Miracle-dz/UniFAR-FTUs huggingface dataset:
Excitation and detection of coherent magnon polarons in a ferromagnetic nanograting

Strong coupling between two quanta of different excitations leads to the formation of a hybridized state which paves a way for exploiting new degrees of freedom to control phenomena with high efficiency and precision. A magnon polaron is the hybridized state of phonon and magnon, the elementary quanta of lattice vibrations and spin waves in a magnetically-ordered material. A magnon polaron can be formed at the intersection of the magnon and phonon dispersions, where their frequencies coincide. The observation of coherent magnon polarons in the time domain has remained extremely challenging because the weak interaction of magnons and phonons and their short lifetime jeopardize the strong coupling required for the formation of a hybridized state. Here, we overcome these limitations by localizing magnons and phonons in a nanograting fabricated from a metallic ferromagnet. The spatial matching of the localized phonon and magnon modes results in high coupling strength, which in combination with their long lifetime allow us to find clear evidence of an optically excited magnon polaron in the magnetic and elastic coherent responses of the nanograting. We show that the symmetries of the localized magnon and phonon states play a crucial role in the magnon polaron formation.
AutoGMM: Automatic Gaussian Mixture Modeling in Python

Gaussian mixture modeling is a fundamental tool in clustering, as well as discriminant analysis and semiparametric density estimation. However, estimating the optimal model for any given number of components is an NP-hard problem, and estimating the number of components is in some respects an even harder problem. In R, a popular package called mclust addresses both of these problems. However, Python has lacked such a package. We therefore introduce AutoGMM, a Python algorithm for automatic Gaussian mixture modeling. AutoGMM builds upon scikit-learn's AgglomerativeClustering and GaussianMixture classes, with certain modifications to make the results more stable. Empirically, on several different applications, AutoGMM performs approximately as well as mclust. This algorithm is freely available and therefore further shrinks the gap between functionality of R and Python for data science.
Chase-escape with death on trees

Chase-escape is a competitive growth process in which red particles spread to adjacent uncolored sites while blue particles overtake adjacent red particles. This can be thought of as prey escaping from pursuing predators. On d-ary trees, we introduce the modification that red particles die and describe the phase diagram for red and blue particle survival as the death rate is varied. Our analysis includes the behavior at criticality, which is different than what occurs in the process without death. Many of our results rely on novel connections to weighted Catalan numbers and analytic combinatorics.
The role of shopping mission in retail customer segmentation

In retailing, it is important to understand customer behavior and determine customer value. A useful tool to achieve such goals is the cluster analysis of transaction data. Typically, a customer segmentation is based on the recency, frequency, and monetary value of shopping or the structure of purchased products. We take a different approach and base our segmentation on the shopping mission—reason why a customer visits the shop. Shopping missions include focused purchases of specific product categories and general purchases of various sizes. In an application to a Czech drugstore chain, we show that the proposed segmentation brings unique information about customers and should be used alongside the traditional methods.
ApproxNet: Content and Contention Aware Video Analytics System for the Edge

Videos take lot of time to transport over the network, hence running analytics on live video at the edge devices, right where it was captured has become an important system driver. However these edge devices, e.g., IoT devices, surveillance cameras, AR/VR gadgets are resource constrained. This makes it impossible to run state-of-the-art heavy Deep Neural Networks (DNNs) on them and yet provide low and stable latency under various circumstances, such as, changes in the resource availability on the device, the content characteristics, or requirements from the user. In this paper we introduce ApproxNet, a video analytics system for the edge. It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under different system conditions and resource contentions, variations in the complexity of the video contents and user requirements. It achieves this by enabling two approximation knobs within a single DNN model, rather than creating and maintaining an ensemble of models (such as in MCDNN [Mobisys-16]). Ensemble models run into memory issues on the lightweight devices and incur large switching penalties among the models in response to runtime changes. We show that ApproxNet can adapt seamlessly at runtime to video content changes and changes in system dynamics to provide low and stable latency for object detection on a video stream. We compare the accuracy and the latency to ResNet [2015], MCDNN, and MobileNets [Google-2017].
A Surgery Formula for the Casson-Seiberg-Witten Invariant of Integral Homology $S^1 \times S^3$

We prove a surgery formula of the Casson-Seiberg-Witten invariant of integral homology S × S along an embedded torus, which could either be regarded as an extension of the product formula for Seiberg-Witten invariants or a manifestation of the surgery exact triangle in 4-dimensional Seiberg-Witten theory of homology S × S. As an application, we compute this invariant for mapping tori of 3-manifolds under diffeomorphisms of finite order and fixed-point set being a simple closed curve. This computation generalizes the result of Lin-Ruberman-Saveliev in [15].
An Acceleration Framework for High Resolution Image Synthesis

Synthesis of high resolution images using Generative Adversarial Networks (GANs) is challenging, which usually requires numbers of high-end graphic cards with large memory and long time of training. In this paper, we propose a two-stage framework to accelerate the training process of synthesizing high resolution images. High resolution images are first transformed to small codes via the trained encoder and decoder networks. The code in latent space is times smaller than the original high resolution images. Then, we train a code generation network to learn the distribution of the latent codes. In this way, the generator only learns to generate small latent codes instead of large images. Finally, we decode the generated latent codes to image space via the decoder networks so as to output the synthesized high resolution images. Experimental results show that the proposed method accelerates the training process significantly and increases the quality of the generated samples. The proposed acceleration framework makes it possible to generate high resolution images using less training time with limited hardware resource. After using the proposed acceleration method, it takes only 3 days to train a 1024 *1024 image generator on Celeba-HQ dataset using just one NVIDIA P100 graphic card.
Efficient Reinforcement Learning with a Thought-Game for StarCraft

StarCraft provides an extremely challenging platform for reinforcement learning due to its huge state-space and game length. The previous fastest method requires days to train a full-length game policy in a single commercial machine. Introduction of background knowledge can accelerate the training of reinforcement learning. But how to effectively introduce background knowledge is still an open question. In this paper, we incorporate the background knowledge to reinforcement learning in the form of a thought-game. With the thought-game, the policy is firstly trained in the thought-game fastly and is then transferred to the real game using mapping functions for the second phase training. In our experiments, the trained agent can achieve a 100\% win-rate on the map \textit{Simple64} against the most difficult non-cheating built-in bot (level-7), and the training is 100 times faster than the previous ones under the same computational resource. To test the generalization performance of the agent, a Golden level of StarCraft~II Ladder human player has competed with the agent. With restricted strategy, the agent wins the human player by 4 out of 5 games. We also apply thought-game idea to another game which is "StarCraft: Brood War", the predecessor of StarCraft II. The thought-game approach might shed some light for further studies of efficient reinforcement learning.
Features in Extractive Supervised Single-document Summarization: Case of Persian News

Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either the abstractive or extractive methods. Extractive methods are more popular, due to their simplicity compared with the more elaborate abstractive methods. In extractive approaches, the system will not generate sentences. Instead, it learns how to score sentences within the text by using some textual features and subsequently selecting those with the highest-rank. Therefore, the core objective is ranking and it highly depends on the document. This dependency has been unnoticed by many state-of-the-art solutions. In this work, the features of the document are integrated into vectors of every sentence. In this way, the system becomes informed about the context, increases the precision of the learned model and consequently produces comprehensive and brief summaries.
Living Near the Edge: A Lower-Bound on the Phase Transition of Total Variation Minimization

This work is about the total variation (TV) minimization which is used for recovering gradient-sparse signals from compressed measurements. Recent studies indicate that TV minimization exhibits a phase transition behavior from failure to success as the number of measurements increases. In fact, in large dimensions, TV minimization succeeds in recovering the gradient-sparse signal with high probability when the number of measurements exceeds a certain threshold; otherwise, it fails almost certainly. Obtaining a closed-form expression that approximates this threshold is a major challenge in this field and has not been appropriately addressed yet. In this work, we derive a tight lower-bound on this threshold in case of any random measurement matrix whose null space is distributed uniformly with respect to the Haar measure. In contrast to the conventional TV phase transition results that depend on the simple gradient-sparsity level, our bound is highly affected by generalized notions of gradient-sparsity. Our proposed bound is very close to the true phase transition of TV minimization confirmed by simulation results.
Chronology of Episodic Accretion in Protostars—An ALMA Survey of the CO and H2O Snowlines

Episodic accretion has been used to explain the wide range of protostellar luminosities, but its origin and influence on the star-forming process are not yet fully understood. We present an ALMA survey of N2H+ (1−0) and HCO+ (3−2) toward 39 Class 0 and Class I sources in the Perseus molecular cloud. N2H+ and HCO+ are destroyed via gas-phase reactions with CO and H2O, respectively, thus tracing the CO and H2O snowline locations. A snowline location at a much larger radius than that expected from the current luminosity suggests that an accretion burst has occurred in the past that has shifted the snowline outward. We identified 18/18 Class 0 and 9/10 Class I post-burst sources from N2H+ and 7/17 Class 0 and 1/8 Class I post-burst sources from HCO+. The accretion luminosities during the past bursts are found to be ∼10–100 L⊙. This result can be interpreted as either evolution of burst frequency or disk evolution. In the former case, assuming that refreeze-out timescales are 1000 yr for H2O and 10,000 yr for CO, we found that the intervals between bursts increase from 2400 yr in the Class 0 stage to 8000 yr in the Class I stage. This decrease in the burst frequency may reflect that fragmentation is more likely to occur at an earlier evolutionary stage when the young stellar object is more prone to instability.
Dripping, jetting and tip streaming

Dripping, jetting and tip streaming have been studied up to a certain point separately by both fluid mechanics and microfluidics communities, the former focusing on fundamental aspects while the latter on applications. Here, we intend to review this field from a global perspective by considering and linking the two sides of the problem. First, we present the theoretical model used to study interfacial flows arising in droplet-based microfluidics, paying attention to three elements commonly present in applications: viscoelasticity, electric fields and surfactants. We review both classical and current results of the stability of jets affected by these elements. Mechanisms leading to the breakup of jets to produce drops are reviewed as well, including some recent advances in this field. We also consider the relatively scarce theoretical studies on the emergence and stability of tip streaming in open systems. Second, we focus on axisymmetric microfluidic configurations which can operate on the dripping and jetting modes either in a direct (standard) way or via tip streaming. We present the dimensionless parameters characterizing these configurations, the scaling laws which allow predicting the size of the resulting droplets and bubbles, as well as those delimiting the parameter windows where tip streaming can be found. Special attention is paid to electrospray and flow focusing, two of the techniques more frequently used in continuous drop production microfluidics. We aim to connect experimental observations described in this section of topics with fundamental and general aspects described in the first part of the review. This work closes with some prospects at both fundamental and practical levels.
Optimal stopping times for a class of Ito diffusion bridges

The scope of this paper is to study the optimal stopping problems associated to a stochastic process, which may represent the gain of an investment, for which information on the final value is available a priori. This information may proceed, for example, from insider trading or from pinning at expiration of stock options. We solve and provide explicit solutions to these optimization problems. As special case, we discuss different processes whose optimal barrier has the same shape as the optimal barrier of the Brownian bridge. So doing we provide a catalogue of alternatives to the Brownian bridge which in practice could be better adapted to the data. Moreover, we investigate if, for any given (decreasing) curve, there exists a process with this curve as optimal barrier. This provides a model for the optimal liquidation time, i.e. the optimal time at which the investor should liquidate a position in order to maximize the gain.
On the discriminative power of hyper-parameters in cross-validation and how to choose them

Hyper-parameters tuning is a crucial task to make a model perform at its best. However, despite the well-established methodologies, some aspects of the tuning remain unexplored. As an example, it may affect not just accuracy but also novelty as well as it may depend on the adopted dataset. Moreover, sometimes it could be sufficient to concentrate on a single parameter only (or a few of them) instead of their overall set. In this paper we report on our investigation on hyper-parameters tuning by performing an extensive 10-Folds Cross-Validation on MovieLens and Amazon Movies for three well-known baselines: User-kNN, Item-kNN, BPR-MF. We adopted a grid search strategy considering approximately 15 values for each parameter, and we then evaluated each combination of parameters in terms of accuracy and novelty. We investigated the discriminative power of nDCG, Precision, Recall, MRR, EFD, EPC, and, finally, we analyzed the role of parameters on model evaluation for Cross-Validation.
Measurement, Characterization, and Modeling of LoRa Technology in Multifloor Buildings

In recent years, we have witnessed the rapid development of the long range (LoRa) technology, together with extensive studies trying to understand its performance in various application settings. In contrast to measurements performed in large outdoor areas, a limited number of attempts have been made to understand the characterization and performance of the LoRa technology in indoor environments. In this article, we present a comprehensive study of the LoRa technology in multifloor buildings. Specifically, we investigate the large-scale fading characteristic, temporal fading characteristic, coverage, and energy consumption of the LoRa technology in four different types of buildings. Moreover, we find that the energy consumption using different parameter settings can vary up to 145 times. These results indicate the importance of parameter selection and enabling the LoRa adaptive data rate feature in energy-limited applications. We hope the results in this article can help both academia and industry understand the performance of the LoRa technology in multifloor buildings to facilitate developing practical indoor applications.
On the connections between algorithmic regularization and penalization for convex losses

In this work we establish the equivalence of algorithmic regularization and explicit convex penalization for generic convex losses. We introduce a geometric condition for the optimization path of a convex function, and show that if such a condition is satisfied, the optimization path of an iterative algorithm on the unregularized optimization problem can be represented as the solution path of a corresponding penalized problem.
Training Deep Neural Networks by optimizing over nonlocal paths in hyperparameter space

Hyperparameter optimization is both a practical issue and an interesting theoretical problem in training of deep architectures. Despite many recent advances the most commonly used methods almost universally involve training multiple and decoupled copies of the model, in effect sampling the hyperparameter space. We show that at a negligible additional computational cost, results can be improved by sampling nonlocal paths instead of points in hyperparameter space. To this end we interpret hyperparameters as controlling the level of correlated noise in training, which can be mapped to an effective temperature. The usually independent instances of the model are coupled and allowed to exchange their hyperparameters throughout the training using the well established parallel tempering technique of statistical physics. Each simulation corresponds then to a unique path, or history, in the joint hyperparameter/model-parameter space. We provide empirical tests of our method, in particular for dropout and learning rate optimization. We observed faster training and improved resistance to overfitting and showed a systematic decrease in the absolute validation error, improving over benchmark results.
A Comprehensive Benchmark for Single Image Compression Artifact Reduction

We present a comprehensive study and evaluation of existing single image compression artifact removal algorithms using a new 4K resolution benchmark. This benchmark is called the Large-Scale Ideal Ultra high-definition 4K (LIU4K), and it includes including diversified foreground objects and background scenes with rich structures. Compression artifact removal, as a common post-processing technique, aims at alleviating undesirable artifacts, such as blockiness, ringing, and banding caused by quantization and approximation in the compression process. In this work, a systematic listing of the reviewed methods is presented based on their basic models (handcrafted models and deep networks). The main contributions and novelties of these methods are highlighted, and the main development directions are summarized, including architectures, multi-domain sources, signal structures, and new targeted units. Furthermore, based on a unified deep learning configuration (i.e. same training data, loss function, optimization algorithm, etc.), we evaluate recent deep learning-based methods based on diversified evaluation measures. The experimental results show state-of-the-art performance comparisons of existing methods based on both full-reference, non-reference, and task-driven metrics. Our survey gives a comprehensive reference source for future research on single image compression artifact removal and inspires new directions in related fields.
Accelerating ADMM for efficient simulation and optimization

The alternating direction method of multipliers (ADMM) is a popular approach for solving optimization problems that are potentially non-smooth and with hard constraints. It has been applied to various computer graphics applications, including physical simulation, geometry processing, and image processing. However, ADMM can take a long time to converge to a solution of high accuracy. Moreover, many computer graphics tasks involve non-convex optimization, and there is often no convergence guarantee for ADMM on such problems since it was originally designed for convex optimization. In this paper, we propose a method to speed up ADMM using Anderson acceleration, an established technique for accelerating fixed-point iterations. We show that in the general case, ADMM is a fixed-point iteration of the second primal variable and the dual variable, and Anderson acceleration can be directly applied. Additionally, when the problem has a separable target function and satisfies certain conditions, ADMM becomes a fixed-point iteration of only one variable, which further reduces the computational overhead of Anderson acceleration. Moreover, we analyze a particular non-convex problem structure that is common in computer graphics, and prove the convergence of ADMM on such problems under mild assumptions. We apply our acceleration technique on a variety of optimization problems in computer graphics, with notable improvement on their convergence speed.
A well-balanced scheme for the simulation tool-kit A-MaZe: implementation, tests, and first applications to stellar structure

Characterizing stellar convection in multiple dimensions is a topic at the forefront of stellar astrophysics. Numerical simulations are an essential tool for this task. We present an extension of the existing numerical tool-kit A-MaZe that enables such simulations of stratified flows in a gravitational field. The finite-volume based, cell-centered, and time-explicit hydrodynamics solver of A-MaZe was extended such that the scheme is now well-balanced in both momentum and energy. The algorithm maintains an initially static balance between gravity and pressure to machine precision. Quasi-stationary convection in slab-geometry preserves gas energy (internal plus kinetic) on average, despite strong local up- and down-drafts. By contrast, a more standard numerical scheme is demonstrated to result in substantial gains of energy within a short time on purely numerical grounds. The test is further used to point out the role of dimensionality, viscosity, and Rayleigh number for compressible convection. Applications to a young sun in 2D and 3D, covering a part of the inner radiative zone, as well as the outer convective zone, demonstrate that the scheme meets its initial design goal. Comparison with results obtained for a physically identical setup with a time-implicit code show qualitative agreement.