Scenario

scenario

Authors:
(1) Tony Lee, Stanford with Equal contribution;
(2) Michihiro Yasunaga, Stanford with Equal contribution;
(3) Chenlin Meng, Stanford with Equal contribution;
(4) Yifan Mai, Stanford;
(5) Joon Sung Park, Stanford;
(6) Agrim Gupta, Stanford;
(7) Yunzhi Zhang, Stanford;
(8) Deepak Narayanan, Microsoft;
(9) Hannah Benita Teufel, Aleph Alpha;
(10) Marco Bellagente, Aleph Alpha;
(11) Minguk Kang, POSTECH;
(12) Taesung Park, Adobe;
(13) Jure Leskovec, Stanford;
(14) Jun-Yan Zhu, CMU;
(15) Li Fei-Fei, Stanford;
(16) Jiajun Wu, Stanford;
(17) Stefano Ermon, Stanford;
(18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction 2 Core framework 3 Aspects 4 Scenarios 5 Metrics 6 Models 7 Experiments and results 8 Related work 9 Conclusion 10 Limitations Author contributions, Acknowledgments and References A Datasheet B Scenario details C Metric details D Model details E Human evaluation procedure 4 Scenarios To evaluate the 12 aspects (§3), we curate diverse and practical scenarios. Table 2 presents an overview of all the scenarios and their descriptions. Each scenario is a set of textual inputs and can be used to evaluate certain aspects. For instance, the “MS-COCO” scenario can be used to assess the alignment, quality, and efficiency aspects, and the “Inappropriate Image Prompts (I2P)” scenario [8] can be used to assess the toxicity aspect. Some scenarios may include sub-scenarios, indicating the sub-level categories or variations within them, such as “Hate” and “Violence” within I2P. We curate these scenarios by leveraging existing datasets and creating new prompts ourselves. In total, we have 62 scenarios, including the sub-scenarios. Notably, we create new scenarios (indicated with “New” in Table 2) for aspects that were previously underexplored and lacked dedicated datasets. These aspects include originality, aesthetics, bias, and fairness. For example, to evaluate originality, we develop scenarios to test the artistic creativity of these models with textual inputs to generate landing pages, logos, and magazine covers. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Authors: Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Core framework 2 Core framework 3 Aspects 3 Aspects 4 Scenarios 4 Scenarios 5 Metrics 5 Metrics 6 Models 6 Models 7 Experiments and results 7 Experiments and results 8 Related work 8 Related work 9 Conclusion 9 Conclusion 10 Limitations 10 Limitations Author contributions, Acknowledgments and References Author contributions, Acknowledgments and References A Datasheet A Datasheet B Scenario details B Scenario details C Metric details C Metric details D Model details D Model details E Human evaluation procedure E Human evaluation procedure 4 Scenarios To evaluate the 12 aspects (§3), we curate diverse and practical scenarios. Table 2 presents an overview of all the scenarios and their descriptions. Each scenario is a set of textual inputs and can be used to evaluate certain aspects. For instance, the “MS-COCO” scenario can be used to assess the alignment, quality, and efficiency aspects, and the “Inappropriate Image Prompts (I2P)” scenario [8] can be used to assess the toxicity aspect. Some scenarios may include sub-scenarios, indicating the sub-level categories or variations within them, such as “Hate” and “Violence” within I2P. We curate these scenarios by leveraging existing datasets and creating new prompts ourselves. In total, we have 62 scenarios, including the sub-scenarios. Notably, we create new scenarios (indicated with “ New ” in Table 2) for aspects that were previously underexplored and lacked dedicated datasets. These aspects include originality, aesthetics, bias, and fairness. For example, to evaluate originality, we develop scenarios to test the artistic creativity of these models with textual inputs to generate landing pages, logos, and magazine covers. New This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Curating 62 Practical Scenarios to Test AI Text-to-Image Models

Author:
(1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es);
(2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es);
(3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Related work The proposed pipeline Evaluation Conclusion & Acknowledgements References & Appendix Abstract This paper presents a pipeline to detect and explain anomalous reviews in online platforms. The pipeline is made up of three modules and allows the detection of reviews that do not generate value for users due to either worthless or malicious composition. The classifications are accompanied by a normality score and an explanation that justifies the decision made. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database. Additionally, a study comparing three explainability techniques involving 241 participants was conducted to assess the explainability module. The study aimed to measure the impact of explanations on the respondents’ ability to reproduce the classification model and their perceived usefulness. This work can be useful to automate tasks in review online platforms, such as those for electronic commerce, and offers inspiration for addressing similar problems in the field of anomaly detection in textual data. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. Keywords: Anomaly Detection, Text Reviews, Transformers, Explainability 1. Introduction Nowadays more than ever in history, the user opinions about products and services have a great impact on the future of the company that offers them. In such a globalized and highly competitive world, online review platforms, such as electronic commerce (e-commerce), play a crucial role in the credibility of products and services. These user reviews usually come in the form of text reviews or numerical ratings, accompanied in some cases by images or videos. These reviews provide users with information about the product or service they are considering purchasing, which directly influences the number of sales. Most people make purchase decisions based on ratings and reviews from other users [1]. In the case of many companies, such as Amazon, each product in the store has a list of text reviews published by customers of the platform. Users can access this list of reviews (opinions) to obtain extra information about the product, being able to mark the reviews as useful, which will position those reviews with the most votes at the top of the list. In addition to this, users can report to Amazon if they feel a review is inappropriate, for example, if its content is incorrect. This procedure to rank reviews based on their usefulness and report inappropriate reviews is carried out manually by platform users. As a result, Amazon reported more than 200 million suspected fake reviews in 2020 alone [2]. This problem does not only occur on Amazon, but affects all platforms that allow their users to post reviews. For example, Tripadvisor uses an automatic system capable of distinguishing between normal, suspicious and inappropriate reviews [3]. Inappropriate ones are automatically removed (3.1% of review submissions in 2020), while those classified as suspicious are reviewed again by a human moderator (5.1% of review submissions in 2020). On the other hand, in machine learning (ML), anomaly detection (AD) is the branch that builds models capable of differentiating between normal and abnormal data [4]. At first, anomaly detection might seem like a classification problem with only two classes. However, anomalies tend to occur infrequently or are non-existent, so normal data prevails in these scenarios. Because of this, it is common for models to be trained only with normal data. The goal of these models is to represent the normal class as well as possible in order to classify new data as normal or anomalous. The technological development of recent years has allowed the construction of very powerful models for Natural Language Processing (NLP) [5]. Contrary to other tasks such as Sentiment Analysis [6] or Question Answering [7], the application of anomaly detection on texts is still at an early stage, probably due to its lower demand. For this reason, we present in this article a pipeline that, given text reviews of a product (in this case from Amazon), addresses opinion filtering as an anomaly detection problem where: • Reviews containing representative information about the product are considered as the normal class. • Reviews whose content has nothing to do with the product to be represented are considered as the anomalous class. The proposed pipeline allows us to carry out the following tasks: • Classify reviews as normal or anomalous, allowing us to locate those that do not describe characteristics of the product to which they are associated, and therefore have no value for the users of the platform. • Issue a normality score associated with each review. • Generate an explanation that justifies the classification made for each review by the system. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database [8]. In addition, to evaluate the explainability module, a study was carried out to compare three explainability techniques in which a total of 241 people had participated. The objective of this study is both to measure the impact of the explanations on the reproducibility of the classification model by the respondents, as well as the usefulness of these explanations. We believe that this work can be useful to automate tasks such as those mentioned in online review platforms, in addition to the existing interest in the application of anomaly detection models on texts, for which it can serve as inspiration to solve similar problems. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. This document is structured as follows. Section 2 contains a brief review of the main anomaly detection works in texts and, more specifically, in text reviews. Section 3 describes the proposed pipeline and its operation. Section 4 collects the experimentation carried out and, finally, conclusions are drawn in Section 5. This paper is available on arxiv under CC 4.0 license. Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Author: Author: (1) David Novoa-Paradela, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain & Corresponding author (Email: david.novoa@udc.es); (2) Oscar Fontenla-Romero, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: oscar.fontenla@udc.es); (3) Bertha Guijarro-Berdiñas, Universidade da Coruña, CITIC, Campus de Elviña s/n, 15008, A Coruña, Spain (Email: berta.guijarro@udc.es). Table of Links Abstract and Introduction Abstract and Introduction Related work Related work The proposed pipeline The proposed pipeline Evaluation Evaluation Conclusion & Acknowledgements Conclusion & Acknowledgements References & Appendix References & Appendix Abstract This paper presents a pipeline to detect and explain anomalous reviews in online platforms. The pipeline is made up of three modules and allows the detection of reviews that do not generate value for users due to either worthless or malicious composition. The classifications are accompanied by a normality score and an explanation that justifies the decision made. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database. Additionally, a study comparing three explainability techniques involving 241 participants was conducted to assess the explainability module. The study aimed to measure the impact of explanations on the respondents’ ability to reproduce the classification model and their perceived usefulness. This work can be useful to automate tasks in review online platforms, such as those for electronic commerce, and offers inspiration for addressing similar problems in the field of anomaly detection in textual data. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. Keywords: Anomaly Detection, Text Reviews, Transformers, Explainability Keywords: 1. Introduction Nowadays more than ever in history, the user opinions about products and services have a great impact on the future of the company that offers them. In such a globalized and highly competitive world, online review platforms, such as electronic commerce (e-commerce), play a crucial role in the credibility of products and services. These user reviews usually come in the form of text reviews or numerical ratings, accompanied in some cases by images or videos. These reviews provide users with information about the product or service they are considering purchasing, which directly influences the number of sales. Most people make purchase decisions based on ratings and reviews from other users [1]. In the case of many companies, such as Amazon, each product in the store has a list of text reviews published by customers of the platform. Users can access this list of reviews (opinions) to obtain extra information about the product, being able to mark the reviews as useful, which will position those reviews with the most votes at the top of the list. In addition to this, users can report to Amazon if they feel a review is inappropriate, for example, if its content is incorrect. This procedure to rank reviews based on their usefulness and report inappropriate reviews is carried out manually by platform users. As a result, Amazon reported more than 200 million suspected fake reviews in 2020 alone [2]. This problem does not only occur on Amazon, but affects all platforms that allow their users to post reviews. For example, Tripadvisor uses an automatic system capable of distinguishing between normal, suspicious and inappropriate reviews [3]. Inappropriate ones are automatically removed (3.1% of review submissions in 2020), while those classified as suspicious are reviewed again by a human moderator (5.1% of review submissions in 2020). On the other hand, in machine learning (ML), anomaly detection (AD) is the branch that builds models capable of differentiating between normal and abnormal data [4]. At first, anomaly detection might seem like a classification problem with only two classes. However, anomalies tend to occur infrequently or are non-existent, so normal data prevails in these scenarios. Because of this, it is common for models to be trained only with normal data. The goal of these models is to represent the normal class as well as possible in order to classify new data as normal or anomalous. The technological development of recent years has allowed the construction of very powerful models for Natural Language Processing (NLP) [5]. Contrary to other tasks such as Sentiment Analysis [6] or Question Answering [7], the application of anomaly detection on texts is still at an early stage, probably due to its lower demand. For this reason, we present in this article a pipeline that, given text reviews of a product (in this case from Amazon), addresses opinion filtering as an anomaly detection problem where: • Reviews containing representative information about the product are considered as the normal class. • Reviews whose content has nothing to do with the product to be represented are considered as the anomalous class. The proposed pipeline allows us to carry out the following tasks: • Classify reviews as normal or anomalous, allowing us to locate those that do not describe characteristics of the product to which they are associated, and therefore have no value for the users of the platform. • Issue a normality score associated with each review. • Generate an explanation that justifies the classification made for each review by the system. The pipeline’s ability to solve the anomaly detection task was evaluated using different datasets created from a large Amazon database [8]. In addition, to evaluate the explainability module, a study was carried out to compare three explainability techniques in which a total of 241 people had participated. The objective of this study is both to measure the impact of the explanations on the reproducibility of the classification model by the respondents, as well as the usefulness of these explanations. We believe that this work can be useful to automate tasks such as those mentioned in online review platforms, in addition to the existing interest in the application of anomaly detection models on texts, for which it can serve as inspiration to solve similar problems. We also consider it interesting to have carried out a human evaluation of the capacity of different explainability techniques in a real and infrequent scenario such as the detection of anomalous reviews, as well as to reflect on whether it is possible to explain tasks as humanly subjective as this one. This document is structured as follows. Section 2 contains a brief review of the main anomaly detection works in texts and, more specifically, in text reviews. Section 3 describes the proposed pipeline and its operation. Section 4 collects the experimentation carried out and, finally, conclusions are drawn in Section 5. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Explained Anomaly Detection in Text Reviews: Can Subjective Scenarios Be Correctly Evaluated?

Unitarity Bound on Dark Matter in Low-temperature Reheating Scenarios: Acknowledgments and Reference

নিম্ন-তাপমাত্রা পুনঃউষ্ণকরণের পরিস্থিতিতে ডার্ক ম্যাটারে ঐক্যবদ্ধতা: স্বীকৃতি এবং রেফারেন্স

Unitaritätsgrenze für Dunkle Materie in Niedertemperatur-Wiedererwärmungsszenarien: Danksagungen und Referenz

Unitaridad unida a la materia oscura en escenarios de recalentamiento a baja temperatura: reconocimientos y referencias

Unitarité liée à la matière noire dans les scénarios de réchauffage à basse température : remerciements et référence

निम्न-तापमान पुनःतापन परिदृश्यों में डार्क मैटर पर यूनिटैरिटी बाउंड: आभार और संदर्भ

低温再加熱シナリオにおける暗黒物質のユニタリー性限界: 謝辞と参考文献

저온 재가열 시나리오에서 암흑물질에 결합된 단일성: 승인 및 참고자료

Unitariedade vinculada à matéria escura em cenários de reaquecimento de baixa temperatura: reconhecimentos e referências

Унитарность, связанная с темной материей в сценариях низкотемпературного разогрева: благодарности и ссылки

Düşük Sıcaklıkta Yeniden Isıtma Senaryolarında Karanlık Maddeye Bağlı Üniterlik: Teşekkür ve Referans

Tính thống nhất bị ràng buộc đối với Vật chất tối trong các kịch bản hâm nóng ở nhiệt độ thấp: Lời cảm ơn và tài liệu tham khảo

低温再加热场景中暗物质的幺正性界限：致谢和参考文献

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Nicolas Bernal, New York University Abu Dhabi;
(2) Partha Konar, Physical Research Laboratory;
(3) Sudipta Show, Physical Research Laboratory. Table of Links Abstract and Intro
S-matrix: Unitarity and its Consequences
Dark Matter Annihilation and Unitarity Bound
Low-temperature Reheating
Freeze-out with a Low-temperature Reheating
Summary and Conclusion
Acknowledgments and References Abstract Model-independent theoretical upper bound on the thermal dark matter (DM) mass can be derived from the maximum inelastic DM cross-section featuring the whole observed DM abundance. We deploy partial-wave unitarity of the scattering matrix to derive the maximal thermally-averaged cross-section for general number-changing processes r → 2 (with r ≥ 2), which may involve standard model particles or occur solely within the dark sector. The usual upper limit on the DM mass for s-wave annihilation is around 130 TeV (1 GeV) for r = 2 (3), only applies in the case of a freeze out occurring in the standard cosmological scenario. We consider the effects of two nonstandard cosmological evolutions, characterized by low-temperature reheating: i) a kination-like scenario and ii) an early matter-dominated scenario. In the first case, the early freeze-out strengthens the unitarity bound to few TeVs for WIMPs; while in the second case, WIMP DM can be as heavy as ∼ 1010 GeV due to a large entropy dilution. 1. Introduction Consideration of a specific DM production paradigm in the early stage of the universe may further constrain the mass range for a viable DM candidate. For instance, the number-changing pair annihilation of DM to SM particles determines its present mass density, where it maintains the chemical and kinetic equilibrium with the thermal soup in the early universe. Interestingly, the requirement of the unitarity of the S-matrix sets a model-independent upper bound on the DM mass for this scenario [12, 13]. The implication of unitarity offers the maximum inelastic cross-section, which fixes the minimum number density of the frozen-out DM. Using this number density, one can establish the maximum allowed DM mass by fulfilling the observed relic density of it. In the theories of DM with long-range forces, bound states of DM can form and therefore relax the unitarity bound by suppressing the inelastic annihilation rate [14–16]. In addition, dark sectors with particle-antiparticle asymmetry enforce the apprehend of the nonzero equilibrium chemical potential for DM, further constraining the unitarity limits due to the demand for an increased effective DM number density at the time of freeze-out [15, 17]. Furthermore, different indirect searches for DM may put a lower limit on DM mass for some specified scenarios. A strong model-independent lower bound for the thermal DM that is annihilating to visible states through an s-wave process is about 20 GeV [18]. In addition, a more restrictive lower limit has recently been found. It has been shown that the lower bound is 200 GeV, considering H.E.S.S. and other updated observational data [19]. In particular, all the DM scenarios mentioned so far pay attention to the 2 → 2 number-changing process where a DM pair annihilates into a pair of SM particles, that is, the Weakly Interacting Massive Particle (WIMP) paradigm [20–22].[1] Moreover, it is not necessary that the number-changing processes involve SM particles, so they may also occur within the dark sector. The minimalistic realization of this scenario is the 3 → 2 process, where this kind of number-changing reaction involves a single DM species. In general, such processes arise in DM theories with new sizable self-interactions, and in several contexts as self-interacting DM [29–31], the Strongly Interacting Massive Particle (SIMP) paradigm [32–49], or even the ELastically DEcoupling Relic (ELDER) scenario [50, 51]. It is essential to mention that the early history of the universe plays a crucial role in DM genesis, since the decoupling of thermal DM occurred at that time. Generally, the studies of DM consider the standard cosmological picture in which the radiation energy density is assumed to dominate the energy budget before the Big Bang Nucleosynthesis (BBN). However, there is no direct evidence for the energy content at very high temperatures. Therefore, it is vital to look at the effects of modified cosmology on the production of DM. In recent times, the evolution of the DM in the period of non-standard expansion usually triggered by the decay of a long-lived massive particle [48, 52–71] or by Hawking evaporation of primordial black holes [72–95] is receiving increasing attention.[2] All such studies point towards the fact that non-standard cosmology alters the value of the thermally averaged cross section needed to satisfy the observed relic of DM. Such a modification in the thermally averaged cross-section may also change the unitarity mass bound of DM. In a recent article, the authors studied the impact of early matter domination on unitarity limits [112]. This article is decorated as follows. In Sections 2 and 3, we present the detailed derivation of the maximum thermally-averaged cross-section allowed by the unitarity of the Smatrix. We discuss two different non-standard cosmological pictures: kination-like and late-time reheating in Section 4. Section 5 shows the analytical expressions for freeze-out and cross sections for the radiation-dominated universe and the mentioned modified cosmologies, and we also demonstrate our results. Finally, we summarize our findings in Section 6. [1] Alternatively, one can also have in the final state a DM and a SM particle (semi-annihilations) [23–27], or in the initial state a DM and another particle of the dark sector (coannihilations) [28]. [2] For studies on baryogenesis with a low reheating temperature or during an early matter-dominated phase, see Refs. [52, 96–100] and [101–104], respectively. Furthermore, the production of primordial gravitational waves in scenarios with an early matter era has recently received particular attention [105–111]. This paper is available on arxiv under CC 4.0 license. Authors: (1) Nicolas Bernal, New York University Abu Dhabi; (2) Partha Konar, Physical Research Laboratory; (3) Sudipta Show, Physical Research Laboratory. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Nicolas Bernal, New York University Abu Dhabi; (2) Partha Konar, Physical Research Laboratory; (3) Sudipta Show, Physical Research Laboratory. Table of Links Abstract and Intro S-matrix: Unitarity and its Consequences Dark Matter Annihilation and Unitarity Bound Low-temperature Reheating Freeze-out with a Low-temperature Reheating Summary and Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro S-matrix: Unitarity and its Consequences S-matrix: Unitarity and its Consequences Dark Matter Annihilation and Unitarity Bound Dark Matter Annihilation and Unitarity Bound Low-temperature Reheating Low-temperature Reheating Freeze-out with a Low-temperature Reheating Freeze-out with a Low-temperature Reheating Summary and Conclusion Summary and Conclusion Acknowledgments and References Acknowledgments and References Abstract Model-independent theoretical upper bound on the thermal dark matter (DM) mass can be derived from the maximum inelastic DM cross-section featuring the whole observed DM abundance. We deploy partial-wave unitarity of the scattering matrix to derive the maximal thermally-averaged cross-section for general number-changing processes r → 2 (with r ≥ 2), which may involve standard model particles or occur solely within the dark sector. The usual upper limit on the DM mass for s-wave annihilation is around 130 TeV (1 GeV) for r = 2 (3), only applies in the case of a freeze out occurring in the standard cosmological scenario. We consider the effects of two nonstandard cosmological evolutions, characterized by low-temperature reheating: i) a kination-like scenario and ii) an early matter-dominated scenario. In the first case, the early freeze-out strengthens the unitarity bound to few TeVs for WIMPs; while in the second case, WIMP DM can be as heavy as ∼ 1010 GeV due to a large entropy dilution. 1. Introduction Consideration of a specific DM production paradigm in the early stage of the universe may further constrain the mass range for a viable DM candidate. For instance, the number-changing pair annihilation of DM to SM particles determines its present mass density, where it maintains the chemical and kinetic equilibrium with the thermal soup in the early universe. Interestingly, the requirement of the unitarity of the S-matrix sets a model-independent upper bound on the DM mass for this scenario [12, 13]. The implication of unitarity offers the maximum inelastic cross-section, which fixes the minimum number density of the frozen-out DM. Using this number density, one can establish the maximum allowed DM mass by fulfilling the observed relic density of it. In the theories of DM with long-range forces, bound states of DM can form and therefore relax the unitarity bound by suppressing the inelastic annihilation rate [14–16]. In addition, dark sectors with particle-antiparticle asymmetry enforce the apprehend of the nonzero equilibrium chemical potential for DM, further constraining the unitarity limits due to the demand for an increased effective DM number density at the time of freeze-out [15, 17]. Furthermore, different indirect searches for DM may put a lower limit on DM mass for some specified scenarios. A strong model-independent lower bound for the thermal DM that is annihilating to visible states through an s-wave process is about 20 GeV [18]. In addition, a more restrictive lower limit has recently been found. It has been shown that the lower bound is 200 GeV, considering H.E.S.S. and other updated observational data [19]. In particular, all the DM scenarios mentioned so far pay attention to the 2 → 2 number-changing process where a DM pair annihilates into a pair of SM particles, that is, the Weakly Interacting Massive Particle (WIMP) paradigm [20–22].[1] Moreover, it is not necessary that the number-changing processes involve SM particles, so they may also occur within the dark sector. The minimalistic realization of this scenario is the 3 → 2 process, where this kind of number-changing reaction involves a single DM species. In general, such processes arise in DM theories with new sizable self-interactions, and in several contexts as self-interacting DM [29–31], the Strongly Interacting Massive Particle (SIMP) paradigm [32–49], or even the ELastically DEcoupling Relic (ELDER) scenario [50, 51]. It is essential to mention that the early history of the universe plays a crucial role in DM genesis, since the decoupling of thermal DM occurred at that time. Generally, the studies of DM consider the standard cosmological picture in which the radiation energy density is assumed to dominate the energy budget before the Big Bang Nucleosynthesis (BBN). However, there is no direct evidence for the energy content at very high temperatures. Therefore, it is vital to look at the effects of modified cosmology on the production of DM. In recent times, the evolution of the DM in the period of non-standard expansion usually triggered by the decay of a long-lived massive particle [48, 52–71] or by Hawking evaporation of primordial black holes [72–95] is receiving increasing attention.[2] All such studies point towards the fact that non-standard cosmology alters the value of the thermally averaged cross section needed to satisfy the observed relic of DM. Such a modification in the thermally averaged cross-section may also change the unitarity mass bound of DM. In a recent article, the authors studied the impact of early matter domination on unitarity limits [112]. This article is decorated as follows. In Sections 2 and 3, we present the detailed derivation of the maximum thermally-averaged cross-section allowed by the unitarity of the Smatrix. We discuss two different non-standard cosmological pictures: kination-like and late-time reheating in Section 4. Section 5 shows the analytical expressions for freeze-out and cross sections for the radiation-dominated universe and the mentioned modified cosmologies, and we also demonstrate our results. Finally, we summarize our findings in Section 6. [1] Alternatively, one can also have in the final state a DM and a SM particle (semi-annihilations) [23–27], or in the initial state a DM and another particle of the dark sector (coannihilations) [28]. [2] For studies on baryogenesis with a low reheating temperature or during an early matter-dominated phase, see Refs. [52, 96–100] and [101–104], respectively. Furthermore, the production of primordial gravitational waves in scenarios with an early matter era has recently received particular attention [105–111].

Unitarity Bound on Dark Matter in Low-temperature Reheating Scenarios: Abstract and Intro

নিম্ন-তাপমাত্রা পুনঃউষ্ণকরণের দৃশ্যে ডার্ক ম্যাটারে ঐক্যবদ্ধতা: বিমূর্ত এবং ভূমিকা

Unitaritätsgrenze für Dunkle Materie in Niedertemperatur-Wiedererwärmungsszenarien: Zusammenfassung und Einführung

Unitaridad ligada a la materia oscura en escenarios de recalentamiento a baja temperatura: resumen e introducción

Unitarité liée à la matière noire dans des scénarios de réchauffage à basse température : résumé et introduction

निम्न-तापमान पुनःतापन परिदृश्यों में डार्क मैटर पर यूनिटैरिटी बाउंड: सार और परिचय

低温再加熱シナリオにおける暗黒物質のユニタリー性限界：概要と序論

저온 재가열 시나리오에서 암흑물질에 결합된 단일성: 개요 및 소개

Unitariedade vinculada à matéria escura em cenários de reaquecimento em baixa temperatura: resumo e introdução

Унитарность, связанная с темной материей в сценариях низкотемпературного повторного нагрева: Аннотация и введение

Düşük Sıcaklıkta Yeniden Isıtma Senaryolarında Karanlık Maddeye Bağlı Birlik: Özet ve Giriş

Tính đơn nhất bị ràng buộc đối với Vật chất tối trong các kịch bản hâm nóng ở nhiệt độ thấp: Tóm tắt và giới thiệu

低温再加热场景中暗物质的幺正性界限：摘要和简介

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Nicolas Bernal, New York University Abu Dhabi;
(2) Partha Konar, Physical Research Laboratory;
(3) Sudipta Show, Physical Research Laboratory. Table of Links Abstract and Intro
S-matrix: Unitarity and its Consequences
Dark Matter Annihilation and Unitarity Bound
Low-temperature Reheating
Freeze-out with a Low-temperature Reheating
Summary and Conclusion
Acknowledgments and References 6. Summary and Conclusion The requirement of the de Broglie wavelength of dark matter (DM) to hold it inside galaxies and the stability of the stellar galaxy cluster collectively put a broad allowed mass range for DM by providing the lower and upper bound of DM mass, respectively. Specifying some properties of DM can further tighten the mass range. Interestingly, one can put the model-independent upper bound by specifying the thermal production of DM in the early universe. The observed DM abundance and unitarity of partial waves from the scattering matrix jointly place an upper limit on DM mass. Primarily, the upper limits on the inelastic cross section for a general number-changing process 2 → r can be derived with the help of the optical theorem, the matrix elements and the elastic scattering cross section for the process. After that, one can obtain the thermally averaged cross section for the r → 2 process by invoking the principle of detailed balance. Finally, the unitarity bounds on the thermally averaged cross section translate to the upper limits on the DM mass satisfying the relic density constraints. It is known that the maximum allowed DM mass for the 2 → 2 and 3 → 2 DM annihilation processes is around 130 TeV and 1 GeV, respectively. However, these bounds not only depend on the particle physics model, but have a strong dependence on the cosmological evolution of the universe, being valid only if the universe followed the so-called “standard cosmological scenario”. Instead, this article explores the DM mass bound in nonstandard cosmological setups characterized by low-temperature reheating. In particular, we focus on i) kination-like scenarios, where the early universe was dominated by a fluid with an energy density that gets diluted faster than free radiation, and ii) early matter-dominated scenarios, where a component with an energy density that scales as nonrelativistic matter dominates the early universe and eventually decays into SM particles. First, we study the kination-like universe, which demands a larger thermally-averaged annihilation cross section to saturate the observed abundance of DM compared to the standard radiation-dominated picture since, in this case, freeze-out occurs early. As a result, the upper bound on the DM mass becomes more stringent than in the standard case. For example, if the reheating temperature is as low as a few MeVs (corresponding to the start of the Big Bang nucleosynthesis epoch), the usual bound of the DM mass m ≲ 130 TeV can be reduced to a few TeVs for WIMPs. Before closing, we want to emphasize that the evolution of the early universe is largely unknown. The standard assumption of a universe dominated by standard-model radiation from the end of cosmological inflation until matter-radiation equality, together with a transition from an inflaton-dominated to a radiation-dominated universe occurring at a very early time, cannot be taken for granted. Having that in mind, here we have studied the impact of the unitarity bound on DM in the case of low-temperature reheating scenarios. This paper is available on arxiv under CC 4.0 license. Authors: (1) Nicolas Bernal, New York University Abu Dhabi; (2) Partha Konar, Physical Research Laboratory; (3) Sudipta Show, Physical Research Laboratory. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Nicolas Bernal, New York University Abu Dhabi; (2) Partha Konar, Physical Research Laboratory; (3) Sudipta Show, Physical Research Laboratory. Table of Links Abstract and Intro S-matrix: Unitarity and its Consequences Dark Matter Annihilation and Unitarity Bound Low-temperature Reheating Freeze-out with a Low-temperature Reheating Summary and Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro S-matrix: Unitarity and its Consequences S-matrix: Unitarity and its Consequences Dark Matter Annihilation and Unitarity Bound Dark Matter Annihilation and Unitarity Bound Low-temperature Reheating Low-temperature Reheating Freeze-out with a Low-temperature Reheating Freeze-out with a Low-temperature Reheating Summary and Conclusion Summary and Conclusion Acknowledgments and References Acknowledgments and References 6. Summary and Conclusion The requirement of the de Broglie wavelength of dark matter (DM) to hold it inside galaxies and the stability of the stellar galaxy cluster collectively put a broad allowed mass range for DM by providing the lower and upper bound of DM mass, respectively. Specifying some properties of DM can further tighten the mass range. Interestingly, one can put the model-independent upper bound by specifying the thermal production of DM in the early universe. The observed DM abundance and unitarity of partial waves from the scattering matrix jointly place an upper limit on DM mass. Primarily, the upper limits on the inelastic cross section for a general number-changing process 2 → r can be derived with the help of the optical theorem, the matrix elements and the elastic scattering cross section for the process. After that, one can obtain the thermally averaged cross section for the r → 2 process by invoking the principle of detailed balance. Finally, the unitarity bounds on the thermally averaged cross section translate to the upper limits on the DM mass satisfying the relic density constraints. It is known that the maximum allowed DM mass for the 2 → 2 and 3 → 2 DM annihilation processes is around 130 TeV and 1 GeV, respectively. However, these bounds not only depend on the particle physics model, but have a strong dependence on the cosmological evolution of the universe, being valid only if the universe followed the so-called “standard cosmological scenario”. Instead, this article explores the DM mass bound in nonstandard cosmological setups characterized by low-temperature reheating. In particular, we focus on i ) kination-like scenarios, where the early universe was dominated by a fluid with an energy density that gets diluted faster than free radiation, and ii ) early matter-dominated scenarios, where a component with an energy density that scales as nonrelativistic matter dominates the early universe and eventually decays into SM particles. i ii First, we study the kination-like universe, which demands a larger thermally-averaged annihilation cross section to saturate the observed abundance of DM compared to the standard radiation-dominated picture since, in this case, freeze-out occurs early. As a result, the upper bound on the DM mass becomes more stringent than in the standard case. For example, if the reheating temperature is as low as a few MeVs (corresponding to the start of the Big Bang nucleosynthesis epoch), the usual bound of the DM mass m ≲ 130 TeV can be reduced to a few TeVs for WIMPs. Before closing, we want to emphasize that the evolution of the early universe is largely unknown. The standard assumption of a universe dominated by standard-model radiation from the end of cosmological inflation until matter-radiation equality, together with a transition from an inflaton-dominated to a radiation-dominated universe occurring at a very early time, cannot be taken for granted. Having that in mind, here we have studied the impact of the unitarity bound on DM in the case of low-temperature reheating scenarios.

Unitarity Bound on Dark Matter in Low-temperature Reheating Scenarios: Summary and Conclusion

নিম্ন-তাপমাত্রা পুনঃউষ্ণকরণের পরিস্থিতিতে ডার্ক ম্যাটারে একতা আবদ্ধ: সারাংশ এবং উপসংহার

Unitaritätsgrenze für Dunkle Materie in Niedertemperatur-Wiedererwärmungsszenarien: Zusammenfassung und Schlussfolgerung

Unitaridad unida a la materia oscura en escenarios de recalentamiento a baja temperatura: resumen y conclusión

Unitarité liée à la matière noire dans les scénarios de réchauffage à basse température : résumé et conclusion

निम्न-तापमान पुनःतापन परिदृश्यों में डार्क मैटर पर यूनिटैरिटी बाउंड: सारांश और निष्कर्ष

低温再加熱シナリオにおける暗黒物質のユニタリー性限界：要約と結論

저온 재가열 시나리오에서 암흑물질에 결합된 단일성: 요약 및 결론

Limitação de unitaridade na matéria escura em cenários de reaquecimento em baixa temperatura: resumo e conclusão

Унитарность, связанная с темной материей в сценариях низкотемпературного разогрева: итоги и выводы

Düşük Sıcaklıkta Yeniden Isıtma Senaryolarında Karanlık Maddeye Bağlı Üniterlik: Özet ve Sonuç

Tính đơn nhất bị ràng buộc đối với Vật chất tối trong các kịch bản hâm nóng ở nhiệt độ thấp: Tóm tắt và kết luận

低温再加热场景中暗物质的幺正性界限：总结和结论

Testing the Extents of AI Empathy: A Nightmare Scenario

এআই সহানুভূতির পরিধি পরীক্ষা করা: একটি দুঃস্বপ্নের দৃশ্য

Testen des Ausmaßes der KI-Empathie: Ein Albtraumszenario

Probando el alcance de la empatía con la IA: un escenario de pesadilla

Tester l’étendue de l’empathie de l’IA : un scénario de cauchemar

एआई सहानुभूति के विस्तार का परीक्षण: एक दुःस्वप्न परिदृश्य

AI 共感の程度をテストする: 悪夢のシナリオ

AI 공감의 범위 테스트: 악몽 시나리오

Testando a extensão da empatia com IA: um cenário de pesadelo

Проверка степени эмпатии ИИ: кошмарный сценарий

Yapay Zeka Empatisinin Kapsamlarını Test Etmek: Bir Kabus Senaryosu

Kiểm tra mức độ đồng cảm của AI: Kịch bản ác mộng

测试人工智能同理心的程度：噩梦场景

This paper is available on arxiv under CC 4.0 license. Authors: (1) Eleonora Alei, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (2) Björn S. Konrad, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (3) Daniel Angerhausen, ETH Zurich, Institute for Particle Physics & Astrophysics, National Center of Competence in Research PlanetS & Blue Marble Space Institute of Science; (4) John Lee Grenfell, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR) (5) Paul Mollière, Max-Planck-Institut für Astronomie; (6) Sascha P. Quanz, ETH Zurich, Institute for Particle Physics & Astrophysics & National Center of Competence in Research PlanetS; (7) Sarah Rugheimer, Department of Physics, University of Oxford; (8) Fabian Wunderlich, Department of Extrasolar Planets and Atmospheres (EPA), Institute for Planetary Research (PF), German Aerospace Centre (DLR); (9) LIFE collaboration, www.life-space-mission.com. Table of Links Abstract & Introduction Methods Results Discussion Conclusions Next Steps & References Appendix A: Scattering of terrestrial exoplanets Appendix B: Corner Plots Appendix C: Bayes’ factor analysis: other epochs Appendix D: Cloudy scenarios: additional figures Appendix D: Cloudy scenarios: additional figures In this section, we provide additional plots for the cloudy scenarios. In Figures D.1 and D.2 we show the retrieved exoplanet parameters and abundances for the different scenarios with varying R and S/N values. Finally, we plot in Figure D.3 the maximum difference ∆ between the cumulative posteriors for the different model parameters, for each combination of the cloudy scenarios and different R-S/N pairs. This paper is available on under CC 4.0 license. arxiv

The Finale of Our Exoplanet LIFE Study: Cloudy Scenarios

https://www.youtube.com/watch?v=JN3KPFbWCy8&embedable=true&transcript=true&long=true

[Transcript] Elon Musk and Lex Fridman on Supreme Beings, Alien Kidnapping Scenarios, and Diablo

This article focuses on extending the property pattern to the next level. Refer prerequisites section for the previous toll fare example used, and the following article extends that example further. Prerequisites Please review the article example below, which calculates a toll fare based on the vehicle type. Advance Property Pattern C# 8.0 The article describes how pattern matching provides an effective way to utilize and process that data in forms that…*medium.com Recap of requirements already covered: If the vehicle is Car => 100 Rs If the vehicle is DeliveryTruck => 200 Rs If the vehicle is Bus => 150 Rs If the vehicle is Taxi => 120 Rs Cars & taxis with “NO” passengers pay an extra 10 Rs. Cars & taxis with two passengers get a 10 Rs discount. Cars & taxis with three or more passengers get a 20 Rs discount. Buses that are less than 50% of passengers pay an extra 30 Rs. Buses with over 90% of passengers get a 40 Rs discount. Trucks over 5000 lbs are charged an extra 100 Rs. Light trucks under 3000 lbs, given a 20 Rs discount. Let’s extend the property pattern. The toll authority wants to add time-sensitive peak pricing for the final feature. Extended Requirements During peak hours, double the price; this rule applies in one direction, i.e., morning into the city and evening out of the city. inbound traffic outbound traffic Late-night & early morning tolls are reduced by 25%. During workday hours, the increasing toll is 50%. During weekends, the toll price is constant concerning time. Reference Table Let’s write some code. Let’s proceed with identifying each column of the reference table above. Identify Day Firstly the program should be aware of whether a particular day is a weekday or weekend. Refer new switch syntax. The above code makes use “ ” enum. System.DaysOfWeek public enum DayOfWeek
{
 Sunday = 0,
 Monday = 1,
 Tuesday = 2,
 Wednesday = 3,
 Thursday = 4,
 Friday = 5,
 Saturday = 6
} Identify Time Firstly, the distinct time slots available. private enum TimeSlots
{
 MorningPeak,
 Daytime,
 EveningPeak,
 Overnight
} Now let’s identify the time slots using switch syntax with time in hours. Identify Direction As there are two possible values of direction, it can directly be used as a boolean flag, i.e., if ‘true’ means inbound and if ‘false’ means outbound. True  => inbound  => Into the city
False => outbound => Out of the city The alternative is to create an enum and add switch syntax, similar to what we have done above. Identify Premium After all table columns are identified, the below switch expression with the tuple pattern calculates the toll premium. Refer to pattern-matching syntax with single & multiple property classes. Link The switch expression. Premium method call var costInbound = PeakPremium.CalculatePeakTimePremium(DateTime.Now, true); var costOutbound = PeakPremium.CalculatePeakTimePremium(DateTime.Now, false); Console.WriteLine("Charges {0} for {1}",costInbound,DateTime.Now); Console.WriteLine("Charges {0} for {1}", costOutbound, DateTime.Now); Output Charges 1.50 for 9/8/2020 12:05:29 PM
Charges 1.50 for 9/8/2020 12:05:29 PM Github Sample ssukhpinder/PropertyPatternExample Thank you for reading. I hope you like the article..!! Also published here.

Pattern Matching in C# for Complex Scenarios: An Extension on Property Patterns

It’s inevitable that at some point the world will end. The only question left is whether it will be in a slow, inexorable heat death of the universe, or something a little more active and exciting. Unsurprisingly most sci fi movies tend to explore the second option (although there are some excellent books and short stories such as Asimov’s which look at the first). The Last Question Since it’s my field, we’re going to look at how cyber security failings could cause these apocalypses (apocalypsi?), and which are most likely. We’ve got the deliberate corruption of global agritech, hijacking of DNA printers, the classic infrastructure hack, and autonomous smart device madness. Those of an anxious or nervous disposition may want to stop reading now. Instead, you can go watch something nice and light. If you’d like a more active distraction, then there’s some great games here to take your mind off the inevitable collapse of, well, everything. Now that we’re left with the incurably curious, lets crack on. Disclaimer: Don’t take these as inspiration. Also, if one of these does happen to be accurate, please note that neither HackerNoon nor I had anything to do with it. 1. Agritech, Agriculture, and Famine Agritech is about technology being applied to farming, especially more advanced and intelligent technology. As agriculture is more and more industrialised, we see traditional farming equipment replaced with advanced gadgetry such as smart tractors, sprinklers, and of course . drones At the 2011 DefCon Chris Roberts and Jesse Diekman demonstrated to the world just how bad cyber security flaws in the ever-expanding world of agritech could be. They’d managed to find a way to access and modify the manufacturer’s software repository, where updates to be pushed out to (in this case) tractors were constructed. The targeted tractor was definitely a labour-saving device, even handling planting of crops at the perfect depth and distance from one another for optimum growth. The software update in question controlled a number of variables, such as the depth and distance for planting. They showed that it could be edited, and staged for update to the full network of tractors. Caption: Not the tractor in question. Instead, an automated harvester for green asparagus developed by Muddy Machines. When dealing with highly industrialised farming measurements are vital. Everything is calculated as perfectly as possible, with very small margins for error. If you’ve gardened at all you’ll know the issues that planting an inch too low can cause. In this case, it could easily have led to grain crop failures across the American Midwest, which may not have been detected until after the planting season when it was too late to correct. There is no doubt difficulties would have quickly followed, especially as one strand of a multi-targeted attack to cause as much damage and disruption as possible. Worryingly, the manufacturers themselves worsen the situation by fighting desperately against the right to repair and trying to force their own central control. If something does go wrong, only the manufacturer can fix by design. Add to this the possibility of of disruption due to other attacks and while it’s a remote possibility, a well-resourced and multi-threaded attack could definitely push into agricultural crisis. Once food is disrupted on a severe enough scale, everything else follows. 2. Pandemics to Order - Bioweapons We’ve now seen what a pandemic can do, at least to some degree. COVID-19 is difficult to judge, but is probably far from in recorded history. Still, at least any attempt to weaponise a virus would surely need highly advanced knowledge and expensive equipment, and any such equipment would never be connected to the internet. the worst pandemic Of course, that ignores desktop DNA synthesisers which come with internet connections. The ones I researched and spoke about in 2021 are, luckily, just used for synthesising base pairs and so couldn’t create an entire custom virus. Still, technology advances, convenience trumps security, and efforts to put protections in place would best be described as struggling. https://www.youtube.com/watch?v=L9vXiqpLkKU?embedable=true While this is unlikely to be how the world ends, the more these technologies advance without putting effective cyber security in place the more likely it becomes. At the very least it would be good to see some sort of fiction about custom-designed killer viruses. Oh, never mind. 3. Out of Power While it’s the most popular option for the average cyber-crisis film about impossible missions, the whole idea of attacking infrastructure isn’t as effective as fiction would lead people to believe. There have been individual incidents of being targeted, more often for ransomware than poisoning, and famously Ukraine’s in 2015, but even in the worst cases these tend to be fairly localised. The good news is, that means even the worst case of a successful attack on national infrastructure is only likely to cause kilodeaths, rather than the megadeaths of something like nuclear warfare. water treatment plants power grid hack The reason for this is that, generally speaking, the people who run national infrastructure have resources, and are very aware of how appealing their systems are for someone wanting to cause harm. There’s also the advantage that different systems are diverse rather than standardised. The time to worry will be if there are significant efforts to standardise and centralise control systems for critical infrastructure - diversity makes systems more resilient, as attacks against one component are less likely to impact others. Frankly, critical national infrastructure failures are more likely to be equipment failures or human error rather than a team of hackers sitting in a room watching green code scroll past. 4. Killer Cars We are a long way from truly self-driving vehicles, despite the hype and enthusiasm we’ve had around them since at least the 1980s. There’s a lot of work before they’re , let alone legal, but it’s one of those things that keeps floating around the collective unconsciousness and popping up as ‘just a few years away’. safe There’s plenty of different models that could be applied to autonomous vehicles, ranging from centralised control to each vehicle making its own decisions. It’s not hard to predict the potential flaws of either model - malicious updates causing cars to become homicidal ala Cory Doctorow’s . We could even see centralised control hijacking as famously shown in . Attack Surface Minority Report That’s only talking about vehicles though, and we already have autonomous drones, including ones which are heavily armed. As with all smart devices, ultimately they do the bidding of whoever controls their software and encryption keys, which puts them as a very definite candidate for at least a local apocalypse. That’s assuming we don’t instead have to battle our own smart homes to the death, becoming more and more popular in film. Or have to deal with our cars locking us in and spontaneously combusting. https://youtu.be/IgZf-auOZxI?embedable=true The Good News In great news, any of these (or other cyber security attack scenarios) are almost certainly not the way the world is going to end. There are plenty of other options out there which are far more likely, and these are largely used as FUD (Fear, Uncertainty, and Doubt) to drum up anxiety rather than anything else - but that doesn’t mean there aren’t serious questions we need to be asking. https://www.youtube.com/watch?v=FcyNNqaXLg8?embedable=true While this is a bit of a light hearted take on things, there are serious conversations we need to have about how we deal with security and how we get from where we are to a place where at least things aren’t getting worse. I’ll just leave you with a little final thought, badly paraphrased from T. S. Elliot’s The Hollow Men. This is the way the world ends This is the way the world ends This is the way the world ends Not with a bang but with a HTTP ERROR 410

How Will The World End? Top 4 Cyber Apocalypse Scenarios

¿Como el mundo terminara? Los 4 mejores escenarios de ciberapocalipsis

Comment le monde finira-t-il ? Top 4 des scénarios de cyber-apocalypse

दुनिया कैसे खत्म होगी? शीर्ष 4 साइबर सर्वनाश परिदृश्य

世界はどのように終わりますか?トップ 4 のサイバー アポカリプス シナリオ

Como o mundo vai acabar? Os 4 principais cenários de apocalipse cibernético

Thế giới sẽ kết thúc như thế nào? 4 kịch bản khải huyền mạng hàng đầu

世界将如何终结？ 4 大网络启示录场景

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