Miracle-dz/UniFAR-FTUs huggingface dataset:
Q-learning with Language Model for Edit-based Unsupervised Summarization

Unsupervised methods are promising for abstractive text summarization in that the parallel corpora is not required. However, their performance is still far from being satisfied, therefore research on promising solutions is on-going. In this paper, we propose a new approach based on Q-learning with an edit-based summarization. The method combines two key modules to form an Editorial Agent and Language Model converter (EALM). The agent predicts edit actions (e.t., delete, keep, and replace), and then the LM converter deterministically generates a summary on the basis of the action signals. Q-learning is leveraged to train the agent to produce proper edit actions. Experimental results show that EALM delivered competitive performance compared with the previous encoder-decoder-based methods, even with truly zero paired data (i.e., no validation set). Defining the task as Q-learning enables us not only to develop a competitive method but also to make the latest techniques in reinforcement learning available for unsupervised summarization. We also conduct qualitative analysis, providing insights into future study on unsupervised summarizers.
Effective Route Scheme of Multicast Probing to Locate High-loss Links in OpenFlow Networks

With the prevalence of cloud computing and contents delivery networking, OpenFlow-based centrally-managed networks with flexible and dynamic traffic engineering are demanded. To maintain a high quality of service of the network, detecting and locating high-loss links is essential. Therefore, in this paper, a measurement framework is proposed to promptly locate all high-loss links with a minimized load on both data-plane and control-plane incurred by the measurement, which assumes only standard OpenFlow functions. It combines an active measurement by probing multicast packets along a designed route and a passive measurement by collecting flow-stats of the probing flow at selected switch ports in an appropriate sequential order to access switches. In particular, by designing the measurement route based on the backbone-and-branch tree (BBT) route scheme, the measurement accuracy and the measurement overhead (i.e., the number of accesses to switches until locating all high-loss links) can be balanced. The numerical simulation demonstrates the effectiveness of our proposal.
Fake Reviews Detection through Analysis of Linguistic Features

Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews. This paper explores a natural language processing approach to identify fake reviews. We present a detailed analysis of linguistic features for distinguishing fake and trustworthy online reviews. We study 15 linguistic features and measure their significance and importance towards the classification schemes employed in this study. Our results indicate that fake reviews tend to include more redundant terms and pauses, and generally contain longer sentences. The application of several machine learning classification algorithms revealed that we were able to discriminate fake from real reviews with high accuracy using these linguistic features.
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers

Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer. Entities are represented as per-token labels with a special structure in order to decode them into spans. Current work eschews prior knowledge of how the span encoding scheme works and relies on the CRF learning which transitions are illegal and which are not to facilitate global coherence. We find that by constraining the output to suppress illegal transitions we can train a tagger with a cross-entropy loss twice as fast as a CRF with differences in F1 that are statistically insignificant, effectively eliminating the need for a CRF. We analyze the dynamics of tag co-occurrence to explain when these constraints are most effective and provide open source implementations of our tagger in both PyTorch and TensorFlow.
Joint State-Action Embedding for Efficient Reinforcement Learning

While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces. Model-free reinforcement learning approaches use some form of state representations and the latest work has explored embedding techniques for actions, both with the aim of achieving better generalization and applicability. However, these approaches consider only states or actions, ignoring the interaction between them when generating embedded representations. In this work, we propose a new approach for jointly embedding states and actions that combines aspects of model-free and model-based reinforcement learning, which can be applied in both discrete and continuous domains. Specifically, we use a model of the environment to obtain embeddings for states and actions and present a generic architecture that uses these to learn a policy. In this way, the embedded representations obtained via our approach enable better generalization over both states and actions by capturing similarities in the embedding spaces. Evaluations of our approach on several gaming and recommender system environments show it significantly outperforms state-of-the-art models in discrete domains with large state/action space, thus confirming the efficacy of joint embedding and its overall superior performance.
HydroDeep - A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis

Floods are one of the major climate-related disasters, leading to substantial economic loss and social safety issue. However, the confidence in predicting changes in fluvial floods remains low due to limited evidence and complex causes of regional climate change. The recent development in machine learning techniques has the potential to improve traditional hydrological models by using monitoring data. Although Recurrent Neural Networks (RNN) perform remarkably with multivariate time series data, these models are blinded to the underlying mechanisms represented in a process-based model for flood prediction. While both process-based models and deep learning networks have their strength, understanding the fundamental mechanisms intrinsic to geo-spatiotemporal information is crucial to improve the prediction accuracy of flood occurrence. This paper demonstrates a neural network architecture (HydroDeep) that couples a process-based hydro-ecological model with a combination of Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to build a hybrid baseline model. HydroDeep outperforms the performance of both the independent networks by 4.8% and 31.8% respectively in Nash-Sutcliffe efficiency. A trained HydroDeep can transfer its knowledge and can learn the Geo-spatiotemporal features of any new region in minimal training iterations.
Quantum measurement induces a many-body transition

The current revolution in quantum technologies relies on the ability to isolate, coherently control, and measure the state of quantum systems. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. Even for ideal detectors, the measurement outcome always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction. Here we report a profound change in the many-body properties of the measured system due to quantum measurements. We observe this backaction-induced transition in a mesoscopic double quantum-dot in the Coulomb-blockade regime, where we switch the electron population through measurement with a charge sensor dot. Our finding showcases the important changes in behaviour that can arise due to quantum detectors, which are ubiquitous in quantum technologies.
Graphing Contributions in Natural Language Processing Research: Intra-Annotator Agreement on a Trial Dataset

Purpose: To stabilize the NLPContributionGraph scheme for the surface structuring of contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: first stage - to define the scheme; and second stage - to stabilize the graphing model. Approach: Re-annotate, a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples. To this end specifically, care was taken in the second annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring scheme. Findings: The application of NLPContributionGraph on the 50 articles resulted in finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples. The intra-annotation agreement between the first and second stages, in terms of F1, was 67.92% for sentences, 41.82% for phrases, and 22.31% for triples indicating that with an increased granularity of the information, the annotation decision variance is greater. Practical Implications: Demonstrate NLPContributionGraph data integrated in the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with compute enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks. Value: NLPContributionGraph is a novel scheme to obtain research contribution-centered graphs from NLP articles which to the best of our knowledge does not exist in the community. And our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.
Robust Adaptive Control of Linear Parameter-Varying Systems With Unmatched Uncertainties

This paper presents a robust adaptive control solution for linear parameter-varying (LPV) systems with unknown input gain and unmatched nonlinear (state-and time-dependent) uncertainties based on the L 1 adaptive control architecture and peak-to-peak gain (PPG) analysis/minimization. Speciﬁcally, we introduce a piecewise-constant estimation law to estimate the lumped uncertainty with quantiﬁable error bounds, which can be systematically improved by reducing the estimation sampling time. We also attenuate the unmatched uncertainty based on the PPG minimization that is applicable to a broad class of systems with nominal linear dynamics. In addition, leveraging the PPG bound of an LPV system that is computable using linear matrix inequality (LMI) techniques, we derive transient and steady-state performance bounds in terms of the input and output signals of the actual closed-loop system as compared to the same signals of a virtual reference system that represents the possibly best achievable performance. Under mild assumptions, we prove that the transient performance bounds can be uniformly reduced by decreasing the estimation sampling time, which is subject only to hardware limitations. The theoretical development is validated by extensive simulations on the short-period dynamics of an F-16 aircraft.
Anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Enormous amounts of data are being produced everyday by submeters and smart sensors installed in different kinds of buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem to become widespread, costly and time-consuming issue. Moreover, this will help in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. Specifically, a comprehensive survey is introduced, in which a novel taxonomy is introduced to classify existing algorithms based on different factors adopted in their implementation, such as the machine learning algorithm, feature extraction approach, detection level, computing platform, application scenario and privacy preservation. To the best of the authors' knowledge, this is the first review article that discusses the anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumptions, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, and (iv) platforms for reproducibility. Following, insights about current research trends that anomaly detection technology needs to target for widespreading its application and facilitate its implementation are described before deriving a set of challenging future directions attracting significant research and development attention.
Exact solution of the position-dependent effective mass and angular frequency Schrödinger equation: harmonic oscillator model with quantized confinement parameter

We present an exact solution of a confined model of the non-relativistic quantum harmonic oscillator, where the effective mass and the angular frequency are dependent on the position. The free Hamiltonian of the proposed model has the form of the BenDaniel–Duke kinetic energy operator. The position-dependency of the mass and the angular frequency is such that the homogeneous nature of the harmonic oscillator force constant k and hence the regular harmonic oscillator potential is preserved. As a consequence thereof, a quantization of the confinement parameter is observed. It is shown that the discrete energy spectrum of the confined harmonic oscillator with position-dependent mass and angular frequency is finite, has a non-equidistant form and depends on the confinement parameter. The wave functions of the stationary states of the confined oscillator with position-dependent mass and angular frequency are expressed in terms of the associated Legendre or Gegenbauer polynomials. In the limit where the confinement parameter tends to ∞, both the energy spectrum and the wave functions converge to the well-known equidistant energy spectrum and the wave functions of the stationary non-relativistic harmonic oscillator expressed in terms of Hermite polynomials. The position-dependent effective mass and angular frequency also become constant under this limit.
Roughness in Spot Variance? A GMM Approach for Estimation of Fractional Log-Normal Stochastic Volatility Models Using Realized Measures

In this paper, we develop a generalized method of moments approach for joint estimation of the parameters of a fractional log-normal stochastic volatility model. We show that with an arbitrary Hurst exponent an estimator based on integrated variance is consistent. Moreover, under stronger conditions we also derive a central limit theorem. These results stand even when integrated variance is replaced with a realized measure of volatility calculated from discrete high-frequency data. However, in practice a realized estimator contains sampling error, the effect of which is to skew the fractal coefficient toward "roughness". We construct an analytical approach to control this error. In a simulation study, we demonstrate convincing small sample properties of our approach based both on integrated and realized variance over the entire memory spectrum. We show that the bias correction attenuates any systematic deviance in the estimated parameters. Our procedure is applied to empirical high-frequency data from numerous leading equity indexes. With our robust approach the Hurst index is estimated around 0.05, confirming roughness in integrated variance.
Gaussian Process (GP)-based Learning Control of Selective Laser Melting Process

Selective laser melting (SLM) is one of emerging processes for effective metal additive manufacturing. Due to complex heat exchange and material phase changes, it is challenging to accurately model the SLM dynamics and design robust control of SLM process. In this paper, we first present a data-driven Gaussian process based dynamic model for SLM process and then design a model predictive control to regulate the melt pool size. Physical and process constraints are considered in the controller design. The learning model and control design are tested and validated with high-fidelity finite element simulation. The comparison results with other control design demonstrate the efficacy of the control design.
Mechanism Design of a Bio-inspired Armwing Mechanism for Mimicking Bat Flapping Gait

The objective of this work is to design and develop a bio-inspired soft and articulated armwing structure which will be an integral component of a morphing aerial co-bot, Aerobat. In our design, we draw inspiration from bats. Bat membranous wings possess unique functions that make them a good example to take inspiration from and transform current aerial drones. In contrast with other flying vertebrates, bats have an extremely articulated musculoskeletal system, key to their body impact-survivability and deliver an impressively adaptive and multimodal locomotion behavior. Bats exclusively use this capability with structural flexibility to generate the controlled force distribution on each wing membrane. The wing flexibility, complex wing kinematics, and fast muscle actuation allow these creatures to change the body configuration within a few tens of milliseconds. These characteristics are crucial to the unrivaled agility of bats and copying them can potentially transform the state-of-the-art aerial drone design.
A Novel ANN Structure for Image Recognition

The paper presents Multi-layer Auto Resonance Networks (ARN), a new neural model, for image recognition. Neurons in ARN, called Nodes, latch on to an incoming pattern and resonate when the input is within its 'coverage. ' Resonance allows the neuron to be noise tolerant and tunable. Coverage of nodes gives them an ability to approximate the incoming pattern. Its latching characteristics allow it to respond to episodic events without disturbing the existing trained network. These networks are capable of addressing problems in varied fields but have not been sufficiently explored. Implementation of an image classification and identification system using two-layer ARN is discussed in this paper. Recognition accuracy of 94% has been achieved for MNIST dataset with only two layers of neurons and just 50 samples per numeral, making it useful in computing at the edge of cloud infrastructure.
Research of Physiology and Heredity on Barley of Chlorophyll-Deficient Mutant

In the article we have studied the ultra structure of chloroplast, the content of chlorophy11, the photosynthetic character, the type of chromosome complement and the heredity controlling in the chlorophy11 deficient mutant barley. The yellowish leaves of the mutant is controlled by a recessive nuclear gene. So the barley is a typical material for studying the heredity and the photosynthesis.
Electrochemically assisted deposition of biodegradable polymer nanoparticles/sol–gel thin films

Biodegradable nanoparticles represent a promising platform for controlled release and other applications in medicine. To alter the interface of the medical device with the living tissue successfully, a thin coating needs to be applied onto the surface. The traits and functionality of the coating depends on its components, which in the presented work are sol–gel and biodegradable nanoparticles (NPs) deposited onto the substrate using the electrochemical sol–gel method. Aspects affecting the deposition process were investigated such as the applied potential and its duration. The properties of the deposits with emphasis on the biodegradable NPs within the deposited films were characterized and studied. Another element examined was the loading ability of the NPs. A fluorescent organic molecule was incorporated in the biodegradable NPs as a drug model, to demonstrate loading capability.
Efficacy and Safety of Fulvestrant 500mg in Hormone-receptor Positive Human Epidermal Receptor 2 Negative Advanced Breast Cancer: A Real-world Study in China

Background: Fulvestrant 500mg has proved its clinical effectiveness in previous trials as primary or second line treatment of hormone receptor positive, human epidermal receptor 2 negative (HR+/HER2˗) post-menopausal advanced breast cancer. This real-world study aimed to investigate the efficacy and safety of Fulvestrant in HR+/HER2˗ Chinese advanced breast cancer patients. Method: HR+/HER2˗ advanced breast cancer patients who received Fulvestrant 500mg from January 2015 to December 2018 in Beijing Cancer Hospital were enrolled in this retrospective study. Progression free survival (PFS), objective response rate (ORR), clinical benefit rate (CBR), overall survival (OS) and adverse events (AEs) of Fulvestrant were investigated. Result: In total 303 enrolled patients [median age was 51 years (range: 21-82)], 255 (84.2%) patients were at postmenopausal status at the start of Fulvestrant treatment and 264 patients (87.1%) had advanced breast cancer. The median PFS (95% confidence interval) was 14.1 months (10.1-18.0) for the first-line, 11.2 months (2.2-20.3) for the second-line and 6.7 months (4.8-8.5) for ≥third-line of Fulvestrant. The ORR and CBR were 3.8% and 86.8% for the first-line, 5.5% and 75.4% for the second-line, 1.1% and 61.1% for ≥third-line of Fulvestrant. The multivariate subgroup analyses showed, PFS was significantly longer for the patients with light tumor burden, less palliative chemotherapy before Fulvestrant and long disease-free interval. For patients receiving Fulvestrant after palliative chemotherapy, the median PFS was numerically greater in maintenance treatment group than those who progressed after chemotherapy. Only 5.0% of patients (15/303) experienced adverse events and majority were grade 1-2. The most common adverse event was headache and palpitation, with merely one patient had severe adverse event (pulmonary embolism). Conclusion: Fulvestrant is an effective, safe and well-tolerated treatment regimen in endocrine therapy for HR+/HER2˗ metastatic breast cancer. Light tumor burden, less palliative chemotherapy before Fulvestrant and long disease-free survival (DFS) might be the ideal condition of Fulvestrant treatment. Fulvestrant can be effective for premenopausal patients with drug-induced menopause. Patients of different luminal subtypes can benefit from Fulvestrant. For patients with visceral metastases, presence of liver metastases rather than lung metastases was poor prognostic factor. Fulvestrant may also be considered as a maintenance treatment after first-line palliative chemotherapy.