Miracle-dz/UniFAR-FTUs huggingface dataset:
Judgment and Decision Making.

The science of judgment and decision making involves three interrelated forms of research: analysis of the decisions facing people, description of their natural responses, and interventions meant to help them do better. After briefly introducing the field's intellectual foundations, we review recent basic research into the three core elements of decision making: judgment, or how people predict the outcomes that will follow possible choices; preference, or how people weigh those outcomes; and choice, or how people combine judgments and preferences to reach a decision. We then review research into two potential sources of behavioral heterogeneity: individual differences in decision-making competence and developmental changes across the life span. Next, we illustrate applications intended to improve individual and organizational decision making in health, public policy, intelligence analysis, and risk management. We emphasize the potential value of coupling analytical and behavioral research and having basic and applied research inform one another. Expected final online publication date for the Annual Review of Psychology Volume 71 is January 4, 2020. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Distinct CRC-associated APC mutations dictate response to Tankyrase inhibition.

The majority of colorectal cancers (CRCs) show hyperactivated WNT signaling due to inactivating mutations in the APC tumor suppressor. Genetically restoring Apc suppresses WNT and induces rapid and sustained tumor regression, implying that re-engaging this endogenous tumor suppressive mechanism may be an effective therapeutic strategy. Here, using new animal models, human cell lines, and ex vivo organoid cultures, we show that Tankyrase (TNKS) inhibition can control WNT hyperactivation and provide long-term tumor control in vivo, but that effective responses are critically dependent on how APC is disrupted. Mutant APC proteins truncated within the Mutation Cluster Region (MCR) region physically engage the destruction complex and suppress the WNT transcriptional program, while early APC truncations (i.e. ApcMin) show limited interaction with AXIN1 and B-catenin, and do not respond to TNKS blockade. Together, this work shows that TNKS inhibition, like APC restoration, can reestablish endogenous control of WNT/B-catenin signaling, but that APC genotype is a crucial determinant of this response.
Changes in Income after an Industrial Accident According to Industry and Return-to-Work Status

Objective: To investigate changes in the incomes of workers, particularly those in the construction sector, who experienced industrial accidents according to their status of return to work. Methods: We used data from the fifth Panel Study of Workers’ Compensation Insurance. A repeated measures ANOVA was used to compare annual differential incomes before and after the industrial accident, and a linear mixed model was used to investigate the changes in income from before to after the industrial accident according to the industry and return-to-work status. Results: A comparison of the industrial categories revealed that construction industry workers exhibited the greatest incomes before the accident and the greatest decrease in income after the industrial accident. Regression analysis for assessing changes in income after the industrial accident showed that a comparison by industry revealed a significantly greater reduction in income in the construction than service industry. A comparison by work status revealed significantly greater decreases in income in the reemployment and non-return to work groups than among those who returned to their original work. Conclusions: The economic statuses of the victims of industrial accidents decreased relative to the pre-accident statuses in all industries. The ability to return to original work is important for preserving the accident victim’s economic status.
Quantifying the use of opioids in the immediate postoperative period after endoscopic sinus surgery

The opioid crisis is a public health emergency. There is limited evidence regarding how much opioid medication is necessary and which patients will require additional pain medication following endoscopic sinus surgery (ESS). The objective of this study was to quantify the use of opioids in the first 24 hours following ESS and determine the risk factors associated with increased need for opioid analgesia.
Where did you come from, where did you go: Refining metagenomic analysis tools for horizontal gene transfer characterisation

Horizontal gene transfer (HGT) has changed the way we regard evolution. Instead of waiting for the next generation to establish new traits, especially bacteria are able to take a shortcut via HGT that enables them to pass on genes from one individual to another, even across species boundaries. The tool Daisy offers the first HGT detection approach based on read mapping that provides complementary evidence compared to existing methods. However, Daisy relies on the acceptor and donor organism involved in the HGT being known. We introduce DaisyGPS, a mapping-based pipeline that is able to identify acceptor and donor reference candidates of an HGT event based on sequencing reads. Acceptor and donor identification is akin to species identification in metagenomic samples based on sequencing reads, a problem addressed by metagenomic profiling tools. However, acceptor and donor references have certain properties such that these methods cannot be directly applied. DaisyGPS uses MicrobeGPS, a metagenomic profiling tool tailored towards estimating the genomic distance between organisms in the sample and the reference database. We enhance the underlying scoring system of MicrobeGPS to account for the sequence patterns in terms of mapping coverage of an acceptor and donor involved in an HGT event, and report a ranked list of reference candidates. These candidates can then be further evaluated by tools like Daisy to establish HGT regions. We successfully validated our approach on both simulated and real data, and show its benefits in an investigation of an outbreak involving Methicillin-resistant Staphylococcus aureus data.
The Best English Teacher According to English Department Students

The study was conducted to analyze who the best English teacher according to English Department student. The participants of the study were a hundred English Department students of any semester or year. This study used quantitative research using a questionnaire, but it was elaborated qualitatively. It focused on the students’ preference of the best English teacher whether it is NESTs or non-NESTs. However this study firstly explored the students’ understanding of NESTs and non-NESTs in order to make sure that they understand those two terms so that they could choose which one they considered as the best English teacher. The first finding showed that the majority of the participants defined NESTs as those from English speaking country that is countries which use English as the mother tongue or L1 and/or as the main language. The second finding, related to the counterpart of NESTs, was almost all participants defined non-NESTs as those from non-English speaking country which also referred to bilingual users of English. The last finding showed that most participants preferred NESTs as the best English teacher. The study suggests that NESTs got more preference for the matter of their speaking skill especially the pronunciation.
Potential Indirect Relationships in Productive Networks

Productive Networks, such as Social Networks Services, organize evidence about human behavior. This evidence is independent of the network content type, and may support the discovery of new relationships between users and content, or with other users. These indirect relationships are important for recommendation systems, and systems where potential relationships between users and content (e.g., locations) is relevant, such as with the emergency management domain, where the discovery of relationships between users and locations on productive networks may enable the identification of population density variations, increasing the accuracy of emergency alerts. This thesis presents a Productive Networks model, which enables the development of a methodology for indirect relationships discovery, using the metadata on the network, and avoiding the computational cost of content analysis. We designed and conducted a set of experiments to evaluate our proposals. Our results are twofold: firstly, the productive network model is sufficiently robust to represent a wide range of networks; secondly, the indirect relationship discovery methodology successfully identifies relevant relationships between users and content. We also present applications of the model and methodology in several contexts.
Direction of arrival estimation using a multiple-input-multiple-output radar with applications to automobiles

The thesis at hand investigates the direction of arrival (DOA) estimation using a Multiple-InputMultiple-Output (MIMO) radar system. The application of MIMO radars in automobiles is studied. A MIMO radar consists of several transmitting (Tx) and receiving (Rx) antennas. We focus on a time division multiplexed (TDM) MIMO radar with colocated Tx and Rx antennas. The motivation is the use of a radar as a security system in automotive applications, e. g. to identify a dangerous situation and react automatically. Security systems must be very reliable. Hence, besides a good estimation of the distance and velocity, a high performance in DOA estimation is necessary. This is a demanding task, since only a small number of antennas is used and the radar is limited to a small geometrical size. Compared to the corresponding Single-Input-Multiple-Output (SIMO) radar, a MIMO radar with colocated antennas can achieve a higher accuracy in DOA estimation due to its larger virtual aperture. Therefore it is a promising technique for the use in automobiles. The obtained results of this thesis enable us to find optimal TDM schemes which yield a very high DOA accuracy for targets which are stationary as well as for targets which are moving relative to the radar system. The results are not confined to MIMO radars in automobiles, but can be used in other applications as well. In Chapter 2 we introduce the basics of radar systems, which are important for this thesis. First we present three different kinds of radars, the pulse Doppler radar, the frequency modulated continuous wave (FMCW) radar, and the chirp sequence radar. After that, the performance measures of a radar system, which are used in the following chapters, are introduced. For that purpose, the concept of parameter estimators are presented. Thereafter, the accuracy is defined and a definition for the resolution is given. Chapter 3 gives an overview on the two types of MIMO radars: The MIMO radar with widely separated antennas and the colocated MIMO radar. A survey on the research already done on MIMO radars is presented. Thereafter, the different multiplexing techniques are discussed, which can be used in a MIMO radar to separate the signals of the different Tx antennas. The last part of Chapter 3 focuses on the application of MIMO radars in automobiles. It shows why TDM MIMO radars are suitable for the use in automotive applications. In Chapter 4 we study the DOA estimation for stationary targets, i. e. the targets are not moving relative to the radar. We investigate SIMO and MIMO radars and compute the deterministic CramerRao Bound (CRB) of the DOA for both types of systems. With these results we can determine the maximal achievable DOA accuracy. We compare both CRBs and show under which conditions
Computational modeling of controlled cortical impact in a mouse

Thresholds for primary vascular injury have not been well-defined. Various animal experimental models are available to study different types of injury and their severity, but quantifying the tissue deformations that produce the injuries is difficult. This project aims at developing a finite element (FE) model of controlled cortical impact (CCI) in a mouse in order to quantify brain deformations associated with the resulting contusion. The predicted deformations will be used to study the correlation between the mechanical responses and the experimental injuries. The model for this study was built by digitizing coronal section images of a mouse brain taken from an online database. The resulting model includes a total of 426,447 brick and shell elements to represent the brain, skull, impactor and the meninges. A contact algorithm was used to model interactions between the mouse brain and the impactor. Impact velocity and depth were governed by a prescribed displacement with respect to time for the impactor. The influence of these loading conditions, along with that of impactor size and shape and craniotomy size, were examined. The significance of mesh density, element integration schemes and contact algorithm was also examined. Not surprisingly, predicted peak mechanical responses in all the parameter studies conducted were localized in and around the region of the impact. Impactor shape and impact depth were the leading factors influencing the mechanical responses.
The Extended Classroom Framework for Teaching Systems Analysis of Food Systems

To further synthesize Systems Action Education (SAE) and adventure learning (AL), a new education framework is presented called the Extended Classroom Framework (ECF) for teaching systems analysis of food systems. Extended Classroom Framework integrates SAE and AL with the circulatory system of science to describe how the classroom interacts with society. In the fall of 2012, the ECF was utilized to design a hybrid course at the undergraduate level that explored four different international agroecoregions through the perspective of on-the-ground collaborators. By utilizing online geographic information systems and an online social network, students digitally explored these agroecosystems as open-ended cases. A pre- and post-test of the Intercultural Development Inventory was given to the students. Students also wrote four reflective journals that were coded and thematically analyzed. Six of seven of students showed positive shifts in their developmental orientation (p < 0.05). Four of seven students showed decreases in their intercultural orientation gap. Every student ended the course similarly or less culturally disengaged to a primary cultural group, with six students in the resolved category compared with four at the beginning of the course. Student reflective journals illustrated growth in considering agroecosystems contextually and as coupled human–environmental systems. These results show that the ECF offers a viable framework for developing student capacities to engage “wicked” problems. Impact Statement The Extended Classroom Framework (ECF) provides a means to synthesize online GIS and social networks to allow students and instructors to experientially learn about human–environment interactions in multiple international contexts in a cost-effective manner. By providing preliminary evidence documenting the framework in action, this article shows the ECFs potential to increase students’ ability to think systemically and work across differences, essential skill sets for practitioners working within “wicked problems. ”
Towards large-scale network analytics

In this thesis, we present a framework for efficient analysis of large-scale network datasets. There are four important components in our framework: a) a high performance computing platform with Graphics Processing Units (GPUs) and efficient implementations of mining algorithms on top of the GPU platform. b) an efficient summarization method to compress the storage space of large-scale streaming and heterogeneous network data with textual content and network topology. c) a complex query engine that depends on the summarized input data and can help to discover new knowledge from network content and topology. d) a visual front-end to present mining results to users. In each of the above components, we propose new methods to either speed up mining tasks or reduce the data storage size in those tasks. We compare our methods with existing approaches on real datasets drawn from various domains.
Improved Coreference Resolution Using Cognitive Insights

Coreference resolution is the task of extracting referential expressions, or mentions, in text and clustering these by the entity or concept they refer to. It is an important component of natural language processing (nlp) pipelines since it enables computational systems to understand that the information from textual attributes and relationships of mentions concern coherent entities, such as particular people or organisations. The sustained research interest in the task reflects the richness of reference expression usage in natural language and the difficulty in encoding insights from linguistic and cognitive theories effectively. In this thesis, we design and implement limeric, a state-of-the-art coreference resolution engine. In the literature, coreference resolution has typically been modelled as a mention pairing problem. However, simple local decoding strategies make errors by failing to account for global consistency constraints, and the two directions to incorporate such constraints – non-local decoding and entity-level modelling – have largely been orthogonal. limeric naturally incorporates both to achieve the highly competitive benchmark performance of 64.22% and 59.99% using gold and automatic preprocessing on the CoNLL-2012 benchmark with a simple model and a baseline feature set. This performance is stronger than any system that only use non-local decoding or entity-level modelling in isolation for global consistency, arguing for their mutual benefit. As well as strong performance, a key contribution of this work is a reconceptualisation of the coreference task. We draw an analogy between shift-reduce parsing and coreference resolution to develop an algorithm which naturally mimics cognitive models of human discourse processing. Leveraging the self-ordering forest of discourse entities as a simple model of the human mind, we redefine how features can be defined and competition in antecedent selection modelled. In our feature development work, we leverage insights from cognitive theories to improve our modelling. Specifically, we exploit the fine-grained typology of the Acces-
Genre analysis of applied linguistics research article introductions: Exploring sub-disciplinary variations

This paper explores sub-disciplinary variations and generic structure of research article introductions (RAIs) in three sub-disciplines of applied linguistics (AL), namely, English for Specific Purposes (ESP), Psycholinguistics, and Sociolinguistics, using Swales’s CARS model. The corpus consisted of 90 RAIs drawn from a wide range of refereed journals in the corresponding subdisciplines. The results indicate sub-disciplinary variations in the structure of this genre that can be justified through the concepts of ‘established’ versus ‘emerging’ fields. The findings underline the need for further in-depth research into sub-disciplinary variation and generic structure of RAIs. The results also have pedagogical implications for ESP/EAP practitioners and course designers as well as material developers.
Performance Evaluation of IEEE 802.15.4k Priority Channel Access with DSSS PHY

Low-energy critical infrastructure monitoring (LECIM) applications are rapidly increasing. A communication protocol developed to fulfill the LECIM requirements is the novel IEEE 802.15. 4k standard. In this paper, we study the impact of priority channel access (PCA), a feature defined for high priority message transmissions in the IEEE 802.15. 4k standard, in LECIM DSSS physical layer (PHY). An LECIM network that employs a Slotted Aloha (S-Aloha) with PCA channel access scheme in conjunction with non-saturated traffic conditions is presented in a varying propagation environment. OPNET simulation results show that the application of a priority access in the network significantly improves the delay performance of high priority messages while imposing less effect on the overall network performance of low priority messages. However, the reliability performance of the PCA scheme is approximately the same as the reliability performance of a non-PCA scheme for high priority messages. Furthermore, in a last-gasp scenario, during which a large amount of high priority data traffic is observed in the network, an LECIM DSSS PHY with PCA system fails to yield the expected network performances.
Feasibility Of A Strategy For Restoring Gait In Paraplegic People

We investigated the utility of the hypothesis that a net extensor moment at the joints of the lower extremity is a necessary feature of human gait. Of particular interest was the application of this idea to the implementation of Functional Electrical Stimulation systems for the restoration of gait in paraplegic people. Mathematical simulations of the double and single limb support phases of gait were used to test the effects of both redistributing joint moments while maintaining a normal support moment and the effects of lowering the support moment. Results showed that this basic concept could be used to compensate for decreased moments (muscle strength) at some joints by increasing the moment at other joints. However, there were limits on the magnitude of the altered joint moments and the results depended on the phase of the gait cycle.
Evaluation of the Role Of bla Genes in Beta Lactam and Methicillin Resistant Staphylococcus aureus

One hundred and nineteen clinical samples were isolated from patients admitted in different hospitals in El-Sharkia Governorate. Sixty six isolates were confirmed to be S. aureus. Susceptibility to different antimicrobial agents and Minimum inhibitory concentration tests showed that all the isolates were resistant to 𝛽-lactam antibiotics, 77.2% (n= 51) isolateswere methicillin resistant S. aureus MRSA, while almost all the isolates were sensitive to vancomycin and tigecycline. Polymerase chain reaction (PCR) of mecA, encoding methicillin resistance, and blaZ, β-lactamase biosynthetic gene, revealed the coexistence of both genes in 56.8% (n= 29/51) of the isolates. Meanwhile, 11.7% (n= 6/51) of MRSA isolates phenotypically resistant to oxacillin were found to be mecA-. This data support the fact that the expression of bla genes enhanced the phenotypic expression of oxacillin resistance as a result of β-lactamase hyperproduction. On the other hand, 33% of MRSA (n=17/51) were blaZ– suggesting a mutation event in blaZ or the existence of an alternative mechanisms for 𝛽-lactam resistance that may compete with mecA gene.
The role of syntactic dependencies in compositional distributional semantics

Abstract This article provides a preliminary semantic framework for Dependency Grammar in which lexical words are semantically defined as contextual distributions (sets of contexts) while syntactic dependencies are compositional operations on word distributions. More precisely, any syntactic dependency uses the contextual distribution of the dependent word to restrict the distribution of the head, and makes use of the contextual distribution of the head to restrict that of the dependent word. The interpretation of composite expressions and sentences, which are analyzed as a tree of binary dependencies, is performed by restricting the contexts of words dependency by dependency in a left-to-right incremental way. Consequently, the meaning of the whole composite expression or sentence is not a single representation, but a list of contextualized senses, namely the restricted distributions of its constituent (lexical) words. We report the results of two large-scale corpus-based experiments on two different natural language processing applications: paraphrasing and compositional translation.