Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory

In this paper we present a technique based on deep reinforcement learning that allows for numerical analytic continuation of integrals that are often encountered in one-loop diagrams in quantum field theory. To extract certain quantities of two-point functions, such as spectral densities, mass poles or multiparticle thresholds, it is necessary to perform an analytic continuation of the correlator in question. At one-loop level in Euclidean space, this results in the necessity to deform the integration contour of the loop integral in the complex plane of the square of the loop momentum, to avoid nonanalyticities in the integration plane. Using a toy model for which an exact solution is known, we train a reinforcement learning agent to perform the required contour deformations. Our study shows great promise for an agent to be deployed in iterative numerical approaches used to compute nonperturbative two-point functions, such as the quark propagator Dyson-Schwinger equation, or more generally, Fredholm equations of the second kind, in the complex domain.
Synthesizing Queries via Interactive Sketching

We propose a novel approach to program synthesis, focusing on synthesizing database queries. At a high level, our proposed algorithm takes as input a sketch with soft constraints encoding user intent, and then iteratively interacts with the user to refine the sketch. At each step, our algorithm proposes a candidate refinement of the sketch, which the user can either accept or reject. By leveraging this rich form of user feedback, our algorithm is able to both resolve ambiguity in user intent and improve scalability. In particular, assuming the user provides accurate inputs and responses, then our algorithm is guaranteed to converge to the true program (i.e., one that the user approves) in polynomial time. We perform a qualitative evaluation of our algorithm, showing how it can be used to synthesize a variety of queries on a database of academic publications.
Fine-grained Object Semantic Understanding from Correspondences

Fine-grained semantic understanding of 3D objects is crucial in many applications such as object manipulation. However, it is hard to give a universal definition of point-level semantics that everyone would agree on. We observe that people are pretty sure about semantic correspondences between two areas from different objects, but less certain about what each area means in semantics. Therefore, we argue that by providing human labeled correspondences between different objects from the same category, one can recover rich semantic information of an object. In this paper, we propose a method that outputs dense semantic embeddings based on a novel geodesic consistency loss. Accordingly, a new dataset named CorresPondenceNet and its corresponding benchmark are designed. Several state-of-the-art networks are evaluated based on our proposed method. We show that our method could boost the fine-grained understanding of heterogeneous objects and the inference of dense semantic information is possible.
Cohn-Leavitt path algebras of bi-separated graphs

Abstract The purpose of this article is to provide a common framework for studying various generalizations of Leavitt path algebras. We first define Cohn-Leavitt path algebras of graphs with an additional structure called bi-separated graphs. We then define and study the category BSG of bi-separated graphs with appropriate morphisms so that the functor which associates bi-separated graphs to their Cohn-Leavitt path algebras is continuous. Next, we define two sub-categories of BSG, compute basis for the algebras corresponding to one of those subcategories and study some algebraic properties in terms of bi-separated graph-theoretic properties. Finally, we compute -monoid for some particular cases.
On a simultaneous parameter inference and missing data imputation for nonstationary autoregressive models

This work addresses the problem of missing data in time-series analysis focusing on (a) estimation of model parameters in the presence of missing data and (b) reconstruction of missing data. Standard approaches used to solve these problems like the maximum likelihood estimation or the Bayesian inference rely on a priori assumptions like the Gaussian or stationary behavior of missing data and might lead to biased results where these assumptions are unfulfilled. In order to go beyond, we extend the Finite Element Methodology (FEM) for Vector Auto-Regressive models with eXogenous factors and bounded variation of the model parameters (FEM-VARX) towards handling the missing data problem. The presented approach estimates the model parameters and reconstructs the missing data in the considered time series and in the involved exogenous factors, simultaneously. The resulting computational framework was compared to the state-of-art methodologies on a set of test-cases and is available as open-source software.
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

In this work, we propose a novel two-stage framework, called FaceShifter, for high fidelity and occlusion aware face swapping. Unlike many existing face swapping works that leverage only limited information from the target image when synthesizing the swapped face, our framework, in its first stage, generates the swapped face in high-fidelity by exploiting and integrating the target attributes thoroughly and adaptively. We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis. To address the challenging facial occlusions, we append a second stage consisting of a novel Heuristic Error Acknowledging Refinement Network (HEAR-Net). It is trained to recover anomaly regions in a self-supervised way without any manual annotations. Extensive experiments on wild faces demonstrate that our face swapping results are not only considerably more perceptually appealing, but also better identity preserving in comparison to other state-of-the-art methods.
Landau polaritons in highly nonparabolic two-dimensional gases in the ultrastrong coupling regime

We probe ultra-strong light matter coupling between metallic terahertz metasurfaces and Landau-level transitions in high mobility 2D electron and hole gases. We utilize heavy-hole cyclotron resonances in strained Ge and electron cyclotron resonances in InSb quantum wells, both within highly non-parabolic bands, and compare our results to well known parabolic AlGaAs/GaAs quantum well (QW) systems. Tuning the coupling strength of the system by two methods, lithographically and by optical pumping, we observe a novel behavior clearly deviating from the standard Hopfield model previously verified in cavity quantum electrodynamics: an opening of a lower polaritonic gap.
Defending from adversarial examples with a two-stream architecture

In recent years, deep learning has shown impressive performance on many tasks. However, recent researches showed that deep learning systems are vulnerable to small, specially crafted perturbations that are imperceptible to humans. Images with such perturbations are the so called adversarial examples, which have proven to be an indisputable threat to the DNN based applications. The lack of better understanding of the DNNs has prevented the development of efficient defenses against adversarial examples. In this paper, we propose a two-stream architecture to protect CNN from attacking by adversarial examples. Our model draws on the idea of "two-stream" which commonly used in the security field, and successfully defends different kinds of attack methods by the differences of "high-resolution" and "low-resolution" networks in feature extraction. We provide a reasonable interpretation on why our two-stream architecture is difficult to defeat, and show experimentally that our method is hard to defeat with state-of-the-art attacks. We demonstrate that our two-stream architecture is robust to adversarial examples built by currently known attacking algorithms.
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject; 2) LA can be used as a preprocessing step before different feature extraction and classification algorithms; and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.
Introduction to Rank-polymorphic Programming in Remora (Draft)

Remora is a higher-order, rank-polymorphic array-processing programming language, in the same general class of languages as APL and J. It is intended for writing programs to be executed on parallel hardware. We provide an example-driven introduction to the language, and its general computational model, originally developed by Iverson for APL. We begin with Dynamic Remora, a variant of the language with a dynamic type system (as in Scheme or Lisp), to introduce the fundamental computational mechanisms of the language, then shift to Explicitly Typed Remora, a variant of the language with a static, dependent type system that permits the shape of the arrays being computed to be captured at compile time. This article can be considered an introduction to the general topic of the rank-polymorphic array-processing computational model, above and beyond the specific details of the Remora language. We do not address the details of type inference in Remora, that is, the assignment of explicit types to programs written without such annotations; this is ongoing research.
Target-less registration of point clouds: A review

Point cloud registration has been one of the basic steps of point cloud processing, which has a lot of applications in remote sensing and robotics. In this report, we summarized the basic workflow of target-less point cloud registration,namely correspondence determination and transformation estimation. Then we reviewed three commonly used groups of registration approaches, namely the feature matching based methods, the iterative closest points algorithm and the randomly hypothesis and verify based methods. Besides, we analyzed the advantage and disadvantage of these methods are introduced their common application scenarios. At last, we discussed the challenges of current point cloud registration methods and proposed several open questions for the future development of automatic registration approaches.
Cloning of zero modes in one-dimensional graphene superlattices

One-dimensional (1D) graphene superlattices have been predicted to exhibit zero-energy modes a decade ago, but an experimental proof has remained missing. Motivated by a recent experiment that could possibly shed light on this, here we perform quantum transport simulations for 1D graphene superlattices, considering electrostatically simulated potential profiles as realistic as possible. Combined with the analysis on the corresponding miniband structures, we find that the zero modes generated by the 1D superlattice potential can be further cloned to higher energies, which are also accessible by tuning the average density. Our multiterminal transverse magnetic focusing simulations further reveal the modulation-controllable ballistic miniband transport for 1D graphene superlattices. A simple idea for creating a perfectly symmetric periodic potential with strong modulation is proposed at the end of this work, generating well aligned zero modes up to 6 within a reasonable gate strength.
Real World Longitudinal iOS App Usage Study at Scale

Given the importance of understanding the interaction between mobile devices and their users, app usage patterns have been studied in various contexts. However, prior work has not fully investigated longitudinal changes to app usage behavior. In this paper, we present a longitudinal, large-scale study of mobile app usage based on a dataset collected from 162,006 iPhones and iPads over 4 years. We explore multiple dimensions of app usage pattern proving useful insights on how app usage changes over time. Our key findings include (i) app usage pattern changes over time both at the individual app level and the app category level (i.e. proportion of time a user spends using an app), (ii) users keep a small set of apps frequently launched (90% of iPhone users launch roughly 14-18 apps weekly), (iii) a small number of apps remain popular while some specific kinds of apps (e.g. Games) have a shorter life cycle compared to other apps of different categories. Finally, we discuss our findings and their implications, for example, a short-term study as an attempt to understand the general needs of mobile devices may not achieve useful results for the long term.
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces

Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While model-based RL aims to learn a dynamics model, in a more general case the learner does not know a priori what the action space is. Here we propose a formalism where the learner induces a world program by learning a dynamics model and the actions in graph-based compositional environments by observing state-state transition examples. Then, the learner can perform RL with the world program as the simulator for complex planning tasks. We highlight a recent application, and propose a challenge for the community to assess world program-based planning.
End-to-end Learning, with or without Labels

We present an approach for end-to-end learning that allows one to jointly learn a feature representation from unlabeled data (with or without labeled data) and predict labels for unlabeled data. The feature representation is assumed to be specified in a differentiable programming framework, that is, as a parameterized mapping amenable to automatic differentiation. The proposed approach can be used with any amount of labeled and unlabeled data, gracefully adjusting to the amount of supervision. We provide experimental results illustrating the effectiveness of the approach.
Water Supply Prediction Based on Initialized Attention Residual Network

Real-time and accurate water supply forecast is crucial for water supply control. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply prediction. In this paper, we address a generic artificial neural network, Initialized Attention Residual Network (IARN), to tackle the time series prediction problem in water supply domain. Specifically, instead of continuing to use the recurrent neural network (RNN) in time-series tasks, we try to build a convolution neural network (CNN) to recede the disturb from other factors and get a more credible results. Experiments show that our method achieves state-of-the-art performance on several datasets in terms of accuracy, robustness and generalization ability. The source code of our method will available at https://github.com/SJTU-L-dd/IARN.
A Modern Introduction to Online Learning

In this monograph, I introduce the basic concepts of Online Learning through a modern view of Online Convex Optimization. Here, online learning refers to the framework of regret minimization under worst-case assumptions. I present first-order and second-order algorithms for online learning with convex losses, in Euclidean and non-Euclidean settings. All the algorithms are clearly presented as instantiation of Online Mirror Descent or Follow-The-Regularized-Leader and their variants. Particular attention is given to the issue of tuning the parameters of the algorithms and learning in unbounded domains, through adaptive and parameter-free online learning algorithms. Non-convex losses are dealt through convex surrogate losses and through randomization. The bandit setting is also briefly discussed, touching on the problem of adversarial and stochastic multi-armed bandits. These notes do not require prior knowledge of convex analysis and all the required mathematical tools are rigorously explained. Moreover, all the proofs have been carefully chosen to be as simple and as short as possible.
Production of nitric oxide by a fragmenting bolide: An exploratory numerical study

A meteoroid's hypersonic passage through the Earth's atmosphere results in ablational and fragmentational mass loss. Potential shock waves associated with a parent object as well as its fragments can modify the surrounding atmosphere and produce a range of physico‐chemical effects. Some of the thermally driven chemical and physical processes induced by meteoroid‐fragment‐generated shock waves, such as nitric oxide (NO) production, are less understood. Any estimates of meteoric NO production depend not only on a quantifiable meteoroid population and a rate of fragmentation, with a size capable of producing high temperature flows, but also on understanding the physical properties of the meteor flows along with their thermal history. We performed an exploratory pilot numerical study using ANSYS Fluent, the computational fluid dynamics (CFD) code, to investigate the production of NO in the upper atmosphere by small meteoroids (or fragments of meteoroids after they undergo a disruption episode) in the size range from 10−2 to 1 m. Our model uses the simulation of a spherical body in the continuum flow at 70‐ and 80‐km altitude to approximate the behaviour of a small meteoroid capable of producing NO. The results presented in this exploratory study are in good agreement with previous studies.
Worst-Case Optimal Radix Triejoin

Relatively recently, the field of join processing has been swayed by the discovery of a new class of multi-way join algorithms. The new algorithms join multiple relations simultaneously rather than perform a series of pairwise joins. The new join algorithms satisfy stronger worst-case runtime complexity guarantees than any of the existing approaches based on pairwise joins -- they are worst-case optimal in data complexity. These research efforts have resulted in a flurry of papers documenting theoretical and some practical contributions. However, there is still the quest of making the new worst-case optimal join algorithms truly practical in terms of (1) ease of implementation and (2) secondary index efficiency in terms of number of indexes created to answer a query. In this paper, we present a simple worst-case optimal multi-way join algorithm called the radix triejoin. Radix triejoin uses a binary encoding for reducing the domain of a database. Our main technical contribution is that domain reduction allows a bit-interleaving of attribute values that gives rise to a query-independent relation representation, permitting the computation of multiple queries over the same relations worst-case optimally without having to construct additional secondary indexes. We also generalise the core algorithm to conjunctive queries with inequality constraints and provide a new proof technique for the worst-case optimal join result.
A parameter choice rule for Tikhonov regularization based on predictive risk

In this work, we propose a new criterion for choosing the regularization parameter in Tikhonov regularization when the noise is white Gaussian. The criterion minimizes a lower bound of the predictive risk, when both data norm and noise variance are known, and the parameter choice involves minimizing a function whose solution depends only on the signal-to-noise ratio. Moreover, when neither noise variance nor data norm is given, we propose an iterative algorithm which alternates between a minimization step of finding the regularization parameter and an estimation step of estimating signal-to-noise ratio. Simulation studies on both small- and large-scale datasets suggest that the approach can provide very accurate and stable regularized inverse solutions and, for small sized samples, it outperforms discrepancy principle, balancing principle, unbiased predictive risk estimator, L-curve method, generalized cross validation, and quasi-optimality criterion, and achieves excellent stability hitherto unavailable.
Encoding word order in complex embeddings

Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or precedence) between individual word positions. We present a novel and principled solution for modeling both the global absolute positions of words and their order relationships. Our solution generalizes word embeddings, previously defined as independent vectors, to continuous word functions over a variable (position). The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions. Hence, word representations in different positions can correlate with each other in a continuous function. The general solution of these functions can be extended to complex-valued variants. We extend CNN, RNN and Transformer NNs to complex-valued versions to incorporate our complex embedding (we make all code available). Experiments on text classification, machine translation and language modeling show gains over both classical word embeddings and position-enriched word embeddings. To our knowledge, this is the first work in NLP to link imaginary numbers in complex-valued representations to concrete meanings (i.e., word order).
Rank-One Hilbert Geometries

We introduce and study the notion of rank-one Hilbert geometries and open properly convex domains in $\mathbb{P}(\mathbb{R}^{d+1})$. This is in the spirit of rank-one non-positively curved Riemannian manifolds. We define rank-one isometries of a Hilbert geometry $\Omega$ and characterize them precisely as the contracting elements in the automorphism group ${\rm Aut}(\Omega)$ of the Hilbert geometry. We prove that if a discrete subgroup of ${\rm Aut}(\Omega)$ contains a rank-one isometry, then the subgroup is either virtually $\mathbb{Z}$ or acylindrically hyperbolic. This leads to some applications like computation of the space of quasimorphisms, genericity results for rank-one isometries and counting results for closed geodesics.