Miracle-dz/UniFAR-FTUs huggingface dataset:
deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search

With the rapid increase of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning-based approaches that provide end-to-end solutions (i.e., accept natural language as queries and show related code fragments), the performance of code search in the large-scale repositories is still low in accuracy because of the code representation (e.g., AST) and modeling (e.g., directly fusing features in the attention stage). In this paper, we propose a novel learnable deep Graph for Code Search (called deGraphCS) to transfer source code into variable-based flow graphs based on an intermediate representation technique, which can model code semantics more precisely than directly processing the code as text or using the syntax tree representation. Furthermore, we propose a graph optimization mechanism to refine the code representation and apply an improved gated graph neural network to model variable-based flow graphs. To evaluate the effectiveness of deGraphCS, we collect a large-scale dataset from GitHub containing 41,152 code snippets written in the C language and reproduce several typical deep code search methods for comparison. The experimental results show that deGraphCS can achieve state-of-the-art performance and accurately retrieve code snippets satisfying the needs of the users.
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning

Customer support via chat requires agents to resolve customer queries with minimum wait time and maximum customer satisfaction. Given that the agents as well as the customers can have varying levels of literacy, the overall quality of responses provided by the agents tend to be poor if they are not predefined. But using only static responses can lead to customer detraction as the customers tend to feel that they are no longer interacting with a human. Hence, it is vital to have variations of the static responses to reduce monotonicity of the responses. However, maintaining a list of such variations can be expensive. Given the conversation context and the agent response, we propose an unsupervised frame-work to generate contextual paraphrases using autoregressive models. We also propose an automated metric based on Semantic Similarity, Textual Entailment, Expression Diversity and Fluency to evaluate the quality of contextual paraphrases and demonstrate performance improvement with Reinforcement Learning (RL) fine-tuning using the automated metric as the reward function.
Epidemic change-point detection in general integer-valued time series

In this paper, we consider the structural change in a class of discrete valued time series, which the true conditional distribution of the observations is assumed to be unknown. The conditional mean of the process depends on a parameter $\theta^*$ which may change over time. We provide sufficient conditions for the consistency and the asymptotic normality of the Poisson quasi-maximum likelihood estimator (QMLE) of the model. We consider an epidemic change-point detection and propose a test statistic based on the QMLE of the parameter. Under the null hypothesis of a constant parameter (no change), the test statistic converges to a distribution obtained from a difference of two Brownian bridge. The test statistic diverges to infinity under the epidemic alternative, which establishes that the proposed procedure is consistent in power. The effectiveness of the proposed procedure is illustrated by simulated and real data examples.
Continuous-Domain Formulation of Inverse Problems for Composite Sparse-Plus-Smooth Signals

We present a novel framework for the reconstruction of 1D composite signals assumed to be a mixture of two additive components, one sparse and the other smooth, given a finite number of linear measurements. We formulate the reconstruction problem as a continuous-domain regularized inverse problem with multiple penalties. We prove that these penalties induce reconstructed signals that indeed take the desired form of the sum of a sparse and a smooth component. We then discretize this problem using Riesz bases, which yields a discrete problem that can be solved by standard algorithms. Our discretization is exact in the sense that we are solving the continuous-domain problem over the search space specified by our bases without any discretization error. We propose a complete algorithmic pipeline and demonstrate its feasibility on simulated data.
Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models

In this paper, we present a Bayesian multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts interacting multiple models (IMM) parameters to describe the mobile agent state dynamics. The time-evolution of the IMM parameters is described by a Markov chain and the parameters are incorporated into the factor graph structure that represents the statistical structure of the SLAM problem. The proposed belief propagation (BP)-based algorithm adapts, in an online manner, to time-varying system models by jointly inferring the model parameters along with the agent and map feature states. The performance of the proposed algorithm is finally evaluating with a simulated scenario. Our numerical simulation results show that the proposed multipath-based SLAM algorithm is able to cope with strongly changing agent state dynamics.
Are Multilingual Models Effective in Code-Switching?

Multilingual language models have shown decent performance in multilingual and cross-lingual natural language understanding tasks. However, the power of these multilingual models in code-switching tasks has not been fully explored. In this paper, we study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting by considering the inference speed, performance, and number of parameters to measure their practicality. We conduct experiments in three language pairs on named entity recognition and part-of-speech tagging and compare them with existing methods, such as using bilingual embeddings and multilingual meta-embeddings. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching, while using meta-embeddings achieves similar results with significantly fewer parameters.
MICROMACHINED PIEZOELECTRIC MICROSPEAKERS FABRICATED WITH HIGH QUALITY ALN THIN FILM

ABSTRACT This paper describes the micromachined piezoelectric microspeakers that can produce the audible signal with 20 V peak-to-peak input voltages. The diaphragm size is 4 × 4 mm2 and the thickness of diaphragm is around 1 micron meter except partially etched piezoelectric area. The maximum sound output pressure of the microspeaker is even higher than ever before with a small diaphragm in high frequency range around 10 kHz. This successful result bases upon using high quality AlN thin film. The deposited AlN thin film shows c-axis oriented columnar structure and very fine grains. The highest SPL (Sound Pressure Level) measured from 300 Hz to 12 kHz shows about 100 dB around 10 kHz in case of circular type microspeaker and about 76 dB in case of cross type, respectively.
Searches for sterile neutrinos and axionlike particles from the Galactic halo with eROSITA

Dark matter might be made of"warm"particles, such as sterile neutrinos in the keV mass range, which can decay into photons through mixing and are consequently detectable by X-ray telescopes. Axionlike particles (ALPs) are detectable by X-ray telescopes too when coupled to standard model particles and decay into photons in the keV range. Both particles could explain the unidentified 3.5 keV line and, interestingly, XENON1T observed an excess of electron recoil events most prominent at 2-3 keV. One explanation could be an ALPs origin, which is not yet excluded by X-ray constraints in an anomaly-free symmetry model in which the photon production is suppressed. We study the diffuse emission coming from the Galactic halo, and calculate the sensitivity of all-sky X-ray survey performed by eROSITA to identify a sterile neutrino or ALP dark matter. We estimate bounds on the mixing angle of the sterile neutrinos and coupling strength of the ALPs. After four years of data-taking by eROSITA, we expect to set stringent constraints, and in particular, we expect to firmly probe mixing angle $\sin^2(2\theta)$ up to nearly two orders magnitude below the best-fit value for explaining the unidentified 3.5 keV line. Moreover, with eROSITA, we will be able to probe the ALP parameter space of couplings to photons and electrons, and potentially confirm an ALP origin of the XENON1T excess.
Pair‐switching rerandomization

Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance‐rejection sampling method used in rerandomization is computationally inefficient. As a result, it is time‐consuming for rerandomization to draw numerous independent assignments, which are necessary for performing Fisher randomization tests and constructing randomization‐based confidence intervals. To address this problem, we propose a pair‐switching rerandomization (PSRR) method to draw balanced assignments efficiently. We obtain the unbiasedness and variance reduction of the difference‐in‐means estimator and show that the Fisher randomization tests are valid under PSRR. Moreover, we propose an exact approach to invert Fisher randomization tests to confidence intervals, which is faster than the existing methods. In addition, our method is applicable to both nonsequentially and sequentially randomized experiments. We conduct comprehensive simulation studies to compare the finite‐sample performance of the proposed method with that of classical rerandomization. Simulation results indicate that PSRR leads to comparable power of Fisher randomization tests and is 3–23 times faster than classical rerandomization. Finally, we apply the PSRR method to analyze two clinical trial datasets, both of which demonstrate the advantages of our method.
Magnetic ordering of the distorted kagome antiferromagnet 
Y3Cu9(OH)18[Cl8
(OH)] prepared via optimal synthesis

W. Sun,1 T. Arh,2, 3 M. Gomiľsek,2 P. Koželj,2, 3 S. Vrtnik,2 M. Herak,4 J.-X. Mi,1 and A. Zorko2, 3, ∗ 1Fujian Provincial Key Laboratory of Advanced Materials, Department of Materials Science and Engineering, College of Materials, Xiamen University, Xiamen 361005, Fujian Province, People’s Republic of China 2Jožef Stefan Institute, Jamova c. 39, SI-1000 Ljubljana, Slovenia 3Faculty of Mathematics and Physics, University of Ljubljana, Jadranska u. 19, SI-1000 Ljubljana, Slovenia 4Institute of Physics, Bijenička c. 46, HR-10000 Zagreb, Croatia (Dated: March 25, 2021)
Bioactive ingredient levels of tea leaves are associated with leaf Al level interactively influenced by acid rain intensity and soil Al supply

The present study aimed to investigate the effects of acid rain (pH 3.0, 4.5 and 6.0) and soil available aluminium (Al) levels (adding 0, 0.5 and 1.0 g kg -1 Al into soil in the form of Al 2 (SO 4 ) 3 solution) on Al cycling in soil-tea plant continuum and the levels of main bioactive ingredients of tea leaves using a pot experiment. Five Al forms in soil were extracted using a sequential extraction procedure, and the Al contents were determined by the inductively coupled plasma-atomic emission spectrometry (ICP-AES). Tea polyphenols, caffeine and amino acids were determined by methods of ferric tartrate, lead acetate precipitation, and ninhydrin colorimetry, respectively, and flavonoids were measured with aluminium chloride colorimetric assay. Two forms of easily absorbable Al including exchangeable Al (E-Al) and organic Al complexes (O-Al) showed higher contents in rhizosphere soil than in bulk soil and their contents in rhizosphere and bulk soils were higher for treatments of simulated acid rains at pH 4.5 and pH 3.0 than for irrigation with normal water at pH 6.0, and higher for treatments added with Al 0.5 and 1.0 g kg -1 in soil than without Al addition. Al assimilation and accumulation in plants were slightly promoted by acid rain with addition of 0.5 g kg -1 Al in soil, while inhibited with addition of 1.0 g kg -1 Al in soil. Al levels in tea shoots, leaves and roots were higher in the treatments with elevated Al level than without Al addition. The contents of tea polyphenols, caffeine and amino acids in tea leaves were higher at pH 4.5 in comparison to the other two pH values, and they increased with increasing Al supply. However, the contents of tea polyphenols, caffeine and amino acids in tea leaves were highest at the leaf Al content of 7~8 mg g -1 . It is therefore concluded that the amount of bioactive ingredients in tea leaves is closely related to leaf Al level, which is interactively influenced by acid rain intensity and soil Al supply. It is feasible to produce tea with reasonable quantity of the bioactive ingredients and a safe Al level by adjusting the acidity and contents of Al in soil.
Magnetic field induced neutrino chiral transport near equilibrium

Based on the recently formulated chiral radiation transport theory for left-handed neutrinos, we study the chiral transport of neutrinos near thermal equilibrium in core-collapse supernovae. We first compute the near-equilibrium solution of the chiral radiation transport equation under the relaxation time approximation, where the relaxation time is directly derived from the effective field theory of the weak interaction. By using such a solution, we systematically derive analytic expressions for the nonequilibrium corrections of the neutrino energy-momentum tensor and neutrino number current induced by magnetic fields via the neutrino absorption on nucleons. In particular, we find the nonequilibrium neutrino energy current proportional to the magnetic field. We also discuss its phenomenological consequences such as the possible relation to pulsar kicks.
An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

Background Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.
PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code

The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representations. Nowadays, a popular starting point to represent code is abstract syntax trees (ASTs). Abstract syntax trees have been used for a long time in various software engineering domains, and in particular in IDEs. The API of modern IDEs allows to manipulate and traverse ASTs, resolve references between code elements, etc. Such algorithms can enrich ASTs with new data and therefore may be useful in ML-based code analysis. In this work, we present PSIMiner— a tool for processing PSI trees from the IntelliJ Platform. PSI trees contain code syntax trees as well as functions to work with them, and therefore can be used to enrich code representation using static analysis algorithms of modern IDEs. To showcase this idea, we use our tool to infer types of identifiers in Java ASTs and extend the code2seq model for the method name prediction problem.
Role of defects in ultrafast charge recombination in monolayer 
MoS2

In this work, we have systematically studied the role of point defects in the recombination time of monolayer MoS2 using time-dependent ab initio non-adiabatic molecular dynamics simulations. Various types of point defects, such as S vacancy, S interstitial, Mo vacancy and Mo interstitial have been considered. We show that defects strongly accelerate the electron-hole recombination, especially interstitial S atoms do that by 3 orders of magnitude higher compared to pristine MoS2. Mo defects (both vacancy and interstitial) introduce a multitude of de-excitation pathways via various defect levels in the energy gap. The results of this study provide some fundamental understanding of photoinduced de-excitation dynamics in presence of defects in highly technologically relevant 2D MoS2.
Efficiently Answering Durability Prediction Queries

We consider a class of queries called durability prediction queries that arise commonly in predictive analytics, where we use a given predictive model to answer questions about possible futures to inform our decisions. Examples of durability prediction queries include "what is the probability that this financial product will keep losing money over the next 12 quarters before turning in any profit? " and "what is the chance for our proposed server cluster to fail the required service-level agreement before its term ends? " We devise a general method called Multi-Level Splitting Sampling (MLSS) that can efficiently handle complex queries and complex models---including those involving black-box functions---as long as the models allow us to simulate possible futures step by step. Our method addresses the inefficiency of standard Monte Carlo (MC) methods by applying the idea of importance splitting to let one "promising" sample path prefix generate multiple "offspring" paths, thereby directing simulation efforts toward more promising paths. We propose practical techniques for designing splitting strategies, freeing users from manual tuning. Experiments show that our approach is able to achieve unbiased estimates and the same error guarantees as standard MC while offering an order-of-magnitude cost reduction.
Practical Effects of Integrating Temperature with Strang Split Reactions

Many astrophysical environments involve convective or explosive flows driven by thermonuclear reactions (Type Ia supernovae, classical novae, X-ray bursts, stellar evolution). Simulation codes need to accurately capture the interactions between reactions and hydrodynamics to produce realistic models of these events. For astrophysical reacting flows, operator splitting is commonly used to couple hydrodynamics and reactions. Each process operates independent of one another, but by staggering the updates in a symmetric fashion (via Strang splitting) second order accuracy in time can be achieved. However, approximations are often made to the reacting system, including the choice of whether or not to integrate temperature with the species. Here we demonstrate through a simple convergence test that integrating an energy equation together with reactions achieves the best convergence when modeling reactive flows with Strang splitting. Additionally, second order convergence cannot be achieved without integrating an energy or temperature equation.
Pricing Energy Derivatives in Markets Driven by Tempered Stable and CGMY Processes of Ornstein–Uhlenbeck Type

In this study, we consider the pricing of energy derivatives when the evolution of spot prices follows a tempered stable or a CGMY-driven Ornstein–Uhlenbeck process. To this end, we first calculate the characteristic function of the transition law of such processes in closed form. This result is instrumental for the derivation of nonarbitrage conditions such that the spot dynamics is consistent with the forward curve. Moreover, we also conceive efficient algorithms for the exact simulation of the skeleton of such processes and propose a novel procedure when they coincide with compound Poisson processes of Ornstein–Uhlenbeck type. We illustrate the applicability of the theoretical findings and the simulation algorithms in the context of pricing different contracts, namely strips of daily call options, Asian options with European style and swing options.
Minimal 
k
-inflation in light of the conformal metric-affine geometry

We motivate a minimal realization of slow-roll k-inflation by incorporating the local conformal symmetry and the broken global SO(1, 1) symmetry in the metric-affine geometry. With use of the metric-affine geometry where both the metric and the affine connection are treated as independent variables, the local conformal symmetry can be preserved in each term of the Lagrangian and thus higher derivatives of scalar fields can be easily added in a conformally invariant way. Predictions of this minimal slow-roll k-inflation, ns ∼ 0.96, r ∼ 0.005, and cs ∼ 0.03, are not only consistent with current observational data but also have a prospect to be tested by forthcoming observations.