Miracle-dz/UniFAR-FTUs huggingface dataset:
Intro-Stabilizing Byzantine Clock Synchronization in Heterogeneous IoT Networks

For reaching dependable high-precision clock synchronization (CS) upon IoT networks, the distributed CS paradigm adopted in ultra-high reliable systems and the master-slave CS paradigm adopted in high-performance but unreliable systems are integrated. Meanwhile, traditional internal clock synchronization is also integrated with external time references to achieve efficient stabilization. Low network connectivity, low complexity, high precision, and high reliability are all considered. To tolerate permanent failures, the Byzantine CS is integrated with the common CS protocols. To tolerate transient failures, the self-stabilizing Byzantine CS is also extended upon open-world IoT networks. With these, the proposed intro-stabilizing Byzantine CS solution can establish and maintain synchronization with arbitrary initial states in the presence of permanent Byzantine faults. With the formal analysis and numerical simulations, it is shown that the best of the CS solutions provided for the ultra-high reliable systems and the high-performance unreliable systems can be well integrated upon IoT networks to derive dependable high-precision CS even across the traditional closed safety-boundary.
FORCE: A Framework of Rule-Based Conversational Recommender System

The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which are largely limited by the requirement of large-scale human-annotated datasets. Such methods are not able to deal with the cold-start scenarios in industrial products. To alleviate the problem, we propose FORCE, a Framework Of Rule-based Conversational rEcommender system that helps developers to quickly build CRS bots by simple configuration. We conduct experiments on two datasets in different languages and domains to verify its effectiveness and usability.
Zipper Codes

Zipper codes are a framework for describing spatially-coupled product-like codes. Many well-known codes, such as staircase codes and braided block codes, are subsumed into this framework. New types of codes such as tiled diagonal and delayed diagonal zipper codes are introduced along with their software simulation results. Stall patterns that can arise in iterative decoding are analyzed, giving a means of error floor estimation.
Anisotropy of Magnetic Field Spectra at Kinetic Scales of Solar Wind Turbulence as Revealed by the Parker Solar Probe in the Inner Heliosphere

Using the Parker Solar Probe data taken in the inner heliosphere, we investigate the power and spatial anisotropy of magnetic field spectra at kinetic scales (i.e., around sub-ion scales) in solar wind turbulence in the inner heliosphere. We find that strong anisotropy of the magnetic spectra occurs at kinetic scales with the strongest power in the perpendicular direction with respect to the local magnetic field (forming an angle θ B with the mean flow velocity). The spectral index of the magnetic spectra varies from −3.2 to −5.8 when the angle θ B changes from 90° to 180° (or 0°), indicating that strong anisotropy of the spectral indices occurs at kinetic scales in the solar wind turbulence. Using a diagnosis based on the magnetic helicity, we show that the anisotropy of the spectral indices can be explained by the nature of the plasma modes that carry the cascade at kinetic scales. We discuss our findings in light of existing theories and current development in the field.
Booting 10K Serverless Functions within One Second via RDMA-based Remote Fork

We present M ITOSIS , an OS primitive to support fast remote fork with minimal resource provisioning for serverless computing. M ITOSIS is the ﬁrst to fork over 10,000 new containers from only one instance across multiple machines within a second. It also allows transparent and efﬁcient intermediate data sharing between producer and consumer serverless functions even across machines. The key enabler for this high speed is an RDMA-OS co-design, which imitates local fork with RDMA’s remote memory read capability bypassing extra software overhead for starting and running containers. We also introduce techniques for lightweight connection and memory management based on advanced RDMA transport and fast containerization in a distributed setting. We have implemented M ITOSIS on Linux and integrated it to F N , a popular serverless computing framework. Under load spikes in real serverless workloads, M ITOSIS reduces the F N tail latency by 95.2% with orders of magnitude smaller memory usage.
Runaway electron generation during tokamak start-up

Tokamak start-up is characterized by low electron densities and strong electric fields, in order to quickly raise the plasma current and temperature, allowing the plasma to fully ionize and magnetic flux surfaces to form. Such conditions are ideal for the formation of superthermal electrons, which may reduce the efficiency of ohmic heating and prevent the formation of a healthy thermal fusion plasma. This is of particular concern in ITER where engineering limitations put restrictions on the allowable electric fields and limit the prefill densities during start-up. In this study, we present a new 0D burn-through simulation tool called STREAM (STart-up Runaway Electron Analysis Model), which self-consistently evolves the plasma density, temperature and electric field, while accounting for the generation and loss of relativistic runaway electrons. After verifying the burn-through model, we investigate conditions under which runaway electrons can form during tokamak start-up as well as their effects on the plasma initiation. We find that Dreicer generation plays a crucial role in determining whether a discharge becomes runaway-dominated or not, and that a large number of runaway electrons could limit the ohmic heating of the plasma, thus preventing successful burn-through or further ramp-up of the plasma current. The runaway generation can be suppressed by raising the density via gas fuelling, but only if done sufficiently early. Otherwise a large runaway seed may have already been built up, which can avalanche even at relatively low electric fields and high densities.
Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural Networks

In this paper, we propose a new adapter network for adapting a pre-trained deep neural network to a target domain with minimal computation. The proposed model, unidirectional thin adapter (UDTA), helps the classifier adapt to new data by providing auxiliary features that complement the backbone network. UDTA takes outputs from multiple layers of the backbone as input features but does not transmit any feature to the backbone. As a result, UDTA can learn without computing the gradient of the backbone, which saves computation for training significantly. In addition, since UDTA learns the target task without modifying the backbone, a single backbone can adapt to multiple tasks by learning only UDTAs separately. In experiments on five fine-grained classification datasets consisting of a small number of samples, UDTA significantly reduced computation and training time required for backpropagation while showing comparable or even improved accuracy compared with conventional adapter models.
Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing: Nonlocal sparse and low-rank modeling

The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employ sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structure of image patches by optimizing their sparse representations or learning deep neural networks while preserving the known or modeled sensing process. Beyond exploiting local image properties, advanced CS schemes adopt nonlocal image modeling by extracting similar or highly correlated patches at different locations of an image to form a group to process jointly. More recent learning-based CS schemes apply nonlocal structured sparsity priors using group sparse (and related) representation (GSR) and/or low-rank (LR) modeling, which have demonstrated promising performance in various computational imaging and image processing applications.
Physically interpretable machine learning for nuclear masses

We present a novel approach to modeling the ground state mass of atomic nuclei based directly on a probabilistic neural network constrained by relevant physics. Our Physically Interpretable Machine Learning (PIML) approach incorporates knowledge of physics by using a physically motivated feature space in addition to a soft physics constraint that is implemented as a penalty to the loss function. We train our PIML model on a random set of $\sim$20\% of the Atomic Mass Evaluation (AME) and predict the remaining $\sim$80\%. The success of our methodology is exhibited by the unprecedented $\sigma_\textrm{RMS}\sim186$ keV match to data for the training set and $\sigma_\textrm{RMS}\sim316$ keV for the entire AME with $Z \geq 20$. We show that our general methodology can be interpreted using feature importance.
Force-matching Coarse-Graining without Forces

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time-and length-scales inaccessible to all-atom simulations. Learning CG force ﬁelds from all-atom data has mainly relied on force-matching and relative entropy minimization. Force-matching is straightforward to implement but requires the forces on the CG particles to be saved during all-atom simulation, and because these instantaneous forces depend on all degrees of freedom, they provide a very noisy signal that makes training the CG force ﬁeld data ineﬃcient. Relative entropy minimization does not require forces to be saved and is more data-eﬃcient, but requires the CG model to be re-simulated during the iterative training procedure, which can make the training procedure extremely costly or lead to failure to converge. Here we present ﬂow-matching , a new training method for CG force ﬁelds that combines the advantages of force-matching and relative entropy minimization by leveraging normalizing ﬂows, a generative
Rational points of rigid-analytic sets: a Pila-Wilkie type theorem

We establish a rigid-analytic analog of the Pila-Wilkie counting theorem, giving sub-polynomial upper bounds for the number of rational points in the transcendental part of a $\mathbb{Q}_p$-analytic set, and the number of rational functions in a $\mathbb{F}_q((t))$-analytic set. For $\mathbb{Z}[[t]]$-analytic sets we prove such bounds uniformly for the specialization to every non-archimedean local field.
Distributionally robust risk evaluation with a causality constraint and structural information

This work studies distributionally robust evaluation of expected function values over temporal data. A set of alternative measures is characterized by the causal optimal transport. We prove the strong duality and recast the causality constraint as minimization over an infinite-dimensional test function space. We approximate test functions by neural networks and prove the sample complexity with Rademacher complexity. Moreover, when structural information is available to further restrict the ambiguity set, we prove the dual formulation and provide efficient optimization methods. Empirical analysis of realized volatility and stock indices demonstrates that our framework offers an attractive alternative to the classic optimal transport formulation.
DSP-Packing: Squeezing Low-precision Arithmetic into FPGA DSP Blocks

The number of Digital Signal Processor (DSP) resources available in Field Programmable Gate Arrays (FPGAs) is often quite limited. Therefore, full utilization of available DSP resources for the computationally intensive parts of an algorithm is paramount for optimizing the non-functional properties of an implementation (i.e., performance, power, and area). The DSPs available in Xilinx devices implement large bit width operators (i.e. a 48-bit accumulator or a 18 × 27 multiplier). However, using such a DSP for low-precision quantized data (as is common in image processing or machine learning applications) leaves the DSP resources underutilized. As a remedy, a method has been proposed to pack and compute four 4-bit multiplications on a single DSP in a single clock cycle. This paper presents a generalization of this scheme to arbitrary bit widths and number of multiplications. We also demonstrate that the previously proposed approach leads to errors (Mean Absolute Error (MAE) = 0.37). Furthermore, we explain where these errors come from and how they can be corrected. On top, we introduce a novel approximate method called “Overpacking” which allows to squeeze even more multiplications into a single DSP at the cost of small errors (MAE = 0.47). Overpacking allows to squeeze six 4-bit multiplications into a single DSP compared to just four in the literature. Finally, we introduce an alternative method for packing multiple small-bit width additions into a single 48-bit accumulator for use in applications such as Spiking Neural Networks.
M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization

Source code summarization aims to generate natural language descriptions of code snippets. Many existing studies learn the syntactic and semantic knowledge of code snippets from their token sequences and Abstract Syntax Trees (ASTs). They use the learned code representations as input to code summarization models, which can accordingly generate summaries describing source code. Traditional models traverse ASTs as sequences or split ASTs into paths as input. However, the former loses the structural properties of ASTs, and the latter destroys the overall structure of ASTs. There-fore, comprehensively capturing the structural features of ASTs in learning code representations for source code summarization remains a challenging problem to be solved. In this paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer for source code Summarization. M2TS uses a multi-scale AST feature extraction method, which can extract the structures of ASTs more completely and accurately at multiple local and global levels. To complement missing semantic information in ASTs, we also obtain code token features, and further combine them with the extracted AST features using a cross modality fusion method that not only fuses the syntactic and contextual semantic information of source code, but also highlights the key features of each modality. We conduct experiments on two Java and one Python datasets, and the experimental results demonstrate that M2TS out-performs current state-of-the-art methods. We release our code at https://github.com/TranSMS/M2TS.
Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the image. It depends on the model of the imaging system and on prior knowledge on the image of interest. In this work, we present a Bayesian reconstruction framework for nonlinear imaging models where we specify the prior knowledge on the image through a deep generative model. We develop a tractable posterior-sampling scheme based on the Metropolis-adjusted Langevin algorithm for the class of nonlinear inverse problems where the forward model has a neural-network-like structure. This class includes most practical imaging modalities. We introduce the notion of augmented deep generative priors in order to suitably handle the recovery of quantitative images. We illustrate the advantages of our framework by applying it to two nonlinear imaging modalities—phase retrieval and optical diffraction tomography.
Field measurement of passing pressure and train induced airflow speed on high speed maglev vehicles

The pressure load caused by passing maglev vehicles is an important aerodynamic problem,which has great effect on safety and comfort of the vehicles. In order to evaluate the vehicle design further,a field measurement testing was conducted in Shanghai in November,2003 to investigate the aerodynamic pressure loads on the maglev vehicle.The test conditions were 5.1m distance between the lines and the speed of passing vehicle varying from 400km/h to 500km/h.In the meantime the train induced airflow speeds were also measured at two positions,0.5m and 1.3m,away from the passing vehicle side.
Some examples of equivalent rearrangement‐invariant quasi‐norms defined via f∗$f^*$ or f∗∗$f^{**}$

We consider Lorentz–Karamata spaces, small and grand Lorentz–Karamata spaces, and the so‐called L$\mathcal {L}$ , R$\mathcal {R}$ , LL$\mathcal {LL}$ , LR$\mathcal {LR}$ , RL$\mathcal {RL}$ , and RR$\mathcal {RR}$ spaces. The quasi‐norms for a function f in each of these spaces can be defined via the nonincreasing rearrangement f∗$f^*$ or via the maximal function f∗∗$f^{**}$ . We investigate when these quasi‐norms are equivalent. Most of the proofs are based on Hardy‐type inequalities. As an application, we demonstrate how our general results can be used to establish interpolation formulae for grand and small Lorentz–Karamata spaces.
AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization

Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretrained on large corpora. While most existing models focus on English, Arabic remains understudied. In this paper we propose AraBART, the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. We show that AraBART achieves the best performance on multiple abstractive summarization datasets, outperforming strong baselines including a pretrained Arabic BERT-based model, multilingual BART, Arabic T5, and a multilingual T5 model. AraBART is publicly available.
Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image Transition

Video frame interpolation (VFI) aims to improve the temporal resolution of a video sequence. Most of the existing deep learning based VFI methods adopt off-the-shelf optical flow algorithms to estimate the bidirectional flows and interpolate the missing frames accordingly. Though having achieved a great success, these methods require much human experience to tune the bidirectional flows and often generate unpleasant results when the estimated flows are not accurate. In this work, we rethink the VFI problem and formulate it as a continuous image transition (CIT) task, whose key issue is to transition an image from one space to another space continuously. More specifically, we learn to implicitly decouple the images into a translatable flow space and a non-translatable feature space. The former depicts the translatable states between the given images, while the later aims to reconstruct the intermediate features that cannot be directly translated. In this way, we can easily perform image interpolation in the flow space and intermediate image synthesis in the feature space, obtaining a CIT model. The proposed space decoupled learning (SDL) approach is simple to implement, while it provides an effective framework to a variety of CIT problems beyond VFI, such as style transfer and image morphing. Our extensive experiments on a variety of CIT tasks demonstrate the superiority of SDL to existing methods. The source code and models can be found at \url{https://github.com/yangxy/SDL}.
Do Deep Networks Transfer Invariances Across Classes?

To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input’s class. Many problems have “class-agnostic” nuisance transformations that apply similarly to all classes, such as lighting and background changes for image classification. Neural networks can learn these invariances given sufficient data, but many real-world datasets are heavily class imbalanced and contain only a few examples for most of the classes. We therefore pose the question: how well do neural networks transfer class-agnostic invariances learned from the large classes to the small ones? Through careful experimentation, we observe that invariance to class-agnostic transformations is still heavily dependent on class size, with the networks being much less invariant on smaller classes. This result holds even when using data balancing techniques, and suggests poor invariance transfer across classes. Our results provide one explanation for why classifiers generalize poorly on unbalanced and long-tailed distributions. Based on this analysis, we show how a generative approach for learning the nuisance transformations can help transfer invariances across classes and improve performance on a set of imbalanced image classification benchmarks. Source code for our experiments is available at https://github.com/AllanYangZhou/ generative-invariance-transfer.
Multigrid-augmented deep learning preconditioners for the Helmholtz equation

In this paper, we present a data-driven approach to iteratively solve the discrete heterogeneous Helmholtz equation at high wavenumbers. In our approach, we combine classical iterative solvers with convolutional neural networks (CNNs) to form a preconditioner which is applied within a Krylov solver. For the preconditioner, we use a CNN of type U-Net that operates in conjunction with multigrid ingredients. Two types of preconditioners are proposed 1) U-Net as a coarse grid solver, and 2) U-Net as a deflation operator with shifted Laplacian V-cycles. Following our training scheme and data-augmentation, our CNN preconditioner can generalize over residuals and a relatively general set of wave slowness models. On top of that, we also offer an encoder-solver framework where an"encoder"network generalizes over the medium and sends context vectors to another"solver"network, which generalizes over the right-hand-sides. We show that this option is more robust and efficient than the stand-alone variant. Lastly, we also offer a mini-retraining procedure, to improve the solver after the model is known. This option is beneficial when solving multiple right-hand-sides, like in inverse problems. We demonstrate the efficiency and generalization abilities of our approach on a variety of 2D problems.
Linear-Quadratic Large-Population Problem with Partial Information: Hamiltonian Approach and Riccati Approach

This paper studies a class of partial information linear-quadratic mean-field game problems. A general stochastic large-population system is considered, where the diffusion term of the dynamic of each agent can depend on the state and control. We study both the control constrained case and unconstrained case. In control constrained case, by using Hamiltonian approach and convex analysis, the explicit decentralized strategies can be obtained through projection operator. The corresponding Hamiltonian type consistency condition system is derived, which turns out to be a nonlinear mean-field forward-backward stochastic differential equation with projection operator. The well-posedness of such kind of equations is proved by using discounting method. Moreover, the corresponding $\varepsilon$-Nash equilibrium property is verified. In control unconstrained case, the decentralized strategies can be further represented explicitly as the feedback of filtered state through Riccati approach. The existence and uniqueness of a solution to a new Riccati type consistency condition system is also discussed. As an application, a general inter-bank borrowing and lending problem is studied to illustrate that the effect of partial information cannot be ignored.