Search Results (7)

Visual target detection under adverse weather conditions presents a fundamental challenge for autonomous driving, particularly in achieving all-weather operational capabilities. Unlike existing reviews that concentrate on individual technical domains such as image restoration or detection robustness, this review introduces an innovative “restoration–detection” collaborative framework. This paper systematically examines state-of-the-art methods for degraded image recovery and improvement of detection model robustness, encompassing from traditional, physically driven approaches as well as contemporary deep learning paradigms. A comprehensive overview and comparative analysis are provided to elucidate these advancements. Regarding the recovery of degraded images, traditional methods demonstrate advantages in interpretability within specific scenarios, such as those based on dark channel prior. In contrast, deep learning methods have achieved significant breakthroughs in modeling complex degradations and enhancing cross-domain generalization through a data-driven paradigm. In the field of enhancing detection robustness, traditional improvement techniques that utilize anisotropic filtering, alongside deep learning methods such as SSD, R-CNN, and the YOLO series, contribute to perceptual stability through feature optimization and end-to-end learning approaches, respectively. This paper summarizes 11 types of mainstream public datasets, examining their multimodal annotation system and addressing issues related to discrepancies. Furthermore, it provides an extensive evaluation of algorithm performance using PSNR, SSIM, mAP, among others. It has been identified that significant bottlenecks persist in dynamic weather coupling modeling, multimodal heterogeneous data fusion, and the efficiency of edge deployment. Future research should focus on establishing a physically guided hybrid learning architecture, developing techniques for dynamic and adaptive timing calibration, and designing a flexible multimodal fusion framework to overcome the limitations associated with complex environment perception. This paper serves as a systematic reference for both the theoretical development and practical implementation of automatic driving vision detection technology under severe weather conditions. Full article

Psychedelic plants and fungi have been traditionally used in many cultures as part of ritual ceremonies and ancient medicinal treatments. In some regions, these psychoactive plants have already entered mainstream discourse through popular literature and art. Today, numerous academic and medical institutions are establishing dedicated departments to examine the benefits and risks of psychedelic-assisted treatments. Entheogens in healing practices and herbal medicine are part of Slavic cultural heritage. However, due to the predominantly oral transmission of this knowledge, there is a significant lack of written sources and a profound gap in documentation regarding entheogen use on the Balkan Peninsula, where many psychoactive plants and mushrooms grow in their natural habitat. Our work aims to bridge indigenous knowledge systems with contemporary therapeutic discourse, while advocating for sustainable, inclusive, and culturally respectful research practices. This review manuscript presents information on Slavic ancient entheogens, and calls for further multidisciplinary, integrative approaches in researching psychoactive plants and mushrooms of the Balkans. Our paper includes the ethnobotanical uses of native Balkan entheogens, outlines the pharmacological mechanisms of their main active compounds, and discusses their impacts on social behavior, mental health, and overall well-being. We also examine their therapeutic potential and risks, contributing to the contemporary understanding of psychoactive and psychedelic use in mental health treatment and beyond, as tools for life enhancement to improve quality of life and well-being. Full article

Low-resource languages remain underserved by contemporary large language models (LLMs) because they lack sizable corpora, bespoke preprocessing tools, and the computing budgets assumed by mainstream alignment pipelines. Focusing on Kazakh, we present a 1.94B parameter LLaMA-based model that demonstrates how strong, culturally aligned performance can be achieved without massive infrastructure. The contribution is threefold. (i) Data and tokenization—we compile a rigorously cleaned, mixed-domain Kazakh corpus and design a tokenizer that respects the language’s agglutinative morphology, mixed-script usage, and diacritics. (ii) Training recipe—the model is built in two stages: causal language modeling from scratch followed by instruction tuning. Alignment is further refined with Direct Preference Optimization (DPO), extended by contrastive and entropy-based regularization to stabilize training under sparse, noisy preference signals. Two complementary resources support this step: ChatTune-DPO, a crowd-sourced set of human preference pairs, and Pseudo-DPO, an automatically generated alternative that repurposes instruction data to reduce annotation cost. (iii) Evaluation and impact—qualitative and task-specific assessments show that targeted monolingual training and the proposed DPO variant markedly improve factuality, coherence, and cultural fidelity over baseline instruction-only and multilingual counterparts. The model and datasets are released under open licenses, offering a reproducible blueprint for extending state-of-the-art language modeling to other under-represented languages and domains. Full article

This essay discusses the genesis, evolution, and impact of the Leningrad Contemporary Music Club (CMC), a pivotal hub for avant-garde and experimental music in the late Soviet Union. Founded amidst the socio-political constraints of the late 1970s, the CMC emerged as a sanctuary for jazz, classical avant-garde, and progressive rock enthusiasts. This paper chronicles the CMC’s unique ability to foster creative expression within the repressive Soviet cultural framework, driven by a coalition of visionaries including such musicians as Sergey Kuryokhin and jazz theoreticians like Efim Barban. The narrative highlights the club’s seminal role in introducing Western avant-garde music to Soviet audiences, hosting groundbreaking performances, and cultivating a vibrant community of musicians, critics, and fans. Through an exploration of the CMC’s organisational strategies, cultural exchanges, and its ultimate closure following state intervention, the paper examines how the Club bridged underground and mainstream music while navigating ideological constraints. The research underscores the CMC’s legacy as a microcosm of resistance and innovation, situating its contributions within broader discussions of Soviet countercultural movements and global avant-garde practices. This work contributes to the historiography of Soviet underground culture, shedding light on the interplay between art, politics, and social transformation in late 20th-century Leningrad. Full article

This paper investigates the narratives involved in the becoming public of an ecological, relational, and culinary culture through artistic mediums. Specifically, the question posed is this: how do food and cooking feature in some selected design and architecture exhibitions? The argument is developed through a series of thematic case studies that aim to affirm the presence in contemporary design, architecture, and exhibition-making of an ecological paradigm. The examples blur the lines of food and art by being proposed as processes of collective authorship happening in atmospheres of conviviality and hospitality. I bring forth the argument that developing exhibitions through the lines of hospitality can improve the quality of public engagement, and amplify a relational model which calls for the collective and entangled nature of all things. Alongside the potential of the arts of sparking a cognitive restructuring and shift in perspective, some risks associated with the mainstream model of society are considered. The final aim is to affirm the importance of relationships to oppose the neoliberal geopolitics of power which foster object-oriented perspectives. Full article

Key–value (KV) stores based on the LSM-tree have become the mainstream of contemporary store engines, but there are problems with high write and read amplification. Moreover, the real-world workload has a high data skew, and the existing KV store lacks hot-awareness, leading to its unreliable and poor performance on the highly skewed real-world workload. In this paper, we propose HoaKV, which unifies the key design ideas of hot issues, KV separation, and hybrid indexing technology in a system. Specifically, HoaKV uses the heat differentiation in KV pairs to manage the hot data and the cold data and conducts real-time dynamic adjustment data classification management. It also uses partial KV separation technology to manage differential KV pairs for large and small KV pairs in the cold data. In addition, HoaKV uses hybrid indexing technology to index the hot data and the cold data, respectively, to improve the performance of reading, writing, and scanning at the same time. In the mixed read and write workloads experments show that HoaKV performs significantly better than several state-of-the-art KV store technologies such as LevelDB, RocksDB, PebblesDB, and WiscKey. Full article

From a small community of pioneering artists who experimented with artificial intelligence (AI) in the 1970s, AI art has expanded, gained visibility, and attained socio-cultural relevance since the second half of the 2010s. Its topics, methodologies, presentational formats, and implications are closely related to a range of disciplines engaged in the research and application of AI. In this paper, I present a comprehensive framework for the critical exploration of AI art. It comprises the context of AI art, its prominent poetic features, major issues, and possible directions. I address the poetic, expressive, and ethical layers of AI art practices within the context of contemporary art, AI research, and related disciplines. I focus on the works that exemplify poetic complexity and manifest the epistemic or political ambiguities indicative of a broader milieu of contemporary culture, AI science/technology, economy, and society. By comparing, acknowledging, and contextualizing both their accomplishments and shortcomings, I outline the prospective strategies to advance the field. The aim of this framework is to expand the existing critical discourse of AI art with new perspectives which can be used to examine the creative attributes of emerging practices and to assess their cultural significance and socio-political impact. It contributes to rethinking and redefining the art/science/technology critique in the age when the arts, together with science and technology, are becoming increasingly responsible for changing ecologies, shaping cultural values, and political normalization. Full article