Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation and Inference in Dynamic Unbalanced Panel-data Models with a Small Number of Individuals

This article describes a new Stata routine, xtlsdvc, that computes bias-corrected least-squares dummy variable (LSDV) estimators and their bootstrap variance–covariance matrix for dynamic (possibly) unbalanced panel-data models with strictly exogenous regressors. A Monte Carlo analysis is carried out to evaluate the finite-sample performance of the bias-corrected LSDV estimators in comparison to the original LSDV estimator and three popular N-consistent estimators: Arellano–Bond, Anderson–Hsiao and Blundell–Bond. Results strongly support the bias-corrected LSDV estimators according to bias and root mean squared error criteria when the number of individuals is small.
DGMED: A Novel Document-Level Graph Convolution Network for Multi-Event Detection

Online news documents can contain thousands 001 of characters and tens of events. To detect 002 events in these documents, it is important to 003 construct long-range context information. Such 004 information, however, is not effectively cre-005 ated in existing event detection methods in-006 cluding DMBERT, MOGANED. As a result, 007 these methods show poor event detection accu-008 racy in production where long documents are 009 common. To address this, this paper proposes 010 a Document-level Graph convolution network 011 for Multi-event Detection (DGMED). DGMED 012 represents each sentence in a long document as 013 a graph, and it interconnects these graphs us-014 ing novel cross-sentence global neural network 015 nodes. These nodes allow DGMED further 016 construct accurate document-level contextual 017 information, thus accurately extracting multi-018 ple events as required. We evaluate DGMED 019 using a public event extraction dataset (i.e., 020 Maven) and a production large-scale dataset 021 (named AML). Evaluation results show that 022 DGMED can out-perform state-of-the-art meth-023 ods BERT+CRF and BiLSTM+CRF up to 0.7% 024 in Maven and 5.7% in AML. 025
Unsupervised Multiview Embedding of Node Embeddings

—In this paper, we propose a comprehensive unsupervised framework that leverages existing and novel multiview learning models, towards obtaining a single node embedding from a collection of node embeddings, combining the best of all worlds. Through extensive experiments, we demonstrate that the proposed multiview node embedding is able to perform on par or better than the best of its constituents and provide reliable performance across downstream tasks including node classification and graph reconstruction
Event Detection via Derangement Reading Comprehension

Event detection (ED), aiming to detect events 001 from texts and categorize them, is vital to 002 understanding the actual happenings in real 003 life. Recently, ED without triggers has been 004 proposed and gained beneﬁts since it relieves 005 the tedious effort of data labeling. However, 006 it still suffers from several formidable chal-007 lenges: multi-label, insufﬁcient clues, and im-008 balanced event types. We, therefore, propose a 009 novel Derangement mechanism on a machine 010 Reading Comprehension (DRC) framework to 011 tackle the above challenges. More specially, 012 we treat the input text as Context and concate-013 nate it with all event types that are deemed 014 as Answers with an omitted default question. 015 Thus, by appending input text and event types 016 simultaneously, we can facilitate the power of 017 self-attention in pre-trained language models, 018 e.g., BERT, to absorb the semantic relation 019 among them. Moreover, we design a simple 020 yet effective derangement mechanism to re-021 lieve the imbalanced training. By introducing 022 such perturbation mainly on major events, we 023 can prohibit major events from excessive learn-024 ing or implicitly under-sample the instances 025 of the major events. This yields a more bal-026 anced training to resolve the imbalanced learn-027 ing issue. The empirical results show that: 028 (1) our proposed framework attains state-of-029 the-art performance over previous competitive 030 models, and (2) by-product, our model can sig-031 nify the connection of triggers and arguments 032 to events for further analysis. 033
Large-scale Multifidelity Aerostructural Optimization of a Transport Aircraft

High-fidelity aerostructural aircraft optimizations have become more widespread in recent years, especially when combined with the efficiencies offered by the adjoint method and gradient-based optimizers. However, it is still computationally expensive to perform such optimizations with hundreds of design variables and multiple flight conditions. In this work, we address some practical issues with large-scale aerostructural optimizations using a multifidelity approach. We perform an optimization of a notional transport aircraft analyzed over ten flight conditions, including close to 1000 design variables and 1000 constraints. We compare the result against a reference single-fidelity optimization, and show that by leveraging multiple lower-fidelity models, we can reduce the computational cost by 14%.
Biosensors Based Diagnosis Treatment of Infectious Diseases

---------------------------------------------------------------------------------------------------------------------------------------ABSTRACT : The development of biosensors has been the focus of scientist's attention for current decades. Biosensors can centrally serve as low-cost and highly able devices for this aim in addition to being applied in other day to day applications. In the past five decades, biosensors have consolidated their impact in several
Condition of California Perennial, Wadeable Streams Based on Algal Indicators

Algae can serve as indicators of stream ecological condition in two ways: (1) Information about algal community composition can be used for assessing overall stream health using indices of biological integrity, and (2) information about algal biomass can be used as an indicator for stream health because excessive algae growth can impact stream health. The assessment, Condition of California Perennial Wadeable Streams Based on Algal Indicators, is funded by SWAMP and uses SWAMP data from 2007 to 2011. It uses information from algae species composition and algal biomass to evaluate the condition of California’s wadeable perennial streams.
Multi-Label Text Classiﬁcation by Graph Neural Network with Mixing Operations

Multi-label text classiﬁcation is one of the fun-001 damental tasks in natural language process-002 ing. Recently, the graph convolution network 003 (GCN) is leveraged to boost the performance 004 of such a task. However, the best way for 005 label correlation modeling and feature learn-006 ing with label system awareness is still un-007 clear. This paper proposes Mix-GCN, a graph 008 network with two mixing operations, to im-009 prove the conventional GCN framework for 010 multi-label text classiﬁcation in the following 011 two steps. Firstly, we model the label correla-012 tions by mixing the graph built from statistical 013 co-occurrence information and the graph con-014 structed from prior knowledge. Secondly, we 015 propose a mixing operation to continuously in-016 ject GCN embedding into LSTM representa-017 tion learning for better label-aware representa-018 tion. Experimental results on four benchmarks 019 demonstrate that Mix-GCN signiﬁcantly out-020 performs the state-of-the-art models and per-021 forms better in long-tail label cases. 022
Exact Paired Permutation Testing Algorithms for NLP Systems

Significance testing has played a vital role in 001 the development of NLP systems, providing 002 confidence that one system is indeed better than 003 another one. However, many significance tests 004 involve hard computation problems, and so we 005 rely on approximation methods such as Monte 006 Carlo sampling. In this paper, we provide an ex-007 act dynamic programming algorithm that runs 008 in quadratic time in the size of the dataset and 009 performs the paired permutation test, a widely 010 used test in comparing two systems, for the 011 case of comparing accuracies between two clas-012 sification systems. We show that Monte Carlo 013 approximations are often too noisy to reliably 014 determine whether we can reject the null hy-015 pothesis. We show that Monte Carlo approx-016 imations are often too noisy to reliably deter-017 mine whether we can reject the null hypothesis 018 with a significance level of α ≈ 0 . 05 for any 019 number of sentence N . Additionally, we show 020 that our exact algorithm is more efficient than 021 the approximation algorithm for N ≤ 10 K . 022
Denoising Large-Scale Image Captioning from Alt-text Data Using Content Selection Models

Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples that are expensive to curate both in terms of time and man-power. Instead, alt-text based captions gathered from the web is a far cheaper alternative to scale with the downside of being noisy. Recent modeling approaches to IC often fall short in terms of performance in leveraging these noisy datasets in favor of clean annotations. We address this problem with a simple yet effective technique of breaking down the task into two smaller, more controllable tasks – skeleton prediction and skeleton-based caption generation. Specifically, we show that sub-selecting content words as skeletons helps in generating improved and denoised captions when leveraging rich yet noisy alt-text–based uncurated datasets. We also show that the predicted English skeletons can further cross-lingually be leveraged to generate non-English captions, and present experimental results covering caption generation in French, Italian, German, Spanish and Hindi. We also show that skeleton-based prediction allows for better control of certain caption properties, such as length, content, and gender expression, providing a handle to perform human-in-the-loop interpretable semi-automatic corrections.
Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis

Combining symbolic and subsymbolic methods has become a promising strategy as research tasks in AI grow increasingly complicated and require higher levels of understanding. Targeted Aspect-based Financial Sentiment Analysis (TABFSA) is an example of such complicated tasks, as it involves processes like information extraction, information specification, and domain adaptation. However, little is known about the design principles of such hybrid models leveraging external lexical knowledge. To fill this gap, we define anterior, parallel, and posterior knowledge integration and propose incorporating multiple lexical knowledge sources strategically into the fine-tuning process of pre-trained transformer models for TABFSA. Experiments on the Financial Opinion mining and Question Answering challenge (FiQA) Task 1 and SemEval 2017 Task 5 datasets show that the knowledge-enabled models systematically improve upon their plain deep learning counterparts, and some outperform state-of-the-art results reported in terms of aspect sentiment analysis error. We discover that parallel knowledge integration is the most effective and domain-specific lexical knowledge is more important according to our ablation analysis.
LipKey: A Large-Scale News Dataset with Abstractive Keyphrases and Their Beneﬁts for Summarization

Summaries, keyphrases, and titles are differ-001 ent ways of concisely capturing the content 002 of a document. While most previous work 003 has addressed them separately, in this work, 004 we jointly use the three elements via multi-005 task training and training as joint structured 006 inputs, in the context of document summa-007 rization. We release LipKey , the largest 008 news corpus with human-written summaries, 009 titles, and keyphrases, as well as being the 010 ﬁrst large-scale Indonesian keyphrase dataset. 011 We ﬁnd that including keyphrases and titles as 012 additional context to the source document im-013 proves transformer-based summarization mod-014 els. 1 015
Names to Rule Them All: Unifying Vehicular Networking via Named Secured Data

Over the last few decades, mobile networking technologies have advanced by leaps and bounds, especially in terms of wireless speeds and mobile device capabilities. Yet advanced mobility support for peer-to-peer networking with ad-hoc encounters, seamless integration with infrastructure, and in particular the ability to secure ad hoc mobile communication is yet to arrive. Today’s commonly-deployed mobile solutions rely heavily on cloud services over cellular connectivity, even when the communicating mobile entities are in close proximity to each other. Vehicular networking is an exemplifying use case of peer-to-peer networking with ad-hoc encounters, which also desires both strong security and seamless integration with infrastructure networks. We take this use case to motivate a new abstraction of networking, via exchanging semantically named and secured data instead of pushing packets to numeric IP addresses. We illustrate how this new abstraction can effectively support vehicular networking, unify mobile ad-hoc and infrastructure communications by the same network protocol, and build security support into all communications. We also illustrate how this new direction of networking can keep local communications local and support the diverse set of today’s vehicular applications, with a potential to support those that are yet to come.
Sharper Reasons: Argument Mining Leveraged with Confluent Knowledge

Relevant to all application domains where it is 001 important to get at the reasons underlying deci-002 sions and sentiments, argument mining seeks 003 to obtain structured arguments from unstruc-004 tured text and has been addressed recently by 005 approaches typically involving some feature 006 and/or neural architecture engineering. 007 By embracing a transfer learning viewpoint, the 008 aim of this paper is to empirically assess the po-009 tential of transferring knowledge learned with 010 confluent tasks to argument mining by means of 011 a systematic study with a wide range of sources 012 of related knowledge possibly suitable to lever-013 age argument mining. 014 This permitted to gain new empirically based 015 insights into the argument mining task while 016 establishing also new state of the art levels of 017 performance for the three main sub-tasks in ar-018 gument mining, viz. identification of argument 019 components, classification of the components, 020 and determination of the relation among them, 021 with a leaner approach that dispenses with heav-022 ier feature and model engineering. 023
A Scalable Holistic approach for Age and Gender inference of Twitter Users

Numerous studies have focused on inference of 001 age and gender. We consider a new approach 002 that takes advantage of contrastive learning 003 methods by using both text and image content 004 for this prediction task. We also consider the 005 case where only text or image data is avail-006 able. Under both of these conditions, we show 007 that our model achieves better performance 008 than the state-of-the-art ones, and still performs 009 well with text/images only. Moreover, because 010 demographic datasets can be small, we also 011 consider combining different datasets to under-012 stand when augmentation is valuable and when 013 it is not. 014
Effectiveness of Bilateral Arm Training on Upper Extremity Function in Sub-Acute Stroke Patients: Pilot Study

: Objective: To see the effect of bilateral arm training on the upper limb function. Subject and Method: 30 subjects diagnosed with sub-acute stroke were assigned to study. Intervention in form of Conventional & Bilateral arm training were given for 5 Days per week for four weeks. Upper extremity function test was evaluated by Fugl Meyer Assessment, Motor activity log scale and action research arm test pre-& post treatment. Relationship and responsiveness of all clinical test were performed by Wilcoxon signed ranks test and Mann-Whitney test respectively. Result: All correlations were significant at 95% C.I. at baseline as well as four-week time. There is increasing bilateral arm training function on upper limb while comparing to conventional therapy in four-week protocol. Conclusion: On the basis of present study, it can be concluded that Bilateral arm training can be beneficial in improving upper limb function in sub-acute stroke patient when it compares to the Conventional therapy by using the Fugl Meyer assessment scale, Motor activity log scale and action research arm test
Defending Textual Neural Networks against Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher

Even though several methods have proposed 001 to defend textual neural network (NN) models 002 against black-box adversarial attacks, they of- 003 ten defend against a speciﬁc text perturbation 004 strategy and/or require re-training the models 005 from scratch. This leads to a lack of general- 006 ization in practice and redundant computation. 007 In particular, the state-of-the-art transformer 008 models (e.g., BERT, RoBERTa) require great 009 time and computation resources. By borrow- 010 ing an idea from software engineering , in or- 011 der to address these limitations, we propose 012 a novel algorithm, S HIELD , which modiﬁes 013 and re-trains only the last layer of a textual 014 NN, and thus it “patches” and “transforms” 015 the NN into a stochastic weighted ensemble 016 of multi-expert prediction heads. Consider- 017 ing that most of current black-box attacks rely 018 on iterative search mechanisms to optimize 019 their adversarial perturbations, S HIELD con- 020 fuses the attackers by automatically utilizing 021 different weighted ensembles of predictors de- 022 pending on the input. In other words, S HIELD 023 breaks a fundamental assumption of the attack, 024 which is a victim NN model remains constant 025 during an attack. By conducting comprehen- 026 sive experiments, we demonstrate that all of 027 CNN, RNN, BERT, and RoBERTa-based tex- 028 tual NNs, once patched by S HIELD , exhibit 029 a relative enhancement of 15%–70% in accu- 030 racy on average against 14 different black-box 031 attacks, outperforming 6 defensive baselines 032 across 3 public datasets. All codes are to be 033 released. 034
Symmetry energy, its density slope, and neutron-proton effective mass splitting at normal density extracted from global nucleon optical potentials

Based on the Hugenholtz-Van Hove theorem, it is shown that both the symmetry energy E{sub sym}({rho}) and its density slope L({rho}) at normal density {rho}{sub 0} are completely determined by the nucleon global optical potentials. The latter can be extracted directly from nucleon-nucleus scatterings, (p,n) charge-exchange reactions, and single-particle energy levels of bound states. Averaging all phenomenological isovector nucleon potentials constrained by world data available in the literature since 1969, the best estimates of E{sub sym}({rho}{sub 0})=31.3 MeV and L({rho}{sub 0})=52.7 MeV are simultaneously obtained. Moreover, the corresponding neutron-proton effective mass splitting in neutron-rich matter of isospin asymmetry {delta} is estimated to be (m{sub n}{sup *}-m{sub p}{sup *})/m=0.32{delta}.
MOMENT STRUCTURE OF A FAMILY OF FIRST-ORDER EXPONENTIAL GARCH MODELS

In this paper we consider the moment structure of a class of first-order exponential generalized autoregressive conditional heteroskedasticity (GARCH) models. This class contains as special cases both the standard exponential GARCH model and the symmetric and asymmetric logarithmic GARCH model. Conditions for the existence of any arbitrary moment are given. Furthermore, the expressions for the kurtosis and the autocorrelations of positive powers of absolute-valued observations are derived. The properties of the autocorrelation structure are discussed and compared to those of the standard first-order GARCH process. In particular, it is seen that, contrary to the standard GARCH case, the decay rate of the autocorrelations of squared errors is not constant and that the rate can be quite rapid in the beginning, depending on the parameters of the model.
Division of Informatics, University of Edinburgh

Summary. The recently developed technique of Iterative Closest Point (ICP) matching has been used for a number of 3D-to-3D and 2D-to-2D point matching applications, and has been further developed in several useful ways, as described below. Central to these applications is the notion of rigid shape matching. This paper extends the concept to projective point matching, in which shapes are related by a projective transform rather than a Euclidean transform. With this extended technique, we show that directly registering 2D Augmented Reality (AR) overlays via a projective transform has greater registration stability than the more usual technique of estimating the 3D position of the overlay and then applying pinhole projection, which can produce noticeable frame-rate jitter of the graphical objects. Moreover, the technique does not rely on explicit feature point correspondence and tracking. We then further extend the technique to directly register 3D shapes pro-jectively by using mutually constrained 2D projective mappings. These two new techniques enhance the repertoire of methods for producing high-detail, stable augmentation of built scenes.
1 Interactions: Do Teacher Behaviors Predict Achievement, Executive Function, and Non- Cognitive Outcomes in Elementary School?

Knowing what teacher characteristics or behaviors consistently predict child outcomes is critical for policy design. We use an experiment with seven consecutive rounds of random assignment and almost perfect compliance to estimate how the quality of teacher-child interactions affects child outcomes in elementary school. We find modest impacts of teacher behaviors on math and language achievement, with somewhat larger effects for children in kindergarten and 1st grade. The impact of teacher behaviors on achievement fades out, but can still be detected seven years later. Better teachers also improve child executive function and reduce the incidence of behavioral problems, but the magnitude of the impacts is small. We do not find clear evidence that better teacher-child interactions improve depression, selfesteem, growth mindset, or grit in a sustained way. Children do not exert more effort in response to differences in teacher quality, and teachers do not exhibit different behaviors when, by chance, they are randomly assigned to children of varying characteristics. We discuss policy implications and avenues for future research. * Inter-American Development Bank +University College London
Improving Document-level Relation Extraction via Context Guided Mention Integration and Inter-pair Reasoning

Document-level Relation Extraction (DRE) 001 aims to recognize the relations between two 002 entities. The entity may correspond to mul-003 tiple mentions that span beyond sentence 004 boundary. Few previous studies have inves-005 tigated the mention integration, which may 006 be problematic because coreferential mentions 007 do not equally contribute to a speciﬁc rela-008 tion. Moreover, prior efforts mainly focus 009 on reasoning at entity-level rather than cap-010 turing the global interactions between entity 011 pairs. In this paper, we propose two novel tech-012 niques, C ontext G uided M ention I ntegration 013 and I nter-pair R easoning (CGM2IR), to im-014 prove the DRE. Instead of simply applying 015 average pooling, the contexts are utilized to 016 guide the integration of coreferential men-017 tions in a weighted sum manner. Addition-018 ally, inter-pair reasoning executes an iterative 019 algorithm on the entity pair graph, so as to 020 model the interdependency of relations. We 021 evaluate our CGM2IR model on three widely 022 used benchmark datasets, namely DocRED, 023 CDR, and GDA. Experimental results show 024 that our model outperforms previous state-of-025 the-art models 1 . 026