Miracle-dz/UniFAR-FTUs huggingface dataset:
Sharpness-aware Quantization for Deep Neural Networks

Network quantization is a dominant paradigm of model compression. However, the abrupt changes in quantized weights during training often lead to severe loss fluctuations and result in a sharp loss landscape, making the gradients unstable and thus degrading the performance. Recently, Sharpness-Aware Minimization (SAM) has been proposed to smooth the loss landscape and improve the generalization performance of the models. Nevertheless, directly applying SAM to the quantized models can lead to perturbation mismatch or diminishment issues, resulting in suboptimal performance. In this paper, we propose a novel method, dubbed Sharpness-Aware Quantization (SAQ), to explore the effect of SAM in model compression, particularly quantization for the first time. Specifically, we first provide a unified view of quantization and SAM by treating them as introducing quantization noises and adversarial perturbations to the model weights, respectively. According to whether the noise and perturbation terms depend on each other, SAQ can be formulated into three cases, which are analyzed and compared comprehensively. Furthermore, by introducing an efficient training strategy, SAQ only incurs a little additional training overhead compared with the default optimizer (e.g., SGD or AdamW). Extensive experiments on both convolutional neural networks and Transformers across various datasets (i.e., ImageNet, CIFAR-10/100, Oxford Flowers-102, Oxford-IIIT Pets) show that SAQ improves the generalization performance of the quantized models, yielding the SOTA results in uniform quantization. For example, on ImageNet, SAQ outperforms AdamW by 1.2% on the Top-1 accuracy for 4-bit ViT-B/16. Our 4-bit ResNet-50 surpasses the previous SOTA method by 0.9% on the Top-1 accuracy.
ViCE: Self-Supervised Visual Concept Embeddings as Contextual and Pixel Appearance Invariant Semantic Representations

This work presents a self-supervised method to learn dense semantically rich visual concept embeddings for images inspired by methods for learning word embeddings in NLP. Our method improves on prior work by generating more expressive embeddings and by being applicable for high-resolution images. Viewing the generation of nat-ural images as a stochastic process where a set of latent visual concepts give rise to observable pixel appearances, our method is formulated to learn the inverse mapping from pixels to concepts. Our method greatly improves the effectiveness of self-supervised learning for dense embedding maps by introducing superpixelization as a natural hierarchical step up from pixels to a small set of visually coherent regions. Additional contributions are regional contextual masking with nonuniform shapes matching visually coherent patches and complexity-based view sampling inspired by masked language models. The enhanced expressiveness of our dense embeddings is demonstrated by signiﬁcantly improving the state-of-the-art representation quality benchmarks on COCO (+12.94 mIoU, +87.6%) and Cityscapes (+16.52 mIoU, +134.2%). Results show favorable scaling and domain generalization properties not demonstrated by prior work.
Horofunction compactifications of symmetric cones under Finsler distances

In this paper we consider symmetric cones equipped with invariant Finsler distances, namely the Thompson distance and the Hilbert distance. We give a complete characterisation of the horofunctions of the symmetric cone \(A_+^\circ\) under the Thompson distance and establish a correspondence between the horofunction compactification of \(A_+^\circ\) and the horofunction compactification of the normed space in the tangent bundle. More precisely, we show that the exponential map extends as a homeomorphism between the horofunction compactification of the normed space in the tangent bundle, which is a JB-algebra, and the horofunction compactification of \(A_+^\circ\). Analogues results are established for the Hilbert distance on the projective symmetric cone \(PA_+^\circ\). The analysis yields a concrete description of the horofunction compactifications of these spaces in terms of the facial structure of the closed unit ball of the dual norm of the norm in the tangent space.
Lepard: Learning partial point cloud matching in rigid and deformable scenes

We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D position space. 2) A position encoding method that explicitly reveals 3D relative distance information through the dot product of vectors. 3) A repositioning technique that modifies the cross-point-cloud relative positions. Ablation studies demonstrate the effectiveness of the above techniques. In rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark. Code and data are available at https://github.com/rabbityl/lepard.
Global well-posedness for 2D fractional inhomogeneous Navier–Stokes equations with rough density

This paper is concerned with the global well-posedness issue of the two-dimensional (2D) incompressible inhomogeneous Navier–Stokes equations with fractional dissipation and rough density. By establishing the Ltq(Lxp) -maximal regularity estimate for the generalized Stokes system and using the Lagrangian approach, we prove the global existence and uniqueness of regular solutions for the 2D fractional inhomogeneous Navier–Stokes equations with large velocity field, provided that the initial density is sufficiently close to the constant 1 in L2∩L∞ and in the norm of some multiplier spaces. Moreover, we also consider the associated density patch problem, and show the global persistence of C1,γ -regularity of the density patch boundary when the piecewise jump of density is small enough.
NAM: Normalization-based Attention Module

Recognizing less salient features is the key for model compression. However, it has not been investigated in the revolutionary attention mechanisms. In this work, we propose a novel normalization-based attention module (NAM), which suppresses less salient weights. It applies a weight sparsity penalty to the attention modules, thus, making them more computational efficient while retaining similar performance. A comparison with three other attention mechanisms on both Resnet and Mobilenet indicates that our method results in higher accuracy. Code for this paper can be publicly accessed at https://github.com/Christian-lyc/NAM.
Efficient Decompositional Rule Extraction for Deep Neural Networks

In recent years, there has been significant work on increasing both interpretability and debuggability of a Deep Neural Network (DNN) by extracting a rule-based model that approximates its decision boundary. Nevertheless, current DNN rule extraction methods that consider a DNN's latent space when extracting rules, known as decompositional algorithms, are either restricted to single-layer DNNs or intractable as the size of the DNN or data grows. In this paper, we address these limitations by introducing ECLAIRE, a novel polynomial-time rule extraction algorithm capable of scaling to both large DNN architectures and large training datasets. We evaluate ECLAIRE on a wide variety of tasks, ranging from breast cancer prognosis to particle detection, and show that it consistently extracts more accurate and comprehensible rule sets than the current state-of-the-art methods while using orders of magnitude less computational resources. We make all of our methods available, including a rule set visualisation interface, through the open-source REMIX library (https://github.com/mateoespinosa/remix).
Piezoresistive micromechanical transducer operation in a pulsed neutron and gamma ray environment

We completed the third in a series of experiments using commercially available, piezoresistive microelectromechanical system (MEMS) transducers to instrument articles exposed to the intense, pulsed neutron and gamma ray radiation environment of Sandia National Laboratories' Annular Core Research Reactor. Based on analyzing the survival of pressure transducers and accelerometers in the first two experiments, we identified candidate piezoresistive, MEMS transducers and then tested them by exposure to pulses, each with 10/sup 15/ neutrons/cm/sup 2/ and 10/sup 4/ Gy (1 Mrad) of gamma radiation. Simultaneous measurement of temperature indicates that the pressure transducers retain their calibrations and may provide a means of quickly identifying temperature-induced offset shifts of accelerometer calibrations. Comparison to other sensors that do not survive in this environment and implications for using these transducers to instrument test articles are also presented.
Projective Robustness for Quantum Channels and Measurements and its Operational Significance

Recently, the projective robustness of quantum states has been introduced in [arXiv:2109.04481(2021)]. It shows that the projective robustness is a useful resource monotone and can comprehensively characterize capabilities and limitations of probabilistic protocols manipulating quantum resources deterministically. In this paper, we will extend the projective robustness to any convex resource theories of quantum channels and measurements. First, we introduce the projective robustness of quantum channels and prove that it satisfies some good properties, especially subor supermultiplicativity under any free quantum process. Moreover, we use the projective robustness of channels to give lower bounds on the errors and overheads in any channel resource distillation. Meanwhile, we show that the projective robustness of channels quantifies the maximal advantage that a given channel outperforms all free channels in simultaneous discrimination and exclusion of a fixed state ensemble. Second, we define the projective robustness of quantum measurements and prove that it exactly quantifies the maximal advantage that a given measurement provides over all free measurements in simultaneous discrimination and exclusion of two fixed state ensembles. Finally, within a specific channel resource setting based on measurement incompatibility, we show that the projective robustness of quantum channels coincides with the projective robustness of measurement incompatibility.
An epidemic model in inhomogeneous environment

The current work deals with an epidemic model on the complete graph 𝕂 𝑛 on 𝑛 vertices in a non-homogeneous setting, where the vertices may have distinct types. Different types differ in the probability of getting infected, and/or in the capacity of infecting other vertices. This generalizes the model in [4]. We prove in Theorem 5.1 and Theorem 6.3 laws of large numbers and central limit theorems for the the total duration of the process and for the number of infected vertices, respectively, when 𝑛 → ∞ . By coupling the epidemic model with a Poisson process, we also obtain continuous-time counterparts of the above-mentioned limit results. Moreover, we also prove that when all individuals have the same spread capacity, then a population with inhomogeneous susceptibility is less affected by the epidemics than a homogeneous population.
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning

Self-supervised contrastive learning frameworks have progressed rapidly over the last few years. In this paper, we propose a novel mutual information optimization-based loss function for contrastive learning. We model our pre-training task as a binary classification problem to induce an implicit contrastive effect and predict whether a pair is positive or negative. We further improve the n\"aive loss function using the Majorize-Minimizer principle and such improvement helps us to track the problem mathematically. Unlike the existing methods, the proposed loss function optimizes the mutual information in both positive and negative pairs. We also present a closed-form expression for the parameter gradient flow and compare the behavior of the proposed loss function using its Hessian eigen-spectrum to analytically study the convergence of SSL frameworks. The proposed method outperforms the SOTA contrastive self-supervised frameworks on benchmark datasets like CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet. After 200 epochs of pre-training with ResNet-18 as the backbone, the proposed model achieves an accuracy of 86.2\%, 58.18\%, 77.49\%, and 30.87\% on CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet datasets, respectively, and surpasses the SOTA contrastive baseline by 1.23\%, 3.57\%, 2.00\%, and 0.33\%, respectively.
MixSyn: Learning Composition and Style for Multi-Source Image Synthesis

Synthetic images created by generative models increase in quality and expressiveness as newer models utilize larger datasets and novel architectures. Although this photorealism is a positive side-effect from a creative standpoint, it becomes problematic when such generative models are used for impersonation without consent. Most of these approaches are built on the partial transfer between source and target pairs, or they generate completely new samples based on an ideal distribution, still resembling the closest real sample in the dataset. We propose MixSyn (read as"mixin'") for learning novel fuzzy compositions from multiple sources and creating novel images as a mix of image regions corresponding to the compositions. MixSyn not only combines uncorrelated regions from multiple source masks into a coherent semantic composition, but also generates mask-aware high quality reconstructions of non-existing images. We compare MixSyn to state-of-the-art single-source sequential generation and collage generation approaches in terms of quality, diversity, realism, and expressive power; while also showcasing interactive synthesis, mix&match, and edit propagation tasks, with no mask dependency.
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features

While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph. In many real-world applications, however, features are only partially available; for example, in social networks, age and gender are available only for a small subset of users. We present a general approach for handling missing features in graph machine learning applications that is based on minimization of the Dirichlet energy and leads to a diffusion-type differential equation on the graph. The discretization of this equation produces a simple, fast and scalable algorithm which we call Feature Propagation. We experimentally show that the proposed approach outperforms previous methods on seven common node-classification benchmarks and can withstand surprisingly high rates of missing features: on average we observe only around 4% relative accuracy drop when 99% of the features are missing. Moreover, it takes only 10 seconds to run on a graph with $\sim$2.5M nodes and $\sim$123M edges on a single GPU.
Scaling Up Vision-Language Pretraining for Image Captioning

In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with moderate sizes (e.g., 12 or 24 layers) on roughly 4 million images. In this paper, we present LEMON O, a LargE-scale iMage captiONer, and provide the first empirical study on the scaling behavior of VLP for image captioning. We use the state-of-the-art Vin VL model as our reference model, which consists of an image feature extractor and a transformer model, and scale the transformer both up and down, with model sizes ranging from 13 to 675 million parameters. In terms of data, we conduct experiments with up to 200 million imagetext pairs which are automatically collected from web based on the alt attribute of the image (dubbed as ALT200M11The dataset is released at https://github.com/xiaoweihu/ALT200M). Extensive analysis helps to characterize the performance trend as the model size and the pre-training data size increase. We also compare different training recipes, especially for training on large-scale noisy data. As a result, LEMON achieves new state of the arts on several major image captioning benchmarks, including COCO Caption, nocaps, and Conceptual Captions. We also show LEMON can generate captions with long-tail vi-sual concepts when used in a zero-shot manner.
Continuous thermomajorization and a complete set of laws for Markovian thermal processes

The standard dynamical approach to quantum thermodynamics is based on Markovian master equations describing the thermalization of a system weakly coupled to a large environment, and on tools such as entropy production relations. Here we develop a new framework overcoming the limitations that the current dynamical and information theory approaches encounter when applied to this setting. More precisely, we introduce the notion of continuous thermomajorization, and employ it to obtain necessary and suﬃcient conditions for the existence of a Markovian thermal process transforming between given initial and ﬁnal energy distributions of the system. These lead to a complete set of generalized entropy production inequalities including the standard one as a special case. Importantly, these conditions can be reduced to a ﬁnitely veriﬁable set of constraints governing non-equilibrium transformations under master equations. What is more, the framework is also constructive, i.e., it returns explicit protocols realizing any allowed transformation. These protocols use as building blocks elementary thermalizations, which we prove to be universal controls. Finally, we also present an algorithm constructing the full set of energy distributions achievable from a given initial state via Markovian thermal processes and provide a Mathematica implementation solving d = 6 on a laptop computer in minutes.
Computer-aided approach of parameters influencing concrete service life and field validation

Over the past decades, an enormous amount of effort has been expended in laboratory and field studies on concrete durability estimation. The results of this research are still either widely scattered in the journal literature or mentioned briefly in the standard textbooks. Moreover, the theoretical approaches of deterioration mechanisms with a predictive character are limited to some complicated mathematical models not widespread in practice. A significant step forward could be the development of appropriate software for computer-based estimation of concrete service life, including reliable mathematical models and adequate experimental data. In the present work, the basis for the development of a computer estimation of the concrete service life is presented. After the definition of concrete mix design and structure characteristics, as well as the consideration regarding the environmental conditions where the structure will be found, the concrete service life can be reliably predicted using fundamental mathematical models that simulate the deterioration mechanisms. The prediction is focused on the basic deterioration phenomena of reinforced concrete, such as carbonation and chloride penetration, that initiate the reinforcing bars corrosion. Aspects on concrete strength and the production cost are also considered. Field observations and data collection from existing structures are compared with predictions of service life using the above model. A first attempt to develop a database of service lives of different types of reinforced concrete structure exposed to varying environments is finally included.
Multifrequency 3D Elasticity Reconstruction withStructured Sparsity and ADMM

—We introduce a model-based iterative method to obtain shear modulus images of tissue using magnetic resonance elastography. The method jointly ﬁnds the displacement ﬁeld that best ﬁts multifrequency tissue displacement data and the corresponding shear modulus. The displacement satisﬁes a viscoelastic wave equation constraint, discretized using the ﬁnite element method. Sparsifying regularization terms in both shear modulus and the displacement are used in the cost function minimized for the best ﬁt. The formulated problem is bi-convex. Its solution can be obtained iteratively by using the alternating direction method of multipliers. Sparsifying regularizations and the wave equation constraint ﬁlter out sensor noise and compressional waves. Our method does not require bandpass ﬁltering as a preprocessing step and converges fast irrespective of the initialization. We evaluate our new method in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal to noise ratios. Re- sults from an in vivo liver imaging study show elastograms with mean elasticity comparable to other values reported in the literature.
Artificial Intelligence Enabled Radio Propagation for Communications—Part I: Channel Characterization and Antenna-Channel Optimization

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence (AI) techniques. In this two-part article, we investigate the application of AI and, in particular, machine learning (ML) to the study of wireless propagation channels. It first provides a comprehensive overview of ML for channel characterization and ML-based antenna–channel optimization in this first part, and then, it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next-generation networks of the topics covered in this part rounds off this article.
Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks

—In recent years there is a growing effort to provide learning algorithms for spectrum collaboration. In this paper we present a medium access control protocol which alllows spectrum collaboration with minimal regret and high spectral efﬁciency in highly loaded networks. We present a fully-distributed algorithm for spectrum collaboration in congested ad-hoc networks. The algorithm jointly solves both the channel allocation and access scheduling problems. We prove that the algorithm has an optimal logarithmic regret. Based on the algorithm we provide a medium access control protocol which allows distributed implementation of the algorithm in ad-hoc networks. The protocol utilizes single-channel opportunistic carrier sensing to carry out a low-complexity distributed auction in time and frequency. We also discuss practical implementation issues such as bounded frame size and speed of convergence. Computer simulations comparing the algorithm to state-of-the-art distributed medium access control protocols show the signiﬁcant advantage of the proposed scheme.