Miracle-dz/UniFAR-FTUs huggingface dataset:
Euler-Heisenberg Lagrangian with axial gauge

Augmentations to the Euler-Heisenberg Lagrangian (QED one-loop eﬀective action in homogeneous electromagnetic ﬁelds) under a constant axial gauge are examined. Two special conﬁgurations admit an exact eigendecomposition, and hence eﬀective action as a spectral sum, of the augmented Dirac operator: one with a magnetic ﬁeld with chiral chemical potential, and the other with an electric ﬁeld with spatial axial gauge. An enhancement to Schwinger pair production is found for the latter, which is more fully analyzed using the worldline instanton formalism. There it is found the overall enhancement is due to the spatial axial gauge serving as a negative mass shift. Finally, we remark on the exactly solvable massless case for arbitrary electromagnetic and axial gauges.
Dynamic Polarizability of the 85Rb 5D3/2-State in 1064 nm Light

We report a measurement of the dynamic (ac) scalar polarizability of the 5D3/2 state in 85Rb atoms at a laser wavelength of 1064 nm. Contrary to a recent measurement in Phys. Rev. A 104, 063304 (2021), the experiments are performed in a low-intensity regime in which the ac shift is less than the 5D3/2 state’s hyperfine structure, as utilized in numerous experiments with cold, trapped atoms. The extracted ac polarizability is α5D3/2=−499±59 a.u. , within the uncertainty of the aforementioned previous result. The calibration of the 1064 nm light intensity, performed by analyzing light shifts of the D1 line, is the main source of uncertainty. Our results are useful for applications of the Rb 5D3/2 state in metrology, quantum sensing, and fundamental-physics research on Rydberg atoms and molecules.
Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou

Customer Life Time Value (LTV) is the expected total revenue that a single user can bring to a business. It is widely used in a variety of business scenarios to make operational decisions when acquiring new customers. Modeling LTV is a challenging problem, due to its complex and mutable data distribution. Existing approaches either directly learn from posterior feature distributions or leverage statistical models that make strong assumption on prior distributions, both of which fail to capture those mutable distributions. In this paper, we propose a complete set of industrial-level LTV modeling solutions. Specifically, we introduce an Order Dependency Monotonic Network (ODMN) that models the ordered dependencies between LTVs of different time spans, which greatly improves model performance. We further introduce a Multi Distribution Multi Experts (MDME) module based on the Divide-and-Conquer idea, which transforms the severely imbalanced distribution modeling problem into a series of relatively balanced sub-distribution modeling problems hence greatly reduces the modeling complexity. In addition, a novel evaluation metric Mutual Gini is introduced to better measure the distribution difference between the estimated value and the ground-truth label based on the Lorenz Curve. The ODMN framework has been successfully deployed in many business scenarios of Kuaishou, and achieved great performance. Extensive experiments on real-world industrial data demonstrate the superiority of the proposed methods compared to state-of-the-art baselines including ZILN and Two-Stage XGBoost models.
An Open-Source P416 Compiler Backend for Reconfigurable Match-Action Table Switches

The P4 language has become the dominant choice for programming the reconfigurable match-action table based programmable switches. V1Model architecture is the most widely available realization of this paradigm. The open-source compiler frontend developed by the P4 consortium can execute syntax analysis and derive a hardware-independent representation of a program written using the latest version of P4 (also known as P416 ). A compiler backend is required to map this intermediate representation to the hardware resources of a V1Model switch. However, there is no open-source compiler backend available to check the realizability of a P416 program over a V1Model switch. Proprietary tools provided by different hardware vendors are available for this purpose. However, they are closed source and do not provide access to the internal mapping mechanisms. Which inhibits experimenting with new mapping algorithms and innovative instruction sets for reconfigurable match-action table architecture. Moreover, the proprietary compiler backends are costly and come with various non-disclosure agreements. These factors pose serious challenges to programmable switch-related research. In this work, we present an open-source P416 compiler backend for the V1Model architecture-based programmable switches. It uses heuristic-based mapping algorithms to map a P416 program over the hardware resources of a V1Model switch. It allows developers to rapidly prototype different mapping algorithms. It also gives various resource usage statistics of a P416 program, enabling comparison among multiple P416 schemes.
Structural Adaptivity of Directed Networks

Network structure plays a critical role in functionality and performance of network systems. This paper examines structural adaptivity of diffusively coupled, directed multi-agent networks that are subject to diffusion performance. Inspired by the observation that the link redundancy in a network may degrade its diffusion performance, a distributed data-driven neighbor selection framework is proposed to adaptively adjust the network structure for improving the diffusion performance of exogenous influence over the network. Specifically, each agent is allowed to interact with only a specific subset of neighbors while global reachability from exogenous influence to all agents of the network is maintained. Both continuous-time and discrete-time directed networks are examined. For each of the two cases, we first examine the reachability properties encoded in the eigenvectors of perturbed variants of graph Laplacian or SIA matrix associated with directed networks, respectively. Then, an eigenvector-based rule for neighbor selection is proposed to derive a reduced network, on which the diffusion performance is enhanced. Finally, motivated by the necessity of distributed and data-driven implementation of the neighbor selection rule, quantitative connections between eigenvectors of the perturbed graph Laplacian and SIA matrix and relative rate of change in agent state are established, respectively. These connections immediately enable a data-driven inference of the reduced neighbor set for each agent using only locally accessible data. As an immediate extension, we further discuss the distributed data-driven construction of directed spanning trees of directed networks using the proposed neighbor selection framework. Numerical simulations are provided to demonstrate the theoretical results.
Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems

At the age of big data, recommender systems have shown remarkable success as a key means of information filtering in our daily life. Recent years have witnessed the technical development of recommender systems, from perception learning to cognition reasoning which intuitively build the task of recommendation as the procedure of logical reasoning and have achieve significant improvement. However, the logical statement in reasoning implicitly admits irrelevance of ordering, even does not consider time information which plays an important role in many recommendation tasks. Furthermore, recommendation model incorporated with temporal context would tend to be self-attentive, i.e., automatically focus more (less) on the relevance (irrelevance), respectively. To address these issues, in this paper, we propose a Time-aware Self-Attention with Neural Collaborative Reasoning (TiSANCR) based recommendation model, which integrates temporal patterns and self-attention mechanism into reasoning-based recommendation. Specially, temporal patterns represented by relative time, provide context and auxiliary information to characterize the user's preference in recommendation, while self-attention is leveraged to distill informative patterns and suppress irrelevances. Therefore, the fusion of self-attentive temporal information provides deeper representation of user's preference. Extensive experiments on benchmark datasets demonstrate that the proposed TiSANCR achieves significant improvement and consistently outperforms the state-of-the-art recommendation methods.
Goal-Conditioned Q-Learning as Knowledge Distillation

Many applications of reinforcement learning can be formalized as goal-conditioned environments, where, in each episode, there is a "goal" that affects the rewards obtained during that episode but does not affect the dynamics. Various techniques have been proposed to improve performance in goal-conditioned environments, such as automatic curriculum generation and goal relabeling. In this work, we explore a connection between off-policy reinforcement learning in goal-conditioned settings and knowledge distillation. In particular: the current Q-value function and the target Q-value estimate are both functions of the goal, and we would like to train the Q-value function to match its target for all goals. We therefore apply Gradient-Based Attention Transfer (Zagoruyko and Komodakis 2017), a knowledge distillation technique, to the Q-function update. We empirically show that this can improve the performance of goal-conditioned off-policy reinforcement learning when the space of goals is high-dimensional. We also show that this technique can be adapted to allow for efficient learning in the case of multiple simultaneous sparse goals, where the agent can attain a reward by achieving any one of a large set of objectives, all specified at test time. Finally, to provide theoretical support, we give examples of classes of environments where (under some assumptions) standard off-policy algorithms such as DDPG require at least O(d^2) replay buffer transitions to learn an optimal policy, while our proposed technique requires only O(d) transitions, where d is the dimensionality of the goal and state space. Code and appendix are available at https://github.com/alevine0/ReenGAGE.
Generally-Altered, -Inflated, -Truncated and -Deflated Regression, With Application to Heaped and Seeped Data

Models such as the zero-inﬂated and zero-altered Poisson and zero-truncated binomial are well-established in modern regression analysis. We propose a super model that jointly and maximally uniﬁes alteration, inﬂation, truncation and deﬂation for counts, given a 1- or 2-parameter parent (base) distribution. Seven disjoint sets of special value types are accommodated because all but truncation have parametric and nonparametric variants. Some highlights include: (i) the mixture distribution is exceeding ﬂexible, e.g., up to seven modes; (ii) under-, equi- and over-dispersion can be handled using a negative binomial (NB) parent, with underdispersion handled by a novel Generally-Truncated-Expansion method; (iii) overdispersion can be studied holistically in terms of the four operators; (iv) an important application: heaped and seeped data from retrospective self-reported surveys are readily handled, e.g., spikes and dips which are located virtually anywhere; (v) while generally-altered regression explains why observations are there, generally-inﬂated regression accounts for why they are there in excess , and generally-deﬂated regression explains why observations are not there; (vi) the VGAM R package implements the methodology based on Fisher scoring and multinomial logit model (Poisson, NB, zeta and logarithmic parents are implemented.) The GAITD-NB has potential to become the Swiss army knife of count distributions.
FFCNN: Fast FPGA based Acceleration for Convolution neural network inference

We present a new efficient OpenCL-based Accelerator for large scale Convolutional Neural Networks called Fast Inference on FPGAs for Convolution Neural Network (FFCNN). FFCNN is based on a deeply pipelined OpenCL kernels architecture. As pointed out before, high-level synthesis tools such as the OpenCL framework can easily port codes originally designed for CPUs and GPUs to FPGAs, but it is still difficult to make OpenCL codes run efficiently on FPGAs. This work aims to propose an efficient FPGA implementation of OpenCL High-Performance Computing Applications. To do so, a Data reuse and task mapping techniques are also presented to improve design efficiency. In addition, the following motivations were taken into account when developing FFCNN: 1) FFCNN has been designed to be easily implemented on Intel OpenCL SDK based FPGA design flow. 2) In FFFCN, different techniques have been integrated to improve the memory band with and throughput. A performance analysis is conducted on two deep CNN for Large-Scale Images classification. The obtained results, and the comparison with other works designed to accelerate the same types of architectures, show the efficiency and the competitiveness of the proposed accelerator design by significantly improved performance and resource utilization.
Neuromorphic Visual Scene Understanding with Resonator Networks

Understanding a visual scene by inferring identities and poses of its individual objects is still and open problem. Here we propose a neuromorphic solution that utilizes an efficient factorization network based on three key concepts: (1) a computational framework based on Vector Symbolic Architectures (VSA) with complex-valued vectors; (2) the design of Hierarchical Resonator Networks (HRN) to deal with the non-commutative nature of translation and rotation in visual scenes, when both are used in combination; (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued resonator networks on neuromorphic hardware. The VSA framework uses vector binding operations to produce generative image models in which binding acts as the equivariant operation for geometric transformations. A scene can therefore be described as a sum of vector products, which in turn can be efficiently factorized by a resonator network to infer objects and their poses. The HRN enables the definition of a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition and for rotation and scaling within the other partition. The spiking neuron model allows mapping the resonator network onto efficient and low-power neuromorphic hardware. Our approach is demonstrated on synthetic scenes composed of simple 2D shapes undergoing rigid geometric transformations and color changes. A companion paper demonstrates the same approach in real-world application scenarios for machine vision and robotics.
Grid Cell Percolation

Grid cells play a principal role in enabling mammalian cognitive representations of ambient environments. The key property of these cells—the regular arrangement of their firing fields—is commonly viewed as means for establishing spatial scales or encoding specific locations. However, using grid cells’ spiking outputs for deducing spatial orderliness proves to be a strenuous task, due to fairly irregular activation patterns triggered by the animal’s sporadic visits to the grid fields. The following discussion addresses statistical mechanisms enabling emergent regularity of grid cell firing activity, from the perspective of percolation theory. In particular, it is shown that the range of neurophysiological parameters required for spiking percolation phenomena matches experimental data, which points at biological viability of the percolation approach and casts a new light on the role of grid cells in organizing the hippocampal map.
Downfolded Configuration Interaction for Chemically Accurate Electron Correlation.

A model subspace configuration interaction method is developed to obtain chemically accurate electron correlations by diagonalizing a very compact effective Hamiltonian of a realistic molecule. The construction of the effective Hamiltonian is deterministic and implemented by iteratively building a sufficiently small model subspace comprising local clusters of a small number of Slater determinants. Through the low-rank reciprocal of interaction Hamiltonian, important determinants can be incrementally identified to couple with selected local pairwise clusters and then downfolded into the model subspace. This method avoids direct ordering and selection of the configurations in the entire space. We demonstrate the efficiency and accuracy of this theory for obtaining the near-FCI ground- and excited-state potential energies by benchmarking the C2 molecule and illustrate its application potential in computing accurate excitation energies of organometallic [Cu(NHC)2(pyridine)2]x+ complexes and other organic molecules of various excitation character.
Compressible Topological Vector Spaces

The optimum subspace decomposition of the infinite-dimensional compressible random processes in the locally convex Hausdorff space has been propose and its dimension has been measured. For this purpose, we conduct extensively topological analysis of finiteand infinite-dimensional compressible vector spaces. We prove that if there are a sufficient number of separating points in compressible topological vector space, then Banach Limit coincides the minimal linear functional. Then, optimum orthogonal decomposition of the compressible topological vector space can be formulated as a limit for which minimal linear functional occurs. Accordingly, we show that the purposed topological analysis method decomposes signal space into separable and inseparable subspace. It has been shown that the separable space, also referred to as an optimum subspace, is the subset of the compressible vector space that contains its extreme points. The inseparable subspace is characterized using a novel absorbing null space property through Minkowski functional and Lebesgue measure. We prove that the absorbing null space is a connected subspace of the given locally convex space. We purpose reflexive homomorphism that establishes a relation between signal space and double dual space. It has been shown that the Banach limit can be determined by the compact convergence of the Cauchy nets on the left and right hands sides of a reflexive homomorphism. Finally, we propose to measure the optimum subspace dimension using the Fréchet distance metric. To apply the Fréchet distance metric to the compressible topological vector space, the Köthe sequence sampled from a continuous function has been proposed. The sampled Köthe sequence generates the Borel σ-algebra proportional to the dimensional growth of the compressible topological vector space. Briefly, the proposed approach measures the sufficient number of separating points of the finiteand infinite-dimensional compressible vector space for the given undersampled operator with respect to Hahn-Banach and Daniell-Kolmogorov theorems. The numerical analysis have been presented for (1) finite-dimensional millimeter-wave channel estimation with and without beam squint, and (2) infinite-dimensional signal with local fluctuation.
An Unsupervised Learning-based Framework for Effective Representation Extraction of Reactor Accidents

: Compared to knowledge-based diagnostic systems, data-based methods tend to perform better in terms of speed and accuracy in diagnosing reactor accidents, and have significant advantages in terms of scalability of models. With the increasing use of high-precision system analysis programs in nuclear engineering, the number of high-fidelity computational data for accident simulation is exploding. Therefore, an algorithm that can achieve both automatic extraction of low-dimensional features from the data and guarantee the validity of the features is needed to improve the performance and confidence of the accident diagnosis system. This study proposes an autoencoder-based autonomous learning framework, namely Padded Auto-Encoder (PAE), which is able to automatically encode accident monitoring data that has been noise-added and with partially missing data into low-dimensional feature vectors via a Vision Transformer-based encoder, and to decode the feature vectors into noise-free and complete reconstructed monitoring data. Thus, the encoder part of the framework is able to automatically infer valid representations from partially missing and noisy monitoring data that reflect the complete and noise-free original data, and the representation vectors can be used for downstream tasks for accident diagnosis or else. In this paper, LOCA of HPR1000 was used as the study object, and the PAE was trained by an unsupervised method using cases with different break locations and sizes as the dataset. The encoder part of the pre-trained PAE was subsequently used as the feature extractor for the monitoring data, and several basic statistical learning algorithms for predicting the break locations and sizes. The results of the study show that the pre-trained
Demystifying the COVID-19 vaccine discourse on Twitter

Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the current COVID-19 pandemic, but also for future pathogen outbreaks. We examine a Twitter dataset containing 75 million English tweets discussing COVID-19 vaccination from March 2020 to March 2021. We train a stance detection algorithm using natural language processing (NLP) techniques to classify tweets as `anti-vax' or `pro-vax', and examine the main topics of discourse using topic modelling techniques. While pro-vax tweets (37 million) far outnumbered anti-vax tweets (10 million), a majority of tweets from both stances (63% anti-vax and 53% pro-vax tweets) came from dual-stance users who posted both pro- and anti-vax tweets during the observation period. Pro-vax tweets focused mostly on vaccine development, while anti-vax tweets covered a wide range of topics, some of which included genuine concerns, though there was a large dose of falsehoods. A number of topics were common to both stances, though pro- and anti-vax tweets discussed them from opposite viewpoints. Memes and jokes were amongst the most retweeted messages. Whereas concerns about polarisation and online prevalence of anti-vax discourse are unfounded, targeted countering of falsehoods is important.
Nanoscale Electric Field Sensing Using Levitated Nano-resonator with Net Charge

: Nanomechanical resonator based on levitated particle exhibits unique advantages in the development of ultrasensitive electric field detector. We demonstrate a three-dimensional, high-sensitivity electric field measurement technology using the optically levitated nanoparticle with a known net charge. By changing the relative position between nanoparticle and parallel electrodes, the three-dimensional electric field distribution is scanned. The measured noise equivalent electric intensity with charge amount of 100 reaches the order of 1μV/cm/Hz 1/2 at 1.4×10 -7 mbar. Linearity analysis near resonance frequency shows a measured linear range over 91dB limited only by the maximum output voltage of the driving equipment. This work may provide avenue for developing a high-sensitive electric field sensor based on optically levitated nano-resonator. feedback scheme, electric driving and field measurement circuit. OBJ: NA microscope objective. SNE: horizontally placed steel