Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-color flat-top solitons in microresonator-based optical parametric oscillators

We studied numerically the generation of the two-color flat-top solitonic pulses, platicons, in the microresonator-based doubly resonant optical parametric oscillator. We revealed that if the signs of the group velocity dispersion (GVD) coefficients at interacting harmonics are opposite, platicon excitation is possible via pump amplitude modulation or controllable mode interaction approach. Upon pump frequency scan generation of platicons was observed at positive pump frequency detunings for the normal GVD at pump frequency and at negative detunings in the opposite case. Interestingly, we found the effect of the transformation of the flat-top platicon profile at half-frequency into the bell-shaped bright soliton profile upon frequency scan. For platicon excitation one needs simultaneous accurate matching of the microresonator free spectral ranges at interacting harmonics and resonant eigenfrequencies. Excitation conditions and platicon generation domains were found for the different generation methods, and properties of the generated platicons were studied for various combinations of the medium parameters.
DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space

It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accepted benchmark. In this work, we propose a new light-weight benchmark task called Diner Dash for evaluating the performance in a complicated task with high dimensional action space. In contrast to the traditional Atari games that only have a flat structure of goals and very few actions, the proposed benchmark task has a hierarchical task structure and size of 57 for the action space and hence can facilitate the development of policy learning in complicated tasks. On top of that, we introduce Decomposed Policy Graph Modelling (DPGM), an algorithm that combines both graph modelling and deep learning to allow explicit domain knowledge embedding and achieves significant improvement comparing to the baseline. In the experiments, we have shown the effectiveness of the domain knowledge injection via a specially designed imitation algorithm as well as results of other popular algorithms.
Low-Complexity Set-Membership Normalized LMS Algorithm for Sparse System Modeling

In this work, we propose two low-complexity set-membership normalized least-mean-square (LCSM-NLMS1 and LCSM-NLMS2) algorithms to exploit the sparsity of an unknown system. For this purpose, in the LCSM-NLMS1 algorithm, we employ a function called the discard function to the adaptive coefficients in order to neglect the coefficients close to zero in the update process. Moreover, in the LCSM-NLMS2 algorithm, to decrease the overall number of computations needed even further, we substitute small coefficients with zero. Numerical results present similar performance of these algorithms when comparing them with some state-of-the-art sparsity-aware algorithms, whereas the proposed algorithms need lower computational cost.
Epigean Tenebrionids (Coleoptera: Tenebrionidae) from the Choros Archipelago (Coquimbo Region, Chile)

ABSTRACT: Using pitfall traps, we studied the taxonomic diversity of epigean tenebrionids in the Chores Archipelago, formed by the Choros, Damas and Gaviota Islands, which are part of the Pingüino de Humboldt National Reserve. We identified fourteen species arranged in eight genera and six tribes, of which seven species were common to the archipelago. Five genera are reported for the first time as occurring in insular habitat islands: Psectrascelis Solier, Entomochilus Solier, Diastoleus Solier, Scotobius Germar, and Thinobatis Eschscholtz, Gyriosomus granulipennis Pizarro-Araya and Flores is recorded as endemic to the Choros Island.
An Alternating Rank-K Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is a prominent technique for data dimensionality reduction that has been widely used for text mining, computer vision, pattern discovery, and bioinformatics. In this paper, a framework called ARkNLS (Alternating Rank-k Nonnegativity constrained Least Squares) is proposed for computing NMF. First, a recursive formula for the solution of the rank-k nonnegativity-constrained least squares (NLS) is established. This recursive formula can be used to derive the closed-form solution for the Rank-k NLS problem for any integer k $\ge$ 1. As a result, each subproblem for an alternating rank-k nonnegative least squares framework can be obtained based on this closed form solution. Assuming that all matrices involved in rank-k NLS in the context of NMF computation are of full rank, two of the currently best NMF algorithms HALS (hierarchical alternating least squares) and ANLS-BPP (Alternating NLS based on Block Principal Pivoting) can be considered as special cases of ARkNLS with k = 1 and k = r for rank r NMF, respectively. This paper is then focused on the framework with k = 3, which leads to a new algorithm for NMF via the closed-form solution of the rank-3 NLS problem. Furthermore, a new strategy that efficiently overcomes the potential singularity problem in rank-3 NLS within the context of NMF computation is also presented. Extensive numerical comparisons using real and synthetic data sets demonstrate that the proposed algorithm provides state-of-the-art performance in terms of computational accuracy and cpu time.
Mismatched Data Detection in Massive MU-MIMO

We investigate mismatched data detection for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems in which the prior distribution of the transmit signal used in the data detector differs from the true prior. In order to minimize the performance loss caused by the prior mismatch, we include a tuning stage into the recently proposed large-MIMO approximate message passing (LAMA) algorithm, which enables the development of data detectors with optimal as well as sub-optimal parameter tuning. We show that carefully-selected priors enable the design of simpler and computationally more efficient data detection algorithms compared to LAMA that uses the optimal prior, while achieving near-optimal error-rate performance. In particular, we demonstrate that a hardware-friendly approximation of the exact prior enables the design of low-complexity data detectors that achieve near individually-optimal performance. Furthermore, for Gaussian priors and uniform priors within a hypercube covering the quadrature amplitude modulation (QAM) constellation, our performance analysis recovers classical and recent results on linear and non-linear massive MU-MIMO data detection, respectively.
Flux Measurements in Trees: Methodological Approach and Application to Vineyards

In this paper a review of two sap flow methods for measuring the transpiration in vineyards is presented. The objective of this work is to examine the potential of detecting transpiration in trees in response to environmental stresses, particularly the high concentration of ozone (O3) in troposphere. The methods described are the stem heat balance and the thermal dissipation probe; advantages and disadvantages of each method are detailed. Applications of both techniques are shown, in two large commercial vineyards in Southern Italy (Apulia and Sicily), submitted to semi-arid climate. Sap flow techniques allow to measure transpiration at plant scale and an upscaling procedure is necessary to calculate the transpiration at the whole stand level. Here a general technique to link the value of transpiration at plant level to the canopy value is presented, based on experimental relationships between transpiration and biometric characteristics of the trees. In both vineyards transpiration measured by sap flow methods compares well with evapotranspiration measured by micrometeorological techniques at canopy scale. Moreover soil evaporation component has been quantified. In conclusion, comments about the suitability of the sap flow methods for studying the interactions between trees and ozone are given.
Graphene Domain Signature of Raman Spectra of sp2 Amorphous Carbons

The standard D-G-2D pattern of Raman spectra of sp2 amorphous carbons is considered from the viewpoint of graphene domains presenting their basic structure units (BSUs) in terms of molecular spectroscopy. The molecular approximation allows connecting the characteristic D-G doublet spectra image of one-phonon spectra with a considerable dispersion of the C=C bond lengths within graphene domains, governed by size, heteroatom necklace of BSUs as well as BSUs packing. The interpretation of 2D two-phonon spectra reveals a particular role of electrical anharmonicity in the spectra formation and attributes this effect to a high degree of the electron density delocalization in graphene domains. A size-stimulated transition from molecular to quasi-particle phonon consideration of Raman spectra was experimentally traced, which allowed evaluation of a free path of optical phonons in graphene crystal.
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning

Stochastic Gradient Descent (SGD) is the key learning algorithm for many machine learning tasks. Because of its computational costs, there is a growing interest in accelerating SGD on HPC resources like GPU clusters. However, the performance of parallel SGD is still bottlenecked by the high communication costs even with a fast connection among the machines. A simple approach to alleviating this problem, used in many existing efforts, is to perform communication every few iterations, using a constant averaging period. In this paper, we show that the optimal averaging period in terms of convergence and communication cost is not a constant, but instead varies over the course of the execution. Specifically, we observe that reducing the variance of model parameters among the computing nodes is critical to the convergence of periodic parameter averaging SGD. Given a fixed communication budget, we show that it is more beneficial to synchronize more frequently in early iterations to reduce the initial large variance and synchronize less frequently in the later phase of the training process. We propose a practical algorithm, named ADaptive Periodic parameter averaging SGD (ADPSGD), to achieve a smaller overall variance of model parameters, and thus better convergence compared with the Constant Periodic parameter averaging SGD (CPSGD). We evaluate our method with several image classification benchmarks and show that our ADPSGD indeed achieves smaller training losses and higher test accuracies with smaller communication compared with CPSGD. Compared with gradient-quantization SGD, we show that our algorithm achieves faster convergence with only half of the communication. Compared with full-communication SGD, our ADPSGD achieves 1:14x to 1:27x speedups with a 100Gbps connection among computing nodes, and the speedups increase to 1:46x ~ 1:95x with a 10Gbps connection.
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection

This paper focuses on the semi-supervised object detection (SSOD) which makes good use of unlabeled data to boost performance. We face the following obstacles when adapting the knowledge distillation (KD) framework in SSOD. (1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may limit the upper bound of the student. (2) The data imbalance issue caused by the large quantity of consistent predictions between the teacher and student hinders an efficient knowledge transfer between them. To mitigate these issues, we propose a novel SSOD model called Temporal Self-Ensembling Teacher (TSET). Our teacher model ensembles its temporal predictions for unlabeled images under stochastic perturbations. Then, our teacher model ensembles its model weights with those of the student model by an exponential moving average. These ensembling strategies ensure data and model diversity, and lead to better teacher predictions for unlabeled images. In addition, we adapt the focal loss to formulate the consistency loss for handling the data imbalance issue. Together with a thresholding method, the focal loss automatically reweights the inconsistent predictions, which preserves the knowledge for difficult objects to detect in the unlabeled images. The mAP of our model reaches 80.73% and 40.52% on the VOC2007 test set and the COCO2014 minival5k set, respectively, and outperforms a strong fully supervised detector by 2.37% and 1.49%, respectively. Furthermore, the mAP of our model (80.73%) sets a new state-of-the-art performance in SSOD on the VOC2007 test set.
HSD Shared Task in VLSP Campaign 2019: Hate Speech Detection for Social Good

The paper describes the organisation of the "HateSpeech Detection" (HSD) task at the VLSP workshop 2019 on detecting the fine-grained presence of hate speech in Vietnamese textual items (i.e., messages) extracted from Facebook, which is the most popular social network site (SNS) in Vietnam. The task is organised as a multi-class classification task and based on a large-scale dataset containing 25,431 Vietnamese textual items from Facebook. The task participants were challenged to build a classification model that is capable of classifying an item to one of 3 classes, i.e., "HATE", "OFFENSIVE" and "CLEAN". HSD attracted a large number of participants and was a popular task at VLSP 2019. In particular, there were 71 teams signed up for the task, 14 of them submitted results with 380 valid submissions from 20th September 2019 to 4th October 2019.
Possible mechanism for multiple changing-look phenomena in active galactic nuclei

Context. The changing-look phenomenon observed in a growing number of active galaxies challenges our understanding of the accretion process close to a black hole. Aims. We propose a simple explanation for the sources where multiple semi-periodic outbursts are observed, and the sources are operating close to the Eddington limit. Methods. The outburst are caused by the radiation pressure instability operating in the narrow ring between the standard gas-dominated outer disk and the hot optically thin inner advection-dominated accretion flow. The corresponding limit cycle is responsible for periodic outbursts, and the timescales are much shorter than the standard viscous timescale due to the narrowness of the unstable radial zone. Results. Our toy model gives quantitative predictions and works well for multiple outbursts like those observed in NGC 1566, NGC 4151, NGC 5548, and GSN 069, although the shapes of the outbursts are not yet well modeled, and further development of the model is necessary.
Cost Minimization for Cooperative Computation Framework in MEC Networks

In this paper, a cooperative task computation framework exploits the computation resource in user equipments (UEs) to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs’ power consumption and the penalty of unaccomplished tasks, and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework.
Morphological Characters and PCR-RFLP Markers in the Interspecific Hybrids between Bactrocera carambolae and B. papayae of the B. dorsalis Species Complex (Diptera: Tephritidae)

Morphological characters and PCR-RFLP markers of interspecific hybrids between Bactrocera carambolae and B. papayae (Diptera: Tephritidae) were investigated. Both species are ec- onomically important pests that belong to the Bactrocera dorsalis species complex. They have never been found in Japan. For the present study, test insects were imported into Japan from Malaysia and Thailand, and they were maintained in the laboratory, with permission from the relevant authority. A pair of flies, B. carambolae female and B. papayae male, were crossed. The aculeus lengths of the F1 and F2 hybrids were found to be intermediate between those of the two parental species. In addition, the range of morphological variation was expanded in the F2 hybrids. Therefore, aculeus length in these two species is considered to be determined by quantitative inheritance. In all F1 hybrids, the restric- tion pattern of the PCR-RFLP fragments of mitochondrial DNA (mtDNA; 16S rRNA) was the same as that of the maternal species, and that for nuclear ribosomal DNA (rDNA; 5.8 S rRNA and ITS) con- tained the restriction fragments of both P1 species in a heterozygous banding pattern (hybrid pattern). The frequency ratio in F2 females of the B. carambolae pattern, the B. papayae pattern and the hybrid re- striction pattern was 14 : 1 : 27. There was no correlation between the morphological characters and the rDNA markers in the F2 hybrids. It is therefore suggested that in both species, the inconsistency be- tween the morphological characters and the DNA markers, and the continuous variation of the aculeus length, were mainly caused by interspecific hybridization in the distribution area.
A functional model for maximum growth of Atlantic Salmon parr, Salmo salar, from two populations in northwest England

1. The chief objective was to develop a functional model for the growth of parr (0+, 1+) of Atlantic Salmon, Salmo salar, from two populations (Rivers Leven and Lune in northwest England), using a model similar to that developed previously for Brown Trout, Salmo trutta. Parr bred from Leven parents were acclimatized to nine fairly constant temperatures (two 0+, two 1+ parr per temperature) in the range 3·8–21·7 °C (variation ± 0·3 at 3·8 °C to ± 1·0 at 21·7 °C). Each fish was kept in a separate tank and fed to satiation on shrimps. The mass and length of each fish was recorded at the start and finish of a growth period of 30 days. Parr (0+, 1+) bred from Lune parents were separated into slow and fast growers, and acclimatized to six very constant temperatures. There were three slow and three fast growers at each of 5·0, 10·0, 15·0 °C for 0+ parr and each of 5·0, 10·0, 13·0, 15·0, 18·0, 20·0 °C for 1+ parr. Procedures were the same as for Leven parr except that the growth period lasted 30 days for the 18 0+ parr and 42 days for the 36 1+ parr. 2. The growth model was an excellent fit (P < 0·001) with no significant differences between parr from the two rivers, fast-growing parr of different ages (0+, 1+), or fast- and slow-growing 1+ parr. The optimum temperature for the combined data (81 parr) was 15·9 °C with a range for growth of 6·0–22·5 °C. The model failed to fit the data for slow-growing 0+ Lune parr in experiments from November to February. Growth was reduced in these fish, even though the temperatures were suitable for growth (10, 15°C). 3. The model described approximately the growth of three year-classes of Atlantic Salmon parr in the River Eden in northwest England. Some of the discrepancies between actual masses and those predicted from the model were consistent and possible reasons for this are discussed.
MFED: A System for Monitoring Family Eating Dynamics

Obesity is a risk factor for many health issues, including heart disease, diabetes, osteoarthritis, and certain cancers. One of the primary behavioral causes, dietary intake, has proven particularly challenging to measure and track. Current behavioral science suggests that family eating dynamics (FED) have high potential to impact child and parent dietary intake, and ultimately the risk of obesity. Monitoring FED requires information about when and where eating events are occurring, the presence or absence of family members during eating events, and some person-level states such as stress, mood, and hunger. To date, there exists no system for real-time monitoring of FED. This paper presents MFED, the first of its kind of system for monitoring FED in the wild in real-time. Smart wearables and Bluetooth beacons are used to monitor and detect eating activities and the location of the users at home. A smartphone is used for the Ecological Momentary Assessment (EMA) of a number of behaviors, states, and situations. While the system itself is novel, we also present a novel and efficient algorithm for detecting eating events from wrist-worn accelerometer data. The algorithm improves eating gesture detection F1-score by 19% with less than 20% computation compared to the state-of-the-art methods. To date, the MFED system has been deployed in 20 homes with a total of 74 participants, and responses from 4750 EMA surveys have been collected. This paper describes the system components, reports on the eating detection results from the deployments, proposes two techniques for improving ground truth collection after the system is deployed, and provides an overview of the FED data, generated from the multi-component system, that can be used to model and more comprehensively understand insights into the monitoring of family eating dynamics.
Design of Transmission Line and Electromagnetic Field Sensors for DC Partial Discharge Analysis

In this paper, a testbed is designed and constructed for the investigation of DC PD pulses. The testbed is equipped with a $50\ \Omega$ transmission line (TL) that terminate to an oscilloscope for measuring the charge displacement current generated by PD pulses. Besides the oscilloscope measurements, two types of electromagnetic field sensors (D-dot and B-dot) were developed to capture the EM fields of the PD pulses propagating through the TL. The main goal of this paper is to investigate the DC PD pulses through the EM fields and the corresponding discharge current pulses that are considered as calibrating signals for the developed D-dot and B-dot sensors. The results of DC cavity discharge measured by the constructed testbed and the EM field sensors demonstrate close agreement with the reference PD pulses measured via oscilloscope.