Hasil untuk "Modern"

J. Nunnally

D. Mackinnon, Amanda J. Fairchild, Matthew S. Fritz

Mediating variables are prominent in psychological theory and research. A mediating variable transmits the effect of an independent variable on a dependent variable. Differences between mediating variables and confounders, moderators, and covariates are outlined. Statistical methods to assess mediation and modern comprehensive approaches are described. Future directions for mediation analysis are discussed.

T. Lyons, C. Reinhard, N. Planavsky

Diederik P. Kingma, S. Mohamed, Danilo Jimenez Rezende et al.

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.

R. Green, J. Krause, Adrian W. Briggs et al.

Mark S. Granovetter

Harry M. Markowitz

Embracing finance, economics, operations research, and computers, this book applies modern techniques of analysis and computation to find combinations of securities that best meet the needs of private or institutional investors.

G. Miller

Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].

John W. Meyer, Brian Rowan

G. Box, G. Jenkins, G. Reinsel et al.

P. Mellars, C. Stringer

Mahdie Hor, Somayeh Rahaiee, Sedighe khanjani Jelodar et al.

Introduction: Castor oil bean (Ricinus communis) belongs to the Euphorbiaceae family and is valued for its oil and medicinal uses. It contains several bioactive compounds including ricin, ricinine and flavonoids, and has attracted considerable attention for its various biological effects, including antioxidant and anti-inflammatory effects. The aim of this article was to investigate the antioxidant, antibacterial effects, and phenolic and flavonoid bioactive compounds of castor bean seeds from different regions. Methods: In this study, methanol and hexane extracts from castor bean seeds were prepared via two different extraction methods (stirring and Soxhlet). The antioxidant effect of these extracts was measured using the DPPH (2, 2-diphenyl-1-picrylhydrazyl) method. For antibacterial effect, both disk diffusion and minimum inhibitory concentration (MIC)/minimum bactericidal concentration (MBC) methods were employed across several types of bacteria. The study also evaluated the total phenolic and flavonoid contents. Results: The results indicated that the stirring extraction method using methanol yielded the highest level of total phenolic and flavonoid content from castor bean seeds sourced from the Mazandaran region. In contrast, the soxhlet- methanolic extraction of castor bean seeds from the Qom region exhibited the most significant free radical scavenging activity. Notably, Bacillus cereus and Staphylococcus aureus strains depicted the highest sensitivity, with mean diameters 21.16 ± 0.84 mm and 15.33 ± 0.47 mm, respectively, at a concentration of 400 mg/mL of stirring-methanolic extraction. Additionally, the methanol extract from the Mazandaran region displayed strong inhibitory and bactericidal effects against various bacterial strains. Conclusion: The results revealed that the cultivation region of the seeds, the used solvent, and the extraction method had a substantial impact on the bioactive properties of the castor bean seeds.

Albanë Mehmetaj, Kosovar Berisha

This paper examines Ibrahim Rugova’s philosophical and theoretical contributions to literary studies, focusing on three central categories that define his aesthetics: objectification, the strategy of meaning, and aesthetic refusal. Through the reworking of phenomenological and ontological concepts, Rugova reformulates objectification as the process by which the inner world of personality becomes externalized in the literary work, thereby affirming the autonomy of art as a mode of being. His notion of the strategy of meaning, developed in dialogue with semiotics and structuralism, explains how literature generates both denotative meanings internal to the work and connotative meanings arising from interpretation. Finally, the concept of aesthetic refusal highlights the tension between literature and politics, showing how literature resists ideological and institutional pressures by affirming its autonomy. The study seeks to analyze and synthesize these concepts by examining Rugova’s theoretical–philosophical works, such as <i>Kah teoria Strategjia e kuptimit</i>, and <i>Refuzimi estetik</i>. <i>letrare</i>, through an interdisciplinary methodology that combines philosophical analysis, literary theory, and cultural critique. Taken together, the categories under discussion form a coherent ontology of the literary work that situates Rugova within multiple intellectual traditions that influenced him—including phenomenology, hermeneutics, information theory, structuralism, and dialectical philosophy—while simultaneously underscoring his originality in adapting these ideas to the Albanian intellectual context. The paper concludes that Rugova’s theoretical legacy, often overshadowed by his political role, offers a significant contribution to modern literary theory by defending the autonomy of literature and reaffirming its function as a distinctive mode of truth and human realization.

Wei Yuan, Shaoqiang Zheng, Zheng Zhang et al.

In this work, we confirm a Pr3+:LiYF4 pulsed laser with high power and high energy at 639 nm based on the acousto-optic cavity dumping technique. The maximum average output power, narrowest pulse width, highest pulse energy and peak power of the pulsed laser at a repetition rate of 0.1 kHz are 532 mW, 112 ns, 5.32 mJ and 47.5 kW, respectively. A 639 nm pulsed laser with such high pulse energy and peak power has not been reported previously. Furthermore, we obtain a widely tunable range of repetition rates from 0.1 to 5000 kHz. The diffracted beam quality factors M2 are 2.18 (in the x direction) and 2.04 (in the y direction). To the best of our knowledge, this is the first time that a cavity-dumped all-solid-state pulsed laser in the visible band has been reported. This work provides a promising method for obtaining high-performance pulsed lasers.

Yue Guan, Yuanwei Fang, Keren Zhou et al.

In this work, we propose KPerfIR, a novel multilevel compiler-centric infrastructure to enable the development of customizable, extendable, and portable profiling tools tailored for modern artificial intelligence (AI) workloads on modern GPUs. Our approach integrates profiling capabilities directly into the compiler workflow, allowing profiling functionalities to be implemented as compiler passes, offering a programmable and reusable framework for performance analysis. This design bridges the gap between compilers and profilers, enabling fine-grained insights into complex optimization challenges such as overlapping the execution of fine-grained function units on GPUs. KPerfIR is integrated into the Triton infrastructure to highlight the power of a compiler-centric approach to advance performance analysis and optimization in the ever-evolving landscape of AI compilers. Our evaluation shows that our tool incurs low overhead (8.2%), provides accurate measurements (2% relative error), and delivers actionable insights into complicated GPU intra-kernel optimizations.

Felix Oberhansl, Marc Schink, Nisha Jacob Kabakci et al.

Smartphones handle sensitive tasks such as messaging and payment and may soon support critical electronic identification through initiatives such as the European Digital Identity (EUDI) wallet, currently under development. Yet the susceptibility of modern smartphones to physical side-channel analysis (SCA) is underexplored, with recent work limited to pre-2019 hardware. Since then, smartphone system on chip (SoC) platforms have grown more complex, with heterogeneous processor clusters, sub 10 nm nodes, and frequencies over 2 GHz, potentially complicating SCA. In this paper, we assess the feasibility of electromagnetic (EM) SCA on a Raspberry Pi 4, featuring a Broadcom BCM2711 SoC and a Fairphone 4 featuring a Snapdragon 750G 5G SoC. Using new attack methodologies tailored to modern SoCs, we recover ECDSA secrets from OpenSSL by mounting the Nonce@Once attack of Alam et al. (Euro S&P 2021) and show that the libgcrypt countermeasure does not fully mitigate it. We present case studies illustrating how hardware and software stacks impact EM SCA feasibility. Motivated by use cases such as the EUDI wallet, we survey Android cryptographic implementations and define representative threat models to assess the attack. Our findings show weaknesses in ECDSA software implementations and underscore the need for independently certified secure elements (SEs) in all smartphones.

Shaonwita Pal, Dibyendu Nandy

Over the past century, the Sun's activity -- which exhibits significant variations -- went through a phase known as the Modern Maximum. Notably, the strongest sunspot cycle on record during this period, and indeed since direct sunspot observations began, was cycle 19; this was followed by a significantly weaker cycle 20. Understanding and reconstructing this extreme variability has remained elusive. Utilizing data-driven, coupled models of magnetic field evolution on the Sun's surface and within its convection zone, here we show that random deviations in the tilt angle and polarity orientation of bipolar sunspot pairs is sufficient to explain these observed, extreme fluctuations during the modern maximum in solar activity. Our results support the theory that perturbation in the poloidal field source of the dynamo mechanism -- mediated via the emergence of anomalously tilted solar active regions - is the primary driver of extreme variations in the Sun's activity. This study has implications for understanding how the Sun may switch from a phase of extreme activity to quiescent, low activity phases -- such as the Maunder Minimum.

Andrea Ceni, Alessio Gravina, Claudio Gallicchio et al.

The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM modules to sequences extracted from graphs, often compromising core properties such as permutation equivariance, message-passing compatibility, and computational efficiency. In this paper, we introduce a new perspective by embedding the key principles of modern SSM computation directly into the Message-Passing Neural Network framework, resulting in a unified methodology for both static and temporal graphs. Our approach, MP-SSM, enables efficient, permutation-equivariant, and long-range information propagation while preserving the architectural simplicity of message passing. Crucially, MP-SSM enables an exact sensitivity analysis, which we use to theoretically characterize information flow and evaluate issues like vanishing gradients and over-squashing in the deep regime. Furthermore, our design choices allow for a highly optimized parallel implementation akin to modern SSMs. We validate MP-SSM across a wide range of tasks, including node classification, graph property prediction, long-range benchmarks, and spatiotemporal forecasting, demonstrating both its versatility and strong empirical performance.

Xuemei Tang, Chengxi Yan, Jinghang Gu et al.

Chinese information extraction (IE) involves multiple tasks across diverse temporal domains, including Classical and Modern documents. Fine-tuning a single model on heterogeneous tasks and across different eras may lead to interference and reduced performance. Therefore, in this paper, we propose Tea-MOELoRA, a parameter-efficient multi-task framework that combines LoRA with a Mixture-of-Experts (MoE) design. Multiple low-rank LoRA experts specialize in different IE tasks and eras, while a task-era-aware router mechanism dynamically allocates expert contributions. Experiments show that Tea-MOELoRA outperforms both single-task and joint LoRA baselines, demonstrating its ability to leverage task and temporal knowledge effectively.

Haonan Wang, Brian Chen, Siquan Li et al.

Parameter-Efficient Fine-Tuning (PEFT) methods have become crucial for rapidly adapting large language models (LLMs) to downstream tasks. Prefix-Tuning, an early and effective PEFT technique, demonstrated the ability to achieve performance comparable to full fine-tuning with significantly reduced computational and memory overhead. However, despite its earlier success, its effectiveness in training modern state-of-the-art LLMs has been very limited. In this work, we demonstrate empirically that Prefix-Tuning underperforms on LLMs because of an inherent tradeoff between input and prefix significance within the attention head. This motivates us to introduce Prefix-Tuning+, a novel architecture that generalizes the principles of Prefix-Tuning while addressing its shortcomings by shifting the prefix module out of the attention head itself. We further provide an overview of our construction process to guide future users when constructing their own context-based methods. Our experiments show that, across a diverse set of benchmarks, Prefix-Tuning+ consistently outperforms existing Prefix-Tuning methods. Notably, it achieves performance on par with the widely adopted LoRA method on several general benchmarks, highlighting the potential modern extension of Prefix-Tuning approaches. Our findings suggest that by overcoming its inherent limitations, Prefix-Tuning can remain a competitive and relevant research direction in the landscape of parameter-efficient LLM adaptation.