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Generated artificial general intelligence--the philosophical principle of artificial general intelligence and give an example
No full textIn recent years, with the development of brain science, neuroscience and cognitive science, artificial intelligence technology has made a series of achievements.however, it still fails to achieve the human level of universal artificial intelligence, and the cognitive structure and consciousness are still unsolved mysteries.this paper integrates the evolutionary laws of the universe, life and thinking, summarizes a model of generated general intelligence and reveal its philosophical principle and algorithm structure, then calculates the functions of thinking and consciousness one by one.the results show that the model and its based principles and algorithms conform to the essential characteristics of biology, physics, neuroscience, cognitive science and philosophy of intelligent species. It is a first implementation model of artificial general intelligence that truly simulates human intelligence.this paper reveals the essence of cognition, thinking and consciousness, which is the first time to find the operation mode of the last mysterious zone that human brain, and also the first answer to the origin of philosophy and the mystery of human mind since human history
Sustainable Assessment of Alternative Sites for the Construction of a Waste Incineration Plant by Applying WASPAS Method with Single-Valued Neutrosophic Set
No full textThe principles of sustainability have become particularly important in the construction, real estate maintenance sector, and all areas of life in recent years. The one of the major problem of urban territories that domestic and construction waste of generated products cannot be removed automatically
Experimental data and software for: Defaults are a double-edged sword in Common Pool Resource governance - An experimental approach
No full text<p>Experimental data and software for the paper: <span>Defaults are a double-edged sword in Common </span><span>Pool Resource governance - An experimental </span><span>approach</span></p>
<p>The experiment consisted in three treatments of the Common Pool Resource Dilemma, where three default interventions were applied: pro-social, self-serving and no default. Plus, the participants had to complete an SVO task and a Risk assessment task.</p>
<h4>Description of the data and file structure</h4>
<p>In the file called <code>all_participants.csv</code> is the full dataset of all participants that took part of the experiment. This includes participants who will end up excluded and dropouts.</p>
<p>The experimental data files come in two formats: wide and long. The wide version, called <code>data_wide_format.csv</code> contains one row per participant and a column for all the fields, including rounds from 1 to 10 of the CPR task. Also, this file includes all demographic information of the participants, times and payments. The ID shown is generated internally and has no relationship with the participants' Prolific ID.</p>
<p>The long version, called <code>data_long_format.csv</code>, contains 10 rows per participant, and columns for the extraction and other variables necessary for analysis. This version contains the necessary data to reproduce all the figures and statistics detailed in the main manuscript.</p>
<p>In both of the previous files, the participants taken into account were the ones who completed the whole experiment. Those who did not complete the comprehension test, dropped out or did not sign the Informed Consent Form were excluded from the experimental data used. More details in the Methods below.</p>
<p>The file "Instructions of the experiment.pdf" contains the instructions of the experiment as shown to participants, also screenshots of the platform.</p><p>The data used in this paper was collected through a behavioural experiment, in which they had to complete three tasks. Task 1 is a Social Value Orientation measure test from Murphy et al. and task 2 was a Risk Assessment measure developed by Eckel and Grossman with the values used by Dave and Eckel. We designed the experiment so we can link extraction behaviour in the CPR in task 3 with their interpersonal preferences of resource allocation and perceived risk of tasks 1 and 2.</p><p>For the main task, or task 3, they played a version of the Common Pool Resource (CPR) dilemma game. This dilemma encapsulates what it is like to have a shared, finite resource and appropriators that extract this resource for financial payoff. The payoff is determined by the extraction made as a group, too much group extraction and the resource will deplete (payoff of zero to everyone) or it can be sustainably utilised, where the resource yields the most payoff for the group. The payoff for participants will be proportional to their extraction, this means that they will get more if they extract a bigger share of the group extraction, hence the participants will be incentivized to extract more.</p><p>In this experiment, we propose a variant of this game where participants have to extract at least one and maximum 30 units (or token, as we call it in the experiment), from the CPR each round. Participants can aim to maximize their individual payoffs by extracting more from the CPR, or they can aim to have a group maximum by coordinating their strategies to do so. The currency used in this experiment is ECU (Experimental Currency Unit). Each ECU is converted to U.K. Pounds (£) with an exchange rate of or 100 ECUs = £1, or 1 ECU = £0.01. The participants for this paper were recruited using Prolific www.prolific.com. Prolific requires a minimum of £6 per hour on average, and our experiments took on average 27 minutes, so we gave £3 as a fixed fee to participants and a variable amount of £6 which represented by the earnings in the experiment, which they could vary depending on their decisions, paid as a bonus at the end of the experiment.</p><p>To help with their decisions, the participants have the following information at all times:</p><ul><li>The round number.</li><li>A timer with two minutes.</li><li>The participant's extraction and payoff in the previous round</li><li>The total group extraction in the previous round</li><li>An interactive sandbox where they can simulate their and others' payoffs and a table with all the possible group extractions, and the ECU's produced.</li></ul><p>Additionally, the participants knew the total number of participants in each group (4), the number of rounds for this task (10) and the maximum time to make a decision (2 minutes). Also, after every round, the participants know how much the other three participants extracted in the previous round, and they are notified if a co-player dropped out of the experiment, in which they continued playing and got paid, although we excluded all data from participants that finished the ten rounds in a group of 4. All participants had to complete a comprehension test for task 3, in which they were given five attempts to get the five questions correct. If they could not complete this test, they were paid for the earnings in the previous two tasks.</p><p>For this implementation, we will use a group of 4 participants, and the treatments will consist in the manipulation of the default extraction, this means that instead of participants choosing how much to extract, an amount of tokens from the shared resource will be already chosen, and the participants have the possibility to override this value.</p><p> </p><p>The values were chosen based on the social optimum and the individualistic extraction:</p><ul><li>Social optimum: <i>xf</i>=a/2<i>bn</i> where <i>n</i> is the group size.</li><li>Individualistic extraction: x<i>h</i>=a/<i>bn</i></li></ul><p> </p><p>In our case, with the chosen parameters <i>xf</i>=11.5 and <i>xh</i>=23, we rounded x<i>f</i> to 11 because participants can only pick an integer from the user interface. If, in any given round,<i>xf</i> is universally chosen, i.e., the group extraction is 44, the CPR will yield its maximum. This also means that participants can get more if they stick with this extraction. However, players will be enticed to pick a higher extraction to get a higher share of the group extraction. When the group extraction reaches 92, or <i>xh</i> is chosen by everyone, the group payoff reaches zero for everyone. The group payoff depending on the level of extraction can be found in Figure labeled "group_extraction_payoff," and the amount that is given back to the players if everyone picks a certain default value.</p><p>Participants are subjected to one of the 3 treatments (control, Self-serving, or Pro-social):</p><p><strong>Control - No default value, </strong><i><strong>n</strong></i><strong>=100:</strong> in this control treatment, participants will play the CPR without default extractions for 10 rounds.</p><p><strong>Pro-social treatment (Pro-social), </strong><i><strong>n</strong></i><strong>=156:</strong> in this treatment, participants are shown a default value representing the social optimum value <i>xf</i> with a label that reads: "Your extraction this round: 11". At each round, each participant has the option to override this value and choose another extraction for themselves. No notion of "fairness" or "fair extraction" is communicated to the participants.</p><p><strong>Self-serving treatment (Self-serving), </strong><i><strong>n</strong></i><strong>=156:</strong> in this treatment, participants are shown a default value representing the individualistic value <i>xh</i> with a label that reads: "Your extraction this round: 23". At each round, each participant has the option to override this value and choose another extraction for themselves. No notion of "individualistic" or "selfish" is communicated to the participants.</p><p>To change the default extraction, participants have to click a button below the extraction and pick a value from a drop-down menu. To measure persistence, in both treatments (Pro-social and Self-serving), participants will be subjected to the default value manipulation for 5 rounds, while in the subsequent 5 rounds, no default option will be presented, i.e., participants have to manually pick the extraction they desire. Participants in treatment Pro-social did not participate in treatment Self-serving nor in the control treatment, and vice versa.</p><p><strong>The experimental data used in this paper are from participants in groups of four who completed all ten rounds, passed the comprehension test, and successfully submitted their work on Prolific. Excluded are those who didn't sign the Informed Consent Form, dropped out, failed the comprehension test, or did not act within the designated time for each round. Participants with group drop-outs are also excluded, even if they finished the experiment themselves.</strong></p><p>All experiments described in this paper followed the guidelines and regulations of data protection and experiments with human participants and were approved by the Ethical Commission for Human Sciences at the Vrije Universiteit Brussel in Brussels, Belgium (ECHW2015_3). Also, all participants who took part in the experiment signed an Informed Consent form for the use of the data collected in the experiment, including decisions and background information, including gender and age. Without signing this form, they could not proceed with the experiment. This experiment design and its hypotheses were pre-registered in OSF: <a href="https://osf.io/gpcnr/">OSF Link</a>.</p><p>For the first task of the experiment, we assessed participants' resource allocation preferences using the Social Value Orientation measure (SVO) referenced in Lange et al. (1999) and the Slider measure by Murphy et al. (2011, 2013). Participants made allocation choices for themselves and another participant, with their role randomly assigned as active or passive for incentivisation. Active deciders were paid their allocated amount, while passive participants received choices from active participants (picked at random). Their payoff was the average of the main six measures (Murphy et al., 2011). Their resulting SVO was represented as an angle from the origin, where a higher angle means greater cooperativeness, and lower angles indicated individualistic or competitive allocations. For an easier analysis, we grouped subjects with SVO angles < 22.45 degrees as "Individualistic+Competitive" and SVO >= 22.45 degrees as "Cooperative+Altruistic" (as done in other works, Roch et al., 1997; Liebrand, 1986) in some sections of the document instead of using the continuous angle.</p><p>In the second task, we implemented a risk-taking measure as done by Eckel and Grossman (2002), with the values from Dave et al. (2010). Participants had to decide between six different gambles, from the least risky to the most risky, of an event happening with a 50% chance. A coin was flipped to pick which of the events would pay them, and that was their payoff for their task. The resulting measure is a discrete range from 1 to 6, where 1 is the safest and 6 is the riskiest.</p>
grp-bork/metadrugs_figures: Release with input data
No full text<p>No description provided.</p>
<p>This repository contains input data and notebooks to plot the figures associated with the manuscript</p>
<p><em>"Combinatorial, additive and dose-dependent drug-microbiome associations"</em></p>
<p>Authors: Sofia K. Forslund*, Rima Chakaroun*, Maria Zimmermann-Kogadeeva*, Lajos Markó*, Judith Aron-Wisnewsky*, Trine Nielsen*, <...> The MetaCardis Consortium, <...> Jens Nielsen, Fredrik Bäckhed, S. Dusko Ehrlich, Marc-Emmanuel Dumas, Jeroen Raes, Oluf Pedersen, Karine Clément, Michael Stumvoll, Peer Bork.</p>
<p>Code contributions by: Sofia K. Forslund, Lucas Moitinho-Silva, Thomas S. B. Schmidt, Till Birkner, Maria Zimmermann-Kogadeeva.</p>
grp-bork/metadrugs_figures: Release of metadrugs_figure scripts and input data
No full text<p>This repository contains input data and notebooks to plot the figures associated with the manuscript</p>
<p><em>"Combinatorial, additive and dose-dependent drug-microbiome associations"</em></p>
<p>Authors: Sofia K. Forslund*, Rima Chakaroun*, Maria Zimmermann-Kogadeeva*, Lajos Markó*, Judith Aron-Wisnewsky*, Trine Nielsen*, <...> The MetaCardis Consortium, <...> Jens Nielsen, Fredrik Bäckhed, S. Dusko Ehrlich, Marc-Emmanuel Dumas, Jeroen Raes, Oluf Pedersen, Karine Clément, Michael Stumvoll, Peer Bork.</p>
<p>Code contributions by: Sofia K. Forslund, Lucas Moitinho-Silva, Thomas S. B. Schmidt, Till Birkner, Maria Zimmermann-Kogadeeva.</p>
Hub genes in a pan-cancer co-expression network show potential for predicting drug responses
No full textDataset 1. Gene co-expression network data. It contains network nodes, weighted network and list of hubs.
Dataset 2. qPCR data from independent validation, including MIQE and additional information.
Dataset 3. Supplementary Figures. Legends are included under each figure.
Dataset 4. Full qPCR data (including raw Cq values)
Aarhus Stiftstidende 1938-1945
No full text<p>This collection contains the OCRed content of the Aarhus Stiftstidende newspaper from 1938-1945 in .txt format. The dataset differs from the original publication. The images have been removed and text collated into monthly documents to facilitate text analysis and distant reading in the Introduction to Archives and Digital Methods course at Aarhus University in 2021</p>
<p>The teaching data are available in two versions, with less and more reduced file size: </p>
<ul>
<li>stiften_monthly_raw where each file represents a month of newspaper content within the 1938-1945 period (large files)</li>
<li>stiften_monthly_cleansed with monthly text files whose content is thinned down so as to support larger-scale analysis in Voyant (less large files) </li>
</ul>
DatasetOpen The dataset of Synoptic Features of the Regional Daily Extreme Precipitation over the Yangtze River Delta during the Summer Seasons of 1979-2021
No full text<p>The dataset contains the SOM results, the start and end dates of the Meiyu season, and the condition of extreme precipitation stations per day during the summer seasons of 1979-2021.</p>
