Miracle-dz/UniFAR-FTUs huggingface dataset:
The Family as a Learning System

Detailed observations of familiy members were undertaken in two museum environments: a partcipatory hall with numerous hands-on opportunities and a tradional hall where artifacts were exhibited behind railings or in glass cases. In both environments family members persistently attended to the information presented and spontaneously engaged in strategies for acquiring and exchanging information about the exhibits. Learning strategies were biased to favor the acquisition of first-hand, factual information. Complementing individualized efforts at information acquisition were behaviors which served to broadcast current perceptions, conceptions, and queries to other family members. By favoring cross-generational interactive partners, family members effectively distributed new information to family members with the least similar knowledge bases. The family emerged as a highly responsive and flexible learning system that adopts well to museum environments.
Superconformal mechanics, black holes, and nonlinear realizations

The OSp(2|2)-invariant planar dynamics of a D = 4 superparticle near the horizon of a large mass extreme black hole is described by an N = 2 superconformal mechanics, with the SO(2) charge being the superparticle’s angular momentum. The non-manifest superconformal invariance of the superpotential term is shown to lead to a shift in the SO(2) charge by the value of its coefficient, which we identify asthe orbital angular momentum. The full SU(1,1|2)-invariant dynamics is found from an extension to N = 4 superconformal mechanics.
Nonlinear compressed sensing based on composite mappings and its pointwise linearization

Classical compressed sensing (CS) allows us to recover structured signals from far few linear measurements than traditionally prescribed, thereby efficiently decreasing sampling rates. However, if there exist nonlinearities in the measurements, is it still possible to recover sparse or structured signals from the nonlinear measurements? The research of nonlinear CS is devoted to answering this question. In this paper, unlike the existing research angles of nonlinear CS, we study it from the perspective of mapping decomposition, and propose a new concept, namely, nonlinear CS based on composite mappings. Through the analysis of two forms of a nonlinear composite mapping Phi, i.e., Phi(x) = F(Ax) and Phi(x) = AF(x), we give the requirements respectively for the sensing matrix A and the nonlinear mapping F when reconstructing all sparse signals exactly from the nonlinear measurements Phi(x). Besides, we also provide a special pointwise linearization method, which can turn the nonlinear composite mapping Phi, at each point in its domain, into an equivalent linear composite mapping. This linearization method can guarantee the exact recovery of all given sparse signals even if Phi is not an injection for all sparse signals. It may help us build an algorithm framework for the composite nonlinear CS in which we can take full advantage of the existing recovery algorithms belonging to linear CS.
Reinvestigating Remarriage: Another Decade of Progress

The article presents an overview of research and theory on remarriages and stepfamilies published in the 1990s. Remarriage is a term that encompasses several different types of relationships--both partners may be in a second marriage or a higher-order marriage. About 75 percent of divorced people remarry and serial remarriages are increasingly common. As people age, however, the divorce rates of first marriages and remarriages converge. The mean length of time between divorce and remarriage is less than four years. Men remarry at higher rates than do women and blacks and Hispanics remarry at lower rates than whites. A substantial proportion of U.S. births occurs in remarriages. Some first marriages create stepfamilies and stepparent-stepchild relationships. In 1992, 15 percent of all children in the U.S. lived with a mother and a stepfather. Although the presence of stepchildren is thought to lower marital quality for remarried adults, the effects are not always strong. A number of intrapersonal, interpersonal and societal-level explanations have been proposed for the greater instability of remarriages. Adolescent stepchildren also generally showed more externalizing behavioral problems than children living with both parents such as using drugs and alcohol, engaging in sexual intercourse, nonmarital childbearing and being arrested.
Cultural beliefs affecting sleep duration

A significant aspect of sleep related studies is formed by the studies that evaluate sufficient sleep duration and the impact of short or long sleep time on health. The determinants of sleep duration that are important for the continuance of both health and daily life are an important discussion area. This study was performed in order to determine the factors related to the sleep duration of individuals aged 18–65, their cultural beliefs related to sleep duration and other variables in this type of descriptive cross-sectional (n = 534). The data were collected through a three-part questionnaire consisting of 44 questions created by the researchers by searching literature. A backward multiple regression analysis was used to identify the determinants of the sleep duration. Being male, (β = −0.11), talking during sleep (β = -0.08), having problems falling asleep (β = −0.08), having the habit of setting the alarm clock (β = −0.13), and having the belief that “The time passed while sleeping is a waste, so the sleep time must be short” (β = −0.26), and “If I have insomnia, I wait to fall asleep without getting up” (β = −0.06) were found to negatively affect sleep duration. Agreeing with the statements; “Sleeping refreshes/clears the mind” (β = 0.22) “Sleeping early and getting up early makes people healthy” (β = 0.09) “Sleeping at noon for a short time is important” (β = 0.09) “I daydream before sleeping” (β = 0.178). “If I have insomnia, I use drugs” (β = 0.110) positively affects and increases sleep duration.
Crowdsourcing argumentation structures in Chinese hotel reviews

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.
Following People's Behavior Across Social Media

To face the new challenge of giving an all-around picture of people's online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how users post their favorite contents and about their centrality on different social media. Specifically posting activities and social sites usage have been gathered from the social media aggregator Alternion. The analysis of social media usage shows that Alternion data capture the typical trend of today's users. However the novelty is the multidimensional and longitudinal nature of the dataset. In fact by performing a rank correlation analysis on the degree in the different social sites, we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals' importance changes from medium to medium.We also investigate the posting activities finding a slightly positive correlation on how often users publish on different social media. Finally we show that users tend to use similar usernames to keep their identifiability across social sites.
Assembling a visa requirement against the Mexican ‘wave’: migrant illegalization, policy and affective ‘crises’ in Canada

Abstract Using the example of a 2009 visa requirement imposed onto Mexican nationals travelling to Canada, this paper frames migrant illegalization as an assemblage that brings together transnational connections, discursive and affective productions, modes of racialization and migrant negotiations. More specifically, the paper maps the events leading to the crafting of the visa requirement and the ensemble of stakeholders – Canadian immigration officials, politicians and business representatives, the Mexican and American governments and Mexican migrants –who responded to it. Through that analysis, the paper argues that migrant illegalization is not always a consistent or predictable set of practices, processes and actors, but instead an often contradictory assemblage of actions and reactions dependent on the social and political context.
Could We Generate Cytology Images from Histopathology Images? An Empirical Study

Automation in medical imaging is quite challenging due to the unavailability of annotated datasets and the scarcity of domain experts. In recent years, deep learning techniques have solved some complex medical imaging tasks like disease classification, important object localization, segmentation, etc. However, most of the task requires a large amount of annotated data for their successful implementation. To mitigate the shortage of data, different generative models are proposed for data augmentation purposes which can boost the classification performances. For this, different synthetic medical image data generation models are developed to increase the dataset. Unpaired image-to-image translation models here shift the source domain to the target domain. In the breast malignancy identification domain, FNAC is one of the low-cost low-invasive modalities normally used by medical practitioners. But availability of public datasets in this domain is very poor. Whereas, for automation of cytology images, we need a large amount of annotated data. Therefore synthetic cytology images are generated by translating breast histopathology samples which are publicly available. In this study, we have explored traditional image-to-image transfer models like CycleGAN, and Neural Style Transfer. Further, it is observed that the generated cytology images are quite similar to real breast cytology samples by measuring FID and KID scores.
Why is difficult to make decisions under multiple criteria ?

The paper makes a survey of the principal difficulties the multiple criteria decision making introduces with a particular emphasis on scheduling problems. Two types of difficulties are considered. The first is of conceptual nature and has to do with the difficulty of defining the concept of optimality in presence of multiple criteria and the impossibility to define universal preference aggregation procedures. The second difficulty is of more technical nature and concerns the increasing computational complexity of multiple criteria decision making problems. A number of examples are introduced in order to explain these issues.
Robust logics

Suppose that we wish to learn from examples and counterexamples a criterion for recognizing whether an assembly of wooden blocks constitutes an arch. Suppose also that we have preprogrammed recognizers for various relationships e.g. on-top-of(z, y), above(z, y), etc. and believe that some quantified expression in terms of these base relationships should suffice to approximate the desired notion of an arch. How can we formulate such a relational learning problem so as to exploit the benefits that are demonstrable in propositional learning, such as attribute-efficient learning by linear separators, and error-resilient learning ? We believe that learning in a general setting that allows for multiple objects and relations in this way is paradigmatic of the more fundamental questions that need to be addressed if one is to resolve the following dilemma that arises in the design of intelligent systems: Mathematical logic is an attractive language of description because it has clear semantics and sound proof procedures. However, as a basis for large programmed systems it leads to brittleness because, in practice, consistent usage of the various predicate names throughout a system cannot be guaranteed, except in application areas such as mathematics where the viability of the axiomatic method has been demonstrated independently. In this paper we develop the following approach to circumventing this problem. We suggest that brittleness can be overcome by using a new kind of logic in which each statement is learnable. By allowing thesystem to learn rules empirically from the environment, relative to any particular programs it may have for recognizing some base predicates, we enable the system to acquire a set of statements approximately consistent with each other and with the world, without the need for a globally knowledgeable and consistent p*OgrUllll~~. We illustrate this approach by describing a simple logic that hzs a sound and efficient proof procedure for reasoning about instances, and that is rendered robust by having the rules learnable. The complexity and accuracy of both ~o,,yri~,,, ACM ,999 l-581 13.067. 8199105. %5.00 learning and deduction are provably polynomial bounded.
Detecting bilateral abnormalities with voxel‐based morphometry

In this article we describe a new method, using SPM99, that searches explicitly for bilateral structural abnormalities. Children with bilateral pathology have a poorer prognosis than children with unilateral damage. After brain injury or disease in childhood, it is thought that rescue of function is only possible if the neuronal substrates of that function are preserved and operational in at least one hemisphere [Vargha‐Khadem and Mishkin, 1997 ]. If this is the case, the detection of bilateral abnormalities would greatly facilitate more accurate prognosis in children with brain injury or developmental disorders. We have therefore developed a technique to detect bilateral abnormalities that uses conjunction analysis with voxel based morphometry. It is illustrated using a group of patients with bilateral hypoxic‐ischaemic damage to the hippocampus. The approach is shown to have enhanced specificity and sensitivity relative to conventional unilateral characterisations. Hum. Brain Mapping 11:223–232, 2000. © 2000 Wiley‐Liss, Inc.
A Self-Paced Motor Imagery Based Brain-Computer Interface for Robotic Wheelchair Control

This paper presents a simple self-paced motor imagery based brain-computer interface (BCI) to control a robotic wheelchair. An innovative control protocol is proposed to enable a 2-class self-paced BCI for wheelchair control, in which the user makes path planning and fully controls the wheelchair except for the automatic obstacle avoidance based on a laser range finder when necessary. In order for the users to train their motor imagery control online safely and easily, simulated robot navigation in a specially designed environment was developed. This allowed the users to practice motor imagery control with the core self-paced BCI system in a simulated scenario before controlling the wheelchair. The self-paced BCI can then be applied to control a real robotic wheelchair using a protocol similar to that controlling the simulated robot. Our emphasis is on allowing more potential users to use the BCI controlled wheelchair with minimal training; a simple 2-class self-paced system is adequate with the novel control protocol, resulting in a better transition from offline training to online control. Experimental results have demonstrated the usefulness of the online practice under the simulated scenario, and the effectiveness of the proposed self-paced BCI for robotic wheelchair control.
Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction

3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information carried by 2D observations, thus suffering from performance degradation and model redundancy. To address the above problem, we propose a novel motion-aware enhancement framework for dynamic scene reconstruction, which mines useful motion cues from optical flow to improve different paradigms of dynamic 3DGS. Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow. Then a novel flow augmentation method is introduced with additional insights into uncertainty and loss collaboration. Moreover, for the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed. We conduct extensive experiments on both multi-view and monocular scenes to verify the merits of our work. Compared with the baselines, our method shows significant superiority in both rendering quality and efficiency.
Evaluating the Population Size Adaptation Mechanism for CMA-ES on the BBOB Noiseless Testbed

The population size adaptation mechanism for CMA-ES is evaluated on the BBOB noiseless testbed. The population size is adapted on the basis of the estimated accuracy of the update of the distribution parameters, i.e., the mean vector and the covariance matrix of the Gaussian distribution. The population size is adapted so that the estimated accuracy of the parameter update keeps a certain level. The CMA- ES with the population size adaptation mechanism could solve well-structured multimodal functions as efficiently as the best 2009 portfolio without a restart strategy that in- creases the population size every restart such as the IPOP strategy.
Text detection on camera acquired document images using supervised classification of connected components in wavelet domain

In this paper we present an algorithm to detect text on video frames consisting of lecture slides. We begin by performing a multi-channel wavelet transform and then merge the channel components for the high frequency sub bands to obtain a composite energy map. Thresholding the energy map results in an edge map consisting of candidate text pixels - some of these correspond to actual text and others correspond to graphics, logo, tables, etc. The connected components in the edge map are then filtered to reject some of the false positives using a trained classifier. Rectangular text blocks compactly surrounding the text regions are then identified using a process of selective dilation and recursive splitting. False positive text blocks still remaining are then rejected using heuristics. Experiments conducted on 890 images show that our scheme has lower false positive rate and misdetection rate when compared with two existing scene text detection methods.
Internet of Things for Electric Vehicle: An Improved Decentralized Charging Scheme

This paper focuses on providing an automatic and efficient electric vehicles (EVs) charging management system (CMS) by exploiting the benefits of Internet of Things (IoT) technology in offering the ubiquitous perception abilities and a real-time interactive view of the physical world by various sensors and radio devices. An improved decentralized charging scheme is also proposed to coordinate the charging of large-scale EVs in multiple residential buildings by leveraging the distributed optimization capability of alternating direction method of multiplier (ADMM). Extensive studies revealed that the proposed work outperforms two other compared decentralized methods by producing higher final mean state-of-charge (SOC) levels to the connected EVs and incurring less electricity bill paid to the utility. Furthermore, the proposed charging scheme is proven better than the centralized approach because of its ability to consume lower communication overhead and offer better robustness in preserving the user privacy.
Functional analysis of Src homology 3‐encoding exon (exon 2) of p130Cas in primary fibroblasts derived from exon 2‐specific knockout mice

p130Cas (Cas, Crk‐associated substrate) is an adaptor molecule composed of a Src homology 3 (SH3) domain, a substrate domain (SD) and a Src binding domain (SBD). The SH3 domain of Cas associates with focal adhesion kinase (FAK), but its role in cellular function has not fully been understood. To address this issue, we established and analyzed primary fibroblasts derived from mice expressing a truncated Cas lacking exon 2, which encodes the SH3 domain (Cas Δexon 2). In comparison to wild‐type cells, Cas exon 2Δ/Δ cells showed reduced motility, which could be due to impaired tyrosine‐phosphorylation of FAK and Cas, reduced FAK/Cas/Src/CrkII binding, and also impaired localization of Cas Δexon 2 to focal adhesions on fibronectin. In addition, to analyze downstream signaling pathways regulated by Cas exon 2, we performed microarray analyses. Interestingly, we found that a deficiency of Cas exon 2 up‐regulated expression of CXC Chemokine Receptor‐4 and CC Chemokine Receptor‐5, which may be regulated by IκBα phosphorylation. These results indicate that the SH3‐encoding exon of Cas participates in cell motility, tyrosine‐phosphorylation of FAK and Cas, FAK/Cas/Src/CrkII complex formation, recruitment of Cas to focal adhesions and regulation of cell motility‐associated gene expression in primary fibroblasts.
Albumin to gamma-glutamyltransferase ratio as a prognostic indicator in intrahepatic cholangiocarcinoma after curative resection

The prognosis of intrahepatic cholangiocarcinoma (ICC) remains poor whereas predictive models for survival prediction in ICC patients following curative resection are limited. Herein, we established a novel inflammation-based score derived from preoperative albumin to gamma-glutamyltransferase ratio (AGR) and evaluated its prognostic significance in ICC patients underwent curative resection. Prognostic value of AGR was retrospectively studied in a cohort comprising 206 ICC patients following curative resection. The predictive performance of AGR was compared with other inflammation-based scores and serological tumor markers in terms of concordance index (C-index). Further, prognostic nomograms incorporating AGR into the tumor-node-metastasis (TNM) staging systems were established to achieve a better discriminatory ability. The optimal cut-off value of AGR was 0.6. Multivariate analysis showed that AGR was an independent predictor for overall survival (OS; P = 0.003) and recurrence-free survival (RFS; P = 0.046). The C-index of AGR was superior to other inflammation-based scores and serological tumor markers in OS and RFS prediction. The established nomograms showed improved predictive accuracy compared with the TNM staging systems alone. These results indicate that AGR is an independent prognostic indicator for ICC underwent curative resection. The incorporation of AGR into the existing TNM staging systems achieved improved predictive accuracy.
Individual Contribution in Brain-storming

Factors affecting individual performance in group tasks such as social loafing and social facilitation have been widely investigated. Past studies compared groups made up of friends or strangers based on prior acquaintance before the experiment, without directly manipulating the level of group cohesiveness. Based on Karau and William's (1997) rationale about the effect of group cohesiveness on social facilitation and social loafing, we tested two hypotheses: (1) When individual members do brain-storming in high cohesiveness group, they work harder and generate more ideas (social facilitation). (2) When individual members do brain-storming in low cohesiveness group, they work less hard and generate fewer ideas (social loafing). Results supported the second hypothesis, but failed to support the first one.