Miracle-dz/UniFAR-FTUs huggingface dataset:
PET Y PP USADO COMO MEDIOS DE CRECIMIENTO DE BIOPELÍCULA FIJA APLICADA AL TRATAMIENTO AEROBIO DE AGUAS RESIDUALES DOMÉSTICAS

RESUMEN – Se evaluó el desempeño de plásticos reciclables como Polietileno Tereftalato PET y Polipropileno PP como medios de soporte para el crecimiento de biopelícula en reactores aerobios de 2.8 L de lecho fijo para el tratamiento de agua residual doméstica. Estos materiales fueron evaluados durante 300 días. Los reactores con PET contenían pedazos de 5cm x 1cm, mientras que los PP con tiras de 80cm x 5cm. Como sustrato se utilizó agua residual del río Machángara (Quito-Ecuador), cuyo DQOs fué de 200 mg/L, a la que se fortificó con melaza entre 795 a 1420 mg/L. El área específica para los materiales PET y PP fue de 654,62 m2/m3 y 882,5 m2/m3 respectivamente. Durante este tiempo la carga orgánica expresada en gramos de DQOs fue para los reactores con material PET de 16,82 g y 17,95 g DQOs para aquellos con PP. Se logró eficiencias máximas de remoción de DQOs del 95% y 96% para PET y PP respectivamente, siendo este último el mejor dado su mayor porcentaje de remoción alcanzado en menor tiempo.
Time-free solution to independent set problem using P systems with active membranes

Membrane computing is a branch of natural computingwhich abstracts fromthe structure and the functioning of living cells. The computation models obtained in the field of membrane computing are usually called P systems. P systems have been used to solve computationally hard problems efficiently on the assumption that the execution of each rule is completed in exactly one time-unit (a global clock is assumed for timing and synchronizing the execution of rules). However, in biological reality, different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, with this biological reality, we give a time-free solution to independent set problemusing P systems with active membranes, which solve the problem independent of the execution time of the involved rules.
Mid-J CO Line Observations of Protostellar Outflows in the Orion Molecular Clouds

Ten protostellar outflows in the Orion molecular clouds were mapped in the 12CO/13CO J = 6 → 5 and 12CO J = 7 → 6 lines. The maps of these mid-J CO lines have an angular resolution of about 10″ and a typical field size of about 100″. The physical parameters of the molecular outflows were derived, including mass transfer rates, kinetic luminosities, and outflow forces. The outflow sample was expanded by reanalyzing archival data of nearby low-luminosity protostars to cover a wide range of bolometric luminosities. Outflow parameters derived from other transitions of CO were compared. The mid-J (J up ≈ 6) and low-J (J up ≤ 3) CO line wings trace essentially the same outflow component. By contrast, the high-J (up to J up ≈ 50) line-emission luminosity of CO shows little correlation with the kinetic luminosity from the J = 6 → 5 line, which suggests that they trace distinct components. The low-/mid-J CO line wings trace long-term outflow behaviors, while the high-J CO lines are sensitive to short-term activities. The correlations between the outflow parameters and protostellar properties are presented, which shows that the strengths of molecular outflows increase with bolometric luminosity and envelope mass.
Elasticity Based Demand Forecasting and Price Optimization for Online Retail

We study a problem of an online retailer who observes the unit sales of a product, and dynamically changes the retail price, in order to maximize the expected revenue. Assuming the demand of the product is price sensitive, we are interested in the optimal pricing policy when future demand is uncertain. We build a system to investigate the relationship between retail price and demand and estimate the demand function. The system predicts demand and revenue at a given retail price. We formulate a revenue maximization problem over a discrete finite time horizon with discrete retail price. The optimal pricing policy is solved based on the predicted demand and revenue values. With computational experiments, we investigate the effect of optimal pricing policy to inventory management.
Evaluating Modules in Graph Contrastive Learning

The recent emergence of contrastive learning approaches facilitates the application on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the literature. These methods contrast semantically similar and dissimilar sample pairs to encode the semantics into node or graph embeddings. However, most existing works only performed \textbf{model-level} evaluation, and did not explore the combination space of modules for more comprehensive and systematic studies. For effective \textbf{module-level} evaluation, we propose a framework that decomposes GCL models into four modules: (1) a \textbf{sampler} to generate anchor, positive and negative data samples (nodes or graphs); (2) an \textbf{encoder} and a \textbf{readout} function to get sample embeddings; (3) a \textbf{discriminator} to score each sample pair (anchor-positive and anchor-negative); and (4) an \textbf{estimator} to define the loss function. Based on this framework, we conduct controlled experiments over a wide range of architectural designs and hyperparameter settings on node and graph classification tasks. Specifically, we manage to quantify the impact of a single module, investigate the interaction between modules, and compare the overall performance with current model architectures. Our key findings include a set of module-level guidelines for GCL, e.g., simple samplers from LINE and DeepWalk are strong and robust; an MLP encoder associated with Sum readout could achieve competitive performance on graph classification. Finally, we release our implementations and results as OpenGCL, a modularized toolkit that allows convenient reproduction, standard model and module evaluation, and easy extension. OpenGCL is available at \url{https://github.com/thunlp/OpenGCL}.
ShortcutFusion: From Tensorflow to FPGA-Based Accelerator With a Reuse-Aware Memory Allocation for Shortcut Data

Residual block is a very common component in recent state-of-the art CNNs such as EfficientNet/EfficientDet. Shortcut data accounts for nearly 40% of feature-maps access in ResNet152. Most of the previous DNN compilers/accelerators ignore the shortcut data optimization. This paper presents <italic>ShortcutFusion</italic>, an optimization tool for FPGA-based accelerator with a reuse-aware static memory allocation for shortcut data, to maximize on-chip data reuse given resource constraints. From TensorFlow DNN models, the proposed design generates instruction sets for a group of nodes which uses an optimized data reuse for each residual block. The accelerator design implemented on the Xilinx KCU1500 FPGA card <inline-formula> <tex-math notation="LaTeX">$2.8\times $ </tex-math></inline-formula> faster and <inline-formula> <tex-math notation="LaTeX">$9.9\times $ </tex-math></inline-formula> more power efficient than NVIDIA RTX 2080 Ti for <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> input size. Compared to the result from baseline, in which the weights/inputs/outputs are accessed from the off-chip memory exactly once per each layer, ShortcutFusion reduces the DRAM access by 47.8-84.8% for RetinaNet, Yolov3, ResNet152, and EfficientNet. Given a similar buffer size to ShortcutMining, which also “mine” the shortcut data in hardware, the proposed work reduces off-chip access for feature-maps <inline-formula> <tex-math notation="LaTeX">$5.27\times $ </tex-math></inline-formula> while accessing weight from off-chip memory exactly once.
Multiwavelength Evidence for a New Flare-mode Transitional Millisecond Pulsar

We report the discovery of a new low-mass X-ray binary near the center of the unassociated Fermi GeV γ-ray source 4FGL J0540.0–7552. The source shows the persistent presence of an optical accretion disk and exhibits extreme X-ray and optical variability. It also has an X-ray spectrum well-fit by a hard power law with Γ = 1.8 and a high ratio of X-ray to γ-ray flux. Together, these properties are consistent with the classification of the binary as a transitional millisecond pulsar (tMSP) in the subluminous disk state. Uniquely among the candidate tMSPs, 4FGL J0540.0–7552 shows consistent optical, X-ray, and γ-ray evidence for having undergone a state change, becoming substantially brighter in the optical and X-rays and fainter in GeV γ-rays sometime in mid-2013. In its current subluminous disk state, and like one other candidate tMSP in the Galactic field, 4FGL J0540.0–7552 appears to always be in an X-ray “flare mode,” indicating that this could be common phenomenology for tMSPs.
Computer-aided Interpretable Features for Leaf Image Classification

Plant species identification is time consuming, costly, and requires lots of efforts, and expertise knowledge. In recent, many researchers use deep learning methods to classify plants directly using plant images. While deep learning models have achieved a great success, the lack of interpretability limit their widespread application. To overcome this, we explore the use of interpretable, measurable and computer-aided features extracted from plant leaf images. Image processing is one of the most challenging, and crucial steps in feature-extraction. The purpose of image processing is to improve the leaf image by removing undesired distortion. The main image processing steps of our algorithm involves: i) Convert original image to RGB (Red-Green-Blue) image, ii) Gray scaling, iii) Gaussian smoothing, iv) Binary thresholding, v) Remove stalk, vi) Closing holes, and vii) Resize image. The next step after image processing is to extract features from plant leaf images. We introduced 52 computationally efficient features to classify plant species. These features are mainly classified into four groups as: i) shape-based features, ii) color-based features, iii) texture-based features, and iv) scagnostic features. Length, width, area, texture correlation, monotonicity and scagnostics are to name few of them. We explore the ability of features to discriminate the classes of interest under supervised learning and unsupervised learning settings. For that, supervised dimensionality reduction technique, Linear Discriminant Analysis (LDA), and unsupervised dimensionality reduction technique, Principal Component Analysis (PCA) are used to convert and visualize the images from digital-image space to feature space. The results show that the features are sufficient to discriminate the classes of interest under both supervised and unsupervised learning settings.
Relation Modeling in Spatio-Temporal Action Localization

This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021. Our solution utilizes multiple types of relation modeling methods for spatio-temporal action detection and adopts a training strategy to integrate multiple relation modeling in end-to-end training over the two large-scale video datasets. Learning with memory bank and finetuning for long-tailed distribution are also investigated to further improve the performance. In this paper, we detail the implementations of our solution and provide experiments results and corresponding discussions. We finally achieve 40.67 mAP on the test set of AVA-Kinetics.
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization

Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training. By examining this masking, or dropout, in the linear case, we uncover a duality between such adaptive methods and regularization through the so-called"$\eta$-trick"that casts both as iteratively reweighted optimizations. We show that any dropout strategy that adapts to the weights in a monotonic way corresponds to an effective subquadratic regularization penalty, and therefore leads to sparse solutions. We obtain the effective penalties for several popular sparsification strategies, which are remarkably similar to classical penalties commonly used in sparse optimization. Considering variational dropout as a case study, we demonstrate similar empirical behavior between the adaptive dropout method and classical methods on the task of deep network sparsification, validating our theory.
Kaizen: Continuously Improving Teacher Using Exponential Moving Average for Semi-Supervised Speech Recognition

In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR). The proposed approach uses a teacher model which is updated as the exponential moving average (EMA) of the student model parameters. We demonstrate that it is critical for EMA to be accumulated with full-precision floating point. The Kaizen framework can be seen as a continuous version of the iterative pseudo-labeling approach for semi-supervised training. It is applicable for different training criteria, and in this paper we demonstrate its effectiveness for frame-level hybrid hidden Markov model-deep neural network (HMM-DNN) systems as well as sequence-level Connectionist Temporal Classification (CTC) based models. For large scale real-world unsupervised public videos in UK English and Italian languages the proposed approach i) shows more than 10% relative word error rate (WER) reduction over standard teacher-student training; ii) using just 10 hours of supervised data and a large amount of unsupervised data closes the gap to the upper-bound supervised ASR system that uses 650h or 2700h respectively.
Unpredictability and entanglement in open quantum systems

We investigate dynamical many-body systems capable of universal computation, which leads to their properties being unpredictable unless the dynamics is simulated from the beginning to the end. Unpredictable behavior can be quantitatively assessed in terms of a data compression of the states occurring during the time evolution, which is closely related to their Kolmogorov complexity. We analyze a master equation embedding of classical cellular automata and demonstrate the existence of a phase transition between predictable and unpredictable behavior as a function of the random error introduced by the probabilistic character of the embedding. We then turn to have this dynamics competing with a second process inducing quantum fluctuations and dissipatively driving the system to a highly entangled steady state. Strikingly, for intermediate strength of the quantum fluctuations, we find that both unpredictability and quantum entanglement can coexist even in the long time limit. Finally, we show that the required many-body interactions for the cellular automaton embedding can be efficiently realized within a variational quantum simulator platform based on ultracold Rydberg atoms with high fidelity.
A geometry-induced topological phase transition in random graphs

Jasper van der Kolk, 2, ∗ M. Ángeles Serrano, 2, 3, † and Marián Boguñá 2, ‡ Departament de F́ısica de la Matèria Condensada, Universitat de Barcelona, Mart́ı i Franquès 1, E-08028 Barcelona, Spain Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona, Spain Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Llúıs Companys 23, E-08010 Barcelona, Spain
User-Specific Adaptive Fine-Tuning for Cross-Domain Recommendations

Making accurate recommendations for cold-start users has been a longstanding and critical challenge for recommender systems (RS). Cross-domain recommendations (CDR) offer a solution to tackle such a cold-start problem when there is no sufficient data for the users who have rarely used the system. An effective approach in CDR is to leverage the knowledge (e.g., user representations) learned from a related but different domain and transfer it to the target domain. Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain. However, current methods are mainly based on the global fine-tuning strategy: the decision of which layers of the pre-trained model to freeze or fine-tune is taken for all users in the target domain. In this paper, we argue that users in RS are personalized and should have their own fine-tuning policies for better preference transfer learning. As such, we propose a novel User-specific Adaptive Fine-tuning method (UAF), selecting which layers of the pre-trained network to fine-tune, on a per user basis. Specifically, we devise a policy network with three alternative strategies to automatically decide which layers to be fine-tuned and which layers to have their parameters frozen for each user. Extensive experiments show that the proposed UAF exhibits significantly better and more robust performance for user cold-start recommendation.
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection

One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties. The remarkable success of word embeddings for this purpose suggests that high-quality representations can be obtained by summarizing the sentence contexts of word mentions. In this paper, we propose a method for learning word representations that follows this basic strategy, but differs from standard word embeddings in two important ways. First, we take advantage of contextualized language models (CLMs) rather than bags of word vectors to encode contexts. Second, rather than learning a word vector directly, we use a topic model to partition the contexts in which words appear, and then learn different topic-specific vectors for each word. Finally, we use a task-specific supervision signal to make a soft selection of the resulting vectors. We show that this simple strategy leads to high-quality word vectors, which are more predictive of semantic properties than word embeddings and existing CLM-based strategies.
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery

The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions between malicious and benign software. Antivirus vendors have also begun to widely utilize malware classifiers based on dynamic and static malware analysis features. Therefore, a malware author might make evasive binary modifications against Machine Learning models as part of the malware development life cycle to execute an attack successfully. This makes the studying of possible classifier evasion strategies an essential part of cyber defense against malice. To this extent, we stage a grey box setup to analyze a scenario where the malware author does not know the target classifier algorithm, and does not have access to decisions made by the classifier, but knows the features used in training. In this experiment, a malicious actor trains a surrogate model using the EMBER-2018 dataset to discover binary mutations that cause an instance to be misclassified via a Monte Carlo tree search. Then, mutated malware is sent to the victim model that takes the place of an antivirus API to test whether it can evade detection.
Domain Adaptive SiamRPN++ for Object Tracking in the Wild

Benefit from large-scale training data, recent advances in Siamese-based object tracking have achieved compelling results on the normal sequences. Whilst Siamese-based trackers assume training and test data follow an identical distribution. Suppose there is a set of foggy or rainy test sequences, it cannot be guaranteed that the trackers trained on the normal images perform well on the data belonging to other domains. The problem of domain shift among training and test data has already been discussed in object detection and semantic segmentation areas, which, however, has not been investigated for visual tracking. To this end, based on SiamRPN++, we introduce a Domain Adaptive SiamRPN++, namely DASiamRPN++, to improve the cross-domain transferability and robustness of a tracker. Inspired by A-distance theory, we present two domain adaptive modules, Pixel Domain Adaptation (PDA) and Semantic Domain Adaptation (SDA). The PDA module aligns the feature maps of template and search region images to eliminate the pixel-level domain shift caused by weather, illumination, etc. The SDA module aligns the feature representations of the tracking target's appearance to eliminate the semantic-level domain shift. PDA and SDA modules reduce the domain disparity by learning domain classifiers in an adversarial training manner. The domain classifiers enforce the network to learn domain-invariant feature representations. Extensive experiments are performed on the standard datasets of two different domains, including synthetic foggy and TIR sequences, which demonstrate the transferability and domain adaptability of the proposed tracker.
Consistency Regularization for Cross-Lingual Fine-Tuning

Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others. In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. Specifically, we use example consistency regularization to penalize the prediction sensitivity to four types of data augmentations, i.e., subword sampling, Gaussian noise, code-switch substitution, and machine translation. In addition, we employ model consistency to regularize the models trained with two augmented versions of the same training set. Experimental results on the XTREME benchmark show that our method significantly improves cross-lingual fine-tuning across various tasks, including text classification, question answering, and sequence labeling.
FIGARO simulation: FIlaments & GAlactic RadiO simulation

Abstract We produce the first low to mid-frequency radio simulation that incorporates both traditional extragalactic radio sources as well as synchrotron cosmic web emission. The FIlaments & GAlactic RadiO (FIGARO) simulation includes 10 unique 4° × 4° fields, incorporating active galactic nucleii (AGNs), star-forming galaxies (SFGs), and synchrotron cosmic web emission out to a redshift of z = 0.8 and over the frequency range 100–1 400 MHz. To do this, the simulation brings together a recent 1003 Mpc3 magnetohydrodynamic simulation (Vazza et al. 2019, A&A, 627, A5), calibrated to match observed radio relic population statistics, alongside updated ‘T-RECS’ code for simulating extragalactic radio sources (Bonaldi et al. 2019, MNRAS, 482, 2). Uniquely, the AGNs and SFGs are populated and positioned in accordance with the underlying matter density of the cosmological simulation. In this way, the simulation provides an accurate understanding of the apparent morphology, angular scales, and brightness of the cosmic web as well as—crucially—the clustering properties of the cosmic web with respect to the embedded extragalactic radio population. We find that the synchrotron cosmic web does not closely trace the underlying mass distribution of the cosmic web, but is instead dominated by shocked shells of emission surrounding dark matter halos and resembles a large, undetected population of radio relics. We also show that, with accurate kernels, the cosmic web radio emission is clearly detectable by cross-correlation techniques and this signal is separable from the embedded extragalactic radio population. We offer the simulation as a public resource towards the development of techniques for detecting and measuring the synchrotron cosmic web.