Input: Claim: Solar panels and wind turbines work efficiently in any weather condition. [SEP] Text: 026 DOI: 10. 7717/peerjcs. 1108/table-4 As shown in Table 4, the absolute value of the Pearson correlation coefficient is the lowest temperature among the six influencing factors at 0. 786. The minimum temperature exerts the maximum influence on the load, and the absolute value of the Pearson correlation coefficient is the weather condition at 0. 026. Hence, weather conditions exert the minimum effect on the load. The influence of wind direction on the load is less than that of other factors. Therefore, wind direction and weather conditions are considered weak influencing factors that will be ignored in this study. The four external factors of minimum temperature, maximum temperature, whether the day is a workday, and whether the day is a holiday are important influencing factors for selecting similar days. Similar day selection August 31, 2003 is used as the date to be forecasted in this section. Data of influencing factors between the date to be forecasted and each historic day are extracted, and Python is utilized for gray correlation analysis and calculation of the gray correlation degree between each historical day and the day to be forecasted (Fig. 6). Because the Pearson correlation coefficient between the lowest temperature and the highest temperature is the largest, and it occupies the largest weight in the selection of similar days, so from the time scale, the closer the historical day to the day to be predicted, the greater the gray correlation value. Huang et al. (2021) sets the threshold to 0. 7 and achieves better prediction results. Therefore, the threshold is set at 0. 7, and the historical days greater than 0. 7 are taken to form a rough set of similar days, including a total of 134 historical days. Figure 6: Gray correlation between days to be predicted and historical days. Download full-size image DOI: 10. 7717/peerjcs. 1108/fig-6 Influencing factor data of each historic day in the rough set of the day to be predicted and similar days are extracted as the input feature dimension according to the rough set of similar days. Python is applied to perform K-means clustering. K-means clustering divides the historical days in the rough set of similar days into different categories according to the number of cluster centres. It obtains the coordinate values of the centre points of each type.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: Full text Figures and data Peer review Side by side Peer review process Decision letter Author response Peer review process This 10. 7554/eLife. 27879. 017 Author response Essential revisions: The paper is a pleasure to read. It is well written, the methods are beyond reproach, and the combination of computational modelling with behaviour, neurophysiological measures and psychopathology is to be commended. However, there were a few important issues for which we would like clarification in order to proceed. 1) Both reviewers noted that the remarkable finding (Figure 3) of a valence-dependent effect of volatility on pupil dilation did not seem to be tested statistically. There was a significant effect for losses and not gains, but we did not see a direct contrast between the two. If we've correctly understood the reported interaction in the subsection “Does Activity of the Central NE System, as Estimated by Pupil Dilation, Track the Volatility of Positive and Negative Outcomes? ”, it does not test specifically for gain/loss asymmetry but is rather analogous to the 2nd and 3rd groups of bars in Figure 2A. The volatility factor is "block volatility" (wins-volatile versus losses-volatile). So if there were merely an overall effect of outcome volatility, it would come through here as an interaction of block type x valence. This could probably be dealt with by setting up the factors as "outcome volatility" and "outcome valence" rather than "block volatility" and "outcome valence", and reporting the interaction. Secondarily, the conclusions section asserts that pupillary response was larger overall for losses (Discussion, third paragraph) but I don't see where this was tested. As suggested we have added an additional analysis to the manuscript which confirms that the difference in magnitude of the volatility effect for the win and loss outcomes was statistically significant: “Indeed a direct statistical comparison of the size of the volatility effect between the positive and negative outcomes indicated a greater effect of volatility on the negative relative to positive outcomes (outcome volatility x valance; F(1,27)=4. 34, p=0. 047). ” Please note that the main block volatility x valence effect reported in the original paper should have been p=0. 02 rather than p=0. 04, we have corrected this and apologise for the error.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: of and Technology > Vol. 13 No. 3, September 2023 Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a () Benjamin Quito, School of Computing and. DOI: 10. 4236/ojsst. 2023. 133006 123 Downloads 604 Views 94. 63% in predicting the images. Secondly, a is established to facilitate the subsequent proposal for Experimental Research, which is the third aspect. Framework incorporates the application of LiDAR and Non-LiDAR technologies mounted on a vehicle that is operated in diverse weather conditions by both a human driver and an autonomous system. tables, which are currently in a blank state, will be populated upon completion of the Experimental Research. Research aims to compare and contrast the performance of LiDAR and Non-LiDAR technologies on a vehicle driven by a human operator versus an Autonomous Vehicle. The findings of this research will be documented in the Scenario table outlined within this study, ultimately shedding light on the safety implications of implementing LiDAR and Non-LiDAR technologies within an AV context. -LiDAR Share and Cite: Quito, B. and Esmahi, L. (2023) Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework. of and Technology, 13, 101-131. doi: 10. 4236/ojsst. 2023. 133006. 1. idea of an (AV) started with Vinci when he designed a self-propelled cart that could move without being pushed [1]. Google started its project in 2009 [2]. In 2015, Tesla released its autopilot [3]. Tesla’s autopilot is not entirely AV; the feature is Autosteer that keeps the car in its current lane; the technology has four different kinds of sensors; forward radar, forward-facing camera, 360-degree ultrasonic sensors, and GPS in combination with high-resolution navigation maps designed to track individual lanes and features of the roads [4]. NHTSA further mentioned that self-driving vehicles would be integrated into U. S. roads by advancing through the six levels of driver assistance technology advancements. The six-level driver assistance technology advancements focus on who does what and when, i. e. , the driver and the systems (ADS). 1. 1.

Output: corroborate


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transaction transparency. [SEP] Text: We know how destabilizing large devaluations following failed currency boards are. The second risk is to the security of the trust account. Despite the allusion to “trust,” funds could be invested in risky or illiquid assets or encumbered as collateral. Redemption of eMoney into fiat currency may not always be possible. The third risk is to the interoperability of eMoney and thus to market contestability. We noted earlier the strong network effects in payments. If eMoney issued by different providers is not interoperable, only the largest providers will survive. The fat cats will eat the nimble and potentially more innovative mice. Even regulation mandating common technological standards will not resolve the issue. It was easier to get cell phones from different providers to talk to each other. In the case of eMoney, interoperability requires a common settlement platform — a way to seamlessly, cheaply, and securely transfer funds between trust accounts. You will not be able to redeem the esend you for fiat currency unless a corresponding amount of fiat currency is transferred from my provider’s trust account to yours. Tackling risks to eMoney—a potential role for central banks? While eMoney inexorably grows — potentially booms — in front of our eyes, major risks also rise. How do we tackle them? One way is for central banks to get involved. Other approaches are also possible; less impactful, though perhaps safer. We will face difficult choices ahead as policymakers. But we will have to make them. Central banks could offer eMoney providers access to their reserve accounts, under strict conditions, of course. Through effective supervision, central banks could check that eMoney issuance is fully backed; there goes risk number one. Moreover, eMoney holdings would become extra safe and liquid for customers, especially if reserve accounts were protected from other creditors of eMoney providers in case of bankruptcy. That would take care of risk number two, minus the hassle of claiming one’s funds. Finally, central banks would ensure interoperability between eMoney issued by different providers by offering a common settlement platform between trust accounts; down with risk number three. Mind you, other risks would be introduced. Most notably, the risk of a potential and partial disintermediation of commercial banks if some depositors preferred holding eMoney. But let us leave that for discussion.

Output: pass


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: A brief description of the above analysis has been added to the Materials and methods section of the paper: “We tested whether the volatility effect was larger for loss than win outcomes using a similar ANOVA in which block volatility was replaced by “outcome volatility” (i. e. outcome volatility is high when the volatility of a given outcome, wins or losses, is high). ” We have also added in the requested analysis confirming that pupil dilation to losses was greater than to wins: “This effect was seen on the background of a generally greater pupil dilation to receipt of a loss relative to a win (main effect of valence; F(1,27)=16. 7, p<0. 001). ” 2) Similarly, the finding that pupil dilation predicts learning rate for losses and not gains does not itself show that the correlation is stronger for one than the other (this can be tested e. g. via Fisher r-to-z transformation). Moreover, it is somewhat unclear what it means that the behavioral effect of volatility is just as strong in win vs. loss conditions but that the pupil doesn't care about wins – can the authors speculate about what this means for separate mechanisms of learning rate adjustment for non-aversive outcomes? (and how does this fit with previous data linking pupil dilation to learning). We have now conducted the Fisher r-to-z transformation analysis which confirms that the correlations are significantly greater for losses than wins: “This correlation was significantly greater for losses than for wins (Fisher r-to-z transformation z=2. 27, p=0. 02). ” We agree with the reviewers that the interpretation of this difference is not straightforward. While we also agree that the results raise the possibility of specificity in the pupillary response to negative vs. positive outcomes we think that caution is warranted in this interpretation. We have elaborated our thoughts on this in a thoroughly revised section in the Discussion of the paper (fifth and sixth paragraphs).

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: 2018 RAND Corporation, through the research of Blanar et al. [10] wrote a report on automated vehicle safety; it was found that there was no definite standard that existed in terms of AV safety, but the information presented a framework wherein the safety of AV can be tested and measured. The framework developed by Blanar et al. [10] focused on the features of Level 4 of SAE J3016 [11] version levels of driving automation. The testing was performed within pre-specified conditions. Level 4, as explained on the website [12] is where the automated system can take over the driving task in each situation. NHTSA [5] mentioned that drivers do not need to pay attention. In the research of Blanar et al. [10] safety was defined as the elimination, minimization, or management of harm to the public; this research focused on comparing AV and conventional vehicles. To further clarify, the comparison is based on the operational design domain (ODD) Blanard& Holliday [13] and ODD was described by Blanar et al. [10] in terms of geography, weather, lighting, roadway markings, previous experience on a specific roadway. Table 2 shows the integrated safety framework developed by Blanar et al. [10] wherein Column 1 is the Setting, Column 2 is the Stage, and Column 3 is the Leading Measures, and Column 4 Lagging Measures. The research can interest stakeholders such as academics, transportation regulators, transportation managers, industries, and the public. The future of logistics will be based on autonomous or semi-autonomous vehicles; this research will help develop a guide on such vehicles’ rules and regulations on the public road. Businesses inclined to use Autonomous vehicles will have an informed choice of whether to go with the technology of LIDAR or Non-LIDAR, which are currently being developed. The two-leading image-gathering technologies for AVs’ image gathering: LiDAR and Non-LiDAR, are essential for the safety of AVs. It serves as the eyes to detect objects on the roads and surroundings; it is paramount that these technologies must assure the public that such detection of objects can be used to make the driving of the AVs safe. For society to trust their lives on AVs, there must be a proper safety measurement. The public needs to know the similarities and differences between the two technologies regarding safety.

Output: corroborate


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transaction transparency. [SEP] Text: Contrarily, blockchain shows immense prospect in the sectors that do not necessarily involve digital payments but uses the characteristics of blockchain, for instance, distinctions of types(public, private, permissioned), access control (centralized, decentralized), persistency, validity, identity control, transparency control(closeness or openness) and superior security (Zheng et al. , 2018). A practical implementation of blockchain-based agri-food traceability has been shown by Caro et al. (2018). They showed how transparent, fault-tolerant, immutable, and audible tracing could be done using blockchain and IoT devices. Manupati et al. (2020) showed how the use of a distributed ledger system of blockchain reduces total cost as well as carbon emission in a multi-echelon supply chain. Blockchain has been used in storing and processing information. File security in the blockchain is far better than any other existing cloud storage system, while the transmission delay is significantly lower (Li, Wu & Chen, 2018). File loss rate in currently available cloud storage architectures can be up to 100%, where it is nearly 0% in the blockchain (Li, Wu & Chen, 2018). Decentralized storage (blockchain) of Interplanetary file system to store and share industrial spare parts data has been implemented by Hasan et al. (2020). Blockchain is one of the most secure ways of dealing with electronic transactions. However, the usage of blockchain in real-world systems that deal with raw data is shown by many researchers. Substantial security in the mobile cloud data of (EPRs) is shown by Nguyen et al. (2019) using blockchain technology. Implementing blockchain in real-world use cases shows excellent future potential, as discussed in this section. The essential idea of blockchain was to create a robust online transaction system. Blockchain’s fundamental aspects have presented so many sturdy prospects of excellent technical support over distributed systems in terms of security and transparency. However, blockchain does not have an integrated system for automatically processing the data in a distributed system. We can achieve this by integrating a smart contract with blockchain. Smart contract With the help of blockchain, we can assuredly discard intermediaries, but the promises and trust boundaries between the contributing parties frequently present the requirement of what is called a smart contract (Macrinici, Cartofeanu & Gao, 2018).

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: In summary, we suggest a) that the previous work describing increased pupil dilation for volatile relative to stable rewards suggests that the central NE system does in fact respond to the volatility of positive outcomes, b) that the smaller effect of volatility for positive outcomes in the current study may relate to fact that the volatility signal in the current study is conditioned on receipt of the outcome and the overall dilation to rewards is smaller than that to losses and finally c) this proposal may be tested in future work by varying the salience (e. g. by varying the magnitude) of positive vs. negative outcomes which would have the effect of varying the dilation to the outcomes or by testing the impact of pharmacological manipulations on win vs. loss learning rate: “The effect of outcome volatility on pupil dilation in the current study was significantly greater for loss than win outcomes with the correlation between this signal and behaviour also significantly greater for losses. […] Specifically, a greater impact of a pharmacological manipulation on learning rates for losses than wins would provide experimental evidence for a preferential role for this system in estimates of the information content of losses. ” 3) Both reviewers also noted that the framing does not quite match up with the main results. The Abstract concludes, "Humans maintain independent estimates of the information content of positive and negative outcomes". This implies people might estimate the informativeness of positive and negative outcomes as general categories, which would go a long way toward explaining affective bias. But what the paper actually shows is quite a bit narrower, that people can track the relative informativeness of two individual outcomes. It's not clear if valence has a privileged role compared to any other feature that might individuate the outcomes (and perhaps there is even a sensory confound in differences between auditory and visual characteristics of the outcomes), nor is it clear if people generalize informativeness estimates across events of the same valence. So it would be appropriate to reduce the breadth of this conclusion (which shows up in a number of places in the paper, including the next-to-last sentence of the Abstract and the first paragraph of the Discussion) and to discuss or qualify this potential limitation. We agree with the reviewers that there is an interesting outstanding question related to the level of abstraction at which the observed effect operates.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: The businesses who invest and use the technology should ensure it is safe to use in any weather conditions. Therefore, there is a need to develop a safety framework to measure the two technologies’ safety. In the research conducted by Blanar et al. [10] it was not mentioned that the AV used in the experiment uses LiDAR or Non-LiDAR. Using LiDAR on Non-LiDAR in terms of safety is essential because this will define the safety of AVs. 2. November 11, 2020, in Japan, Co. , Ltd. announced the approval of an SAE Level 3 autonomous vehicle [14]. According to the SAE International standard, a level 3 autonomous vehicle means the driver is not driving, and the automated features of autonomous driving are engaged, even though the driver is in the driver’s seat. According to the 2021. 2021, Honda made the available to the public, the first SAE Level 3 certified AV and considered the world’s first [15]. 2021, an 2. The integrated safety framework. autonomous technology and compares it to Tesla being in Level 2 in autonomous technology. 2. 1. according to Wasser [17] is an active remote sensing system, and an active system is a system that produces energy or light to quantify things on the ground. Wasser further explained that remote sensing is measuring things, not using our hands; sensors are used to obtain information regarding a landscape and record things to measure characteristics and conditions. Light is emitted from a quick-firing laser, which makes a LiDAR system; according to Wasser, this light goes to the ground, and the light bounces off obstacles like buildings or trees; the bounced light then goes back to the LiDAR sensor then records it. LiDAR sensors can achieve mapping precision of up to 1 cm horizontally (x, y), and 2 cm vertically (z) and range accuracy of 0. 5 to 10 mm relative to the sensor. As a result, they serve as particularly advantageous remote sensing equipment for mobile mapping. (Vectornav n. d. ). Furthermore, LiDAR sensors may capture several returns from a single light pulse. This is due to the fact that as the light pulses travel from the sensor, they may come into contact with many objects that will reflect the pulse, such as the leaves and branches of a tree canopy.

Output: corroborate


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transaction transparency. [SEP] Text: This claim can easily be refuted. Bitcoin is a global, decentralized, highly liquid, and pseudo-anonymous asset. Therefore, in any transaction, which requires all these properties, the benefits of using Bitcoin over other currencies are clear. Moreover, that is exactly the reason that most people do not appreciate these properties. First, most people are unaware of the damage caused by centralized monetary systems. Second, only rarely do they perform international financial transactions in large volumes. Third, most people are against anonymous transactions. However, the benefits of Bitcoin are widely recognized in the following cases: (1) Where the centralized monetary system completely collapses (e. g. , Venezuela and Zimbabwe). (2) Where the government confiscates its own citizen’s assets (e. g. , Cyprus and Saudi Arabia). (3) Among populations, which are excluded from the financial and banking systems (e. g. , bankrupted citizens and minors). (4) Among outcasted populations (e. g. , drug users and gamblers). (5) Among people who are keener on their privacy (e. g. , tax evaders and porn users). (6) Among frequent travelers/flyers. In general, it is clear that the Bitcoin technology has a clear value wherever censorship-resistance is required. Money is one example, social media is another (e. g. , ). As was mentioned above, in a market economy, when an asset is valuable to some people, then it is valuable to the entire economy. Consequently, people who do not belong to any of these populations can still regard Bitcoin as a safe haven for their money. However, the fact that Bitcoin is valuable only increases the mystery as for the origin of its value. In the previous sections, we have presented the problems in arguments, which claim that Bitcoin should be worthless. Before we present the last argument, which will lead us to the origin of the Bitcoin’s value, it is instructive to discuss incorrect explanations, from which we will learn what cannot explain the origin of the value of Bitcoin. 20. Backed by energy It is repeatedly mentioned by crypto advocates that Bitcoin is backed by proof-of-work, or, that it is backed by energy or by the mining’s electricity. In particular, such an explanation was used by John McAfee to justify his prediction of the Bitcoin price at the end of 2020 [30].

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: We have highlighted the question of effect generalisation in a new Discussion section in which we describe the outstanding questions (also related to point 3 above) and how they might be answered using different study designs: “The results of the current study provide evidence that the information content of different events can be estimated in parallel during learning, however the level of abstraction at which these estimates function is not clear. […] The intervention used in the ongoing clinical study described above involves repeatedly completing the “positive volatile” block from the current study over the course of two weeks (see; Browning et al. , 2012 for a similar design) which will provide an initial assessment of this question. ” 5) For the control experiment described in Figure 2—figure supplement 2, it would be helpful to have additional information about how noise and volatility were parameterized, and what parameters were used. In other words, what was the generative model for the outcome schedules? I also didn't understand why this was referred to as a control experiment. It seems instead like a second test of the main hypothesis: if people adjust learning based on outcome informativeness, then noise level should make a difference. Given that it doesn't, it seems like this should be interpreted as a caveat or limitation on adaptive learning (rather than as a control for an alternative explanation). We have added in details of the generative process used to determine magnitude size in this task (added to the legend of Figure 2—figure supplement 2): “This design allowed us to present participants with schedules in which the volatility (i. e. unexpected uncertainty) of win and loss magnitudes was constant (three change points occurred per block) but the noise (expected uncertainty) varied (Panel b; the standard deviation of the magnitudes was 17. 5 for the high noise outcomes and 5 for the low noise outcomes). ” We had added this task to tease apart the effects of expected and unexpected uncertainty reported in the main paper, but agree that perhaps calling this a “control” task is confusing. We have now renamed it descriptively as the “magnitude” task which is described in the main paper as an “additional” task. We have also added in an additional discussion about the results from this task and how it might be interpreted in light of the paper by Nassar and colleagues which reported an effect of expected uncertainty on reward learning using a very different task to that reported here.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: [21] indicated that when using a camera, there are problems with changing lighting and weather conditions and that the depth data using algorithms are not as accurate as the LiDAR. From the research, the researchers concluded that LiDAR and cameras on their own are not safe and that it was suggested in the 2. 4. website of Fritz. ai [22] defines image recognition as a computer vision technique that allows machines to decode and classify what they see on an image or video. This is often called image classification or image labelling; in machine learning, Du, Guo, & Simpson [23] wrote an 2. 5. neural network architectures are used in image recognition, and it is typical for the neural network to be used in different image recognition problems; it can also be tested on object detection or segmentation. Kumar [24] explained that CNN architectures are a well-known deep learning framework and the application of which ranges from computer vision to natural language processing (NLP); based on an 3. Karl Benz’s invention of the automobile in Germany in 1885/1886 [26] human drivers have consistently assumed control of vehicles. Among the paramount safety requirements in driving is the ability to visually perceive the road, discern obstacles, and maintain situational awareness, all of which necessitate the use of our visual faculties. This fundamental principle also applies to autonomous vehicles (AVs) as they, too, require a means of “seeing. ” The integration of LiDAR and Non-LiDAR technology empowers AVs to perceive the road, identify obstacles, and maintain environmental awareness. Pertinent terminologies in the realm of AVs encompass perception, localization, prediction, and decision-making [27]. The focus of this research lies specifically in the realm of perception, which enables AVs to accurately identify and categorize the objects within their visual field. In terms of data acquisition for identification and categorization purposes, the two dominant technologies employed are the Camera, which falls under the Non-LiDAR category, and the LiDAR itself. Through an extensive literature review, this research endeavour seeks to juxtapose and examine the relative merits and limitations of these two technologies. Furthermore, the research entails a comparative evaluation of proposed experimental methodologies for data collection, which will serve as inputs for an image processing model.

Output: corroborate


Input: Claim: Cryptocurrencies will completely replace traditional currency by 2030. [SEP] Text: downloadthe Chinese yuan going to replace the US dollar as the top international currency? Enes Özcan2021, Academia LettersSee full papersYuan: Towards an : A Journal of International Affairs, 2013The emergence of China as a powerhouse of the world economy has added a new dimension to the global currency market. China’s impressive economic growth and phenomenal rise in exports have given considerable strength to its currency yuan in recent years. To what extent will China push to make the yuan the world’s reserve currency has become a burning issue. Currently, the share of yuan in total world reserves is hardly 1 per cent. The reasons are obvious. The yuan is not fully convertible, the capital market is shallow, the exchange rate is not flexible and international acceptance of yuan as a store of value is non-existent. In the face of these constraints, China is showing an inclination to make the yuan an international currency and to rival the US dollar and euro. Increase in the inflow of foreign direct investment as well as outflow of investment abroad is enabling China to move towards internationalisation of yuan in the near future. Regionalisation of the yuan is gaining impo. . . downloadDownload free _rightWhat are the prospects of the Yuan becoming the world's new reserve currency? Danko GaricIndustrijadownloadDownload free _rightChina’s ‘Yuan’ in IMF: A Boon or a Bane? Dr. Amrita JashWhen the world still anticipates the decline of China faced by a new normal in economic growth concomitant with decades of debates over China’s ‘Yuan’ to be included in the elite basket of currencies, the Fund’s (IMF) acceptance of Yuan to its reserve currency basket clarifies China’s emergence in the global economic power club. Given China’s reform over the decades and its greater export-led economy and robust growth, on November 30, IMF granted yuan/renminbi with the prestigious designation of ‘Rights’ (SDR)- finally imparting global importance to renminbi to the stature of US dollar, Euro, British pound and Japanese yen.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: Given this finding we don’t think the result of our task can be taken as strong evidence for a limitation to the adaptive learning account, but rather that our task is more sensitive to differences in unexpected than expected uncertainty. This additional discussion has been added to the legend for Figure 2—figure supplement 2: “Interestingly a previous study (Nassar et al. , 2012) described a learning tasks in which a normative effect of outcome noise was seen (i. e. a higher learning rate was used by participants when the outcome had lower noise). […] Regardless of the exact reason for the lack of effect of noise in the magnitude task, it suggests that the effect described in the main paper is likely to be driven by an effect of unexpected rather than expected uncertainty. ” [Editors' note: further revisions were requested prior to acceptance, as described below. ] The reviewers both agreed that the manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below: In particular, reviewer 1 points out that the framing of the manuscript in terms of depression – given the lack of association with clinical measures – continues to miss out on an opportunity to highlight what the findings speak to more directly. In addition to the reviewer comments below, in the consultation session amongst reviewers and editor it was suggested that you could emphasize any or all of the following aspects: The study shows that people are able to adjust learning rates in a way that is more clearly indicative of the latent causes compared to previous studies in this domain (i. e. it is feasible that the typical volatility learning rate association could have arisen from a generalized response to arousal or overall changeability of the world leading to more learning writ large, but here adjustment is clearly more sophisticated and rational). Other aspects that the study is relevant to mentioned by reviewers are the known neurobiology of learning from rewards and losses more generally, loss aversion, unified scalar value codes, the relative roles of dopamine, acetylcholine and noradrenaline in this sort of task. All of these are entirely independent of any depression/psychopathology. If you feel strongly to maintain the current framing in terms of depression given that this clearly was your motivation in the first place, that would be fine as such normative approaches to prominent and important issues in psychopathology are indeed very promising and rare. But we felt that discussing/ motivating the above and below issues could further improve your paper.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Transform, a method commonly employed for isolating features of specific shapes in images, including lines, circles, and ellipses [32] was utilized to transform the object point cloud into Hough space. Subsequently, the Hough space was rasterized, transforming the electronic data into a sequence of evenly sized grids [33]. The count of accumulators in each grid was computed and fed into a CNN model for the classification of 3D objects. Moreover, the researchers developed a semi-automatic 3D object labeling tool to construct a LiDAR point cloud object labeling library. Following the initialization of the CNN model, the dataset from the object labeling library was employed to train the neural network. The outcome of this approach yielded an object classification accuracy of 93. 3% [31]. The process of gathering data, as illustrated by the research conducted by Song et al. [31] involves transforming the object point cloud into Hough space using the algorithm. Subsequently, the Hough space is Figure 1. Research concept framework. rasterized into evenly sized grids, and the count of accumulators in each grid is computed. These accumulated counts are then utilized as input for a CNN model to classify 3D objects. Additionally, a semi-automatic 3D object labelling tool is employed to construct a LiDAR point cloud object labelling library. The CNN model is initialized, and the dataset from the object labelling library is used for training the neural network. The proposed methodology demonstrates promising results in achieving accurate object classification in LiDAR-based systems. The process of gathering data based on the research of Song et al. [31]. Figure 2 shows the process of gathering data of LiDAR in preparation for the CNN input [31]. 3. 2. Non-LiDAR for of 2021, Tesla, a prominent advocate of Non-LiDAR technology, made a significant transition to a vision-only model by removing all Radar sensors from their vehicles. This decision was aimed at demonstrating the company’s belief that cameras alone are sufficient for computer vision applications. According to information from the Tesla website, the Model 3 and Model Y, manufactured in North America, became the first vehicles to rely on camera vision and neural net processing for their Autopilot self-driving capabilities [34].

Output: corroborate


Input: Claim: Cryptocurrencies will completely replace traditional currency by 2030. [SEP] Text: downloadDownload free _rightWho cares about the Chinese YuanVimal Balasubramaniam2011downloadDownload free _rightThe US in the Era of a Yuan: Implications for dollar has enjoyed a long-standing dominance as the global reserve currency, serving as a medium of exchange, store of value, and unit of account for international trade and financial transactions. However, the economic rise of China and the growing internationalization of the Chinese yuan have posed significant challenges to the US dollar's supremacy. This research paper examines the implications of a rising Chinese yuan on the global reserve currency system, highlighting the potential challenges and opportunities that arise as a result. Through an analysis of historical trends, economic indicators, and policy developments, this paper provides insights into the future trajectory of global reserve currencies and the potential shifts in the international monetary system. downloadDownload free _rightInternationalisation of the Chinese Currency, Act II: -francoisAfter two waves of important changes in the Chinese system of foreign exchange, one in July 2005, when the Chinese currency was allowed to fluctuate more widely against the US dollar, the other in June 2010, when it was then quoted against an undisclosed basket of currencies, but moreover, was allowed to be partially traded in with the invention of a 'twin' the 'offshore yuan' (CNH), a deep reform could have taken place. The creation of the CNH, allowing debt to be issued internationally in yuan, initially led to the belief that a real set of deep reforms was under way. Yet, like some reforms of the past, which were hesitant or even annulled, the path towards monetary and financial reform is encountering difficulty. The steps taken towards the 'regionalisation' or possibly the 'internationalisation' of the Chinese currency may well be taking the same route. Before the reforms have had time to come to fruition and deliver deep changes, there seems to be some indecisiveness, a sense of relative failure, or at least discovery of the limitations in a process which many, at least at the beginning, believed would be quick and decisive. This 10. 5937/industrija42-6410 = To be or renminbi: Trend in the evolution of the international monetary systemMarko MalovicIndustrija, 2014downloadDownload free _right& Journal (IBER), 2011In the past two years, the Bush Administration, members of Congress and a few senators, such as Sen. of and Sen.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: 1 IntroductionOver the last few years, the application domain of robots has expanded from installations in caged factory environments to deployments in environments shared with humans. The behavior of humans in these environments is typically guided by explicit (rules) and implicit regulations. We jointly refer to both types of regulations as social norms which represent shared standards of acceptable behavior (Lapinski & Rimal, 2005). Asides from reaching a goal, all (artificial) agents should respect the applicable social norms to assure safety and predictability (Christian, 2020). Reinforcement learning (RL) is a general framework for decision making and control. It is a training method for learning a mapping from states to actions (i. e. policy) in order to maximize a predefined reward signal. However, fully defining the desired behavior of an artificial agent by a single reward function is often a burdensome task (Amodei et al. , 2016; Russell, 2019; Everitt & Hutter, 2016). The framework of constrained reinforcement learning specifies an agent’s desired behavior by a reward function in combination with a set of hard constraints. For instance, the reward function can be expressed in terms of the distance from the goal and the time needed to reach it, complemented with a hard constraint to not crash into other vehicles. In the Markov decision process representing the environment, constraints are states or state-action pairs that in principle could be reached by a certain trajectory, but that are considered as invalid by latent information about the environment, such as social norms. In contrast with reward functions, hard constraints provide strong safety guarantees such that a constrained state will never be visited, this makes them better suited for decision making in safety-critical environments. Because social norms are often implicit and environment-specific, it is complicated to define them as a set of constraints. A promising direction is to learn social norms from human observations. To this end, recent studies have shown principles of inverse reinforcement learning (Ng & Russell, 2000) can be adopted to infer both reward function and constraints from observed trajectories (Scobee & Sastry, 2019; Malik et al. , 2021). A shortcoming of current methods is the lack of interpretability and hence verifiability of what an agent has actually learned. When learning from human observations in the wild, there is a chance that the dataset will contain trajectories of humans violating to a smaller or larger extent the social norms, e. g.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: The outcomes of this experiment will serve as the foundation for conducting a comparative analysis between LiDAR and Non-LiDAR technologies implemented in a driver-operated vehicle. The collected data will be utilized for the purpose of comparing and contrasting these technologies with an system. By examining and contrasting the performance of LiDAR and Non-LiDAR in the context of a human-driven vehicle, insights can be gained regarding their effectiveness and potential advantages when compared to autonomous driving systems. 4. 3. for The forthcoming experiment will employ the same weather conditions and scenarios as the previous one, albeit without the involvement of a human driver. The focus will be on evaluating the performance of LiDAR and Non-LiDAR technologies within an autonomous vehicle (AV) context. To facilitate this research, a collaboration with Waymo and Tesla, proponents of LiDAR and Non-LiDAR technologies respectively, is proposed. These companies possess extensive training and test data, thereby minimizing the training duration since their AVs are already trained. The experiment will be conducted under comparable conditions as those involving a human driver. Subsequently, the results from both experiments will be compared and contrasted. The primary objective is to establish a baseline for assessing whether an AV can effectively navigate around obstacles on the road, akin to the actions of a human driver. While a human driver would slow down or halt upon encountering an obstruction, LiDAR and Non-LiDAR will record obstacle data for comparison with AV performance. The proposed experimental research will adhere to the safety principles outlined in Blanar et al. ’s [10] study on Safety (RSS). These principles encompass maintaining a safe distance in front of and laterally to obstacles, respecting right-of-way, and exercising caution when encountering roadblocks or signs. Comparable to the actions of a human driver, these principles will be contrasted with AV behavior. The experimental research aims to enhance the existing Model developed in this study. The enhancements include incorporating distance and speed measurements, evaluating the time taken to stop relative to obstacle distance, utilizing GPS measurements for navigating or avoiding obstacles, determining the time required for turning or obstacle avoidance, and identifying and adhering to road signs. These enhancements will significantly contribute to the safety capabilities of AVs in the future. In summary, the proposed experimental research serves as a suggestion for the future advancement of the image processing model. The model established in this study forms a foundational framework for subsequent research.

Output: corroborate


Input: Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: Published by January 29, 2024 Purchase 10. 1515/eng-2022-0509 Download 79. 5% of the global cryptocurrency market capitalization [3]. Deep learning (DL) refers to powerful Machine learning (ML) algorithms that specialize in solving nonlinear and complex problems by exploiting large amounts of data to become efficient predictor models, and it has proven to be a reliable tool for analysis [9,10]. The study of data gathering, analysis, interpretation, forecasting, visualization, and organization is known as statistics. ML is utilized efficiently in numerous fields, including forecasting [11], real-time advertising, malware detection [12], and text-based sentiment analysis [13]. Recurrent neural networks (RNNs) have emerged as highly effective models in the field of deep learning, demonstrating remarkable performance in recent years. These models are specifically designed to tackle sequential data by leveraging a memory-based architecture [14]. In this research, we will be studying recent literature on the prediction of currency prices, which is considered a major risk factor in currency trading. The studies will be presented in detail in Section 2. We will learn about the most important risks and features of currencies, take a quick look at RNNs, make comparisons between studies, analyze the results, and discuss them. 2 Literature review This study presents a comparative analysis of published studies on predicting BTC prices using neural networks (NNs) and statistical methods. The selected publications were chosen from recent years, and the analysis was conducted by considering the frequency of the dataset, the causal association, and the methodology used in each study Dutta et al. have been using a combination of exogenous and endogenous variables to forecast the price of BTC. The authors proposed two DL models, RNN and Gated recurrent units (GRU), to make BTC price predictions. The study found that GRU was more effective in learning and training BTC price changes, and both RNN and Long-Short-(LSTM) were slower in comparison. The root mean square error (RMSE) values for the training dataset were NN 0. 02, LSTM 0. 010, and GRU 0. 010, and for the test dataset, they were NN 0. 31, LSTM 0. 024, and GRU 0. 019. The study concluded that DL models like LSTM and GRU outperform traditional ML models [15]. Jay et al.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: tailgating, accelerating when the traffic light turns orange, etc. To guarantee safety, we should be able to validate which aspects were extracted from the observations since we want to refrain an agent from picking up undesired behavior in the dataset. Although a single constraint, represented by a state-action pair, could be easily interpretable, it is difficult to obtain a general view on the environmental restrictions from a large set of constrained state-action pairs. Another limitation is the difficulty to transfer learned constraints in one environment to a similar environment, since constraints are defined in the state-action space of a particular environment. We hypothesize that defining constraints in terms of more generalized, human interpretable concepts would improve transferability. For example, when inferring constraints from a traffic environment, all states corresponding to off-road positions should be added to the set of constraints. However, when humans learn to drive, we use the concept “road” and learn a single rule which prohibits us to drive off the road. In contrast with the set of constraints defined as prohibited state-action pairs, this rule also applies in traffic environments other than the one which was used to learn the rule. Inductive logic programming (ILP) (Muggleton, 1991) offers a computational framework to induce a logic program (i. e. hypothesis) which generalizes a set of training examples. ILP also provides the possibility to specify background knowledge as a logic program. Because the induced hypothesis is comprehensible by humans and providing background knowledge allows us to introduce human concepts, ILP could be a worthy alternative for IRL and constraint inference. However, ILP requires both positive and negative examples while we consider observations only consist of positive examples. Negative examples correspond to anomalous behavior which is rarely observed in real-world environments or would be dangerous to collect. If the observations contain anomalous behavior, this will be reflected in the induced rules hence can be detected. In this paper, we combine ILP and constraint inference and propose a hybrid method to infer restrictive rules (which represent social norms) from observations (positive examples) and represent them by a logic program. The presented method is an iterative procedure, where each iteration consists of an inference and an induction stage. During inference, a constraint is inferred from the observed trajectories and added to a set of constraints.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: While the model primarily focuses on image identification—an essential attribute for AV safety—the suggested enhancements will bolster AV safety measures. The results obtained from the developed image processing model will serve as the groundwork for formulating the safety framework for AVs. However, these outcomes alone are insufficient to instill trust and confidence among drivers, pedestrians, and society at large. Since LiDAR and Non-LiDAR technologies serve as the “eyes” of AVs, similar to human vision, it is crucial to compare the reactions of AVs with those of human drivers based on established rules. This comparative analysis will aid in establishing a certain level of trust in AVs. The tables presented within the safety framework will provide tangible and verifiable data, facilitating a clear comparison and contrast between the two technologies through the developed image processing model in this research. 5. the introductory section, the study provided an overview of different LiDAR and Non-LiDAR technologies, as well as an exploration of Technologies and the Framework on Vehicle Safety. The background section examined existing research on the two prominent technologies in Automated Vehicles, namely LiDAR and Non-LiDAR, and highlighted current advancements in the field. Additionally, insights into models and architectures, including Networks (CNNs), were discussed. The methodology section established the research framework and presented the theoretical background on with a specific focus on Image Processing. Furthermore, the Model was elaborated upon, involving the development of an algorithm to identify input images from both LiDAR and Non-LiDAR technologies. Subsequently, the images collected from the two technologies were fed into the algorithm for prediction. Given that the algorithm employed a supervised learning model, the images were appropriately labeled, enabling accurate measurement of input accuracy for each technology. The collected images were then organized into tables based on weather conditions, and each technology underwent 25 runs under various weather conditions. The results from these 25 runs were averaged and compiled into tables, facilitating a comprehensive comparison and contrast between the two technologies. The image processing model developed in this research served as the foundation for data collection from both technologies using a vehicle driven by a human driver in a simulated environment. The same tests and scenarios outlined in the were also conducted with (AVs). The resulting data was subsequently compared and contrasted, taking into account the safety framework known as Safety (RSS).

Output: corroborate


Input: Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: introduced layer-wise randomness into the activations of observed features in multilayer perceptron (MLP) and LSTM to simulate market volatility. Their best-case trial, which used 23 features and a window size of 7 days, resulted in an average improvement of no more than 5% in (MAPE) [16]. Othman et al. used LSTM to create a model for predicting the BTC stock market. The study concludes by discussing future work based on the methodology used and techniques and tools employed to predict BTC’s price on Yahoo Finance’s stock market. According to their prediction, BTC’s price will exceed USD 12,600 with an RMSE of 288. 59866 in the days following the prediction [17]. Cavalli and Amoretti have introduced a new method for predicting BTC trends using the 1D convolutional neural network (CNN). Their method includes combining social media data, blockchain transaction history, and financial metrics to construct datasets, and using a cloud-based system to collect and analyze large amounts of data. The 1D CNN model they created achieved a prediction accuracy of 74. 2%, which is reported to be better than LSTM models. They also presented a trading strategy that maximizes profit during bullish trends and minimizes losses during bearish trends based on their model. Their contribution to the field of BTC trend prediction is significant [18]. Yang suggested a method that combines (EEMD) and LSTM to investigate the subject of BTC price forecasting for the following day. Using the EEMD-LSTM approach requires additional work, as indicated by the outcome [19]. Sebastião and Godinho explored the predictability of and Litecoin using ML approaches. They validated and tested their models during a period of high volatility and bear markets to evaluate the accuracy of their predictions. They utilized trading and network activity data from August 15, 2015, through March 3, 2019, with the test sample starting on April 13, 2018. Five out of 18 different models had success rates of less than 50% during the test period. However, the ensemble of five models that generate identical signals performed well for ETH and Litecoin, with high positive ratios and annual rate returns. The study concludes that ML can be used to develop profitable trading strategies in cryptocurrency markets, even during tough times in the market [20].

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: The set of constraints is extended with the state-action pairs which correspond to the interpretations which are an answer set of B but are no answer set of \(B \cup H\), i. e. the state-action pairs which are no longer valid after imposing H on B. After \(\eta\) iterations, the hypothesis H is returned. The complete procedure is depicted in Algorithm 1. 4 ExperimentsFig. 2The different SUMO traffic environments. The agents learn social norms such as dealing with priority to the right and traffic lights, purely from observed trajectoriesFull size imageIn this section we perform an experimental evaluation of the proposed method. Section 4. 1 provides a brief overview of the used environments. In Sect. 4. 2, a qualitative evaluation follows of the induced hypotheses. Section 4. 3 covers a quantitative evaluation of the learned rules and the effect of the hyperparameter \(\eta\) and the number of trajectories available. In Sect. 4. 4, we provide a model-free RL agent with rules induced using our method as prior knowledge. First, we examine to what extent this prior knowledge speeds up the learning process. Next, we evaluate the applicability in safety critical environments by measuring the empirical violation probability both during and after training. At last, in Sect. 4. 5 we inspect the transferability of the induced rules to other but similar environments. 4. 1 EnvironmentsWe conduct experiments in the towers of Hanoi environment (Fig. 1) and simulated traffic environments (Fig. 2). We chose these environments because the behavior of agents in these environments is constrained by formal or informal rules. Towers of Hanoi is a canonical recursive problem where a stack of disks has to be moved from one place to another, without placing a larger disk on a smaller one and only moving the upper disk of any stack. Although all rules are explicitly known and could in principle be integrated upfront in the MDP definition, the simplicity of this problem allows to illustrate the working of our algorithm. In this environment, there is only one agent which plays the game. The state of the game is represented by the xy-coordinates of the three disks. The available actions are the displacement of any disk on the top position of one of the stacks to the top position of another stack.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: , Raison, M. , Tejani, A. , Chilamkurthy, S. , Steiner, B. , Fang, L. , Bai, J. and Chintala, S. (2019) PyTorch: Style, High-Learning Library. In: Wallach, H. , Larochelle, H. , Beygelzimer, A. , d’Alché-Buc, F. , Fox, E. and Garnett, R. , Eds. , Advances in. (NeurIPS), Vancouver, 8024-8035. [42] Simonyan, K. and Zisserman, A. (2015) for Large-Scale Recognition. arXiv: 1409. 1556. [43] He, K. , Zhang, X. , Ren, S. and Sun, J. (2016) Learning for Image Recognition. 2016 IEEE Conference on (CVPR), Las Vegas, 27-30 June 2016, 770-778. [44] Szegedy, C. , Liu, W. , Jia, Y. , Sermanet, P. , Reed, S. , Anguelov, D. , Erhan, D. , Vanchoucke, V. and Rabinovich, A. (2015) with Convolutions. 2015 IEEE Conference on (CVPR), Boston, 7-12 June 2015, 1-9. [45] Krizhevsky, A. , Sutskever, I. and Hinton, G. E. (2012) Imagewith Neural Networks. Proceedings of the 25th on 3-6 December 2012, 1097-1105. [46] Boesch, G. (2022) (VGGNet)—Need to Know. [47] IBM (2020) Machine Learning. . [48] Zhao, J. , Liang, B. and Chen, Q. (2018) Technology toward the Self-Driving Car. of Systems, 6, 2-20. [49] Fernandes, J. (2018) Training. [50] (2020) Unit (ReLu)? Introduction to ReFunction. [51] Jia, Y. , Shelhamer, E. , Donahue, J. , Karayev, S. , Long, J.

Output: corroborate


Input: Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: Schulte and Eggert aim to find markers for BTC price fluctuations and create an hourly price prediction model using a trained LSTM. They identify major price prediction signs and compare the LSTM results with other techniques, demonstrating an impressive accuracy rate of 96. 48%. The study utilized 47 input features over 10 months, leading to discussions on the use of NNs in predicting stock prices. The research makes valuable contributions to the field of BTC price prediction and suggests the potential of LSTM models for accurate and reliable predictions [21]. Jaquart et al. investigate the predictability of the BTC market across different time horizons using ML models. Technical aspects are found to be the most important for most techniques, followed by blockchain-based and sentiment/interest-based elements. Predictability improves with longer horizons, and a long-short trading strategy based on quantiles can yield up to 39% monthly profits before transaction costs, but the accuracy of predictions is around 50%. RNNs and gradient-boosting classifiers perform well in the predictive task, outperforming a random classifier [22]. Das et al. proposed a method for predicting BTC prices using a combination of DL techniques, involving -and Bi-RNN. While individual models like --RNN, or autoregressive integrated moving average achieve satisfactory results, the hybrid method proposed in the study outperforms them with and mean absolute error (MAE) values of 2. 69, 1. 78, 2. 20, and 1. 29, respectively. The study demonstrates the efficacy of combining different DL building blocks for more reliable BTC price prediction [23]. Aljojo et al. used a nonlinear autoregressive exogenous NN model to investigate the impact of timestamps on BTC’s value. They find that transaction timestamps significantly influence BTC’s performance variance with 96% forecast accuracy. Certain transaction events show repeating patterns, allowing for the anticipation of BTC’s price with less accuracy. Incorrect timestamps can cause uncertainty and have a negative impact on the BTC market. The study highlights the importance of accurate time stamping for participants in the BTC market [24]. Andi’s study used to predict BTC prices using a normalized dataset. The study found that using a combination of ML techniques leads to better results. The accuracy of the prediction model was found to be 97. 2% [25]. Buslim et al.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: To establish a mapping to first-order logic using \(\psi\), we use three instances (one for each disk) of the predicate at(D, X, Y). This predicate evaluates true if disk D is at position (X, Y). Disks are numbered in order of decreasing size, the disk for which D=1 corresponds with the largest disk. For the second set of experiments, we constructed three traffic environments: an intersection with bidirectional traffic which is controlled by traffic lights (Fig. 2a), an intersection with unidirectional traffic where the priority is given to cars coming from the right (Fig. 2b) and an environment combining both previous scenarios (Fig. 2c). In these environments, the cars represent the different agents. From any position the agent can choose between 5 actions which correspond to the 4 cardinal directions and standing still. The mapping \(\psi\) also uses these directions to map the xy-coordinate of an agent to a specific road, e. g. onRoad(north) signifies the agent is on the road on the north of the intersection. The unary predicate beforeJunction denotes if the agent is in front of a junction. The action of the agent is represented by the predicate go. The state of the traffic light is represented by tls0 and tls1. If a car is approaching from the right, the predicate carOnTheRight is true. The ground truth rules (to be learned constraints) consist of not driving off-road, giving priority to the right on a junction without traffic lights, stopping at a red light and, in case of unidirectional traffic, not driving in the opposite direction. These scenarios are built in the SUMO traffic simulator (Lopez et al. , 2018) which comes with internal car control algorithms, representing the expert policy \(\pi _E\), to generate realistic car trajectories. All possible (valid) trajectories are generated with the same probability and all results are averaged over 20 trials with different random seeds. 4. 2 Induced hypothesesIn this subsection we carry out a qualitative evaluation of the induced hypotheses. In the towers of Hanoi environment, only one rule is learned which states that it is invalid that a larger disk V1 is on a disk V2:Note that disks are numbered in order of decreasing size. For the towers of Hanoi environment the background knowledge and search space are depicted in Fig. 1.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Notes on contributoris a scholar in planning and urban studies, based at the Centre for. Her research is about displacement and dispossession in contemporary cities, particularly focused on Indigenous land justice in cities, the impacts of urban regeneration, critical urban governance, and the politics of urban informality. Her most recent book is Planning in Indigenous Australia: From imperial foundations to postcolonial futures with co-authors. Libby is of ‘Interface’ in the journal and Practice. Email: libby. porter@rmit. edu. auImagine it is sometime in the year 2025 and you are getting ready for the daily commute. Your mode of transport is car and while it is not far in distance, the roads are congested and the journey often takes an hour. But this is not lost time, for you can work on the way as your car is an autonomous vehicle, doing the driving for you. Having taken you to work, the car drives back home, saving the cost and hassle of car parking, but the cost of fuel is high and rising. You have set the car to return to your office later in the day to pick you up and take you home. Alternatively, you could take one of the share cars from the fleet outside your office, which will return itself to the fleet space. The fleet is also the way you will move about in the city during your day, as you book an AV on a phone app and meet it on the street. This is an entirely plausible future. Autonomous vehicles (AV) – vehicles powered by autonomous technologies, without requiring human control – are, according to many, just around the corner. And the implications are vast. While fully autonomous vehicles are still being tested, in reality a range of levels of automation already exist. Many of them are already on the road in the form of adaptive cruise control and other technologies (Peng, Citation2018). A fully autonomous ‘level 5ʹ vehicle is one able to move safely and reliably anywhere in any conditions without human intervention. While the debate continues about whether this level of full autonomy in a vehicle is a long time away or just around the corner, what is clear is that AV is set to have significant implications for our mobility and the way we think, plan and manage our cities, towns and infrastructure. AV is of course big business. According to industry estimates, four in 10 vehicles will be autonomous by 2040 (Accenture Digital, Citation2014).

Output: corroborate


Input: Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: ’s study contrasts the effectiveness of and LSTM models using a dataset from August 17, 2017 to April 13, 2021. are utilized to determine the best-performing model. The results show that GRU with has the best performance, with MAE training of 0. 0043 and a testing of 0. 0594 [26]. Zhang et al. combined SDAB, which uses the advanced deep neural network model (SDAE), with an effective integration method to predict price integration. The study used various exogenous variables and found that SDAE-B was more accurate and had fewer errors compared to traditional ML methods such as Machine (LSSVM) and back propagation. The SDAE-B method had a MAPE of 0. 016, RMSE of 131. 643, and DA of 0. 817, making it a more effective method in handling the randomness and nonlinearity of the BTC price [27]. Zhang et al. proposed an LSTM prediction (LSTM-P) neural network model for BTC and gold price prediction. To smooth out price data fluctuations, a noise decrease technique based on the wavelet transform has been used, and an optimized LSTM-P model has been created utilizing historical gold and BTC price data. With a MAPE of 6. 08% for LSTM-P and 4. 81% for standard LSTM, the LSTM-P model outperforms standard LSTM models and other time series forecasting models [28]. Luo et al. used ML and multiscale analysis to create ensemble prediction models by matching various techniques to their respective multiscale components. BTC price data from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27 were used for training and prediction. The ensemble models achieved 95. 12% forecast accuracy and outperformed benchmark models. The suggested models were robust in both rising and falling market conditions, and various algorithms were applied to components with different time scales [29]. Kang et al. suggested a hybrid DL model, 1D CNN-GRU, for forecasting cryptocurrency prices. The model combines a 1D CNN and stacked GRU to encode BTC price data over time and capture its long-range dependencies.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: For reasons of space, the complete background knowledge and search space are omitted for the traffic environments. In the traffic environment with priority to the right the following rules are induced:The first rule states that it is invalid for an agent to be off-road. This rule makes use of a conditional literal and is equivalent to the conjunction of not onRoad(V1) for all directions V1 (dir(V1) evaluates true if V1 is a variable which represents a direction). The following four rules invalidate being in a state on-road and transitioning to a state off the road. Since all roads in this environment are unidirectional, the action of going west is invalid as stated by the second last rule. The last rule represents the “priority to the right"-rule, i. e. when there is a car on the right, the only valid action is go(zero) which represents being stationary. The rules learned in the traffic light controlled junction environment are displayed below:The first rule invalidates being off-road and the following 8 rules prohibit transitioning to a state off-road, similar rules were induced in the environment with priority to the right. The last four rules state cars have to stop at a red traffic light. 4. 3 Accuracy of the learned hypothesesFig. 3False positive rate, false negative rate and accuracy of the induced rules for the traffic light controlled intersection environment (a, c, e respectively) and for the intersection with priority to the right environment (b, d, f respectively) as a function of the number of observed trajectories. Mind the different y-axis scaleFull size imageFig. 4Number of state-action pairs which are constrained by the inferred rules for increasing values of \(\eta\). Line styles indicate the number of expert trajectories available. Results are from the traffic light controlled intersection environmentFull size imageIn this section, we validate the induced rules of the traffic environment by calculating all answer sets of the ASP program represented by the union of the background knowledge and the induced hypothesis. These answer sets represent the state-action pairs which are considered valid by the induced hypothesis and can hence be used to classify a state-action pair as allowed or prohibited. We gather 10 000 trajectories from the SUMO simulator and validated that all correct actions of cars in any of the possible on-road position were observed at least once in this dataset.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: This is a lucrative new industry, and while the estimates vary, the predicted global value of the autonomous vehicle industry has been projected at $US54billion in 2019, rising to $US556billion by 2026 (Garsten, Citation2018). This will increasingly include trucks and other non-domestic uses of AV such as military and defence. In Australia, is already using driverless trucks in its mining operations by retrofitting an System to its fleet, set to make 30% of its fleet autonomous by 2019 (Pash, Citation2017). The major automotive companies are all in a race to get AV on the road and embedded into the market. These are digital disruptions indeed, and the implications are enormous. Debate continues to rage about whether autonomous vehicles are safer than human-controlled vehicles, and whether or not this represents massive advantages for cities of the future, or a dystopia of more freeways, trips and congestion. There are plans to launch on-demand driverless taxis in the US in 2019, contributing to a trend already visible where consumers want to purchase on-demand mobility, through platform technologies, thus enhancing a mode shift from public transit toward rideshare. There is much anticipation of AV and its possibilities. The AV revolution could be combined with a mass shift to carbon neutral vehicles, presenting new possibilities for carbon reductions. Many pundits claim AV is safer than human drivers, though recent high profile accidents have dented public confidence. For people with disabilities and impairments to mobility, AV presents transformative possibilities, with a recent survey in Australia demonstrating that more than 85% of people felt AV would bring mobility impaired people significant benefits (Regan et al. , Citation2017). Yet the challenges and risks are very significant. AV may well result in perverse mobility outcomes, reducing mobility for some such as pedestrians and cyclists and actually increasing the number of trips as wealthy households able to afford the technology unleash more private trips on the road. Even greater urban sprawl may result as drivers capitalise on the benefits of being relieved of the task of driving. Perhaps this means we will be more comfortable with more and longer trip times, potentially fuelling urban sprawl. The unrolling of AV might reconstitute roads as real estate through fleets of rideshare, drones and delivery services, all supported by apps and other platform technologies that harvest our data and pitch ever more products to us. In this Interface, practitioners, scholars and transport commentators from around the world provide commentary on these dimensions of this impending technological revolution.

Output: corroborate


Input: Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: The execution of the 1D CNN-GRU model has been evaluated on and XRP datasets, and it was found to outperform previous approaches with RMSE values of 43. 933 for BTC, 3. 511 for ETH, and 0. 00128 for XRP [30]. Aljadani’s study applies Bidirectional LSTM and GRU-based DL algorithms to three public real-time BTC datasets. GRU outperforms Bi-LSTM for and Cardano, with the GRU model achieving the lowest RMSE values of 0. 01711, 0. 02662, and 0. 00852 for the three cryptocurrencies, respectively. The proposed framework achieves significant performance in predicting cryptocurrency prices [31]. Nematallah et al. proposed using DL processes such as RNNs and LSTMs to predict BTC market price trends. The models are evaluated based on their MAPE and RMSE values for predicting the next 15, 30, and 60 days. The LSTM model is found to be the superior method with a lower MAPE of 3. 01% compared to RNN’s MAPE of 7. 10%, even though it takes longer to assemble [32]. Mehta and Sasikala’s research focusses on using ML techniques to forecast BTC prices. By analyzing daily data for 1,681 cryptocurrencies between November 2015 and April 2018, the authors show that their approach, which employs simple trading techniques supported by cutting-edge ML algorithms, outperforms traditional benchmarks. The study’s goal is to forecast the cryptocurrency market’s short-term future and provide real-time BTC price predictions. The use of on the available dataset yielded an impressive 92% accuracy [33]. 3 Characteristics of cryptocurrencies Cryptocurrencies offer many benefits, such as decentralization, security, and privacy, but they also have some downsides, such as high volatility and susceptibility to hacking and fraud. Users can evaluate whether these currencies are suitable for their financial objectives by assessing each feature listed in Table 1. Table 1 Important features of cryptocurrencies Cryptocurrencies are characterized by decentralization where there is no intermediary or entity that controls payment and transfer operations Böhme et al. [41] provide high security for users, as payment transactions are protected using advanced encryption techniques Antonopoulos [42] allow complete privacy for users, as no personal information is recorded when making payment transactions Böhme et al.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: Accordingly, the ground truth validity of a state-action pair is determined by the presence in at least one of the 10 000 trajectories. We report three statistics. The false positive rate (FPR) is the fraction of the state-action space that is incorrectly prohibited by the set of induced rules. Accordingly, the false negative rate (FNR) is the fraction that is incorrectly allowed. The accuracy is the ratio between the sum of false positive and false negative state-action pairs divided by the total number of state-action pairs. We calculate these statistics for the traffic light controlled intersection and the intersection with priority to the right environments for increasing numbers of trajectories (dataset sizes) and iterations \(\eta\). The results are depicted in Fig. 3. The FPR decreases with the number of observed trajectories. When few expert observations are available, some allowed state-action combinations may not be observed. If an allowed but unvisited state-action pair has high likelihood under the nominal policy it will incorrectly be selected as a constraint which will lead to incorrect rules. When, for instance, in none of the trajectories a car stopped in front of a red traffic light (because the light was always green), the system could induce that it is invalid for a car to be in front of a red traffic light. Provided that the number of iterations \(\eta\) is not too small, small number of observations will result in a low FNR because rules are inferred which invalidate large regions of the state-action space which also include the true constraints. In our example, when the system infers the rule, all states which include the agent in front of a red traffic light are invalid, it also invalidates the true constraint of driving a red light. This phenomenon can also be observed in Fig. 4 which depicts the number of state-action pairs which are invalidated by the learned rules for increasing values of \(\eta\) and number of expert trajectories. With fewer iterations \(\eta\), fewer constraints are inferred accordingly the induced rules invalidate less state-action pairs hence a smaller FPR but a higher FNR. It is self-evident the number of constrained state-action pairs increase when the number of iterations increase. Notably, in the traffic light scenario of Fig. 3c, the FNR first increases for smaller number of trajectories.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Email: C. Curtis@exchange. curtin. edu. auThe PromiseOur cities are changing. One place where change is most rapid is in the development and convergence of communications and information technologies with transport technologies. This convergence is presenting a challenge to the forces that shaped the automobile city throughout the 20th century by promising new forms of shared mobility and autonomous vehicles (AVs). Many analysts are upbeat about the role autonomous vehicles and mobility on demand will play in our future cities. This anticipation is finding wide expression in policy documents, and government programs (see for instance Authority, Citation2018; Infrastructure Victoria, Citation2016) where autonomous vehicles and mobility on demand are being celebrated for their potential to reduce fatalities on the road (Glaser, Vanholme, Mammar, Gruyer, & Nouveliere, Citation2010), replace private ownership of a vehicle with vehicles that will be shared (Firnkorn & Müller, Citation2015), and reclaiming valuable land currently used to accommodate parked cars (Burns, Citation2013; Zhang, Guhathakurta, Fang, & Zhang, Citation2015). To better understand the potential of shared mobility and AVs, industry and universities are investing billions of dollars to test, standardise, and ultimately mass produce the new technologies. In our home institutions, the future of AVs is being anticipated with the creation of new research centres to test and refine the complex communications technologies need to operate AVs on city streets. However, the anticipatory research and industry activity around AVs and shared mobility sits in tension with a regulatory environment that is struggling to understand the potential downsides of these potential futures. One possible side effect from the convergence between the IT and automobile sectors is the development of increased capability to collect data about human practice, and then use these capacities to commercialise movement around cities in new ways. This presents a challenge for planners who might see value in minimising the extent of commercialisation of future mobility in order to ensure that benefits are distributed equally across the city. Ultimately this is a question about urban governance in the 21st Century city and region, and the role planners will play in an era that will be marked by transport disruptions. To help move the debate forward, in this short piece we ask, how might planners be better positioned to anticipate and then plan for these new technologies and the cities they will create?

Output: corroborate


Input: Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: [41] enable easy access to payment and transfer services Antonopoulos [42] Enhancing international transfers Cryptocurrencies help enhance international transfer operations due to their ease of use Antonopoulos [42] Improving transparency Cryptocurrencies provide great transparency in payment and transfer operations, as all transactions are recorded in an encrypted and transparent ledger Narayanan et al. [43] Hedge against inflation Cryptocurrencies are a good option for hedging against traditional currency inflation Böhme et al. [41] High volatility Cryptocurrencies are characterized by high volatility in their value, as their value can be significantly affected by changes in the global market, which can result in financial losses for users Narayanan et al. [43] High probability of hacking Despite the security offered by cryptocurrencies, there is still a high probability of hacking due to vulnerabilities in software or user errors Narayanan et al. [43] Table 1 introduces the characteristics of cryptocurrencies, which are digital assets that support online payments and transfers. Due to the encryption of transactions, cryptocurrencies are characterized by decentralization and high security. They provide users with privacy and easy access to financial services. They can also improve international transfers and act as a hedge against inflation. However, their high volatility and vulnerability to hacking calls for precautionary measures. Overall, cryptocurrencies represent a significant development in the field of digital finance. 4 Risk of cryptocurrency Cryptocurrencies, as an emerging asset class, present unique risks for investors and traders. Understanding these risks is essential for making informed decisions and mitigating potential losses. The following points highlight the most significant risks associated with investing in cryptocurrencies and provide insights for navigating the volatile digital currency landscape. Investing in cryptocurrencies carries several risks, one of which is hype risk. This occurs when investors make impulsive decisions without fully understanding the technology or market dynamics, leading to significant losses when the market self-corrects. Therefore, thorough market analysis is crucial before investing, and the risks involved must be considered. Another significant risk is a security risk, which stems from the high risk of scams, hacking, and theft in the cryptocurrency market. Investors must choose secure blockchain protocols, exchanges, and wallets to safeguard their investments. Cryptocurrencies are known for their high volatility, which poses a considerable risk for investors. Having a long-term investing plan and being equipped to deal with volatility are both crucial.

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: All agents adopt an \(\epsilon\)-greedy exploration strategy during training (\(\epsilon =0. 05\)), but become fully deterministic afterwards (\(\epsilon = 0\)). We adopt a learning rate of 0. 9 and a discount factor 0. 75. The Q-table is initialized with zeros. Experimental results are the average of 400 trained agents. We define the cost signal (negative reward) as the L1-distance from the agent’s position to its goal, which has a maximum of 4. Violating a ground truth rule (i. e. traffic regulations) incurs a cost of 10. Figure 5 shows the total cost received by an agent trained for a different number of episodes in the intersection with traffic lights environment (Fig. 5a) and the intersection with priority to the right environment (Fig. 5b). We conclude from these results that constraining an agent with a logic program inferred using our method leads to a considerable speed-up of the Q-learning convergence. Using only constraint inference barely improves the agent’s required number of episodes during training which confirms the importance of the induction step. Moreover, we notice a much smaller variation in performance between LC-QL agents. Table 1 shows the empirical probability of an agent violating at least one rule during and after training for both intersections. These results show only our method guarantees no rules will be violated both at deployment and during exploration, thus, would qualify for safety-critical applications. At last, we measured the execution time of the different steps of our algorithm, the results for the traffic light controlled junction environment are depicted in Fig. 6. We observe that the induction step takes up the largest part of the execution time, especially for large values of \(\eta\). The green curve represents the time until convergence of LC-QL when provided with rules inferred using our algorithm for different values of \(\eta\). Although our method improves the speed to convergence when constraining a learning agent with the learned rules (as shown in Fig. 5a, b) there is no overall time saving compared to training an agent without prior knowledge. 4. 5 Transfer learningFig. 6Execution time of the different steps of our algorithm applied to the traffic light controlled intersection environmentFull size imageFig. 7Transfer learning experiment: total cost received in the combo environment (see Fig.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: Equally, there is very little about how these different futures can then equip planners, policy makers, and different publics across the vast geographies of cities and regions to challenge the dominant discourses around AVs. There is great potential to explore these futures more comprehensively in a manner that accounts for the health, urban design, ecological, social and built environment impacts this future will create, and to then consider planning responses to these challenges. This needs support through research funding for projects that go beyond workings of the technologies, so that society may be furnished with a fuller evidence base. This would also fill the knowledge gap that planners and decision makers have identified. Also, in this research, we must not conceive of the state as a single entity with one view or perspective. Planning is now multiple in its orientation. It stretches across public and private sector bodies, each with different - often competing - motivations, mandates and stakeholders. We need to examine the socio-technical disruptions of the kind presented by AVs across a range of social, political, institutional and land use contexts. The roll out of AVs in Australia will be very different to that in Germany and so on. Developing an evidence base from different planning systems will bring to light the ways in which AVs shape and are shaped by different governance settings and public and private sector arrangements. New evidence is also needed to show us how different scenarios of AV ownership and control might shape the land use patterns and urban structure, equity of access and opportunities and incentives for active travel. Planners, in practice and in academia, need to articulate our understandings of the pitfalls and possibilities of different AV futures in plain language and engage with citizens to shape our expectations of governments and the private sector. Governance PathwaysThe lure of the shiny new gadget-driven autonomous mobility future is certainly attracting a great deal of public and private R&D investment. It is whetting the appetite of time poor overworked families in a cash-strapped age who see possibilities to use their travel time more usefully. It is also attracting new private sector players to a new ‘mobility market’, captured under the banner of Mobility as a Service (MaaS). But the governance issues are currently under-played. Many governments are actively courting AV trials for fear of being left behind in the technology race and are scrambling for regulations to address complex legal questions about safety and liability when an algorithm, rather than an individual, controls a vehicle.

Output: corroborate


Input: Claim: Cryptocurrency investments guarantee high returns with no risks. [SEP] Text: Liquidity risk is also a concern, particularly for lesser-known altcoins, which may not have enough buyers or sellers, making it challenging to sell investments quickly at a fair price. Vanishing risk refers to the possibility of cryptocurrencies failing and becoming worthless, resulting in significant losses for investors. Therefore, investors should conduct thorough research and invest in promising and established projects. Regulatory risk is also a possibility as regulatory changes can impact the market and create volatility as cryptocurrencies become more mainstream. Keeping up with legislative changes is essential so that investing plans may be modified. Finally, tax risk includes the possibility of cryptocurrency investments being subject to capital gains tax, and tax authorities becoming more stringent in enforcing tax regulations. Complying with tax regulations and seeking professional advice can help avoid potential penalties and losses [34]. 5 Artificial neural network (ANN) There are some similarities between ANNs and biological brains. Data are received, processed, and transmitted from and to nearby neurons by a group of simple signal processing units (artificial neurons). In the training phase, when ANN learns from the dataset given, the strength (weighting) of the connections among neurons is determined. ANNs are a vast category of algorithms. The main classes for supervised learning include [35] (Figures 1–3): MLP network – This has layers of artificial neurons that are normally fully coupled. Data are transmitted among the levels in a feed-forward fashion. ANNs’ inputs are vectors. These ANNs are primarily employed for situations involving organized or unstructured data of limited complexity [36]. CNN – CNN is one of the DL techniques. A network that organizes artificial neurons in feed-forward layers that are often thinly linked. Convolution layers enable the use of multi-dimensional data (tensor matrices) and the detection of complicated patterns. As a result, such ANNs are mainly well adapted to huge issues involving unstructured data [37,38]. RNNs are a form of ANN capable of handling sequential data such as text, speech, and time series data. They use memory cells to store information from the past, allowing them to carry information forward through time. The best type of RNN for price prediction depends on the specific task and data quality [39]. Figure 1MLP neural network structure [36]. Figure 2Convolutional neural networks [38]. Figure 3The simple RNN design schematic [39].

Output: contrast


Input: Claim: Reinforcement learning algorithms mimic the way humans learn through rewards and punishments. [SEP] Text: (2021) adopted the principle of maximum causal entropy to extend the applicability to stochastic environments. IRL often serves the goal of aligning an artificial agent’s behavior with values respected during demonstrations. Noothigattu et al. (2018) propose to learn two policies, one maximizing the reward through classic RL and one obeying behavioral constraints through IRL. Next, a contextual-bandit-based orchestrator is used to blend the two policies. Other work introduces an approach to model the navigation behavior of interacting pedestrians in terms of a joint mixture distribution over the trajectories of all agents (Kretzschmar et al. , 2016). Next, this model is adopted by a robot to interact with humans in a socially compliant way. However, no existing work focuses on true interpretability of the learned concepts which is, by our opinion, a critical aspect in the development of safe artificial agents. Our work is also related to safe RL as the induced hypothesis can be adopted as external knowledge to avoid unsafe situations during the exploration process. The exploration process can also be modified by initializing the Q-function with recorded trajectories of a teacher (de Lope, 2009). Other work suggests to decompose the Q-function in a task and a survival component (et al. , 2017). The survival component is task-independent and is used to safely navigate the environment. Other techniques take risks into account by adapting the optimization criteria (Geibel, 2006). However, in contrast with the proposed method, these techniques do not completely exclude the occurrence of an invalid state-action pair. Related to our approach is the technique of a teacher giving advice to the agent when unsafe situations could occur (Alshiekh et al. , 2018). In our case the teacher is represented by the learned rules which restricts the possible actions in certain states. Safety is also closely related to interpretability which is why other research focuses on explaining RL policies. Coppens et al. (2020) proposed a policy distilling algorithm which extracts a set of rules from a deep RL policy. However, in contrast to our work, they assume a RL policy is already available. Alternatively, relational RL (Džeroski et al. , 2001), learns an optimal policy starting from a description of the environment based on objects and relations which makes it interpretable “by design”. It is also possible to learn a reward function (Munzer et al.

Output: pass


Input: Claim: Several car manufacturers are actively developing autonomous vehicle technology. [SEP] Text: The first relates to day-to-day mobility, with an explicit mandate to record and theorise shifts away from private car use towards more sustainable transport modes in car oriented cities. The second is on the general links between the built environment and health. The various health impacts of transport, including the advantages and disadvantages to health of private car dependency, exist as the nexus between these two research themes. Jennifer publishes regularly in high ranking scholarly journals and her work has been used to inform policy development in NSW and Australia. Email: Jennifer. kent@sydney. edu. auIn their comprehensive research report on autonomous vehicles (AVs) in Australia, Austroads paint positive and negative scenarios for the futureFootnote1 (Austroads, Citation2017). It is the positive scenario that suggests AVs could lead to less car ownership and use. In this scenario, a homogenous fleet of highly automated vehicles roams the city. Cars and rides are shared, a practice enabled through technologies of connectivity, matching people and trips. And, just like car sharing cars today, AVs are primarily used to fill the gaps in the timetables and fixed routes of the public transport network, which is also enhanced by technologies of connectivity (Duarte & Ratti, Citation2018). The critical points here for reduced car ownership and use are that: AVs are shared, andAVs complement a robust public transport system that accommodates most trips. I have been researching the cultural and structural contours of private car dependency for almost ten years now. In this piece I draw from three case studies in the low-density, car-dependent city of Sydney, to highlight three warning signs that show AVs will not lead to less car ownership and use. is used here as an example, many of the structures and practices described are common internationally, particularly in nations that are seeking to transition away from car-dependence. One: to the Expediency of OwnershipReduced private car ownership requires that we own less cars – hence the assumption that AVs will be shared vehicles. Shared. Really? You see, sharing a ride does not only mean sharing the space that is the car. It means sharing your car boot, or whatever other space in the car where you stash your gym clothes, spare umbrella and shopping bags. It means sharing your music, climate control, car-based conversations and habits.

Output: pass