Miracle-dz/UniFAR-FTUs huggingface dataset:
Compounding Reconstruction for Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) images are usually corrupted by strongly speckle noise,which blocks scene information abstracting and the application of SAR images.A combination despeckle reconstruction algorithm is proposed based on generation principles and statistic characters of speckle noise.The despeckle and reconstruction performance of this algorithm is as good as simulated annealing algorithm,but it reduces the computation cost and still has excellent despeckle and scene structure keeping performance.It is a practical algorithm for SAR image reconstruction.
Open AdS/CFT via a Double Trace Deformation

A concrete model of extracting the physics from the bulk of a gravitational universe is important to the study of quantum gravity and its possible relationship with experiments. Such a model can be constructed in the AdS/CFT correspondence by gluing a bath on the asymptotic boundary of the bulk anti-de Sitter (AdS) spacetime. This bath models a laboratory and is described by a quantum field theory. In the dual conformal field theory (CFT) description this coupling is achieved by a double-trace deformation that couples the CFT with the bath. This suggests that the physics observed by the laboratory is fully unitary. In this paper, we analyze the quantum aspects of this model in detail which conveys new lessons about the AdS/CFT correspondence, and we discuss the potential usefulness of this model in understanding subregion physics in a gravitational universe.
A rate-independent model for geomaterials under compression coupling strain gradient plasticity and damage

We study a strain gradient-enhanced version of a model for geomaterials under compression by Marigo and Kazymyrenko (2019) coupling damage and small-strain associative plasticity. We prove that the jumps in time of the plastic variable may happen only along jumps of the damage variable. Moreover, we perform a vanishing-viscosity analysis showing existence of Balanced Viscosity quasistatic solutions \`a la Mielke-Rossi-Savar\'e.
One-loop evolution of twist-2 generalized parton distributions

We revisit the evolution of generalised parton distributions (GPDs) at the leading order in the strong coupling constant $\alpha_s$ for all of the twist-2 quark and gluon operators. We rederive the relevant one-loop evolution kernels, expressing them in a form suitable for implementation, and check analytically that some basic properties, such as DGLAP/ERBL limits and polynomiality conservation, are fulfilled. We also present a number of numerical results obtained with a public implementation of the evolution in the library {\tt APFEL++} and available within the {\tt PARTONS} framework.
Emergent Topology in Many-Body Dissipative Quantum Chaos

The identification, description, and classification of topological features is an engine of discovery and innovation in several fields of physics. This research encompasses a broad variety of systems, from the integer and fractional Chern insulators in condensed matter, to protected states in complex photonic lattices in optics, and the structure of the QCD vacuum. Here, we introduce another playground for topology: the dissipative dynamics of the Sachdev-Ye-Kitaev (SYK) model, $N$ fermions in zero dimensions with strong $q$-body interactions coupled to a Markovian bath. For $q = 4, 8, \ldots$ and certain choices of $N$ and bath details, involving pseudo-Hermiticity, we find a rectangular block representation of the vectorized Liouvillian that is directly related to the existence of an anomalous trace of the unitary operator implementing fermionic exchange. As a consequence of this rectangularization, the Liouvillian has purely real modes for any coupling to the bath. Some of them are demonstrated to be topological by an explicit calculation of the spectral flow, leading to a symmetry-dependent topological index $\nu$. Topological properties have universal features: they are robust to changes in the Liouvillian provided that the symmetries are respected and they are also observed if the SYK model is replaced by a quantum chaotic dephasing spin chain in the same symmetry class. Moreover, the topological symmetry class can be robustly characterized by the level statistics of the corresponding random matrix ensemble. In the limit of weak coupling to the bath, topological modes govern the approach to equilibrium, which may enable a direct path for experimental confirmation of topology in dissipative many-body quantum chaotic systems.
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training

Multimodal reasoning is a challenging task that requires models to reason across multiple modalities to answer questions. Existing approaches have made progress by incorporating language and visual modalities into a two-stage reasoning framework, separating rationale generation from answer inference. However, these approaches often fall short due to the inadequate quality of the generated rationales. In this work, we delve into the importance of rationales in model reasoning. We observe that when rationales are completely accurate, the model's accuracy significantly improves, highlighting the need for high-quality rationale generation. Motivated by this, we propose MC-CoT, a self-consistency training strategy that generates multiple rationales and answers, subsequently selecting the most accurate through a voting process. This approach not only enhances the quality of generated rationales but also leads to more accurate and robust answers. Through extensive experiments, we demonstrate that our approach significantly improves model performance across various benchmarks. Remarkably, we show that even smaller base models, when equipped with our proposed approach, can achieve results comparable to those of larger models, illustrating the potential of our approach in harnessing the power of rationales for improved multimodal reasoning. The code is available at https://github.com/chengtan9907/mc-cot.
Constant-Time Wasmtime, for Real This Time: End-to-End Verified Zero-Overhead Constant-Time Programming for the Web and Beyond

We claim that existing techniques and tools for generating and verifying constant-time code are incomplete, since they rely on assumptions that compiler optimization passes do not break constant-timeness or that certain operations execute in constant time on the hardware. We present the first end-to-end constant-time-aware compilation process that preserves constant-time semantics at every step from a high-level language down to microarchitectural guarantees, provided by the forthcoming ARM PSTATE.DIT feature. First, we present a new compiler-verifier suite based on the JIT-style runtime Wasmtime, modified to compile ct-wasm, a preexisting type-safe constant-time extension of WebAssembly, into ARM machine code while maintaining the constant-time property throughout all optimization passes. The resulting machine code is then fed into an automated verifier that requires no human intervention and uses static dataflow analysis in Ghidra to check the constant-timeness of the output. Our verifier leverages characteristics unique to ct-wasm-generated code in order to speed up verification while preserving both soundness and wide applicability. We also consider the resistance of our compilation and verification against speculative timing leakages such as Spectre. Finally, in order to expose ct-Wasmtime at a high level, we present a port of FaCT, a preexisting constant-time-aware DSL, to target ct-wasm.
Towards cosmology with Void Lensing: how to find voids sensitive to weak-lensing and numerically interpret them

In this work, we present a study of the void lensing signal or the excess surface mass density (ESMD) around cosmic voids. First, we propose a new void-finder algorithm that is designed to capture the ESMD around voids. We compare our algorithm applied to projected slices with the ZOBOV void finder and find significantly deeper weak-lensing profiles for voids defined by our algorithm in the context of a realistic galaxy mock. Then we test the consistency between the measurements of the ESMD as measured through the shear of background galaxies and directly calculated through the dark matter density profiles of the same voids. We found inconsistencies for voids with diameter $\geq 100h^{-1}\mathrm{Mpc}$ along the line-of-sight, but the consistency holds for smaller voids, meaning that we are indeed probing the underlying dark matter field by measuring the shear around these voids. Moreover, we show that voids found in the projected slices, which are highly sensitive to lensing, are correlated to $3$D voids exhibiting intrinsic alignments between them.
RAISE - Radiology AI Safety, an End-to-end lifecycle approach

The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology. Beginning with rigorous pre-deployment evaluation and validation, the focus should be on ensuring models meet the highest standards of safety, effectiveness and efficacy for their intended applications. Input and output guardrails implemented during production usage act as an additional layer of protection, identifying and addressing individual failures as they occur. Continuous post-deployment monitoring allows for tracking population-level performance (data drift), fairness, and value delivery over time. Scheduling reviews of post-deployment model performance and educating radiologists about new algorithmic-driven findings is critical for AI to be effective in clinical practice. Recognizing that no single AI solution can provide absolute assurance even when limited to its intended use, the synergistic application of quality assurance at multiple levels - regulatory, clinical, technical, and ethical - is emphasized. Collaborative efforts between stakeholders spanning healthcare systems, industry, academia, and government are imperative to address the multifaceted challenges involved. Trust in AI is an earned privilege, contingent on a broad set of goals, among them transparently demonstrating that the AI adheres to the same rigorous safety, effectiveness and efficacy standards as other established medical technologies. By doing so, developers can instil confidence among providers and patients alike, enabling the responsible scaling of AI and the realization of its potential benefits. The roadmap presented herein aims to expedite the achievement of deployable, reliable, and safe AI in radiology.
GAN-Avatar: Controllable Personalized GAN-based Human Head Avatar

Digital humans and, especially, 3D facial avatars have raised a lot of attention in the past years, as they are the backbone of several applications like immersive telepresence in AR or VR. Despite the progress, facial avatars reconstructed from commodity hardware are incomplete and miss out on parts of the side and back of the head, severely limiting the usability of the avatar. This limitation in prior work stems from their requirement of face tracking, which fails for profile and back views. To address this issue, we propose to learn person-specific animatable avatars from images without assuming to have access to precise facial expression tracking. At the core of our method, we leverage a 3D-aware generative model that is trained to reproduce the distribution of facial expressions from the training data. To train this appearance model, we only assume to have a collection of 2D images with the corresponding camera parameters. For controlling the model, we learn a mapping from 3DMM facial expression parameters to the latent space of the generative model. This mapping can be learned by sampling the latent space of the appearance model and reconstructing the facial parameters from a normalized frontal view, where facial expression estimation performs well. With this scheme, we decouple 3D appearance reconstruction and animation control to achieve high fidelity in image synthesis. In a series of experiments, we compare our proposed technique to state-of-the-art monocular methods and show superior quality while not requiring expression tracking of the training data.
Fast Policy Learning for Linear Quadratic Regulator with Entropy Regularization

This paper proposes and analyzes two new policy learning methods: regularized policy gradient (RPG) and iterative policy optimization (IPO), for a class of discounted linear-quadratic regulator (LQR) problems over an infinite time horizon with entropy regularization. Assuming access to the exact policy evaluation, both proposed approaches are proved to converge linearly in finding optimal policies of the regularized LQR. Moreover, the IPO method can achieve a super-linear convergence rate once it enters a local region around the optimal policy. Finally, when the optimal policy from a well-understood environment in an RL problem is appropriately transferred as the initial policy to an RL problem with an unknown environment, the IPO method is shown to enable a super-linear convergence rate if the latter is sufficiently close to the former. The performances of these proposed algorithms are supported by numerical examples
High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed POPCORN, a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number of aggregate population counts over coarse census districts for calibration. Despite the minimal data requirements our approach surpasses the mapping accuracy of existing schemes, including several that rely on building footprints derived from high-resolution imagery. E.g., we were able to produce population maps for Rwanda with 100m GSD based on less than 400 regional census counts. In Kigali, those maps reach an $R^2$ score of 66% w.r. t. a ground truth reference map, with an average error of only $\pm$10 inhabitants/ha. Conveniently, POPCORN retrieves explicit maps of built-up areas and of local building occupancy rates, making the mapping process interpretable and offering additional insights, for instance about the distribution of built-up, but unpopulated areas, e.g., industrial warehouses. Moreover, we find that, once trained, the model can be applied repeatedly to track population changes; and that it can be transferred to geographically similar regions, e.g., from Uganda to Rwanda). With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns.
On the Galactic radio signal from stimulated decay of axion dark matter

We study the full-sky distribution of the radio emission from the stimulated decay of axions which are assumed to compose the dark matter in the Galaxy. Besides the constant extragalactic and CMB components, the decays are stimulated by a Galactic radio emission with a spatial distribution that we empirically determine from observations. We compare the diffuse emission to the counterimages of the brightest supernov\ae\ remnants, and take into account the effects of free-free absorption. We show that, if the dark matter halo is described by a cuspy NFW profile, the expected signal from the Galactic center is the strongest. Interestingly, the emission from the Galactic anti-center provides competitive constraints that do not depend on assumptions on the uncertain dark matter density in the inner region. Furthermore, the anti-center of the Galaxy is the brightest spot if the Galactic dark matter density follows a cored profile. The expected signal from stimulated decays of axions of mass $m _{a} \sim 10 ^{-6}$ eV is within reach of the Square Kilometer Array for an axion-photon coupling $g _{a\gamma} \gtrsim (2-3) \times 10 ^{-11}$ GeV$^{-1}$.
Benchmarking Multimodal Variational Autoencoders: CdSprites+ Dataset and Toolkit

Multimodal Variational Autoencoders (VAEs) have been the subject of intense research in the past years as they can integrate multiple modalities into a joint representation and can thus serve as a promising tool for both data classification and generation. Several approaches toward multimodal VAE learning have been proposed so far, their comparison and evaluation have however been rather inconsistent. One reason is that the models differ at the implementation level, another problem is that the datasets commonly used in these cases were not initially designed to evaluate multimodal generative models. This paper addresses both mentioned issues. First, we propose a toolkit for systematic multimodal VAE training and comparison. The toolkit currently comprises 4 existing multimodal VAEs and 6 commonly used benchmark datasets along with instructions on how to easily add a new model or a dataset. Second, we present a disentangled bimodal dataset designed to comprehensively evaluate the joint generation and cross-generation capabilities across multiple difficulty levels. We demonstrate the utility of our dataset by comparing the implemented state-of-the-art models.
FViT-Grasp: Grasping Objects With Using Fast Vision Transformers

This study addresses the challenge of manipulation, a prominent issue in robotics. We have devised a novel methodology for swiftly and precisely identifying the optimal grasp point for a robot to manipulate an object. Our approach leverages a Fast Vision Transformer (FViT), a type of neural network designed for processing visual data and predicting the most suitable grasp location. Demonstrating state-of-the-art performance in terms of speed while maintaining a high level of accuracy, our method holds promise for potential deployment in real-time robotic grasping applications. We believe that this study provides a baseline for future research in vision-based robotic grasp applications. Its high speed and accuracy bring researchers closer to real-life applications.
Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction

Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming. The study aims to accelerate MRI and enhance PET image quality. Conventional approaches involve the separate reconstruction of each modality within PET-MRI systems. However, there exists complementary information among multi-modal images. The complementary information can contribute to image reconstruction. In this study, we propose a novel PET-MRI joint reconstruction model employing a mutual consistency-driven diffusion mode, namely MC-Diffusion. MC-Diffusion learns the joint probability distribution of PET and MRI for utilizing complementary information. We conducted a series of contrast experiments about LPLS, Joint ISAT-net and MC-Diffusion by the ADNI dataset. The results underscore the qualitative and quantitative improvements achieved by MC-Diffusion, surpassing the state-of-the-art method.
Variational Annealing on Graphs for Combinatorial Optimization

Several recent unsupervised learning methods use probabilistic approaches to solve combinatorial optimization (CO) problems based on the assumption of statistically independent solution variables. We demonstrate that this assumption imposes performance limitations in particular on difficult problem instances. Our results corroborate that an autoregressive approach which captures statistical dependencies among solution variables yields superior performance on many popular CO problems. We introduce subgraph tokenization in which the configuration of a set of solution variables is represented by a single token. This tokenization technique alleviates the drawback of the long sequential sampling procedure which is inherent to autoregressive methods without sacrificing expressivity. Importantly, we theoretically motivate an annealed entropy regularization and show empirically that it is essential for efficient and stable learning.
Archiving Body Movements: Collective Generation of Chinese Calligraphy

As a communication channel, body movements have been widely explored in behavioral studies and kinesics. Performing and visual arts share the same interests but focus on documenting and representing human body movements, such as for dance notation and visual work creation. This paper investigates body movements in oriental calligraphy and how to apply calligraphy principles to stimulate and archive body movements. Through an artwork (Wushu), the authors experiment with an interactive and generative approach to engage the audience's bodily participation and archive the body movements as a compendium of generated calligraphy. The audience assumes the role of both writers and readers; creating ("writing") and appreciating ("reading") the generated calligraphy becomes a cyclical process within this infinite"Book,"which can motivate further attention and discussions concerning Chinese characters and calligraphy.
JWST’s PEARLS: Improved Flux Calibration for NIRCam

The Prime Extragalactic Areas for Reionization and Lensing Science, a James Webb Space Telescope (JWST) GTO program, obtained a set of unique NIRCam observations that have enabled us to significantly improve the default photometric calibration across both NIRCam modules. The observations consisted of three epochs of 4-band (F150W, F200W, F356W, and F444W) NIRCam imaging in the Spitzer IRAC Dark Field (IDF). The three epochs were six months apart and spanned the full duration of Cycle 1. As the IDF is in the JWST continuous viewing zone, we were able to design the observations such that the two modules of NIRCam, modules A and B, were flipped by 180° and completely overlapped each other’s footprints in alternate epochs. We were therefore able to directly compare the photometry of the same objects observed with different modules and detectors, and we found significant photometric residuals up to ∼0.05 mag in some detectors and filters, for the default version of the calibration files that we used (jwst_1039.pmap). Moreover, there are multiplicative gradients present in the data obtained in the two long-wavelength bands. The problem is less severe in the data reduced using the latest pmap (jwst_1130.pmap as of 2023 September), but it is still present, and is non-negligible. We provide a recipe to correct for this systematic effect to bring the two modules onto a more consistent calibration, to a photometric precision better than ∼0.02 mag.