Miracle-dz/UniFAR-FTUs huggingface dataset:
An Exploration of Dual Systems via Time Pressure Manipulation in Decision-making Problems

Every day, decisions need to be made where time is a limiting factor. Regardless of situation, time constraints often place a premium on rapid decision-making. Researchers have been interested in studying this human behavior and understanding its underlying cognitive processes. In previous studies, scientists have believed that the cognitive processes underlying decision-making behavior were consistent with dual-process modes of thinking. Critics of dual-process theory question the vagueness of its definition, and claim that single-process accounts can explain the data just as well. My aim is to elucidate the cognitive processes that underlie decisions which involve some level of risk through the experimental manipulation of time pressure. Using this method, I hope to distinguish between competing hypotheses related to the origin of the effect. I will explore three types of decisions that illustrate these concepts: risky decision-making involving gambles, intertemporal choice, and one-shot public goods games involving social cooperation. In our experiments, participants made decisions about gambles framed as either gains or losses; decided upon intertemporal choices for smaller but sooner rewards or larger but later rewards; and played a one-shot public goods game involving social cooperation and contributing an amount of money to a group. In each case, we experimentally manipulated time pressure, either within subjects or among individuals. Results showed under time pressure, increased framing effects under in both hypothetical and incentivized choices; and greater contributions and cooperation among individuals, lending support to the dual process hypothesis that these effects arise from a fast, intuitive system. However, our intertemporal choice experiment showed that time constraints led to increased selection of the larger but later options, which suggests that the magnitude of the reward may play larger role in choice selection under cognitive load than previously studied. This diverges from the current dual-process interpretation that myopic choices under time pressure favor smaller but sooner rewards, and suggests that more studies are needed in this realm to disentangle the intuitive from the deliberative system through the manipulation of cognitive load.
Working Memory for Conjunctions Relies on the Medial Temporal Lobe

A prominent theory of hippocampal function proposes that the hippocampus is importantly involved in relating or binding together separate pieces of information to form an episodic representation. This hypothesis has only been applied to studies of long-term memory because the paradigmatic view of the hippocampus is that it is not critical for short-term forms of memory. However, relational processing is important in many working memory tasks, especially tasks using visual stimuli. Here, we test the hypothesis that the medial temporal lobes are important for relational memory even over short delays. The task required patients with medial temporal lobe amnesia and controls to remember three objects, locations, or object-location conjunctions over 1 or 8 s delays. The results show that working memory for objects and locations was at normal levels, but that memory for conjunctions was severely impaired at 8 s delays. Additional analyses suggest that the hippocampus per se is critical for accurate conjunction working memory. We propose that the hippocampus is critically involved in memory for conjunctions at both short and long delays.
Do Incentive Contracts Undermine Voluntary Cooperation?

In this paper we provide experimental evidence indicating that incentive contracts may undermine voluntary cooperation. This suggests that explicit incentives may have costly side effects that have been largely neglected by economists. In our experiments the undermining effect is so strong that the incentive contracts are less efficient than contracts without any incentives. Buyers, who are in the role of principals, nonetheless, prefer the incentive contracts because they allow them to appropriate a much larger share of the (smaller) total surplus and are, hence, more profitable for them. The undermining of voluntary cooperation through incentives is, in principle, consistent with models of inequity aversion and reciprocity. Additional experiments show, however, that the reduction of voluntary cooperation through incentives is partly due to a framing effect. If the incentive is framed as a price deduction the reduction of voluntary cooperation is much stronger compared to a situation where the incentive is framed as a bonus paid on top of a base price.
Word of mouth communication within online communities: Conceptualizing the online social network

Word of mouth (WOM) communication is a major part of online consumer interactions, particularly within the environment of online communities. Nevertheless, existing (offline) theory may be inappropriate to describe online WOM and its influence on evaluation and purchase. The authors report the results of a two-stage study aimed at investigating online WOM: a set of in-depth qualitative interviews followed by a social network analysis of a single online community. Combined, the results provide strong evidence that individuals behave as if Web sites themselves are primary “actors” in online social networks and that online communities can act as a social proxy for individual identification. The authors offer a conceptualization of online social networks which takes the Web site into account as an actor, an initial exploration of the concept of a consumer–Web site relationship, and a conceptual model of the online interaction and information evaluation process.
Numerical Modeling for Multiphase Incompressible Flow with Phase Change

ABSTRACT A general formula for the second-order projection method combined with the level set method is developed to simulate unsteady, incompressible multifluid flow with phase change. A subcell conception is introduced in a modified mass transfer model to accurately calculate the mass transfer across the interface. The third-order essentially nonoscillatory (ENO) scheme and second-order semi-implicit Crank-Nicholson scheme is employed to update the convective and diffusion terms, respectively. The projection method has second-order temporal accuracy for variable-density unsteady incompressible flows as well. The level set approach is employed to implicitly capture the interface for multiphase flows. A continuum surface force (CSF) tension model is used in the present cases. Phase change and dynamics associated with single bubble and multibubbles in two and three dimensions during nucleate boiling are studied numerically via the present modeling. The numerical results show that this method can handle complex deformation of the interface and account for the effect of liquid–vapor phase change.
Quantifying the degree of sharing of genetic and non-genetic causes of gene expression variability across four tissues

Gene expression can provide biological mechanisms which underlie genetic associations with complex traits and diseases, but often the most relevant tissue for the trait is inaccessible and a proxy is the only alternative. Here, we investigate shared and tissue specific patterns of variability in expression in multiple tissues, to quantify the degree of sharing of causes (genetic or non-genetic) of variability in gene expression among tissues. Using gene expression in ~800 female twins from the TwinsUK cohort in skin, fat, whole blood and lymphoblastoid cell lines (LCLs), we identified 9166 significant cis-eQTLs in fat, 9551 in LCLs, 8731 in skin and 5313 in blood (1% FDR). We observed up to 80% of cis-eQTLs are shared in pairs of tissues. In addition, the cis genetic correlation between tissues is > 90% for 35% of the genes, indicating for these genes a largely tissue-shared component of cis regulation. However, variance components show that cis genetic signals explain only a small fraction of the variation in expression, with from 67–87% of the variance explained by environmental factors, and 53% of the genetic effects occurring in trans. We observe a trans genetic correlation of 0 for all genes except a few which show correlation between fat and skin expression. The environmental effects are also observed to be entirely tissue specific, despite related tissues largely sharing exposures. These results demonstrate that patterns of gene expression are largely tissue specific, strongly supporting the need to study higher order regulatory interactions in the appropriate tissue context with large samples sizes and diversity of environmental contexts.
The Prevalence of Skilled Birth Attendant Utilization and Its Correlates in North West Ethiopia

The low utilization of skilled birth attendants sustained high maternal mortality. The aim of this study was to assess its magnitude and correlates in Northwest Ethiopia. A study was conducted on 373 randomly selected women who gave birth in the 12 months preceding the survey. Correlates were identified using binary logistic regression. Skilled birth attendance was 18.8%. Inability to perform cultural practices in health facilities (65.5%), expecting smooth delivery (63.4%), and far distance (62%) were the main barriers. Women with urban residence (AOR = 5.46: 95% CI [2.21–13.49]), primary (AOR = 2.10: 95% CI [0.71–6.16]) and secondary-plus (AOR = 6.12: [1.39–26.92]) educational level, four-plus ANC visits (AOR = 17.33: 95% CI [4.22–71.29]), and proximity to health centers (AOR = 5.67: 95% CI [1.47–25.67]) had higher odds of using skilled birth attendants though women with no labor complications had lower odds (AOR = 0.02: 95% CI [0.01–0.05]). Skilled birth attendance use was low. Urban residence, primary-plus level of education, frequent ANC visits, living nearby the health centers, and a problem during labor were positively correlated with skilled birth attendance utilization. Stakeholders should enhance girls' education beyond primary level and ANC services and shorten distances to health facilities.
RoadRunner: Towards Automatic Data Extraction from Large Web Sites

The paper investigates techniques for extracting data from HTML sites through the use of automatically generated wrappers. To automate the wrapper generation and the data extraction process, the paper develops a novel technique to compare HTML pages and generate a wrapper based on their similarities and dierences. Experimental results on real-life data-intensive Web sites confirm the feasibility of the approach.
Cultural Expressions of Social Class and Their Implications for Group‐Related Beliefs and Behaviors

In the wake of the Great Recession, rising inequality has increased social-class disparities between people in society. In this research, we examine how differences in social class shape unique patterns of cultural expression, and how these cultural expressions affirm ingroup beliefs. In Study 1 (N = 113), we provide evidence that cultural expressions of social class on an online social network can signal the social class of targets: by simply viewing the cultural practices of individuals captured in uploaded Facebook photographs, individuals express their social class in ways that allow it to be perceived by strangers at levels that are above chance accuracy. In Study 2 (N = 78), we provide evidence that individuals express their own ingroup space differently based on social class: Class-specific cultural practices (including interests in education, arts, newspapers, TV, and shopping) have implications for ingroup-related beliefs and political organizing. Individuals who reported being lower in subjective social class, relative to those reporting higher subjective social class, show cultural practices that relate to recognizing the ingroup's relative lack of control (lower group efficacy) and, in turn, a tendency to remain politically inactive when faced with an ingroup-related social disadvantage. In sum, our research provides evidence suggesting that expressions of culture derived from one's social class have the capacity to create and maintain social-class boundaries between individuals. Practical and political implications are discussed.
Exenatide once weekly for the treatment of type 2 diabetes: effectiveness and tolerability in patient subpopulations

Objective: Patient numbers in individual diabetes trials are often too limited to assess the effect of a treatment by different patient characteristics, and meta‐analyses often do not include patient‐level data. The purpose of this pooled analysis was to evaluate the efficacy and tolerability of exenatide once weekly (EQW) in patients with type 2 diabetes grouped into subpopulations by key demographic characteristics.
Reorganization of the composition of brain oscillations and their temporal characteristics during opioid withdrawal

Majority of the opioid-dependence and withdrawal studies are dominated with many inconsistencies and contradictions. One of the reasons for such inconsistencies may be methodological while performing EEG analysis. To overcome methodological limitations, in the present study we examined the composition of electroencephalographic (EEG) brain oscillations in broad frequency band (0.5—30 Hz) in 13 withdrawal opioid-dependent patients and 14 healthy subjects during resting condition (closed eyes). The exact compositions of brain oscillations and their temporal behaviour were assessed by the probability-classification analysis of short-term EEG spectral patterns (SPs). It was reported that early withdrawal had a generalized effect: the activity in all EEG channels was affected nearly equally. EEG of withdrawal patients was characterized by (a) different dominant SP types (had unique SP types which describe β-frequency band), (b) increased number of SP types observed in each EEG channel, (c) a larger percentage of α2-, β- and poly-rhythmic activity, and by a smaller percentage of δ-, θ- and α1-rhythmic activity, (d) predominantly right-sided asymmetry and (e) longer periods of temporal stabilization for α- and β-brain oscillations and by shorter periods of temporal stabilization for θ-activity when compared with control subjects. When taken together, these findings suggest a considerable reorganization of composition of brain oscillations, which reflects a disorganization process and an allostatic state with neuronal activation in EEG of opioid withdrawal patients.
Toward a “Meaningful Self” at the Workplace: Multinational Evidence From Asia, Europe, and North America

The quest for meaning has long concerned thinkers of all cultures. According to Austrian Jewish neurologist and Nazi death-camp survivor Viktor E. Frankl (1905-1997), man’s search for meaning is our paramount motivational driving force. It has become common knowledge that experiencing meaning is positively associated with well-being in general. The main focus of the present article, however, is the workplace as the scene where it arguably makes a difference whether workers are able to materialize their inherent will to meaning. Extrinsic reward systems often constitute a psychological quid pro quo contract between managers and employees in the workforce. Although incentives are well established, accepted, expected, and easily deployable, their value is both deficient and unsustainable for employee motivation, for employee performance, and subsequently for firm performance. Firms therefore need to counterbalance the above negative ends. The counterweight this work comes up with is meaning at work as a key driver of intrinsic motivation. In the course of a territory-mapping, large-scale, around-the-globe investigation, we offer a ranking of framework conditions that are most fruitful to promote the management of meaning. On the basis of fresh primary data from Europe, America, and Asia (specifically from China, Germany, India, Korea, and the United States), the perception of the relation between self and leaders is identified as the most important facilitator of a meaningful self at the workplace. Our findings are discussed, limitations are admitted, and future research avenues are illuminated.
Elliptical anomalies in wireless sensor networks

Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an important task in managing these types of networks. A key challenge is how to detect anomalies with few false alarms while preserving the limited energy in the network. In this article, we define different types of anomalies that occur in wireless sensor networks and provide formal models for them. We illustrate the model using statistical parameters on a dataset gathered from a real wireless sensor network deployment at the Intel Berkeley Research Laboratory. Our experiments with a novel distributed anomaly detection algorithm show that it can detect elliptical anomalies with exactly the same accuracy as that of a centralized scheme, while achieving a significant reduction in energy consumption in the network. Finally, we demonstrate that our model compares favorably to four other well-known schemes on four datasets.
Improved Model to Predict the Free Energy Contribution of Trinucleotide Bulges to RNA Duplex Stability

Trinucleotide bulges in RNA commonly occur in nature. Yet, little data exists concerning the thermodynamic parameters of this motif. Algorithms that predict RNA secondary structure from sequence currently attribute a constant free energy value of 3.2 kcal/mol to all trinucleotide bulges, regardless of bulge sequence. To test the accuracy of this model, RNA duplexes that contain frequent naturally occurring trinucleotide bulges were optically melted, and their thermodynamic parameters—enthalpy, entropy, free energy, and melting temperature—were determined. The thermodynamic data were used to derive a new model to predict the free energy contribution of trinucleotide bulges to RNA duplex stability: ΔG°37, trint bulge = ΔG°37, bulge + ΔG°37, AU + ΔG°37, GU. The parameter ΔG°37, bulge is variable depending upon the purine and pyrimidine composition of the bulge, ΔG°37, AU is a 0.49 kcal/mol penalty for an A-U closing pair, and ΔG° 37, GU is a −0.56 kcal/mol bonus for a G-U closing pair. With both closing pair and bulge sequence taken into account, this new model predicts free energy values within 0.30 kcal/mol of the experimental value. The new model can be used by algorithms that predict RNA free energies as well as algorithms that use free energy minimization to predict RNA secondary structure from sequence.
Dynamic Maintenance of a Shortest-Path Tree on Homogeneous Batches of Updates

A dynamic graph algorithm is called batch if it is able to update efficiently the solution of a given graph problem after multiple updates at a time (i.e., a batch) take place on the input graph. In this article, we study batch algorithms for maintaining a single-source shortest-path tree in graphs with positive real edge weights. In particular, we focus our attention on homogeneous batches, that is, either incremental (containing only edge insertion and weight decrease operations) or decremental (containing only edge deletion and weight increase operations) batches, which model realistic dynamic scenarios like transient vertex failures in communication networks and traffic congestion/decongestion phenomena in road networks. We propose two new algorithms to process either incremental or decremental batches, respectively, and a combination of these two algorithms that is able to process arbitrary sequences of incremental and decremental batches. All these algorithms are update sensitive; namely, they are efficient with respect to the number of vertices in the shortest-path tree that change their parents and/or their distances from the source as a consequence of a batch. This makes unfeasible an effective comparison on a theoretical basis of our new algorithms with the solutions known in the literature, which in turn are analyzed with respect to others and different parameters. For this reason, in order to evaluate the quality of our approach, we provide also an extensive experimental study including our new algorithms and the most efficient previous batch algorithms. Our experimental results complement previous studies and show that the various solutions can be consistently ranked on the basis of the type of homogeneous batch and of the underlying network. As a result, our work can be helpful in selecting a proper solution depending on the specific application scenario.
Matrix-Group Algorithm via Improved Whitening Process for Extracting Statistically Independent Sources From Array Signals

This paper addresses the problem of blind separation of multiple independent sources from observed array output signals. The main contributions in this paper include an improved whitening scheme for estimation of signal subspace, a novel biquadratic contrast function for extraction of independent sources, and an efficient alterative method for joint implementation of a set of approximate diagonalization-structural matrices. Specifically, an improved whitening scheme is first developed by estimating the signal subspace jointly from a set of diagonalization-structural matrices based on the proposed cyclic maximizer of an interesting cost function. Moreover, the globally asymptotical convergence of the proposed cyclic maximizer is analyzed and proved. Next, a novel biquadratic contrast function is proposed for extracting one single independent component from a slice matrix group of any order cumulant of the array signals in the presence of temporally white noise. A fast fixed-point algorithm that is a cyclic minimizer is constructed for searching a minimum point of the proposed contrast function. The globally asymptotical convergence of the proposed fixed-point algorithm is analyzed. Then, multiple independent components are obtained by using repeatedly the proposed fixed-point algorithm for extracting one single independent component, and the orthogonality among them is achieved by the well-known QR factorization. The performance of the proposed algorithms is illustrated by simulation results and is compared with three related blind source separation algorithms
Can recursive neural tensor networks learn logical reasoning?

Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning. To evaluate this, I train a recursive model on a new corpus of constructed examples of logical reasoning in short sentences, like the inference of "some animal walks" from "some dog walks" or "some cat walks," given that dogs and cats are animals. This model learns representations that generalize well to new types of reasoning pattern in all but a few cases, a result which is promising for the ability of learned representation models to capture logical reasoning.