Index Structure Research Articles

Hierarchical Indexing for Retrieval-Augmented Opinion Summarization

Abstract We propose a method for unsupervised abstractive opinion summarization, that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs). Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews. Then, we use a pretrained LLM to generate a readable summary that is grounded in these extracted evidential clusters. The modularity of our approach allows us to evaluate its efficacy at each stage. We show that HIRO learns an encoding space that is more semantically structured than prior work, and generates summaries that are more representative of the opinions in the input reviews. Human evaluation confirms that HIRO generates significantly more coherent, detailed, and accurate summaries.

The Impact of IT-Technologies on the Re-actualization and Research of a Once Forgotten Cultural Phenomenon: Ukrainian Epigraphic Embroidery in the Digital World

Abstract Today’s digital world inevitably affects both the study of culture and the processes taking place in culture itself. Such a widespread phenomenon of Ukrainian folk culture of the first half of the twentieth century as epigraphic embroidery (textile products with embroidered folk songs, proverbs, etc.) has not attracted significant attention for several decades. Now, largely thanks to digital technologies, embroidered inscriptions are being updated in Ukraine. This paper is devoted to the influence of IT-methods (particularly, the principles and structure of the Interactive Index of Folklore Formulas (Epigraphic Embroidery)) on cultural studies. The main idea of the Index is to quickly find all the items that contain a specific formula which helps to analyze the changes in a specific embroidered plot as accurately as possible. Also, the paper reviews Internet re-actualization (“new life”) of century-old embroidered products as their photos are used as a means of communication in social networks.

ActiveReach: an active learning framework for approximate reachability query answering in large-scale graphs.

With graph reachability query, one can answer whether there exists a path between two query vertices in a given graph. The existing reachability query processing solutions use traditional reachability index structures and can only compute exact answers, which may take a long time to resolve in large graphs. In contrast, with an approximate reachability query, one can offer a compromise by enabling users to strike a trade-off between query time and the accuracy of the query result. In this study, we propose a framework, dubbed ActiveReach, for learning index structures to answer approximate reachability query. ActiveReach is a two-phase framework that focuses on embedding nodes in a reachability space. In the first phase, we leverage node attributes and positional information to create reachability-aware embeddings for each node. These embeddings are then used as nodes' attributes in the second phase. In the second phase, we incorporate the new attributes and include reachability information as labels in the training data to generate embeddings in a reachability space. In addition, computing reachability for all training data may not be practical. Therefore, selecting a subset of data to compute reachability effectively and enhance reachability prediction performance is challenging. ActiveReach addresses this challenge by employing an active learning approach in the second phase to selectively compute reachability for a subset of node pairs, thus learning the approximate reachability for the entire graph. Our extensive experimental study with various real attributed large-scale graphs demonstrates the effectiveness of each component of our framework.

A personalized query method for spatial keywords in indoor environments

AbstractWith the rapid development of indoor Location Based Services (LBS), a growing volume of textual data is being generated in indoor environments. Consequently, indoor spatial keyword query holds significant potential for development in the coming years. However, existing methods for indoor spatial keyword queries often neglect the personalized needs of users. To solve this problem, we propose an Indoor Spatial Keyword Personalized Query (ISKPQ) method. First, a novel index structure called ISKIR-tree has been designed. This index integrates Hilbert encoding techniques and introduces Bloom filters and distance matrices to enhance the efficiency of processing indoor spatial keyword problems. Subsequently, an efficient pruning algorithm based on the ISKIR-tree index is proposed to refine the dataset effectively. Finally, a comprehensive scoring function that considers text similarity, spatial proximity, and user preferences is introduced to score and rank the pruned data points, thereby filtering out the optimal query results that meet users’ personalized requirements. Theoretical analysis and experimental studies demonstrate the outstanding performance of the proposed method in terms of both efficiency and accuracy.

Crystal Growth, Optical, Mechanical and Dielectric Analysis of Semiorganic Glycine Manganese Sulphate Single Crystal for Opto‐Electronic Device Application

AbstractGlycine manganese sulphate (GMS) crystals are produced by a slow evaporation technique at room temperature. This is confirmed that triclinic of the crystal lattice system by using single crystal X‐ray diffraction analysis and to determine the lattice parameters of the synthesised crystals. Powder XRD is used to confirm the planar indexing and crystalline structure. The Fourier Transform Infrared (FT‐IR) spectra are examined to verify that functional groups are present in the generated GMS crystals. By establishing a cut‐off wavelength of 253 nm, spectra of visible, near‐infrared, and Ultra Violet (UV) light from 200 to 1100 nm are analyzed. At frequencies ranging from 100 Hz to 8 MHz, the grown crystal's dielectric response is studied. Vickers microhardness tester is utilized to find out how strong the grown crystal is mechanically. Photoluminescence (PL) investigations often aim to detect crystal formation faults and contaminants. Etching analysis is used to find surface flaws and dislocations on the formed crystal's surface. The internal surface property of the produced crystal is investigated with scanning electron microscopy (SEM). The findings contradicted each other, suggesting that the created GMS crystal be used in opto‐electronic device applications.

A Coverless Image Steganography Based on Huffman Encoding and Robust Image Wavelet Hashing

Data hiding approaches are crucial in insecure communication to protect sensi- tive information from illegal access. The robustness of existing coverless image steganogra- phy methods that utilize mapping rules is derived from their ability to extract feature patterns within the spatial domain. In order to protect sensitive data more securely and to increase its durability against attacks, a new coverless steganography is introduced in this study. The me- thod used is based on the frequency domain. A coverless image steganography based on ro- bust image wavelet hashing and Huffman encoding. Initially, the confidential data is encoded through lossless compression using Huffman Encoding, which reduces the payload and en- hances the capacity for embedding. Second, segments of an 8-bit size are created from the compressed secret data. The efficient DWT hashing technique has been employed to generate a hash sequence for an image. An inverted index structure is established, allowing for the se- lection of the image whose hash corresponds to the secret data segment. Subsequently, all chosen images along with supplementary information are transmitted to the recipient. Testing has shown that the proposed method is robust against various image processing attacks, in- cluding scaling, low pass filtering, JPEG compression, rotation, noise introduction, and me- dian/mean filtering. Research findings and analysis indicate that this method enhances the latest coverless steganography algorithms in terms of capacity, resilience, and security.

Atmospheric Turbulence Parameters Deduced from VerticalProfile of Cn2

The proposed framework can capture realistic spatiotemporal variations in atmospheric turbulence parameters based on local meteorological conditions, such as Fried's parameter r0, isoplanatic angles θ0, seeing ε, scintillation rate , and the wavefront coherence time τ0. Those parameters have been deduced from the refractive index structure parameter Two Iraqi locations were compared, specifically the middle and southern borders, and atmospheric turbulence parameters relevant to astronomical observation were measured, lines were drawn at latitudes 34.26° and 28.31°, respectively. The methodology included collecting meteorological data from both sites and performing numerical simulations based on atmospheric turbulence parameters. According to the criteria of air turbulence, the results showed that the middle of Iraq was better than the southern location for astronomical observation.

Size and vitality characteristics of bilberry (Vaccinium myrtillus L.) populations in the Central Bohemian region of the Czech Republic: A case study of non-timber forest products

Bilberry (Vaccinium myrtillus L.) is a key non-timber forest product that plays an important role in forest biodiversity and ecosystem function. This study evaluated the vitality levels of eight bilberry populations in the Central Bohemian region of the Czech Republic, examining whether their vitality depends on the habitat type. Morphometric analyses were conducted, measuring 13 static metric and 16 static allometric morphological parameters of bilberry shoots. Statistical methods, including correlation, factor analysis, and ANOVA, were employed to determine the vitality index and population vitality structure based on the algorithm developed by Y.A. Zlobin. The results indicated that bilberry plants exhibit significant morphological variability based on forest conditions, forming plants with specific habitus traits. High positive correlations were found between many morphological characteristics, which were grouped into five clusters, each comprising 2–4 parameters. Among these, phytomass, total leaf surface area, and plant height were identified as the key determinants of vitality levels. Bilberry populations in Central Bohemian forests were classified into three vitality categories: prosperous, balanced, and depressed. The study found that the most vigorous populations, with Q-index values between 0.3333 and 0.4375, were located in associations such as Pinetum (sylvestris) vaccinio (myrtilli)-oxalidosum (acetosellae), Pinetum (sylvestris) vaccinio (myrtilli), and Pineto (sylvestris)-Piceetum (abietis) vaccinio (myrtilli)-oxalidosum (acetosellae). These populations are also of high economic value due to their bioresource potential. The study underscores the importance of habitat type in influencing bilberry vitality and offers insights into future research on bilberry populations as a vital component of non-timber forest products.

Wind turbine blade health state assessment based on fuzzy evaluation

Abstract Due to the health state of wind turbine blade being affected by many factors and the mechanism being unclear, a fuzzy evaluation method using SCADA information is proposed. Firstly, the index structure of blade health state assessment is determined by correlation analysis of SCADA data. Secondly, entropy weight is used to allocate weights to all levels of parameters, and then the index data degradation function is optimized by the 3σ criterion. Thirdly, the ridge distribution function is utilized to establish the membership function of the health indexs. Finally, a mathematical model is created for the fuzzy comprehensive assessment of the blade health status. The described techniques is employed to assess the blade health state in the Qingdao wind farm. The “healthy” and “dangerous” states of the blade can be distinguished. The assessment result aligns with reality, verifying that the method in this paper is applicable to the blade health state assessment of wind farms.

Transmission characteristics of vortex light superposition in atmospheric turbulence disturbed by plane acoustic waves

Herein, we derive the expression for the atmospheric refractive index structure constant under the influence of planar acoustic wave perturbations under the influence of the acoustic field on the refractive index and energy of the atmosphere. Utilizing the low-frequency compensated power spectrum inversion technique, we simulate the refractive index power spectrum of atmospheric turbulence perturbed by a planar acoustic wave. Numerical analysis is conducted on the transmission characteristics of the vortex light superposition states in atmospheric turbulence perturbed by a plane acoustic wave under different acoustic wave transmission heights, acoustic pressure amplitudes, and frequencies. Results indicate that introducing an acoustic field induces fluctuations in the atmospheric refractive index structure constant, with a more pronounced impact on the refractive index than on energy. Compared with the sole consideration of the impact of the acoustic field on the atmospheric refractive index, incorporating its effect on atmospheric energy results in a decrease in the atmospheric refractive index structure constant. The impact of the acoustic field on atmospheric turbulence is directly proportional to both acoustic pressure amplitude and frequency. The influence of the acoustic field on the transmission properties of the vortex optical superposition state varies with different acoustic transmission distances. Consequently, the transmission characteristics of the vortex light superposition state can be actively modulated according to varying acoustic wave transmission distances. This study offers a theoretical basis for modulating the optical field transmission characteristics via acoustic fields.

Estimation of Subsurface Structure Using Euler Deconvolution Method of Magnetic Data at the Geothermal Area of Sonai Village and its Surroundings, Konawe Regency

It has been carried out research with the aim of determining the subsurface structure at the geothermal area of Sonai Village and its surroundings, Konawe Regency. The data used are magnetic data obtained through field measurements at 126 points in the N180oS direction. After the data were subjected to diurnal and IGRF corrections, a residual (local) magnetic field anomaly of around -150 nT to 90 nT was obtained. On the residual magnetic anomaly map which has been reduced to the Pole (RTP), the Euler Deconvolution (ED) method is applied to the Index Structure N=0 to estimate the subsurface structure in the form of the presence of minor faults, and it is known that there are 5 minor faults at a depth of around 9 to 38 meters. Information on the existence of these faults is then used in 2D modeling. Modeling results show that these minor faults cut through two rock layers, which are the layers composed of conglomerate rocks from the Alangga Formation and peridotites as bedrock from the Ultramafic Complex. One of the minor faults closest to the manifestation area (hot spring) is at coordinates around 4o1’12.412” S and 122o7’24.263” E to 4o1’15.532” S and 122o7’19.561” E with a distance of ±15 meters. The existence of these minor faults is thought to be the migration routes for heat flow or conduction to the surface at the geothermal area of Sonai Village and its surroundings.

BWBEV: A Bitwise Query Processing Algorithm for Approximate Prefix Search

We tackle the challenge of conducting an approximate prefix search within datasets of strings. We explore using a bit-parallelism technique to compute the edit distance between distinct strings and illustrate its adaptation for an approximate prefix search procedure referred to as BWBEV. This technique employs a unary representation of edit vectors alongside bitwise operations to efficiently update these vectors during the edit distance computation. We also show how to apply our new bit-parallelism technique strategy to online edit distance computation between strings without index structure. Our experiments with BWBEV applied to approximate prefix search for a query autocompletion task revealed a substantial acceleration of over 36% when contrasted against state-of-the-art methods.

Backbone Index and GNN Models for Skyline Path Query Evaluation over Multi-cost Road Networks

Skyline path queries (SPQs) extend skyline queries to multi-dimensional networks, such as multi-cost road networks (MCRNs). Such queries return a set of non-dominated paths between two given network nodes. Despite the existence of extensive works on evaluating different SPQ variants, SPQ evaluation is still very inefficient due to the nonexistence of efficient index structures to support such queries. Existing index building approaches for supporting shortest-path query execution, when directly extended to support SPQs, use an unreasonable amount of space and time to build, making them impractical for processing large graphs. In this article, we propose a novel index structure, backbone index , and a corresponding index construction method that condenses an initial MCRN to multiple smaller summarized graphs with different granularity. We present efficient approaches to find approximate solutions to SPQs by utilizing the backbone index structure. Furthermore, considering making good use of historical query and query results, we propose two models, S kyline P ath G raph N eural N etwork (SP-GNN) and T ransfer SP-GNN (TSP-GNN), to support effective SPQ processing. Our extensive experiments on real-world large road networks show that the backbone index can support finding meaningful approximate SPQ solutions efficiently. The backbone index can be constructed in a reasonable time, which dramatically outperforms the construction of other types of indexes for road networks. As far as we know, this is the first compact index structure that can support efficient approximate SPQ evaluation on large MCRNs. The results on the SP-GNN and TSP-GNN models also show that both models can help get approximate SPQ answers efficiently.

GLMI: An Efficient Spatiotemporal Index Leveraging Geohash and Piecewise Linear Models for Optimized Query Performance

Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency.

Random Raman Lasing in Diode-Pumped Multi-Mode Graded-Index Fiber with Femtosecond Laser-Inscribed Random Refractive Index Structures of Various Shapes

Diode-pumped multi-mode graded-index (GRIN) fiber Raman lasers provide prominent brightness enhancement both in linear and half-open cavities with random distributed feedback via natural Rayleigh backscattering. Femtosecond laser-inscribed random refractive index structures allow for the sufficient reduction in the Raman threshold by means of Rayleigh backscattering signal enhancement by +50 + 66 dB relative to the intrinsic fiber level. At the same time, they offer an opportunity to generate Stokes beams with a shape close to fundamental transverse mode (LP01), as well as to select higher-order modes such as LP11 with a near-1D longitudinal random structure shifted off the fiber axis. Further development of the inscription technology includes the fabrication of 3D ring-shaped random structures using a spatial light modulator (SLM) in a 100/140 μm GRIN multi-mode fiber. This allows for the generation of a multi-mode diode-pumped GRIN fiber random Raman laser at 976 nm with a ring-shaped output beam at a relatively low pumping threshold (~160 W), demonstrated for the first time to our knowledge.

Nonseparable panel models with index structure and correlated random effects

. To facilitate semiparametric estimation of general discrete-choice, censored, sample selection, and other complex panel data models, we study identification and estimation of nonseparable multiple-index models in panel data with correlated random effects and a fixed number of time periods. The parameter vectors of interest are shown to be identified up to multiplicative constants and the average marginal effects are identified under the assumption that the distribution of individual effects depends on the explanatory variables only through their averages across time. Under this assumption, we propose to estimate the unknown parameters by the generalized method of moments based on the average and outer product of the difference of derivatives of the regression function. The rate of convergence and asymptotic distribution is established both for the proposed parameter estimates and the average marginal effects. We conduct Monte Carlo simulation studies to assess finite-sample performance of the proposed estimator. We also use it to model dynamic earnings of women, and using our general identification results, we find a negative effect of the number of children not only on the selection into employment as usual, but also on the average earnings.

Secure Blockchain Assisted Attribute-Based Keyword Search for Collaborative E-Healthcare

In the age of technological advancement, collaborative E-healthcare emerges as a transformative system eliminating traditional location and accessibility barriers in healthcare services. Here, Searchable Encryption (SE) plays a key role in enabling healthcare providers to outsource encrypted medical data and search services to third parties like cloud servers, thereby reducing storage and management expenses. This intermediary approach poses challenges of single-point failure, privacy breaches, and potentially untrustworthy results. State-of-the-art public key-based SE methods use a cloud-assisted architecture that doesn’t support reliable and practical searches with fine-grained permissions. Also, such systems require additional support to address potential privacy leakages and ensure data availability at the storage server. To address these concerns, we propose a B lockchain-assisted E fficient and S ecure K eyword S earch (BESKS) scheme to enforce fine-grained keyword search privilege control while achieving practical search complexity. Our scheme employs a ciphertext policy attribute-based keyword search mechanism where keywords are encrypted using expressive access policies to build an inverted index structure. The encrypted indexes are stored on the blockchain while encrypted medical documents are stored on InterPlanetary File System (IPFS) nodes to enhance availability and ensure the reliability and scalability of our approach. Our scheme utilizes blockchain-based smart contract for efficient, secure search operations and ensures financial fairness in fine-grained searches. Search tokens are generated based on user attributes and query keywords to facilitate private searches on-chain. To enhance the search process, our secure index enables exact match for a query keyword in constant time to ensure expensive authorization operations are performed only once. Theoretical analysis suggests that our BESKS is more efficient and secure than state-of-the-art schemes. Prototype implementation results on the Ethereum blockchain network further validate its feasibility for real-world applications, demonstrating the scheme's practical applicability in collaborative E-Healthcare systems.

Validity and reliability of the Sustainable HEalthy Diet (SHED) index by comparison with EAT-Lancet diet, Mediterranean diet in Turkish adults.

Food consumption and diet are strongly associated with sustainability. The Sustainable HEalthy Diet index was developed to measure the nutritional, environmental, and sociocultural components of sustainable diets and healthy eating patterns. However, a methodological approach has yet to be proposed for Turkish adults. This study aimed to determine the validity and reliability of the SHED index in Turkish adults. Data were collected from 558 healthy adults using a web-based questionnaire. Internal consistency reliability was evaluated using Cronbach's alpha coefficient, and repeatability was evaluated using the test-retest method. Construct validity was investigated using the EAT-Lancet diet and the Mediterranean Diet Adherence Screener (MEDAS), and the adapted SHED index structures' accordance was evaluated with confirmatory factor analysis. Good reliability and repeatability were found (r = 0.758 and 0.795, respectively). A higher SHED index score was related to a greater intake of grains, fruits, and vegetables and a lower intake of meat, eggs, and dairy compared to EAT-Lancet diet food groups. A higher SHED index score was associated with a lower saturated fat and added sugar intake. While the SHED index was associated with greater adherence to the Mediterranean diet (r = 0.334, p < 0.001), it was negatively associated with non-alcoholic and diet non-alcoholic beverage consumption (r = -0.257 and -0.264, respectively; p < 0.001). The SHED index showed good validity and reliability in Turkish adults. Our results suggest that the SHED index can be used in epidemiological and intervention studies because it allows the measurement of diets in terms of health and sustainability to propose adaptations accordingly.

Research on the effect of thermal disturbance on temporal and spatial distributions of refractive index structure constant by moiré deflection tomography

Research on the effect of thermal disturbance on temporal and spatial distributions of refractive index structure constant by moiré deflection tomography

Brain-body mitochondrial distribution patterns lack coherence and point to tissue-specific and individualized regulatory mechanisms.

Energy transformation capacity is generally assumed to be a coherent individual trait driven by genetic and environmental factors. This predicts that some individuals should have high and others low mitochondrial oxidative phosphorylation (OxPhos) capacity across organ systems. Here, we test this assumption using multi-tissue molecular and enzymatic activities in mice and humans. Across up to 22 mouse tissues, neither mitochondrial OxPhos capacity nor mtDNA density were correlated between tissues (median r = -0.01-0.16), indicating that animals with high mitochondrial capacity in one tissue can have low capacity in other tissues. Similarly, the multi-tissue correlation structure of RNAseq-based indices of mitochondrial gene expression across 45 tissues from 948 women and men (GTEx) showed small to moderate coherence between only some tissues (regions of the same brain), but not between brain-body tissue pairs in the same person (median r = 0.01). Mitochondrial DNA copy number (mtDNAcn) also lacked coherence across organs and tissues. Mechanistically, tissue-specific differences in mitochondrial gene expression were attributable in part to i) tissue-specific activation of canonical energy sensing pathways including the transcriptional coactivator PGC-1 and the integrated stress response (ISR), and ii) proliferative activity across tissues. Finally, we identify subgroups of individuals with high mitochondrial gene expression in some tissues (e.g., heart) but low expression in others (e.g., skeletal muscle) who display different clinical phenotypic patterns. Taken together, these data raise the possibility that tissue-specific energy sensing pathways may contribute to the idiosyncratic mitochondrial distribution patterns associated with the inter-organ heterogeneity and phenotypic diversity among individuals.