this work, we study a novel kagome metal based on Mn magnetic sites in a KMn$_3$Sb$_5$ stoichiometry. By means of first-principles density functional theory calculations, we demonstrate that the studied compound is dynamically stable, locking the ferromagnetic order as the ground state configuration, thus preventing the charge-density-wave state as reported in its vanadium-based counterpart KV$_3$Sb$_5$. Our calculations predict that KMn$_3$Sb$_5$ exhibits an out-of-plane (001) ferromagnetic response as the ground state, allowing for the emergence of topologically protected Weyl nodes near the Fermi level and nonzero anomalous Hall conductivity ($\sigma_{ij}$) in this centrosymmetric system. We obtain a tangible $\sigma_{xy} = 314$ S$\cdot$cm$^{-1}$ component, which is comparable to that of other kagome metals. Finally, we explore the effect of the on-site Coulomb repulsion ($+U$) on the structural and electronic properties and find that, although the lattice parameters and $\sigma_{xy}$ moderately vary with increasing $+U$, KMn$_3$Sb$_5$ stands as an ideal stable ferromagnetic kagome metal with a large anomalous Hall conductivity response.
Projected Multi-Agent Consensus Equilibrium (PMACE) With Application to Ptychography

Multi-Agent Consensus Equilibrium (MACE) formulates an inverse imaging problem as a balance among multiple update agents such as data-fitting terms and denoisers. However, each such agent operates on a separate copy of the full image, leading to redundant memory use and slow convergence when each agent affects only a small subset of the full image. In this article, we extend MACE to Projected Multi-Agent Consensus Equilibrium (PMACE), in which each agent updates only a projected component of the full image, thus greatly reducing memory use for some applications. We describe PMACE in terms of an equilibrium problem and an equivalent fixed point problem and show that in most cases the PMACE equilibrium is not the solution of an optimization problem. To demonstrate the value of PMACE, we apply it to the problem of ptychography, in which a sample is reconstructed from the diffraction patterns resulting from coherent X-ray illumination at multiple overlapping spots. In our PMACE formulation, each spot corresponds to a separate data-fitting agent, with the final solution found as an equilibrium among all the agents. Our results demonstrate that the PMACE reconstruction algorithm generates more accurate reconstructions at a lower computational cost than existing ptychography algorithms when the spots are sparsely sampled.
Temperature signature of high latitude Atlantic boundary currents revealed by marine mammal‐borne sensor and Argo data

Results from the development and analysis of a novel temperature dataset for the high latitude North‐West Atlantic are presented. The new 1° gridded dataset (“ATLAS”) has been produced from about 13,000 Argo and 48,000 marine mammal (hooded seal, harp seal, grey seal and beluga) profiles spanning 2004–8. These data sources are highly complementary as marine mammals greatly enhance shelf region coverage where Argo floats are absent. ATLAS reveals distinctive boundary current related temperature minima in the Labrador Sea (−1.1°C) and at the east Greenland coast (1.8°C), largely absent in the widely‐used Levitus'09 and EN3v2a datasets. The ATLAS 0–500 m average temperature is lower than Levitus'09 and EN3v2a by up to 3°C locally. Differences are strongest from 0–300 m and persist at reduced amplitude from 300–500 m. Our results clearly reveal the value of marine mammal‐borne sensors for a reliable description of the North‐West Atlantic at a time of rapid change.
Accelerating decay with acceleration

We investigate accelerated Unruh-deWitt detectors as a model for particle decay. We find non-trivial decay rates, including a pattern of peaks in decay rate that extends to lower accelerations. Applying our model to the alpha decay of $\mathrm{^{210}Po}$, we find that effects could be observed with an acceleration of $a\approx 10^{26} \frac{\mathrm{m}}{\mathrm{s}^2}$ as long as that acceleration is controlled to within 1 percent. Although still out of reach of current experimental setups, other decay processes at lower energy, such as beta decay, could result in the peaks we find being within range of future experiments.
Magnetic hysteresis properties of interacting and noninteracting micron‐sized magnetite produced by electron beam lithography

The magnetic hysteresis properties for well‐defined micron‐sized magnetite samples produced by electron beam lithography (EBL) are presented. In addition to measuring standard hysteresis parameters, first‐order reversal curve (FORC) diagrams are also reported. EBL produces samples that consist of particles with very tightly constrained size distributions, and spatial distributions that govern the degree of intergrain magnetostatic interactions are accurately controlled and known. Thus EBL samples are significantly better characterized compared to powdered samples, which are conventionally used to characterize the size dependency of magnetic hysteresis properties of naturally occurring magnetic minerals. Compared with the hysteresis properties of powdered samples of the same nominal sizes, EBL samples display more multidomain‐like (MD) behavior. The influence of magnetostatic interactions fields on hysteresis properties is analyzed. When magnetostatic interactions are effectively in only one direction, the hysteresis properties become more single domain‐like, and if the interactions are in more than one direction, hysteresis becomes more MD‐like, in agreement with numerical models.
Towards Foundation Models for Knowledge Graph Reasoning

Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of tokens in language. Knowledge graphs (KGs) have different entity and relation vocabularies that generally do not overlap. The key challenge of designing foundation models on KGs is to learn such transferable representations that enable inference on any graph with arbitrary entity and relation vocabularies. In this work, we make a step towards such foundation models and present ULTRA, an approach for learning universal and transferable graph representations. ULTRA builds relational representations as a function conditioned on their interactions. Such a conditioning strategy allows a pre-trained ULTRA model to inductively generalize to any unseen KG with any relation vocabulary and to be fine-tuned on any graph. Conducting link prediction experiments on 57 different KGs, we find that the zero-shot inductive inference performance of a single pre-trained ULTRA model on unseen graphs of various sizes is often on par or better than strong baselines trained on specific graphs. Fine-tuning further boosts the performance.
Asymmetrically Decentralized Federated Learning

To address the communication burden and privacy concerns associated with the centralized server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged, which discards the server with a peer-to-peer (P2P) communication framework. However, most existing DFL algorithms are based on symmetric topologies, such as ring and grid topologies, which can easily lead to deadlocks and are susceptible to the impact of network link quality in practice. To address these issues, this paper proposes the DFedSGPSM algorithm, which is based on asymmetric topologies and utilizes the Push-Sum protocol to effectively solve consensus optimization problems. To further improve algorithm performance and alleviate local heterogeneous overfitting in Federated Learning (FL), our algorithm combines the Sharpness Aware Minimization (SAM) optimizer and local momentum. The SAM optimizer employs gradient perturbations to generate locally flat models and searches for models with uniformly low loss values, mitigating local heterogeneous overfitting. The local momentum accelerates the optimization process of the SAM optimizer. Theoretical analysis proves that DFedSGPSM achieves a convergence rate of $\mathcal{O}(\frac{1}{\sqrt{T}})$ in a non-convex smooth setting under mild assumptions. This analysis also reveals that better topological connectivity achieves tighter upper bounds. Empirically, extensive experiments are conducted on the MNIST, CIFAR10, and CIFAR100 datasets, demonstrating the superior performance of our algorithm compared to state-of-the-art optimizers.
Imaginary-Temperature Zeros for Quantum Phase Transitions

While the zeros of complex partition functions, such as Lee-Yang zeros and Fisher zeros, have been instrumental in characterizing temperature-driven phase transitions, the extension of this concept to zero temperature remains an open and active topic. In this paper, we introduce the concept of Imaginary-Temperature Zeros (ITZs) by calculating the zeros of the imaginary-temperature partition function. We delve into the properties of ITZs for the transverse-field Ising chain, and find that the ITZs are distributed in sectorial structures, accompanied by universal singular behaviors for such quantities as the edge density of ITZs and the magnetization. These singularities remarkably differ from those in the Lee-Yang theory, and are demonstrated to be intimately related to quantum criticality and the Ising critical exponents. We further illuminate the correspondence between ITZs and the zeros of the spectrum form factor, which offers a practical way for detecting ITZs and zero-temperature phase transitions through quantum dynamics.
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

The aligned Large Language Models (LLMs) are powerful language understanding and decision-making tools that are created through extensive alignment with human feedback. However, these large models remain susceptible to jailbreak attacks, where adversaries manipulate prompts to elicit malicious outputs that should not be given by aligned LLMs. Investigating jailbreak prompts can lead us to delve into the limitations of LLMs and further guide us to secure them. Unfortunately, existing jailbreak techniques suffer from either (1) scalability issues, where attacks heavily rely on manual crafting of prompts, or (2) stealthiness problems, as attacks depend on token-based algorithms to generate prompts that are often semantically meaningless, making them susceptible to detection through basic perplexity testing. In light of these challenges, we intend to answer this question: Can we develop an approach that can automatically generate stealthy jailbreak prompts? In this paper, we introduce AutoDAN, a novel jailbreak attack against aligned LLMs. AutoDAN can automatically generate stealthy jailbreak prompts by the carefully designed hierarchical genetic algorithm. Extensive evaluations demonstrate that AutoDAN not only automates the process while preserving semantic meaningfulness, but also demonstrates superior attack strength in cross-model transferability, and cross-sample universality compared with the baseline. Moreover, we also compare AutoDAN with perplexity-based defense methods and show that AutoDAN can bypass them effectively.
Effect of seepage forces on tunnel face stability

In this study, two factors are simultaneously considered for assessing tunnel face stability. The first is the effective stress acting on the tunnel face calculated by upper bound solution, and the other is the seepage force calculated by numerical analysis under the condition of steady-state groundwater flow. The seepage forces calculated by numerical analysis are compared with the results of a model test. The upper bound solution taking into consideration the seepage force acting on the tunnel face, shows that the minimum support pressure for the face stability is equal to the sum of the effective support pressure that is obtained from the upper bound solution based on effective stress and the seepage pressure acting on the tunnel face. It was found that the average seepage pressure acting on the tunnel face is proportional to the hydrostatic pressure at the same elevation, and the magnitude is about 22% of the hydrostatic pressure for the drainage type tunnel and about 28% for the waterproof type tunne.