Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence

We propose a new dynamic average consensus algorithm that is robust to information-sharing noise arising from differential-privacy design. Not only is dynamic average consensus widely used in cooperative control and distributed tracking, it is also a fundamental building block in numerous distributed computation algorithms such as multi-agent optimization and distributed Nash equilibrium seeking. We propose a new dynamic average consensus algorithm that is robust to persistent and independent information-sharing noise added for the purpose of differential-privacy protection. In fact, the algorithm can ensure both provable convergence to the exact average reference signal and rigorous ϵ-differential privacy (even when the number of iterations tends to infinity), which, to our knowledge, has not been achieved before in average consensus algorithms. Given that channel noise in communication can be viewed as a special case of differential-privacy noise, the algorithm can also be used to counteract communication imperfections. Numerical simulation results confirm the effectiveness of the proposed approach.
Traveling wave solutions of the generalized scale-invariant analog of the KdV equation by tanh–coth method

Abstract In this work, the generalized scale-invariant analog of the Korteweg–de Vries equation is studied. For the first time, the tanh–coth methodology is used to find traveling wave solutions for this nonlinear equation. The considered generalized equation is a connection between the well-known Korteweg–de Vries (KdV) equation and the recently investigated scale-invariant of the dependent variable (SIdV) equation. The obtained results show many families of solutions for the model, indicating that this equation also shares bell-shaped solutions with KdV and SIdV, as previously documented by other researchers. Finally, by executing the symbolic computation, we demonstrate that the used technique is a valuable and effective mathematical tool that can be used to solve problems that arise in the cross-disciplinary nonlinear sciences.
Compressing Volumetric Radiance Fields to 1 MB

Approximating radiance fields with discretized volumetric grids is one of promising directions for improving NeRFs, represented by methods like DVGO, Plenoxels and TensoRF, which achieve super-fast training convergence and real-time rendering. However, these methods typically require a tremendous storage overhead, costing up to hundreds of megabytes of disk space and runtime memory for a single scene. We address this issue in this paper by introducing a simple yet effective framework, called vector quantized radiance fields (VQRF), for compressing these volume-grid-based radiance fields. We first present a robust and adaptive metric for estimating redundancy in grid models and performing voxel pruning by better exploring intermediate outputs of volumetric rendering. A trainable vector quantization is further proposed to improve the compactness of grid models. In combination with an efficient joint tuning strategy and post-processing, our method can achieve a compression ratio of 100× by reducing the overall model size to 1 MB with negligible loss on visual quality. Extensive experiments demonstrate that the proposed framework is capable of achieving unrivaled performance and well generalization across multiple methods with distinct volumetric structures, facilitating the wide use of volumetric radiance fields methods in real-world applications. Code is available at https://github.com/AlgoHunt/VQRF.
Ultrasensitive Ozone Detection Using Single-Walled Carbon Nanotube Networks

We demonstrated ultrasensitive detection of ozone using single-walled carbon nanotube (SWNT) networks directly grown on a conventional sensor substrate. Ozone was detected down to the ppb level (∼6 ppb) at room-temperature operation with a fast response. Using an electrical breakdown technique, gas sensitivity was improved. This result clearly indicates that owing to its high sensitivity, simplicity in fabrication and compact size, an SWNT sensor provides a feasible route to ultrasensitive ozone detection surpassing existing methods.
Fourier-Net: Fast Image Registration with Band-limited Deformation

Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, named TransMorph, our Fourier-Net, which only uses 2.2% of its parameters and 6.66% of the multiply-add operations, achieves a 0.5% higher Dice score and an 11.48 times faster inference speed. Code is available at https://github.com/xi-jia/Fourier-Net.
Curriculum Temperature for Knowledge Distillation

Most existing distillation methods ignore the flexible role of the temperature in the loss function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In general, the temperature controls the discrepancy between two distributions and can faithfully determine the difficulty level of the distillation task. Keeping a constant temperature, i.e., a fixed level of task difficulty, is usually sub-optimal for a growing student during its progressive learning stages. In this paper, we propose a simple curriculum-based technique, termed Curriculum Temperature for Knowledge Distillation (CTKD), which controls the task difficulty level during the student's learning career through a dynamic and learnable temperature. Specifically, following an easy-to-hard curriculum, we gradually increase the distillation loss w.r. t. the temperature, leading to increased distillation difficulty in an adversarial manner. As an easy-to-use plug-in technique, CTKD can be seamlessly integrated into existing knowledge distillation frameworks and brings general improvements at a negligible additional computation cost. Extensive experiments on CIFAR-100, ImageNet-2012, and MS-COCO demonstrate the effectiveness of our method.
Active 3D Double-RIS-Aided Multi-User Communications: Two-Timescale-Based Separate Channel Estimation via Bayesian Learning

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels’ sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead. Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework.
Coder Reviewer Reranking for Code Generation

Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions. Inspired by collaborative programming, we propose Coder-Reviewer reranking. We augment Coder language models from past work, which generate programs given language instructions, with Reviewer models, which evaluate the likelihood of the instruction given the generated programs. We perform an extensive study across six datasets with eight models from three model families. Experimental results show that Coder-Reviewer reranking leads to consistent and significant improvement (up to 17% absolute accuracy gain) over reranking with the Coder model only. When combined with executability filtering, Coder-Reviewer reranking can often outperform the minimum Bayes risk method. Coder-Reviewer reranking is easy to implement by prompting, can generalize to different programming languages, and works well with off-the-shelf hyperparameters.
Gravitational collapse to extremal black holes and the third law of black hole thermodynamics

We construct examples of black hole formation from regular, one-ended asymptotically flat Cauchy data for the Einstein-Maxwell-charged scalar field system in spherical symmetry which are exactly isometric to extremal Reissner-Nordstr\"om after a finite advanced time along the event horizon. Moreover, in each of these examples the apparent horizon of the black hole coincides with that of a Schwarzschild solution at earlier advanced times. In particular, our result can be viewed as a definitive disproof of the"third law of black hole thermodynamics. "The main step in the construction is a novel $C^k$ characteristic gluing procedure, which interpolates between a light cone in Minkowski space and a Reissner-Nordstr\"om event horizon with specified charge to mass ratio $e/M$. Our setup is inspired by the recent work of Aretakis-Czimek-Rodnianski on perturbative characteristic gluing for the Einstein vacuum equations. However, our construction is fundamentally nonperturbative and is based on a finite collection of scalar field pulses which are modulated by the Borsuk-Ulam theorem.
The performance of the MAGIC telescopes using deep convolutional neural networks with CTLearn

The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescope system is located on the Canary Island of La Palma and inspects the very high-energy (VHE, few tens of GeV and above) gamma-ray sky. MAGIC consists of two imaging atmospheric Cherenkov telescopes (IACTs), which capture images of the air showers originating from the absorption of gamma rays and cosmic rays by the atmosphere, through the detection of Cherenkov photons emitted in the shower. The sensitivity of IACTs to gamma-ray sources is mainly determined by the ability to reconstruct the properties (type, energy, and arrival direction) of the primary particle generating the air shower. The state-of-the-art IACT pipeline for shower reconstruction is based on the parameterization of the shower images by extracting geometric and stereoscopic features and machine learning algorithms like random forest or boosted decision trees. In this contribution, we explore deep convolutional neural networks applied directly to the pixelized images of the camera as a promising method for IACT full-event reconstruction and present the performance of the method on observational data using CTLearn, a package for IACT event reconstruction that exploits deep learning.
Reasoning about Promises in Weak Memory Models with Event Structures (Extended Version)

Modern processors such as ARMv8 and RISC-V allow executions in which independent instructions within a process may be reordered. To cope with such phenomena, so called promising semantics have been developed, which permit threads to read values that have not yet been written. Each promise is a speculative update that is later validated (fulfilled) by an actual write. Promising semantics are operational, providing a pathway for developing proof calculi. In this paper, we develop an incorrectness-style logic, resulting in a framework for reasoning about state reachability. Like incorrectness logic, our assertions are underapproximating, since the set of all valid promises are not known at the start of execution. Our logic uses event structures as assertions to compactly represent the ordering among events such as promised and fulfilled writes. We prove soundness and completeness of our proof calculus and demonstrate its applicability by proving reachability properties of standard weak memory litmus tests.
Controlled Language Generation for Language Learning Items

This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24%) and complexity (9%) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.
Gauge-invariant uniqueness theorems for $P$-graphs

We prove a version of the result in the title that makes use of maximal coactions in the context of discrete groups. Earlier Gauge-Invariant Uniqueness theorems for $C^*$-algebras associated to $P$-graphs and similar $C^*$-algebras exploited a property of coactions known as normality. In the present paper, the view point is that maximal coactions provide a more natural starting point to state and prove such uniqueness theorems. A byproduct of our approach consists of an abstract characterization of co-universal representations for a Fell bundle over a discrete group.
Transferability Estimation Based On Principal Gradient Expectation

Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks. The standard approach is pre-training followed by fine-tuning or linear probing. Especially, selecting a proper source domain for a specific target domain under predefined tasks is crucial for improving efficiency and effectiveness. It is conventional to solve this problem via estimating transferability. However, existing methods can not reach a trade-off between performance and cost. To comprehensively evaluate estimation methods, we summarize three properties: stability, reliability and efficiency. Building upon them, we propose Principal Gradient Expectation(PGE), a simple yet effective method for assessing transferability. Specifically, we calculate the gradient over each weight unit multiple times with a restart scheme, and then we compute the expectation of all gradients. Finally, the transferability between the source and target is estimated by computing the gap of normalized principal gradients. Extensive experiments show that the proposed metric is superior to state-of-the-art methods on all properties.
PHANGS–JWST First Results: Duration of the Early Phase of Massive Star Formation in NGC 628

The earliest stages of star formation, when young stars are still deeply embedded in their natal clouds, represent a critical phase in the matter cycle between gas clouds and young stellar regions. Until now, the high-resolution infrared observations required for characterizing this heavily obscured phase (during which massive stars have formed, but optical emission is not detected) could only be obtained for a handful of the most nearby galaxies. One of the main hurdles has been the limited angular resolution of the Spitzer Space Telescope. With the revolutionary capabilities of the James Webb Space Telescope (JWST), it is now possible to investigate the matter cycle during the earliest phases of star formation as a function of the galactic environment. In this Letter, we demonstrate this by measuring the duration of the embedded phase of star formation and the implied time over which molecular clouds remain inert in the galaxy NGC 628 at a distance of 9.8 Mpc, demonstrating that the cosmic volume where this measurement can be made has increased by a factor of >100 compared to Spitzer. We show that young massive stars remain embedded for 5.1−1.4+2.7 Myr ( 2.3−1.4+2.7 Myr of which being heavily obscured), representing ∼20% of the total cloud lifetime. These values are in broad agreement with previous measurements in five nearby (D < 3.5 Mpc) galaxies and constitute a proof of concept for the systematic characterization of the early phase of star formation across the nearby galaxy population with the PHANGS–JWST survey.
A Brief Introduction to Robust Bilevel Optimization

Bilevel optimization is a powerful tool for modeling hierarchical decision making processes. However, the resulting problems are challenging to solve - both in theory and practice. Fortunately, there have been significant algorithmic advances in the field so that we can solve much larger and also more complicated problems today compared to what was possible to solve two decades ago. This results in more and more challenging bilevel problems that researchers try to solve today. In this article, we give a brief introduction to one of these more challenging classes of bilevel problems: bilevel optimization under uncertainty using robust optimization techniques. To this end, we briefly state different versions of uncertain bilevel problems that result from different levels of cooperation of the follower as well as on when the uncertainty is revealed. We highlight these concepts using an academic example and discuss recent results from the literature concerning complexity as well as solution approaches. Finally, we discuss that the sources of uncertainty in bilevel optimization are much richer than in single-level optimization and, to this end, introduce the concept of decision uncertainty.
On the Evolution of Vorticity and Potential Vorticity in the Presence of Diabatic Heating and Frictional or Other Forces

Abstract Some consequences of regarding potential vorticity as a tracer are considered. It is shown that neither diabatic heating, nor frictional forces, nor external forces such as might be used to model gravity-wave drag, can bring about any net transport or Rossby-Ertel potential vorticity (PV) across an isotropic surface—notwithstanding the diabatic, cross-isentropic transport of mass and chemical tracers. Nor can PV be created or destroyed within a layer bounded by two isentropic surface. It can only be transported along the layer. and diluted or concentrated by cross-isentropic mass inflow or outflow. This constitutes a systematic difference between the behavior of PV and that of other tracers, recognition of which simplifies thinking about PV budgets and gives insight into the relationships between dynamical processes, departures from radiatively determined temperatures, and chemical tracer transport including stratosphere-troposphere exchange. The results just stated are true by virtue of the way .
On the use of senders for asymmetric tuples of cliques in Ramsey theory

A graph $G$ is $q$-Ramsey for a $q$-tuple of graphs $(H_1,\ldots,H_q)$ if for every $q$-coloring of the edges of $G$ there exists a monochromatic copy of $H_i$ in color $i$ for some $i\in[q]$. Over the last few decades, researchers have investigated a number of questions related to this notion, aiming to understand the properties of graphs that are $q$-Ramsey for a fixed tuple. Among the tools developed while studying questions of this type are gadget graphs, called signal senders and determiners, which have proven invaluable for building Ramsey graphs with certain properties. However, until now these gadgets have been shown to exist and used mainly in the two-color setting or in the symmetric multicolor setting, and our knowledge about their existence for multicolor asymmetric tuples is extremely limited. In this paper, we construct such gadgets for any tuple of cliques. We then use these gadgets to generalize three classical theorems in this area to the asymmetric multicolor setting.
T HE U NION OF M ANIFOLDS H YPOTHESIS

Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the manifold hypothesis, which states that the data lies on an unknown manifold of low intrinsic dimension. In this paper, we argue that this hypothesis does not properly capture the low-dimensional structure typically present in image data. Assuming that data lies on a single manifold implies intrinsic dimension is identical across the entire data space, and does not allow for subregions of this space to have a different number of factors of variation. To address this deficiency, we put forth the union of manifolds hypothesis, which states that data lies on a disjoint union of manifolds of varying intrinsic dimensions. We empirically verify this hypothesis on commonly-used image datasets, finding that indeed, observed data lies on a disconnected set and that intrinsic dimension is not constant. We also provide insights into the implications the union of manifolds hypothesis has for deep learning, both supervised and unsupervised, showing that designing models with an inductive bias for this structure improves performance across classification and generative modelling tasks.
Categories of quantum liquids II

We continue to develop the theory of separable higher categories, including center functors, higher centralizers, modular extensions and group theoretical higher fusion categories. Moreover, we outline a theory of orthogonal higher categories to treat anti-unitary symmetries. Using these results we derive a systematic classification of gapped quantum liquids and predict many new SPT orders in spacetime dimension ≥ 3.
Perfect simulation and backward coupling

Algorithms for perfect or exact simulation of random samples from the invariant measure of a Markov chain have received considerable recent attention following the introduction of the “coupling-from-the-past” (CFTP) technique of Propp and Wilson. Here we place such algorithms in the context of backward coupling of stochastically recursive sequences. We show that although general backward couplings can be constructed for chains with finite mean forward coupling times, and can even be thought of as extending the classical “Loynes schemes” from queueing theory, successful “vertical” CFTP algorithms such as those of Propp and Wilson can be constructed if and only if the chain is uniformly geometric ergodic. We also relate the convergence moments for backward coupling methods to those of forward coupling times: the former typically lose at most one moment compared to the latter