This article examines how ethnographic methodology and literary theory can advance research engines and artificial intelligence systems beyond the reductive computational approaches that dominate contemporary AI development. Drawing on recent Stanford research revealing fundamental gaps in large language models’ ability to distinguish factual knowledge from belief, I argue that contemporary AI systems enact what I term “abducted semantics”—appropriating the inferential logic of human meaning-making while systematically attenuating the culturally embedded, phenomenologically grounded capacities that generate authentic understanding. Through close analysis of Clifford Geertz’s thick description, Charles Sanders Peirce’s triadic semiotics, and canonical literary works—Miguel de Cervantes’ <i>Don Quixote</i> and Gabriel García Márquez’s <i>One Hundred Years of Solitude</i>—I demonstrate that human understanding operates through complex semiotic processes irreducible to pattern-matching and statistical prediction. The article proposes concrete interventions to transform research engines from tools of semantic extraction into technologies that preserve and enhance interpretive richness, arguing that ethnographic and literary methodologies offer essential correctives to the epistemological impoverishment inherent in current AI architectures.
Chandrima B. Pushpan, Tanoy Kanti Konar, Aditi Sen De
et al.
We propose a single auxiliary-assisted purification-based framework for quantum error correction, capable of correcting errors that drive a system from its ground-state subspace into excited-state sectors. The protocol consists of a joint time evolution of the system-auxiliary duo under a specially engineered interaction Hamiltonian, followed by a single measurement of the auxiliary in its energy eigenbasis and a subsequent post-selection of one of the measurement outcomes. We show that the resulting purified state always achieves unit fidelity, while the probability of obtaining any energy of the auxiliary other than its ground state energy yields the success rate of the protocol. We demonstrate the power of this proposed method for several low-distance quantum codes, including the three-, four-, and five-qubit codes, and for the one-dimensional isotropic Heisenberg model, subjected to bit-flip, phase-flip, and amplitude-damping noises acting on all qubits. Notably, the protocol expands the class of correctable errors for a given code, particularly in the presence of amplitude-damping noise. We further analyze the impact of replacing the auxiliary qudit with a single auxiliary qubit, and the changes in the performance of the protocol under the realistic scenario where noise remains active during the correction cycle.
This paper is dedicated to the issue of two multicultural and multi-temporal necropolises in the riverside part of Vodyanskoye settlement. A study conducted in 2011 revealed several Muslim burials in the site which was previously thought to be a Christian cemetery. The presented tables summarize for the fi rst time the data on all burials from this area and excavated between 1968 and 2014. This allows us to confirm the previously expressed opinion about the presence of two necropolises in the riverside part of Vodyanskoye settlement. The northern section is a Christian cemetery where burials were made until the 30s of the XIV century. The southern cemetery of the second half of the XIV century was an urban cemetery, where there are burials with Mongoloid and Uraloid features, beshik deformation, one burial has an inventory, some of the burials were made according to Muslim burial rite. Perhaps, this place was abandoned during the crisis of the 60-70s of the XIV century, and during the restoration of the city under Toktamysh Khan this site could be used for burials.
Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li
et al.
Offline imitation learning enables learning a policy solely from a set of expert demonstrations, without any environment interaction. To alleviate the issue of distribution shift arising due to the small amount of expert data, recent works incorporate large numbers of auxiliary demonstrations alongside the expert data. However, the performance of these approaches rely on assumptions about the quality and composition of the auxiliary data, and they are rarely successful when those assumptions do not hold. To address this limitation, we propose Robust Offline Imitation from Diverse Auxiliary Data (ROIDA). ROIDA first identifies high-quality transitions from the entire auxiliary dataset using a learned reward function. These high-reward samples are combined with the expert demonstrations for weighted behavioral cloning. For lower-quality samples, ROIDA applies temporal difference learning to steer the policy towards high-reward states, improving long-term returns. This two-pronged approach enables our framework to effectively leverage both high and low-quality data without any assumptions. Extensive experiments validate that ROIDA achieves robust and consistent performance across multiple auxiliary datasets with diverse ratios of expert and non-expert demonstrations. ROIDA effectively leverages unlabeled auxiliary data, outperforming prior methods reliant on specific data assumptions. Our code is available at https://github.com/uditaghosh/roida.
The scalar auxiliary variable (SAV) approach is a highly efficient method widely used for solving gradient flow systems. This approach offers several advantages, including linearity, unconditional energy stability, and ease of implementation. By introducing scalar auxiliary variables, a modified system that is equivalent to the original system is constructed at the continuous level. However, during temporal discretization, computational errors can lead to a loss of equivalence and accuracy. In this paper, we introduce a new Constant Scalar Auxiliary Variable (CSAV) approach in which we derive an Ordinary Differential Equation (ODE) for the constant scalar auxiliary variable r. We also introduce a stabilization parameter (α) to improve the stability of the scheme by slowing down the dynamics of r. The CSAV approach provides additional benefits as well. We explicitly discretize the auxiliary variable in combination with the nonlinear term, enabling the solution of a single linear system with constant coefficients at each time step. This new approach also eliminates the need for assumptions about the free energy potential, removing the bounded-from-below restriction imposed by the nonlinear free energy potential in the original SAV approach. Finally, we validate the proposed method through extensive numerical simulations, demonstrating its effectiveness and accuracy.
Thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment. However, detecting these fractures on rib CT scans poses considerable challenges, involving the analysis of many image slices in sequence. Despite notable advancements in algorithms for automated fracture segmentation, the persisting challenges stem from the diverse shapes and sizes of these fractures. To address these issues, this study introduces a sophisticated deep-learning model with an auxiliary classification task designed to enhance the accuracy of rib fracture segmentation. The auxiliary classification task is crucial in distinguishing between fractured ribs and negative regions, encompassing non-fractured ribs and surrounding tissues, from the patches obtained from CT scans. By leveraging this auxiliary task, the model aims to improve feature representation at the bottleneck layer by highlighting the regions of interest. Experimental results on the RibFrac dataset demonstrate significant improvement in segmentation performance.
To fully leverage the multi-task optimization paradigm for accelerating the solution of expensive scheduling problems, this study has effectively tackled three vital concerns. The primary issue is identifying auxiliary tasks that closely resemble the original expensive task. We suggested a sampling strategy based on job importance, creating a compact matrix by extracting crucial rows from the entire problem specification matrix of the expensive task. This matrix serves as an economical auxiliary task. Mathematically, we proved that this economical auxiliary task bears similarity to its corresponding expensive task. The subsequent concern revolves around making auxiliary tasks more cost-effective. We determined the sampling proportions for the entire problem specification matrix through factorial design experiments, resulting in a more compact auxiliary task. With a reduced search space and shorter function evaluation time, it can rapidly furnish high-quality transferable information for the primary task. The last aspect involves designing transferable deep information from auxiliary tasks. We regarded the job priorities in the (sub-) optimal solutions to the economical auxiliary task as transferable invariants. By adopting a partial solution patching strategy, we augmented specificity knowledge onto the common knowledge to adapt to the target expensive task. The strategies devised for constructing task pairs and facilitating knowledge transfer, when incorporated into various evolutionary multitasking algorithms, were utilized to address expensive instances of permutation flow shop scheduling. Extensive experiments and statistical comparisons have validated that, with the collaborative synergy of these strategies, the performance of evolutionary multitasking algorithms is significantly enhanced in handling expensive scheduling tasks.
The article is devoted to the consideration of the ancestral legends of the Russian nobility contained in the genealogy record collections of the second half of the 19th century. An
attempt was made to identify noble families, the origin of which is associated with the era of Dmitry Donskoy and the Battle of Kulikovo in 1380. The most famous genealogy record collections compiled by A. Bobrinskii, V. Arsen'ev, P. Dolgorukov, A. Lobanov-Rostovskii, V. Rummel', M. Yablochkov, and V. Chernopyatov are used as sources for the analysis. Ancestral legends of Russian noble families are considered as part of family memory. Special attention is paid to the reflections of famous historical figures of the Battle of Kulikovo era, such as Metropolitan Cyprian, Prince Dmitry Olgerdovich, Prince Andrei Olgerdovich, and Sergius of Radonezh, in the family legends of the Russian nobility. The introduction presents the characteristics of ancestral memory and analyzes the approaches to it by individual researchers. The main part of the article deals with ancestral legends preserved in genealogy record collections of the second half of the 19th century, with special emphasis on the historical figures of the 12th – 15th centuries, mentioned therein. In the final part of the article, a conclusion is made about the great popularity of the name of Prince Dmitry and his associates, participants in the Battle of
Kulikovo, among the Russian nobility of the 18th – 19th centuries. The preferences of the people of the 19th century could not but tell on the legends that existed in the second half of the 19th century, which is also reflected in the diversity of legends about the origin of one and the same noble family. The analysis of the ancestral legends of the Russian nobility presented in the genealogy record collections makes it possible to understand the mechanisms of formation and transformation of the ancestral memory of Russian nobles over several centuries and also gives a vivid description of the worldview of the Russian nobility in the second half of the 19th century.
History of Civilization, History (General) and history of Europe
Knowledge graph entity typing (KGET) is a task to predict the missing entity types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to solve the KGET task by introducing an auxiliary relation, 'hasType', to model the relationship between entities and their types. However, a single auxiliary relation has limited expressiveness for diverse entity-type patterns. We improve the expressiveness of KGE methods by introducing multiple auxiliary relations in this work. Similar entity types are grouped to reduce the number of auxiliary relations and improve their capability to model entity-type patterns with different granularities. With the presence of multiple auxiliary relations, we propose a method adopting an Asynchronous learning scheme for Entity Typing, named AsyncET, which updates the entity and type embeddings alternatively to keep the learned entity embedding up-to-date and informative for entity type prediction. Experiments are conducted on two commonly used KGET datasets to show that the performance of KGE methods on the KGET task can be substantially improved by the proposed multiple auxiliary relations and asynchronous embedding learning. Furthermore, our method has a significant advantage over state-of-the-art methods in model sizes and time complexity.
I apply Dawid's Meta-Empirical Assessment (MEA) methodology to the theory of cosmological inflation. I argue that applying this methodology does not currently offer a compelling case for ascribing non-empirical confirmation to cosmological inflation. In particular, I argue that despite displaying strong instances of Unexpected Explanatory Coherence (UEA), it is premature to evaluate the theory on the basis of the No Alternatives Argument (NAA). More significantly though, I argue that the theory of cosmological inflation fails to sustain a convincing Meta-Inductive Argument (MIA) because the empirical evidence and theoretical successes that it seeks to draw meta-empirical support from do not warrant a meta-inductive inference to inflation. I conclude by assessing how future developments could pave the way towards crafting a more compelling case for the non-empirical confirmation of cosmological inflation.
We address the problem of the existence of a Lagrangian for a given system of linear PDEs with constant coefficients. As a subtask, this involves bringing the system into a pre-Lagrangian form, wherein the number of equations matches the number of unknowns. We introduce a class of overdetermined systems, called co-flat, and show that they always admit a pre-Lagrangian form, which can be explicitly constructed using auxiliary variables. Moreover, we argue that such systems enjoy pre-Lagrangian formulations without auxiliary variables at all. As an application of our method, we construct new pre-Lagrangian and Lagrangian formulations for free massive fields of arbitrary integer spin. In contrast to the well-known models of Singh and Hagen, our Lagrangians involve much fewer auxiliary fields.
The article examines the genre of magtal in the aspect of the greatness of bladed weapons on the examples of magtal poems in the poetry of Aksen Suseev, David Kugultinov and Mikhail Honinov. If “Uldin magtal” (“The Greatness of the Sword”) is included in A. Suseev’s poem “Teegin urn” (“Son of the Steppes”, 1939) as part of three magtals addressed to the greatness of the hero, horse and his weapons, before 12 songs-chapters, then D. Kugultinov’s poems “Aakun shor-uld taig” (“The sword-cane of Oka”, 1956) and M. Honinov’s “Khazg Chashkin ir ...” (“The blade of the Cossack saber ...”) are separate magtals dedicated to the heroes of fellow countrymen. A. Suseev and D. Kugultinov have magtals addressed to the hero of the Civil War O.I. Gorodovikov, M. Honinov — the hero of the Patriotic War B.B. Gorodovikov, defenders of the Fatherland. Comparative-typological analysis of folklore analogues in the epic “Dzhangar” and literary magtals revealed both common and different in the glorification of the hero through the glorification of his weapons — sword, saber, Cossack saber.
Manolis I. STEFANAKIS, Stella SPANTIDAKI, Ioannis MPARDANIS
<p>The paper discusses the methodology, some preliminary results and scientific problems of the HISTIA research project studying the production, maintenance, and administration of a neglected area of research, namely the sails, rope and any textile equipment needed for the military ships of Rhodes, mainly based on similar studies carried out for the Athenian navy, as well as research on the naval power of Rhodes. By focusing on this previously unstudied field of naval studies, this project, not only aims at breaching a significant research gap, but also establishes a new field of textile archaeology that studies textiles intended for the ships.</p>
Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover supervisory signals from the raw data without human annotation, which greatly mitigates the problem of sparse user-item interactions. However, most SSL-based recommendation models rely on general-purpose auxiliary tasks, e.g., maximizing correspondence between node representations learned from the original and perturbed interaction graphs, which are explicitly irrelevant to the recommendation task. Accordingly, the rich semantics reflected by social relationships and item categories, which lie in the recommendation data-based heterogeneous graphs, are not fully exploited. To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths. Based on the finding, we design two auxiliary tasks that are tightly coupled with the target task (one is predictive and the other one is contrastive) towards connecting recommendation with the self-supervision signals hiding in the positive correlation. Finally, a model-agnostic DUal-Auxiliary Learning (DUAL) framework which unifies the SSL and recommendation tasks is developed. The extensive experiments conducted on three real-world datasets demonstrate that DUAL can significantly improve recommendation, reaching the state-of-the-art performance.
Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a redesigned UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scans with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity index metric computed at multiple scales (MS-SSIM), were used for objective assessment of the ADC-Net performance. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The five-input channels implementation was observed as the optimal mode for ADC-Net training to achieve robust dispersion compensation in OCT
INTRODUCTION Since dentists are strategically positioned to promote tobacco abstinence and cessation, we assessed their commitment through their patients’ dental history in one of the busiest tertiary dental clinics in Nigeria, and also aimed to assess factors associated with screening for tobacco use. METHODS This retrospective and descriptive study utilized the dental records of patients (aged 12–80 years) at the oral diagnosis unit of the Lagos State University Teaching Hospital (LSUTH), Nigeria, from 2017–2018. Descriptive statistics were used to quantify variables such as age, gender, history of tobacco use, while the outcome variable was provision of cessation assistance through referral to the preventive dentistry cessation clinic. Data were analyzed by χ2 tests, t-test, and regression analysis. The significant level for statistical analysis was set at 5% (p≤0.05). RESULTS A total of 15786 new patients, mean age 49.3±12.8 years, were reviewed. Only 4104 (26%) of the patients had their tobacco use history documented; of these, 656 (16%) indicated past or current tobacco use; only 120 (18.3%) of these were referred for cessation counseling in the preventive dentistry unit. Males (n=611; 93.1%) had a higher proportion of past or present tobacco use. Patients presenting with oral ulcers (OR=1.94) and jaw tumors (OR=2.45) were significantly more likely to be screened for tobacco use. CONCLUSIONS Less than 0.01% of new patients were provided with tobacco use cessation advice, and opportunities for screening were essentially unexploited implying an urgent need to incorporate tobacco cessation interventions as part of standard clerking sheets for patients. INTRODUCTION Tobacco consumption occurs in many forms such as cigarettes, pipes, cigars, cigarillos, bidis, kreteks, smokeless tobacco (chew, snuff, and dissolvable strips, sticks, or lozenges), and through a hookah or waterpipe. The most common and referenced version of tobacco use is smoking. According to the U.S. Food and Drug Administration (FDA), the categories of smoked tobacco products are cigars, hookah, pipe tobacco, roll-your-own tobacco, electronic nicotine delivery systems and cigarettes. A cigarette is a thin cylinder of finely cut tobacco rolled in paper for smoking with chemical additives and a filter. There are 93 known harmful and potentially harmful chemicals in cigarettes including nicotine, cadmium, lead, acetaldehyde, ammonia, and benzene, and more than 7000 chemicals in cigarette smoke itself1. Close to 80% of the world’s 1 billion smokers live in lowto middle-income countries, like Nigeria2. Due to poor regulatory framework and weak governing systems, easy distribution of tobacco products, the initiation and maintenence of tobacco use is widely prevalent in Nigeria. Currently, about 1 in every 5 Nigerians smokes3, with high prevalence of consumption, as high as 50% among highrisk occupations like truck drivers4. Over 20 billion sticks of cigarettes are consumed annually in the country3. The World Health Organization (WHO) estimates that tobacco kills nearly 7 million people annually while 100 million tobacco related deaths were recorded over the course of the 20th century5. Tobacco is the leading preventable cause of mortality globally, contributing to cancer, cardiac disease, stroke, chronic lung diseases and other non-communicable Research Paper| Population Medicine Popul. Med. 2021;3(February):6 https://doi.org/10.18332/popmed/132292 2 diseases6. Furthermore, the consumption of tobacco products also has a negative impact on oral health; tobacco smoking is a significant etiological factor in periodontal diseases, deficits in postoperative oral healing and recovery, and plays a role in the failure of dental implants7. These diseases not only affect disability adjusted life years (DALYs), but also lead to a significant economic burden on societies8,9. One of the six measures developed to advance tobacco control is to ‘offer help to quit tobacco use’10 in the WHO Framework Convention for Tobacco Control, which is ratified by 177 countries worldwide. Substantial evidence shows that smoking cessation reduces mortality from tobaccorelated diseases and improves health11. However, smoking is a powerful addiction and despite numerous quit attempts, many individuals who smoke frequently fail to stop smoking12. Smoking cessation programs are therefore necessary to provide the support required for smokers to quit13. An analysis of 17 trials investigating physicians’ advice as an intervention concluded that even brief advice was effective in increasing the odds ratio for quitting smoking (1.74)14. National health organizations and organized dentistry have long advocated for practitioners to take a more active role in tobacco interventions with their patients13,15. Carr and Ebbert16 have reported that abstinence rates may increase among tobacco users as a result of behavioral interventions with an oral examination component provided by dentists and their auxiliaries in clinics or community settings. Dental healthcare providers are a great resource to aid in tobacco cessation. The patient–dentist relationship is unique; the dentist has the opportunity to meet his/her patient over many visits, which provides the opportunity to initiate and reinforce tobacco cessation practices. Dentists are experienced and knowledgeable in diagnosing oral disease4, particularly the adverse oral health effects of tobacco smoking, which on many occasions are present as potentially malignant or malignant lesions. Dentists can provide cessation assistance to their patients by identifying the oral signs of tobacco use, informing patients of these and assessing their willingness to quit smoking. They can also refer patients who desire to quit for smoking cessation services. It is estimated that about 50% of patients visit their dentist at least once a year17. In many countries, dentistry may be a potential setting for several aspects of clinical public health interventions because of their regular recall system of patients, presenting opportunity for promoting life style changes. In a recent Nigerian study, about half of the patients visiting the teaching hospital attended the dental clinic, a very high proportion18. Thus, the dental office visit provides a unique opportunity for dental professionals to point out the detrimental effects of tobacco and to discuss and assist in quitting. Cessation assistance within the dental clinic can be defined as referring patients for cessation counseling in the preventive dentistry unit. Since dentists are strategically positioned to promote tobacco abstinence and cessation of tobacco use, we aimed to review patients’ dental records in one of the busiest tertiary dental clinics in Nigeria to determine if tobacco use was documented and referral services for tobacco cessation were made. METHODS This retrospective and descriptive study utilized the dental records and case histories of patients (aged 12–80 years) seen at the oral diagnosis unit of the Lagos State University Teaching Hospital, Nigeria, over 2 years. The study was done at the dental center of the Lagos State University Teaching Hospital (LASUTH), which is the clinical division of Faculty of Dentistry, Lagos State University College of Medicine. Ethical approval for the study was obtained from the Health Research and Ethics Committee of the Lagos State University Teaching Hospital, Ikeja. The study was implemented in line with the Declarations of Helsinki and the confidentiality of all the participants was assured by the researchers. An official written request, which was approved, was also obtained from the medical records department of the Hospital. Written informed consent was not taken since the data were retrieved anonymously from the patients’ records and confidentiality was guaranteed by omitting personal identifiers. The inclusion criteria were male and female subjects presenting for the first time to the dental center. The exclusion criteria included case files with incomplete information with respect to age and gender and missing diagnosis or treatment plan. The principal investigator and a second researcher were responsible for data collection using 40 randomly selected dental records of patients at the oral diagnosis unit of the dental center which were not included in the final analysis. Inter-examiner reliability for both examiners was 0.90, whereas the intra-examiner reliability was 0.92 and 0.87 for the two examiners, respectively. The dental records of the patients were subsequently retrieved by the dental record officers after they obtained the written permission from the medical records department. The principal investigator used a proforma to record the date of birth, gender, the presenting complaint and diagnosis made by the attending dentist, documentation of tobacco use history and treatment plan, including referral of the patient to the Preventive Dentistry unit for smoking cessation. The chart review procedure was replicated by the other examiner and the records were compared for reproducibility and consistency. Other sociodemographic parameters such as socioeconomic status, religion, and occupation were not included during the assessment of the patients’ dental records. Statistical analysis Data were entered and analyzed using the Statistical Package for the Social Sciences (SPSS) software, version 20 (IBM, Armonk, New York). Descriptive statistics were used to quantify variables such as age, gender, history of tobacco use and referral to the preventive dentistry cessation clinic. Descriptive statistics included the mean age, sex distribution, Research Paper| Population Medicine Popul. Med. 2021;3(February):6 https://doi.org/10.18332/popmed/132292 3 and smoking status of the survey respondents. Data were analyzed by χ2 tests, t-test, and linear regression analysis. The confidence and significant levels for statistical analysis were set at 95% and 5%