Hasil untuk "Modern"

H. Reineck, I. Singh

J. Mcdowell, Luis Eduardo Hoyos

Modern philosophy finds it difficult to give a satisfactory picture of the place of minds in the world. In "Mind and World", based on the 1991 John Locke Lectures, John McDowell offers his diagnosis of this difficulty and points to a cure. He illustrates a major problem of modern philosophy - the insidious persistence of dualism - in his discussion of empirical thought. Much as we would like to conceive empirical thought as rationally grounded in experience, pitfalls await anyone who tries to articulate this position, and McDowell exposes these traps by exploiting the work of contemporary philosophers from Wilfrid Sellars to Donald Davidson. These difficulties, he contends, reflect an understandable - but surmountable - failure to see how we might integrate what Sellars calls "the logical space of reasons" into the natural world. What underlies this impasse is a conception of nature that has certain attractions for the modern age, a conception that McDowell proposes to put aside, thus circumventing these philosophical difficulties. By returning to a pre-modern conception of nature but retaining the intellectual advance of modernity that has mistakenly been viewed as dislodging it, he makes room for a fully satisfying conception of experience as a rational openness to independent reality. This approach also overcomes other obstacles that impede a generally satisfying understanding of how we are placed in the world.

Giyeong Oh, Junghyun Lee, Jaehyun Park et al.

Modern LLMs inherit strong priors from web-scale pretraining, which can limit the headroom of post-training data-selection strategies. While Active Preference Learning (APL) seeks to optimize query efficiency in online Direct Preference Optimization (DPO), the inherent richness of on-policy candidate pools often renders simple Random sampling a surprisingly formidable baseline. We evaluate uncertainty-based APL against Random across harmlessness, helpfulness, and instruction-following settings, utilizing both reward models and LLM-as-a-judge proxies. We find that APL yields negligible improvements in proxy win-rates compared to Random. Crucially, we observe a dissociation where win-rate improves even as general capability -- measured by standard benchmarks -- degrades. APL fails to mitigate this capability collapse or reduce variance significantly better than random sampling. Our findings suggest that in the regime of strong pre-trained priors, the computational overhead of active selection is difficult to justify against the ``cheap diversity'' provided by simple random samples. Our code is available at https://github.com/BootsofLagrangian/random-vs-apl.

Benjamin Hamblin, Victor M. Calo

Modern Yo-Yo play has developed into a sophisticated international subculture, featuring elite competition and intricate tricks. Despite this, no systematic knot-theoretic treatment has yet been applied to the numerous string configurations realised in contemporary Yo-Yo play. This paper takes initial steps in addressing this gap, recalling fundamental results from knot theory and embeddings to develop a methodology for classifying string arrangements, known as `mounts', in both beginner and advanced single Yo-Yo play. We classify a range of mounts according to the knots they form under appropriate post-processing procedures and identify Yo-Yo maneuvers that correspond to Reidemeister moves. Furthermore, we analyse the impact of certain mounts on the writhe of their diagrammatic projections and introduce operations to facilitate discussion of composite mounts. This work seeks to initiate a dialogue between Yo-Yo practitioners and knot theorists, fostering further advancements in high-level Yo-Yo play and enabling novel physical realisations of knots, links, braids, surgeries, and other topological transformations.

Yinfang Chen, Jiaqi Pan, Jackson Clark et al.

In cloud-scale systems, failures are the norm. A distributed computing cluster exhibits hundreds of machine failures and thousands of disk failures; software bugs and misconfigurations are reported to be more frequent. The demand for autonomous, AI-driven reliability engineering continues to grow, as existing humanin-the-loop practices can hardly keep up with the scale of modern clouds. This paper presents STRATUS, an LLM-based multi-agent system for realizing autonomous Site Reliability Engineering (SRE) of cloud services. STRATUS consists of multiple specialized agents (e.g., for failure detection, diagnosis, mitigation), organized in a state machine to assist system-level safety reasoning and enforcement. We formalize a key safety specification of agentic SRE systems like STRATUS, termed Transactional No-Regression (TNR), which enables safe exploration and iteration. We show that TNR can effectively improve autonomous failure mitigation. STRATUS significantly outperforms state-of-the-art SRE agents in terms of success rate of failure mitigation problems in AIOpsLab and ITBench (two SRE benchmark suites), by at least 1.5 times across various models. STRATUS shows a promising path toward practical deployment of agentic systems for cloud reliability.

M. Polzin, M. Guzman

When modernizing a legacy application, it is easy to fall back on a like-for-like replica with new tools and updated design stylings, but this is an opportunity to explore making a more intuitive application that supports user tasks and efficiency. Rather than having a blank canvas-unburdened by legacy tech debt-to create a new application, you are working with an existing application that is integral to accelerator operations and one that expert users are already familiar with. Due to this, you might assume people will prefer the like-for-like, but you could be carrying forward the pain points, processes that are inefficient, and ultimately wind up with an application that no one wants to use because it doesn't solve existing problems. Getting users involved can make all the difference in your approach to modernizing a legacy application that caters to both newer and expert users. It also can bridge the gap between like-for-like and introducing new GUI design. Having a legacy application doesn't have to make the modernized one difficult to develop, as the existing application is a tool in how you move forward with the new application. It provides insight into areas that an application with a clean slate doesn't give you.

Xiaohong Chen, Haitian Xie

This paper studies nonparametric local (over-)identification and the semiparametric efficiency in modern causal frameworks. We develop a unified approach that begins by translating structural models with latent variables into their induced statistical models of observables and then analyzes local overidentification through conditional moment restrictions. We apply this approach to three popular classes of causal models: (1) the general treatment model under unconfoundedness; (2) the negative control model, and (3) the long-term causal inference model under unobserved confounding. The first model yields a locally just-identified statistical model, implying that all regular asymptotically linear estimators of the treatment effect have the same asymptotic variance, which equals the (trivial) semiparametric efficient variance bound. In contrast, the latter two models involve nonparametric endogeneity and are naturally locally overidentified; consequently, some doubly robust orthogonal moment estimators of the average treatment effect are inefficient. Whereas existing work typically imposes strong conditions to restore local just-identification to justify the efficiency of their doubly robust orthogonal moment estimators, we characterize the semiparametric efficient variance bounds, along with efficient estimators, for the (locally) overidentified models (2) and (3). A small real data application, along with a simulation study, illustrates the semiparametric efficiency gains in model (3).

Haoran Li, Xingye Cheng, Ziyang Huang et al.

Traditional Chinese Medicine diagnosis and treatment principles, established through centuries of trial-and-error clinical practice, directly maps patient-specific symptom patterns to personalised herbal therapies. These empirical holistic mapping principles offer valuable strategies to address remaining challenges of reductionism methodologies in modern biomedicine. However, the lack of a quantitative framework and molecular-level evidence has limited their interpretability and reliability. Here, we present an AI framework trained on ancient and classical TCM formula records to quantify the symptom pattern-herbal therapy mappings. Interestingly, we find that empirical TCM diagnosis and treatment are consistent with the encoding-decoding processes in the AI model. This enables us to construct an interpretable TCM embedding space (TCM-ES) using the model's quantitative representation of TCM principles. Validated through broad and extensive TCM patient data, the TCM-ES offers universal quantification of the TCM practice and therapeutic efficacy. We further map biomedical entities into the TCM-ES through correspondence alignment. We find that the principal directions of the TCM-ES are significantly associated with key biological functions (such as metabolism, immune, and homeostasis), and that the disease and herb embedding proximity aligns with their genetic relationships in the human protein interactome, which demonstrate the biological significance of TCM principles. Moreover, the TCM-ES uncovers latent disease relationships, and provides alternative metric to assess clinical efficacy for modern disease-drug pairs. Finally, we construct a comprehensive and integrative TCM knowledge graph, which predicts potential associations between diseases and targets, drugs, herbal compounds, and herbal therapies, providing TCM-informed opportunities for disease analysis and drug development.

Dinan Li, Panagiotis Kakosimos, Luca Peretti

The recent technological advances in digitalization have revolutionized the industrial sector. Leveraging data analytics has now enabled the collection of deep insights into the performance and, as a result, the optimization of assets. Industrial drives, for example, already accumulate all the necessary information to control electric machines. These signals include but are not limited to currents, frequency, and temperature. Integrating machine learning (ML) models responsible for predicting the evolution of those directly collected or implicitly derived parameters enhances the smartness of industrial systems even further. In this article, data already residing in most modern electric drives has been used to develop a data-driven thermal model of a power module. A test bench has been designed and used specifically for training and validating the thermal digital twin undergoing various static and dynamic operating profiles. Different approaches, from traditional linear models to deep neural networks, have been implemented to emanate the best ML model for estimating the case temperature of a power module. Several evaluation metrics were then used to assess the investigated methods' performance and implementation in industrial embedded systems.

K. A. Beyer, T. E. Cocolios, C. Costache et al.

Recent advances in muonic x-ray experiments have reinvigorated efforts in measurements of absolute nuclear charge radii. Here, a modern approach is presented, and demonstrated through determination of the charge radii of the two stable chlorine nuclides $^{35}$Cl and $^{37}$Cl. Knowledge of these radii has implications for fundamental studies in nuclear and atomic physics. For this purpose, a state-of-the-art experiment was performed at the $π$E1 beamline in the Paul Scherrer Institute (Switzerland), using a large-scale HPGe detector array in order to extract precise energies of the muonic $^{35}$Cl and $^{37}$Cl $np1s$ transitions. The nuclear charge radius extraction relies on modern calculations for QED effects and nuclear polarization with rigorous uncertainty quantification, including effects that were not accounted for in older studies. Additionally, we established a new method for applying the nuclear shape correction directly from energy density functionals, which are amenable to isotopes for which no high-quality electron scattering experiments are available. The resulting charge radii are $3.3335(23) fm$ for $^{35}$Cl and $3.3445(23) fm$ for $^{37}$Cl, thus improving the uncertainty of the available electron scattering values by a factor of seven. The correlation of several observables was evaluated between the different isotopes in order to produce a more precise value of the differential mean square charge radius $δ\langle r^2 \rangle^{37, 35}=+0.0771(66) fm^{2}$. In this case, improvement of the uncertainty by more than one order of magnitude was achieved compared to the literature value. This precision is sufficient to use this differential as input for isotope shift factor determination.

Nathan J. LeRoy, Donald R. Campbell, Seth Stadick et al.

Introduction: Epigenomic datasets from high-throughput sequencing experiments are commonly summarized as genomic intervals. As the volume of this data grows, so does interest in analyzing it through deep learning. However, the heterogeneity of genomic interval data, where each dataset defines its own regions, creates barriers for machine learning methods that require consistent, discrete vocabularies. Methods: We introduce gtars-tokenizers, a high-performance library that maps genomic intervals to a predefined universe or vocabulary of regions, analogous to text tokenization in natural language processing. Built in Rust with bindings for Python, R, CLI, and WebAssembly, gtars-tokenizers implements two overlap methods (BITS and AIList) and integrates seamlessly with modern ML frameworks through Hugging Face-compatible APIs. Results: The gtars-tokenizers package achieves top efficiency for large-scale datasets, while enabling genomic intervals to be processed using standard ML workflows in PyTorch and TensorFlow without ad hoc preprocessing. This token-based approach bridges genomics and machine learning, supporting scalable and standardized analysis of interval data across diverse computational environments. Availability: PyPI and GitHub: https://github.com/databio/gtars.

Olexandr Bieliaiev

The article shows the history of the life and activities of the organisers and members of the literary studio «Brama», which was founded in Kyiv in 1963 and was created to unite creative youth who did not accept Soviet rule and ideology, around the problems of creative development, preservation of national culture and opposition to the ruling regime. The biographies of the poets of the sixties such as Viktor Mohylnyi, Hryhorii Tymenko, Vasyl Solovia, artist and fashion designer Liubov Panchenko, public and political figure Oles Shevchenko, dissident and teacher Yurii Murashov and others are considered. The study should fill the gap in studies of the history of the Ukrainian dissident movement and cultural organizations, which exists due to the absence in modern Ukrainian historical science of separate works devoted to the history of the studio “Brama” and biographies of personalities who were its members. The life story of the members of the literary studio “Brama” is a direct reflection of the entire spectrum of problems and life circumstances that the Ukrainian creative intelligentsia and opposition to the Soviet totalitarian regime faced. The biographies of the individuals who made up the personal group of the studio “Brama” indicate a high level of their involvement in the socio-political and national-cultural life of Ukraine of their time, their significant contribution to the development of culture, the preservation of national identity and the fight against communist-Russian colonial rule. Thus, the biographies of the members of the studio “Brama” can be exemplary in considering the history of the dissident movement in Ukraine and the development of cultural and national life during the Soviet occupation in the second half of the twentieth century.

Aliza U. Siddiqui, Kaitlin Gili, Chris Ballance

Quantum hardware is progressing at a rapid pace and, alongside this progression, it is vital to challenge the capabilities of these machines using functionally complex algorithms. Doing so provides direct insights into the current capabilities of modern quantum hardware and where its breaking points lie. Stress testing is a technique used to evaluate a system by giving it a computational load beyond its specified thresholds and identifying the capacity under which it fails. We conduct a qualitative and quantitative evaluation of the Quantinuum H1 ion trap device using a stress test based protocol. Specifically, we utilize the quantum machine learning algorithm, the Quantum Neuron Born Machine, as the computationally intensive load for the device. Then, we linearly scale the number of repeat-until-success subroutines within the algorithm to determine the load under which the hardware fails and where the failure occurred within the quantum stack. Using this proposed method, we assess the hardware capacity to manage a computationally intensive QML algorithm and evaluate the hardware performance as the functional complexity of the algorithm is scaled. Alongside the quantitative performance results, we provide a qualitative discussion and resource estimation based on the insights obtained from conducting the stress test with the QNBM.

Jinying Zhang, Yifan Shi, Yexiaotong Zhang et al.

Photoacoustic imaging has emerged as a promising modality for medical imaging since its introduction. Photoacoustic microscopy (PAM), which is based on the photoacoustic effect, combines the advantages of both optical and acoustic imaging modalities. PAM facilitates high-sensitivity, high-resolution, non-contact, and non-invasive imaging by employing optical absorption as its primary contrast mechanism. The ability of PAM to specifically image parameters such as blood oxygenation and melanin content makes it a valuable addition to the suite of modern biomedical imaging techniques. This review aims to provide a comprehensive overview of the diverse technical approaches and methods employed by researchers to enhance the resolution of photoacoustic microscopy. Firstly, the fundamental principles of the photoacoustic effect and photoacoustic imaging will be presented. Subsequently, resolution enhancement methods for both acoustic-resolution photoacoustic microscopy (AR-PAM) and optical-resolution photoacoustic microscopy (OR-PAM) will be discussed independently. Finally, the aforementioned resolution enhancement methods for photoacoustic microscopy will be critically evaluated, and the current challenges and future prospects of this technology will be summarized.

Yu-Chieh Chou, Ting-Hsuan Sung, Shi-Jing Hou et al.

<i>Cordyceps militaris</i> is a valuable medicinal fungus which has been widely used as a traditional medicine in East Asia. Compared to the well-known medicinal fungus <i>C. sinensis</i>, <i>C. militaris</i> can produce similar fermented metabolites with various biological activities, but it requires a shorter culture time and simpler culture conditions, and therefore, it has attracted increasing attention in recent years. The purpose of this review was to organize the current studies regarding metabolite production from <i>C. militaris</i> relative to their biological functions. We combined findings of metabolite production to correlate with different fermentation modes to obtain a full view of production processes used to yield the product. While research on <i>C. militaris</i> fermentation is not uncommon to date, its high value still highlights the importance of developing more modern fermentation processes for industrial production.

Rebecca M. M. Hicke, David Mimno

Large language models have shown breakthrough potential in many NLP domains. Here we consider their use for stylometry, specifically authorship identification in Early Modern English drama. We find both promising and concerning results; LLMs are able to accurately predict the author of surprisingly short passages but are also prone to confidently misattribute texts to specific authors. A fine-tuned t5-large model outperforms all tested baselines, including logistic regression, SVM with a linear kernel, and cosine delta, at attributing small passages. However, we see indications that the presence of certain authors in the model's pre-training data affects predictive results in ways that are difficult to assess.

Ben Goertzel

A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are. It is found that many of the practical weaknesses of these AI systems can be tied specifically to lacks in the basic cognitive architectures according to which these systems are built. It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI, in practical terms given realizable amounts of compute resources. This does not imply there is nothing to learn about human-level AGI from studying and experimenting with LLMs, nor that LLMs cannot form significant parts of human-level AGI architectures that also incorporate other ideas. Social and ethical matters regarding LLMs are very briefly touched from this perspective, which implies that while care should be taken regarding misinformation and other issues, and economic upheavals will need their own social remedies based on their unpredictable course as with any powerfully impactful technology, overall the sort of policy needed as regards modern LLMs is quite different than would be the case if a more credible approximation to human-level AGI were at hand.

Harold Erbin

This book provides an introduction to string field theory (SFT). String theory is usually formulated in the worldsheet formalism, which describes a single string (first-quantization). While this approach is intuitive and could be pushed far due to the exceptional properties of two-dimensional theories, it becomes cumbersome for some questions or even fails at a more fundamental level. These motivations have led to the development of SFT, a description of string theory using the field theory formalism (second-quantization). As a field theory, SFT provides a rigorous and constructive formulation of string theory. The main objective is to construct the closed bosonic SFT and to explain how to assess the consistency of string theory with it. The accent is put on providing the reader with the foundations, conceptual understanding and intuition of what SFT is. After reading this book, they should be able to study the applications from the literature. The book is organized in two parts. The first part reviews the topics of the worldsheet theory that are necessary to build SFT (worldsheet path integral, CFT and BRST quantization). The second part starts by introducing general concepts of SFT from the BRST quantization. Then, it introduces off-shell string amplitudes before providing a Feynman diagrams interpretation from which the building blocks of SFT are extracted. After constructing the closed SFT, it is used to outline the proofs of several important consistency properties, such as background independence, unitarity and crossing symmetry. Finally, the generalization to the superstring is also discussed. This book grew up from lecture notes for a course given at the Ludwig-Maximilians-Universität LMU (winter semesters 2017-2018 and 2018-2019). The current document is the draft of the manuscript published by Springer.

Grigoreva Julia G.

Introduction. The relevance of the study is determined by the increasing role of North-East Asia as one of the world and political centers, as well as the growth of various challenges and threats in the region, affecting safe and stable development of the world community as a whole. The study of the problem of regional security in Northeast Asia and the participation of Mongolia in its ensuring is important for the formation of theoretical and practical conclusions and assessments regarding its international status. Since the 1980s Mongolia has been consistently pursuing the policy of creating a mechanism for dialogue in Northeastern Asia. The result of these efforts was the Ulaanbaatar Northeast Asia Security Dialogue initiative. Mongolia's active foreign policy and the will to fully participate in regional cooperation in Northeastern Asia and in as many international and multilateral organizations as possible is one of the hallmarks of the phenomenon of modern Mongolia. The purpose of the study is to review the “Ulaanbaatar Dialogue on Security in Northeast Asia” and analyze its role in creating conditions for the interaction of all stakeholders in the interests of maintaining peace in Northeastern Asia. Results. This study presents a brief history of the formation and development of the Ulaanbaatar Dialogue, identifies advantages over similar discussion platforms in the region, and shows the importance of this event in increasing the international status of Mongolia. It is concluded that in the nearest future Mongolia may become an analogue of Asian Switzerland, the main platform for negotiations between countries in Northeast Asia due to the fact that Ulaanbaatar pursues an open, multifaceted foreign policy, and the adherence of this country to the “third neighbor” doctrine makes Mongolia a neutral state that does not participate in military-political blocks.

Sesenlo Kath, Ruokuovilie Mezhatsu

The agricultural extension services in the North East region of India are not only hindered by limited resources and scarcity of trained staff at state & regional level but also by the remoteness of the villages. Many villages remain inaccessible particularly during monsoon, due to poor road connectivity. The major technology dissemination approach adopted so far had been the traditional direct interaction and field level practical demonstration at the community level. The facility of toll-free modern smart phone based information and communication technology (ICT) service has been started and is gaining momentum. An attempt has been made to collect the data from 200 farmers of Tseminyu district of Nagaland State based on proportionate random sampling (PPS) technique to know the impact of mobile based extension agro advisory services in the region. Majority of the farmer respondents had perceived ‘yield increase’ , and ‘information of new agricultural technology’ as the major benefits of using the mobile -based agro- advisory services.