Miracle-dz/UniFAR-FTUs huggingface dataset:
Priming Effects of Focus in Mandarin Chinese

Psycholinguistic research has long established that focus-marked words have a processing advantage over other words in an utterance, e.g., they are recognized more quickly and remembered better. More recently, studies have shown that listeners infer contextual alternatives to a focused word in a spoken utterance, when marked with a contrastive accent, even when the alternatives are not explicitly mentioned in the discourse. This has been shown by strengthened priming of contextual alternatives to the word, but not other non-contrastive semantic associates, when it is contrastively accented, e.g., after hearing “The customer opened the window," salesman is strongly primed, but not product. This is consistent with Rooth's (1992) theory that focus-marking signals the presence of alternatives to the focus. However, almost all of the research carried out in this area has been on Germanic languages. Further, most of this work has looked only at one kind of focus-marking, by contrastive accenting (prosody). This paper reports on a cross-modal lexical priming study in Mandarin Chinese, looking at whether focus-marking heightens activation, i.e., priming, of words and their alternatives. Two kinds of focus-marking were investigated: prosodic and syntactic. Prosodic prominence is an important means of focus-marking in Chinese, however, it is realized through pitch range expansion, rather than accenting. The results showed that focused words, as well as their alternatives, were primed when the subject prime word carried contrastive prosodic prominence. Syntactic focus-marking, however, did not enhance priming of focused words or their alternatives. Non-contrastive semantic associates were not primed with either kind of focus-marking. These results extend previous findings on focus and alternative priming for the first time to Chinese. They also suggest that the processing advantages of focus, including priming alternatives, are particularly related to prosodic prominence, at least in Chinese and Germanic languages. This research sheds light on what linguistic mechanisms listeners use to identify important information, generate alternatives, and understand implicature necessary for successful communication.
Mindfulness‐based interventions for psychological and physical health outcomes in cancer patients and survivors: A systematic review and meta‐analysis of randomized controlled trials

Mindfulness‐based interventions (MBIs) are increasingly used within psycho‐oncology. Since the publication of the most recent comprehensive meta‐analysis on MBIs in cancer in 2012, the number of published trials has more than doubled. We therefore conducted a systematic review and meta‐analysis of randomized controlled trials (RCTs), testing the efficacy of MBIs on measures of psychological distress (primary outcome) and other health outcomes in cancer patients and survivors.
Tech Report: Efficient and Exact Collision Detection for Circular Agents

A common problem in multi-robot algorithms is collision detection. Many solutions exist, but often suffer from problems such as high computational cost or causing false negative or false positive detections. In practice, these problems can result in results that range from inefficiency and annoyance to catastrophic. The main contribution of this paper is to provide a high-level overview of major categories of collision detection, provide insight into the need for accurate and computationally efficient collision detection, and provide methods of collision detection and anticipatory collision avoidance that are both computationally efficient and exact -- that is, they will never yield false negative or false positive detections. Supplementary reference for mathematics behind collision detection.
Enhancing Context-Awareness in Autonomous Fog Nodes for IoT Systems

The main aim of this research is to lay the foundation work for the development of a communication protocol which will enhance the context awareness of autonomous Fog nodes in IoT systems, by enabling them to share information on their status. Analysis is being conducted on numerous basic and current architectures, models and communication patterns used by IoT systems. A new, adjusted, model is proposed as a protocol for communication in IoT systems encompassing autonomous agents. Finally, modelization of the adjusted protocol (ECTORAS) is conducted, with main criteria the challenges of IoT, Fog, and Cloud computing.
Simplified Network Traffic Visualization for Real-Time Security Analysis

Although traditional methods of network security analysis used in investigating network traffic and log files are essential to mitigating malicious network activity, these methods alone cannot keep up with constant increases in malevolent network traffic. Many visualization tools have been created as a supplement to traditional analysis and intrusion detection systems. Even though these tools are useful, each tool tends to have a niche use. Also, many network administrators fill dual roles as administrators and security analysts and have little time to learn different complex visualization tools. We therefore observe a need for a simple out-of-the-box solution for general network security visualization. We hope to fill this need with our tool called VNR, which in addition to its simplicity embeds transport layer data within visualizations allowing for better intra-host analysis. VNR can also be used for real-time or auditing purposes by configuring the amount of data visualized within specific time frames.
A tractable formulation for multi-period linearized optimal power flow in presence of thermostatically controlled loads

This paper presents a convex reformulation of a nonlinear constrained optimization problem for Markov decision processes, and applies the technical findings to optimal control problems for an ensemble of thermostatically controlled loads (TCLs). The paper further explores the formulation and solution of a (linearized) AC optimal power flow problem when one or more ensembles of TCLs are connected to a power network. In particular, a receding horizon controller is proposed, to simultaneously compute the optimal set-points of distributed energy resources (DERs) in the grid and the optimal switching signal for the TCLs. This formulation takes into account hardware constraints of the DERs, operational constraints of the grid (e.g., voltage limits), comfort of the TCL users, and ancillary services provision at the substation. Numerical results are provided to verify the effectiveness of the proposed methodology.
Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid–Binding Proteins

Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.
Improving Museum Accessibility through Storytelling in Wearable Immersive Virtual Reality

Our research aims at helping people with Neurodevelopmental Disorders (NDD) enjoy the experience with artistic or cultural artefacts while visiting museums. We adopted a digital storytelling approach that exploits Wearable Immersive Virtual Reality (WIVR) technology and the social story method. A social story is a short textual and visual narrative that describes a social situation of everyday life using visuals (and text) on paper sheets. This method is widely used in interventions for persons with NDD (particularly autism) to help them gain an accurate understanding of social situations, develop autonomy and learn appropriate behaviour. The paper describes how the traditional concept of social story can evolve when the narrative is implemented using a different medium (virtual reality) and a different interaction paradigm (pointing with head movements). We present the concept of Wearable Immersive Social Stories (WISS for short) and describe the design process - performed in collaboration with NDD experts - that led to the definition of WISS and to the development of new technological tools.
Multilingual Neural Machine Translation with Language Clustering

Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the training process, reducing online maintenance costs, and enhancing low-resource and zero-shot translation. Given there are thousands of languages in the world and some of them are very different, it is extremely burdensome to handle them all in a single model or use a separate model for each language pair. Therefore, given a fixed resource budget, e.g., the number of models, how to determine which languages should be supported by one model is critical to multilingual NMT, which, unfortunately, has been ignored by previous work. In this work, we develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space. In particular, we obtain the embedding vectors of all the languages by training a universal neural machine translation model. Our experiments on 23 languages show that the first clustering method is simple and easy to understand but leading to suboptimal translation accuracy, while the second method sufficiently captures the relationship among languages well and improves the translation accuracy for almost all the languages over baseline methods.
Data-driven deep learning for proactive terminal process management

Big data offers tremendous opportunities for transport process innovation. One key enabling big data technology is predictive data analytics. Predictive data analytics supports business process management by facilitating the proactive adaptation of process instances to mitigate or prevent problems. We present an industry case employing big data for process management innovation at duisport, the world’s largest inland container port. In particular, we show how data-driven deep learning facilitates proactive port terminal process management. We demonstrate the feasibility of our deep learning approach by implementing it as part of a terminal productivity cockpit prototype. The terminal productivity cockpit provides decision support to terminal operators for proactive process adaptation. We confirm the desirability of our approach via interviews. We assess the viability of our approach by estimating the improvements in a key business KPI, as well as experimentally measuring the cost savings when compared to terminal operations without using proactive adaptation. We also present our main technical lessons learned regarding the use of big data for predictive analytics.
Choice Under Risk: How Occupation Influences Preferences

In the last decade, a number of studies in the behavioral sciences, particularly in psychology and economics, have explored the complexity of individual risk behavior and its underlying factors. Most previous studies have examined the influences of various socio-economic, cognitive, biological and psychological factors on human decision-making, however, the relationship between the decision-makers’ risk preferences and occupational background has not received much empirical attention. Accordingly, in the current study, we investigated how occupational background, together with decision-making framing (e.g., variations in decision domain, context, presentation of risk, and utility ratios), influence participants’ risk preferences for decision options with equivalent expected utility. Our novel findings indicate that risk preferences may vary among individuals from different occupational backgrounds. As such, when the task was framed in gain terms, participants who mostly deal with health/safety-related risks on a day-to-day basis (high-risk occupations) were predominantly risk-averse (avoiding risky options), while participants who mostly deal with financial/social risks (white-collar occupations) were prone to risk-seeking behavior (avoiding certain options). Specifically, in “high-risk” occupations, participants’ pattern of choices changed from risk-averse in gain scenarios to risk-seeking in loss scenarios. However, the opposite pattern of risk preferences was found in participants with “white-collar” occupations. Our findings indicate that decision-makers’ occupational backgrounds influence risk preferences under some circumstances.
Evaluation of the nutritional composition of Myrothamnus flabellifolius (Welw.) herbal tea and its protective effect against oxidative hepatic cell injury.

The nutrient composition of Myrothamnus flabellifolius leaf tea extract (MLTE) and its protective effect against oxidative hepatic cell injury were evaluated. Gallic acid, caffeic acid, ferulic acid, methyl gallate, and epicatechin were identified in MLTE by high-performance liquid chromatography (HPLC). The tea extract showed an appreciable nutritional content of proximate, sugar, vitamin E, monounsaturated fatty acids, omega 6 and 9 unsaturated fatty acids, as well as considerable amounts of various mineral elements. Nineteen amino acids were found. Moreover, MLTE exhibited potent in vitro antioxidant activities, presumably because of its richness in polyphenols (gallic acid and ferulic acid) and vitamin E. In Chang liver cells, pretreatment with MLTE suppressed oxidative lipid peroxidation (IC50 = 113.11 μg/ml) and GSH depletion (IC50 = 70.49 μg/ml) without causing cytotoxicity. These data support the local consumption of M. flabellifolius herbal tea, which may be used against oxidative stress-induced diseases while providing the body with necessary nutrients. PRACTICAL APPLICATION: Herbal teas are one of the most consumed beverages in the world today, due to their refreshing taste and additional health benefits. Myrothamnus flabellifolius herbal tea is a widely used traditional herbal tea in Southern Africa with potentials for commercialization due to its pleasant flavor. This study, for the first time, reported the nutritional composition of the leaf decoction of M. flabellifolius and its protective effect on hepatic oxidative insults. These results can inform the dietary and nutritional use of the tea for optimum benefits, as well as provide preliminary scientific validation of the use of the herbal tea as an antioxidant beverage with good nutritional value.
An Automatic Classification Tool for Non-destructive Testing for use in Structural Maintenance

New non-destructive testing methods based on ultrasonic waves involve analysing a large amount of time series of data to classify the structural state of the system being evaluated. In North America, thousands of civil infrastructures have to be replaced to maintain the networks in good working order. Detecting faulty structures from good ones is a time consuming, error prone, and costly activity. Although, modern acoustic signal based tools are now available for such work, analysing the data automatically using computational tools is an ongoing research. We have designed a decision support system with classification ability for non-destructive testing of materials for defect classification and characterisation. The classification is performed using artificial intelligence (back propagation neural network and adaptive neuro-fuzzy inference system) and statistical techniques (k-nearest neighbor and linear discriminate analysis). The research involves identifying appropriate features for defect characterisation as there are a large number of possibilities. From the classification results, we found that k-NN gave the highest accuracy of 94% in identification of a defect and 81% in determining the size of the defect. The classification results establish the applicability of simplified methods such as k-NN in defect characterisation.
CELLULAR AUTOMATA OVER ALGEBRAIC STRUCTURES

Abstract Let G be a group and A a set equipped with a collection of finitary operations. We study cellular automata $$\tau :{A^G} \to {A^G}$$ that preserve the operations AG of induced componentwise from the operations of A. We show τ that is an endomorphism of AG if and only if its local function is a homomorphism. When A is entropic (i.e. all finitary operations are homomorphisms), we establish that the set EndCA(G;A), consisting of all such endomorphic cellular automata, is isomorphic to the direct limit of Hom(AS, A), where S runs among all finite subsets of G. In particular, when A is an R-module, we show that EndCA(G;A) is isomorphic to the group algebra $${\rm{End}}(A)[G]$$ . Moreover, when A is a finite Boolean algebra, we establish that the number of endomorphic cellular automata over AG admitting a memory set S is precisely $${(k|S|)^k}$$ , where k is the number of atoms of A.
Correlation of publication frequency with impact factors in 1058 medical journals in SCI-expanded database

We collected the impact factor (IF) and 5-year impact factor (5-IF) of 1058 medical journals in SCI-expanded database, and explore their changes with the increase of publication frequency (PF). Our studies shows that there are positive correlation of PF with IF (r=0.313, P=0.000) and 5-IF (r=0.298, P=0.000). According to the levels of journals’ PF, the journals were divided into four groups. The journals with PF from 1 to 5 were divided into group I ，t hat from 6 to 11, 12 to 23, 24 to 58 were divided into group II, group III and group IV, respectively. The IF of the journals in group I were 1.94±1.71，2.16±4.30 in group II, 3.17±2.93 in group III, and 8.00±9.68 in group IV, respectively. The 5-IF of the journals in group I were 2.080±1.668，2.194±3.046 in group II, 3.253±3.045 in group III, and 8.00±10.015 in group IV, respectively. With comparison of mean ranks of IF and 5-IF among four groups with different PF, we had found that the higher the PF the higher the IF (H=114.075, P=0.000) and 5-IF (H=102.796, P=0.000). From 2006 to 2008, PF increased at various degrees in 22 journals, of which the total citation increased too, and just 18 journals of which IF increased. After PF increased in 22 journals, IF and 5-year IF were obviously higher than those before PF increased (Z=2.386, P=0.017; Z=4.015, P=0.000). We concluded that the IF and 5-IF are increased with the increasing of journals’ PF. The mechanism of the positive correlation between IF and PF are discussed.
KRAS-Driven Metabolic Rewiring Reveals Novel Actionable Targets in Cancer

Tumors driven by mutant KRAS are among the most aggressive and refractory to treatment. Unfortunately, despite the efforts, targeting alterations of this GTPase, either directly or by acting on the downstream signaling cascades, has been, so far, largely unsuccessful. However, recently, novel therapeutic opportunities are emerging based on the effect that this oncogenic lesion exerts in rewiring the cancer cell metabolism. Cancer cells that become dependent on KRAS-driven metabolic adaptations are sensitive to the inhibition of these metabolic routes, revealing novel therapeutic windows of intervention. In general, mutant KRAS fosters tumor growth by shifting cancer cell metabolism toward anabolic pathways. Depending on the tumor, KRAS-driven metabolic rewiring occurs by up-regulating rate-limiting enzymes involved in amino acid, fatty acid, or nucleotide biosynthesis, and by stimulating scavenging pathways such as macropinocytosis and autophagy, which, in turn, provide building blocks to the anabolic routes, also maintaining the energy levels and the cell redox potential (1). This review will discuss the most recent findings on mutant KRAS metabolic reliance in tumor models of pancreatic and non-small-cell lung cancer, also highlighting the role that these metabolic adaptations play in resistance to target therapy. The effects of constitutive KRAS activation in glycolysis elevation, amino acids metabolism reprogramming, fatty acid turnover, and nucleotide biosynthesis will be discussed also in the context of different genetic landscapes.
Explorations in Stochastic Diusion Search: soft- and hardware implementations of biologically inspired Spiking Neuron Stochastic Diusion Networks

Stochastic Diffusion Search, a probabilistic pattern matching algorithm, has been applied to a variety of problems with great success, and its basic properties have been studied in detail. Although parallel in nature, most of the applications are implemented in software on serial machines. This report aims at giving an overview of possible modifications towards efficient implementation in parallel hardware and towards implementations as networks of biologically inspired Spiking Neurons, in both hardand software.
GIS and the humanities: Presenting a path to digital scholarship with the Story Map app

ABSTRACT Working collaboratively, a scholarly resources librarian and a subject liaison librarian at a small, public liberal arts college have created a GIS story mapping workshop for fellow information professionals. Using GIS, the librarians are bridging the gap between humanities and sciences in a liberal arts environment. The librarians' experience of being introduced to GIS and creating training for others enabled them to support their library's data services and digital scholarship initiatives. By helping connect the humanities with science and technology using story maps, librarians can create a neutral environment for learners to conduct interdisciplinary research and develop spatial literacy.
Adversarial Domain Adaptation for Machine Reading Comprehension

In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. To this end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where (i) pseudo questions are first generated for unlabeled passages in the target domain, and then (ii) a domain classifier is incorporated into an MRC model to predict which domain a given passage-question pair comes from. The classifier and the passage-question encoder are jointly trained using adversarial learning to enforce domain-invariant representation learning. Comprehensive evaluations demonstrate that our approach (i) is generalizable to different MRC models and datasets, (ii) can be combined with pre-trained large-scale language models (such as ELMo and BERT), and (iii) can be extended to semi-supervised learning.