Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum phase transition and Resurgence: Lessons from 3d N = 4 SQED

We study a resurgence structure of a quantum field theory with a phase transition to uncover relations between resurgence and phase transitions. In particular, we focus on three-dimensional N = 4 supersymmetric quantum electrodynamics (SQED) with multiple hypermultiplets, where a second-order quantum phase transition has been recently proposed in the large-flavor limit. We provide interpretations of the phase transition from the viewpoints of Lefschetz thimbles and resurgence. For this purpose, we study the Lefschetz thimble structure and properties of the large-flavor expansion for the partition function obtained by the supersymmetric localization. We show that the second-order phase transition is understood as a phenomenon where a Stokes and anti-Stokes phenomenon occurs simultaneously. The order of the phase transition is determined by how saddles collide at the critical point. In addition, the phase transition accompanies an infinite number of Stokes phenomena due to the supersymmetry. These features are appropriately mapped to the Borel plane structures as the resurgence theory expects. Given the lessons from the SQED, we provide a more general discussion on the relationship between the resurgence and phase transitions. In particular, we show how the information on the phase transition is decoded from the Borel resummation technique.
Data Generation in Low Sample Size Setting Using Manifold Sampling and a Geometry-Aware VAE

While much efforts have been focused on improving Variational Autoencoders through richer posterior and prior distributions, little interest was shown in amending the way we generate the data. In this paper, we develop two non prior-dependent generation procedures based on the geometry of the latent space seen as a Riemannian manifold. The ﬁrst one consists in sampling along geodesic paths which is a natural way to explore the latent space while the second one consists in sampling from the inverse of the metric volume element which is easier to use in practice. Both methods are then compared to prior-based methods on various data sets and appear well suited for a limited data regime. Finally, the latter method is used to perform data augmentation in a small sample size setting and is validated across various standard and real-life data sets. In particular, this scheme allows to greatly improve classiﬁcation results on the OASIS database where balanced accuracy jumps from 80.7% for a classiﬁer trained with the raw data to 89.1% when trained only with the synthetic data generated by our method. Such results were also observed on 4 standard data sets.
Novel Percolation Mechanism in PMMA Matrix Composites Containing Segregated ITO Nanowire Networks

Poly(methyl methacrylate) (PMMA)/indium tin oxide (ITO) nanocomposites were prepared by mechanical mixing and compression molding in order to study the properties and microstructure of the composites. The composites were examined by optical and scanning electron microscopy, impedance spectroscopy, and UV‐VIS spectrophotometry. It was observed that upon compaction of the powders above the glass‐transition temperature of the matrix, the PMMA transforms from spherical to polyhedral‐shaped, and develops sharp edges and flat faces. The ITO nanoparticles do not penetrate the polymer particles, resulting in a novel segregated network microstructure. Excellent correlation between the electrical, optical, and microscopy data also provide good insight about the behavior of the filler as the content is increased in the nanocomposites. There is strong evidence that the ITO nanoparticles are extensively displaced during compaction as the PMMA powders become polyhedral‐shaped. Our results indicate that percolation occurs due to the ITO forming a continuous network along the edges of the faceted PMMA particles. The ITO nanoparticles do not appear on the faces of the PMMA particles until after a percolation path has formed and a marked increase in electrical conductivity has occurred. This behavior significantly diverges from previous models for segregated network microstructures which proposed that percolation occurred as the result of limited displacement of the filler during compaction of the mixed powders.
CrossFix: Collaborative bug fixing by recommending similar bugs

Many automated program repair techniques have been proposed for fixing bugs. Some of these techniques use the information beyond the given buggy program and test suite to improve the quality of generated patches. However, there are several limitations that hinder the wide adoption of these techniques, including (1) they rely on a fixed set of repair templates for patch generation or reference implementation, (2) searching for the suitable reference implementation is challenging, (3) generated patches are not explainable. Meanwhile, a recent approach shows that similar bugs exist across different projects and one could use the GitHub issue from a different project for finding new bugs for a related project. We propose collaborative bug fixing, a novelapproach that suggests bug reports that describe a similar bug. Our studyredefines similar bugs as bugs that share the (1) same libraries, (2) same functionalities, (3) same reproduction steps, (4) same configurations, (5) sameoutcomes, or (6) same errors. Moreover, our study revealed the usefulness of similar bugs in helping developers in finding more context about the bug and fixing. Based on our study, we design CrossFix, a tool that automatically suggests relevant GitHub issues based on an open GitHub issue. Our evaluation on 249 open issues from Java and Android projects shows that CrossFix could suggest similar bugs to help developers in debugging and fixing.
Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation

Prior works on formalizing explanations of a graph neural network (GNN) focus on a single use case - to preserve the prediction results through identifying important edges and nodes. In this paper, we develop a multi-purpose interpretation framework by acquiring a mask that indicates topology perturbations of the input graphs. We pack the framework into an interactive visualization system (GNNViz) which can fulfill multiple purposes: Preserve,Promote, or Attack GNN's predictions. We illustrate our approach's novelty and effectiveness with three case studies: First, GNNViz can assist non expert users to easily explore the relationship between graph topology and GNN's decision (Preserve), or to manipulate the prediction (Promote or Attack) for an image classification task on MS-COCO; Second, on the Pokec social network dataset, our framework can uncover unfairness and demographic biases; Lastly, it compares with state-of-the-art GNN explainer baseline on a synthetic dataset.
Constrained Deep Learning-based Model Predictive Control with Improved Constraint Satisfaction

— Machine learning technique can help reduce computational cost of model predictive control (MPC). In this paper, a constrained deep neural networks design is proposed to learn and construct MPC policies for nonlinear input afﬁne dynamic systems. Using constrained training of neural networks helps enforce MPC constraints effectively. We show the asymptotic stability of the learned policies. Additionally, different data sampling strategies are compared in terms of their generalization errors on the learned policy. Furthermore, probabilistic feasibility and optimality guarantees are provided for the learned control policy. The proposed algorithm is implemented on a rotary inverted pendulum experimentally and control performance is demonstrated and compared with the exact MPC and the normally trained learning MPC. The results show that the proposed algorithm improves constraint satisfaction while preserves computational efﬁciency of the learned policy.
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors

Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the region proposal mechanisms and ROI-based instance-level features to design fine-grained feature alignment modules with respect to the foreground objects. However, for one-stage detectors, it is hard or even impossible to obtain explicit instance-level features in the detection pipelines. Motivated by this, we propose an Implicit Instance-Invariant Network (I3Net), which is tailored for adapting one-stage detectors and implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers. Specifically, we facilitate the adaptation from three aspects: (1) Dynamic and Class-Balanced Reweighting (DCBR) strategy, which considers the coexistence of intra-domain and intra-class variations to assign larger weights to those sample-scarce categories and easy-to-adapt samples; (2) Category-aware Object Pattern Matching (COPM) module, which boosts the cross-domain foreground objects matching guided by the categorical information and suppresses the uninformative background features; (3) Regularized Joint Category Alignment (RJCA) module, which jointly enforces the category alignment at different domain-specific layers with a consistency regularization. Experiments reveal that I3Net exceeds the state-of-the-art performance on benchmark datasets.
Nonreciprocal guided waves in the presence of swift electron beams

Breaking the reciprocity of electromagnetic interactions is of paramount importance in photonic and microwave technologies, as it enables unidirectional power flows and other unique electromagnetic phenomena. Here we explore a method to break the reciprocity of electromagnetic guided waves utilizing an electron beam with a constant velocity. By introducing an effective dynamic conductivity for the beam, we theoretically demonstrate how nonreciprocal guided waves and a one-way propagating regime can be achieved through the interaction of swift electrons with electromagnetic waves in two-dimensional (2D) parallel-plate and three-dimensional (3D) circular-cylindrical waveguides. Unlike the conventional electron beam structures such as traveling wave tubes and electron accelerators, here the goal is neither to generate and/or amplify the wave nor to accelerate electrons. Instead, we study the salient features of nonreciprocity and unidirectionality of guided waves in such structures. The relevant electromagnetic properties such as the modal dispersion, the field distributions, the operating frequency range, and the nonreciprocity strength and its dependence on the electron velocity and number density are presented and discussed. Moreover, we compare the dispersion characteristics of waves in such structures with some electric-current-based scenarios in materials reported earlier. This broadband tunable magnet-free method offers a unique opportunity to have a switchable strong nonreciprocal response in optoelectronics, nanophotonics, and THz systems.
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

In this letter, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.
Error Identification and Recovery in Robotic Snap Assembly

Existing methods for predicting robotic snap joint assembly cannot predict failures before their occurrence. To address this limitation, this paper proposes a method for predicting error states before the occurence of error, thereby enabling timely recovery. Robotic snap joint assembly requires precise positioning; therefore, even a slight offset between parts can lead to assembly failure. To correctly predict error states, we apply functional principal component analysis (fPCA) to 6Dforce/torque profiles that are terminated before the occurence of an error. The error state is identified by applying a feature vector to a decision tree, wherein the support vector machine (SVM) is employed at each node. If the estimation accuracy is low, we perform additional probing to more correctly identify the error state. Finally, after identifying the error state, a robot performs the error recovery motion based on the identified error state. Through the experimental results of assembling plastic parts with four snap joints, we show that the error states can be correctly estimated and a robot can recover from the identified error state.
Spirit Distillation: Precise Real-time Prediction with Insufficient Data

—Recent trend demonstrates the effectiveness of deep neural networks (DNNs) apply on the task of environment perception in autonomous driving system. While large-scale and complete data can train out ﬁne DNNs, collecting it is always difﬁcult, expensive, and time-consuming. Also, the signiﬁcance of both accuracy and efﬁciency cannot be over-emphasized due to the requirement of real-time recognition. To alleviate the conﬂicts between weak data and high computational consumption of DNNs, we propose a new training framework named Spirit Distillation(SD). It extends the ideas of ﬁne-tuning-based transfer learning(FTT) and feature-based knowledge distillation. By allowing the student to mimic its teacher in feature extraction, the gap of general features between the teacher-student networks is bridged. The Image Party distillation enhancement method(IP) is also proposed, which shufﬂing images from various domains, and randomly selecting a few as mini-batch. With this approach, the overﬁtting that the student network to the general features of the teacher network can be easily avoided. Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI. Results demonstrate the boosting performance in segmentation(mIOU and high-precision accuracy boost by 1.4% and 8.2% respectively, with 78.2% output variance), and can gain a precise compact network with only 41.8% FLOPs(see Fig. 1). This paper is a pioneering work on knowledge distillation applied to few-shot learning. The proposed methods signiﬁcantly reduce the dependence on data of DNNs training, and improves the robustness of DNNs when facing rare situations, with real-time requirement satisﬁed. We provide important technical support for the advancement of scene perception technology for autonomous driving.
Intraday trading strategy based on time series and machine learning for Chinese stock market

This article comes up with an intraday trading strategy under T+1 using Markowitz optimization and Multilayer Perceptron (MLP) with published stock data obtained from the Shenzhen Stock Exchange and Shanghai Stock Exchange. The empirical results reveal the profitability of Markowitz portfolio optimization and validate the intraday stock price prediction using MLP. The findings further combine the Markowitz optimization, an MLP with the trading strategy, to clarify this strategy's feasibility.
Do Geopolymers Actually Contain Nanocrystalline Zeolites? A Reexamination of Existing Results

Geopolymers are a class of aluminosilicate materials synthesized by alkaline or alkali-silicate activation of solid alumina- and silica-containing precursor materials at ambient or higher temperature. These products have highly significant commercial and technological potential, but the exact physicochemical nature of the geopolymeric binder phase has never before been determined. Through analysis of existing experimental results from the literature and comparison with related systems, in particular the hydrothermal synthesis of zeolites, geopolymeric binders are identified as being comprised of agglomerates of nanocrystalline zeolites compacted by an amorphous gel phase. The degree of crystallinity is largely determined by product formulation and synthesis conditions. Results from powder X-ray diffraction, microscopy, electron diffraction, mechanical strength testing, and calorimetry are analyzed and shown to be consistent with the theory presented. The implications of nanocrystallinity are discussed, an.
Effects of pH Control On Composting of Garbage

Composting experiments of garbage were conducted by using a laboratory scale reactor under well controlled experimental conditions and the effects of pH control were quantitatively analysed. In the pH controlled experiment, lime was added to prevent pH decreasing below 7, especially at the early stage of composting. The degradation rate of organic matter in the pH controlled experiment was faster than that without. Nitrogen loss was enhanced by the control of pH value, but the amount of promotion was relatively small. The pH dependency on the activity of microorganisms, which contribute to the composting rate, was investigated by using a liquid medium containing glucose and proteins as nutrients. The optimum pH for the growth rate and the degradation activity of proteins of the microorganisms was in the range of 7-8, while the decomposition of glucose proceeded rapidly at an early stage of composting in a pH range from 6 to 9.
Generative-Adversarial-Networks-based Ghost Recognition

—Nowadays, target recognition technique plays an important role in many ﬁelds. However, the current target image information based methods suffer from the inﬂuence of image quality and the time cost of image reconstruction. In this paper, we propose a novel imaging-free target recognition method combining ghost imaging (GI) and generative adversarial networks (GAN). Based on the mechanism of GI, a set of random speckles sequence is employed to illuminate target, and a bucket detector without resolution is utilized to receive echo signal. The bucket signal sequence formed after continuous detections is constructed into a bucket signal array, which is regarded as the sample of GAN. Then, conditional GAN is used to map bucket signal array and target category. In practical application, the speckles sequence in training step is employed to illuminate target, and the bucket signal array is input GAN for recognition. The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability. Extensive experiments show that the proposed method achieves promising performance.
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies

Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability in an application. To address the problem, this paper presents the first imitation learning framework that incorporates Bayesian variational inference for learning flexible nonparametric multi-action policies, while simultaneously robustifying the policies against sources of error, by introducing and optimizing disturbances to create a richer demonstration dataset. This combinatorial approach forces the policy to adapt to challenging situations, enabling stable multi-action policies to be learned efficiently. The effectiveness of our proposed method is evaluated through simulations and real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm. Results show that, through improved flexibility and robustness, the learning performance and control safety are better than comparison methods.
On sign changes of primitive Fourier coefficients of Siegel cusp forms

In this article, we establish quantitative results for sign changes in certain subsequences of primitive Fourier coefficients of a non-zero Siegel cusp form of arbitrary degree over congruence subgroups. As a corollary of our result for degree two Siegel cusp forms, we get sign changes of its diagonal Fourier coefficients. In the course of our proofs, we prove the non-vanishing of certain type of Fourier-Jacobi coefficients of a Siegel cusp form and all theta components of certain Jacobi cusp forms of arbitrary degree over congruence subgroups, which are also of independent interest.
Directed Data-Processing Inequalities for Systems with Feedback

We present novel data-processing inequalities relating the mutual information and the directed information in systems with feedback. The internal deterministic blocks within such systems are restricted only to be causal mappings, but are allowed to be non-linear and time varying, and randomized by their own external random input, can yield any stochastic mapping. These randomized blocks can for example represent source encoders, decoders, or even communication channels. Moreover, the involved signals can be arbitrarily distributed. Our first main result relates mutual and directed information and can be interpreted as a law of conservation of information flow. Our second main result is a pair of data-processing inequalities (one the conditional version of the other) between nested pairs of random sequences entirely within the closed loop. Our third main result introduces and characterizes the notion of in-the-loop (ITL) transmission rate for channel coding scenarios in which the messages are internal to the loop. Interestingly, in this case the conventional notions of transmission rate associated with the entropy of the messages and of channel capacity based on maximizing the mutual information between the messages and the output turn out to be inadequate. Instead, as we show, the ITL transmission rate is the unique notion of rate for which a channel code attains zero error probability if and only if such an ITL rate does not exceed the corresponding directed information rate from messages to decoded messages. We apply our data-processing inequalities to show that the supremum of achievable (in the usual channel coding sense) ITL transmission rates is upper bounded by the supremum of the directed information rate across the communication channel. Moreover, we present an example in which this upper bound is attained. Finally, we further illustrate the applicability of our results by discussing how they make possible the generalization of two fundamental inequalities known in networked control literature.
Alterações nos teores nutricionais foliares de bananeira 'prata-anã' adubada com composto orgânico em cinco ciclos de produção

This work aimed to assess the nutritional status of banana 'Prata-ana' leaves for five cycles of cultivation with organic fertilization, in Botucatu, SP, Brazil. Plants were fertilized with organic compost produced with wood residue and cattle manure. Treatments consisted of the compost rates (0, 98.5, 197.0, 290.5 and 394.0 g of K2O per plant). The experiment was arranged in a randomized block design, with 5 treatments and 5 replications. At blooming, leaves of two plants were collected to analyze the levels of nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, boron, copper, iron, manganese and zinc. The content of most nutrients in leaves was not influenced by the increasing rates of organic compost. During the cycles assessed the levels of leaf nitrogen, phosphorus, potassium, sulfur, boron, iron and manganese decreased, whereas there was an increase of the calcium and magnesium contents. The levels of potassium were below the standards for the cultivar in the state of Sao Paulo, for all the years studied, and yet plants did not show symptoms of weakness or collapse of production. It can be conclude that the range considered appropriate for the culture may be lower than the standards currently adopted.