Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Alignment between the Information Needs of Developers and the Documentation of Programming Languages: A Case Study on Rust

Programming language documentation refers to the set of technical documents that provide application developers with a description of the high-level concepts of a language (e.g., manuals, tutorials, and API references). Such documentation is essential to support application developers in effectively using a programming language. One of the challenges faced by documenters (i.e., personnel that design and produce documentation for a programming language) is to ensure that documentation has relevant information that aligns with the concrete needs of developers, defined as the missing knowledge that developers acquire via voluntary search. In this article, we present an automated approach to support documenters in evaluating the differences and similarities between the concrete information need of developers and the current state of documentation (a problem that we refer to as the topical alignment of a programming language documentation). Our approach leverages semi-supervised topic modelling that uses domain knowledge to guide the derivation of topics. We initially train a baseline topic model from a set of Rust-related Q&A posts. We then use this baseline model to determine the distribution of topic probabilities of each document of the official Rust documentation. Afterwards, we assess the similarities and differences between the topics of the Q&A posts and the official documentation. Our results show a relatively high level of topical alignment in Rust documentation. Still, information about specific topics is scarce in both the Q&A websites and the documentation, particularly related topics with programming niches such as network, game, and database development. For other topics (e.g., related topics with language features such as structs, patterns and matchings, and foreign function interface), information is only available on Q&A websites while lacking in the official documentation. Finally, we discuss implications for programming language documenters, particularly how to leverage our approach to prioritize topics that should be added to the documentation.
Large Transverse and Longitudinal Magneto‐Thermoelectric Effect in Polycrystalline Nodal‐Line Semimetal Mg3Bi2

Topological semimetals provide new opportunities for exploring novel thermoelectric phenomena, owing to their exotic and nontrivial electronic structure topology around the Fermi surface. Herein, the discovery of large transverse and longitudinal magneto‐thermoelectric (MTE) effects in Mg3Bi2 is reported and predicted to be a type‐II nodal‐line semimetal in the absence of spin‐orbit coupling (SOC). The maximum transverse power factor is 2182 μW m−1K−2 at 13.5 K and 6 Tesla. The longitudinal power factor reaches up to 3043 μW m−1K−2, which is 20 times higher than that in a zero‐strength magnetic field and is also comparable to state‐of‐the‐art MTE materials. By compensating the Mg loss in Mg‐rich conditions for tuning the carrier concentration close to intrinsic state, the sample fabricated in this study exhibits a large linear non‐saturating magnetoresistance of 940% under a field of 14 Tesla. Using density functional calculations, the authors attribute the underlying mechanism to the parent linear‐dispersed nodal‐line electronic structure without SOC and the anisotropic Fermi surface shape with SOC, highlighting the essential role of high carrier mobility and open electron orbits in the moment space. This work offers a new avenue toward highly efficient MTE materials through defect engineering in polycrystalline topological semimetals.
Cohomological DT invariants from localization

Given a quiver with potential associated with a toric Calabi–Yau threefold, the numerical Donaldson–Thomas invariants for the moduli space of framed representations can be computed by using toric localization, which reduces the problem to the enumeration of pyramid partitions. We provide a refinement of this localization procedure, which allows to compute cohomological Donaldson–Thomas invariants. Using this approach, we prove a universal formula which gives the BPS invariants of any toric quiver, up to undetermined contributions which are invariant under Poincaré duality. When the toric Calabi–Yau threefold has compact divisors, these self‐Poincaré dual contributions have a complicated dependence on the stability parameters, but explicit computations suggest that they drastically simplify for the self‐stability condition (also called attractor chamber). We conjecture a universal formula for the attractor invariants, which applies to any toric Calabi–Yau singularity with compact divisors.
Static Inference Meets Deep learning: A Hybrid Type Inference Approach for Python

Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based approaches are feature-agnostic, but they can-not guarantee the correctness of the predicted types. Their per-formance significantly depends on the quality of the training data (i.e., DL models perform poorly on some common types that rarely appear in the training dataset). It is interesting to note that the static and DL-based approaches offer complementary benefits. Un-fortunately, to our knowledge, precise type inference based on both static inference and neural predictions has not been exploited and remains an open challenge. In particular, it is hard to integrate DL models into the framework of rule-based static approaches. This paper fills the gap and proposes a hybrid type inference approach named Hityper based on both static inference and deep learning. Specifically, our key insight is to record type dependen-cies among variables in each function and encode the dependency information in type dependency graphs (TDGs). Based on TDGs, we can easily integrate type inference rules in the nodes to conduct static inference and type rejection rules to inspect the correctness of neural predictions. Hityper iteratively conducts static inference and DL-based prediction until the TDG is fully inferred. Experi-ments on two benchmark datasets show that Hityper outperforms state-of-the-art DL models by exactly matching 10% more human annotations. Hityper also achieves an increase of more than 30% on inferring rare types. Considering only the static part of Hityper, it infers 2× ~3× more types than existing static type inference tools. Moreover, Hityper successfully corrected seven wrong human an-notations in six GitHub projects, and two of them have already been approved by the repository owners.
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration

Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong dependency on hyperparameters. In this work, we introduce Decoupled RL (DeRL) as a general framework which trains separate policies for intrinsically-motivated exploration and exploitation. Such decoupling allows DeRL to leverage the benefits of intrinsic rewards for exploration while demonstrating improved robustness and sample efficiency. We evaluate DeRL algorithms in two sparse-reward environments with multiple types of intrinsic rewards. Our results show that DeRL is more robust to varying scale and rate of decay of intrinsic rewards and converges to the same evaluation returns than intrinsically-motivated baselines in fewer interactions. Lastly, we discuss the challenge of distribution shift and show that divergence constraint regularisers can successfully minimise instability caused by divergence of exploration and exploitation policies.
Heavy-Traffic Universality of Redundancy Systems with Assignment Constraints

Modern service systems, like cloud computing platforms or data center environments, commonly face a high degree of heterogeneity. This heterogeneity is not only caused by different server speeds but also, by binding task-server relations that must be taken into account when assigning incoming tasks. Unfortunately, there are hardly any theoretical performance guarantees as these systems do not fall within the typical supermarket modeling framework which heavily relies on strong symmetry and homogeneity assumptions. In “Heavy-traffic universality of redundancy systems with assignment constraints,” Cardinaels, Borst, and van Leeuwaarden provide insight in the performance of these systems operating under redundancy scheduling policies. Surprisingly, when experiencing high demand, these systems exhibit state space collapse and can achieve a similar level of resource pooling and performance as a fully flexible system, even subject to quite strict task-server constraints.
Human Pose Transfer with Augmented Disentangled Feature Consistency

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring the poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.
Vortex solutions in the presence of Dark Portals

The existence of hidden sectors weakly coupled to the visible one has been extensively studied as a way to extend the Standard Model (SM) and to provide a good dark matter candidate. In this work we analyze two models in which gauge and scalar hidden fields interact with the visible sector which, for simplicity we take as an Abelian Higgs model. In one of them, the hidden sector consists of an uncharged scalar. The connection with the visible Abelian Higgs sector is in this case provided by the interaction between the scalars in the two sectors and also by the coupling between the hidden scalar and the visible squared field strength. This model can be seen as an extension of previously studied ones both in high energy and in condensed matter physics and the solutions that we find indicate that it could be relevant in connection to establishing the dark matter relic density. In the second model, both sectors correspond to spontaneously broken gauge theories and hence they feature classical vortex solutions. Introducing three different portals between the two sectors, we analyze the dependence of the model dynamics on the portal parameters and discuss the physical implications in connection with dark matter and gravitational radiation issues.
Optimal Evaluation of Symmetry-Adapted n-Correlations Via Recursive Contraction of Sparse Symmetric Tensors

We present a comprehensive analysis of an algorithm for evaluating high-dimensional polynomials that are invariant (or equi-variant) under permutations and rotations. This task arises in the evaluation linear models as well as equivariant neural network models of many-particle systems. The theoretical bottleneck is the contraction of a high-dimensional symmetric and sparse tensor with a specific sparsity pattern that is directly related to the symmetries imposed on the polynomial. The sparsity of this tensor makes it challenging to construct a highly efficient evaluation scheme. The references [10, 11] introduced a recursive evaluation strategy that relied on a number of heuristics, but performed well in tests. In the present work, we propose an explicit construction of such a recursive evaluation strategy and show that it is in fact optimal in the limit of infinite polynomial degree.
A Priori Error Estimates of a Discontinuous Galerkin Finite Element Method for the Kelvin-Voigt Viscoelastic Fluid Motion Equations

This paper applies a discontinuous Galerkin finite element method to the Kelvin-Voigt viscoelastic fluid motion equations when the forcing function is in $L^\infty({\bf L}^2)$-space. Optimal a priori error estimates in $L^\infty({\bf L}^2)$-norm for the velocity and in $L^\infty(L^2)$-norm for the pressure approximations for the semi-discrete discontinuous Galerkin method are derived here. The main ingredients for establishing the error estimates are the standard elliptic duality argument and a modified version of the Sobolev-Stokes operator defined on appropriate broken Sobolev spaces. Further, under the smallness assumption on the data, it has been proved that these estimates are valid uniformly in time. Then, a first-order accurate backward Euler method is employed to discretize the semi-discrete discontinuous Galerkin Kelvin-Voigt formulation completely. The fully discrete optimal error estimates for the velocity and pressure are established. Finally, using the numerical experiments, theoretical results are verified. It is worth highlighting here that the error results in this article for the discontinuous Galerkin method applied to the Kelvin-Voigt model using finite element analysis are the first attempt in this direction.
On the Uniqueness and Stability of the Equilibrium Price in Quasi-Linear Economies

In this paper, we show that if every consumer in an economy has a quasi-linear utility function, then the normalized equilibrium price is unique, and it is locally stable with respect to the t\^atonnement process. If the dimension of the consumption space is two, then this result can be expressed by the corresponding partial equilibrium model. Our study can be seen as that extends the results in partial equilibrium theory to economies with more than two dimensional consumption space.
Task Modifiers for HTN Planning and Acting

The ability of an agent to change its objectives in response to unexpected events is desirable in dynamic environments. In order to provide this capability to hierarchical task network (HTN) planning, we propose an extension of the paradigm called task modifiers, which are functions that receive a task list and a state and produce a new task list. We focus on a particular type of problems in which planning and execution are interleaved and the ability to handle exogenous events is crucial. To determine the efficacy of this approach, we evaluate the performance of our task modifier implementation in two environments, one of which is a simulation that differs substantially from traditional HTN domains.
A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP

We study the risk-sensitive exponential cost Markov decision process (MDP) formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies. We derive a formula that can be used to compute the policy gradient from (state, action, cost) information collected from sample paths of the MDP for each fixed parameterized policy. Unlike the traditional average cost problem, standard stochastic approximation theory cannot be used to exploit this formula. To address the issue, we introduce a truncated and smooth version of the risk-sensitive cost and show that this new cost criterion can be used to approximate the risk-sensitive cost and its gradient uniformly under some mild assumptions. We then develop a trajectory-based gradient algorithm to minimize the smooth truncated estimation of the risk-sensitive cost and derive conditions under which a sequence of truncations can be used to solve the original, untruncated cost problem. Funding: This work was supported by the Office of Naval Research Global [Grant N0001419-1-2566], the Division of Computer and Network Systems [Grant 21-06801], the Army Research Office [Grant W911NF-19-1-0379], and the Division of Computing and Communication Foundations [Grants 17-04970 and 19-34986].
Improving Computational Complexity in Statistical Models with Second-Order Information

It is known that when the statistical models are singular, i.e., the Fisher information matrix at the true parameter is degenerate, the fixed step-size gradient descent algorithm takes polynomial number of steps in terms of the sample size $n$ to converge to a final statistical radius around the true parameter, which can be unsatisfactory for the application. To further improve that computational complexity, we consider the utilization of the second-order information in the design of optimization algorithms. Specifically, we study the normalized gradient descent (NormGD) algorithm for solving parameter estimation in parametric statistical models, which is a variant of gradient descent algorithm whose step size is scaled by the maximum eigenvalue of the Hessian matrix of the empirical loss function of statistical models. When the population loss function, i.e., the limit of the empirical loss function when $n$ goes to infinity, is homogeneous in all directions, we demonstrate that the NormGD iterates reach a final statistical radius around the true parameter after a logarithmic number of iterations in terms of $n$. Therefore, for fixed dimension $d$, the NormGD algorithm achieves the optimal overall computational complexity $\mathcal{O}(n)$ to reach the final statistical radius. This computational complexity is cheaper than that of the fixed step-size gradient descent algorithm, which is of the order $\mathcal{O}(n^{\tau})$ for some $\tau>1$, to reach the same statistical radius. We illustrate our general theory under two statistical models: generalized linear models and mixture models, and experimental results support our prediction with general theory.
A Mini-Block Natural Gradient Method for Deep Neural Networks

Currently, the training of deep neural networks (DNNs) is mostly done using ﬁrst-order methods. Some of these methods (e.g., Adam, AdaGrad, and RMSprop, and their variants) incorporate a small amount of curvature information by using a diagonal matrix to precondition the stochastic gradient. Recently, effective second-order meth-ods, such as KFAC, K-BFGS, Shampoo, and TNT, have been developed for training DNNs, by pre-conditioning the stochastic gradient by layer-wise block-diagonal matrices. Here we propose and analyze the convergence of an approximate nat-ural gradient method, mini-block Fisher (MBF), that lies in between these two classes of methods. Speciﬁcally, our method uses a block-diagonal approximation to the Fisher matrix, where for each DNN layer, whether it is convolutional or feed-forward and fully connected, the associated diagonal block is also block-diagonal and is composed of a large number of mini-blocks of modest size. Our novel approach utilizes the parallelism of GPUs to efﬁciently perform computations on the large number of matrices in each layer. Consequently, MBF’s per-iteration computational cost is only slightly higher than it is for ﬁrst-order methods. Finally, MBF’s performance is compared to that of several baseline methods, on both Auto-encoder and CNN problems, to validate its effectiveness both in terms of time efﬁciency and generalization power.
Time–Frequency Mask-Aware Bidirectional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation

Underwater acoustic signal separation is a key technique for underwater communications. The existing methods are mostly model-based, and cannot accurately characterize the practical underwater acoustic communication environment. They are only suitable for binary signal separation and cannot handle multivariate signal separation. However, recurrent neural networks (RNNs) show a powerful ability to extract the features of temporal sequences. Inspired by this, in this paper, we present a data-driven approach for underwater acoustic signal separation using deep learning technology. We use a bidirectional long short-term memory (Bi-LSTM) approach to explore the features of a time–frequency (T-F) mask, and propose a T-F-mask-aware Bi-LSTM for signal separation. Taking advantage of the sparseness of the T-F image, the designed Bi-LSTM network is able to extract the discriminative features for separation, which further improves the separation performance. In particular, this method breaks through the limitations of the existing methods and not only achieves good results in multivariate separation but also effectively separates signals when they are mixed with 40 dB Gaussian noise signals. The experimental results show that this method can achieve a 97% guarantee ratio (PSR), and the average similarity coefficient of the multivariate signal separation is stable above 0.8 under high noise conditions. It should be noted that our model can only handle known signals such as test signals for calibration.
Optically excited spin dynamics of thermally metastable skyrmions in Fe$_{0.75}$Co$_{0.25}$Si

We investigate the microwave spin excitations of the cubic chiral magnet Fe 0 . 75 Co 0 . 25 Si as driven by the thermal modulation of magnetic interactions via laser heating and probed by time-resolved measurements of the magneto-optical Kerr effect. Focusing on the topologically nontrivial skyrmion lattice state, the dynamic properties in thermodynamic equilibrium are compared with those of a metastable state prepared by means of rapid ﬁeld cooling. In both cases, we ﬁnd precessional and exponential contributions to the dynamic response, characteristic of a breathing mode and energy dissipation, respectively. When taking into account the universal scaling as a function of temperature, the precession frequencies in the equilibrium and metastable skyrmion state are in excellent quantitative agreement. This ﬁnding highlights that skyrmion states far from thermal equilibrium promise great ﬂexibility, for instance with respect to temperature and ﬁeld scales, both for possible microwave applications and the study of fundamental properties. equilibrium and metastable skyrmion state using time-resolved magneto-optical Kerr effect measurements. The skyrmion dynamics are well modeled by a phenomenological approach, which separates the characteristic for the skyrmion the exponential denotes the We observe an excellent quantitative agreement of the precession frequency in the equilibrium and metastable skyrmion state considering the universal scaling with Our results strongly indicate that the characteristics of are preserved far from this fundamental sig-niﬁcance, the larger and enhanced mode point higher potential possible compared to the equilibrium SkL.