Miracle-dz/UniFAR-FTUs huggingface dataset:
Speed Reading: Learning to Read ForBackward via Shuttle

We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension. In contrast to previous work, LSTM-Shuttle not only reads shuttling forward but also goes back. Shuttling forward enables high efficiency, and going backward gives the model a chance to recover lost information, ensuring better prediction. We evaluate LSTM-Shuttle on sentiment analysis, news classification, and cloze on IMDB, Rotten Tomatoes, AG, and Children’s Book Test datasets. We show that LSTM-Shuttle predicts both better and more quickly. To demonstrate how LSTM-Shuttle actually behaves, we also analyze the shuttling operation and present a case study.
Computing the Projected Reachable Set of Stochastic Biochemical Reaction Networks Modeled by Switched Affine Systems

A fundamental question in systems biology is what combinations of mean and variance of the species present in a stochastic biochemical reaction network are attainable by perturbing the system with an external signal. To address this question, we show that the moments evolution in any generic network can be either approximated or, under suitable assumptions, computed exactly as the solution of a switched affine system. We then propose a new method to approximate the reachable set of such a switched affine system. A remarkable feature of our approach is that it allows one to easily compute projections of the reachable set for pairs of moments of interest, without requiring the computation of the full reachable set, which can be prohibitive for large networks. As a second contribution, we also show how to select the external signal in order to maximize the probability of reaching a target set. To illustrate the method, we study a renowned model of the controlled gene expression and we derive estimates of the reachable set, for the protein mean and variance, that are more accurate than those available in the literature and consistent with experimental data.
Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching

Event detection (ED) and word sense disambiguation (WSD) are two similar tasks in that they both involve identifying the classes (i.e. event types or word senses) of some word in a given sentence. It is thus possible to extract the knowledge hidden in the data for WSD, and utilize it to improve the performance on ED. In this work, we propose a method to transfer the knowledge learned on WSD to ED by matching the neural representations learned for the two tasks. Our experiments on two widely used datasets for ED demonstrate the effectiveness of the proposed method.
Psychotherapeutic Group Intervention for Traumatized Male Refugees Using Imaginative Stabilization Techniques—A Pilot Study in a German Reception Center

Background: Due to persecution, human rights violations and armed conflicts, the prevalence of post-traumatic stress disorder (PTSD) is high in refugee populations. Previous studies indicate that trauma-focused treatments are highly effective in treating PTSD in refugees. However, these approaches rely on the stability of the therapeutic setting, treatment continuity, and safe housing. Although early treatment of PTSD is recommended, these requirements are not met in reception centers. Therefore, we conducted a pilot study to examine the effect of imaginative stabilization techniques derived from psychodynamic psychotraumatology therapy for the early stabilization of traumatized refugees in a reception center. Methods: From May 2017 to April 2018, 86 imaginative stabilization group therapy sessions have taken place. A sample of 43 out of 46 traumatized refugees completed self-report questionnaires assessing PTSD, depression, and anxiety symptoms prior to attending open imaginative stabilization group therapy sessions. Furthermore, participants filled in self-report questionnaires on distress and emotional state (valence/arousal/dominance) before and after each session. After having participated in four consecutive sessions, a sub-group of 17 participants completed a follow-up assessment of PTSD, depression, and anxiety symptoms. Follow-up interviews were conducted with 25 participants 2 weeks after their last session attendance to explore self-practice habits post intervention. Results: The pre-post-intervention comparison of scores indicated a significant reduction of distress (z = −3.35, p < 0.001, r = −0.51) and an improvement of affective reports for valence (z = −4.79, p < 0.001, r = −0.82) and dominance (z = −3.89, p < 0.001, r = −0.59), whereas arousal scores were not affected. We found a significant reduction of anxiety symptoms (z = −2.04, p < 0.05, r = −0.49), whereas PTSD and depression scores remained unchanged. Follow-up interviews revealed that 80% of the participants continued to practice the imaginative stabilization techniques after redistribution to other accommodation. Conclusion: The results indicate that imaginative stabilization techniques are a promising and feasible approach to treat refugees in unstable reception center settings. In regular imaginative stabilization group therapy sessions, we were able to reduce the participants' distress and anxiety symptoms while strengthening their internal resources and increasing their emotional stability.
CMOS Nanophotonic Sensor With Integrated Readout System

The measurement of nanophotonic sensors currently requires the use of external measuring equipment for their read-out such as an optical spectrum analyzer, spectrophotometer, or detectors. This requirement of external laboratory-based measuring equipment creates a “chip-in-a-lab” dilemma and hinders the use of nanophotonic sensors in practical applications. Making nanophotonic sensors usable in everyday life requires miniaturization of not only the sensor chip itself but also the equipment used for its measurement. In this paper, we have removed the need of external measuring equipment by monolithically integrating 1-D grating structures with a complementary metal-oxide-semiconductor (CMOS) integrated circuit having an array of photodiodes. By doing so, we get a direct electrical read-out of the refractive index changes induced when applying different analytes to grating structures. The gratings are made of CMOS compatible silicon nitride. Employing a nanophotonic sensor made of CMOS compatible material allows fabrication of the integrated sensor chip in a commercial CMOS foundry, enabling mass production for commercialization with low cost. Our results present a significant step toward transforming present laboratory-based nanophotonic sensors into practical portable devices to enable applications away from the analytical laboratory. We anticipate the work will have a major impact on technology for personalized medicine, environmental, and industrial sensing.
Extracting Entities and Relations with Joint Minimum Risk Training

We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.
Adaptive Multi-pass Decoder for Neural Machine Translation

Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored. In this paper, we propose a novel architecture called adaptive multi-pass decoder, which introduces a flexible multi-pass polishing mechanism to extend the capacity of NMT via reinforcement learning. More specifically, we adopt an extra policy network to automatically choose a suitable and effective number of decoding passes, according to the complexity of source sentences and the quality of the generated translations. Extensive experiments on Chinese-English translation demonstrate the effectiveness of our proposed adaptive multi-pass decoder upon the conventional NMT with a significant improvement about 1.55 BLEU.
Efficient and High-Quality Seeded Graph Matching: Employing Higher-order Structural Information

Driven by many real applications, we study the problem of seeded graph matching. Given two graphs and , and a small set of pre-matched node pairs where and , the problem is to identify a matching between and growing from , such that each pair in the matching corresponds to the same underlying entity. Recent studies on efficient and effective seeded graph matching have drawn a great deal of attention and many popular methods are largely based on exploring the similarity between local structures to identify matching pairs. While these recent techniques work provably well on random graphs, their accuracy is low over many real networks. In this work, we propose to utilize higher-order neighboring information to improve the matching accuracy and efficiency. As a result, a new framework of seeded graph matching is proposed, which employs Personalized PageRank (PPR) to quantify the matching score of each node pair. To further boost the matching accuracy, we propose a novel postponing strategy, which postpones the selection of pairs that have competitors with similar matching scores. We show that the postpone strategy indeed significantly improves the matching accuracy. To improve the scalability of matching large graphs, we also propose efficient approximation techniques based on algorithms for computing PPR heavy hitters. Our comprehensive experimental studies on large-scale real datasets demonstrate that, compared with state-of-the-art approaches, our framework not only increases the precision and recall both by a significant margin but also achieves speed-up up to more than one order of magnitude.
3D Human Action Recognition Using a Single Depth Feature and Locality-Constrained Affine Subspace Coding

This paper addresses the problem of recognizing human actions from depth videos. We propose a depth-based local descriptor and affine subspace coding representation with locality-constrained affine subspace coding (LASC) for 3D action recognition. First, each depth video sequence is divided into a set of subsequences (i.e., multi-scale sub-actions) based on the normalized motion energy vector. Next, depth motion map-based gradient local auto-correlation features are employed to capture the shape information and motion cues of each sub-action. In order to obtain discriminative and compact representation, we extract the local high-order information of the depth video using LASC. Through experiments, we show that the use of LASC exhibits better performance compared with existing methods such as locality-constrained linear coding. We compared LASC with the state-of-the-art methods based on similar principle, using features extracted from a single modality, on four datasets, and with those using multiple features or nonlinear recognition machines. The results on four datasets clearly show the effectiveness of the proposed method.
Detection of Short Circuits in the Rotor Field Winding in Large Hydro Generator

A method for detecting inter-turn short circuit in the rotor field winding of a large hydro generator is presented. The proposed method is based on the use of two inductive probes installed at two different locations in the machine, exploiting therefore the existing magnetic symmetry. This latter consists of monitoring the algebraic sum of the two induced voltages of these two probes. The studied hydro generator has a rating of 310 MVA with 408 slots and 56 poles. Many advanced electromagnetic simulations based on finite element method were carried out in order to validate the proposed approach. The simulation results were compared to the experimentally measured data collected of the considered generator and obtained respectively at no-load and rated load.
Protective Effects of Silymarin and Silibinin against DNA Damage in Human Blood Cells

Silymarin (SM), a standardized extract derived from Silybum marianum (L.) Gaertn, is primarily composed of flavonolignans, with silibinin (SB) as its major active constituent. The present study aimed to evaluate the antigenotoxic activities of SM and SB using the alkaline comet assay in whole blood cells and to assess their effects on the expression of genes associated with carcinogenesis and chemopreventive processes. Different concentrations of SM or SB (1.0, 2.5, 5.0, and 7.5 mg/ml) were used in combination with the DNA damage-inducing agent methyl methanesulfonate (MMS, 800 μM) to evaluate their genoprotective potential. To investigate the role of SM and SB in modulating gene expression, we performed quantitative real-time PCR (qRT-PCR) analysis of five genes that are known to be involved in DNA damage, carcinogenesis, and/or chemopreventive mechanisms. Treatment with SM or SB was found to significantly reduce the genotoxicity of MMS, upregulate the expression of PTEN and BCL2, and downregulate the expression of BAX and ABL1. We observed no significant changes in ETV6 expression levels following treatment with SM or SB. In conclusion, both SM and SB exerted antigenotoxic activities and modulated the expression of genes related to cell protection against DNA damage.
Scalable Discrete Supervised Multimedia Hash Learning With Clustering

The hashing method maps similar data of various types to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low-storage cost and fast retrieval speed. However, the existing limitations make the present algorithms difficult to deal with for large-scale data sets: 1) discrete constraints are involved in the learning of the hash function and 2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting in both time and space complexity greater than <inline-formula> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula>. To address these issues, we propose a novel discrete supervised hash learning framework that can be scalable to large-scale data sets of various types. First, the discrete learning procedure is decomposed into a binary classifier learning scheme and binary codes learning scheme, which makes the learning procedure more efficient. Second, by adopting the <italic>asymmetric low-rank matrix factorization</italic>, we propose the <italic>fast clustering-based batch coordinate descent</italic> method, such that the time and space complexity are reduced to <inline-formula> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula>. The proposed framework also provides a flexible paradigm to incorporate with arbitrary hash function, including deep neural networks and kernel methods, as well as any types of data to hash, including images and videos. Experiments on large-scale data sets demonstrate that the proposed method is superior or comparable with the state-of-the-art hashing algorithms.
Model transformation languages under a magnifying glass: a controlled experiment with Xtend, ATL, and QVT

In Model-Driven Software Development, models are automatically processed to support the creation, build, and execution of systems. A large variety of dedicated model-transformation languages exists, promising to efficiently realize the automated processing of models. To investigate the actual benefit of using such specialized languages, we performed a large-scale controlled experiment in which over 78 subjects solve 231 individual tasks using three languages. The experiment sheds light on commonalities and differences between model transformation languages (ATL, QVT-O) and on benefits of using them in common development tasks (comprehension, change, and creation) against a modern general-purpose language (Xtend). Our results show no statistically significant benefit of using a dedicated transformation language over a modern general-purpose language. However, we were able to identify several aspects of transformation programming where domain-specific transformation languages do appear to help, including copying objects, context identification, and conditioning the computation on types.
Is Nike female? Exploring the role of sound symbolism in predicting brand name gender

Are brand names such as Nike female or male? Previous research suggests that the sound of a person’s first name is associated with the person’s gender, but no research has tried to use this knowledge to assess the gender of brand names. We present a simple computational approach that uses sound symbolism to address this open issue. Consistent with previous research, a model trained on various linguistic features of name endings predicts human gender with high accuracy. Applying this model to a data set of over a thousand commercially-traded brands in 17 product categories, our results reveal an overall bias toward male names, cutting across both male-oriented product categories as well as female-oriented categories. In addition, we find variation within categories, suggesting that firms might be seeking to imbue their brands with differentiating characteristics as part of their competitive strategy.
Knowledge Base Question Answering via Encoding of Complex Query Graphs

Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions.
Robust Visual Tracking via Multi-Scale Spatio-Temporal Context Learning

In order to tackle the incomplete and inaccurate of the samples in most tracking-by-detection algorithms, this paper presents an object tracking algorithm, termed as multi-scale spatio-temporal context (MSTC) learning tracking. MSTC collaboratively explores three different types of spatio-temporal contexts, named the long-term historical targets, the medium-term stable scene (i.e., a short continuous and stable video sequence), and the short-term overall samples to improve the tracking efficiency and reduce the drift phenomenon. Different from conventional multi-timescale tracking paradigm that chooses samples in a fixed manner, MSTC formulates a low-dimensional representation named fast perceptual hash algorithm to update long-term historical targets and the medium-term stable scene dynamically with image similarity. MSTC also differs from most tracking-by-detection algorithms that label samples as positive or negative, it investigates a fusion salient sample detection to fuse weights of the samples not only by the distance information, but also by the visual spatial attention, such as color, intensity, and texture. Numerous experimental evaluations with most state-of-the-art algorithms on the standard 50 video benchmark demonstrate the superiority of the proposed algorithm.
On the Abstractiveness of Neural Document Summarization

Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries. We attempted to verify the degree of abstractiveness of modern neural abstractive summarization systems by calculating overlaps in terms of various types of units. Upon the observation that many abstractive systems tend to be near-extractive in practice, we also implemented a pure copy system, which achieved comparable results as abstractive summarizers while being far more computationally efficient. These findings suggest the possibility for future efforts towards more efficient systems that could better utilize the vocabulary in the original document.
Weakly Supervised Learning for Fake News Detection on Twitter

The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor. In this paper, we discuss a weakly supervised approach, which automatically collects a large-scale, but very noisy training dataset comprising hundreds of thousands of tweets. During collection, we automatically label tweets by their source, i.e., trustworthy or untrustworthy source, and train a classifier on this dataset. We then use that classifier for a different classification target, i.e., the classification of fake and non-fake tweets. Although the labels are not accurate according to the new classification target (not all tweets by an untrustworthy source need to be fake news, and vice versa), we show that despite this unclean inaccurate dataset, it is possible to detect fake news with an F1 score of up to 0.9.
Influence of Strategic Cortical Infarctions on Pupillary Function

Objective: Cortical activity, including cognitive and emotional processes, may influence pupillary function. The exact pathways and the site of cortical pupillary innervation remain elusive, however. We investigated the effects of select cortical strokes, i.e. ischemic infarcts affecting the insular cortex and prefrontal eye field, on pupillary function. Methods: Seventy-four patients with acute ischemic stroke, consecutively admitted to our institution from March to July 2018, were assessed 24 h after endovascular recanalization therapy (i.e., day 2 after the stroke), using automated pupillometry. Stroke location and volume and clinical severity (estimated by the Alberta Stroke Program Early CT Score and National Institute of Health Stroke Scale) were recorded. We excluded patients with posterior circulation stroke, intracranial pathology other than ischemic stroke, midline shift on computed tomography exceeding 5 millimeters or a history of eye disease. Pupillometry data from 25 neurologically normal patients with acute myocardial infarction were acquired for control. Results: Fifty stroke patients after thrombectomy were included for analysis. Twenty-five patients (50%) had insular cortex or prefrontal eye field involvement (group 1, strategic infarcts); 25 patients had infarcts located in other cerebral areas (group 2, other infarcts). The pupillary light reflex, as measured by constriction velocity and maximal/minimal pupillary diameters, was within physiological limits in all patients, including controls. However, while pupillary size and constriction velocities were correlated in all subjects, the correlation of size and dilatation velocity was absent in right-hemispheric infarcts (left hemisphere infarcts, group 1 (r2 = 0.15, p = 0.04), group 2 (r2 = 0.41, p = 0.0007); right hemisphere infarcts, group 1 (r2 = 0.008, p = 0.69); group 2 (r2 = 0.12, p = 0.08); controls (r2 = 0.29, p ≤ 0.0001). Conclusions: Cortical infarcts of the prefrontal eye field or insula do not impair the pupillary light reflex in humans. However, subtle changes may occur when the pupils dilate back to baseline, probably due to autonomic dysfunction. Replication is needed to explore the possible influence of hemispheric lateralization. We suggest that endovascular therapy for acute ischemic stroke may serve as a clinical research model for the study of acquired cortical lesions in humans.