Miracle-dz/UniFAR-FTUs huggingface dataset:
StruQ: Defending Against Prompt Injection with Structured Queries

Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications, which perform text-based tasks by utilizing their advanced language understanding capabilities. However, as LLMs have improved, so have the attacks against them. Prompt injection attacks are an important threat: they trick the model to deviate from the original application's instructions and instead follow user directives. These attacks rely on the LLM's ability to follow instructions and inability to separate the prompts and user data. We introduce structured queries, a general approach to tackle this problem. Structured queries separate prompts and data into two channels. We implement a system that supports structured queries. This system is made of (1) a secure front-end that formats a prompt and user data into a special format, and (2) a specially trained LLM that can produce high-quality outputs from these inputs. The LLM is trained using a novel fine-tuning strategy: we convert a base (non-instruction-tuned) LLM to a structured instruction-tuned model that will only follow instructions in the prompt portion of a query. To do so, we augment standard instruction tuning datasets with examples that also include instructions in the data portion of the query, and fine-tune the model to ignore these. Our system significantly improves resistance to prompt injection attacks, with little or no impact on utility. Our code is released at https://github.com/Sizhe-Chen/PromptInjectionDefense.
Selective Radiance in Super-Wavelength Atomic Arrays

A novel way to create efficient atom-light interfaces is to engineer collective atomic states that selectively radiate into a target optical mode by suppressing emission into undesired modes through destructive interference. While it is generally assumed that this approach requires dense atomic arrays with sub-wavelength lattice constants, here we show that selective radiance can also be achieved in arrays with super-wavelength spacing. By stacking multiple two-dimensional arrays we find super-wavelength mirror configurations where one can eliminate emission into unwanted diffraction orders while enhancing emission into the desired specular mode, leading to near-perfect reflection of weak resonant light. These super-wavelength arrays can also be functionalized into efficient quantum memories, with error probabilities on the order of ~1 for a trilayer with only around ~100 atoms per layer. Relaxing the previous constraint of sub-wavelength spacing could potentially ease the technical requirements for realizing efficient atom-light interfaces, such as enabling the use of tweezer arrays.
POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition

We study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods -- most of which rely crucially on reward-regression models or importance-weighted policy gradients -- fail due to excessive bias or variance. To overcome these issues in OPL, we propose a novel two-stage algorithm, called Policy Optimization via Two-Stage Policy Decomposition (POTEC). It leverages clustering in the action space and learns two different policies via policy- and regression-based approaches, respectively. In particular, we derive a novel low-variance gradient estimator that enables to learn a first-stage policy for cluster selection efficiently via a policy-based approach. To select a specific action within the cluster sampled by the first-stage policy, POTEC uses a second-stage policy derived from a regression-based approach within each cluster. We show that a local correctness condition, which only requires that the regression model preserves the relative expected reward differences of the actions within each cluster, ensures that our policy-gradient estimator is unbiased and the second-stage policy is optimal. We also show that POTEC provides a strict generalization of policy- and regression-based approaches and their associated assumptions. Comprehensive experiments demonstrate that POTEC provides substantial improvements in OPL effectiveness particularly in large and structured action spaces.
Optical and soft X-ray light-curve analysis during the 2022 eruption of U Scorpii: Structural changes in the accretion disk

We present our optical photometric observations of the 2022 eruption of the recurrent nova U Scorpii (U Sco) using 49152 data points over 70 d following the optical peak. We have also analyzed its soft X-ray (0.3–1 keV) light curve by the Neil Gehrels Swift Observatory. During the 2022 eruption, the optical plateau stage started 13.8–15.0 d and ended 23.8–25.0 d after the optical peak. The soft X-ray stage started 14.6–15.3 d and ended 38.7–39.5 d after the optical peak. Both stages started later and had shorter durations, and the soft X-ray light curve peaked earlier and was less luminous compared to those during the U Sco 2010 eruption. These points suggest that there were differences in the envelope mass between the different cycles of the nova eruption. Furthermore, we have analyzed the optical eclipses during the 2022 eruption. The primary eclipse was first observed 10.4–11.6 d after the optical peak, earlier than the beginning of the optical plateau stage. This sequence of events can be explained by the receding ejecta photosphere associated with the expanding nova ejecta. We have determined the ingress and egress phases of the primary eclipses and estimated the outer radius of the optical light source centered at the white dwarf (WD). During the optical plateau stage, the source radius remained ∼1.2 times larger than the Roche volume radius of the primary WD, being close to the L1 point. When the optical plateau stage ended, the source radius drastically shrank to the tidal truncation radius within a few orbital periods. This previously unresolved phenomenon can be interpreted as a structural change in U Sco where the temporarily expanded accretion disk due to the nova wind returned to a steady state.
Probabilistic Forecasting of Irregular Time Series via Conditional Flows

Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate. State-of-the-art methods for the task estimate only marginal distributions of observations in single channels and at single timepoints, assuming a fixed-shape parametric distribution. In this work, we propose a novel model, ProFITi, for probabilistic forecasting of irregularly sampled time series with missing values using conditional normalizing flows. The model learns joint distributions over the future values of the time series conditioned on past observations and queried channels and times, without assuming any fixed shape of the underlying distribution. As model components, we introduce a novel invertible triangular attention layer and an invertible non-linear activation function on and onto the whole real line. We conduct extensive experiments on four datasets and demonstrate that the proposed model provides $4$ times higher likelihood over the previously best model.
On the irreducibility and convergence of a class of nonsmooth nonlinear state-space models on manifolds

In this paper, we analyze a large class of general nonlinear state-space models on a state-space X, defined by the recursion $\phi_{k+1} = F(\phi_k,\alpha(\phi_k,U_{k+1}))$, $k \in\bN$, where $F,\alpha$ are some functions and $\{U_{k+1}\}_{k\in\bN}$ is a sequence of i.i. d. random variables. More precisely, we extend conditions under which this class of Markov chains is irreducible, aperiodic and satisfies important continuity properties, relaxing two key assumptions from prior works. First, the state-space X is supposed to be a smooth manifold instead of an open subset of a Euclidean space. Second, we only suppose that $F is locally Lipschitz continuous. We demonstrate the significance of our results through their application to Markov chains underlying optimization algorithms. These schemes belong to the class of evolution strategies with covariance matrix adaptation and step-size adaptation.
Accelerating PDE Data Generation via Differential Operator Action in Solution Space

Recent advancements in data-driven approaches, such as Neural Operator (NO), have demonstrated their effectiveness in reducing the solving time of Partial Differential Equations (PDEs). However, one major challenge faced by these approaches is the requirement for a large amount of high-precision training data, which needs significant computational costs during the generation process. To address this challenge, we propose a novel PDE dataset generation algorithm, namely Differential Operator Action in Solution space (DiffOAS), which speeds up the data generation process and enhances the precision of the generated data simultaneously. Specifically, DiffOAS obtains a few basic PDE solutions and then combines them to get solutions. It applies differential operators on these solutions, a process we call 'operator action', to efficiently generate precise PDE data points. Theoretical analysis shows that the time complexity of DiffOAS method is one order lower than the existing generation method. Experimental results show that DiffOAS accelerates the generation of large-scale datasets with 10,000 instances by 300 times. Even with just 5% of the generation time, NO trained on the data generated by DiffOAS exhibits comparable performance to that using the existing generation method, which highlights the efficiency of DiffOAS.
SAE: Single Architecture Ensemble Neural Networks

Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks. Recent methods compress ensembles within a single network via early exits or multi-input multi-output frameworks. However, the landscape of these methods is fragmented thus far, making it difficult to choose the right approach for a given task. Furthermore, the algorithmic performance of these methods is behind the ensemble of separate NNs and requires extensive architecture tuning. We propose a novel methodology unifying these approaches into a Single Architecture Ensemble (SAE). Our method learns the optimal number and depth of exits per ensemble input in a single NN. This enables the SAE framework to flexibly tailor its configuration for a given architecture or application. We evaluate SAEs on image classification and regression across various network architecture types and sizes. We demonstrate competitive accuracy or confidence calibration to baselines while reducing the compute operations or parameter count by up to $1.5{\sim}3.7\times$.
Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method

Controlled text generation (CTG) seeks to guide large language model (LLM) output, that statistical language generation would conform to desired criteria. The current study presents a novel CTG algorithm that enforces adherence toward specific rhetorical relations in an LLM sentence-completion context by a parser-driven decoding scheme that requires no model fine-tuning. The method is validated both with automatic and human evaluation.
Teleoscope: Exploring Themes in Large Document Sets By Example

Qualitative thematic exploration of data by hand does not scale and researchers create and update a personalized point of view as they explore data. As a result, machine learning (ML) approaches that might help with exploration are challenging to apply. We developed Teleoscope, a web-based system that supports interactive exploration of large corpora (100K-1M) of short documents (1-3 paragraphs). Teleoscope provides visual programming workflows that have semantic and computational meaning; helping researchers to retrace, share, and recompute their sense-making process. Attempting to create qualitative"themes"rather than"topics,"our NLP approach tunes an ML model to"think like you"without significant retraining. Here, we present our two-year design process and validation of Teleoscope, including a multi-week study with qualitative researchers (N = 5), a six-month field deployment with a qualitative research group, and an on-going public release.
Iris-SAM: Iris Segmentation Using a Foundational Model

Iris segmentation is a critical component of an iris biometric system and it involves extracting the annular iris region from an ocular image. In this work, we develop a pixel-level iris segmentation model from a foundational model, viz., Segment Anything Model (SAM), that has been successfully used for segmenting arbitrary objects. The primary contribution of this work lies in the integration of different loss functions during the fine-tuning of SAM on ocular images. In particular, the importance of Focal Loss is borne out in the fine-tuning process since it strategically addresses the class imbalance problem (i.e., iris versus non-iris pixels). Experiments on ND-IRIS-0405, CASIA-Iris-Interval-v3, and IIT-Delhi-Iris datasets convey the efficacy of the trained model for the task of iris segmentation. For instance, on the ND-IRIS-0405 dataset, an average segmentation accuracy of 99.58% was achieved, compared to the best baseline performance of 89.75%.
ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation

Data generation is recognized as a potent strategy for unsupervised domain adaptation (UDA) pertaining semantic segmentation in adverse weathers. Nevertheless, these adverse weather scenarios encompass multiple possibilities, and high-fidelity data synthesis with controllable weather is under-researched in previous UDA works. The recent strides in large-scale text-to-image diffusion models (DM) have ushered in a novel avenue for research, enabling the generation of realistic images conditioned on semantic labels. This capability proves instrumental for cross-domain data synthesis from source to target domain owing to their shared label space. Thus, source domain labels can be paired with those generated pseudo target data for training UDA. However, from the UDA perspective, there exists several challenges for DM training: (i) ground-truth labels from target domain are missing; (ii) the prompt generator may produce vague or noisy descriptions of images from adverse weathers; (iii) existing arts often struggle to well handle the complex scene structure and geometry of urban scenes when conditioned only on semantic labels. To tackle the above issues, we propose ControlUDA, a diffusion-assisted framework tailored for UDA segmentation under adverse weather conditions. It first leverages target prior from a pre-trained segmentor for tuning the DM, compensating the missing target domain labels; It also contains UDAControlNet, a condition-fused multi-scale and prompt-enhanced network targeted at high-fidelity data generation in adverse weathers. Training UDA with our generated data brings the model performances to a new milestone (72.0 mIoU) on the popular Cityscapes-to-ACDC benchmark for adverse weathers. Furthermore, ControlUDA helps to achieve good model generalizability on unseen data.
Spatial calibration of high-density absorption imaging

The accurate determination of atom numbers is an ubiquitous problem in the field of ultracold atoms. For modest atom numbers, absolute calibration techniques are available, however, for large numbers and high densities, the available techniques neglect many-body scattering processes. Here, a spatial calibration technique for time-of-flight absorption images of ultracold atomic clouds is presented. The calibration is obtained from radially averaged absorption images and we provide a practical guide to the calibration process. It is shown that the calibration coefficient scales linearly with optical density and depends on the absorbed photon number for the experimental conditions explored here. This allows for the direct inclusion of a spatially dependent calibration in the image analysis. For typical ultracold atom clouds the spatial calibration technique leads to corrections in the detected atom number up to $\approx\! 12\,\%$ and temperature up to $\approx \! 14\,\%$ in comparison to previous calibration techniques. The technique presented here addresses a major difficulty in absorption imaging of ultracold atomic clouds and prompts further theoretical work to understand the scattering processes in ultracold dense clouds of atoms for accurate atom number calibration.
Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study

Large Language Models (LLMs) have been widely employed in programming language analysis to enhance human productivity. Yet, their reliability can be compromised by various code distribution shifts, leading to inconsistent outputs. While probabilistic methods are known to mitigate such impact through uncertainty calibration and estimation, their efficacy in the language domain remains underexplored compared to their application in image-based tasks. In this work, we first introduce a large-scale benchmark dataset, incorporating three realistic patterns of code distribution shifts at varying intensities. Then we thoroughly investigate state-of-the-art probabilistic methods applied to CodeLlama using these shifted code snippets. We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision. However, our study further reveals varied performance dynamics across different criteria (e.g., calibration error vs misclassification detection) and trade-off between efficacy and efficiency, highlighting necessary methodological selection tailored to specific contexts.
Safe Guaranteed Exploration for Non-linear Systems

Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficient exploration are also crucial for ensuring autonomous task completion. To address these challenges, we propose a novel safe guaranteed exploration framework using optimal control, which achieves first-of-its-kind results: guaranteed exploration for non-linear systems with finite time sample complexity bounds, while being provably safe with arbitrarily high probability. The framework is general and applicable to many real-world scenarios with complex non-linear dynamics and unknown domains. Based on this framework we propose an efficient algorithm, SageMPC, SAfe Guaranteed Exploration using Model Predictive Control. SageMPC improves efficiency by incorporating three techniques: i) exploiting a Lipschitz bound, ii) goal-directed exploration, and iii) receding horizon style re-planning, all while maintaining the desired sample complexity, safety and exploration guarantees of the framework. Lastly, we demonstrate safe efficient exploration in challenging unknown environments using SageMPC with a car model.
Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments

Unmanned Aerial Vehicles need an online path planning capability to move in high-risk missions in unknown and complex environments to complete them safely. However, many algorithms reported in the literature may not return reliable trajectories to solve online problems in these scenarios. The Q-Learning algorithm, a Reinforcement Learning Technique, can generate trajectories in real-time and has demonstrated fast and reliable results. This technique, however, has the disadvantage of defining the iteration number. If this value is not well defined, it will take a long time or not return an optimal trajectory. Therefore, we propose a method to dynamically choose the number of iterations to obtain the best performance of Q-Learning. The proposed method is compared to the Q-Learning algorithm with a fixed number of iterations, A*, Rapid-Exploring Random Tree, and Particle Swarm Optimization. As a result, the proposed Q-learning algorithm demonstrates the efficacy and reliability of online path planning with a dynamic number of iterations to carry out online missions in unknown and complex environments.
A Method for Decrypting Data Infected with Rhysida Ransomware

Ransomware is malicious software that is a prominent global cybersecurity threat. Typically, ransomware encrypts data on a system, rendering the victim unable to decrypt it without the attacker's private key. Subsequently, victims often pay a substantial ransom to recover their data, yet some may still incur damage or loss. This study examines Rhysida ransomware, which caused significant damage in the second half of 2023, and proposes a decryption method. Rhysida ransomware employed a secure random number generator to generate the encryption key and subsequently encrypt the data. However, an implementation vulnerability existed that enabled us to regenerate the internal state of the random number generator at the time of infection. We successfully decrypted the data using the regenerated random number generator. To the best of our knowledge, this is the first successful decryption of Rhysida ransomware. We aspire for our work to contribute to mitigating the damage inflicted by the Rhysida ransomware.
Where is the Truth? The Risk of Getting Confounded in a Continual World

A dataset is confounded if it is most easily solved via a spurious correlation which fails to generalize to new data. We will show that, in a continual learning setting where confounders may vary in time across tasks, the resulting challenge far exceeds the standard forgetting problem normally considered. In particular, we derive mathematically the effect of such confounders on the space of valid joint solutions to sets of confounded tasks. Interestingly, our theory predicts that for many such continual datasets, spurious correlations are easily ignored when the tasks are trained on jointly, but it is far harder to avoid confounding when they are considered sequentially. We construct such a dataset and demonstrate empirically that standard continual learning methods fail to ignore confounders, while training jointly on all tasks is successful. Our continually confounded dataset, ConCon, is based on CLEVR images and demonstrates the need for continual learning methods with more robust behavior with respect to confounding.
Dynamics of a strongly coupled quantum heat engine-Computing bath observables from the hierarchy of pure states.

We present a fully quantum dynamical treatment of a quantum heat engine and its baths based on the Hierarchy of Pure States (HOPS), an exact and general method for open quantum system dynamics. We show how the change of the bath energy and the interaction energy can be determined within HOPS for arbitrary coupling strength and smooth time dependence of the modulation protocol. The dynamics of all energetic contributions during the operation can be carefully examined both in its initial transient phase and, also later, in its periodic steady state. A quantum Otto engine with a qubit as an inherently nonlinear work medium is studied in a regime where the energy associated with the interaction Hamiltonian plays an important role for the global energy balance and, thus, must not be neglected when calculating its power and efficiency. We confirm that the work required to drive the coupling with the baths sensitively depends on the speed of the modulation protocol. Remarkably, departing from the conventional scheme of well-separated phases by allowing for temporal overlap, we discover that one can even gain energy from the modulation of bath interactions. We visualize these various work contributions using the analog of state change diagrams of thermodynamic cycles. We offer a concise, full presentation of HOPS with its extension to bath observables, as it serves as a universal tool for the numerically exact description of general quantum dynamical (thermodynamic) scenarios far from the weak-coupling limit.