Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Structured Noise Removal with Variational Lossy Autoencoder

Most unsupervised denoising methods are based on the assumption that imaging noise is either pixel-independent, i.e., spatially uncorrelated, or signal-independent, i.e., purely additive. However, in practice many imaging setups, especially in microscopy, suffer from a combination of signal-dependent noise (e.g. Poisson shot noise) and axis-aligned correlated noise (e.g. stripe shaped scanning or readout artifacts). In this paper, we present the first unsupervised deep learning-based denoiser that can remove this type of noise without access to any clean images or a noise model. Unlike self-supervised techniques, our method does not rely on removing pixels by masking or subsampling so can utilize all available information. We implement a Variational Autoencoder (VAE) with a specially designed autoregressive decoder capable of modelling the noise component of an image but incapable of independently modelling the underlying clean signal component. As a consequence, our VAE's encoder learns to encode only underlying clean signal content and to discard imaging noise. We also propose an additional decoder for mapping the encoder's latent variables back into image space, thereby sampling denoised images. Experimental results demonstrate that our approach surpasses existing methods for self- and unsupervised image denoising while being robust with respect to the size of the autoregressive receptive field. Code for this project can be found at https://github.com/krulllab/DVLAE.
On the Computational Complexity of Private High-dimensional Model Selection via the Exponential Mechanism

We consider the problem of model selection in a high-dimensional sparse linear regression model under privacy constraints. We propose a differentially private best subset selection method with strong utility properties by adopting the well-known exponential mechanism for selecting the best model. We propose an efficient Metropolis-Hastings algorithm and establish that it enjoys polynomial mixing time to its stationary distribution. Furthermore, we also establish approximate differential privacy for the final estimates of the Metropolis-Hastings random walk using its mixing property. Finally, we perform some illustrative experiments that show the strong utility of our algorithm.
Very-Large-Scale Integrated High-$Q$ Nanoantenna Pixels (VINPix)

Metasurfaces provide a versatile and compact approach to free-space optical manipulation and wavefront shaping. Comprised of arrays of judiciously-arranged dipolar resonators, metasurfaces precisely control the amplitude, polarization, and phase of light, with applications spanning imaging, sensing, modulation, and computing. Three crucial performance metrics of metasurfaces and their constituent resonators are the quality factor ($Q$-factor), mode-volume ($V_m$), and the ability to control far-field radiation. Often, resonators face a trade-off between these parameters: a reduction in $V_m$ leads to an equivalent reduction in $Q$, albeit with more control over radiation. Here, we demonstrate that this perceived compromise is not inevitable $-$ high-$Q$, subwavelength $V_m$, and controlled dipole-like radiation can be achieved, simultaneously. We design high-$Q$, very-large-scale integrated silicon nanoantenna pixels $-$ VINPix $-$ that combine guided mode resonance waveguides with photonic crystal cavities. With optimized nanoantennas, we achieve $Q$-factors exceeding 1500 with $V_m$ less than 0.1 $(\lambda/n_{air})^3$. Each nanoantenna is individually addressable by free-space light, and exhibits dipole-like scattering to the far-field. Resonator densities exceeding a million nanoantennas per $cm^2$ can be achieved. As a proof-of-concept application, we demonstrate spectrometer-free, spatially localized, refractive-index sensing utilizing VINPix metasurfaces. Our platform provides a foundation for compact, densely multiplexed devices such as spatial light modulators, computational spectrometers, and in-situ environmental sensors.
Non-symmetric stable processes: Dirichlet heat kernel, Martin kernel and Yaglom limit

We study a $d$-dimensional non-symmetric strictly $\alpha$-stable L\'{e}vy process $\mathbf{X}$, whose spherical density is bounded and bounded away from the origin. First, we give sharp two-sided estimates on the transition density of $\mathbf{X}$ killed when leaving an arbitrary $\kappa$-fat set. We apply these results to get the existence of the Yaglom limit for arbitrary $\kappa$-fat cone. In the meantime we also obtain the spacial asymptotics of the survival probability at the vertex of the cone expressed by means of the Martin kernel for $\Gamma$ and its homogeneity exponent. Our results hold for the dual process $\widehat{\mathbf{X}}$, too.
Would you Trust a Vehicle Merging into Your Lane? Subjective Evaluation of Negotiating Behaviour in a Congested Merging Scenario

Aiming for a society where humans and auto-mated vehicles can coexist cooperatively, understanding what constitutes cooperative and trustworthy behaviour is essential to designing automated vehicle controllers that enable the integration of highly automated vehicles into the real world. This study investigates how merging vehicles can gain trust from human-driven vehicles in a congested merging situation that requires explicit and implicit communication. Specifically, this study examines how the different behaviours of merging vehicles in the preparatory phase of the merge affect perceived trust from the perspective of the host vehicle in the main-stream lane. The findings suggest that transparent longitudinal positioning could improve the chance of successful merging, and cooperative deceleration during merging preparation could enhance the trust perceived by the host vehicle. Furthermore, the results reveal that, in time-sensitive situations where the merging vehicle approaches a lane closing point, prompt and decisive action of the merging vehicle encourages establishing trust with the host vehicle; any delay or hesitation can result in a lower level of trust. The results can provide valuable insights towards developing collaborative automated vehicles that improve safety and efficiency in real-world traffic situations that involve humans.
An Experience-based TAMP Framework for Foliated Manifolds

Due to their complexity, foliated structure problems often pose intricate challenges to task and motion planning in robotics manipulation. To counter this, our study presents the ``Foliated Repetition Roadmap. '' This roadmap assists task and motion planners by transforming the complex foliated structure problem into a more accessible graph format. By leveraging query experiences from different foliated manifolds, our framework can dynamically and efficiently update this graph. The refined graph can generate distribution sets, optimizing motion planning performance in foliated structure problems. In our paper, we lay down the theoretical groundwork and illustrate its practical applications through real-world examples.
How connectivity structure shapes rich and lazy learning in neural circuits

In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learning dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity could exhibit a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights -- in particular their effective rank -- influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.
Harnessing the Power of LLM to Support Binary Taint Analysis

This paper proposes LATTE, the first static binary taint analysis that is powered by a large language model (LLM). LATTE is superior to the state of the art (e.g., Emtaint, Arbiter, Karonte) in three aspects. First, LATTE is fully automated while prior static binary taint analyzers need rely on human expertise to manually customize taint propagation rules and vulnerability inspection rules. Second, LATTE is significantly effective in vulnerability detection, demonstrated by our comprehensive evaluations. For example, LATTE has found 37 new bugs in real-world firmware which the baselines failed to find, and 7 of them have been assigned CVE numbers. Lastly, LATTE incurs remarkably low engineering cost, making it a cost-efficient and scalable solution for security researchers and practitioners. We strongly believe that LATTE opens up a new direction to harness the recent advance in LLMs to improve vulnerability analysis for binary programs.
MeanAP-Guided Reinforced Active Learning for Object Detection

Active learning presents a promising avenue for training high-performance models with minimal labeled data, achieved by judiciously selecting the most informative instances to label and incorporating them into the task learner. Despite notable advancements in active learning for image recognition, metrics devised or learned to gauge the information gain of data, crucial for query strategy design, do not consistently align with task model performance metrics, such as Mean Average Precision (MeanAP) in object detection tasks. This paper introduces MeanAP-Guided Reinforced Active Learning for Object Detection (MAGRAL), a novel approach that directly utilizes the MeanAP metric of the task model to devise a sampling strategy employing a reinforcement learning-based sampling agent. Built upon LSTM architecture, the agent efficiently explores and selects subsequent training instances, and optimizes the process through policy gradient with MeanAP serving as reward. Recognizing the time-intensive nature of MeanAP computation at each step, we propose fast look-up tables to expedite agent training. We assess MAGRAL's efficacy across popular benchmarks, PASCAL VOC and MS COCO, utilizing different backbone architectures. Empirical findings substantiate MAGRAL's superiority over recent state-of-the-art methods, showcasing substantial performance gains. MAGRAL establishes a robust baseline for reinforced active object detection, signifying its potential in advancing the field.
Nonrelativistic Limit of Normalized Solutions to a class of nonlinear Dirac equations

In this paper, we investigate the nonrelativistic limit of normalized solutions to a nonlinear Dirac equation as given below: \begin{equation*} \begin{cases}&-i c\sum\limits_{k=1}^3\alpha_k\partial_k u +mc^2 \beta {u}- \Gamma * (K |{u}|^\kappa) K|{u}|^{\kappa-2}{u}- P |{u}|^{s-2}{u}=\omega {u}, \\&\displaystyle\int_{\mathbb{R}^3}\vert u \vert^2 dx =1. \end{cases} \end{equation*} Here, $c>0$ represents the speed of light, $m>0$ is the mass of the Dirac particle, $\omega\in\mathbb{R}$ emerges as an indeterminate Lagrange multiplier, $\Gamma$, $K$, $P$ are real-valued function defined on $\mathbb{R}^3$, also known as potential functions. Our research first confirms the presence of normalized solutions to the Dirac equation under high-speed light conditions. We then illustrate that these solutions progress to become the ground states of a system of nonlinear Schr\"odinger equations with a normalized constraint, exhibiting uniform boundedness and exponential decay irrespective of the light speed. Our results form the first discussion on nonrelativistic limit of normalized solutions to nonlinear Dirac equations. This not only aids in the study of normalized solutions of the nonlinear Schr\"odinger equations, but also physically explains that the normalized ground states of high-speed particles and low-speed motion particles are consistent.
Detecting and Fixing Violations of Modification Terms in Open Source Licenses during Forking

Open source software brings benefit to software community, but also introduces legal risks caused by license violations, which result in serious consequences such as lawsuits and financial losses. To mitigate legal risks, some approaches have been proposed to identify licenses, detect license incompatibilities and inconsistencies, and recommend licenses. As far as we know, however, there is no prior work to understand modification terms in open source licenses or to detect and fix violations of modification terms. To bridge this gap, we first empirically characterize modification terms in 47 open source licenses. These licenses all require certain forms of"notice"to describe the modifications made to the original work. Inspired by our study, we then design LiVo to automatically detect and fix violations of modification terms in open source licenses during forking. Our evaluation has shown the effectiveness and efficiency of LiVo. 18 pull requests of fixing modification term violations have received positive responses. 8 have been merged.
GRASP: Accelerating Shortest Path Attacks via Graph Attention

Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical combinatorial optimization algorithms. ML-based speed ups that aim to learn in an end to end manner (i.e., directly output the solution) tend to trade off run time with solution quality. Therefore, solutions that are able to accelerate existing solvers while maintaining their performance guarantees, are of great interest. We consider an APX-hard problem, where an adversary aims to attack shortest paths in a graph by removing the minimum number of edges. We propose the GRASP algorithm: Graph Attention Accelerated Shortest Path Attack, an ML aided optimization algorithm that achieves run times up to 10x faster, while maintaining the quality of solution generated. GRASP uses a graph attention network to identify a smaller subgraph containing the combinatorial solution, thus effectively reducing the input problem size. Additionally, we demonstrate how careful representation of the input graph, including node features that correlate well with the optimization task, can highlight important structure in the optimization solution.
Testing multi-field inflation with LiteBIRD

We investigate expected constraints on the primordial tensor power spectrum from the future cosmic microwave background polarization experiment LiteBIRD as a test of multi-field inflation, where we specifically consider spectator models as representative examples. We argue that the measurements of the tensor-to-scalar ratio and the tensor spectral index, in combination with the constraints on the scalar spectral index from the Planck observation, are useful in testing multi-field inflation models. We also discuss implications for multi-field inflationary model building.
A Survey of Feature Types and Their Contributions for Camera Tampering Detection

Camera tamper detection is the ability to detect unauthorized and unintentional alterations in surveillance cameras by analyzing the video. Camera tampering can occur due to natural events or it can be caused intentionally to disrupt surveillance. We cast tampering detection as a change detection problem, and perform a review of the existing literature with emphasis on feature types. We formulate tampering detection as a time series analysis problem, and design experiments to study the robustness and capability of various feature types. We compute ten features on real-world surveillance video and apply time series analysis to ascertain their predictability, and their capability to detect tampering. Finally, we quantify the performance of various time series models using each feature type to detect tampering.
An SiO Toroid and Wide-angle Outflow Associated with the Massive Protostar W75N(B)-VLA2

We have carried out Atacama Large Millimeter/submillimeter Array observations of the massive star-forming region W75N(B), which contains the massive protostars VLA1, VLA2, and VLA3. Particularly, VLA2 is an enigmatic protostar associated with a wind-driven H2O maser shell, which has evolved from an almost isotropic outflow to a collimated one in just 20 yr. The shell expansion seemed to be halted by an obstacle located to the northeast of VLA2. Here we present our findings from observing the 1.3 mm continuum and H2CO and SiO emission lines. Within a region of ∼30″ (∼39,000 au) diameter, we have detected 40 compact millimeter continuum sources, three of them coinciding with VLA1, VLA2, and VLA3. While the H2CO emission is mainly distributed in a fragmented structure around the three massive protostars, but without any of the main H2CO clumps spatially coinciding with them, the SiO is highly concentrated on VLA2, indicating the presence of very strong shocks generated near this protostar. The SiO emission is clearly resolved into an elongated structure (∼0.″6 × 0.″3; ∼780 au×390 au) perpendicular to the major axis of the wind-driven maser shell. The structure and kinematics of the SiO emission are consistent with a toroid and a wide-angle outflow surrounding a central mass of ∼10 M ⊙, thus supporting previous theoretical predictions regarding the evolution of the outflow. Additionally, we have identified the expected location and estimated the gas density of the obstacle that is hindering the expansion of the maser shell.
Development of salt-related structures in the Central North Sea: results from section balancing

Abstract The geometric and kinematic evolution of salt-related structures in the Central North Sea has been studied using structural restoration and balancing techniques. Strain associated with fault displacements, compaction and isostasy has been systematically removed, thus producing a graphical representation of the temporal and spatial distribution of salt structures through time. The results clearly demonstrate that the Central North Sea can be sub-divided into three structural provinces based on the type of salt structure present: (1) the Central Graben which is characterized by large offset extensional faults with broad salt swells and tall salt diapirs, (2) the Eastern platform comprising the Norwegian-Danish Basin which is dominated by symmetrical salt swells that are typically welded in intervening lows and deeply buried, and (3) the Western platform which is characterized by salt swells and gravity-driven, thin-skinned extension. By restoring the deformation at both a local and a regional scale, it becomes apparent that the Permian salt has controlled the development of structures throughout the Mesozoic and Tertiary in the Central Graben region and the Western Platform, whereas on the Eastern Platform the salt was primarily active during the Triassic only. This has led to a variation in the evolution of structural styles across the Central North Sea. The restorations also suggest that salt dissolution has not been a significant feature during the development of diapirs in this area.
Hydrological trend analysis in the Yellow River basin using a distributed hydrological model

The hydrological cycle has been highly influenced by climate change and human activities, and it is significant for analyzing the hydrological trends that occurred in past decades in order to understand past changes and to predict future trends. The water crisis of the Yellow River basin has drawn much attention from around the world, especially the drying up of the main river along the lower reaches during the 1990s. By incorporating historical meteorological data and available geographic information related to the conditions of the landscape, a distributed hydrological model has been employed to simulate the natural runoff without consideration of artificial water intake. On the basis of the data observed and the results simulated by the model, the hydrological trends have been analyzed quantitatively for evaluating the impact from climate change and human activity. It is found that the simulated natural runoff follows a similar trend as the precipitation in the entire area being studied during the last half century, and this implies that changes in the natural runoff are mainly controlled by the climate change rather than land use change. Changes in actual evapotranspiration upstream of the Lanzhou gauge are controlled by changes in both precipitation and potential evaporation, while changes of actual evapotranspiration downstream of the Lanzhou gauge are controlled mainly by the changes in precipitation. The difference between the annual observed runoff and the simulated runoff indicates that there is little artificial water consumption upstream of the Lanzhou gauge, but the artificial water consumption becomes larger downstream of the Lanzhou gauge. The artificial water consumption shows a significant increasing trend during the past 50 years and is the main cause of the drying up of the Yellow River. However, in contrast to the common perception that the serious drying up downstream of the Yellow River during the 1990s is caused by the rapid increase of artificial water consumption during the same period, it has been found that the main cause of this aggravation is the drier climate that has existed since the 1990s. The main reason that the drying‐up situation became better in the 21st century is because of the enhanced water resources management since 2000.
Applications of the vortex-surface field to flow visualization, modelling and simulation

Abstract We review the progress on the applications of the vortex-surface field (VSF). The VSF isosurface is a vortex surface consisting of vortex lines. Based on the generalized Helmholtz theorem, the VSF isosurfaces of the same threshold at different times have strong coherence. As a general flow diagnostic tool for studying vortex evolution, the numerical VSF solution is first constructed in a given flow field by solving a pseudo-transport equation driven by the instantaneous frozen vorticity, and then the VSF evolution is calculated by the two-time method. From the database of numerical simulations or experiments, the VSF can elucidate mechanisms in the flows with essential vortex dynamics, such as isotropic turbulence, wall flow transition, flow past a flapping plate and turbulence–flame interaction. The characterization of VSFs reveals the correlation between robust statistical features and the critical quantities needed to be predicted in engineering applications, such as the friction coefficient in transition, thrust in bio-propulsion and growth rate in interface instability. Since the VSF evolution captures the essential Lagrangian-based dynamics of vortical flows, it inspires novel numerical methods on cutting-edge hardware, e.g. graphic and quantum processors.