Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Learning for Obstacle Avoidance

We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations of uncertainty in the environment or a stationary stochastic model of uncertainty, we propose a method that is efficient to implement and provably grants instance-optimality with respect to perturbations of trajectories generated from an open-loop planner (in the sense of minimizing worst-case regret). The resulting policy adapts online to realizations of uncertainty and provably compares well with the best obstacle avoidance policy in hindsight from a rich class of policies. The method is validated in simulation on a dynamical system environment and compared to baseline open-loop planning and robust Hamilton- Jacobi reachability techniques. Further, it is implemented on a hardware example where a quadruped robot traverses a dense obstacle field and encounters input disturbances due to time delays, model uncertainty, and dynamics nonlinearities.
Steady-state edge burst: From free-particle systems to interaction-induced phenomena

The interplay between the non-Hermitian skin effect and the imaginary gap of lossy lattices results in the edge burst, a boundary-induced dynamical phenomenon in which an exceptionally large portion of particle loss occurs at the edge. Here, we find that this intriguing non-Hermitian dynamical phenomenon can be exactly mapped into the steady-state density distribution of a corresponding open quantum system. Consequently, the bulk-edge scaling relation of loss probability in the edge burst maps to that of steady-state density. Furthermore, we introduce a many-body open-system model in which the two-body loss generates an interaction-induced non-Hermitian skin effect. Using the positive-$P$ method, we demonstrate the validity of the scaling relation for steady-state correlators. These results provide a unique perspective on the interaction-induced many-body non-Hermitian skin effect. Our predictions are testable in state-of-the-art experimental platforms.
ArtWhisperer: A Dataset for Characterizing Human-AI Interactions in Artistic Creations

As generative AI becomes more prevalent, it is important to study how human users interact with such models. In this work, we investigate how people use text-to-image models to generate desired target images. To study this interaction, we created ArtWhisperer, an online game where users are given a target image and are tasked with iteratively finding a prompt that creates a similar-looking image as the target. Through this game, we recorded over 50,000 human-AI interactions; each interaction corresponds to one text prompt created by a user and the corresponding generated image. The majority of these are repeated interactions where a user iterates to find the best prompt for their target image, making this a unique sequential dataset for studying human-AI collaborations. In an initial analysis of this dataset, we identify several characteristics of prompt interactions and user strategies. People submit diverse prompts and are able to discover a variety of text descriptions that generate similar images. Interestingly, prompt diversity does not decrease as users find better prompts. We further propose a new metric to quantify the steerability of AI using our dataset. We define steerability as the expected number of interactions required to adequately complete a task. We estimate this value by fitting a Markov chain for each target task and calculating the expected time to reach an adequate score in the Markov chain. We quantify and compare AI steerability across different types of target images and two different models, finding that images of cities and natural world images are more steerable than artistic and fantasy images. These findings provide insights into human-AI interaction behavior, present a concrete method of assessing AI steerability, and demonstrate the general utility of the ArtWhisperer dataset.
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer

Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency. However, such descriptions may not always be available or may pose privacy concerns. To tackle this bottleneck, we introduce PersonaPKT, a lightweight transfer learning approach that can build persona-consistent dialogue models without explicit persona descriptions. By representing each persona as a continuous vector, PersonaPKT learns implicit persona-specific features directly from a small number of dialogue samples produced by the same persona, adding less than 0.1% trainable parameters for each persona on top of the PLM backbone. Empirical results demonstrate that PersonaPKT effectively builds personalized DAs with high storage efficiency, outperforming various baselines in terms of persona consistency while maintaining good response generation quality. In addition, it enhances privacy protection by avoiding explicit persona descriptions. Overall, PersonaPKT is an effective solution for creating personalized DAs that respect user privacy.
Exotic Dehn twists on 4-manifolds

We initiate the study of exotic Dehn twists along 3-manifolds $\neq S^3$ inside $4$-manifolds, which produces the first known examples of exotic diffeomorphisms of contractible 4-manifolds, more generally of definite 4-manifolds, and exotic diffeomorphisms of 4-manifolds with $\neq S^3$ boundary that survive after one stabilization. We also construct the smallest closed 4-manifold known to support an exotic diffeomorphism. These exotic diffeomorphisms are the Dehn twists along certain Seifert fibered 3-manifolds. As a consequence, we get loops of diffeomorphisms of 3-manifolds that topologically extend to some 4-manifolds $X$ but not smoothly so, implying the non-surjectivity of $\pi_1(\mathrm{Diff}(X)) \to \pi_1(\mathrm{Homeo}(X))$. Our method uses $2$-parameter families Seiberg-Witten theory over $\mathbb{RP}^2$, while known methods to detect exotic diffeomorphisms used $1$-parameter families gauge-theoretic invariants. Using a similar strategy, we construct a new kind of exotic diffeomorphisms of 4-manifolds, given as commutators of diffeomorphisms.
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness

Machine learning models are susceptible to a variety of attacks that can erode trust in their deployment. These threats include attacks against the privacy of training data and adversarial examples that jeopardize model accuracy. Differential privacy and randomized smoothing are effective defenses that provide certifiable guarantees for each of these threats, however, it is not well understood how implementing either defense impacts the other. In this work, we argue that it is possible to achieve both privacy guarantees and certified robustness simultaneously. We provide a framework called DP-CERT for integrating certified robustness through randomized smoothing into differentially private model training. For instance, compared to differentially private stochastic gradient descent on CIFAR10, DP-CERT leads to a 12-fold increase in certified accuracy and a 10-fold increase in the average certified radius at the expense of a drop in accuracy of 1.2%. Through in-depth per-sample metric analysis, we show that the certified radius correlates with the local Lipschitz constant and smoothness of the loss surface. This provides a new way to diagnose when private models will fail to be robust.
Multi-target Backdoor Attacks for Code Pre-trained Models

Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence. However, most existing works insert triggers into task-specific data for code-related downstream tasks, thereby limiting the scope of attacks. Moreover, the majority of attacks for pre-trained models are designed for understanding tasks. In this paper, we propose task-agnostic backdoor attacks for code pre-trained models. Our backdoored model is pre-trained with two learning strategies (i.e., Poisoned Seq2Seq learning and token representation learning) to support the multi-target attack of downstream code understanding and generation tasks. During the deployment phase, the implanted backdoors in the victim models can be activated by the designed triggers to achieve the targeted attack. We evaluate our approach on two code understanding tasks and three code generation tasks over seven datasets. Extensive experimental results demonstrate that our approach effectively and stealthily attacks code-related downstream tasks.
A sharp H\"{o}rmander estimate for multi-parameter and multi-linear Fourier multiplier operators

In this paper, we investigate the H\"ormander type theorems for the multi-linear and multi-parameter Fourier multipliers. When the multipliers are characterized by $L^u$-based Sobolev norms for $1<u\le 2$ , our results on the smoothness assumptions are sharp in the multi-parameter and bilinear case. In the multi-parameter and multi-linear case, our results are almost sharp. Moreover, even in the one-parameter and multi-linear case, our results improve earlier ones in the literature.
Enhanced ferromagnetism in artificially stretched lattice in quasi two-dimensional Cr2Ge2Te6

In the fundamental understanding of magnetic interactions between atoms in solids, the crystal lattice is one of the key parameters. As the effective tool for controlling the lattice using tensile stress is limited, there are only few demonstrations of the control in magnetic properties with expanding the lattice structure. Here, we observe that the Curie temperature (Tc) of quasi two-dimensional Cr2Ge2Te6 with NiO overlayer doubles from ~60 K to ~120 K, describe a clear correlation of magnetic properties with lattice expansion, which is characterized by several probes and computational approaches, and address on the mechanisms leading to the increase in Tc via the change in exchange interactions.
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models

While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning. Here, we propose InfoDiffusion, an algorithm that augments diffusion models with low-dimensional latent variables that capture high-level factors of variation in the data. InfoDiffusion relies on a learning objective regularized with the mutual information between observed and hidden variables, which improves latent space quality and prevents the latents from being ignored by expressive diffusion-based decoders. Empirically, we find that InfoDiffusion learns disentangled and human-interpretable latent representations that are competitive with state-of-the-art generative and contrastive methods, while retaining the high sample quality of diffusion models. Our method enables manipulating the attributes of generated images and has the potential to assist tasks that require exploring a learned latent space to generate quality samples, e.g., generative design.
INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation

We introduce a method that dramatically reduces fine-tuning VRAM requirements and rectifies quantization errors in quantized Large Language Models. First, we develop an extremely memory-efficient fine-tuning (EMEF) method for quantized models using Low-Rank Adaptation (LoRA), and drawing upon it, we construct an error-correcting algorithm designed to minimize errors induced by the quantization process. Our method reduces the memory requirements by up to 5.6 times, which enables fine-tuning a 7 billion parameter Large Language Model (LLM) on consumer laptops. At the same time, we propose a Low-Rank Error Correction (LREC) method that exploits the added LoRA layers to ameliorate the gap between the quantized model and its float point counterpart. Our error correction framework leads to a fully functional INT2 quantized LLM with the capacity to generate coherent English text. To the best of our knowledge, this is the first INT2 Large Language Model that has been able to reach such a performance. The overhead of our method is merely a 1.05 times increase in model size, which translates to an effective precision of INT2.1. Also, our method readily generalizes to other quantization standards, such as INT3, INT4, and INT8, restoring their lost performance, which marks a significant milestone in the field of model quantization. The strategies delineated in this paper hold promising implications for the future development and optimization of quantized models, marking a pivotal shift in the landscape of low-resource machine learning computations.
The Upper Bound of Information Diffusion in Code Review

Background: Code review, the discussion around a code change among humans, forms a communication network that enables its participants to exchange and spread information. Although reported by qualitative studies, our understanding of the capability of code review as a communication network is still limited. Objective: In this article, we report on a first step towards evaluating the capability of code review as a communication network by quantifying how fast and how far information can spread through code review: the upper bound of information diffusion in code review. Method: In an in-silico experiment, we simulate an artificial information diffusion within large (Microsoft), mid-sized (Spotify), and small code review systems (Trivago) modelled as communication networks. We then measure the minimal topological and temporal distances between the participants to quantify how far and how fast information can spread in code review. Results: An average code review participants in the small and mid-sized code review systems can spread information to between 72% and 85% of all code review participants within four weeks independently of network size and tooling; for the large code review systems, we found an absolute boundary of about 11000 reachable participants. On average (median), information can spread between two participants in code review in less than five hops and less than five days. Conclusion: We found evidence that the communication network emerging from code review scales well and spreads information fast and broadly, corroborating the findings of prior qualitative work. The study lays the foundation for understanding and improving code review as a communication network.
A Luminous Red Supergiant and Dusty Long-period Variable Progenitor for SN 2023ixf

We analyze pre-explosion near- and mid-infrared (IR) imaging of the site of SN 2023ixf in the nearby spiral galaxy M101 and characterize the candidate progenitor star. The star displays compelling evidence of variability with a possible period of ≈1000 days and an amplitude of Δm ≈ 0.6 mag in extensive monitoring with the Spitzer Space Telescope since 2004, likely indicative of radial pulsations. Variability consistent with this period is also seen in the near-IR J and K s bands between 2010 and 2023, up to just 10 days before the explosion. Beyond the periodic variability, we do not find evidence for any IR-bright pre-supernova outbursts in this time period. The IR brightness ( MKs=−10.7 mag) and color (J − K s = 1.6 mag) of the star suggest a luminous and dusty red supergiant. Modeling of the phase-averaged spectral energy distribution (SED) yields constraints on the stellar temperature ( Teff=3500−1400+800 K) and luminosity ( logL/L⊙=5.1±0.2 ). This places the candidate among the most luminous Type II supernova progenitors with direct imaging constraints, with the caveat that many of these rely only on optical measurements. Comparison with stellar evolution models gives an initial mass of M init = 17 ± 4 M ⊙. We estimate the pre-supernova mass-loss rate of the star between 3 and 19 yr before explosion from the SED modeling at Ṁ≈3×10−5 to 3 × 10−4 M ⊙ yr−1 for an assumed wind velocity of v w = 10 km s−1, perhaps pointing to enhanced mass loss in a pulsation-driven wind.
Geometric Active Disturbance Rejection Control of Rotorcraft on $SE(3)$ with Fast Finite-Time Stability

This article presents a tracking control framework enhanced by an extended state observer for a rotorcraft aerial vehicle modeled as a rigid body in three-dimensional translational and rotational motions. The system is considered as an underactuated system on the tangent bundle of the six-dimensional Lie group of rigid body motions, $SE(3)$. The extended state observer is designed to estimate the resultant external disturbance force and disturbance torque acting on the vehicle. It guarantees stable convergence of disturbance estimation errors in finite time when the disturbances are constant and finite time convergence to a bounded neighborhood of zero errors for time-varying disturbances. This extended state observer design is based on a H\"{o}lder-continuous fast finite time stable differentiator that is similar to the super-twisting algorithm, to obtain fast convergence. A tracking control scheme that uses the estimated disturbances from extended state observer for disturbance rejection, is designed to achieve fast finite-time stable tracking control. Numerical simulations are conducted to validate the proposed extended state observer and tracking control scheme with disturbance rejection. The proposed extended state observer is compared with other existing research to show its supremacy.
Differentially Private Domain Adaptation with Theoretical Guarantees

In many applications, the labeled data at the learner's disposal is subject to privacy constraints and is relatively limited. To derive a more accurate predictor for the target domain, it is often beneficial to leverage publicly available labeled data from an alternative domain, somewhat close to the target domain. This is the modern problem of supervised domain adaptation from a public source to a private target domain. We present two $(\epsilon, \delta)$-differentially private adaptation algorithms for supervised adaptation, for which we make use of a general optimization problem, recently shown to benefit from favorable theoretical learning guarantees. Our first algorithm is designed for regression with linear predictors and shown to solve a convex optimization problem. Our second algorithm is a more general solution for loss functions that may be non-convex but Lipschitz and smooth. While our main objective is a theoretical analysis, we also report the results of several experiments first demonstrating that the non-private versions of our algorithms outperform adaptation baselines and next showing that, for larger values of the target sample size or $\epsilon$, the performance of our private algorithms remains close to that of the non-private formulation.
The Dynamics and Energetics of Remnant and Restarting RLAGN

In this article, I review past, current, and future advances on the study of radio-loud AGN (RLAGN; radio-loud quasars and radio galaxies) lifecycles exclusively in the remnant and restarting phases. I focus on their dynamics and energetics as inferred from radio observations while discussing their radiative lifetimes, population statistics, and trends in their physical characteristics. I briefly summarise multi-wavelength observations, particularly X-rays, that have enabled studies of the large-scale environments of RLAGN in order to understand their role in feedback. Furthermore, I discuss analytic and numerical simulations that predict key properties of remnant and restarting sources as found in wide-area surveys, and discuss the prospects of future surveys that may shed further light on these elusive subpopulations of RLAGN.
Multiple-distribution-function finite-difference lattice Boltzmann method for incompressible Navier-Stokes equation

In this paper, a multiple-distribution-function finite-difference lattice Boltzmann method (MDF-FDLBM) is proposed for the convection-diffusion system based incompressible Navier-Stokes equations (NSEs). By Chapman Enskog analysis, the convection-diffusion system based incompressible NSEs can be recovered from MDF-FDLBM. Some quantities, including the velocity gradient, velocity divergence, strain rate tensor, shear stress and vorticity, can be computed locally by the first-order moment of the non-equilibrium distribution function. Through the von Neumann analysis, we conduct the stability analysis for the MDF-FDLBM and incompressible finite-difference lattice Boltzmann method (IFDLBM). It is found that the IFDLBM will be more stable than that of MDF-FDLBM with small kinematic viscosity, and the MDF-FDLBM will be more stable than that of IFDLBM with large Courant-Friedrichs-Lewy condition number. Finally, some simulations are conducted to validate the MDF-FDLBM. The results agree well with the analytical solutions and previous results. Through the numerical testing, we find that the MDF-FDLBM has a second-order convergence rate in space and time. The MDF-FDLBMcombined with non-uniform grid also works well. Meanwhile, compared with IFDLBM, it can be found that MDF-FDLBM offers higher accuracy and computational efficiency, reducing computation time by more than 36%.
Doing classic grounded theory: the data analysis process

This case study shares some of the methodological challenges I faced during my PhD research from 2007 to 2010. My study focused on Condition Management Programmes, part of the UK Government's Pathways to Work initiative, which provided work-focused interventions for people claiming health-related benefits. In 2007, there had been very little research on Condition Management Programmes, and there was little understanding about how they were actually being delivered at a ground level. As an inductive methodology suited to researching new areas, I decided to use classic grounded theory. The aim of classic grounded theory is to identify participants’ main concern and develop a theory that explains their behaviour. I interviewed health-care practitioners working in Condition Management Programmes and observed their treatment sessions with clients. By following the key stages of classic grounded theory (theoretical sampling, substantive coding, memo writing and theoretical coding), I developed a theory that explained practitioners’ decision-making processes. This case study provides a detailed account of some of the difficulties I encountered as I analysed my data and how these were resolved. I address the five key areas of data analysis that I found challenging: getting conceptual, choosing a core category, recognising theoretical saturation, achieving theoretical integration and manual versus computer-assisted analysis.
Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement

The goal of this study is to implement diffusion models for speech enhancement (SE). The first step is to emphasize the theoretical foundation of variance-preserving (VP)-based interpolation diffusion under continuous conditions. Subsequently, we present a more concise framework that encapsulates both the VP- and variance-exploding (VE)-based interpolation diffusion methods. We demonstrate that these two methods are special cases of the proposed framework. Additionally, we provide a practical example of VP-based interpolation diffusion for the SE task. To improve performance and ease model training, we analyze the common difficulties encountered in diffusion models and suggest amenable hyper-parameters. Finally, we evaluate our model against several methods using a public benchmark to showcase the effectiveness of our approach