Miracle-dz/UniFAR-FTUs huggingface dataset:
The Application and Development of Construal Level Theory

Construal level denotes the different abstract levels of the representation of objects, and this abstract level is of different degree. The highlighting research in the early stage of Construal Level Theory is the exploration of the underlying psychological mechanism, which researches the implication of psychological distance for construal level. In recent years, researchers highlight the applied researches of the Construal Level Theory, which survey its application in the territories of persuasion, negotiation, and consumer behaviors etc. And its application reflects on the researches of marketing and consumer behavior, social cognition, decision making and negotiation, socialization. Besides, the applied researches of Construal Level Theory put forward the research of the underlying psychological mechanism, which improves the development of Construal Level Theory.
Generalizing Torque Control Concepts: Using Well-Established Torque Control Methods on Variable Stiffness Robots

Strict requirements must be met before robotic systems can be implemented in a human environment, for example, as service robots. Robustness, task adaptability, and energy efficiency are key aspects in this regard. Variable stiffness robots have been shown to be one step toward achieving these standards. In this article, we elaborate on the essential control aspects required to operate these robots and generalize well-established torque control methods to a variable stiffness robot, the DLR Hand Arm System (HASy). The adaptation and implementation of several control approaches for the compliant robots are also presented, with a focus on the experimental validation.
RIC: Parameter-free noise-robust clustering

How do we find a natural clustering of a real-world point set which contains an unknown number of clusters with different shapes, and which may be contaminated by noise? As most clustering algorithms were designed with certain assumptions (Gaussianity), they often require the user to give input parameters, and are sensitive to noise. In this article, we propose a robust framework for determining a natural clustering of a given dataset, based on the minimum description length (MDL) principle. The proposed framework, robust information-theoretic clustering (RIC), is orthogonal to any known clustering algorithm: Given a preliminary clustering, RIC purifies these clusters from noise, and adjusts the clusterings such that it simultaneously determines the most natural amount and shape (subspace) of the clusters. Our RIC method can be combined with any clustering technique ranging from K-means and K-medoids to advanced methods such as spectral clustering. In fact, RIC is even able to purify and improve an initial coarse clustering, even if we start with very simple methods. In an extension, we propose a fully automatic stand-alone clustering method and efficiency improvements. RIC scales well with the dataset size. Extensive experiments on synthetic and real-world datasets validate the proposed RIC framework.
Analyzing Partially Confounded Factorial Conjoint Choice Experiments Using SAS/IML®

Completely Confounded Factorial Conjoint Choice Experiment (CCFCCE) design allows estimation of main effects and interaction effects. Since some of the effects will be confounded with the blocks in completely confounded factorial design, the conjoint choice design was extended to use of partially confounded factorial design, which is call Partially Confounded Factorial Conjoint Choice Experiment(PCFCCE). A 2 8 partially confounded factorial design with two replicate was applied to CCE in this study. In this study, all the responses were assume to be independent and hence the multinomial logit model follows. The log likelihood is nonlinear and hence the newton-raphson method is needed to estimate the parameters. PROC IML was used to generated the Newton-Raphson procedures. The result showed that all main effects and some of the first-order interaction effects were significant.
Developmental Regulation of Protein O-GlcNAcylation, O-GlcNAc Transferase, and O-GlcNAcase in Mammalian Brain

O-GlcNAcylation is a common posttranslational modification of nucleocytoplasmic proteins by β-N-acetylglucosamine (GlcNAc). The dynamic addition and removal of O-GlcNAc groups to and from proteins are catalyzed by O-linked N-acetylglucosamine transferase (O-GlcNAc transferase, OGT) and β-N-acetylglucosaminidase (O-GlcNAcase, OGA), respectively. O-GlcNAcylation often modulates protein phosphorylation and regulates several cellular signaling and functions, especially in the brain. However, its developmental regulation is not well known. Here, we studied protein O-GlcNAcylation, OGT, and OGA in the rat brain at various ages from embryonic day 15 to the age of 2 years. We found a gradual decline of global protein O-GlcNAcylation during developmental stages and adulthood. This decline correlated positively to the total protein phosphorylation at serine residues, but not at threonine residues. The expression of OGT and OGA isoforms was regulated differently at various ages. Immunohistochemical studies revealed ubiquitous distribution of O-GlcNAcylation at all ages. Strong immunostaining of O-GlcNAc, OGT, and OGA was observed mostly in neuronal cell bodies and processes, further suggesting the role of O-GlcNAc modification of neuronal proteins in the brain. These studies provide fundamental knowledge of age-dependent protein modification by O-GlcNAc and will help guide future studies on the role of O-GlcNAcylation in the mammalian brain.
The Secret Life of Online Moms: Anonymity and Disinhibition on YouBeMom.com

Moms are one of the fastest growing demographics online. While much is known about where they spend their time, little is known about how they spend it. Using a dataset of over 51 million posts and comments from the website YouBeMom.com, this paper explores what kinds of topics moms talk about when they are not constrained by norms and expectations of face-to-face culture. Results show that almost 5% of posts are about dh, or “dear husband,” but these posts tend to express more negative emotion than other posts. The average post is only 124 characters long and family and daily life are common categories of posting. This suggests that YouBeMom is used as a fast-paced social outlet that may not be available to moms in other parts of their lives. This work concludes with a discussion of anonymity and disinhibition and puts forth a new provocation that moms, too, spend time online “for the lulz. ”
Surface Approximation Based on Two Types of Geometric Features

The complex models, in 3D computer graphics, can provide a convincing level of realism but rather cause problems in real-time applications focusing on real-time interactivity. Therefore, it is useful to have various simplified versions of the model according to the performance of the system. In this paper, we present a surface approximation algorithm based on two types of geometric features, which can excellently preserve the characteristic features of the original model, even after drastic simplification process.
Use of event detection approaches for outlier detection in wireless sensor networks

Outliers or anomalies are generally considered to be those observations that are considerably diverged from normal pattern of data. Due to their special characteristics, e.g. constrained available resources, frequent physical failure, and often harsh deployment area, wireless sensor networks (WSNs) are more likely to generate outliers compared to their other wireless counterparts. Potential sources of deviated data in a series of measurements are errors, events, and/or malicious attacks on the network. Current studies tend to handle events and errors separately and propose different techniques for event detection as for outlier detection. By bringing the concept of outlier and event close together and assuming that events are some sorts of outliers, in this paper, we investigate applicability of pattern matching-based event detection techniques for outlier detection. Through extensive experiments, we evaluate performance of various event detection techniques to detect outliers and compare them with a recent outlier detection study.
Influence Process Structural Learning and the Emergence of Collective Intelligence

Recent work [Hazy 2012] has demonstrated computationally that collectives that are organized into networks which govern the flow of resources can learn to recognize newly emerging opportunities distributed in the environment. This paper argues that the system does this through a process analogous to neural network learning with relative status playing the role of synaptic weights. Hazy showed computationally that learning of this type can occur even when resource allocation decision makers have no direct visibility into the environment, have no direct understanding of the opportunity, and are not involved in their exploitation except to the extent that they evaluate the success or failure of funded projects. Effectively, the system of interactions learns which individuals have the best access to information and other resources within the ecosystem. Hazy [2012] calls this previously unidentified emergence phenomenon: Influence Process Structural Learning (IPSL). In the prior model of IPSL, a three-tiered organizational structure was predetermined in the model design [Hazy 2012]. These initial conditions delimit the extent to which the emergence of collective intelligence can be posited because the model itself assumes a defined structure. This work contributes to the field by extending the IPSL argument for collective intelligence to a holistic emergence argument. It begins by briefly reviewing previously published work. It continues the conversation by adding two additional steps: Firstly, it shows how a three-tier organizing structure might emerge through known complexity mechanisms. In this case the mechanism identified is preferential attachment [Barabasi 2002]. Secondly, the paper shows how collective intelligence can emerge within a system of agents when the influence structure among these agents is treated as a the genetic algorithm.
Resonance Structure in a Weakly detuned Laser with Injected Signal

We describe the qualitative dynamics and bifurcation set for a laser with injected signal for small cavity detunings. The main organizing center is the Hopf-saddle-node bifurcation from where a secondary Hopf bifurcation of a periodic orbit originates. We show that the laser's stable cw solution existing for low injections, also suffers a secondary Hopf bifurcation. The resonance structure of both tori interact, and homoclinic orbits to the "off" state are found inside each Arnold tongue. The accumulation of all the above resonances towards the Hopf-saddle-node singularity points to the occurrence of a highly degenerate global bifurcation at the codimension-2 point.
Receiver Grouping with Local Recovery for Scalable Reliable Multicast

Scalable Reliable Multicast Protocols that reduce processing requirements on receivers and senders have been the subject of much research in recent years. In [12] and [6] we propose a new protocol that groups receivers for error recovery into fixed-size groups, thus reducing their processing requirement to O(1). We expand on this work by applying the concept of Local Recovery to receiver groups, which further improves the processing requirements on the sender while maintaining a constant O(1) requirement on the receivers. Furthermore, we extrapolate the concept of receiver grouping to repair servers resulting in further reduction in the sender’s processing requirements. The processing requirements on both the sender and the receiver for both cases are analytically studied.
Adding intelligence to non-corpus based word sense disambiguation

Natural language processing applications invariably perform word sense disambiguation as one of its processing steps. The accuracy of sense disambiguation depends upon an efficient algorithm as well as a reliable knowledge-base in the form of annotated corpus and/or dictionaries in machine readable form. Algorithms working on corpus for sense disambiguation are generally employed as supervised machine learning systems. But such systems need ample training on the corpus before being applied on the actual data set. This paper discusses an unsupervised approach of a graph-based technique that solely works on a machine-readable dictionary as the knowledge source. This approach can improve the bottleneck problem that persists in corpus-based word sense disambiguation. The method described here attempts to make the algorithm more intelligent by considering various WordNet semantic relations and auto-filtration of content words before graph generation.
Literature survey on dynamic community detection and models of social networks

In this paper, we realize a literature survey on the issue of community detection over time, first we present some basic concepts about networks modeled as graphs, then we state in an non exhaustive way the research fields arising from social networks. We present some of the existing models and methods to track communities over time. Community detection in networks is a prevailing subject in the area of social network analysis. There exist numerous methods and algorithms in the literature that have been dealing with that issue.
Quenching Cluster Cooling Flows with Recurrent Hot Plasma Bubbles

The observed cooling rate of hot gas in clusters is much lower than that inferred from the gas density profiles. This suggests that the gas is being heated by some source. We use an adaptive-mesh refinement code (FLASH) to simulate the effect of multiple, randomly positioned, injections of thermal energy within 50 kpc of the centre of an initially isothermal cluster with mass M 200 = 3 × 10 14 M ○. and kT = 3.1 keV. We have performed eight simulations with spherical bubbles of energy generated every 10 8 yr, over a total of 1.5 Gyr. Each bubble is created by injecting thermal energy steadily for 10 7 yr; the total energy of each bubble lies in the range (0.1-3) ×10 60 erg, depending on the simulation. We find that 2 x 10 60 erg per bubble (corresponding to an average power of 6.3 x 10 44 erg s -1 ) effectively balances energy loss in the cluster and prevents the accumulation of gas below kT = 1 keV from exceeding the observational limits. This injection rate is comparable to the radiated luminosity of the cluster, and the required energy and periodic time-scale of events are consistent with observations of bubbles produced by central active galactic nuclei in clusters. The effectiveness of this process depends primarily on the total amount of injected energy and the initial location of the bubbles, but is relatively insensitive to the exact duty cycle of events.
Explaining Labour Market Inactivity in Migrant-Sending Families: Housework, Hammock, or Higher Education*

This article presents a new perspective on the impact of migration and remittances on labour market participation and time allocation in migrant-sending families. Departing from the common finding that labour market participation is lower in migrant households, we investigate whether the reasons for inactivity, i.e. leisure consumption, home production and higher education are affected by migration. Based on household survey data from Moldova, our results challenge the assertion that those who stay behind consume more leisure. Instead, living in a migrant household implies higher probabilities of intra-household labour substitution and a substantially higher likelihood of university enrolment.
A Novel Solution Scheme for the Kernel MSE Model

In this paper we first show that the minimum squared-error solutions of kernel minimum squared-error (KMSE) models are neither unique nor numerically stable. We then propose a novel scheme for KMSE. This solution scheme can produce the unique solution and the maximum between-class margin. This solution has a highly accurate classification rate. The experimental result illustrates the feasibility and effectiveness of MNMSE solution of KMSE.
What can be Found on the Web and How: A Characterization of Web Browsing Patterns

In this paper, we suggest a novel approach to studying user browsing behavior, i.e., the ways users get to different pages on the Web. Namely, we classified all user browsing paths leading to web pages into several types or browsing patterns. In order to define browsing patterns, we consider several important points of the browsing path: its origin, the last page before the user gets to the domain of the target page, and the target page referrer. Each point can be of several types, which leads to 56 possible patterns. The distribution of the browsing paths over these patterns forms the navigational profile of a web page. We conducted a comprehensive large-scale study of navigational profiles of different web pages. First, we demonstrated that the navigational profile of a web page carry crucial information about the properties of this page (e.g., its popularity and age). Second, we found that the Web consists of several typical non-overlapping clusters formed by pages of similar ranges of incoming traffic. These clusters can be characterized by the functionality of their pages.
Sentinel surveillance of imported dengue via travellers to Europe 2012 to 2014: TropNet data from the DengueTools Research Initiative

We describe the epidemiological pattern and genetic characteristics of 242 acute dengue infections imported to Europe by returning travellers from 2012 to 2014. The overall geographical pattern of imported dengue (South-east Asia > Americas > western Pacific region > Africa) remained stable compared with 1999 to 2010. We isolated the majority of dengue virus genotypes and epidemic lineages causing outbreaks and epidemics in Asia, America and Africa during the study period. Travellers acted as sentinels for four unusual dengue outbreaks (Madeira, 2012–13; Luanda, 2013; Dar es Salaam, 2014; Tokyo, 2014). We were able to characterise dengue viruses imported from regions where currently no virological surveillance data are available. Up to 36% of travellers infected with dengue while travelling returned during the acute phase of the infection (up to 7 days after symptom onset) or became symptomatic after returning to Europe, and 58% of the patients with acute dengue infection were viraemic when seeking medical care. Epidemiological and virological data from dengue-infected international travellers can add an important layer to global surveillance efforts. A considerable number of dengue-infected travellers are viraemic after arrival back home, which poses a risk for dengue introduction and autochthonous transmission in European regions where suitable mosquito vectors are prevalent.
Actuation-assisted External Calibration of Distributed Camera Sensor Networks Anonymous Authors

While cameras have the potential to enable many applications in sensor networks, to be effective they first must be externally calibrated. In prior systems, cameras, identified by controllable light sources, utilized angular measurements amongst themselves to determine their relative positions and orientations. However, the typical camera's narrow field of view makes such systems susceptible to failures in the presence of occlusions or non-ideal configurations. Actuation-assistance helps to overcome such issues by essentially broadening each camera's view. In this paper we discuss and implement a prototype system that uses actua-tion to aid in the external calibration of camera networks. We evaluate our system using simulations and a testbed of MicaZ nodes, equipped with Cyclops camera modules mounted on custom pan-tilt platforms. Our results show that actuation-assistance can dramatically reduce node density requirements for localization convergence.
Contextual sensitivity in scientific reproducibility

Significance Scientific progress requires that findings can be reproduced by other scientists. However, there is widespread debate in psychology (and other fields) about how to interpret failed replications. Many have argued that contextual factors might account for several of these failed replications. We analyzed 100 replication attempts in psychology and found that the extent to which the research topic was likely to be contextually sensitive (varying in time, culture, or location) was associated with replication success. This relationship remained a significant predictor of replication success even after adjusting for characteristics of the original and replication studies that previously had been associated with replication success (e.g., effect size, statistical power). We offer recommendations for psychologists and other scientists interested in reproducibility. In recent years, scientists have paid increasing attention to reproducibility. For example, the Reproducibility Project, a large-scale replication attempt of 100 studies published in top psychology journals found that only 39% could be unambiguously reproduced. There is a growing consensus among scientists that the lack of reproducibility in psychology and other fields stems from various methodological factors, including low statistical power, researcher’s degrees of freedom, and an emphasis on publishing surprising positive results. However, there is a contentious debate about the extent to which failures to reproduce certain results might also reflect contextual differences (often termed “hidden moderators”) between the original research and the replication attempt. Although psychologists have found extensive evidence that contextual factors alter behavior, some have argued that context is unlikely to influence the results of direct replications precisely because these studies use the same methods as those used in the original research. To help resolve this debate, we recoded the 100 original studies from the Reproducibility Project on the extent to which the research topic of each study was contextually sensitive. Results suggested that the contextual sensitivity of the research topic was associated with replication success, even after statistically adjusting for several methodological characteristics (e.g., statistical power, effect size). The association between contextual sensitivity and replication success did not differ across psychological subdisciplines. These results suggest that researchers, replicators, and consumers should be mindful of contextual factors that might influence a psychological process. We offer several guidelines for dealing with contextual sensitivity in reproducibility.
Studying Fault Propagation Patterns in Distributed Applications Using Dyninst

In this paper we explore the idea of fault injection in parallel software. Transient faults present unique challenges to parallel systems, due to the complexity and scale of those systems. While fault analysis in general is a large and well-documented field, its application to parallel systems is less well understood. Here we present a framework using Dyninst binary instrumentation tool which allows the injection of faults into any software using the MPI library, and provide a discussion of how the techniques discussed could be extended to include other libraries. We then detail several experiments run on actual parallel applications which demonstrate wildly varying fault propagation patterns. By injecting various faults into two different applications, a Laplace Equation Solver and a Game of Life implementation, we show how fault injection affects different applications in different ways and conclude that, for successful fault analysis, it is in many cases necessary to possess some domain knowledge in order to introduce a fault that will produce a detrimental effect.