Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Intermittent Wetting on Concentration Profiles and Release from a Cement-Based Waste Matrix

The objective of the research presented here was to assess the impact on constituent release from cement-based waste materials caused by intermittent leaching with storage (i.e., nonleaching period) carried out under 100% relative humidity and an inert atmosphere to prevent carbonation processes. The test matrix of concern was a synthetic Portland cement matrix containing a mixture of metal oxides (i.e., arsenic, cadmium, copper, lead, and zinc). The impact of intermittent wetting was studied by comparison of (1) continuous tank leaching with (2) leaching having interspersed periods of storage under 100% relative humidity and an inert atmosphere. Interpretations of constituent release were conducted by simulation of leaching and nonleaching periods using adjustment of the (1) diffusion model for sodium and chloride (i.e., highly soluble species), and (2) coupled dissolution-diffusion model for arsenic, cadmium, and lead whose solubilities exhibit a strong dependence on pore water pH. Results showed that t.
Co-Design for A64FX Manycore Processor and ”Fugaku”

We have been carrying out the FLAGSHIP 2020 Project to develop the Japanese next-generation flagship supercomputer, the Post-K, recently named “Fugaku”. We have designed an original many core processor based on Armv8 instruction sets with the Scalable Vector Extension (SVE), an A64FX processor, as well as a system including interconnect and a storage subsystem with the industry partner, Fujitsu. The “co-design” of the system and applications is a key to making it power efficient and high performance. We determined many architectural parameters by reflecting an analysis of a set of target applications provided by applications teams. In this paper, we present the pragmatic practice of our co-design effort for “Fugaku”. As a result, the system has been proven to be a very power-efficient system, and it is confirmed that the performance of some target applications using the whole system is more than 100 times the performance of the K computer.
Can I get your (robot) attention? Human sensitivity to subtle hints of human-likeness in a humanoid robot's behavior

Designing artificial agents that can closely imitate human behavior, might influence humans in perceiving them as intentional agents. Nonetheless, the factors that are crucial for an artificial agent to be perceived as an animated and anthropomorphic being still need to be addressed. In the current study, we investigated some of the factors that might affect the perception of a robot's behavior as human-like or intentional. To meet this aim, seventy-nine participants were exposed to two different behaviors of a humanoid robot under two different instructions. Before the experiment, participants' biases towards robotics as well as their personality traits were assessed. Our results suggest that participants’ sensitivity to human-likeness relies more on their expectations rather than on perceptual cues.
Context Recognition by Wireless Sensing: A Comprehensive Survey

: Context recognition is a topic that has garnered considerable interest in the ubiquitous and pervasive computing research community. A wide variety of Internet-of-things devices with micro-electromechanical system (MEMS) sensors are used to obtain sensor data (e.g., acceleration, vibration, and sound) related to target contexts. However, devices for context recognition also have limitations such as deployment cost, battery maintenance cost, and the requirement for wearing / carrying the devices. To solve this problem, wireless sensing has attracted the attention of many researchers because it enables device-free and / or maintenance-free context recognition. In this study, we will comprehensively review studies on context recognition by wireless sensing, focusing on WiFi channel state information (CSI), radio-frequency identiﬁcation (RFID), and backscatter. We will also discuss the design choices of wireless sensing with their pros and cons through a review of the state-of-the-art.
Crowdsourcing to Analyze Belief Systems Underlying Social Issues

People’s beliefs and attitudes about social and scientiﬁc issues, such as capital punishment and climate change, appear to form complex but generally coherent networks. Understanding the nature of these networks is a prerequisite for designing interventions for changing beliefs on the basis of rational arguments and evidence. It is therefore important to develop methods to represent and analyze the form and nature of belief networks, which may not be explicitly verbalizable. Adopting an emerging approach that utilizes crowdsourcing to develop educational interventions, we mined discussions from the Reddit forum Change My View to determine which beliefs and types of information underlie people’s attitudes about capital punishment. By combining computational analyses based on a topic model with more qualitative assessments of the extracted topics, we found that moral arguments are more prevalent than statistical or data-based arguments. The present study serves as a test case for the open sourced software crowdpy , a Python toolkit for running naturalistic studies on the web, which will enable other researchers to use crowdsourcing in their research. This approach sets the stage for research exploring potential interventions to change people’s beliefs.
Discovering Conceptual Hierarchy Through Explicit and Implicit Cues in Child-Directed Speech

In order for children to understand and reason about the world in a mature fashion, they need to learn that conceptual categories are organized in a hierarchical fashion (e.g., a dog is also an animal). The caregiver linguistic input can play an important role in this learning, and previous studies have documented several cues in parental talk that can help children learn a conceptual hierarchy. However, these previous studies used different datasets and methods which made difficult the systematic comparison of these cues and the study of their relative contribution. Here, we use a large-scale corpus of child-directed speech and a classification-based evaluation method which allowed us to investigate, within the same framework, various cues that varied radically in terms of how explicit the information they offer is. We found the most explicit cues to be too sparse or too noisy to support robust learning (though part of the noise may be due to imperfect operationalization). In contrast, the implicit cues offered, overall, a reliable source of information. Our work confirms the utility of caregiver talk for conveying conceptual information. It provides a stepping stone towards a cognitive model that would use this information in a principled way, possibly leading to testable predictions about children's conceptual development.
Students Perception about the Use of Jupyter Notebook in Power Systems Education

The purpose of this study was to examine students’ computer attitudes and experience, as well as students’ perceptions about the use of Jupyter notebook in the Electrical Engineering context, specifically in teaching Power Systems. Forty-five power systems students in the department of Electrical Engineering Science at the University of Johannesburg took part in this study. Results suggest that students hold favorable computer attitudes and perception about the use of the Jupyter notebook in power systems education. Furthermore, the feedback from respondents can help software developers make informed decision about aspects of the application to improve.
A Computational Analysis of the Constraints on Parallel Word Identification

The debate about how attention is allocated during reading has been framed in as: Either attention is allocated in a strictly serial manner, to support the identification of one word at a time, or it is allocated as a gradient, to support the concurrent processing of multiple words. The first part of this article reviews reading models to examine the feasibility of both positions. Although word-identification and sentence-processing models assume that words are identified serially to incrementally build larger units of representation, discourse-processing model allow several propositions to be co-active in working memory. The remainder of this article then describes an instance-based model of word identification, Über-Reader, and simulations comparing the identification of single words and word pairs. These simulations indicate that, although word pairs can be identified, accurate identification is restricted to short high-frequency words due to the computational demands of both memory retrieval and limited visual acuity.
Modeling Gestalt Visual Reasoning on Raven's Progressive Matrices Using Generative Image Inpainting Techniques

Psychologists recognize Raven’s Progressive Matrices as a useful test of general human intelligence. While many computational models investigate various forms of top-down, deliberative reasoning on the test, there has been less research on bottom-up perceptual processes, like Gestalt image completion, that are also critical in human test performance. In this work, we investigate how Gestalt visual reasoning on the Raven’s test can be modeled using generative image inpainting techniques from computer vision. We demonstrate that a reasoning agent that has access to an off-the-shelf inpainting model trained only on photorealistic images of objects achieves a score of 27/36 on the Colored Progressive Matrices, which corresponds to average performance for nine-year-old children. We also show that when our agent uses inpainting models trained on other datasets (faces, places, and textures), it does not perform as well. Our results illustrate how learning visual regularities in real-world images can translate into successful reasoning about artiﬁcial test stimuli. On the ﬂip side, our results also highlight the limitations of such transfer, which may contribute to explanations for why intelligence tests like the Raven’s are often sensitive to people’s individual sociocultural backgrounds.
Modeling word interpretation with deep language models: The interaction between expectations and lexical information

How a word is interpreted depends on the context it appears in. We study word interpretation leveraging deep language models, tracing the contribution and interaction of two sources of information that have been shown to be central to it: context-invariant lexical information, represented by the word embed-dings of a model, and a listener’s contextual expectations, represented by its predictions. We deﬁne operations to combine these components to obtain representations of word interpretations. We instantiate our framework using two English language models, and evaluate the resulting representations in the extent by which they reﬂect contextual word substitutes provided by human subjects. Our results suggest that both lexical information and expectations codify information pivotal to word interpretation; however, their combination is better than either on its own. Moreover, the division of labor between expectations and the lexicon appears to change across contexts.
Abstract strategy learning underlies flexible transfer in physical problem solving

strategy learning underlies flexible transfer in physical problem solving Kelsey R. Allen krallen@mit.edu Brain and Cognitive Sciences Massachusetts Institute of Technology Kevin A. Smith k2smith@mit.edu Brain and Cognitive Sciences Massachusetts Institute of Technology Ulyana Piterbarg upiter@mit.edu Brain and Cognitive Sciences Massachusetts Institute of Technology Robert Chen robertcc@mit.edu Brain and Cognitive Sciences Massachusetts Institute of Technology Joshua B. Tenenbaum jbt@mit.edu Brain and Cognitive Sciences Massachusetts Institute of Technology
Forming Concepts of Mozart and Homer Using Short-Term and Long-Term Memory: A Computational Model Based on Chunking

A fundamental issue in cognitive science concerns the mental processes that underlie the formation and retrieval of concepts in the short-term and long-term memory (STM and LTM respectively). This study advances Chunking Theory and its computational embodiment CHREST to propose a single model that accounts for significant aspects of concept formation in the domains of literature and music. The proposed model inherits CHREST’s architecture with its integrated STM/LTM stores, while also adding a moving attention window and an “LTM chunk activation” mechanism. These additions address the overly destructive nature of primacy effect in discrimination network based architectures and expand Chunking Theory to account for learning, retrieval and categorisation of complex sequential symbolic patterns – like real-life text and written music scores. The model was trained through exposure to labelled stimuli and learned to categorise classical poets/writers and composers. The model categorised previously unseen literature pieces by Homer, Chaucer, Shakespeare, Walter Scott, Dickens and Joyce, as well as unseen sheet music scores by Bach, Mozart, Beethoven and Chopin. These findings offer further support to mechanisms proposed by Chunking Theory and expand it into the psychology of music.
This manuscript has been accepted for publication in the Journal of the Association for Information Systems, 9 Oct 2020. © 2020 Association for Information Systems ONLINE CHILD SEXUAL EXPLOITATION: A NEW MIS CHALLENGE

This paper deals with the difficult yet increasingly important MIS phenomenon of online child sexual exploitation (online CSE). Through the use of secondary and publicly available data from the Federal Bureau of Investigation (FBI), as well as primary data from a cybercrime police unit in the United Kingdom, this study takes a grounded theory approach and organises the role that technologies and social actors play in shaping online CSE. The paper contributes to IS theory by providing a consolidated model for online CSE, which we label as the Technology and Imagery Dimensions Model (TIDM). This combines the staging of the phenomenon and the key dimensions that depict how the use of technology and imagery both fuels and defuses the phenomenon. In informing the construction of the model, the paper extracts, organises, and generalises the affordances of technology and discusses the role of information systems in detecting online CSE.
Simulating Feature- and Relation-Based Categorisation with a Symbolic-Connectionist Model

Participants in Goldwater et al. (2018) reported using either feature-or relation-based strategy during a series of category learning tasks. A computational modeling study was conducted to investigate whether performance on Experiments 1 and 2 of Goldwater et al. (2018) might be explained by the assumption that participants used either feature-or relation-based representational encoding during learning. Human participant s’ and model performance are compared and implications are discussed.
Atmospheric‐Pressure Plasma Deposited Epoxy‐Rich Thin Films as Platforms for Biomolecule Immobilization—Application for Anti‐Biofouling and Xenobiotic‐Degrading Surfaces

In this work, an atmospheric-pressure dielectric barrier discharge process is exploited for the fast deposition of adherent epoxy-rich layers acting as a versatile platform for the efficient one-step biomolecule immobilization in mild aqueous conditions. Particular attention is given to the influence of the plasma process parameters on the chemical and morphological properties of the deposited layers and on their subsequent exploitation for chemical interfacial reactions. As a proof-of-concept, two enzymes with drastically different biological properties, namely dispersin B and a laccase, are immobilized onto functionalized metallic surfaces. The pH of the enzyme solution appears as a key parameter to control the amount of immobilized enzyme on the plasma functionalized surfaces, thus leading to bioactive surfaces with improved stability and activity.
Mannich-type reactions in water using anionic water-soluble calixarenes as recoverable and reusable catalysts

Mannich-type reactions in water can be carried out without the need for any added surfactants and organic solvents. The water-soluble calix[4]resorcinarene sulfonic acid, 2,8,14,20-tetramethyl-5,11,17,23-tetrakis(sulfomethyl)calix[4]resorcinarene, which functions as an efficient inverse phase-transfer catalyst, can be recovered as an aqueous solution and recycled after the simple extraction of the water-insoluble products.
The relationship of irisin with metabolic syndrome components and insulin secretion and resistance in schoolchildren

Abstract Irisin, a novel myokine, is believed to be the crucial factor in converting white adipose tissue to beige adipose tissue. For this paper, we studied the relationship among irisin and components of metabolic syndrome (MetS), and insulin secretion and resistance in schoolchildren of Taiwan. Subjects receiving routine annual health examination at elementary school were enrolled. Demographic data, anthropometry, MetS components, irisin, and insulin secretion and resistance were collected. Subjects were divided into normal, overweight, and obese groups for evaluation of irisin in obesity. Finally, the relationship between irisin and MetS was analyzed. There were 376 children (179 boys and 197 girls), aged 10.3 ± 1.5 years, were enrolled. In boys, irisin levels were not associated with body mass index percentile, body fat, blood pressure, lipid profiles, insulin secretion or resistance. After adjusting for age, the irisin level in boys was negatively related to fasting plasma glucose (FPG) (r = –0.21, P = .006). In girls, after adjusting for age, the irisin levels were positively related only to FPG (r = 1.49, P = .038). In both genders, irisin levels were similar among normal, overweight, and obese groups, and between subjects with and without MetS. The irisin levels were not associated with MetS in either boys or girls. In girls, circulating irisin levels have a nonsignificant declining trend in overweight and obese girls. However, irisin levels were negatively related to FPG in boys and positively related to FPG in girls. The contrary relationship between irisin and FPG in boys and girls needs further exploration.
A protocol of systematic review

Background: The purpose of this study is to explore the association between extravascular lung water (EVLW) and prognosis of sepsis (PS). Methods: We will carry out comprehensive literature search in electronic databases (PUBMED/MEDLINE, EMBASE, CENTRAL, WorldSciNet, PsycINFO, Allied and Complementary Medicine Database, CBM, and CNKI) and additional sources. All electronic databases will be searched from their initial to the present without language restrictions. Case-controlled studies reporting the association between EVLW and PS will be evaluated for inclusion. Outcomes of interest will include mortality rate, extravascular lung water index, pulmonary vascular permeability index, blood lactate clearance, oxygenation index, blood gas analysis, PaO2/FiO2, cardiac output index, global end diastolic volume index, intrathoracic blood volume index, systemic resistance index, acute physiology and chronic health scoring system II, and infection-related organ failure scoring system. Study quality will be evaluated using Newcastle-Ottawa Tool, and statistical analysis will be performed utilizing RevMan 5.4 software. Results: This study will summarize the most recent evidence to investigate the association between EVLW and PS. Conclusions: The results of this study will provide an exhaustive view of the association between EVLW and PS. Study registration OSF: osf.io/vhnxw. Abbreviations: CCSs = case-controlled studies, CI = cardiac output index, EVLW = extravascular lung water, PS = prognosis of sepsis.
Measuring memory integration: A metric tapping memory representation rather than inference

Our ability to link related events could be supported either by connecting their representations in memory, or by storing them separately but integrating their content when later drawing inferences. Here, we adapted classic memory contingency analyses to develop and validate an integration index designed to tap stored representations. We conducted three preregistered experiments adopting this metric. We found positive recall dependency for associations experienced both within the same and across different events. Compared to a conventional inference test, we found that recall dependency was more sensitive to a manipulation of memory integration. Leveraging recall dependency to investigate individual differences revealed that better memory for contextual detail was associated with faster inference judgments, consistent with high-fidelity representations of related memories—but only for people who tended to store memories separately. Our approach, thus, provides an important tool to illuminate how related events are represented in memory.
Children hear more about what is atypical than what is typical

How do children learn the typical features of objects in the world? For many objects, this information must come from the language they hear. However, language does not veridically reflect the world: People are more likely to talk about atypical features (e.g., "purple carrot") than typical features (e.g., "orange carrot"). Does the speech children hear from their parents also overrepresent atypical features? We examined the typicality of adjectives produced by parents in a large, longitudinal corpus of parent-child interaction. Across nearly 2000 unique adjective–noun pairs, we found parents’ adjectives predominantly mark atypical features of objects, although parents of very young children are relatively more likely to comment on typical features as well. We then used vector space models to show that learning the typical features of common categories from linguistic input alone is challenging even with sophisticated statistical inference techniques.
Investigating the Role of Verb Frequency in Factive and Manner-of-speaking Islands

Frequency plays a central role in human cognition, and in language processing in particular. There is growing evidence that acceptability judgements are shaped by the statistics of the input. In this paper, we focus on a type of constraint operative in long-distance dependencies (e.g. wh-questions, relative clauses, topicalizations, etc.) which has been claimed to result from verb subcategorization frequency effects. We take a closer look at this hypothesis, and conclude that it does not account for the sentence acceptability contrasts. Rather, the evidence we find suggests that the acceptability of these dependencies hinges on clause-level semantic-pragmatic factors.
Runtime-Guided ECC Protection using Online Estimation of Memory Vulnerability

Diminishing reliability of semiconductor technologies and decreasing power budgets per component hinder designing next-generation high performance computing (HPC) systems. Both constraints strongly impact memory subsystems, as DRAM main memory accounts for up to 30 to 50 percent of a node’s overall power consumption, and is the subsystem that is most subject to faults. Improving reliability requires stronger error correcting codes (ECCs), which incur additional power and storage costs. It is critical to develop strategies to uphold memory reliability while minimising these costs, with the goal of improving the power efficiency of computing machines.We introduce a methodology to dynamically estimate the vulnerability of data, and adjust ECC protection accordingly. Our methodology relies on information readily available to runtime systems in task-based dataflow programming models, and the existing Virtualized Error Correcting Code (VECC) schemes to provide adaptable protection. Guiding VECC using vulnerability estimates offers a wide range of reliabilityredundancy trade-offs, as reliable as using expensive offline profiling for guidance and up to to 25% safer than VECC without guidance. Runtime-guided VECC is more efficient than a stronger uniform ECC, reducing DIMM lifetime failure from 1.84% down to 1.26% while increasing DRAM energy consumption by only 1.03×.