Miracle-dz/UniFAR-FTUs huggingface dataset:
What just happened? Evaluating retrofitted distributional word vectors

Recent work has attempted to enhance vector space representations using information from structured semantic resources. This process, dubbed retrofitting (Faruqui et al., 2015), has yielded improvements in word similarity performance. Research has largely focused on the retrofitting algorithm, or on the kind of structured semantic resources used, but little research has explored why some resources perform better than others. We conducted a fine-grained analysis of the original retrofitting process, and found that the utility of different lexical resources for retrofitting depends on two factors: the coverage of the resource and the evaluation metric. Our assessment suggests that the common practice of using correlation measures to evaluate increases in performance against full word similarity benchmarks 1) obscures the benefits offered by smaller resources, and 2) overlooks incremental gains in word similarity performance. We propose root-mean-square error (RMSE) as an alternative evaluation metric, and demonstrate that correlation measures and RMSE sometimes yield opposite conclusions concerning the efficacy of retrofitting. This point is illustrated by word vectors retrofitted with novel treatments of the FrameNet data (Fillmore and Baker, 2010).
The Impact of Social-Cognitive Stress on Speech Variability, Determinism, and Stability in Adults Who Do and Do Not Stutter.

Purpose This study examined the impact of social-cognitive stress on sentence-level speech variability, determinism, and stability in adults who stutter (AWS) and adults who do not stutter (AWNS). We demonstrated that complementing the spatiotemporal index (STI) with recurrence quantification analysis (RQA) provides a novel approach to both assessing and interpreting speech variability in stuttering. Method Twenty AWS and 21 AWNS repeated sentences in audience and nonaudience conditions while their lip movements were tracked. Across-sentence variability was assessed via the STI; within-sentence determinism and stability were assessed via RQA. Results Compared with the AWNS, the AWS produced speech that was more variable across sentences and more deterministic and stable within sentences. Audience presence contributed to greater within-sentence determinism and stability in the AWS. A subset of AWS who were more susceptible to experiencing anxiety exhibited reduced across-sentence variability in the audience condition compared with the nonaudience condition. Conclusions This study extends the assessment of speech variability in AWS and AWNS into the social-cognitive domain and demonstrates that the characterization of speech within sentences using RQA is complementary to the across-sentence STI measure. AWS seem to adopt a more restrictive, less flexible speaking approach in response to social-cognitive stress, which is presumably a strategy for maintaining observably fluent speech.
Recovering Variable Names for Minified Code with Usage Contexts

To avoid the exposure of original source code in a Web application, the variable names in JS code deployed in the wild are often replaced by short, meaningless names, thus making the code extremely difficult to manually understand and analysis. This paper presents JSNeat, an information retrieval (IR)-based approach to recover the variable names in minified JS code. JSNeat follows a data-driven approach to recover names by searching for them in a large corpus of open-source JS code. We use three types of contexts to match a variable in given minified code against the corpus including the context of the properties and roles of the variable, the context of that variable and relations with other variables under recovery, and the context of the task of the function to which the variable contributes. We performed several empirical experiments to evaluate JSNeat on the dataset of more than 322K JS files with 1M functions, and 3.5M variables with 176K unique variable names. We found that JSNeat achieves a high accuracy of 69.1%, which is the relative improvements of 66.1% and 43% over two state-of-the-art approaches JSNice and JSNaughty, respectively. The time to recover for a file or a variable with JSNeat is twice as fast as with JSNice and 4x as fast as with JNaughty, respectively.
Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.
Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models.
Adaptive Convolution for Multi-Relational Learning

We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein. Convolutional neural networks have recently shown their superiority for this problem, bringing increased model expressiveness while remaining parameter efficient. Despite the success, previous convolution designs fail to model full interactions between input entities and relations, which potentially limits the performance of link prediction. In this work we introduce ConvR, an adaptive convolutional network designed to maximize entity-relation interactions in a convolutional fashion. ConvR adaptively constructs convolution filters from relation representations, and applies these filters across entity representations to generate convolutional features. As such, ConvR enables rich interactions between entity and relation representations at diverse regions, and all the convolutional features generated will be able to capture such interactions. We evaluate ConvR on multiple benchmark datasets. Experimental results show that: (1) ConvR performs substantially better than competitive baselines in almost all the metrics and on all the datasets; (2) Compared with state-of-the-art convolutional models, ConvR is not only more effective but also more efficient. It offers a 7% increase in MRR and a 6% increase in Hits@10, while saving 12% in parameter storage.
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations

Nonconvex regularizers have been successfully used in low-rank matrix learning. In this paper, we extend this to the more challenging problem of low-rank tensor completion. Based on the proximal average algorithm, we develop an efficient solver that avoids expensive tensor folding and unfolding. A special “sparse plus low-rank” structure, which is essential for fast computation of individual proximal steps, is maintained throughout the iterations. We also incorporate adaptive momentum to further speed up empirical convergence. Convergence results to critical points are provided under smoothness and Kurdyka-Lojasiewicz conditions. Experimental results on a number of synthetic and realworld data sets show that the proposed algorithm is more efficient in both time and space, and is also more accurate than existing approaches.
Kernel Normalized Cut: a Theoretical Revisit

In this paper, we study the theoretical properties of clustering based on the kernel normalized cut. Our first contribution is to derive a nonasymptotic upper bound on the expected distortion rate of the kernel normalized cut. From this result, we show that the solution of the kernel normalized cut converges to that of the population-level weighted k-means clustering on a certain reproducing kernel Hilbert space (RKHS). Our second contribution is the discover of the interesting fact that the population-level weighted k-means clustering in the RKHS is equivalent to the population-level normalized cut. Combining these results, we can see that the kernel normalized cut converges to the population-level normalized cut. The criterion of the population-level normalized cut can be considered as an indivisibility of the population distribution, and this criterion plays an important role in the theoretical analysis of spectral clustering in Schiebinger et al. (2015). We believe that our results will provide deep insights into the behavior of both normalized cut and spectral clustering.
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling

Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger. The task is highly correlative with semantic role labeling (SRL), which identifies important semantic arguments such as agent and patient for a given predicate. As predicate agents and patients usually correspond to opinion holders and targets respectively, SRL could be valuable for ORL. In this work, we propose a simple and novel method to enhance ORL by utilizing SRL, presenting semantic-aware word representations which are learned from SRL. The representations are then fed into a baseline neural ORL model as basic inputs. We verify the proposed method on a benchmark MPQA corpus. Experimental results show that the proposed method is highly effective. In addition, we compare the method with two representative methods of SRL integration as well, finding that our method can outperform the two methods significantly, achieving 1.47% higher F-scores than the better one.
Step-size Analyses of the Mitochondrial Hsp70 Import Motor Reveal the Brownian Ratchet in Operation*

Newly synthesized mitochondrial precursor proteins have to become unfolded by the mitochondrial Hsp70 (mtHsp70) import motor to cross the mitochondrial membranes. To assess the mechanism of unfolding of precursor proteins by mtHsp70, we designed a system to measure step sizes of the mtHsp70 import motor, which are distances at which the motor system moves along polypeptide chains during a single turnover of ATP. We made a series of fusion proteins consisting of a mitochondrial presequence containing the first mtHsp70 binding site, a spacer sequence containing an Hsp70 avoidance segment followed by the second mtHsp70 binding site, and different folded mature domains. Analyses of the dependence of the import rates of those fusion proteins on the lengths of Hsp70 avoidance segments allowed us to estimate the step sizes, which differ for different mature domains and different lengths of the spacers. These results suggest that the mtHsp70 import motor functions at least as a molecular Brownian ratchet to unfold mitochondrial precursor proteins.
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets

We consider the problem of handling missing data with deep latent variable models (DLVMs). First, we present a simple technique to train DLVMs when the training set contains missing-at-random data. Our approach, called MIWAE, is based on the importance-weighted autoencoder (IWAE), and maximises a potentially tight lower bound of the log-likelihood of the observed data. Compared to the original IWAE, our algorithm does not induce any additional computational overhead due to the missing data. We also develop Monte Carlo techniques for single and multiple imputation using a DLVM trained on an incomplete data set. We illustrate our approach by training a convolutional DLVM on incomplete static binarisations of MNIST. Moreover, on various continuous data sets, we show that MIWAE provides extremely accurate single imputations, and is highly competitive with state-of-the-art methods.
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization

As it was mentioned in Section 5 to show the pure effect of redundancy in distributed training we start with a warm-up experiment. In this experiment RI-SGD has only 1 round of communication at the end of training, hence each device does the training locally with a bit of redundancy infused. We choose different redundancy values from μ ∈ {0.0, 0.05, 0.1, 0.25} and do the training and compare the results with syncSGD. Please note that when μ = 0, our algorithm is equivalant to PR-SGD (Yu et al., 2018). Figure 5 shows the result of this experiment. When we do not have any redundancy, there is a gap between training error of this setting and SyncSGD, which is evident. However, as we add more redundancy this gap is diminished and we can reach smaller error rate way sooner than SyncSGD. That is the most interesting part that we are gaining the speed-up with respect to SyncSGD (almost twice as fast as SyncSGD) with only adding redundancy to the training process. Increasing the redundancy rate would slightly increase the time of training, however, reduces the final error. Hence, as we move from fully synchronous SGD to distributed local SGD we are trading accuracy with speed. On the other hand, redundancy can increase the accuracy, with roughly the same speed-up, thus we can benefit from the advantages of the two settings in our RI-SGD.
MiR‐503 regulates cisplatin resistance of human gastric cancer cell lines by targeting IGF1R and BCL2

Background Studies have shown that the drug resistance of gastric cancer cells can be modulated by abnormal expression of microRNAs (miRNAs). We investigated the role of miR‐503 in the development of cisplatin resistance in human gastric cancer cell lines. Methods MiR‐503 expression was measured by quantitative real‐time PCR. MTT (3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide) and clonogenic assays were used to examine changes in cell viability and the drug resistance phenotype of cancer cells associated with upregulation or downregulation of the miRNA. A dual‐luciferase activity assay was used to verify target genes of miR‐503. Immunohistochemistry, Western blotting analysis, and a flow cytometric apoptosis assay were used to elucidate the mechanism by which miR‐503 modulates drug resistance in cancer cells. Results MiR‐503 was significantly downregulated in gastric cancer tissues and several gastric cancer cell lines. Additionally, downregulation of miR‐503 in the cisplatin (DDP)‐resistant gastric cancer cell line SGC7901/DDP was concurrent with the upregulation of insulin‐like growth factor‐1 receptor (IGF1R) and B‐cell lymphoma 2 (BCL2) expression compared with the parental SGC7901 cell line. An in vitro drug sensitivity assay showed that overexpression of miR‐503 sensitized SGC7901/DDP cells to cisplatin. The luciferase activity of reporters driven by IGF1R and BCL2 3′‐untranslated regions in SGC7901/DDP cells suggested that IGF1R and BCL2 were both direct target genes of miR‐503. Enforced miR‐503 expression in SGC7901/DDP cells reduced expression of the target proteins, inhibited proliferation, and sensitized the cells to DDP‐induced apoptosis. Conclusion Our findings suggest that hsa‐miR‐503 modulates cisplatin resistance of human gastric cancer cells at least in part by targeting IGF1R and BCL2.
Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text

Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.
On Learning Invariant Representations for Domain Adaptation

Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source domain, can generalize to the target domain. In this paper, we first construct a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation. In particular, the counterexample exhibits conditional shift: the class-conditional distributions of input features change between source and target domains. To give a sufficient condition for domain adaptation, we propose a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift. Moreover, we shed new light on the problem by proving an information-theoretic lower bound on the joint error of any domain adaptation method that attempts to learn invariant representations. Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target. Finally, we conduct experiments on real-world datasets that corroborate our theoretical findings. We believe these insights are helpful in guiding the future design of domain adaptation and representation learning algorithms.
Uncertainty Characterization and Propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves multiple Essential Climate Variables (ECV) about the vertical atmosphere from hyperspectral infrared measurements made by the Atmospheric InfraRed Sounder (AIRS, 2002–present) and its successor, the Cross-track Infrared Sounder (CrIS, 2011–present). CLIMCAPS ECVs are profiles of temperature and water vapor, column amounts of greenhouse gases (CO2, CH4), ozone (O3) and precursor gases (CO, SO2) as well as cloud properties. AIRS (and CrIS) spectral measurements are highly correlated signals of many atmospheric state variables. CLIMCAPS inverts an AIRS (and CrIS) measurement into a set of discrete ECVs by employing a sequential Bayesian approach in which scene-dependent uncertainty is rigorously propagated. This not only linearizes the inversion problem but explicitly accounts for spectral interference from other state variables so that the correlation among ECVs (and their uncertainty) may be minimized. Here, we outline the CLIMCAPS retrieval methodology with specific focus given to its sequential scene-dependent uncertainty propagation system. We conclude by demonstrating continuity in two CLIMCAPS ECVs across AIRS and CrIS so that a long-term data record may be generated to study the feedback cycles characterizing our climate system.
Unreproducible Research is Reproducible

The apparent contradiction in the title is a word-play on the different meanings attributed to the word reproducible across different scientiﬁc ﬁelds. What we imply is that unreproducible ﬁndings can be built upon reproducible methods . Without denying the importance of facilitating the reproduction of methods , we deem important to reassert that reproduction of ﬁndings is a fundamental step of the scientiﬁc inquiry. We argue that the commendable quest towards easy deterministic reproducibility of methods and numerical results should not have us forget the even more important necessity of ensuring the reproducibility of empirical ﬁndings and conclusions by properly accounting for essential sources of variations. We provide experiments to exemplify the brittleness of current common practice in the evaluation of models in the ﬁeld of deep learning, showing that even if the results could be reproduced, a slightly different experiment would not support the ﬁndings. We hope to help clarify the distinction between exploratory and empirical research in the ﬁeld of deep learning and believe more energy should be devoted to proper empirical research in our community. This work is an attempt to promote the use of more rigorous and diversiﬁed methodologies. It is not an attempt to impose a new methodology and it is not a critique on the nature of exploratory research.
Discussing a 'Design-led' Approach to Pervasive Healthcare

This paper attempt to trigger a debate about the possible existence of a design approach or a design process that can deliver a pervasive healthcare system that is focused on acceptance by its users, that is about improving their daily lives and the healthcare they receive. while at the same time such approach or process should support the healthcare industry in cost-cutting and efficiency gains