Miracle-dz/UniFAR-FTUs huggingface dataset:
Language and Arithmetic: A Failure to Find Cross Cognitive Domain Semantic Priming Between Exception Phrases and Subtraction or Addition

We examined cross-domain semantic priming effects between arithmetic and language. We paired subtractions with their linguistic equivalent, exception phrases (EPs) with positive quantifiers (e.g., “everybody except John”) while pairing additions with their own linguistic equivalent, EPs with negative quantifiers (e.g., “nobody except John”; Moltmann, 1995). We hypothesized that EPs with positive quantifiers prime subtractions and inhibit additions while EPs with negative quantifiers prime additions and inhibit subtractions. Furthermore, we expected similar priming and inhibition effects from arithmetic into semantics. Our design allowed for a bidirectional analysis by using one trial's target as the prime for the next trial. Two experiments failed to show significant priming effects in either direction. Implications and possible shortcomings are explored in the general discussion.
Nanobiosensing Platforms for Real-Time and Non-Invasive Monitoring of Stem Cell Pluripotency and Differentiation

Breakthroughs in the biomedical and regenerative therapy fields have led to the influential ability of stem cells to differentiate into specific types of cells that enable the replacement of injured tissues/organs in the human body. Non-destructive identification of stem cell differentiation is highly necessary to avoid losses of differentiated cells, because most of the techniques generally used as confirmation tools for the successful differentiation of stem cells can result in valuable cells becoming irrecoverable. Regarding this issue, recent studies reported that both Raman spectroscopy and electrochemical sensing possess excellent characteristics for monitoring the behavior of stem cells, including differentiation. In this review, we focus on numerous studies that have investigated the detection of stem cell pluripotency and differentiation in non-invasive and non-destructive manner, mainly by using the Raman and electrochemical methods. Through this review, we present information that could provide scientific or technical motivation to employ or further develop these two techniques for stem cell research and its application.
SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition

In this work, we propose a Spiking Neural Network (SNN) consisting of input neurons sparsely connected by plastic synapses to a randomly interlinked liquid, referred to as Liquid-SNN, for unsupervised speech and image recognition. We adapt the strength of the synapses interconnecting the input and liquid using Spike Timing Dependent Plasticity (STDP), which enables the neurons to self-learn a general representation of unique classes of input patterns. The presented unsupervised learning methodology makes it possible to infer the class of a test input directly using the liquid neuronal spiking activity. This is in contrast to standard Liquid State Machines (LSMs) that have fixed synaptic connections between the input and liquid followed by a readout layer (trained in a supervised manner) to extract the liquid states and infer the class of the input patterns. Moreover, the utility of LSMs has primarily been demonstrated for speech recognition. We find that training such LSMs is challenging for complex pattern recognition tasks because of the information loss incurred by using fixed input to liquid synaptic connections. We show that our Liquid-SNN is capable of efficiently recognizing both speech and image patterns by learning the rich temporal information contained in the respective input patterns. However, the need to enlarge the liquid for improving the accuracy introduces scalability challenges and training inefficiencies. We propose SpiLinC that is composed of an ensemble of multiple liquids operating in parallel. We use a “divide and learn” strategy for SpiLinC, where each liquid is trained on a unique segment of the input patterns that causes the neurons to self-learn distinctive input features. SpiLinC effectively recognizes a test pattern by combining the spiking activity of the constituent liquids, each of which identifies characteristic input features. As a result, SpiLinC offers competitive classification accuracy compared to the Liquid-SNN with added sparsity in synaptic connectivity and faster training convergence, both of which lead to improved energy efficiency in neuromorphic hardware implementations. We validate the efficacy of the proposed Liquid-SNN and SpiLinC on the entire digit subset of the TI46 speech corpus and handwritten digits from the MNIST dataset.
Magnetic resonance imaging T1- and T2-mapping to assess renal structure and function: a systematic review and statement paper

Abstract This systematic review, initiated by the European Cooperation in Science and Technology Action Magnetic Resonance Imaging Biomarkers for Chronic Kidney Disease (PARENCHIMA), focuses on potential clinical applications of magnetic resonance imaging in renal non-tumour disease using magnetic resonance relaxometry (MRR), specifically, the measurement of the independent quantitative magnetic resonance relaxation times T1 and T2 at 1.5 and 3Tesla (T), respectively. Healthy subjects show a distinguishable cortico-medullary differentiation (CMD) in T1 and a slight CMD in T2. Increased cortical T1 values, that is, reduced T1 CMD, were reported in acute allograft rejection (AAR) and diminished T1 CMD in chronic allograft rejection. However, ambiguous findings were reported and AAR could not be sufficiently differentiated from acute tubular necrosis and cyclosporine nephrotoxicity. Despite this, one recent quantitative study showed in renal transplants a direct correlation between fibrosis and T1 CMD. Additionally, various renal diseases, including renal transplants, showed a moderate to strong correlation between T1 CMD and renal function. Recent T2 studies observed increased values in renal transplants compared with healthy subjects and in early-stage autosomal dominant polycystic kidney disease (ADPKD), which could improve diagnosis and progression assessment compared with total kidney volume alone in early-stage ADPKD. Renal MRR is suggested to be sensitive to renal perfusion, ischaemia/oxygenation, oedema, fibrosis, hydration and comorbidities, which reduce specificity. Due to the lack of standardization in patient preparation, acquisition protocols and adequate patient selection, no widely accepted reference values are currently available. Therefore this review encourages efforts to optimize and standardize (multi-parametric) protocols to increase specificity and to tap the full potential of renal MRR in future research.
rs495139 in the TYMS-ENOSF1 Region and Risk of Ovarian Carcinoma of Mucinous Histology

Thymidylate synthase (TYMS) is a crucial enzyme for DNA synthesis. TYMS expression is regulated by its antisense mRNA, ENOSF1. Disrupted regulation may promote uncontrolled DNA synthesis and tumor growth. We sought to replicate our previously reported association between rs495139 in the TYMS-ENOSF1 3′ gene region and increased risk of mucinous ovarian carcinoma (MOC) in an independent sample. Genotypes from 24,351 controls to 15,000 women with invasive OC, including 665 MOC, were available. We estimated per-allele odds ratios (OR) and 95% confidence intervals (CI) using unconditional logistic regression, and meta-analysis when combining these data with our previous report. The association between rs495139 and MOC was not significant in the independent sample (OR = 1.09; 95% CI = 0.97–1.22; p = 0.15; N = 665 cases). Meta-analysis suggested a weak association (OR = 1.13; 95% CI = 1.03–1.24; p = 0.01; N = 1019 cases). No significant association with risk of other OC histologic types was observed (p = 0.05 for tumor heterogeneity). In expression quantitative trait locus (eQTL) analysis, the rs495139 allele was positively associated with ENOSF1 mRNA expression in normal tissues of the gastrointestinal system, particularly esophageal mucosa (r = 0.51, p = 1.7 × 10−28), and nonsignificantly in five MOC tumors. The association results, along with inconclusive tumor eQTL findings, suggest that a true effect of rs495139 might be small.
Applying graph kernels to model-driven engineering problems

Machine Learning (ML) can be used to analyze and classify large collections of graph-based information, e.g. images, location information, the structure of molecules and proteins, . Graph kernels is one of the ML techniques typically used for such tasks. In a software engineering context, models of a system such as structural or architectural diagrams can be viewed as labeled graphs. Thus, in this paper we propose to employ graph kernels for clustering software modeling artifacts. Among other benefits, this would improve the efficiency and usability of a variety of software modeling activities, e.g., design space exploration, testing or verification and validation.
Reducing Gender Bias in Abusive Language Detection

Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, “You are a good woman” was considered “sexist” when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure them on models trained with different datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce model bias by 90-98% and can be extended to correct model bias in other scenarios.
Neural relevance model using similarities with elite documents for effective clinical decision support

Clinical Decision Support (CDS) is regarded as an information retrieval (IR) task, where medical records are used to retrieve full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship is modelled, resulting in non-optimal results in that a medical record can barely reflect the information included in a relevant biomedical article which is usually much longer. Therefore, in addition to the document-query relationship, we propose a neural relevance model (DNRM) based on similarities to a set of elite documents, addressing the information mismatch by utilising the content of relevant articles as a complete picture of the given medical record. Specifically, our DNRM model evaluates a document relative to a query and to several pseudo relevant documents for the query at the same time, capturing the interactions from both parts with a feed forward network. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed DNRM model.
Workload-aware Parameter Selection and Performance Prediction for In-memory Databases

In-memory databases, just as hard drive ones, may offer hundreds of customizable settings, making the task of system tuning overwhelming for a database administrator. Even worse, the number of parameters continues to grow over the years and they can affect performance in a not intuitive manner. Models that capture their behavior can assist automatic tuning mechanisms to obtain optimal performance. In this work, we propose a learning-based approach to select the most meaningful parameters and generate a performance model based on both the workload and the database configurations. Experimental results confirm that our approach can create accurate performance models using only a reduced set of selected parameters.
Semi-Trained Memristive Crossbar Computing Engine with In Situ Learning Accelerator

On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption. In this research, an on-device training circuitry for threshold-current memristors integrated in a crossbar structure is proposed. Furthermore, alternate approaches of mapping the synaptic weights into fully trained and semi-trained crossbars are investigated. In a semi-trained crossbar, a confined subset of memristors are tuned and the remaining subset of memristors are not programmed. This translates to optimal resource utilization and power consumption, compared to a fully programmed crossbar. The semi-trained crossbar architecture is applicable to a broad class of neural networks. System level verification is performed with an extreme learning machine for binomial and multinomial classification. The total power for a single 4 × 4 layer network, when implemented in IBM 65nm node, is estimated to be ≈42. 16μW and the area is estimated to be 26. 48μm × 22. 35μm.
A Framework for Identification and Monitoring of Profiles and Behaviors of Users Based on Mobile App Usage

representem situac¸˜oes de risco para fabricantes de dispositivos m´oveis. Abstract. Over the last years, the use of mobile devices and applications (apps) signiﬁcantly grow. In addition, the technological innovation and ﬁerce dispute to conquer the mobile market make companies increase their attention to their clients’ loyalty. These clients perform daily many activities through apps generating a large number of events in real time. In this sense, the identiﬁcation and monitoring of proﬁles and behavior of these clients can contribute to minimize risk situations, such as turnover. Therefore, this study proposes a new framework for the identiﬁcation and monitoring of proﬁles and behaviors based on users’ app usage. It aims to segment users into proﬁles seeking to identify infrequent behaviors that could represent risk situations for mobile device manufacturers
Secure k-objects selection for a keyword query based on MapReduce skyline algorithm

Keyword query interface has become a de-facto standard and such systems have been used by the community for decades. The process of selecting necessary objects for a keyword query is considered as one of the most precious query problems. In top-k query, a user specifies scoring functions and k, the number of objects to be retrieved. Based on the user's scoring function, k-objects are then selected. However, the top-k objects are valuable only for users whose scoring functions are similar. In some cases, parties may not want to disclose any information during the processing. In this paper, we propose k-object selection procedure that selects various k-objects that are preferable for all users whose scoring functions are not identical. The proposed method prevents disclosures of sensitive information. The idea of skyline and top-k query along with perturbed cipher has been used to select the k-objects securely by using MapReduce framework.
The Effect of Naoxintong Capsule in the Treatment of Patients with Cerebral Infarction and Carotid Atherosclerosis: A Systematic Review and Meta-Analysis of Randomized Trials

Objective Naoxintong capsule (NXT) has been widely used to treat patients with cerebral infarction and carotid atherosclerosis. However, it is uncertain whether there is robust evidence on the effects of NXT for cerebral infarction and carotid atherosclerosis. A systematic review and meta-analysis of randomized trials were performed to assess the efficacy of NXT in the treatment of cerebral infarction and carotid atherosclerosis. Methods The Cochrane Library, EMBASE, the Medline database, the Wanfang database, the China National Knowledge Infrastructure, and the VIP database were searched up to January 2018 with no language restrictions. Study selection, data extraction, quality assessment, and data analyses were performed according to the Cochrane standards. Results Eleven studies (N=1141) in total satisfied the inclusion criteria for the meta-analysis. The results of meta-analysis showed that compared with the conventional therapy alone, NXT combined with conventional therapy could significantly improve national institutes of health stroke scale (NIHSS) score (MD= -3.92, 95%CI: -4.31~-3.52, P<0.00001), plaque area (MD= -0.16, 95%CI: -0.20~-0.13, P<0.00001), carotid intima-media thickness (IMT) (MD= -0.23, 95%CI: -0.26~-0.20, P<0.00001), total cholesterol (TC) (MD= -0.16, 95%CI: -0.79~-0.42, P<0.00001), triglyceride (TG) (MD= -0.69, 95%CI: -0.88~-0.51, P<0.00001), high-density lipoprotein cholesterol (HDL-C) (MD= 0.23, 95%CI: 0.15~0.31, P<0.00001), and low-density lipoprotein cholesterol (LDL-C) (MD= -0.42, 95%CI: -0.58~-0.25, P<0.00001). There were no reported adverse events in the studies. Conclusions NXT is an effective and safe therapy option for patients with cerebral infarction and carotid atherosclerosis. However, due to the high clinical heterogeneity and small sample size of the included trials, further standardized preparation, large-scale and rigorously designed trials are needed.
Signature-based Non-orthogonal Multiple Access (S-NOMA) for Massive Machine-Type Communications in 5G

The problem of providing massive connectivity in Internet-of-Things (IoT) with a limited number of available resources motivates the non-orthogonal multiple access (NOMA) solutions. In this article, we provide a comprehensive review of the signature-based NOMA (S-NOMA) schemes as potential candidates for IoT. The signature in S-NOMA represents the way the data stream of an active device is spread over available resources in a non-orthogonal manner. It can be designed based on device-specific codebook structures, delay patterns, spreading sequences, interleaving patterns, and scrambling sequences. Additionally, we present the detection algorithms employed to decode each device's data from non-orthogonally superimposed signals at the receiver. The bit error rate of different S-NOMA schemes is simulated in impulsive noise environments, which can be important in machine-type communications. Simulation results show that the performance of the S-NOMA schemes degrades under such conditions. Finally, research challenges in S-NOMA oriented IoT are presented.
To Extend or not to Extend: On the Uniqueness of Browser Extensions and Web Logins

Recent works showed that websites can detect browser extensions that users install and websites they are logged into. This poses significant privacy risks, since extensions and Web logins that reflect user's behavior, can be used to uniquely identify users on the Web. This paper reports on the first large-scale behavioral uniqueness study based on 16,393 users who visited our website. We test and detect the presence of 16,743 Chrome extensions, covering 28% of all free Chrome extensions. We also detect whether the user is connected to 60 different websites. We analyze how unique users are based on their behavior, and find out that 54.86% of users that have installed at least one detectable extension are unique; 19.53% of users are unique among those who have logged into one or more detectable websites; and 89.23% are unique among users with at least one extension and one login. We use an advanced fingerprinting algorithm and show that it is possible to identify a user in less than 625 milliseconds by selecting the most unique combinations of extensions. Because privacy extensions contribute to the uniqueness of users, we study the trade-off between the amount of trackers blocked by such extensions and how unique the users of these extensions are. We have found that privacy extensions should be considered more useful than harmful. The paper concludes with possible countermeasures.
Co‐occurrence network analysis reveals thermodynamics‐driven microbial interactions in methanogenic bioreactors

Methanogenic bioreactors have been applied to treat purified terephthalic acid (PTA) wastewater containing complex aromatic compounds, such as terephthalic acid, para-toluic acid and benzoic acid. This study characterized the interaction of microbial populations in 42 samples obtained from 10 PTA-degrading methanogenic bioreactors. Approximately, 54 dominant populations (11 methanogens, 8 syntrophs and 35 functionally unknown clades) that represented 73.9% of total 16S rRNA gene iTag sequence reads were identified. Co-occurrence analysis based on the abundance of dominant OTUs showed two non-overlapping networks centred around aromatic compound- (group AR: Syntrophorhabdaceae, Syntrophus and Pelotomaculum) and fatty acid- (group FA: Smithella and Syntrophobacter) degrading syntrophs. Group AR syntrophs have no direct correlation with hydrogenotrophic methanogens, while those from group FA do. As degradation of aromatic compounds has a wider thermodynamic window than fatty acids, Group AR syntrophs may be less influenced by fluctuations in hydrogenotrophic methanogen abundance or may non-specifically interact with diverse methanogens. In both groups, network analysis reveals full-scale- and lab-scale-specific uncultivated taxa that may mediate interactions between syntrophs and methanogens, suggesting that those uncultivated taxa may support the degradation of aromatic compounds through uncharted ecophysiological traits. These observations suggest that organisms from multiple niches orchestrate their metabolic capacity in multiple interaction networks to effectively degrade PTA wastewater.
Caracterização e Comparação das Campanhas do Outubro Rosa e Novembro Azul no Twitter

As campanhas Outubro Rosa e Novembro Azul buscam disseminar a educação e a conscientização sobre os cânceres de mama e de próstata. Dado o sucesso das campanhas do Outubro Rosa e o baixo engajamento masculino no Novembro Azul, apresentamos uma avaliação comparativa pioneira dessas campanhas no contexto online. Considerando o Twitter como uma plataforma que fornece engajamento da comunidade, estudamos a caracterização demográfica de gênero e idade, localização geográfica, períodos de atividade e engajamento do usuário usando tweets das campanhas Outubro Rosa 2017 e Novembro Azul. Identificamos que essas campanhas atingiram seu público-alvo, ou seja, homens e mulheres com mais de 40 anos. Também descobrimos padrões nas atividades ao longo do respectivo período dentro das campanhas, bem como diferenças quando são considerados diferentes países. O alcance do Outubro Rosa é definitivamente maior, medido em termos do número de tweets, re-tweets, bem como a capacidade de se espalhar pela rede social.