Improving access to health: learning from the field. While ‘the field’ often denotes people, patients, communities, and healthcare workers, we challenge the notion and its usage. A group of like-minded conference participants set up a working group to examine the term ‘the field’ and look at questions related to language, power, participation, and rights. By highlighting deficiencies of existing terms and jargon, we explain why language is a form of power that matters in public health. We describe global, regional, and national case studies that facilitate full participation to achieve more equitable health outcomes. By concluding with concrete recommendations, we hope to contribute to these shared goals: to correct power imbalances between health authorities and the people that they intend, and are expected, to serve. The authors are all members of the working group.The theme of the 8th edition of the Geneva Health Forum (GHF) was  Improving access to health: learning from the field.” While ‘the field’ may denote people, patients, communities, healthcare workers, and others, we challenge the notion of ‘the field.’ The term limits those ‘in the field’ to research objects to be studied, as those requiring assistance, or as passive beneficiaries of services and policies. “Learning from the field” implies that learning or knowledge and related action and change remain with an ‘external’ or ‘superior’ actor . At its extreme, in health research and international development cooperation (‘aid’), people use the term ‘field’ to objectify others, reflecting unequal distribution of power and resources. This inequality persists. In health research, it leaves members of researched communities out of study design and implementation, or retains ownership of data and resultant publications in higher-income countries of the researchers, not those of study participants.The Geneva Health Forum (GHF), a bilateral event bringing together diverse stakeholders in global health, describes itself as “the forum of innovative practices in Global Health” and “one of the most important international conferences on Global Health” . The theResolving power imbalances will require efficient strategies to address root causes and manifestations. In our experience, the term ‘in the field’ most commonly refers to low- and middle-income countries (LMICs) as targets of public health interventions or subjects of research led by outside groups. Here, power differentials between researcher and study subjects are often greatest. For this reason, and due to the limits and focus of our analysis, we restrict our analysis of ‘in the field’ to LMICs, rather than to high-income countries.To examine power implications of the term ‘in the field,’ this Viewpoint focuses on deficiencies in communication in public health policymaking, research, and implementation of public health interventions. It involves case studies of stakeholder groups working towards alternatives. We conclude with policy recommendations for health research and funding institutions to improve collaboration ‘in the field.’learning from the field to one focused on jointly driving change. That is, to move from taking lessons back from ‘from the field’ to actively promoting equity-affirming public health advocacy, research, policy, and practice.A multisectoral group of representatives from civil society, academia, and funding organizations organized a workshop titled “From ‘learning from the field’ to addressing power imbalances and jointly driving change.” We aim to provide an alternative voice at the GHF, raising questions about power, rights, and ownership of the processes, management, and dissemination of public health research and policymaking. First we highlight the deficiencies in the relationship between health authorities, donors, service providers, research institutions, and the people that they intend, and are expected, to serve. We examine language and its use to explore people’s participation and power in health policymaking and research. Next we review global, regional, and national level case studies provided by working group members that may contribute to overcoming power imbalances and achieving a new agenda for ‘people’s participation.’ Our ultimate aim is to prompt reflection among participants in the GHF and to help the conference evolve from an approach of Literature review and analysis of key concepts, including communities, empowerment, ownership, political participation, social accountability, and community engagement (This is not an exhaustive list and relates only to the main topics under discussion).Introspective observation by sharing our personal experiences in the field of public health. We drew on previous analytical models, including the work of Xue et al., and Olgunick on the introspective observation method , 4.Group discussions in the form of coordinated virtual exchanges between experts around the world on the themes noted above.We identified case studies at national, regional, and global levels through our literature review and complemented it with our personal experience and involvement in many of them as researchers, advocates, and funders of public health interventions. Although far less than a comprehensive list of the universe of work in this area, we believe the initiatives we describe provide a broad overview of some of the most salient examples of ongoing efforts to advance equity in global health research, funding, and practice. Our group held five virtual consensus meetings from October 2019 to February 2020 to debate and arrive at the views expressed here. Debates during our meetings allowed us to combine our thinking and draft this Viewpoint using an iterative process and sharing opinions to reach consensus.From September 2019 to January 2020, our group followed appreciative inquiry technique to explore our own understanding and perspectives on “learning from the field to jointly driving change.” Appreciative inquiry can be used as a tool to “support discovery, dreaming, design, and creation of a vision that inspires people…to move to a collective destiny” [In 2018, primary health care (PHC) attracted renewed attention on the fortieth anniversary of the Alma-Ata Declaration. WHO defines PHC as a “whole-of-society approach to health and well-being centered on the needs and preferences of individuals, families, and communities. It addresses the broader determinants of health and focuses on the comprehensive and interrelated aspects of physical, mental, and social health and well-being” . The annWe see the first challenge as a rhetorical one: inaccurate terms lacking shared meaning inundate the global health vocabulary, littering the field with jargon and hollow buzzwords about people, participation, and power. Observed communication gaps could be a symptom, not the cause, of a larger problem: a power imbalance. ‘Poor wording’ could be a manifestation of broken or unequal relationships resulting from power imbalances. Language matters; acknowledging its power is a key step for building people-centered health research and systems for more equitable outcomes.For example, ‘citizens’ in different contexts may be individuals or organized groups. With the rise of displacement, migration, and the increasing challenge of providing health for all, despite global commitments for universal health coverage (UHC), the term ‘citizens’ is not always clear. Galjour and Russell have defined ‘civil society’ as a “diverse and complex sector, comprising non-state actors from an array of interest groups, networks, organizations, and institutions” that often share goals . The expRegional and national authorities commonly specify public health programs and policies, yet public health services are typically implemented at a local level. We adopt the definition of “public health” as put forward by the Editors of this journal that refers to “all activities that affect the health of the population: the environments where people carry on their personal and work lives and that produce the food, air, water, transportation systems, and services on which they depend; nutrition, climate, education, income, race, and poverty, and so on” . Logical‘Empowerment’ is a buzzword in health promotion and health cooperation. ‘Community empowerment,’ as defined by the World Health Organization (WHO), “refers to the process of enabling communities to increase control over their lives” . But if through the enjoyment of individual or collective rights and responsibilities [‘Power’ does not refer to the expression or enjoyment of rights. We need to broaden our understanding of power in its varied dimensions, such as psychological, cultural, social, political, and economic—by not limiting the definition of power to rights, or their expression, alone. Molm summarizes decades of research on power balances , defining power as a “structural potential created by the mutual control that persons exercise over each other’s outcomes.” Molm differentiates between power and its use . Our objbilities .‘Ownership’ in health policies and health promotion is based on its common-sense definition “as the act, state, or right of possessing something” . It inclPolitical participation “includes a broad range of activities through which people develop and express their opinions on the world and how it is governed and try to take part in and shape the decisions that affect their lives” . To fullWe return to our first objective, to explore language and its use in people’s participation and power in health policymaking. Policies influence local implementation of research findings. These policies then influence practice. The presence of empowered and engaged people with roles in designing and implementing health policies is relatively new in many countries. Expressions of this are as diverse as the states and interpretations and instruments of democracy. These range from a high level of formalization and institutionalization at national and subnational levels to realities of oppression or poor governance where participation has little space and must be expanded through resistance and social action. In claiming their rights, people’s movements often call for policy change at national and even international levels. People’s participation and engagement in planning, decision-making, and implementing policies lead to empowerment and redistribution of power, but also to increased knowledge, authority, and problem solving , 20.“…political decentralization…” to “…give citizens or their elected representatives more power in public decision-making. Its goal is to introduce more participatory forms of governance by giving citizens, or their representatives, more influence in the formulation and implementation of health policies and plans” .Malena et al. define social accountability as “an approach toward building accountability that relies on civic engagement, i.e., in which it is ordinary citizens and/or civil society organizations who participate directly or indirectly in exacting accountability.” [Decentralization of national health systems moves decision-making away from centralized control and closer to the users of health services. The World Health Organization (WHO) has promoted this for decades to overcome the ineffectiveness of national health administrations . WHO’s dbility.” . We apprbility.” . This moUHC means that all individuals and communities receive the health services they need without suffering financial hardship. It includes the full spectrum of essential, quality health services, from health promotion to prevention, treatment, rehabilitation, and palliative care .The objective of this Handbook is to assist WHO Members States increase social participation and community engagement in the health sector, acknowledging that social participation requires skills in which governments will have to invest further to reach UHC goals [Indeed, proliferation of movements, initiatives, guidelines, tools, and methodological advances from 2010 to 2020 illustrate growing interest in social accountability. These movements for participation and social accountability directly question power imbalances and demand (re)distribution of power. At the time of writing this paper, the Partnership for Universal Health Coverage (UHC2030) had been working closely with the WHO to draft a “Handbook on Social Participation for UHC,” launched in December 2020 . AccordiHC goals .‘Community engagement’ is an important concept in achieving the PHC vision enshrined in the Alma-Ata Declaration that stretches back more than forty years. The WHO Member States clarified in 2016 that “empowering and engaging people and communities” is the first strategic approach in the WHO Framework on Integrated People-Centered Health Services . The WHODoes aid (particularly when it situates people and ‘communities’ at its core) allow people to better claim their rights and properly engage in changing the root causes of their realities?Alternatively, do aid and its actors distort people-centered national health policies, systems, and negotiation processes?Voices of those most affected by health inequity are regularly excluded from the conversation. This discussion has been taken up most recently in the “Kampala Initiative,” a civil society initiative aiming to “advance cooperation and solidarity for health equity within and beyond aid” [In addressing power imbalances in the design and implementation of health policies, the role of international cooperation (‘aid’) and its actors deserve attention. While certain patient groups and ‘communities’ in low- and middle-income countries might benefit from attention and support given to them by the aid sector, the key question to be answered is the same as for other aspects of ‘aid.’ This question is part of a critical analysis of the “relevance, legitimacy, and effectiveness”—in a debate that is as old as aid itself .Does aidond aid” .  , bottom-The People’s Health Movement formed in 2000 when concerned individuals from diverse backgrounds in health, civil society, and academia gathered to create the first People’s Health Assembly . From thThe World Federation of Public Health Associations (WFPHA), founded in 1967, is one of the oldest global networking groups, representing about five million public health professionals worldwide and working with the WHO and others in “promoting global public health” . In collEQUINET, the Regional Network on Equity in Health in East and Southern Africa, is “a network of professionals, civil society members, policy makers, state officials, and others within the region who have come together as an equity catalyst, to promote and realize shared values of equity and social justice in health” . In ZimbPower imbalances in research collaborations between researchers in high-income countries and study subjects in low-income ones are well documented in the literature, yet power imbalances persist. To be most effective, health research should consistently address issues of benefits, representation, legitimacy, and accountability, building on generations of lessons learned “from the field”– and then go one step further. If health research is to move from an approach of “learning from the field” to jointly driving change, then proactively identifying—and correcting—power imbalances between “researchers” and “researched” is crucial. If people  effectively claim their rights to participate in all stages of research processes whose outcomes affect their own health , this might even lead to overcoming an artificial dichotomy between researchers and those ‘researched.’Is there a minimum owed to individual study participants and their communities when outside researchers conduct research in collaboration with local researchers?Is there a minimum set of ethical obligations and benefits owed by outside researchers to study subjects or communities under study?Lairumbi et al. reviewed ethics guidelines internationally and found broad agreement on the sharing of benefits as a standard practice of global health research partnerships. International guidelines, however, fail to indicate a consensus on any standard or on specific ethical obligations to ensure the social value of the research, which, for Lairumbi et al., “at a minimum refers to efforts aimed at ensuring global health research contributes to improvements in human health, through for instance the generation and application of generalisable knowledge” [Despite ethical standards for the conduct of health research, insufficient consideration has been given to the larger, macro-level ethical implications of health research.owledge” . They reowledge” . There aowledge” .As a step toward basing collaborative partnerships based on trust, it may be important, particularly for multi-year studies, to take a short- and long-term view. Establishing trust with people and communities takes time, beyond short-term cycles. Too often public health researchers and professionals lack understanding and investment in developing methods to provide accurate short-term and near-term predictions in public health. Yet, a science-based public health strategy requires us all to recognize and assess the consequences of any changes in public health. An ecological study from Alaska identified a key factor in its success as “reconceptualization of intervention research time frames beyond the typical three- to five-year grant funding cycle, to an enhanced understanding of how change in complex systems occurs over a time span of decades, not years” . In the In our experience, participatory action research (PAR) is the most convincing methodology for changing patterns and closing the gap between researchers and those ‘researched.’ PAR arose in part, as described by Baum, through Freire’s work in the 1970s in low-income settings, where he worked with communities to question and analyze their oppression with the aim of bringing about social change . WritingThe progress in overcoming power imbalances in health research remains varied. Full and unfettered participation of affected groups persists; box-ticking “community involvement” as “tokenization” continues. A systematic review of community participation in health systems research in LMICs from 2000 to 2012 suggests that "community participation in health systems interventions” remains uneven, “with few being truly community directed” . In a niPublic health professionals should avoid references to “the field” as the term distances the people whose concerns should be at the center. Whenever possible, people should use names of actual people, places, and organizations in lieu of generalizations that can seem to dismiss needs, challenges, and realities of actual people, individuals, and groups, and of their life experiences.Map comprehensively and analyze stakeholders’ interests to ensure all avenues of engagement are explored and pursued among affected populations;Involve communities in defining research questions and disseminate research findings to the study populations, formally documenting this step;Publish study findings in local languages of the study population and always acknowledge the contribution of communities;Engage with local organizations that conduct health research to advocate for equity-affirming practices in public health through the use of inclusive and transparent language. Use plain language easily understood by affected people facilitates their participation, empowerment, and ultimately their ownership of study or policy results.We offer five concrete recommendations for public health researchers and practitioners to facilitate people’s participation in health research and policymaking. Acting on these recommendations can begin to address inadequacies and inequities that remain. Research and science should drive people’s active participation in ‘owning’ their health and their health systems.We provide an alternative perspective on the term “in the field” using an appreciative inquiry approach to raise fundamental questions about power, rights, and knowledge ownership in global health. Far from being an optional ‘extra,’ we believe that people must be the first point of reference in analyzing barriers to access to health care, in the design of new health products or strategies, and in piloting, implementing, and monitoring health programs and health research. Many initiatives to improve people’s participation in health policymaking and research, such as those profiled above, and more broadly, the growing attention given to people’s involvement in health, health policy and planning, health practice and services, and research about all of these, illustrate a growing recognition that people must lead in analyzing and overcoming power imbalances in pursuit of more equitable health outcomes. Creating responsive public institutions that provide people with basic services is fundamental to the contract between governments and people. The next step beyond people ‘receiving’ the right services is people’s active participation in prioritizing, planning, and managing all elements of their own health systems  and the equitable (or not) outcomes that those systems produce. We advocate greater awareness of language and its uses, as a foundational element of behaviors that spring from it, and of minimum standards to establish a common language and behavior for governments, local populations, donors, implementors, and policy makers to promote health equity. While some progress has been made in the last two decades in improving the quality of people’s engagement in health policymaking and health research, including examples above, much remains to be done to move from an approach that seeks to “learn from the field” to one that proactively and “jointly drives change” in the pursuit of more equitable health outcomes.


Neurooncology is a young specialty which initially dealt mostly with glioblastoma patients with a short overall survival (OS). Yet, recently the scope gradually expanded by taking care of lower-grade glioma (LGG) patients with a longer OS . HistoriTwenty years later, it is now admitted that (i) beyond seizures, LGG patients suffer from cognitive and behavioral deficits at diagnosis even in incidental cases  (ii) thiMoreover, neurooncologists had to pay more attention to quality of life (QoL) for patients who must learn to live with a chronic neoplastic disease.On the other hand, because LGG will systematically recur, further adapted treatments have to be administrated . HoweverTo provide more reliable prognostic factors, advances in molecular biology led to a new classification designed for more appropriate decisions . SurprisHere, the main purpose is to redefine what “best quality of care” means by considering both tumor characteristics and patient’s personal criteria. The ultimate goal is to give the choice of therapeutic orientation at each step thanks to honest although complex and time-consuming information highlighting oncofunctional balance and various strategies individually adapted over time in parallel with changes in tumor behavior and patient’s expectations.Official guidelines, elaborated on EBM and mostly relying on randomized controlled trials (RCTs), were primarily designed to help physicians within a framework facilitating decision making and thus defining a “quality of care”.Particularly, progress allowed a refinement of the WHO classification increasingly based on genetic profiling , 16. ThiThese examples illustrate the drift in the utilization of EBM originally defined as “the conscientious, explicit, and judicious use of the current best evidence in making decisions about the care of individual patients” . Yet, thThird, EBM was not designed to validate a multistep strategy over years. Indeed, time-scales are different between the long life expectancy of patients and many RCTs with only a short follow-up which optionally use surrogates  to demonstrate within the time allowed a significant difference regarding investigated parameters. This “reality of the moment” does not reflect long-term OS and QoL, e.g., early RT may have an impact on PFS but not on OS  while geFourthly, whereas the quality of care relying on RCT depends on a reductionist panel of criteria, the selection of parameters “officially recognized” as decreed under the guise of EBM is questionable. For example, velocity expansion diameter is not incorporated in trials while it is an independent prognostic marker not correlated to molecular profile  and moreTo sum up, due to a new orientation of EBM different from the Sackett et al. seminal concept  this “prBecause LGG patients live one to two decades, neurooncologists should learn to anticipate functional considerations. Indeed, a major lack of “precision-medicine” in gliomas is to prioritize analysis of PFS and OS as first endpoints at the expense of QoL. However, if a patient is doing well, this means that he/she is still alive, while the reverse is not true. Therefore, QoL should be more systematically considered as the main endpoint since LGG patients should have an active life . Yet, phà la carte , these criteria are nonetheless essential to elaborate new guidelines paving the way for “quality of care.” Neuro-oncologists should ask the patient to define his/her own expectations and adjust the management accordingly , 33, e.gla carte . Indeed,la carte , 56. Thela carte , 11.Beyond the fundamental opposition between precision medicine relying on molecular EBM and individualized multistep therapeutic approach adapted over years, “best quality of care” starts by giving the choice to the patient and family and by honestly detailing both philosophies. This approach of complexity is time-consuming and poorly suited to productionist practices of our care systems. It is, nevertheless, possible, independent of the socio-cultural level of each patient, and it represents the condition of a true interactive patient-centered medicine, far from a “unique solution” dogma.The other risk of a single thought is to disempower the physicians who will not continue to actively discuss the best therapeutic option tailored to each patient but only passively apply a “standardized protocol”. This could lead to an impoverishment of knowledge, failing to see the full picture if all alternatives are not critically considered anymore. The ultimate danger would be to end up with strategies exclusively dictated by processing of large databanks with pre-defined reductive parameters or to use artificial intelligence methods disconnected from clinical practice and real life: this may turn doctors into uncritical executing agents.Therefore, official recommendations should only be a guide, and tumor boards should provide consultative proposals but not become too oppressive ; otherwise, a rigid EBM might kill innovation, which is still essential because glioma patients cannot yet be cured.In summary, although efforts have been made to excavate different molecular subtypes from the formerly not well-defined mix of gliomas LGG , 58, morLT and HD contributed to conception and design of the study. HD wrote the first draft of the manuscript. LT and TO wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mouse dynamics is a hot topic under study in session-based authentication and intrusion detection in privileged access management systems. We contribute to this topic, which develops with the increase of big data capabilities, with a missing data set in the field. This data article describes an extensive dataset with free usage mouse dynamics data. This dataset has seven variables and 24 users with 2550 h of active usage data. Each user has training data, internal attack test data, and external attack test data. An application in Python programming language that continuously listens to mouse movements and mouse clicks and writes them into a file with a timestamp, foremost window name, and mouse action details, is implemented. Among 24 unique users, we labeled five users with the least amount of data as external users since their data would be weak for training purposes, and we can test external threat detection. Each user has training data consisting of 10 days with the most frequent mouse usage, and reminder days of data are used for internal attack test data. This dataset is highly suitable for testing the under-development procedures against the insider threat, remote unauthorised access, and physical access. Differently from similar datasets, it has an excessive amount of sessions. Also, it includes more columns that can be used to infer more accurate results.•Dataset can be valuable for students, engineers, and especially researchers studying behavioral biometrics. Similar datasets have small amounts of data with a low number of users, where we have 24 unique users with 157.377 different sessions.•This dataset can be used to analyze mouse usage patterns, train neural networks and deep learning models for user identification and verification.1This dataset includes mouse behavior data which is collected from 24 different participants. Dataset is split into three categories; training, external test, and internal test, with 157.377 sessions in total. These categories are explained in detail in the next chapter. In order to measure classification performance, we have to know which test sessions really belongs to the corresponding user and which test sessions do not. We created a file in the dataset directory named “labels.csv” for this purpose, which has two columns; session name and authenticity of the session. Session names are unique. If a test session belongs to the corresponding user, we label it 0 (authorized). If that session belongs to another user, we label it 1 (unauthorised). Each user has its own directory for session files. There are three directories for training data, external test data, and internal test data in each users' directory, which include session files.Collected data has 7 columns; action type, timestamp, mouse cursor X location, mouse cursor Y location, clicked button (None if it is not a click), click state  and foremost application window name respectively. Dataset columns are listed with detailed descriptions in For all users, browsing  is the most time spent action by far with 51 percent. Development applications  are second with 19 percent, Office applications  are third with 17 percent, filesystem applications  are fourth by 11 percent, and finally, entertainment applications  are fifth by 2 percent.2Experimental users were selected from different positions from a software company, allowing users to use different programs and tools in the office environment. Each user participating in the experiment is loaded with a program that is launched at startup. This program is designed to collect the user's mouse movements without being tied to a specific task. This program is based on the listener callbacks are invoked directly from an operating thread on OS platforms. Thus, users are not prevented from performing daily activities since very little processing power, and memory are needed while collecting mouse movements. Each user uses this program on their computer for one month, and the mouse movements are recorded into .csv files. The raw data records are encrypted with the user's password and taken to the platform where the preprocessing is performed securely.The raw data records contain the recording time, x and y coordinates, button, state, and the process name of the window that is open while the motion is executed. The first preprocessing is done to divide the process names of the users into the activity type. We have collected all the process names in 5 groups: file system, browsing, development, office, entertainment. This is achieved by manually tagging all process names in an Excel file. Operating system-induced noises and incomprehensible process names in process names were collected under the file system label. We have determined that incomprehensible process names are related to the library we are using, having problems catching “explorer.exe” in the Windows operating system.After the raw data is tagged, it is divided into sessions. To ensure that training and test data are separate for all experiments, we decided to separate training and test data to be collected in separate folders. Overall, each user has their own training sessions, “internal_threat” sessions in which performance against internal threats is measured, and “external_threat” sessions where performance against external threats is measured.Preparation of training sessions starts with selecting the target user's ten most active days. These active days are the days with the most data pertaining to the sessions. The data of each day are shared in the training folder as separate sessions. Thus, 10 training sessions for each user are obtained. Before dividing the sessions into folders to be created for performance measurement against internal and external threats, sessions are created with 30 to 100 actions selected from each user's remaining days. Action description is essential here. We split the behavior of users into five main actions. These are free mouse movement (MM), drag and drop (DD), left single click (LC), right single click (RC), double click (DC). Rules are defined to interpret lower-level events into these higher-level actions.Although this data set is offered as benchmark data for detecting unauthorised account usages, during its creation, no such misuses were carried out. To measure performance against internal and external attacks, the test sessions belonging to the target user and some of the test sessions of other users are randomly selected and collected in the same folder. Thus, the attacks are imitated. Here, all sessions in the “internal_test” folder are selected from the test sessions of the users in the training folder. However, from the sessions in the “external_test” folder, those representing unauthorised sessions are selected from users who are not in the training set. We make the distinction here by selecting 19 users with the most active data from 24 users' data pool for training sessions. We convert all data of the remaining five users with relatively less data into test sessions and distribute them to the “external_test” folder. In conclusion, we have 19 users' data for training, 19 users' data for internal testing, and 5 users' data for external testing.The authors declare that they have followed the general ethics rules of scientific research performance and publishing. Participants were volunteers and they were informed. In addition, data is anonymized in terms of process names and usernames.  and 2.FiArjen Aykan Kılıç: Software, Data curation, Visualization; Metehan Yıldırım: Data gathering, Methodology, Conceptualization; Emin Anarım: Supervision.The authors declare that they have no known competing for financial interests or personal relationships that could have appeared to influence the work reported in this paper.


In 1886, Bernhard von Gudden and three other expert psychiatrists diagnosed the Bavarian King Ludwig II with “paranoia (madness),” a diagnosis that the Bavarian government used to justify removing Ludwig from power. Although Ludwig was not evaluated in detail by the psychiatrists, in their opinion, sworn eyewitness accounts and general knowledge about Ludwig’s behavior provided sufficient grounds for the diagnosis. Ludwig was a great admirer of the musician, Richard Wagner, and shared some of his ideas of an idealistic society. At first, he identified with Wagner’s opera heroes, and he became Wagner’s patron sponsor for life. However, he grew increasingly interested in an absolutist state, envisioning himself as a monarch with a role similar to that of Louis XIV. His multiple building projects, for which he incurred much debt, his conviction that he was descended from the Bourbons through baptism, his increasingly abnormal behavior, and his hallucinations together formed the basis for the psychiatrists’ diagnosis. Although not mentioned in the expert opinion, Ludwig’s homophilic behavior—a scandal at the time—was probably also an important reason for his removal from office. A review of the psychiatric knowledge and societal philosophy of the time indicates that the psychiatrists were correct with their diagnosis in their time. For more than 150 years, people have questioned whether Ludwig II, the King of Bavaria (1845–1886), was actually ill. An alternative explanation for his behavior is that he was a young man who was certainly eccentric but also highly gifted, not only a patron of the arts but also fond of them, and who was driven into extreme, ultimately fatal isolation by his contemporaries’ lack of understanding. Or, even worse, was psychiatry, acting on behalf of the government, responsible for the King’s death? .Most of those who study Ludwig think he was mentally ill –6. HowevWhen attempting to answer this question, one enters the “minefield of retrospective diagnosis” . ImposinAfter the death of Maximilian II in 1864, 18-year-old Ludwig became king, even though he was largely unprepared for the role . The monHis first governmental action was to have someone find Richard Wagner 1813–1883) and invite him to Munich. Ludwig had become an early “Wagnerian” and had read his Kunstwerk der Zukunft (The Artwork of the Future). He internalized especially “Tannhäuser” and “Lohengrin,” with whom he almost identified, much to the amazement of the people around him. Early on, in Wagner and his view of art and the state Ludwig saw a person of his own art-oriented nature who also had similar thoughts about the concept of an absolutist state , 18. Wag13–1883 aLudwig had in mind an absolutist monarchy like that of Louis XIV, in which the king shines over everything like the sun as a provider of energy and a role model. Thus, in his imaginings, there would have been no need for either a government or a parliament but only for idealistic co-helpers. This anachronistic idea was opposed by the constitutional monarchical state order imposed by the Bavarian Constitution of 1818 . Under WWagner, royal saxon court music director at the time, was a revolutionary from the barricades in Dresden who had been sought by warrant since 1849. His concept of a state thus clearly differed from that of the young Ludwig II, but his influence on his younger friend made the scandal predictable. When Wagner demanded the dismissal of the ministers Pfistermeister and von der Pfordten (“Pfi and Pfo” ), it wasWere Ludwig’s points of view already unsound early in his monarchy? Probably not. But they were generally considered to be anachronistic and dangerous. Ludwig enabled the premiere of “Tristan und Isolde” in 1865, financed the “Ring” and the Bayreuth company and supported Wagner as one of the greatest patrons in the history of music. Any attempt to reduce the relationship between Ludwig and Wagner, which was very close only until 1869, to a giver and taker role or even to a homoerotic relationship would considerably reduce Wagner’s ongoing role in Ludwig’s ideas and artistic desires , 18.Ludwig started with his fantastic castles, which are marveled at today, his self-presentations, the “scenery” of his life. His “identification with Lohengrin,” which annoyed and ultimately led to the loss of his bride ‘Sophie in Bavaria’ because he demanded that she play the role of “Elsa,” Lohengrin’s bride, became tangible with the building of Neuschwanstein (1869), together with the “Tannhäuser world.” Schloss Linderhof (1870), the purest French Rococo, represented Ludwig’s second major theme, the world of the Bourbons, which he acted out in lonely nocturnal sleigh rides dressed up as Louis XIV, even with a scepter and crown. The Schloss Herrenchiemsee (1873) houses a representation of Ludwig’s godfather genealogy, which became increasingly important to him but which he tried to hide from the public . He feltWas this art world already outside the norm? Did it border on the paranoid? It probably did! For example, Wagner said to his wife Cosima , “…we wCosima’s diary  often reWe do not know when the perhaps legitimate but “overvalued” idea of the baptismal lineage from the Bourbons transformed into a systematic delusion. In any case, Ludwig did not attend the 700-year Wittelsbach jubilee in 1880 because of his high state of arousal and highly pathological ambivalence that lasted for months . Witness” (forbidden love): “I do not know them/and I do not want to know them, who call themselves the benefactors of my days…an eternal enigma…” This quote describes Beatrice’s denial of her ancestry [With a rather mysterious aura, Ludwig quoted more and more often a verse from Schiller: “I wish to remain an eternal enigma to myself and others,…” This shortened version rather appears to hide the actual meaning. The literary connoisseur Ludwig quotes Beatrice from Schiller’s “The Bride of Messinaancestry .On February 5, 1884, Ludwig was treated by Dr. Franz Carl Gerster, a young doctor and dentist who, at the request of the Bavarian administrative government, was also trained in psychiatry by Charcot in Paris. Because it was no longer possible to use gut strings to attach any kind of artificial teeth to Ludwig’s few remaining molars, Gerster suggested fitting a palate plate. Ludwig immediately asked if Louis XIV also wore a palate plate. Gerster describes with psychopathological competence the 4-h-long night meeting with Ludwig. He determined that Ludwig had an accelerated flow of ideas, flight of thoughts, alogical stringing together of thoughts, delusions, and illusory and hallucinatory phenomena. He informed the responsible authorities, who told him clearly that his strong suspicion of mental illness “was interpreted and branded by all as high treason.” Gerster published his experience anonymously in 1886, using only his first and middle name . Gudden Plans to remove Ludwig from office began no later than fall 1885. The loans to build his palaces, the uncontrollability of further debts, the behavioral problems, the discontinuation of contact with his government, the holding of court in a manner not befitting his status, and in particular the rumors about a somewhat homophile life with the “Chevaux-legers,” a light cavalry unit, and the transfer of Bavarian cavalry soldiers to the valet service, forced the government to act . The staBernhard von Gudden, Professor of Psychiatry at the Ludwig Maximilians University in Munich since 1874 and director of the asylum, was the treating psychiatrist for Ludwig’s brother Prince Otto, who had had schizophrenia since the age of 20 , 24. TheThe report lists Ludwig’s abnormalities from birth onwards Table . AlthougThe report mentions birth, early childhood development, heredity, then the first early symptoms in Ludwig’s development that may have been psychopathological. No later than 1881, however, behaviors are mentioned that suggest the existence of a psychotic illness not only in the psychiatry of the time. The episode with Gerster in early 1884 suggests that Ludwig’s self-imposed isolation at least played a role in preventing an earlier diagnosis. The symptoms documented from March 1886 on after witness questioning then justify the diagnosis of “paranoia (madness)” .Ludwig’s unstoppable construction-related debt, however, played only a minor role, or it was just a trigger. From a psychiatric perspective, his swiftly changing ideas, ranging from ways to procure loans to giving orders to rob a bank, were part of the paranoid illness. The financial scandal—Ludwig was threatened by an “auction,” i.e., insolvency proceedings before civil courts—was surpassed by Ludwig’s homophilic practices, which were only hinted at between the lines and never explicated, even after his death. The transfer of cavalry soldiers to the valet service was increasingly scandalized in the press , 12. At Gudden was considered to be an outstanding academic teacher, scientist, and clinician , 28. He The term “démence précoce” (Morel 1860) was not yet in use in German psychiatry; paranoia only became “dementia praecox” under Kraepelin in 1893. Bleuler then coined the term “schizophrenia” in 1908. In the “degeneration doctrine,” Gudden’s contemporaries discussed possible connections between psychological performance requirements and the price that would have to be paid for them , 33. KraIf one considers Ludwig’s symptoms, which developed with a clear crescendo, one can find traces of sociopathy in his increasing avoidance of contact. The fact that he showed empathy not only at a young age is apparent from his relationship with his schizophrenic brother Otto , 24, andThe frequent allegation that Ludwig was not properly evaluated is not completely true. Gudden performed an unplanned assessment when the commission he was in charge of took Ludwig into custody in Neuschwanstein on the night of June 12, 1886. Because of Ludwig’s acute suicidal behavior—he threatened to jump from the tower—the first contact with him, originally planned for 4 a.m., had been brought forward to midnight. Between then and the scheduled departure with the police at 4 a.m., Ludwig and Gudden spoke for almost 4 h . Gudden In a statement in Der Nervenarzt in 2019, Haefner , one of The few days of direct contact with Ludwig reaffirmed Gudden’s diagnosis , and a fToday, extensive, partly new information is available. Ludwig’s autopsy revealed that the purulent meningitis he suffered as a 7-month-old infant, which was followed by a long failure to thrive, left behind scars in the frontal lobes, and that he also showed frontotemporal atrophy, which could be the basis for an incipient organic delusional psychosis , 23 , possibly also as “schizotypal personality disorder” . The lat


After an initial phase of playful exploration, the agent can execute kinematic simulations of the environment's future and use those for action planning. We demonstrate its performance on a set of several related, but different one-shot imitation tasks, which the agent flexibly solves in an active inference style.Human learning and intelligence work differently from the supervised pattern recognition approach adopted in most deep learning architectures. Humans seem to learn rich representations by exploration and imitation, build causal models of the world, and use both to flexibly solve new tasks. We suggest a simple but effective unsupervised model which develops such characteristics. The agent learns to represent the dynamical physical properties of its environment by intrinsically motivated exploration and performs inference on this representation to reach goals. For this, a set of self-organizing maps which represent state-action pairs is combined with a causal model for sequence prediction. The proposed system is evaluated in the  During the last decade, rapid progress in the field of deep learning has led to a number of remarkable achievements in many fields of artificial intelligence (AI) . HoweverHere we suggest a simple neural network architecture which learns to represent the dynamic physical characteristics of its environment in an unsupervised, exploratory way. By inference on the basis of this representation, the system can plan actions to reach externally given or intrinsically generated goals. In the following, we summarize related work and outline the proposed model.Impressive performance of deep learning approaches has been demonstrated in some classical supervised tasks such as object  or speecDeep learning approaches are in essence model-free and require massive amounts of labelled examples—orders of magnitude more than humans—in order to learn a complex task , 10.In most deep learning approaches, the original problem is being reformulated in a clever way as a related, supervised task which can be tackled by deep multilayer perceptrons. Hence, the intelligent, creative part is done by the human designer, not by the algorithm.Deep learning approaches work on associations and cannot grasp cause and effect .Despite of all these achievements, studying deep learning might be less useful when it comes to understanding humanlike intelligence with the goal to create artificial general intelligence . Major iThe first issue has been addressed in many ways with approaches summarized as “few-shot learning” . GeneraYet, the systems trained are extremely complex in terms of the amount of parameters, and consequently the amount of data required, when summing over the different tasks, is still very high.Addressing the second and third issues seems to require a qualitative change in the paradigm of an artificial intelligent system, compared to deep learning.Recently, Lake et al.  formulat“Cognition is about using these models to understand the world, to explain what we see, to imagine what could have happened and did not, or what could be true and is not, and then planning actions to make it so.” .Lake et al.  have devIn our approach, which we outline in the next but one section, we address the first issue by deliberately designing a very simple  model. Despite being simple, the model allows addressing the second and third issues by being able to actively explore the causal structure of its environment, to learn intuitive physics, and to plan actions in order to achieve goals.In this work, we suggest a very simple neural architecture, which learns in completely unsupervised fashion and incorporates several of the mentioned principles: it learns a model of the dynamics of its environment by playful exploration (“intuitive physics”), can play virtual, predicted episodes (“what could be true and is not”), and can plan action sequences to bring the environment closer to a target state that has been given extrinsically by a one-shot demonstration (“planning actions to make it so”). Conceptually, the same mechanism could be utilised, if the agent could generate intrinsic goals.state network, which learns a representation of the environment's states, an action module which represents possible motor commands, and a sensorimotor integrating state-action network which learns and represents associations between both  : a perceeen both . During In the proposed setup, playing virtual episodes or “kinematic mental simulation,” for which evidence has been found in human reasoning , correspA goal is defined as a target state or group of states in latent state space. Action planning corresponds to searching in action sequence space in the style of active inference, such that predicted resulting states approximate as good as possible a desired region in latent space .The active inference paradigm adopted here –21 repreThe paper is organized as follows. An introductory section on related work is followed by the Model section, where we specify the model architecture including the representation and prediction learning procedures and the mechanism of inference and action planning. In the Method and Results section thereafter, we provide a proof of concept using the Open AI's gym cartpole environment. The section is subdivided into three parts: First, SapSom is trained by playfully exploring the cartpole environment using random action sequences. It is shown that the agent correctly learns to represent the phase space structure of the cartpole system, referred to as “intuitive physics.” In the second part, the ability to perform kinematic simulation is examined by comparing real and simulated dynamics of the environment under three prespecified deterministic action sequences as well as under many random action sequences. A quantitative error measure is provided which demonstrates that predicted state sequences closely resemble the actual temporal state sequences provided by the environment. In the third part of the Method and Results section, we analyze the ability of the agent to reach various goals, which are extrinsically given as successful example state sequences. Three classes of goals are considered—the balancing task, the controlled-tilt task, and the tilted-balancing task—and individual runs for visual inspection as well as a quantitative performance assessment are given. It is shown that the agent flexibly and most of the time successfully solves many instances of these tasks after observing only one demonstration. We conclude the paper by discussing how our model might be extended to yield more powerful architectures and relating our model to biological findings. A preprint of this work has been made available under .In contrast to current trends in deep learning, human like learning systems need to build causal models of their environments , acquire an intuitive understanding of the underlying physics (intuitive physics), and develop learning-to-learn skills and combining constituents  to generalize knowledge to new tasks and events . As suchThe SapSom model presented below emphasizes simplicity in the sense of strongly constrained and therefore easy-to-train architecture. A set of SOMs is equipped with a simple yet powerful additive, Hebb-trained prediction system. In spirit, it shows close relationship to model-predictive control. There are similar approaches which address intuitive physics learning on the basis of self-organizing maps. For example, Toussaint  designedA common approach to learn intuitive physics is via handcrafted simulation engines  or via pAs sensor signals are often fraught with uncertainty, models of the outside world need to integrate sensory inputs with internal prior beliefs, in accordance with Bayesian inference. Hence, probabilistic approaches are able to deal with uncertainty and, combined with system identification techniques like particle filters, achieve robust long-term predictions of object dynamics while simultaneously learning the underlying intuitive physics , 42.Several recent approaches refer to learning physics from observations alone. Applying exploratory learning of causal relationships was the intention of Kansky et al.  when desRather than relying on physics engines, Nguyen et al.  design aK action-conditioned state-only maps is trained, where K is the number of actions. All maps share the same perceptual (state) representation, but on top of each map an individual, action-conditioned state transition matrix is learned. This prohibits dimensional control of the sensorimotor space but makes sure that all state-action pairs are readily represented.In this work, we actually implement a reduced version of the model outlined in We wish to learn a sparse representation of the input space, because it is believed that this will facilitate the learning of unimodal sharply peaked state transition distributions. As discussed above, many powerful techniques for representation learning and even several possibilities for obtaining sparse representations are around . Here ws ∈ ℝ2 maintains a codebook vector w(s) with the same dimension as the input space. On presentation of an input vector or “environmental state” u, the winning unit s∗ is found, defined by its codebook vector being closest to the input·‖ denotes Euclidean norm, and the notation on the left-hand side of ‖ is just the prediction error in input space (the term “prediction” being used in the sense of “predicting the presence”), often referred to as quantization error. Finding s∗(u) according to  causes a large portion of the map to rearrange to better represent this novel input.Similar to , we defiThe normalized activation pattern in map space can then be interpreted as recognition density, wherep(u| s)=δ(u − w(s)), δ denoting Kronecker's delta. For a comprehensive treatment of generative and recognition models, see, for example, , and the vector of inverse variances or precisions, πg, to the system . For the pole balancing example, the set of goal values for x and θ and x and θ and ug= and a precision vector of πg ≈  for this example.One-shot imitation refers to the ability of a system “to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering” . Here weu and ug: d=∑iπig(ui − uig)2. For action planning, one-step greedy forward search is applied .Reaching the target state then means to search for a sequence of actions, which drive the environment's actual state towards the target state(s) and keep it there. For simplicity, we avoid explicitly determining the distance between the actual state and all target states, but instead use the precision-weighted distance between ug=, πg=. The controlled-tilt task consists of deliberately letting the pole tilt to a specified side, until the maximum tilt of θg=±0.2 rad tolerated by the gym environment is reached. The challenge to the agent is to control the tilt process such that a specific angular velocity θg ≠ 0. This task, which is also not easy to achieve by humans, involves letting the pole tilt a little bit at the beginning, followed by a controlled acceleration of the cart to the corresponding side in order to keep the tilt stationary afterwards. This task is assigned to the agent by imprinting ug=, πg=.In this section, we consider three different types of tasks, namely, (i) the (pole) balancing task, (ii) the controlled-tilt task, and (iii) the tilted-balancing task, which are explained in the following. The balancing task consists of keeping the pole upright as stationary and as long as possible. As pointed out above, presenting the goal of a balanced pole results in imprinting x . The green traces arose under another controlled-tilt task with high negative goal angular velocity ug=. Figures  and angl Figures  for fourThe final angular velocities were  for the moderate tilt case and  for the rapid tilt case. The latter angular velocities are about the maximum which can be achieved when pushing to one side all the time, which was correctly predicted by the agent: all but one action over the five rapid tilt trials were “push right.”θg=0.15 rad. To solve this task, the agent had to constantly accelerate the cart into the direction of the tilt in a controlled way such that the cart leaves the regime previously explored and learned. The traces indicate that the agent manages to keep the pole steadily tilted over a considerable number of time steps, although it does not quite reach the goal of θg=0.15 rad but instead stabilizes values around θ=0.08 rad. Also, when approaching the screen boundaries, the agent fails to stabilize any longer, because this configuration is far from what it experienced during playful exploration with random actions only.Figures From visual inspection of these examples, we find that the system is quite flexible in solving different related tasks and generalizes satisfactorily to previously unseen regimes. In the following, we quantitatively analyze the performance and variability thereof for a larger number of specific goals for these tasks.OpenAI defines “solving” the cartpole problem as being able to balance the pole over an average of 195 time steps per episode, taken over the last 100 episodes, where each episode ends in case of failure or after 200 time steps otherwise. In order to characterize the performance of the agent, we analyzed 100 episodes under the balancing task , blue. Wθg=0.2 rad, we systematically increased the goal angular velocity nleft − nright)/(nleft+nright))  after only one presentation of the goal state. This is possible, because task solution is done via inference over the intuitive physics learned, rather than via slow modification of weights. Hence, in comparison with reinforcement learning, the presented system provides two major advantages: (i) it does not need any extrinsic reward structure (which has often to be engineered in a tedious process), and (ii) it can flexibly solve various tasks, which would require both a new reward structure and retraining in reinforcement learning. The latter flexibility has also been specified as an important feature of humanlike learning and performance .An adaptive and sparse sensorimotor representation, in particular the possibility to act on an environment rather than just observing, in order to learn by exploration.A temporal sequence learning and prediction mechanism at the sensorimotor level.A short-term memory mechanism to store target states in state representation space.A mechanism to intrinsically generate action and state representations and to reason on their temporal evolution on the basis of temporal sequence prediction.A distance measure between states. If reasoning is to be done at the representation level, a distance measure between sparse state representations, such as a topographic map, is required.The goal of this work was to suggest a minimal model that shows important properties of artificial intelligence: learning from experience, comprehension of its surroundings, reasoning, planning, and flexible solution of different tasks. It does so by learning a representation of the dynamical physical properties of its environment by exploration and by performing inference on this representation to reach goals. The philosophy behind this approach is related to the KISS principle and Occam's razor at the model level: we identified minimal ingredients which seem both plausible and important for achieving the mentioned properties . The key ingredients of SapSom are as follows:In the following, we first compare our model to a standard Q-learning setup for pole balancing, then discuss possible extensions of our model, and relate its mechanisms to biological findings.The proposed agent learns to achieve various different goals on the basis of a state-action and state-transition models that are learned in a completely unsupervised manner. Goals are imprinted by simple activation of states' latent space, for example, as a result of a single presentation of a target environmental state. Hence, it is very easy to switch forth and back between different goals on a per-episode basis for SapSom. In contrast, almost all state-of-the-art methods are related either to explicit physical modelling or to reinforcement learning (RL) in some sense (see Related Work section). While physical simulation engines require the human expert to design this engine, RL-methods, which might be most closely related to our approach, heavily rely on adequate reward signals provided by the environment, which have to be manually designed by the human expert as well. Moreover, the characteristics of the reward signal provided by the environment usually encode or encourage one specific goal only. An RL agent can only learn a new, different task, if (a) the experimenter handcrafts a new reward structure provided to the agent, and (b) the agent undergoes retraining in order to learn to exploit the new reward structure. For that reason, the high flexibility and generalization ability of SapSom alone renders our approach superior to physical engines and RL agents for the context addressed.θ and α=0.1 and an ε-greedy policy with ε=1 − log10(n/25) constrained to ε ∈  for episode number n ≥ 1 was used. As SapSom, the agent was trained over 4000 episodes. Of the last 1000 episodes, 87 percent reached the limit of 200 steps of balancing; the average number of time steps was 186. When comparing this to 61 percent completed episodes and 188 average steps of SapSom (see Method and Results section), we find that SapSom reaches a performance comparable to Q-learning, but without manual binning of the input space, without taking any benefit of the reward signal, and while still being flexible enough to immediately perform a different task from the very next episode on. It can be concluded that our model is clearly more effective and efficient than Q-learning and probably related RL methods on the cartpole task.However, it is possible to compare the performance of SapSom to that of an RL agent for a stationary task with defined reward structure. Therefore, we reimplemented standard tabular Q-learning  for the A simple set of one-layer network architectures was used  are derived from normalized activations of state neurons  is interpreted as connection from state-action neuron with index s,  a to target neuron s′, the learning rule reads Ta will be increased, when s′ becomes active after s, a (stronger than expected), and is decreased, if s′ is expected active but remains silent.The state transition matrices optimized during prediction learning can be interpreted as lateral  or top-dequation ). When TMoreover, there is evidence that prefrontal cortex is capable of actively maintaining information by robust, persistent neural activity, which, according to prominent models, might be rapidly updatable by a striatum-driven gating mechanism . The sysLearning more about the principles of this orchestration process of cortical states, or, in SapSom terminology, learning about how search and prediction should be ideally designed given an implicit world model, might lead to a better understanding of the principles of human thinking in general—an exciting field for future research.


Wilkerson RC, Linton Y-M, Strickman DMosquitoes of the WorldJohns Hopkins University Press, Baltimore; 2021, Vol. 1 and Vol. 2, 1332 pagesISBN 978-1-421438-14-6Mosquitoes of the World,” Vols. 1 and 2  flew on this planet some 260 million years ago. The presented timeline of events of culicid history, which covers almost three entire pages, ends 28 years ago, with the introduction of the Asian tiger mosquito in Italy. In the chapter on nomenclature, the authors point to a critical issue when they state that “The level of knowledge may be decreasing as traditional taxonomy is less and less considered a core part of the biological curriculum, a danger that has been recognized for decades.” This is so true. When trapping mosquitoes out in the field, there’s always that pile of “unidentified” mosquitoes that remains. And only recently have we discovered that some of these “ignored” species may actually play a significant role in disease transmission, even though their abundance may be low .Volume I of “y be low , 2. ThisMosquitoes of the World” as primarily a comprehensive guide to the taxonomy and systematics of mosquitoes is probably the best way to assess the contribution of these volumes to the field. For a more in-depth overview of mosquito biology and control, one should consult Alan Clements’ three-volume “Biology of Mosquitoes” [Mosquito Ecology: field sampling methods” [Interestingly, the authors decided not to include information on mosquito control. For the sake of completeness, they could have at least included the fundamentals underpinning contemporary control strategies. Moreover, not only is the topic of mosquito control missing, but also monitoring and the vast discipline of mosquito ecology are only marginally addressed. Considering “quitoes” –6, whichmethods” , would bAnopheles, with which this section starts, I noticed that the bionomics section reports the recently detected high-altitude wind-borne passive migration of adult mosquitoes of species within this genus. It is somewhat surprising that this recent finding, which is not referenced, was selected from the numerous, influential (review) articles on the bionomics of this genus. The pictures and drawings of mosquito genitals and other morphological features, however, are of a very high standard and quality. It is a joy to flip through the pages and read up on the selected species, for which distribution maps have been included. Not all of this material is easy to understand when you are a novice in mosquito taxonomy, though. For instance, the section on the Asian tiger mosquito’s diagnostic characters includes the following:“(b) Thorax (side): Antealar area with patch of broad pale scales (AnS); mesepimeron with lower scales (LMSc); paratergite with scales (PaSc), postpronotal scales (PpSc) present; postspiracular scales absent (PoSc); proepisternal scales (PeSc) present; scutal angles (ScA) without pale scales; subspiracular area with broad white scales (SSc).”The second part of volume 1 is a marvel for the die-hard mosquito biologist. It comprises a series of beautifully illustrated pages that summarize the diagnostic morphological characters, systematics, distributions, bionomics, associated pathogens and exemplar DNA sequences for each of the 41 genera and 128 globally important mosquito species. When reading about the genus This example clearly shows that this material is not for the faint-hearted . Although this treatment of the morphological diversity of mosquitoes is accompanied by a glossary of all the morphological terms used, someone new to the field will not find it easy to use.Volume 2 is the first printed catalog of all described mosquito species (including fossils and synonyms) in over 40 years, and follows Knight and Stone’s seminal work published in 1977 . During Mosquitoes of the World” would help me find details about them. First was Sabethes belisarioi, a species that I tried to rear during my PhD because it (supposedly) prefers to land on and bite the nose of humans. It is perhaps also one of the most beautiful mosquitoes in the world, with its magnificent metallic blue color and “feathered” legs  that assist in its amazing courtship behavior. Within seconds, Vol. 2 helped me to locate the tribe Sabethini in the contents overview on p. ix, and from the genus Sabethes I was directed to p. 993. And there, on that very page, I immediately located Sabethes belisarioi. I was then referred back to Vol. 1, p. 278, for a description of the genus, its systematics and distribution, bionomics, associated pathogens and diagnostic characters. Although the two volumes comprise 1332 pages, I reached my “destination” within seconds, which reveals the ease with which one can maneuver through the catalog. I discovered that species of the genus Sabethes are called “canopy mosquitoes,” and that Sabethes belisarioi also has an informal name: the “Belisario Brazilian canopy mosquito.” The type location for this species is Bicudos, Minas Gerais, Brazil, although the location where the type specimen was deposited is unknown. Seven references accompany the description, one of which, “Mattingly 1971a,” surprised me. When I looked this up in the reference list, it referred to “Contributions to the mosquito fauna of South East Asia.” To my knowledge, there are no Sabethes belisarioi in Southeast Asia, thus I presume that this reference was cited in error.I decided to mine my memory for three mosquito species that crossed my path during my 30 years of mosquito research, and see how “Anopheles sundaicus, a species that featured in an extensive review of the taxonomy and bionomics of Indonesian anophelines that I worked on during my MSc. [Mosquitoes of the World” mentions the crepuscular biting activity and lists some of the preferred breeding sites of An. sundaicus, but its extensive breeding in coastal areas with fish ponds and irrigated rice fields is not mentioned.The second species I dug up from memory was  my MSc. . On lookAedes albopictus, and again within seconds I found myself reading about the type location, Calcutta , India, and that the type specimen is kept in the United Sates National Museum in Washington DC. There was also half a page listing the countries from which it has been reported, followed by half a page of references. I work with this species every day in the Maldives, where we are trying to eliminate its populations from small coral islands. The descriptions of its bionomics in terms of breeding sites, biting habits, diapausing and dessication-resistant eggs are all highly informative and accurate. Some references to guide the reader further would have been useful, though.Lastly, I selected the Asian tiger mosquito, Mosquitoes of the World” is an extremely useful resource for entomologists, public health professionals and epidemiologists, and for inclusion in reference libraries. The fully updated catalog, which includes many new species, puts this work at the forefront of mosquito taxonomy and systematics. The style, layout and organization fit the book’s purpose, as evidenced by my searches for the three mosquito species mentioned above.As the most complete reference work on mosquitoes ever produced, “Mosquitoes of the World” will surely be considered one of the many great achievements of his career [Mosquitoes of the World” was “to facilitate the future fight against mosquitoes through a holistic understanding of their diversity, systematics, and associated biology.” There is no doubt that they succeeded in doing just that, and I foresee that this work will serve as the gold standard for decades to come in the quest to “know thy enemy” before going into battle against mosquitoes. Mosquito taxonomy remains a grossly underrated and underrepresented discipline that few develop a passion for, but virtually all in the field depend on when trying to understand the bug that they are dealing with. “Mosquitoes of the World” will hopefully enthuse a new generation of biologists to venture into this fascinating field!The authors are world experts in this field, and it is sad to note that one of them, Dr. Dan Strickman, did not live to see this massive work in print. “s career . Accordi


Symptom phrase recognition is essential to improve the use of unstructured medical consultation corpora for the development of automated question answering systems. A majority of previous works typically require enough manually annotated training data or as complete a symptom dictionary as possible. However, when applied to real scenarios, they will face a dilemma due to the scarcity of the annotated textual resources and the diversity of the spoken language expressions.In this paper, we propose a composition-driven method to recognize the symptom phrases from Chinese medical consultation corpora without any annotations. The basic idea is to directly learn models that capture the composition, i.e., the arrangement of the symptom components (semantic units of words). We introduce an automatic annotation strategy for the standard symptom phrases which are collected from multiple data sources. In particular, we combine the position information and the interaction scores between symptom components to characterize the symptom phrases. Equipped with such models, we are allowed to robustly extract symptom phrases that are not seen before.Without any manual annotations, our method achieves strong positive results on symptom phrase recognition tasks. Experiments also show that our method enjoys great potential with access to plenty of corpora.Compositionality offers a feasible solution for extracting information from unstructured free text with scarce labels. The high-speed development of internet is changing the habits of individuals to harvest information and obtain answers to the questions. Nowadays, more and more people try to figure out their physical problems via online consultation with medical professionalsThe majority of automatic QA systems rely on named entity recognition (NER) as the first step , 4. In c(ENG: “shortness of breath and in a state of discomfort”) describes the typical symptom of (ENG: “chest distres”). It is often the case that most symptom mentions appearing in the consultation corpus fail to match the symptom phrases in the vocabulary list. An alternative way is to rely on tokenization and match individual words to acquire symptom information. However, this may lead to either incorrect boundaries  symptom phrase recognition method for Chinese medical consultation corpora without any annotations. The basic idea is to directly learn models that capture the composition, i.e., the arrangement of the symptom components. We introduce an automatic annotation strategy for the standard symptom phrases which are collected from medical publications, electronic medical records, and web resources. Specifically, we establish a position recognition model based on the relative positions between the symptom components (semantic units of words). Afterwards, we learn the embedding representations for the components, which are then used to estimate the interaction scores between them. By integrating the position outputs and the interaction scores, we are allowed to recognize the symptom phrases in medical consultation corpora. Experimental results demonstrate the feasibility and effectiveness of the proposed method which further improves the overall performance.To tackle these issues, we go beyond word matching and propose a We view each symptom phrase in the vocabulary as composition of words and their interactions. This allows us to deduce the symptom phrases that are not seen before, without dependence upon any annotated corpus.We incorporate the position outputs and the interaction scores to judge the compositionality between individual words during symptom prediction, which can be viewed as an innovative attempt in the field of data mining.Experiments have shown that for symptom phrases recognition tasks, the proposed method can achieve strong positive results, and have great potential with access to increasing online textual corpus.The contributions of our paper can be summarized into three aspects:For a medical question answering system, understanding the symptom phrases from the patient’s input is the most critical step in providing an effective solution. A significant way to address the issues of symptom phrases recognition is named entity recognition (NER), which is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization, etc. Over the past few decades, NER has made great strides in a wide range of areas with the help of technologies such as artificial rules, traditional machine learning (ML), and deep learning (DL). Here, we classify these existing NER techniques into three levels from the attributes of methods, and conduct a brief analysis of the pros and cons of some representative strategies related to our work below.Some hand-crafted rules, for example, domain-specific gazetteers and syntactic-lexical patterns, are commonly used to design the rule-based NER systems. Kim  adopted In machine learning, NER is cast into a multi-class classification or clustering task, which learns a model that identifies similar patterns from unseen data. Among the supervised approaches, Bikel et al. , 12 propIn recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding state-of-the-art performance . The appAlthough NER has been extensively studied in the biomedical field, symptom phrases seem to have been shelved because there is so little work being done on the subject, especially for Chinese medical texts, which we are mainly discussing here. In our work, we make full use of public resources to mine medical knowledge, and does not require manual annotations. By devising a novel automatic annotation strategy, the proposed approach can save a lot of labour and material costs. More specifically, we combine the position information and the interaction scores between the components of the symptom phrases, and then employ them to identify new symptom phrases in the corpus.We begin by introducing a general framework for unsupervised symptom phrase recognition. We then describe three crucial components in detail, i.e., (1) how to characterize the positions of the symptom components, (2) how to calculate the interaction scores between the symptom components, and (3) how to recognize unobserved symptom phrases.From the perspective of position, we provide an automatic annotation scheme to indicate demarcation of symptom phrases, followed by characterizing the probabilities of each component (semantic units of words) arranged on different positions within a symptom phrase.From the perspective of interaction, we learn the embedding for each component to capture the contextual associations between them. If two components tend to co-occur or appear in similar contexts, they will be mapped into similar word vectors.When medical consultation corpus is concerned, we use a basic pipeline to pre-process the texts involved. First, we segmented each text into sentences using a Chinese text analysis toolFor this study, we build a symptom dictionary in advance by aggregating a number of standard symptom phrases from multiple resources . Equipped with the dictionary, we are allowed to mine useful knowledge, which facilitates the downstream task of symptom extraction from medical consultation corpus., ENG: “chest distress”) and compound words . In addition, a compound word can be split into multiple simple words by the tokenizer mentioned before. Therefore, we adopt the basic annotation signs “BIES”  to represent the position information of a simple word in the phrase. For convenience, we abbreviate a “simple word” as a “word”, unless otherwise stated.Different from traditional annotation methods  which m(ENG: “fever”), then it is annotated as “S”. If there is more than one simple word, such as (ENG: “persistent low back pain”), then the word (ENG: “persistent”) is annotated as “B” indicating its beginning position, the word (ENG: “pain”) is annotated as “E” indicating its end position, and the other word (ENG: “low back”) is annotated as “I” indicating its intermediate position.Table Besides word-based annotations, we can also conduct character-based annotations whose signs “BIES” represent the position information of a character in the symptom phrase, as illustrated in Table (ENG: “intermittent”) appears 60 times at the beginning and 12 times in the middle. We model the probability of counts as a multinomial distribution n independent trials. For example, in terms of (ENG: “intermittent”), there are 72 independent trials, each of which leads to a success for exactly one of the four positions “BIES”. The parameters can be derived based on maximum likelihood estimation. In the above example, the estimations are . Note that these results allow us to infer candidate symptom phrases from the perspective of position arrangements.Given a component in a symptom phrase, we count how many times it appears on a particular position. For example, the word We exploit the vector representation of words computed on the symptom dictionary, and implement certain metric to estimate an interaction score between any two words in the embedding space.Word embedding For this study, we build the embedding using the Word2Vec implementation proposed by Mikolov et al. [(a) v et al. , a shallv et al. . As is wv et al. , 37. We Interaction scoring Once the embeddings are built, we are in a position to predict interaction scores based on the distribution of word vectors in the word embedding. A straightforward way to measure proximity between two components is to use a common distance metric such as Euclidean distance, Manhattan distance or Cosine similarity, and regard them to interact with each other when the metric is within a certain threshold. However, this is problematic due to the fact that the close proximity in high dimensional space does not necessarily imply strong semantic interaction. As high dimensionality often carries rich and diverse semantics, the same distance may arise from different regions of the semantic space. Inspired by [(b) pired by , we asse(ENG: “persistent”) and (ENG: “fever”), (ENG: “persistent”) and (ENG: “fever”).Following , we firsIn order to circumvent the asymmetry of KL divergence, we use a score function based on the JSD, defined as follows ,2\documeIn order to measure the interactions between components  follows :3\documeWe combine the position information and the interaction scores to judge the compositionality between individual words. Suppose the words are independent of each other. Let (ENG: “intermittent”) appears in (ENG: “intermittent urinary protein”) and (ENG: “upper limb intermittent dyskinesia”) respectively). This also makes sense from the view of morphology.Note that there may be consecutive occurrences of words with annotation “B”. We treat the leftmost word as the beginning. Similarly, for the consecutive occurrences of words with annotation “E”, we treat the rightmost word as the end. In general, the words that can act as boundaries can also act as intermediate components  which is annotated as “B−I−I−I−E”. Let (ENG: “at first”) and (ENG: “so”) are discarded due to their utility values are less than (ENG: “An ear infection is itchy and painful”). The details are shown in Table For example, an input sentence contains a subsequence In this section, we investigate the performance of the proposed method ComD on the medical consultation corpus crawled from public websites. The goals of our experiments are threefold, where one is to investigate the contribution of each module by performing the detailed ablation study, one is to examine whether ComD is able to achieve satisfactory results compared to other baseline approaches and the other is to analyze the effect of the threshold parameter The datasets consist of the symptom dictionary for training and the medical consultation data for prediction.Symptom Dictionary contains 732,855 symptom phrases which were collected from multiple data sources including medical publications7During pre-processing, we first determined whether the sources have a clear delineation of symptom phrases. If so, we directly loaded them into the dictionary. Otherwise, we predefined several lexico-syntactic patterns to detect symptom phrases in texts. For instance, patterns like “The principal manifestation of NPMedConSult is a collection of nearly one thousand medical consultation records derived from the websiteCurated Data from MedConSult is a subset of the medical consultation data MedConSult by removing the records that have no annotation signs “I”. In other words, we only kept the records that contain symptom phrases with at least three components. It has been stated that if a record only contains B−E subsequences, we directly combine the boundary components into a symptom phrase without judging their semantic interaction. Therefore, this subset serves the purpose of demonstrating the necessity of each training operation , and exploring how the recognition performance is sensitive against the variations of the interaction parameter.This section describes the metrics that are used to quantitatively evaluate our method.Interaction over Union (IoU) is the most commonly used metric for comparing the similarity between two strings. The higher the IoU, the closer the extracted result is to the ground truth. In our case, if IoU between the extracted result and the ground truth exceeds a certain threshold Micro-averaging (Micro) is an average metric that computes the total number of false positives (FP), false negatives (FN), and true positives (TP) over all consultation records, and then computes the precision, recall, and FMacro-averaging (Macro) is an average metric that treats all records equally, no matter how many symptom phrases they contain. Specifically, it computes the precision and recall independently for each consultation record S. The results are then combined to obtain the s respectively.ComD: We leverage the symptom dictionary to learn the arrangements of the symptom components. By incorporating the position outputs and the interaction scores, we are allowed to deduce the symptom phrases that are not seen before.BERT-CRF : This meBiLSTM-CRF : This isBDMM : This isDictionary-based: This is a dictionary lookup method that relies heavily on exact string matching, where the words, and order of words should be exactly the same as the entry in the symptom dictionary.We implemented ComD in python. To investigate the principle of compositionality, we simplify ComD to the case in which characters are taken as symptom components. We call this method ComD-Character.We consider such a setting that only a symptom dictionary including numerous standard symptom phrases is available, while there is no annotated dataset for clinically motivated symptom extraction. In this new setting, we compare our proposed method ComD against the well-developed dictionary-based method and the deep learning method which have achieved the current state-of-the-art on the NER datasets.k-nearest neighbours, with We performed a grid search to tune hyperparameter values for ComD. Each of these parameters is varied from low to high at a fixed interval, and the performance on the medical consultation data is measured. Through trial and error tuning in our implementation, the following choices of hyperparameters are preferred: We set the dimension of word embeddings to 500. We used K-means with 500 clusters, and identified the To investigate the individual contribution of our position modeling and semantic interaction in training, we removed them to offer the methods ComD-NoPos and ComD-NoInt. Without position modeling, the components that interact with each other and appear in a sentence are extracted as a symptom phrase. Without semantic interaction, the components within the boundaries are directly combined into a symptom phrase.Table We now evaluate the performance of the proposed method and other baselines on MedConSult. The detailed results are summarized in Table In addition to the JSD-based scores presented in Sect. We examine how the extraction performance is sensitive against parameter variations. Figure In this section, we analyze several representative examples to illustrate the advantages and disadvantages of the proposed ComD, as shown in Table . In fact, the phrase  is a Chinese idiom which expresses a certain denotation as a whole, and cannot be split into multiple words by the tokenizer. As such, the proposed model fails to recognize the symptom phrase.Sentence S1 makes a claim about a patient experiencing a symptom (ENG: “ingrown toenails”) experienced by a patient, which is further segmented into three words, i.e., (ENG: “ingrown toenails”). In our case, they are out-of-vocabulary (OOV) words, and have no word embedding representation learned. Hence, they are ignored when the proposed model is applied.Sentence S2 describes a phenomenon (ENG: “waist pain and soreness”), which composes of two words, i.e., (ENG: “waist pain”) and (ENG: “soreness”). Here, (ENG: “waist pain”) is an OOV word and ignored during extraction. Although (ENG: “soreness”) is a valid component of symptom phrases, it rarely appears at the starting position. Without detecting appropriate starting boundaries, the model outputs no extraction results.Sentence S3 makes a claim about a patient experiencing the symptom . The subsequences are annotated as and . For this case, the proposed model chooses the most probable boundary, i.e., (ENG: “right side”) and (ENG: “cleft”), and leave out the annotated component (ENG: “to”) and (ENG: “throat”) beyond the boundaries.Sentence S4 contains two subsequences relevant to patient symptoms, with the primary symptom being (ENG: “headache and a snotty nose”). However, our method only identifies and extracts (ENG: “headache) , leaving (ENG: “a snotty nose”) unrecognized. This is because the two words appearing in the phrase (ENG: “a snotty nose”) are OOV words, and ignored accordingly.Sentence S5 makes a claim about a patient experiencing the symptoms , where the former three relate to the symptoms. Through annotating, (ENG: “upper”) and (ENG: “teeth”) are both considered as candidate boundaries at the beginning. The proposed model discards the word (ENG: “upper”) by mistake according to the estimated boundary scores.Sentence S6 is segmented into five words, i.e., (ENG: “a pimple growing on the eyelid”) observed by a patient. After finding the most probable boundaries, i.e., (ENG: “Something growing on the eyelid is a pimple”), the proposed model calculates the interaction scores between each intermediate component and the boundaries, and then keeps both intermediate components, one of which is yet semantically redundant and unnecessary.Sentence S7 describes a phenomenon (ENG: “feet”), (ENG: “icy cold”) and (ENG: “pain”) in sentence S8 are disjoint but are all key components of the symptom (ENG: “icy cold feet & pain”), similar cases in sentence S9. Our model captures such composition and concatenates them as the extraction result.Sentence S8 and S9 are positive examples that show our model can correctly recognize the symptom phrases. As can be seen, there are disjoint words present in a sentence, which are integral parts of a symptom mention. This is a common phenomenon in colloquial expressions. For example, the words In this paper, we explore how using symptom dictionary can facilitate identifying symptom phrases from medical consultation corpus. The basic idea is to learn models for semantic compositionality over linguistic units according to the observed symptom phrases. Our method can not only support computer-assisted diagnosis systems, but can also promote the medical knowledge graph construction. Experimental results prove the superiority of our method. A special emphasis for our future work is placed on more effective design patterns of composing words/characters to form sound symptom expressions. We believe that compositionality provides a feasible solution for extracting information from unstructured free text with scarce labels. Another focus concerns development of computer-assisted medical consultation systems integrated with the proposed symptom recognition method.