Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Activity of the Long Point Fault in Houston, Texas Constrained by Continuous GPS Measurements (2013-2018)

The Long Point Fault is one of the most active urban faults in Houston, Texas, which belong to a complex system of normal growth faults along the Texas Gulf Coast. To assess the activity of the Long Point Fault, a GPS array with 12 permanent stations was installed along the two sides of the 16-km-long fault scarp in 2013. GPS datasets were processed with the Precise Point Positioning (PPP) and Double-Difference (DD) methods. The daily PPP solutions with respect to the International Global Navigation Satellite System (GNSS) Reference Frame 2014 (IGS14) were converted to the Stable Houston Reference Frame (Houston16). The six-year continuous GPS observations indicate that the Long Point Fault is currently inactive, with the rates of down-dip-slip and along-strike-slip being below 1 mm/year. The Long Point Fault area is experiencing moderate subsidence varying from 5 to 11 mm/year and a coherent horizontal movement towards the northwest at a rate of approximately 2 to 4 mm/year. The horizontal movement is induced by the subsidence bowl that has been developing since the 1980s in the Jersey Village area. Current surficial damages in the Long Point Fault area are more likely caused by ongoing uneven subsidence and its induced horizontal strains, as well as the significant seasonal ground deformation, rather than deep-seated or tectonic-controlled fault movements. The results from this study suggest a cause-and-effect relationship between groundwater withdrawals and local faulting, which is pertinent to plans for future urban development, use of groundwater resources, and minimization of urban geological hazards.
Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning

Active learning for multi-label classification poses fundamental challenges given the complex label correlations and a potentially large and sparse label space. We propose a novel CS-BPCA process that integrates compressed sensing and Bayesian principal component analysis to perform a twolevel label transformation, resulting in an optimally compressed continuous target space. Besides leveraging correlation and sparsity of a large label space for effective compression, an optimal compressing rate and the relative importance of the resultant targets are automatically determined through Bayesian inference. Furthermore, the orthogonality of the transformed space completely decouples the correlations among targets, which significantly simplifies multi-label sampling in the target space. We define a novel sampling function that leverages a multi-output Gaussian Process (MOGP). Gradient-free optimization strategies are developed to achieve fast online hyperparameter learning and model retraining for active learning. Experimental results over multiple realworld datasets and comparison with competitive multi-label active learning models demonstrate the effectiveness of the proposed framework.
Estimation of Translational Motion Parameters in Terahertz Interferometric Inverse Synthetic Aperture Radar (InISAR) Imaging Based on a Strong Scattering Centers Fusion Technique

Translational motion of a target will lead to image misregistration in interferometric inverse synthetic aperture radar (InISAR) imaging. In this paper, a strong scattering centers fusion (SSCF) technique is proposed to estimate translational motion parameters of a maneuvering target. Compared to past InISAR image registration methods, the SSCF technique is advantageous in its high computing efficiency, excellent antinoise performance, high registration precision, and simple system structure. With a one-input three-output terahertz InISAR system, translational motion parameters in both the azimuth and height direction are precisely estimated. Firstly, the motion measurement curves are extracted from the spatial spectrums of mutually independent strong scattering centers, which avoids the unfavorable influences of noise and the “angle scintillation” phenomenon. Then, the translational motion parameters are obtained by fitting the motion measurement curves with phase unwrapping and intensity-weighted fusion processing. Finally, ISAR images are registered precisely by compensating the estimated translational motion parameters, and high-quality InISAR imaging results are achieved. Both simulation and experimental results are used to verify the validity of the proposed method.
ENRE: A Tool Framework for Extensible eNtity Relation Extraction

Understanding the dependencies among code entities is fundamental to many software analysis tools and techniques. However, with the emergence of new programming languages and paradigms, the increasingly common practice of writing systems in multiple languages, and the increasing popularity of dynamic languages, no existing framework can reliably extract this information. That is, no tools exist to accurately extract dependencies from systems written in multiple and dynamic languages. To address this problem, we have designed and implemented the Extensible eNtity Relation Extraction (ENRE) framework. ENRE supports the extraction of entities and their dependencies from systems written in multiple languages, enables the customization of dependencies of interest to the user, and makes implicit dependencies explicit. To demonstrate feasibility of this framework, we developed two ENRE instances for analyzing Python and Golang programs. Our experiments on 12 Python and Golang projects demonstrated the effectiveness and flexibility of ENRE. By comparing with a commercial static analysis tool, we show that we can extract dependencies from Golang programs which are not supported by existing tools and we can reveal implicit dependencies in Python. (Demo Video: https://youtu.be/BfXp5bb1yqc)
TASKWORK: A Cloud-aware Runtime System for Elastic Task-parallel HPC Applications

With the capability of employing virtually unlimited compute resources, the cloud evolved into an attractive execution environment for applications from the High Performance Computing (HPC) domain. By means of elastic scaling, compute resources can be provisioned and decommissioned at runtime. This gives rise to a new concept in HPC: Elasticity of parallel computations. However, it is still an open research question to which extent HPC applications can benefit from elastic scaling and how to leverage elasticity of parallel computations. In this paper, we discuss how to address these challenges for HPC applications with dynamic task parallelism and present TASKWORK, a cloud-aware runtime system based on our findings. TASKWORK enables the implementation of elastic HPC applications by means of higher-level development frameworks and solves corresponding coordination problems based on Apache ZooKeeper. For evaluation purposes, we discuss a development framework for parallel branch-and-bound based on TASKWORK, show how to implement an elastic HPC application, and report on measurements with respect to parallel efficiency and elastic scaling.
A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling

In this paper we present a new method for intent recognition for complex dialog management in low resource situations. Complex dialog management is required because our target domain is real world mixed initiative food ordering between agents and their customers, where individual customer utterances may contain multiple intents and refer to food items with complex structure. For example, a customer might say “Can I get a deluxe burger with large fries and oh put extra mayo on the burger would you? ” We approach this task as a multi-level sequence labeling problem, with the constraint of limited real training data. Both traditional methods like HMM, MEMM, or CRF and newer methods like DNN or BiLSTM use only homogeneous feature sets. Newer methods perform better but also require considerably more data. Previous research has done pseudo-data synthesis to obtain the required amounts of training data. We propose to use a k-NN learner with heterogeneous feature set. We used windowed word n-grams, POS tag n-grams and pre-trained word embeddings as features. For the experiments we perform a comparison between using pseudo-data and real world data. We also perform semi-supervised self-training to obtain additional labeled data, in order to better model real world scenarios. Instead of using massive pseudo-data, we show that with only less than 1% of the data size, we can achieve better result than any of the methods above by annotating real world data. We achieve labeled bracketed F-scores of 75.46, 52.84 and 49.66 for the three levels of sequence labeling where each level has a longer word span than its previous level. Overall we achieve 60.71F. In comparison, two previous systems, MEMM and DNN-ELMO, achieved 52.32 and 45.25 respectively.
Towards Intra-Datacentre High-Availability in CloudDBAppliance

In the context of the CloudDBAppliance (CDBA) project, fault tolerance and high-availability are provided in layers: within each appliance, within a data centre and between data centres. This paper presents the proposed replication architecture for providing fault tolerance and high availability within a data centre. This layer configuration, along with specific deployment constraints require a custom replication architecture. In particular, replication must be implemented at the middleware-level, to avoid constraining the backing operational database. This paper is focused on the design of the CDBA Replication Manager along with an evaluation, using micro-benchmarking, of components for the replication middleware. Results show the impact, on both throughput and latency, of the replication mechanisms in place.
Doc2hash: Learning Discrete Latent variables for Documents Retrieval

Learning to hash via generative model has become a powerful paradigm for fast similarity search in documents retrieval. To get binary representation (i.e., hash codes), the discrete distribution prior (i.e., Bernoulli Distribution) is applied to train the variational autoencoder (VAE). However, the discrete stochastic layer is usually incompatible with the backpropagation in the training stage, and thus causes a gradient flow problem because of non-differentiable operators. The reparameterization trick of sampling from a discrete distribution usually inc non-differentiable operators. In this paper, we propose a method, Doc2hash, that solves the gradient flow problem of the discrete stochastic layer by using continuous relaxation on priors, and trains the generative model in an end-to-end manner to generate hash codes. In qualitative and quantitative experiments, we show the proposed model outperforms other state-of-art methods.
Handling Packet Losses in Cloud-based MPTCP Application Traffic

Internet traffic is comprised of data flows from various applications with unique traffic characteristics. For many cloud applications, end-to-end latency is a primary factor affecting the perceived user experience. As packet losses cause delays in the communication they impact user experience, making efficient handling of packet losses an important function of transport layer protocols. Multipath TCP (MPTCP) is a modification to TCP that enables simultaneous use of several paths for a TCP flow. MPTCP is known to improve throughput. However, the performance of MPTCP is not optimal when handling certain loss scenarios. Efficient packet loss recovery is thus important to achieve desirable flow completion times for interactive cloud-based applications. In this paper we evaluate the performance of MPTCP in handling tail losses using traffic traces from various cloud-based applications. Tail losses, losses that occur at the end of a flow or traffic burst, are particularly challenging from a latency perspective as they are difficult to detect and recover in a timely manner. Tail losses in TCP are handled by using a tail loss probe (TLP) mechanism which was adapted to MPTCP from TCP. We investigate the performance of TLP in MPTCP, comparing the standard implementation to a recently proposed, less conservative approach. Our experimental results show that a less conservative implementation of TLP performs significantly better than the standard implementation in handling tail losses, reducing the average burst completion time of cloud based applications when tail loss occurs by up to 50% in certain cases.
Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization

Batch Bayesian optimization has been shown to be an efficient and successful approach for blackbox function optimization, especially when the evaluation of cost function is highly expensive but can be efficiently parallelized. In this paper, we introduce a novel variational framework for batch query optimization, based on the argument that the query batch should be selected to have both high diversity and good worst case performance. This motivates us to introduce a variational objective that combines a quantile-based risk measure (for worst case performance) and entropy regularization (for enforcing diversity). We derive a gradient-based particle optimization algorithm for solving our quantile-based variational objective, which generalizes Stein variational gradient descent (SVGD) by Liu & Wang (2016). We evaluate our method on a number of real-world applications, and show that it consistently outperforms other recent state-of-the-art batch Bayesian optimization methods.
Learning from a Learner

In this paper, we propose a novel setting for Inverse Reinforcement Learning (IRL), namely “Learning from a Learner” (LfL). As opposed to standard IRL, it does not consist in learning a reward by observing an optimal agent, but from observations of another learning (and thus sub-optimal) agent. To do so, we leverage the fact that the observed agent’s policy is assumed to improve over time. The ultimate goal of this approach is to recover the actual environment’s reward and to allow the observer to outperform the learner . To recover that reward in practice, we propose methods based on the entropy-regularized policy iteration framework. We discuss different approaches to learn solely from trajectories in the state-action space. We demonstrate the genericity of our method by observing agents implementing various reinforcement learning algorithms. Finally, we show that, on both discrete and continuous state/action tasks, the observer ’s performance (that optimizes the recovered reward) can surpass those of the observed learner .
Analyzing and Repairing Compilation Errors

Resolving a build failure consumes developer time both in finding a suitable resolution and in rerunning the build. Our goal is to develop automated repair tools that can automatically resolve build errors and therefore improve developer productivity. We collected data on the resolution of Java build failures to discover how long developers spend resolving different kinds of diagnostics at Google. We found that the diagnostic reporting an unresolved symbol consumes 47% of the total time spent resolving broken builds. We found that choice of tool has a significant impact: 26% of command line builds fail whereas only 3% of IDE builds fail. However, the set of most costly diagnostic kinds remains the same for both. We trained a Neural Machine Translation model on the Abstract Syntax Tree changes made when resolving an unresolved symbol failure. This generates a correct fix with a true positive rate of 50%.
Analysis of Filipino Mood Swings within a Day using Tweets

We infer moods and how they vary with time using a dataset from the social media application Twitter. We used Python text mining techniques to gather all tweets originating from the Philippines within a span of 24 hours. From the dataset of around 130,000 tweets, we gathered the highest-frequency words and filtered out neutral words to come up with words that imply mood levels. We then plotted the density of keyword usage with respect to time, distinguishing between positive and negative moods. Our initial results of positive mood and negative mood trends are consistent with published studies regarding microblogging mood scales. The emergence of Big Data and the Internet of Things has greatly amplifed our ability not only to express ourselves but to understand each other.
A 0.32V, 55fJ per bit access energy, CMOS 65nm bit-interleaved SRAM with radiation Soft Error tolerance

A 32kb memory is presented with an Ultra Low Voltage optimized 10 transistors bitcell designed to withstand an extended voltage range from 1.2V down to 0.35V, achieving 1.77pJ low energy access. A validation circuit was fabricated in 65nm CMOS and exhibits wafer level yield above 95% at 0.4V, 1MHz. Packaged parts show 0.32V minimum voltage at 490kHz and up to 17X energy gain per operation. The memory terrestrial radiation Soft Error Rate was characterized with no multibit errors reported, enabling future medical appplications radiation reliability through bit-interleaving combined with error correcting code.
Learning Distance for Sequences by Learning a Ground Metric

Learning distances that operate directly on multidimensional sequences is challenging because such distances are structural by nature and the vectors in sequences are not independent. Generally, distances for sequences heavily depend on the ground metric between the vectors in sequences. We propose to learn the distance for sequences through learning a ground Mahalanobis metric for the vectors in sequences. The learning samples are sequences of vectors for which how the ground metric between vectors induces the overall distance is given, and the objective is that the distance induced by the learned ground metric produces large values for sequences from different classes and small values for those from the same class. We formulate the metric as a parameter of the distance, bring closer each sequence to an associated virtual sequence w.r. t. the distance to reduce the number of constraints, and develop a general iterative solution for any ground-metricbased sequence distance. Experiments on several sequence datasets demonstrate the effectiveness and efficiency of our method.
Incorporating Emoji Descriptions Improves Tweet Classification

Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.
Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling

There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.
ARepair: A Repair Framework for Alloy

Researchers have proposed many automated program repair techniques for imperative languages, e.g. Java. However, little work has been done to repair programs written in declarative languages, e.g. Alloy. We proposed ARepair, the first automated program repair technique for faulty Alloy models. ARepair takes as input a faulty Alloy model and a set of tests that capture the desired model properties, and produces a fixed model that passes all tests. ARepair uses tests written for the recently introduced AUnit framework, which provides a notion of unit testing for Alloy models. In this paper, we describes our Java implementation of ARepair, which is a command-line tool, released as an open-source project on GitHub. Our experimental results show that ARepair is able to fix 28 out of 38 real-world faulty models we collected. The demo video for ARepair can be found at https://youtu.be/436drvWvbEU.
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

A broad range of cross-$m$-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as $m$ increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i.e., MMI-ALI for $m$-domain joint distribution matching. As an $m$-domain ensemble model of ALIs \cite{dumoulin2016adversarially}, MMI-ALI is adversarially trained with maximizing Multivariate Mutual Information (MMI) w.r. t. joint variables of each pair of domains and their shared feature. The negative MMIs are upper bounded by a series of feasible losses that provably lead to matching $m$-domain joint distributions. MMI-ALI linearly scales as $m$ increases and thus, strikes a right balance between efficacy and scalability. We evaluate MMI-ALI in diverse challenging $m$-domain scenarios and verify its superiority.
#Drink Or #Drunk: Multimodal Signals and Drinking Practices on Instagram

The understanding of alcohol consumption patterns, especially those indicating negative drinking behavior, is an important issue to researchers and health policymakers. On social media, people share daily activities, including alcohol consumption, representing these moments through images and text. This work, using a five-year dataset from Instagram, analyzes what machine-extracted textual and visual cues reveal about trends of casual drinking (concepts gathered around #drink) and possible negative drinking (concepts gathered around #drunk). Our analysis reveals that #drunk posts occur more frequently in party occasions and nightlife locations, with a higher presence of people, while #drink posts occur at food locations, with a higher presence of drink containers. Manual coding further shows that #drunk posts have a higher chance of being perceived as potentially objectionable. A random forest classifier shows that #drink and #drunk posts can be discriminated with accuracy up to 82.3%. These results have important implications for alcohol research among youth.
Differential Functioning on the Illingworth by Home- vs Institution-Reared 3-Month-Old Infants

The performance on the Illingworth of 32 3-mo. -old home-reared Greek infants was compared to that of 22 infants reared in a modern institution. Home-reared infants were superior to institutional infants in every aspect of development except on Manipulations (fine motor coordination). It is speculated that the superiority of the home infants is attributed to the quality of the mother/caretaker-infant relationship.