Articolul abordează aspectele stilistice ale textelor de pe site-uri turistice, cu scopul de a analiza funcția persuasivă a elementelor lexicale precum metafora, epitetul și comparația. Scopul cercetării constă în identificarea strategiilor de traducere care permit păstrarea expresivității și a impactului emoțional al textului-sursă în limba-țintă. Cercetarea de monstrează că utilizarea frecventă a adjectivelor evaluative, a superlativelor și a figurilor de stil intensifică impactul emoțional asupra cititorului, contribuind la construirea unei imagini pozitive și memorabile a destinației promovate. Ipoteza studiului pornește de la ideea că traducerea comunicativă, conform abordării lui Peter Newmark, facilitează obținerea unui efect pragmatic și estetic echivalent. Analiza corpusului de texte turistice demonstrează că utilizarea strategiilor precum reproducerea imaginii metaforice, echivalența culturală și compensarea contribuie la menținerea funcției persuasive a textelor localizate. Rezultatele obținute validează ipoteza formulată și subliniază importanța păstrării expresivității în procesul de traducere a textului turistic, esențială pentru menținerea atractivității ofertei turistice.
Cuvinte-cheie: turism, stilistică, metaforă, epitet, comparație, traducere comunicativă, echivalență culturală
DOI: https://doi.org/10.59295/sum10(220)2025_13
History of scholarship and learning. The humanities
Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what can be seen as a value bonus. The value bonus can be learned by estimating a value function on reward bonuses, propagating local uncertainties around rewards. However, this approach only increases the value bonus for an action retroactively, after seeing a higher reward bonus from that state and action. Such an approach does not encourage the agent to visit a state and action for the first time. In this work, we introduce an algorithm for exploration called Value Bonuses with Ensemble errors (VBE), that maintains an ensemble of random action-value functions (RQFs). VBE uses the errors in the estimation of these RQFs to design value bonuses that provide first-visit optimism and deep exploration. The key idea is to design the rewards for these RQFs in such a way that the value bonus can decrease to zero. We show that VBE outperforms Bootstrap DQN and two reward bonus approaches (RND and ACB) on several classic environments used to test exploration and provide demonstrative experiments that it can scale easily to more complex environments like Atari.
هدفت الدراسة إلى بيان قادح فساد الاعتبار، والوقوف على جملة من الأقيسة التي تعقبها الإمام ابن حجر بأنها فاسدة الاعتبار، ومحاولة الجواب عنها. وقد جعلت الدراسة في مقدمة ومبحثين: الأول في التعريف بقادح فساد الاعتبار وأدلة اعتباره والجواب عنه، والثاني في تطبيقاته الفقهية من كتاب فتح الباري. واعتمدت في البحث المنهج الوصفي التحليلي لنماذج من الأقيسة التي تعقبها الإمام ابن حجر، وذكرت من الأقوال والأدلة ما يظهر معه هذا القادح وتأثيره في الخلاف الفقهي. ثم ختمت البحث بنتائج كان من أهمها: التأكيد على أن اجتهاد الأئمة الأعلام -رحمهم الله- دائرٌ مع النص الشرعي، متى ظهر لهم الدليل اتبعوه وعدلوا عن النظر، وقد سبق من كلام العلماء ما يدل على هذا المعنى. وغالب الأقيسة المذكورة -هنا- التي اُعترض عليها بأنها مخالفة للنص عند التدقيق يتبين للناظر فيها وجود أدلة أخرى غير القياس استندوا إليها؛ فيكون القياس معتضدًا بالأدلة النقلية، وحينئذٍ يُنظر في تلك الأدلة فإن صحت تقوّى بها القياس وكان ذلك جوابًا يندفع معه الاعتراض، وإن لم يصح الاستدلال بها -ثبوتًا أو دلالة- سقط القياس؛ لبقائه وحيدًا أمام النص، والقياس لا يقوى على معارضته.
History of scholarship and learning. The humanities
Abstract This article explores historical video games as a system modeling medium, and how they provide space for a new form of historical agency that thrives on spatial navigations and intense interactions. The counterfactuals embedded in game designs offer alternative narrative structures for players to think and to imagine the past differently. Such shared immersion in the analog-digital remediation foster a diverse array of “collectives” and expand the traditional scope of collective memory thus historical consciousness. Drawing on a series of video games based on Romance of the Three Kingdoms as illustrative cases, this study argues that historical video games have emerged as complex public history in Asia.
In this paper, we investigate the challenges of complementary-label learning (CLL), a specialized form of weakly-supervised learning (WSL) where models are trained with labels indicating classes to which instances do not belong, rather than standard ordinary labels. This alternative supervision is appealing because collecting complementary labels is generally cheaper and less labor-intensive. Although most existing research in CLL emphasizes the development of novel loss functions, the potential of data augmentation in this domain remains largely underexplored. In this work, we uncover that the widely-used Mixup data augmentation technique is ineffective when directly applied to CLL. Through in-depth analysis, we identify that the complementary-label noise generated by Mixup negatively impacts the performance of CLL models. We then propose an improved technique called Intra-Cluster Mixup (ICM), which only synthesizes augmented data from nearby examples, to mitigate the noise effect. ICM carries the benefits of encouraging complementary label sharing of nearby examples, and leads to substantial performance improvements across synthetic and real-world labeled datasets. In particular, our wide spectrum of experimental results on both balanced and imbalanced CLL settings justifies the potential of ICM in allying with state-of-the-art CLL algorithms, achieving significant accuracy increases of 30% and 10% on MNIST and CIFAR datasets, respectively.
Artikkelen omhandler barnehagens hverdagsliv under covid-19-pandemien. Oppmerksomheten rettes mot hvordan barna ble berørt av de nasjonale smitteverntiltakene barnehagene ble pålagt. Det empiriske materialet som ligger til grunn for artikkelen, er dagboknotater fra barnehageansatte nedtegnet i april, mai og juni 2020. I diskusjonen av de empiriske funnene trekker vi veksler på Foucaults teori om biopolitikk, der hygiene betraktes som verktøy for sosial kontroll, og Nadesans forståelse av biopolitikk-begrepet, der barndom og oppvekst vies spesiell oppmerksomhet. Smitteverntiltakene i barnehagene ble innført «ovenfra», men måtte forvaltes og praktiseres «nedenfra». Resultatene viser at tiltakene både var disiplinerende (fjerning av leker, forbud mot å leke med bestemte andre) og selvdisiplinerende (barna ble opplært til å passe på egen og andres håndvask). Vi ser også hvordan en institusjon som er regulert i tid og rom, under pandemien i enda større grad ble regulert gjennom krav om «redusert kontakthyppighet» og segregering av barnegruppene. Tiltak som var motivert ut fra helse og sikkerhet for alle landets innbyggere, satte preg på barns hverdagsliv både gjennom konkrete begrensninger og ved at de måtte forholde seg til ekstraordinære hygienekrav og frykt for smitte.
English abstract
Infection Control, Biopolitics and Early Childhood Education and Care. How Children Were Directly Affected by Infection Prevention Measures in Kindergartens During Spring 2020
The article concerns everyday life in Early Childhood Education and Care (ECEC) institutions during the COVID-19 pandemic. Attention is focused on how children were affected by the infection control measures that kindergartens were required to implement. The empirical data that forms the basis of the article are diary notes from kindergarten staff recorded in April, May, and June 2020. In the discussion of the empirical findings, we draw on Foucault’s theory of biopolitics, where hygiene is considered as a tool for social control. The infection control measures in the kindergartens were introduced “from above”, but had to be managed and practiced “from below”. The measures were both about discipline (removal of toys, restrictions on allowed playmates) and dissemination of knowledge and self-discipline (the children were taught to take responsibility for their own hand-washing, as well as that of the other children). We also consider how an institution regulated in time and space was regulated to an even greater extent during the pandemic through requirements for reduced frequency of contact and segregation of smaller groups of children. Measures motivated by the protection of public health and safety affected children’s everyday lives, both through specific restrictions and due to the fact that they had to deal with extraordinary hygiene requirements and fear of infection.
History of scholarship and learning. The humanities, Social sciences (General)
Till Koebe, Zinnya del Villar, Brahmani Nutakki
et al.
Abstract Child pornography—better known as child sexual abuse material (CSAM)—represents a severe form of exploitation and victimization of children, leaving the victims with emotional and physical trauma. In this study, we aim to analyze local patterns of CSAM consumption across 1341 French communes in 20 metropolitan regions of France between March 16 to May 31, 2019 using fine-grained mobile traffic data of Tor network-related web services. We estimate that approx. 0.08% of Tor mobile download traffic observed in France is linked to the consumption of CSAM by correlating it with local-level temporal porn consumption patterns. This compares to 0.19% of what we conservatively estimate to be the share of CSAM content in global Tor traffic. In line with existing literature on the link between sexual child abuse and the consumption of image-based content thereof, we observe a positive and statistically significant effect of our CSAM consumption estimates on the reported number of victims of sexual violence and vice versa, which validates our findings, after controlling for a set of geographically disaggregated features including socio-demographic characteristics, voting behavior, nearby points of interest and Google Trends queries. While this is a first, exploratory attempt to look at CSAM from a spatial epidemiological angle, we believe this research provides public health officials with valuable information to prioritize target areas for public awareness campaigns as another step to fulfill the global community’s pledge to target 16.2 of the sustainable development goals: “end abuse, exploitation, trafficking and all forms of violence and torture against children".
History of scholarship and learning. The humanities, Social Sciences
The early history of string theory is marked by a shift from strong interaction physics to quantum gravity. The first string models and associated theoretical framework were formulated in the late 1960s and early 1970s in the context of the S-matrix program for the strong interactions. In the mid-1970s, the models were reinterpreted as a potential theory unifying the four fundamental forces. This paper provides a historical analysis of how string theory was developed out of S-matrix physics, aiming to clarify how modern string theory, as a theory detached from experimental data, grew out of an S-matrix program that was strongly dependent upon observable quantities. Surprisingly, the theoretical practice of physicists already turned away from experiment before string theory was recast as a potential unified quantum gravity theory. With the formulation of dual resonance models (the "hadronic string theory"), physicists were able to determine almost all of the models' parameters on the basis of theoretical reasoning. It was this commitment to "non-arbitrariness", i.e., a lack of free parameters in the theory, that initially drove string theorists away from experimental input, and not the practical inaccessibility of experimental data in the context of quantum gravity physics. This is an important observation when assessing the role of experimental data in string theory.
Chi Ian Tang, Lorena Qendro, Dimitris Spathis
et al.
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature of human behaviours, continual learning promises HAR systems that are tailored to users' needs. However, because of the difficulty in collecting labelled data with wearable sensors, existing approaches that focus on supervised continual learning have limited applicability, while unsupervised continual learning methods only handle representation learning while delaying classifier training to a later stage. This work explores the adoption and adaptation of CaSSLe, a continual self-supervised learning model, and Kaizen, a semi-supervised continual learning model that balances representation learning and down-stream classification, for the task of wearable-based HAR. These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning. In addition to comparing state-of-the-art self-supervised continual learning schemes, we further investigated the importance of different loss terms and explored the trade-off between knowledge retention and learning from new tasks. In particular, our extensive evaluation demonstrated that the use of a weighting factor that reflects the ratio between learned and new classes achieves the best overall trade-off in continual learning.
Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite
et al.
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by $2 \times$ relative to dense FL. Finally, in a real-world federated learning deployment, SSFL delivers over $2.3 \times$ faster communication time, underscoring its practical efficiency.
The Robust Regularized Markov Decision Process (RRMDP) is proposed to learn policies robust to dynamics shifts by adding regularization to the transition dynamics in the value function. Existing methods mostly use unstructured regularization, potentially leading to conservative policies under unrealistic transitions. To address this limitation, we propose a novel framework, the $d$-rectangular linear RRMDP ($d$-RRMDP), which introduces latent structures into both transition kernels and regularization. We focus on offline reinforcement learning, where an agent learns policies from a precollected dataset in the nominal environment. We develop the Robust Regularized Pessimistic Value Iteration (R2PVI) algorithm that employs linear function approximation for robust policy learning in $d$-RRMDPs with $f$-divergence based regularization terms on transition kernels. We provide instance-dependent upper bounds on the suboptimality gap of R2PVI policies, demonstrating that these bounds are influenced by how well the dataset covers state-action spaces visited by the optimal robust policy under robustly admissible transitions. We establish information-theoretic lower bounds to verify that our algorithm is near-optimal. Finally, numerical experiments validate that R2PVI learns robust policies and exhibits superior computational efficiency compared to baseline methods.
Johannes O. Ferstad, Emily B. Fox, David Scheinker
et al.
Digital health interventions (DHIs) and remote patient monitoring (RPM) have shown great potential in improving chronic disease management through personalized care. However, barriers like limited efficacy and workload concerns hinder adoption of existing DHIs; while limited sample sizes and lack of interpretability limit the effectiveness and adoption of purely black-box algorithmic DHIs. In this paper, we address these challenges by developing a pipeline for learning explainable treatment policies for RPM-enabled DHIs. We apply our approach in the real-world setting of RPM using a DHI to improve glycemic control of youth with type 1 diabetes. Our main contribution is to reveal the importance of clinical domain knowledge in developing state and action representations for effective, efficient, and interpretable targeting policies. We observe that policies learned from clinician-informed representations are significantly more efficacious and efficient than policies learned from black-box representations. This work emphasizes the importance of collaboration between ML researchers and clinicians for developing effective DHIs in the real world.
Learning navigation capabilities in different environments has long been one of the major challenges in decision-making. In this work, we focus on zero-shot navigation ability using given abstract $2$-D top-down maps. Like human navigation by reading a paper map, the agent reads the map as an image when navigating in a novel layout, after learning to navigate on a set of training maps. We propose a model-based reinforcement learning approach for this multi-task learning problem, where it jointly learns a hypermodel that takes top-down maps as input and predicts the weights of the transition network. We use the DeepMind Lab environment and customize layouts using generated maps. Our method can adapt better to novel environments in zero-shot and is more robust to noise.
Subhojyoti Mukherjee, Josiah P. Hanna, Qiaomin Xie
et al.
We study learning to learn for the multi-task structured bandit problem where the goal is to learn a near-optimal algorithm that minimizes cumulative regret. The tasks share a common structure and an algorithm should exploit the shared structure to minimize the cumulative regret for an unseen but related test task. We use a transformer as a decision-making algorithm to learn this shared structure from data collected by a demonstrator on a set of training task instances. Our objective is to devise a training procedure such that the transformer will learn to outperform the demonstrator's learning algorithm on unseen test task instances. Prior work on pretraining decision transformers either requires privileged information like access to optimal arms or cannot outperform the demonstrator. Going beyond these approaches, we introduce a pre-training approach that trains a transformer network to learn a near-optimal policy in-context. This approach leverages the shared structure across tasks, does not require access to optimal actions, and can outperform the demonstrator. We validate these claims over a wide variety of structured bandit problems to show that our proposed solution is general and can quickly identify expected rewards on unseen test tasks to support effective exploration.
Abstract Until recently, most research in the Digital Humanities (DH) was monomodal, meaning that the object of analysis was either textual or visual. Seeking to integrate multimodality theory into the DH, this article demonstrates that recently developed multimodal deep learning models, such as Contrastive Language Image Pre-training (CLIP), offer new possibilities to explore and analyze image–text combinations at scale. These models, which are trained on image and text pairs, can be applied to a wide range of text-to-image, image-to-image, and image-to-text prediction tasks. Moreover, multimodal models show high accuracy in zero-shot classification, i.e. predicting unseen categories across heterogeneous datasets. Based on three exploratory case studies, we argue that this zero-shot capability opens up the way for a multimodal turn in DH research. Moreover, multimodal models allow scholars to move past the artificial separation of text and images that was dominant in the field and analyze multimodal meaning at scale. However, we also need to be aware of the specific (historical) bias of multimodal deep learning that stems from biases in the training data used to train these models.
The article deals with the influence of the Protestant community of the Weimar Republic on the presidential elections of 1925. The use of religious contradictions within German society to mobilize the electorate in support of P. Hindenburg’s candidacy is analyzed. The religious aspect of elections is especially relevant in the study of state-confessional relations in Germany in the 1920s and understanding the degree of influence of religion on politics.The reasons and features of the formation of preelection inter-party coalitions are revealed. The role of the religious factor in the creation of the «Imperial block» is noted. Changes in the electoral base of candidates after the first round of elections are analyzed. The differences in political sympathies in the industrial and agrarian regions of Germany, as well as the contradictions in the ranks of the Bavarian People’s Party under the influence of the religious factor, are explained. The participation of the Protestant clergy in the election campaign in support of the candidate from the nationalist forces is characterized. The influence of the nomination from the coalition of the SPD and the Center Party of the Catholic V. Marx on the election results is indicated. A significant part of the Protestant clergy and Protestant organizations came out in support of P. Hindenburg. Campaigning for a Protestant candidate from church pulpits led to the mobilization of the conservative religious electorate and further victory of P. Hindenburg in the second round of the presidential elections in 1925.
Law, History of scholarship and learning. The humanities
Every year many scholars are funded by the China Scholarship Council (CSC). The CSC is a funding agency established by the Chinese government with the main initiative of training Chinese scholars to conduct research abroad and to promote international collaboration. In this study, we identified these CSC-funded scholars sponsored by the China Scholarship Council based on the acknowledgments text indexed by the Web of Science. Bibliometric data of their publications were collected to track their scientific mobility in different fields, and to evaluate the performance of the CSC scholarship in promoting international collaboration by sponsoring the mobility of scholars. Papers funded by the China Scholarship Council are mainly from the fields of natural sciences and engineering sciences. There are few CSC-funded papers in the field of social sciences and humanities. CSC-funded scholars from mainland China have the United States, Australia, Canada, and some European countries, such as Germany, the UK, and the Netherlands, as their preferential mobility destinations across all fields of science. CSC-funded scholars published most of their papers with international collaboration during the mobility period, with a decrease in the share of international collaboration after the support of the scholarship.
Gaps and requirements for multi-modal interfaces for humanities can be explored by observing the configuration of real-world environments and the tasks of visitors within them compared to digital environments. Examples include stores, museums, galleries, and stages with tasks similar to visualization tasks such as overview, zoom and detail; multi-dimensional reduction; collaboration; and comparison; with real-world environments offering much richer interactions. Some of these capabilities exist with the technology and visualization research, but not routinely available in implementations.